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b/lambda0.004/elic-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample4-layer4-item1.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2a5fb05265eea0cc19c7c204ca0cced16f4d0423176f098298869a084f46b92 +size 4174713 diff --git a/lambda0.004/elic-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample40-layer4-item1.zst b/lambda0.004/elic-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample40-layer4-item1.zst new file mode 100644 index 0000000000000000000000000000000000000000..cbd92ffb707beffd855446707e4fffe17d7e8ae0 --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample40-layer4-item1.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56c17242d0933af75b9510b6f59c00c302e7dfa595681fea6019340e2009ffd7 +size 2642490 diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/dtufc_hyperprior-featurecoding_qwen_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/dtufc_hyperprior-featurecoding_qwen_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..82051ba5ca474dff4d2e7a2c2cb413ffbe709503 --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/dtufc_hyperprior-featurecoding_qwen_individual.log @@ -0,0 +1,16958 @@ +Experiment: dtufc_hyperprior-featurecoding_qwen_individual +Log file: output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/dtufc_hyperprior-featurecoding_qwen_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: qwen + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.0.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.0.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.1.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.1.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.2.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.2.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.3.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.3.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.4.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.4.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_feature.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.4.output' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_feature.json +Loaded per-key mappings: model=qwen + Keys: ['layer.0.k_cache', 'layer.0.v_cache', 'layer.1.k_cache', 'layer.1.v_cache', 'layer.2.k_cache', 'layer.2.v_cache', 'layer.3.k_cache', 'layer.3.v_cache', 'layer.4.k_cache', 'layer.4.v_cache', 'layer.4.output'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1 +Output output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1 +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample1-layer4-item1.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample1-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 276, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.015s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 276, 128) +Output shape: (1, 276, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.0.v_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.1.k_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.1.v_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.2.k_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.2.v_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.3.k_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.3.v_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.4.k_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.4.v_cache: torch.Size([1, 8, 276, 128]) -> torch.Size([1, 1, 276, 1024]) + layer.4.output: torch.Size([1, 276, 4096]) -> torch.Size([1, 1, 276, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 40,212B, BPFP=1.1382 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 90,268B, BPFP=2.5551 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 66,548B, BPFP=1.8837 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 94,880B, BPFP=2.6857 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 73,684B, BPFP=2.0857 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 95,512B, BPFP=2.7036 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 75,364B, BPFP=2.1333 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 94,016B, BPFP=2.6612 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 66,448B, BPFP=1.8809 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 95,592B, BPFP=2.7058 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 281,916B, BPFP=1.9950 +⌛️ [2/4] FRONTEND: Frontend time: 0.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 276, 128]) + layer.0.v_cache: torch.Size([1, 8, 276, 128]) + layer.1.k_cache: torch.Size([1, 8, 276, 128]) + layer.1.v_cache: torch.Size([1, 8, 276, 128]) + layer.2.k_cache: torch.Size([1, 8, 276, 128]) + layer.2.v_cache: torch.Size([1, 8, 276, 128]) + layer.3.k_cache: torch.Size([1, 8, 276, 128]) + layer.3.v_cache: torch.Size([1, 8, 276, 128]) + layer.4.k_cache: torch.Size([1, 8, 276, 128]) + layer.4.v_cache: torch.Size([1, 8, 276, 128]) + layer.4.output: torch.Size([1, 276, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.653s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 276, 128]) + layer.0.v_cache: torch.Size([1, 8, 276, 128]) + layer.1.k_cache: torch.Size([1, 8, 276, 128]) + layer.1.v_cache: torch.Size([1, 8, 276, 128]) + layer.2.k_cache: torch.Size([1, 8, 276, 128]) + layer.2.v_cache: torch.Size([1, 8, 276, 128]) + layer.3.k_cache: torch.Size([1, 8, 276, 128]) + layer.3.v_cache: torch.Size([1, 8, 276, 128]) + layer.4.k_cache: torch.Size([1, 8, 276, 128]) + layer.4.v_cache: torch.Size([1, 8, 276, 128]) + layer.4.output: torch.Size([1, 276, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02420060 4.55245220 + layer.0.v_cache 0.00000027 0.00014328 + layer.1.k_cache 0.00304789 0.40858482 + layer.1.v_cache 0.00000080 0.00049525 + layer.2.k_cache 0.00119192 0.23761451 + layer.2.v_cache 0.00000117 0.00069756 + layer.3.k_cache 0.00127914 0.25744300 + layer.3.v_cache 0.00000214 0.00108687 + layer.4.k_cache 0.00374547 0.47307482 + layer.4.v_cache 0.00000302 0.00187518 + layer.4.output 0.00013416 0.03162352 + ------------------------------------------------------------------------------------- + TOTAL 0.00242922 0.43285440 + (elements=3,956,736) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3956736 +Total Bytes 1074440 +BPFP 2.1724 bits/point +EBPFP 4.3448 equivalent bits/point +MSE 0.432854 +---------------------- -------------------------------------------------------- +Time: 1.278s Load: 0.015s, Pack+Encode: 0.610s, Decode+Unpack: 0.653s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 276, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 276, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4329 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample1-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample1-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample10-layer4-item1.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample10-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 283, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.016s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 283, 128) +Output shape: (1, 283, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.0.v_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.1.k_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.1.v_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.2.k_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.2.v_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.3.k_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.3.v_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.4.k_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.4.v_cache: torch.Size([1, 8, 283, 128]) -> torch.Size([1, 1, 283, 1024]) + layer.4.output: torch.Size([1, 283, 4096]) -> torch.Size([1, 1, 283, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 39,684B, BPFP=1.0955 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 90,704B, BPFP=2.5040 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 67,936B, BPFP=1.8754 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 95,336B, BPFP=2.6318 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 74,904B, BPFP=2.0678 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 96,660B, BPFP=2.6684 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 77,056B, BPFP=2.1272 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 95,456B, BPFP=2.6352 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 67,764B, BPFP=1.8707 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 96,604B, BPFP=2.6669 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 295,976B, BPFP=2.0427 +⌛️ [2/4] FRONTEND: Frontend time: 0.377s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 283, 128]) + layer.0.v_cache: torch.Size([1, 8, 283, 128]) + layer.1.k_cache: torch.Size([1, 8, 283, 128]) + layer.1.v_cache: torch.Size([1, 8, 283, 128]) + layer.2.k_cache: torch.Size([1, 8, 283, 128]) + layer.2.v_cache: torch.Size([1, 8, 283, 128]) + layer.3.k_cache: torch.Size([1, 8, 283, 128]) + layer.3.v_cache: torch.Size([1, 8, 283, 128]) + layer.4.k_cache: torch.Size([1, 8, 283, 128]) + layer.4.v_cache: torch.Size([1, 8, 283, 128]) + layer.4.output: torch.Size([1, 283, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.613s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 283, 128]) + layer.0.v_cache: torch.Size([1, 8, 283, 128]) + layer.1.k_cache: torch.Size([1, 8, 283, 128]) + layer.1.v_cache: torch.Size([1, 8, 283, 128]) + layer.2.k_cache: torch.Size([1, 8, 283, 128]) + layer.2.v_cache: torch.Size([1, 8, 283, 128]) + layer.3.k_cache: torch.Size([1, 8, 283, 128]) + layer.3.v_cache: torch.Size([1, 8, 283, 128]) + layer.4.k_cache: torch.Size([1, 8, 283, 128]) + layer.4.v_cache: torch.Size([1, 8, 283, 128]) + layer.4.output: torch.Size([1, 283, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02559670 4.45063787 + layer.0.v_cache 0.00000030 0.00013996 + layer.1.k_cache 0.00287003 0.40121638 + layer.1.v_cache 0.00000078 0.00045416 + layer.2.k_cache 0.00118918 0.23568782 + layer.2.v_cache 0.00000127 0.00067078 + layer.3.k_cache 0.00127329 0.25655432 + layer.3.v_cache 0.00000227 0.00105653 + layer.4.k_cache 0.00368869 0.45657095 + layer.4.v_cache 0.00000310 0.00174283 + layer.4.output 0.00017183 0.04134002 + ------------------------------------------------------------------------------------- + TOTAL 0.00252235 0.42643512 + (elements=4,057,088) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 4057088 +Total Bytes 1098080 +BPFP 2.1653 bits/point +EBPFP 4.3305 equivalent bits/point +MSE 0.426435 +---------------------- -------------------------------------------------------- +Time: 1.006s Load: 0.016s, Pack+Encode: 0.377s, Decode+Unpack: 0.613s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 283, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 283, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4264 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample10-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample10-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample100-layer4-item1.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample100-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 158, 128) +Output shape: (1, 158, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.output: torch.Size([1, 158, 4096]) -> torch.Size([1, 1, 158, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,636B, BPFP=1.2182 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,016B, BPFP=2.6709 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,688B, BPFP=1.9624 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,476B, BPFP=2.7925 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,984B, BPFP=2.1748 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 56,964B, BPFP=2.8167 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,268B, BPFP=2.2383 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,028B, BPFP=2.7704 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,176B, BPFP=1.9866 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,020B, BPFP=2.8194 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 179,268B, BPFP=2.2160 +⌛️ [2/4] FRONTEND: Frontend time: 0.338s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.416s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02645765 4.87300477 + layer.0.v_cache 0.00000027 0.00014701 + layer.1.k_cache 0.00311677 0.47703639 + layer.1.v_cache 0.00000085 0.00048149 + layer.2.k_cache 0.00117315 0.25557231 + layer.2.v_cache 0.00000112 0.00070766 + layer.3.k_cache 0.00135825 0.29048550 + layer.3.v_cache 0.00000212 0.00106812 + layer.4.k_cache 0.00352137 0.52263144 + layer.4.v_cache 0.00000299 0.00175039 + layer.4.output 0.00021777 0.05366382 + ------------------------------------------------------------------------------------- + TOTAL 0.00260754 0.47411002 + (elements=2,265,088) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2265088 +Total Bytes 653524 +BPFP 2.3082 bits/point +EBPFP 4.6163 equivalent bits/point +MSE 0.474110 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.009s, Pack+Encode: 0.338s, Decode+Unpack: 0.416s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4741 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample100-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample100-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample101-layer4-item1.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample101-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 154, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 154, 128) +Output shape: (1, 154, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.output: torch.Size([1, 154, 4096]) -> torch.Size([1, 1, 154, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,800B, BPFP=1.2581 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,580B, BPFP=2.6674 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,012B, BPFP=1.9791 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 55,212B, BPFP=2.8009 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,392B, BPFP=2.2013 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 55,968B, BPFP=2.8393 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,568B, BPFP=2.2610 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 55,052B, BPFP=2.7928 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,312B, BPFP=1.9943 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 55,876B, BPFP=2.8346 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 173,708B, BPFP=2.2031 +⌛️ [2/4] FRONTEND: Frontend time: 0.266s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.output: torch.Size([1, 154, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.output: torch.Size([1, 154, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02866718 4.67296174 + layer.0.v_cache 0.00000026 0.00014905 + layer.1.k_cache 0.00317794 0.46681818 + layer.1.v_cache 0.00000104 0.00052495 + layer.2.k_cache 0.00114871 0.26878097 + layer.2.v_cache 0.00000119 0.00075734 + layer.3.k_cache 0.00136607 0.28853716 + layer.3.v_cache 0.00000217 0.00116006 + layer.4.k_cache 0.00338641 0.51848102 + layer.4.v_cache 0.00000303 0.00193144 + layer.4.output 0.00020569 0.05760104 + ------------------------------------------------------------------------------------- + TOTAL 0.00275548 0.46075043 + (elements=2,207,744) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2207744 +Total Bytes 639480 +BPFP 2.3172 bits/point +EBPFP 4.6345 equivalent bits/point +MSE 0.460750 +---------------------- -------------------------------------------------------- +Time: 0.686s Load: 0.009s, Pack+Encode: 0.266s, Decode+Unpack: 0.411s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 154, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4608 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample101-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample101-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample102-layer4-item1.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample102-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 150, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 150, 128) +Output shape: (1, 150, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.0.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.1.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.1.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.2.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.2.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.3.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.3.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.4.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.4.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.4.output: torch.Size([1, 150, 4096]) -> torch.Size([1, 1, 150, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,080B, BPFP=1.2542 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,144B, BPFP=2.7158 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 38,704B, BPFP=2.0158 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 55,324B, BPFP=2.8815 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,444B, BPFP=2.2627 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 55,908B, BPFP=2.9119 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,088B, BPFP=2.2963 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 54,916B, BPFP=2.8602 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,116B, BPFP=2.0373 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 55,892B, BPFP=2.9110 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 173,224B, BPFP=2.2555 +⌛️ [2/4] FRONTEND: Frontend time: 0.263s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 150, 128]) + layer.0.v_cache: torch.Size([1, 8, 150, 128]) + layer.1.k_cache: torch.Size([1, 8, 150, 128]) + layer.1.v_cache: torch.Size([1, 8, 150, 128]) + layer.2.k_cache: torch.Size([1, 8, 150, 128]) + layer.2.v_cache: torch.Size([1, 8, 150, 128]) + layer.3.k_cache: torch.Size([1, 8, 150, 128]) + layer.3.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.k_cache: torch.Size([1, 8, 150, 128]) + layer.4.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.output: torch.Size([1, 150, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.407s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 150, 128]) + layer.0.v_cache: torch.Size([1, 8, 150, 128]) + layer.1.k_cache: torch.Size([1, 8, 150, 128]) + layer.1.v_cache: torch.Size([1, 8, 150, 128]) + layer.2.k_cache: torch.Size([1, 8, 150, 128]) + layer.2.v_cache: torch.Size([1, 8, 150, 128]) + layer.3.k_cache: torch.Size([1, 8, 150, 128]) + layer.3.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.k_cache: torch.Size([1, 8, 150, 128]) + layer.4.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.output: torch.Size([1, 150, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02593268 4.27808919 + layer.0.v_cache 0.00000026 0.00014083 + layer.1.k_cache 0.00305171 0.47285004 + layer.1.v_cache 0.00000092 0.00055688 + layer.2.k_cache 0.00119162 0.25255564 + layer.2.v_cache 0.00000123 0.00082120 + layer.3.k_cache 0.00128981 0.28133906 + layer.3.v_cache 0.00000247 0.00124971 + layer.4.k_cache 0.00345981 0.47829956 + layer.4.v_cache 0.00000333 0.00207372 + layer.4.output 0.00015266 0.05434195 + ------------------------------------------------------------------------------------- + TOTAL 0.00253889 0.42752454 + (elements=2,150,400) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2150400 +Total Bytes 636840 +BPFP 2.3692 bits/point +EBPFP 4.7384 equivalent bits/point +MSE 0.427525 +---------------------- -------------------------------------------------------- +Time: 0.679s Load: 0.008s, Pack+Encode: 0.263s, Decode+Unpack: 0.407s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 150, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4275 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample102-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample102-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample103-layer4-item1.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample103-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 169, 128) +Output shape: (1, 169, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.output: torch.Size([1, 169, 4096]) -> torch.Size([1, 1, 169, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,964B, BPFP=1.2465 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,132B, BPFP=2.6411 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,764B, BPFP=1.9307 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,904B, BPFP=2.7692 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,704B, BPFP=2.1590 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,508B, BPFP=2.7972 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,704B, BPFP=2.2053 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,532B, BPFP=2.7520 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,176B, BPFP=1.9497 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,444B, BPFP=2.7942 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 183,904B, BPFP=2.1254 +⌛️ [2/4] FRONTEND: Frontend time: 0.267s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02810763 5.39194033 + layer.0.v_cache 0.00000027 0.00015591 + layer.1.k_cache 0.00304808 0.45676129 + layer.1.v_cache 0.00000079 0.00051604 + layer.2.k_cache 0.00115532 0.25099281 + layer.2.v_cache 0.00000117 0.00073922 + layer.3.k_cache 0.00130126 0.28362741 + layer.3.v_cache 0.00000227 0.00113442 + layer.4.k_cache 0.00349612 0.49322857 + layer.4.v_cache 0.00000313 0.00196137 + layer.4.output 0.00017973 0.04219008 + ------------------------------------------------------------------------------------- + TOTAL 0.00270250 0.50355841 + (elements=2,422,784) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2422784 +Total Bytes 686736 +BPFP 2.2676 bits/point +EBPFP 4.5352 equivalent bits/point +MSE 0.503558 +---------------------- -------------------------------------------------------- +Time: 0.689s Load: 0.012s, Pack+Encode: 0.267s, Decode+Unpack: 0.411s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5036 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample103-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample103-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample105-layer4-item1.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample105-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 150, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 150, 128) +Output shape: (1, 150, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.0.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.1.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.1.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.2.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.2.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.3.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.3.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.4.k_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.4.v_cache: torch.Size([1, 8, 150, 128]) -> torch.Size([1, 1, 150, 1024]) + layer.4.output: torch.Size([1, 150, 4096]) -> torch.Size([1, 1, 150, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 23,896B, BPFP=1.2446 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,124B, BPFP=2.7148 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 38,336B, BPFP=1.9967 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 54,712B, BPFP=2.8496 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 42,816B, BPFP=2.2300 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 55,136B, BPFP=2.8717 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 43,532B, BPFP=2.2673 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 54,340B, BPFP=2.8302 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 38,588B, BPFP=2.0098 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 55,292B, BPFP=2.8798 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 168,820B, BPFP=2.1982 +⌛️ [2/4] FRONTEND: Frontend time: 0.269s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 150, 128]) + layer.0.v_cache: torch.Size([1, 8, 150, 128]) + layer.1.k_cache: torch.Size([1, 8, 150, 128]) + layer.1.v_cache: torch.Size([1, 8, 150, 128]) + layer.2.k_cache: torch.Size([1, 8, 150, 128]) + layer.2.v_cache: torch.Size([1, 8, 150, 128]) + layer.3.k_cache: torch.Size([1, 8, 150, 128]) + layer.3.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.k_cache: torch.Size([1, 8, 150, 128]) + layer.4.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.output: torch.Size([1, 150, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.409s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 150, 128]) + layer.0.v_cache: torch.Size([1, 8, 150, 128]) + layer.1.k_cache: torch.Size([1, 8, 150, 128]) + layer.1.v_cache: torch.Size([1, 8, 150, 128]) + layer.2.k_cache: torch.Size([1, 8, 150, 128]) + layer.2.v_cache: torch.Size([1, 8, 150, 128]) + layer.3.k_cache: torch.Size([1, 8, 150, 128]) + layer.3.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.k_cache: torch.Size([1, 8, 150, 128]) + layer.4.v_cache: torch.Size([1, 8, 150, 128]) + layer.4.output: torch.Size([1, 150, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02945780 4.81071899 + layer.0.v_cache 0.00000027 0.00014643 + layer.1.k_cache 0.00309326 0.47446762 + layer.1.v_cache 0.00000086 0.00051141 + layer.2.k_cache 0.00115289 0.25491666 + layer.2.v_cache 0.00000113 0.00073355 + layer.3.k_cache 0.00132321 0.28666827 + layer.3.v_cache 0.00000214 0.00112132 + layer.4.k_cache 0.00338825 0.48143473 + layer.4.v_cache 0.00000324 0.00201827 + layer.4.output 0.00014331 0.05270640 + ------------------------------------------------------------------------------------- + TOTAL 0.00278545 0.46596878 + (elements=2,150,400) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2150400 +Total Bytes 627592 +BPFP 2.3348 bits/point +EBPFP 4.6696 equivalent bits/point +MSE 0.465969 +---------------------- -------------------------------------------------------- +Time: 0.688s Load: 0.010s, Pack+Encode: 0.269s, Decode+Unpack: 0.409s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 150, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 150, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4660 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample105-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample105-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample106-layer4-item1.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample106-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 178, 128) +Output shape: (1, 178, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.output: torch.Size([1, 178, 4096]) -> torch.Size([1, 1, 178, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,452B, BPFP=1.2049 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,768B, BPFP=2.5355 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,160B, BPFP=1.8943 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,600B, BPFP=2.6598 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,000B, BPFP=2.1067 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,524B, BPFP=2.7003 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,364B, BPFP=2.1666 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,740B, BPFP=2.6659 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,580B, BPFP=1.9127 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,512B, BPFP=2.6998 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 195,164B, BPFP=2.1415 +⌛️ [2/4] FRONTEND: Frontend time: 0.267s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.413s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02793351 4.99948977 + layer.0.v_cache 0.00000028 0.00014832 + layer.1.k_cache 0.00336537 0.42726414 + layer.1.v_cache 0.00000081 0.00048638 + layer.2.k_cache 0.00115814 0.24883716 + layer.2.v_cache 0.00000111 0.00069781 + layer.3.k_cache 0.00131051 0.27326325 + layer.3.v_cache 0.00000213 0.00108825 + layer.4.k_cache 0.00357284 0.48502727 + layer.4.v_cache 0.00000305 0.00183280 + layer.4.output 0.00019607 0.04404358 + ------------------------------------------------------------------------------------- + TOTAL 0.00272372 0.47245068 + (elements=2,551,808) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2551808 +Total Bytes 708864 +BPFP 2.2223 bits/point +EBPFP 4.4446 equivalent bits/point +MSE 0.472451 +---------------------- -------------------------------------------------------- +Time: 0.691s Load: 0.011s, Pack+Encode: 0.267s, Decode+Unpack: 0.413s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4725 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample106-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample106-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample11-layer4-item1.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample11-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 217, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 217, 128) +Output shape: (1, 217, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.0.v_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.1.k_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.1.v_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.2.k_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.2.v_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.3.k_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.3.v_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.4.k_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.4.v_cache: torch.Size([1, 8, 217, 128]) -> torch.Size([1, 1, 217, 1024]) + layer.4.output: torch.Size([1, 217, 4096]) -> torch.Size([1, 1, 217, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 33,792B, BPFP=1.2166 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 71,844B, BPFP=2.5865 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 53,492B, BPFP=1.9258 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 75,840B, BPFP=2.7304 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 59,700B, BPFP=2.1493 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 76,780B, BPFP=2.7643 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 61,016B, BPFP=2.1967 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 75,916B, BPFP=2.7332 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 54,128B, BPFP=1.9487 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 76,760B, BPFP=2.7635 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 245,384B, BPFP=2.2086 +⌛️ [2/4] FRONTEND: Frontend time: 0.378s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 217, 128]) + layer.0.v_cache: torch.Size([1, 8, 217, 128]) + layer.1.k_cache: torch.Size([1, 8, 217, 128]) + layer.1.v_cache: torch.Size([1, 8, 217, 128]) + layer.2.k_cache: torch.Size([1, 8, 217, 128]) + layer.2.v_cache: torch.Size([1, 8, 217, 128]) + layer.3.k_cache: torch.Size([1, 8, 217, 128]) + layer.3.v_cache: torch.Size([1, 8, 217, 128]) + layer.4.k_cache: torch.Size([1, 8, 217, 128]) + layer.4.v_cache: torch.Size([1, 8, 217, 128]) + layer.4.output: torch.Size([1, 217, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 217, 128]) + layer.0.v_cache: torch.Size([1, 8, 217, 128]) + layer.1.k_cache: torch.Size([1, 8, 217, 128]) + layer.1.v_cache: torch.Size([1, 8, 217, 128]) + layer.2.k_cache: torch.Size([1, 8, 217, 128]) + layer.2.v_cache: torch.Size([1, 8, 217, 128]) + layer.3.k_cache: torch.Size([1, 8, 217, 128]) + layer.3.v_cache: torch.Size([1, 8, 217, 128]) + layer.4.k_cache: torch.Size([1, 8, 217, 128]) + layer.4.v_cache: torch.Size([1, 8, 217, 128]) + layer.4.output: torch.Size([1, 217, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02570072 4.40970060 + layer.0.v_cache 0.00000028 0.00014092 + layer.1.k_cache 0.00315590 0.45294211 + layer.1.v_cache 0.00000089 0.00053050 + layer.2.k_cache 0.00124266 0.24962338 + layer.2.v_cache 0.00000119 0.00073031 + layer.3.k_cache 0.00129581 0.26685562 + layer.3.v_cache 0.00000363 0.00125606 + layer.4.k_cache 0.00355111 0.48463598 + layer.4.v_cache 0.00000379 0.00198667 + layer.4.output 0.00017838 0.05053327 + ------------------------------------------------------------------------------------- + TOTAL 0.00254782 0.43360966 + (elements=3,110,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3110912 +Total Bytes 884652 +BPFP 2.2750 bits/point +EBPFP 4.5499 equivalent bits/point +MSE 0.433610 +---------------------- -------------------------------------------------------- +Time: 0.911s Load: 0.012s, Pack+Encode: 0.378s, Decode+Unpack: 0.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 217, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 217, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4336 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample11-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample11-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample110-layer4-item1.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample110-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 173, 128) +Output shape: (1, 173, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.output: torch.Size([1, 173, 4096]) -> torch.Size([1, 1, 173, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,280B, BPFP=1.2319 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,876B, BPFP=2.6136 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,564B, BPFP=1.9221 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,572B, BPFP=2.7354 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,460B, BPFP=2.1432 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,356B, BPFP=2.7708 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,672B, BPFP=2.1980 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,412B, BPFP=2.7281 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,092B, BPFP=1.9460 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,196B, BPFP=2.7635 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 195,432B, BPFP=2.2064 +⌛️ [2/4] FRONTEND: Frontend time: 0.267s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.416s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02750326 4.77867241 + layer.0.v_cache 0.00000027 0.00016021 + layer.1.k_cache 0.00310050 0.45267495 + layer.1.v_cache 0.00000106 0.00050917 + layer.2.k_cache 0.00119927 0.25410827 + layer.2.v_cache 0.00000121 0.00080464 + layer.3.k_cache 0.00131341 0.28292236 + layer.3.v_cache 0.00000232 0.00116383 + layer.4.k_cache 0.00354461 0.51004902 + layer.4.v_cache 0.00000318 0.00193631 + layer.4.output 0.00022996 0.05585569 + ------------------------------------------------------------------------------------- + TOTAL 0.00268492 0.46474457 + (elements=2,480,128) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2480128 +Total Bytes 705912 +BPFP 2.2770 bits/point +EBPFP 4.5540 equivalent bits/point +MSE 0.464745 +---------------------- -------------------------------------------------------- +Time: 0.695s Load: 0.012s, Pack+Encode: 0.267s, Decode+Unpack: 0.416s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4647 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample110-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample110-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample111-layer4-item1.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample111-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 178, 128) +Output shape: (1, 178, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.output: torch.Size([1, 178, 4096]) -> torch.Size([1, 1, 178, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,736B, BPFP=1.2173 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,348B, BPFP=2.5609 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,232B, BPFP=1.8975 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,260B, BPFP=2.6887 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,056B, BPFP=2.1092 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,668B, BPFP=2.7066 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,320B, BPFP=2.1647 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,924B, BPFP=2.6740 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,736B, BPFP=1.9196 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,736B, BPFP=2.7096 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 200,420B, BPFP=2.1991 +⌛️ [2/4] FRONTEND: Frontend time: 0.264s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.405s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02635046 4.92153931 + layer.0.v_cache 0.00000029 0.00016228 + layer.1.k_cache 0.00306277 0.42479727 + layer.1.v_cache 0.00000083 0.00053174 + layer.2.k_cache 0.00121657 0.25343558 + layer.2.v_cache 0.00000121 0.00073825 + layer.3.k_cache 0.00128658 0.27404616 + layer.3.v_cache 0.00000219 0.00115414 + layer.4.k_cache 0.00351850 0.48741493 + layer.4.v_cache 0.00000322 0.00196252 + layer.4.output 0.00018894 0.04915206 + ------------------------------------------------------------------------------------- + TOTAL 0.00258560 0.46874217 + (elements=2,551,808) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2551808 +Total Bytes 716436 +BPFP 2.2460 bits/point +EBPFP 4.4921 equivalent bits/point +MSE 0.468742 +---------------------- -------------------------------------------------------- +Time: 0.678s Load: 0.010s, Pack+Encode: 0.264s, Decode+Unpack: 0.405s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4687 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample111-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample111-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample112-layer4-item1.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample112-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 164, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 164, 128) +Output shape: (1, 164, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.0.v_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.1.k_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.1.v_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.2.k_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.2.v_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.3.k_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.3.v_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.4.k_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.4.v_cache: torch.Size([1, 8, 164, 128]) -> torch.Size([1, 1, 164, 1024]) + layer.4.output: torch.Size([1, 164, 4096]) -> torch.Size([1, 1, 164, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,408B, BPFP=1.2580 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 55,788B, BPFP=2.6576 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,168B, BPFP=1.9611 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 58,560B, BPFP=2.7896 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 45,856B, BPFP=2.1845 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,036B, BPFP=2.8123 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 46,912B, BPFP=2.2348 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 58,200B, BPFP=2.7725 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 41,476B, BPFP=1.9758 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,384B, BPFP=2.8289 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 182,424B, BPFP=2.1725 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 164, 128]) + layer.0.v_cache: torch.Size([1, 8, 164, 128]) + layer.1.k_cache: torch.Size([1, 8, 164, 128]) + layer.1.v_cache: torch.Size([1, 8, 164, 128]) + layer.2.k_cache: torch.Size([1, 8, 164, 128]) + layer.2.v_cache: torch.Size([1, 8, 164, 128]) + layer.3.k_cache: torch.Size([1, 8, 164, 128]) + layer.3.v_cache: torch.Size([1, 8, 164, 128]) + layer.4.k_cache: torch.Size([1, 8, 164, 128]) + layer.4.v_cache: torch.Size([1, 8, 164, 128]) + layer.4.output: torch.Size([1, 164, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 164, 128]) + layer.0.v_cache: torch.Size([1, 8, 164, 128]) + layer.1.k_cache: torch.Size([1, 8, 164, 128]) + layer.1.v_cache: torch.Size([1, 8, 164, 128]) + layer.2.k_cache: torch.Size([1, 8, 164, 128]) + layer.2.v_cache: torch.Size([1, 8, 164, 128]) + layer.3.k_cache: torch.Size([1, 8, 164, 128]) + layer.3.v_cache: torch.Size([1, 8, 164, 128]) + layer.4.k_cache: torch.Size([1, 8, 164, 128]) + layer.4.v_cache: torch.Size([1, 8, 164, 128]) + layer.4.output: torch.Size([1, 164, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02515400 4.59993241 + layer.0.v_cache 0.00000027 0.00014501 + layer.1.k_cache 0.00312787 0.43874089 + layer.1.v_cache 0.00000077 0.00048993 + layer.2.k_cache 0.00115327 0.26231010 + layer.2.v_cache 0.00000115 0.00071544 + layer.3.k_cache 0.00131309 0.27926317 + layer.3.v_cache 0.00000211 0.00107936 + layer.4.k_cache 0.00347769 0.49705468 + layer.4.v_cache 0.00000342 0.00196298 + layer.4.output 0.00018780 0.04242913 + ------------------------------------------------------------------------------------- + TOTAL 0.00249892 0.44652932 + (elements=2,351,104) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2351104 +Total Bytes 675212 +BPFP 2.2975 bits/point +EBPFP 4.5950 equivalent bits/point +MSE 0.446529 +---------------------- -------------------------------------------------------- +Time: 0.670s Load: 0.010s, Pack+Encode: 0.260s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 164, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 164, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4465 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample112-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample112-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample114-layer4-item1.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample114-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 144, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 144, 128) +Output shape: (1, 144, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.0.v_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.1.k_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.1.v_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.2.k_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.2.v_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.3.k_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.3.v_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.4.k_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.4.v_cache: torch.Size([1, 8, 144, 128]) -> torch.Size([1, 1, 144, 1024]) + layer.4.output: torch.Size([1, 144, 4096]) -> torch.Size([1, 1, 144, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 22,792B, BPFP=1.2365 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 51,200B, BPFP=2.7778 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 37,760B, BPFP=2.0486 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 53,920B, BPFP=2.9253 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 42,088B, BPFP=2.2834 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 54,192B, BPFP=2.9401 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 42,820B, BPFP=2.3231 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 53,492B, BPFP=2.9021 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 37,664B, BPFP=2.0434 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 54,312B, BPFP=2.9466 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 166,340B, BPFP=2.2561 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 144, 128]) + layer.0.v_cache: torch.Size([1, 8, 144, 128]) + layer.1.k_cache: torch.Size([1, 8, 144, 128]) + layer.1.v_cache: torch.Size([1, 8, 144, 128]) + layer.2.k_cache: torch.Size([1, 8, 144, 128]) + layer.2.v_cache: torch.Size([1, 8, 144, 128]) + layer.3.k_cache: torch.Size([1, 8, 144, 128]) + layer.3.v_cache: torch.Size([1, 8, 144, 128]) + layer.4.k_cache: torch.Size([1, 8, 144, 128]) + layer.4.v_cache: torch.Size([1, 8, 144, 128]) + layer.4.output: torch.Size([1, 144, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 144, 128]) + layer.0.v_cache: torch.Size([1, 8, 144, 128]) + layer.1.k_cache: torch.Size([1, 8, 144, 128]) + layer.1.v_cache: torch.Size([1, 8, 144, 128]) + layer.2.k_cache: torch.Size([1, 8, 144, 128]) + layer.2.v_cache: torch.Size([1, 8, 144, 128]) + layer.3.k_cache: torch.Size([1, 8, 144, 128]) + layer.3.v_cache: torch.Size([1, 8, 144, 128]) + layer.4.k_cache: torch.Size([1, 8, 144, 128]) + layer.4.v_cache: torch.Size([1, 8, 144, 128]) + layer.4.output: torch.Size([1, 144, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02690511 4.13336309 + layer.0.v_cache 0.00000027 0.00014667 + layer.1.k_cache 0.00313749 0.46918742 + layer.1.v_cache 0.00000108 0.00056921 + layer.2.k_cache 0.00118318 0.25824730 + layer.2.v_cache 0.00000114 0.00074005 + layer.3.k_cache 0.00131682 0.27675523 + layer.3.v_cache 0.00000220 0.00121704 + layer.4.k_cache 0.00346269 0.49126588 + layer.4.v_cache 0.00000333 0.00202030 + layer.4.output 0.00016306 0.04280473 + ------------------------------------------------------------------------------------- + TOTAL 0.00261897 0.41462365 + (elements=2,064,384) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2064384 +Total Bytes 616580 +BPFP 2.3894 bits/point +EBPFP 4.7788 equivalent bits/point +MSE 0.414624 +---------------------- -------------------------------------------------------- +Time: 0.666s Load: 0.011s, Pack+Encode: 0.258s, Decode+Unpack: 0.398s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 144, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 144, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4146 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample114-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample114-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample116-layer4-item1.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample116-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 171, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 171, 128) +Output shape: (1, 171, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.output: torch.Size([1, 171, 4096]) -> torch.Size([1, 1, 171, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,364B, BPFP=1.2502 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,116B, BPFP=2.6095 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,288B, BPFP=1.9320 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,456B, BPFP=2.7621 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,032B, BPFP=2.1488 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,552B, BPFP=2.7664 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,084B, BPFP=2.1968 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,924B, BPFP=2.7378 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,560B, BPFP=1.9444 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,876B, BPFP=2.7812 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 186,520B, BPFP=2.1304 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.output: torch.Size([1, 171, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.403s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.output: torch.Size([1, 171, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02584258 4.82255331 + layer.0.v_cache 0.00000026 0.00014465 + layer.1.k_cache 0.00320420 0.43819182 + layer.1.v_cache 0.00000080 0.00052357 + layer.2.k_cache 0.00119855 0.25624357 + layer.2.v_cache 0.00000108 0.00070183 + layer.3.k_cache 0.00129062 0.26877518 + layer.3.v_cache 0.00000212 0.00113513 + layer.4.k_cache 0.00351305 0.50471693 + layer.4.v_cache 0.00000315 0.00201293 + layer.4.output 0.00016406 0.04081076 + ------------------------------------------------------------------------------------- + TOTAL 0.00255090 0.46130300 + (elements=2,451,456) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2451456 +Total Bytes 692772 +BPFP 2.2608 bits/point +EBPFP 4.5215 equivalent bits/point +MSE 0.461303 +---------------------- -------------------------------------------------------- +Time: 0.675s Load: 0.011s, Pack+Encode: 0.260s, Decode+Unpack: 0.403s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 171, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4613 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample116-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample116-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample117-layer4-item1.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample117-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 175, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 175, 128) +Output shape: (1, 175, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.output: torch.Size([1, 175, 4096]) -> torch.Size([1, 1, 175, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,644B, BPFP=1.2341 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,592B, BPFP=2.5711 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,944B, BPFP=1.9171 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,348B, BPFP=2.6941 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,908B, BPFP=2.1387 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,904B, BPFP=2.7189 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,944B, BPFP=2.1850 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,312B, BPFP=2.6925 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,400B, BPFP=1.9375 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,196B, BPFP=2.7320 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 191,148B, BPFP=2.1333 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.output: torch.Size([1, 175, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.output: torch.Size([1, 175, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02705980 4.75391044 + layer.0.v_cache 0.00000027 0.00015456 + layer.1.k_cache 0.00311821 0.45759665 + layer.1.v_cache 0.00000085 0.00052517 + layer.2.k_cache 0.00114782 0.26842176 + layer.2.v_cache 0.00000111 0.00071648 + layer.3.k_cache 0.00131325 0.28685699 + layer.3.v_cache 0.00000221 0.00117311 + layer.4.k_cache 0.00342930 0.51350856 + layer.4.v_cache 0.00000305 0.00197461 + layer.4.output 0.00022257 0.04885064 + ------------------------------------------------------------------------------------- + TOTAL 0.00264044 0.46287435 + (elements=2,508,800) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2508800 +Total Bytes 702340 +BPFP 2.2396 bits/point +EBPFP 4.4792 equivalent bits/point +MSE 0.462874 +---------------------- -------------------------------------------------------- +Time: 0.673s Load: 0.011s, Pack+Encode: 0.259s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 175, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4629 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample117-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample117-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample12-layer4-item1.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample12-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 218, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 218, 128) +Output shape: (1, 218, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.0.v_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.1.k_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.1.v_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.2.k_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.2.v_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.3.k_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.3.v_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.4.k_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.4.v_cache: torch.Size([1, 8, 218, 128]) -> torch.Size([1, 1, 218, 1024]) + layer.4.output: torch.Size([1, 218, 4096]) -> torch.Size([1, 1, 218, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 32,684B, BPFP=1.1713 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 72,284B, BPFP=2.5905 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 53,984B, BPFP=1.9346 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 76,264B, BPFP=2.7331 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 59,760B, BPFP=2.1416 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 76,600B, BPFP=2.7451 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 60,892B, BPFP=2.1822 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 75,468B, BPFP=2.7046 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 53,732B, BPFP=1.9256 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 76,684B, BPFP=2.7481 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 231,596B, BPFP=2.0749 +⌛️ [2/4] FRONTEND: Frontend time: 0.313s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 218, 128]) + layer.0.v_cache: torch.Size([1, 8, 218, 128]) + layer.1.k_cache: torch.Size([1, 8, 218, 128]) + layer.1.v_cache: torch.Size([1, 8, 218, 128]) + layer.2.k_cache: torch.Size([1, 8, 218, 128]) + layer.2.v_cache: torch.Size([1, 8, 218, 128]) + layer.3.k_cache: torch.Size([1, 8, 218, 128]) + layer.3.v_cache: torch.Size([1, 8, 218, 128]) + layer.4.k_cache: torch.Size([1, 8, 218, 128]) + layer.4.v_cache: torch.Size([1, 8, 218, 128]) + layer.4.output: torch.Size([1, 218, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 218, 128]) + layer.0.v_cache: torch.Size([1, 8, 218, 128]) + layer.1.k_cache: torch.Size([1, 8, 218, 128]) + layer.1.v_cache: torch.Size([1, 8, 218, 128]) + layer.2.k_cache: torch.Size([1, 8, 218, 128]) + layer.2.v_cache: torch.Size([1, 8, 218, 128]) + layer.3.k_cache: torch.Size([1, 8, 218, 128]) + layer.3.v_cache: torch.Size([1, 8, 218, 128]) + layer.4.k_cache: torch.Size([1, 8, 218, 128]) + layer.4.v_cache: torch.Size([1, 8, 218, 128]) + layer.4.output: torch.Size([1, 218, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02786819 4.38846245 + layer.0.v_cache 0.00000028 0.00014738 + layer.1.k_cache 0.00304396 0.44049363 + layer.1.v_cache 0.00000081 0.00053621 + layer.2.k_cache 0.00116087 0.26702356 + layer.2.v_cache 0.00000117 0.00072696 + layer.3.k_cache 0.00132673 0.28495040 + layer.3.v_cache 0.00000218 0.00116968 + layer.4.k_cache 0.00351200 0.49155013 + layer.4.v_cache 0.00000322 0.00200037 + layer.4.output 0.00018944 0.04240304 + ------------------------------------------------------------------------------------- + TOTAL 0.00269123 0.43190521 + (elements=3,125,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3125248 +Total Bytes 869948 +BPFP 2.2269 bits/point +EBPFP 4.4538 equivalent bits/point +MSE 0.431905 +---------------------- -------------------------------------------------------- +Time: 0.831s Load: 0.012s, Pack+Encode: 0.313s, Decode+Unpack: 0.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 218, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 218, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4319 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample12-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample12-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample120-layer4-item1.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample120-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 171, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 171, 128) +Output shape: (1, 171, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.output: torch.Size([1, 171, 4096]) -> torch.Size([1, 1, 171, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,016B, BPFP=1.2343 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,428B, BPFP=2.6237 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,500B, BPFP=1.9417 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,148B, BPFP=2.7480 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,136B, BPFP=2.1535 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,412B, BPFP=2.7601 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,200B, BPFP=2.2021 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,608B, BPFP=2.7233 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,568B, BPFP=1.9448 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,408B, BPFP=2.7599 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 185,644B, BPFP=2.1204 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.output: torch.Size([1, 171, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.399s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.output: torch.Size([1, 171, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02655128 4.59619105 + layer.0.v_cache 0.00000026 0.00014198 + layer.1.k_cache 0.00304961 0.44944362 + layer.1.v_cache 0.00000080 0.00051568 + layer.2.k_cache 0.00119340 0.25544453 + layer.2.v_cache 0.00000107 0.00070399 + layer.3.k_cache 0.00127930 0.26868305 + layer.3.v_cache 0.00000211 0.00108528 + layer.4.k_cache 0.00352197 0.50239679 + layer.4.v_cache 0.00000299 0.00183998 + layer.4.output 0.00016153 0.04047564 + ------------------------------------------------------------------------------------- + TOTAL 0.00258921 0.44559632 + (elements=2,451,456) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2451456 +Total Bytes 691068 +BPFP 2.2552 bits/point +EBPFP 4.5104 equivalent bits/point +MSE 0.445596 +---------------------- -------------------------------------------------------- +Time: 0.669s Load: 0.009s, Pack+Encode: 0.260s, Decode+Unpack: 0.399s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 171, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4456 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample120-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample120-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample122-layer4-item1.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample122-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 190, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.013s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 190, 128) +Output shape: (1, 190, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.0.v_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.1.k_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.1.v_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.2.k_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.2.v_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.3.k_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.3.v_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.4.k_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.4.v_cache: torch.Size([1, 8, 190, 128]) -> torch.Size([1, 1, 190, 1024]) + layer.4.output: torch.Size([1, 190, 4096]) -> torch.Size([1, 1, 190, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,676B, BPFP=1.1380 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,776B, BPFP=2.3757 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,000B, BPFP=1.8092 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,280B, BPFP=2.4786 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,416B, BPFP=1.9908 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,432B, BPFP=2.5260 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,964B, BPFP=2.0544 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,784B, BPFP=2.4993 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,392B, BPFP=1.8253 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,508B, BPFP=2.5291 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 196,808B, BPFP=2.0231 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 190, 128]) + layer.0.v_cache: torch.Size([1, 8, 190, 128]) + layer.1.k_cache: torch.Size([1, 8, 190, 128]) + layer.1.v_cache: torch.Size([1, 8, 190, 128]) + layer.2.k_cache: torch.Size([1, 8, 190, 128]) + layer.2.v_cache: torch.Size([1, 8, 190, 128]) + layer.3.k_cache: torch.Size([1, 8, 190, 128]) + layer.3.v_cache: torch.Size([1, 8, 190, 128]) + layer.4.k_cache: torch.Size([1, 8, 190, 128]) + layer.4.v_cache: torch.Size([1, 8, 190, 128]) + layer.4.output: torch.Size([1, 190, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 190, 128]) + layer.0.v_cache: torch.Size([1, 8, 190, 128]) + layer.1.k_cache: torch.Size([1, 8, 190, 128]) + layer.1.v_cache: torch.Size([1, 8, 190, 128]) + layer.2.k_cache: torch.Size([1, 8, 190, 128]) + layer.2.v_cache: torch.Size([1, 8, 190, 128]) + layer.3.k_cache: torch.Size([1, 8, 190, 128]) + layer.3.v_cache: torch.Size([1, 8, 190, 128]) + layer.4.k_cache: torch.Size([1, 8, 190, 128]) + layer.4.v_cache: torch.Size([1, 8, 190, 128]) + layer.4.output: torch.Size([1, 190, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02682430 4.56486367 + layer.0.v_cache 0.00000028 0.00014166 + layer.1.k_cache 0.00318839 0.42947821 + layer.1.v_cache 0.00000076 0.00044318 + layer.2.k_cache 0.00117212 0.23501129 + layer.2.v_cache 0.00000110 0.00064164 + layer.3.k_cache 0.00129629 0.27238733 + layer.3.v_cache 0.00000212 0.00101760 + layer.4.k_cache 0.00366745 0.49836675 + layer.4.v_cache 0.00000296 0.00167839 + layer.4.output 0.00018717 0.03969610 + ------------------------------------------------------------------------------------- + TOTAL 0.00263603 0.44020101 + (elements=2,723,840) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2723840 +Total Bytes 713036 +BPFP 2.0942 bits/point +EBPFP 4.1884 equivalent bits/point +MSE 0.440201 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.013s, Pack+Encode: 0.260s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 190, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 190, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4402 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample122-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample122-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample125-layer4-item1.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample125-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 172, 128) +Output shape: (1, 172, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.output: torch.Size([1, 172, 4096]) -> torch.Size([1, 1, 172, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,620B, BPFP=1.2091 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,172B, BPFP=2.5968 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,172B, BPFP=1.9155 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,776B, BPFP=2.7151 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,772B, BPFP=2.1245 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,488B, BPFP=2.7475 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,088B, BPFP=2.1842 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,824B, BPFP=2.7173 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,480B, BPFP=1.9295 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,836B, BPFP=2.7633 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 189,680B, BPFP=2.1539 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02742122 5.19149567 + layer.0.v_cache 0.00000027 0.00015382 + layer.1.k_cache 0.00311153 0.44567596 + layer.1.v_cache 0.00000078 0.00049236 + layer.2.k_cache 0.00118723 0.25690405 + layer.2.v_cache 0.00000107 0.00069361 + layer.3.k_cache 0.00131758 0.27495103 + layer.3.v_cache 0.00000212 0.00108883 + layer.4.k_cache 0.00355508 0.49213538 + layer.4.v_cache 0.00000305 0.00183851 + layer.4.output 0.00016643 0.04540451 + ------------------------------------------------------------------------------------- + TOTAL 0.00266183 0.48907480 + (elements=2,465,792) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2465792 +Total Bytes 693908 +BPFP 2.2513 bits/point +EBPFP 4.5026 equivalent bits/point +MSE 0.489075 +---------------------- -------------------------------------------------------- +Time: 0.668s Load: 0.010s, Pack+Encode: 0.258s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4891 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample125-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample125-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample126-layer4-item1.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample126-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 165, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 165, 128) +Output shape: (1, 165, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.output: torch.Size([1, 165, 4096]) -> torch.Size([1, 1, 165, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,472B, BPFP=1.2534 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 56,456B, BPFP=2.6731 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,340B, BPFP=1.9574 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,004B, BPFP=2.7938 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,224B, BPFP=2.1886 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,616B, BPFP=2.8227 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,332B, BPFP=2.2411 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 58,824B, BPFP=2.7852 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 41,760B, BPFP=1.9773 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,660B, BPFP=2.8248 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,380B, BPFP=2.2536 +⌛️ [2/4] FRONTEND: Frontend time: 0.264s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.output: torch.Size([1, 165, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.output: torch.Size([1, 165, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02563144 5.36925382 + layer.0.v_cache 0.00000028 0.00016357 + layer.1.k_cache 0.00322103 0.46451176 + layer.1.v_cache 0.00000086 0.00051841 + layer.2.k_cache 0.00118144 0.26392212 + layer.2.v_cache 0.00000111 0.00073015 + layer.3.k_cache 0.00130833 0.29035395 + layer.3.v_cache 0.00000230 0.00123220 + layer.4.k_cache 0.00357125 0.51872059 + layer.4.v_cache 0.00000311 0.00195266 + layer.4.output 0.00020081 0.05736919 + ------------------------------------------------------------------------------------- + TOTAL 0.00255174 0.51005971 + (elements=2,365,440) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2365440 +Total Bytes 687068 +BPFP 2.3237 bits/point +EBPFP 4.6474 equivalent bits/point +MSE 0.510060 +---------------------- -------------------------------------------------------- +Time: 0.675s Load: 0.011s, Pack+Encode: 0.264s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 165, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5101 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample126-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample126-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample129-layer4-item1.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample129-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 174, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 174, 128) +Output shape: (1, 174, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.0.v_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.1.k_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.1.v_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.2.k_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.2.v_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.3.k_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.3.v_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.4.k_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.4.v_cache: torch.Size([1, 8, 174, 128]) -> torch.Size([1, 1, 174, 1024]) + layer.4.output: torch.Size([1, 174, 4096]) -> torch.Size([1, 1, 174, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,064B, BPFP=1.2152 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,532B, BPFP=2.5832 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,664B, BPFP=1.9156 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,268B, BPFP=2.7060 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,336B, BPFP=2.1254 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,740B, BPFP=2.7272 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,396B, BPFP=2.1730 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,952B, BPFP=2.6918 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,896B, BPFP=1.9260 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,868B, BPFP=2.7329 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 188,588B, BPFP=2.1169 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 174, 128]) + layer.0.v_cache: torch.Size([1, 8, 174, 128]) + layer.1.k_cache: torch.Size([1, 8, 174, 128]) + layer.1.v_cache: torch.Size([1, 8, 174, 128]) + layer.2.k_cache: torch.Size([1, 8, 174, 128]) + layer.2.v_cache: torch.Size([1, 8, 174, 128]) + layer.3.k_cache: torch.Size([1, 8, 174, 128]) + layer.3.v_cache: torch.Size([1, 8, 174, 128]) + layer.4.k_cache: torch.Size([1, 8, 174, 128]) + layer.4.v_cache: torch.Size([1, 8, 174, 128]) + layer.4.output: torch.Size([1, 174, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 174, 128]) + layer.0.v_cache: torch.Size([1, 8, 174, 128]) + layer.1.k_cache: torch.Size([1, 8, 174, 128]) + layer.1.v_cache: torch.Size([1, 8, 174, 128]) + layer.2.k_cache: torch.Size([1, 8, 174, 128]) + layer.2.v_cache: torch.Size([1, 8, 174, 128]) + layer.3.k_cache: torch.Size([1, 8, 174, 128]) + layer.3.v_cache: torch.Size([1, 8, 174, 128]) + layer.4.k_cache: torch.Size([1, 8, 174, 128]) + layer.4.v_cache: torch.Size([1, 8, 174, 128]) + layer.4.output: torch.Size([1, 174, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02884222 4.82711757 + layer.0.v_cache 0.00000026 0.00014753 + layer.1.k_cache 0.00305335 0.45482842 + layer.1.v_cache 0.00000075 0.00047210 + layer.2.k_cache 0.00114914 0.24788703 + layer.2.v_cache 0.00000112 0.00067150 + layer.3.k_cache 0.00130410 0.28100974 + layer.3.v_cache 0.00000204 0.00104963 + layer.4.k_cache 0.00362435 0.49924570 + layer.4.v_cache 0.00000301 0.00172342 + layer.4.output 0.00017507 0.04958478 + ------------------------------------------------------------------------------------- + TOTAL 0.00276290 0.46517798 + (elements=2,494,464) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2494464 +Total Bytes 696304 +BPFP 2.2331 bits/point +EBPFP 4.4662 equivalent bits/point +MSE 0.465178 +---------------------- -------------------------------------------------------- +Time: 0.670s Load: 0.010s, Pack+Encode: 0.260s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 174, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 174, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4652 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample129-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample129-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample130-layer4-item1.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample130-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 152, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 152, 128) +Output shape: (1, 152, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.0.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.1.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.1.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.2.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.2.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.3.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.3.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.4.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.4.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.4.output: torch.Size([1, 152, 4096]) -> torch.Size([1, 1, 152, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,912B, BPFP=1.2804 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,328B, BPFP=2.6896 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 38,860B, BPFP=1.9973 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 55,140B, BPFP=2.8341 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,348B, BPFP=2.2280 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 55,772B, BPFP=2.8666 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,156B, BPFP=2.2695 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 54,624B, BPFP=2.8076 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 38,952B, BPFP=2.0021 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 55,616B, BPFP=2.8586 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 172,368B, BPFP=2.2148 +⌛️ [2/4] FRONTEND: Frontend time: 0.257s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 152, 128]) + layer.0.v_cache: torch.Size([1, 8, 152, 128]) + layer.1.k_cache: torch.Size([1, 8, 152, 128]) + layer.1.v_cache: torch.Size([1, 8, 152, 128]) + layer.2.k_cache: torch.Size([1, 8, 152, 128]) + layer.2.v_cache: torch.Size([1, 8, 152, 128]) + layer.3.k_cache: torch.Size([1, 8, 152, 128]) + layer.3.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.k_cache: torch.Size([1, 8, 152, 128]) + layer.4.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.output: torch.Size([1, 152, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 152, 128]) + layer.0.v_cache: torch.Size([1, 8, 152, 128]) + layer.1.k_cache: torch.Size([1, 8, 152, 128]) + layer.1.v_cache: torch.Size([1, 8, 152, 128]) + layer.2.k_cache: torch.Size([1, 8, 152, 128]) + layer.2.v_cache: torch.Size([1, 8, 152, 128]) + layer.3.k_cache: torch.Size([1, 8, 152, 128]) + layer.3.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.k_cache: torch.Size([1, 8, 152, 128]) + layer.4.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.output: torch.Size([1, 152, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02732493 4.73196210 + layer.0.v_cache 0.00000027 0.00014836 + layer.1.k_cache 0.00311055 0.45005658 + layer.1.v_cache 0.00000090 0.00054378 + layer.2.k_cache 0.00122034 0.25876371 + layer.2.v_cache 0.00000121 0.00078363 + layer.3.k_cache 0.00130561 0.28264666 + layer.3.v_cache 0.00000231 0.00122278 + layer.4.k_cache 0.00351005 0.49305991 + layer.4.v_cache 0.00000314 0.00198798 + layer.4.output 0.00016281 0.04135112 + ------------------------------------------------------------------------------------- + TOTAL 0.00265218 0.45618428 + (elements=2,179,072) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2179072 +Total Bytes 636076 +BPFP 2.3352 bits/point +EBPFP 4.6704 equivalent bits/point +MSE 0.456184 +---------------------- -------------------------------------------------------- +Time: 0.664s Load: 0.008s, Pack+Encode: 0.257s, Decode+Unpack: 0.398s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 152, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4562 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample130-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample130-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample144-layer4-item1.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample144-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 154, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 154, 128) +Output shape: (1, 154, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.output: torch.Size([1, 154, 4096]) -> torch.Size([1, 1, 154, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,136B, BPFP=1.2244 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,592B, BPFP=2.6680 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,068B, BPFP=1.9819 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 55,256B, BPFP=2.8032 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,248B, BPFP=2.1940 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 55,832B, BPFP=2.8324 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,436B, BPFP=2.2543 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 55,032B, BPFP=2.7918 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,356B, BPFP=1.9966 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 56,000B, BPFP=2.8409 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 173,956B, BPFP=2.2062 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.output: torch.Size([1, 154, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.output: torch.Size([1, 154, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02760743 4.45968430 + layer.0.v_cache 0.00000026 0.00014378 + layer.1.k_cache 0.00317114 0.46283217 + layer.1.v_cache 0.00000086 0.00048734 + layer.2.k_cache 0.00117006 0.25397972 + layer.2.v_cache 0.00000109 0.00068164 + layer.3.k_cache 0.00134568 0.28022494 + layer.3.v_cache 0.00000205 0.00109832 + layer.4.k_cache 0.00345514 0.50331086 + layer.4.v_cache 0.00000304 0.00189151 + layer.4.output 0.00019092 0.05415227 + ------------------------------------------------------------------------------------- + TOTAL 0.00268003 0.44149598 + (elements=2,207,744) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2207744 +Total Bytes 638912 +BPFP 2.3152 bits/point +EBPFP 4.6303 equivalent bits/point +MSE 0.441496 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.010s, Pack+Encode: 0.258s, Decode+Unpack: 0.398s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 154, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4415 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample144-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample144-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample147-layer4-item1.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample147-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 165, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 165, 128) +Output shape: (1, 165, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.output: torch.Size([1, 165, 4096]) -> torch.Size([1, 1, 165, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,672B, BPFP=1.2629 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 55,944B, BPFP=2.6489 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,232B, BPFP=1.9523 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 58,852B, BPFP=2.7866 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 45,828B, BPFP=2.1699 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,512B, BPFP=2.8178 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,312B, BPFP=2.2402 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 58,432B, BPFP=2.7667 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 41,780B, BPFP=1.9782 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,464B, BPFP=2.8155 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 183,796B, BPFP=2.1756 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.output: torch.Size([1, 165, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.output: torch.Size([1, 165, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02663295 5.13421039 + layer.0.v_cache 0.00000027 0.00015005 + layer.1.k_cache 0.00314363 0.47160584 + layer.1.v_cache 0.00000082 0.00049155 + layer.2.k_cache 0.00118021 0.25937715 + layer.2.v_cache 0.00000116 0.00071804 + layer.3.k_cache 0.00128344 0.28000872 + layer.3.v_cache 0.00000214 0.00107324 + layer.4.k_cache 0.00361995 0.51501386 + layer.4.v_cache 0.00000298 0.00182493 + layer.4.output 0.00016477 0.04971175 + ------------------------------------------------------------------------------------- + TOTAL 0.00260904 0.49023720 + (elements=2,365,440) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2365440 +Total Bytes 678824 +BPFP 2.2958 bits/point +EBPFP 4.5916 equivalent bits/point +MSE 0.490237 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.009s, Pack+Encode: 0.258s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 165, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4902 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample147-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample147-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample15-layer4-item1.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample15-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 209, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 209, 128) +Output shape: (1, 209, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.0.v_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.1.k_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.1.v_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.2.k_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.2.v_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.3.k_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.3.v_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.4.k_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.4.v_cache: torch.Size([1, 8, 209, 128]) -> torch.Size([1, 1, 209, 1024]) + layer.4.output: torch.Size([1, 209, 4096]) -> torch.Size([1, 1, 209, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 30,084B, BPFP=1.1246 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 70,128B, BPFP=2.6214 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 51,820B, BPFP=1.9371 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 73,936B, BPFP=2.7638 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 57,812B, BPFP=2.1610 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 74,400B, BPFP=2.7811 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 58,876B, BPFP=2.2008 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 73,484B, BPFP=2.7469 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 51,952B, BPFP=1.9420 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 74,696B, BPFP=2.7922 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 223,148B, BPFP=2.0853 +⌛️ [2/4] FRONTEND: Frontend time: 0.309s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 209, 128]) + layer.0.v_cache: torch.Size([1, 8, 209, 128]) + layer.1.k_cache: torch.Size([1, 8, 209, 128]) + layer.1.v_cache: torch.Size([1, 8, 209, 128]) + layer.2.k_cache: torch.Size([1, 8, 209, 128]) + layer.2.v_cache: torch.Size([1, 8, 209, 128]) + layer.3.k_cache: torch.Size([1, 8, 209, 128]) + layer.3.v_cache: torch.Size([1, 8, 209, 128]) + layer.4.k_cache: torch.Size([1, 8, 209, 128]) + layer.4.v_cache: torch.Size([1, 8, 209, 128]) + layer.4.output: torch.Size([1, 209, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 209, 128]) + layer.0.v_cache: torch.Size([1, 8, 209, 128]) + layer.1.k_cache: torch.Size([1, 8, 209, 128]) + layer.1.v_cache: torch.Size([1, 8, 209, 128]) + layer.2.k_cache: torch.Size([1, 8, 209, 128]) + layer.2.v_cache: torch.Size([1, 8, 209, 128]) + layer.3.k_cache: torch.Size([1, 8, 209, 128]) + layer.3.v_cache: torch.Size([1, 8, 209, 128]) + layer.4.k_cache: torch.Size([1, 8, 209, 128]) + layer.4.v_cache: torch.Size([1, 8, 209, 128]) + layer.4.output: torch.Size([1, 209, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02572924 4.98420620 + layer.0.v_cache 0.00000027 0.00014705 + layer.1.k_cache 0.00299828 0.43904895 + layer.1.v_cache 0.00000076 0.00046438 + layer.2.k_cache 0.00126581 0.25728357 + layer.2.v_cache 0.00000114 0.00066834 + layer.3.k_cache 0.00131754 0.27062267 + layer.3.v_cache 0.00000208 0.00107829 + layer.4.k_cache 0.00354781 0.48249828 + layer.4.v_cache 0.00000302 0.00179254 + layer.4.output 0.00016605 0.04087235 + ------------------------------------------------------------------------------------- + TOTAL 0.00253787 0.47152141 + (elements=2,996,224) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2996224 +Total Bytes 840336 +BPFP 2.2437 bits/point +EBPFP 4.4874 equivalent bits/point +MSE 0.471521 +---------------------- -------------------------------------------------------- +Time: 0.822s Load: 0.011s, Pack+Encode: 0.309s, Decode+Unpack: 0.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 209, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 209, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4715 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample15-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample15-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample152-layer4-item1.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample152-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 171, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 171, 128) +Output shape: (1, 171, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) -> torch.Size([1, 1, 171, 1024]) + layer.4.output: torch.Size([1, 171, 4096]) -> torch.Size([1, 1, 171, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,920B, BPFP=1.2756 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,560B, BPFP=2.6298 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,360B, BPFP=1.9353 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,384B, BPFP=2.7588 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,104B, BPFP=2.1520 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,800B, BPFP=2.7778 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,384B, BPFP=2.2105 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,816B, BPFP=2.7328 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,648B, BPFP=1.9485 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,768B, BPFP=2.7763 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 193,156B, BPFP=2.2062 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.output: torch.Size([1, 171, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.403s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 171, 128]) + layer.0.v_cache: torch.Size([1, 8, 171, 128]) + layer.1.k_cache: torch.Size([1, 8, 171, 128]) + layer.1.v_cache: torch.Size([1, 8, 171, 128]) + layer.2.k_cache: torch.Size([1, 8, 171, 128]) + layer.2.v_cache: torch.Size([1, 8, 171, 128]) + layer.3.k_cache: torch.Size([1, 8, 171, 128]) + layer.3.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.k_cache: torch.Size([1, 8, 171, 128]) + layer.4.v_cache: torch.Size([1, 8, 171, 128]) + layer.4.output: torch.Size([1, 171, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02710774 4.62869031 + layer.0.v_cache 0.00000028 0.00016098 + layer.1.k_cache 0.00313176 0.44729882 + layer.1.v_cache 0.00000087 0.00055535 + layer.2.k_cache 0.00119500 0.25227041 + layer.2.v_cache 0.00000129 0.00076513 + layer.3.k_cache 0.00128502 0.28020138 + layer.3.v_cache 0.00000223 0.00122040 + layer.4.k_cache 0.00358421 0.50254643 + layer.4.v_cache 0.00000308 0.00187584 + layer.4.output 0.00020665 0.04575746 + ------------------------------------------------------------------------------------- + TOTAL 0.00265272 0.44990106 + (elements=2,451,456) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2451456 +Total Bytes 700900 +BPFP 2.2873 bits/point +EBPFP 4.5746 equivalent bits/point +MSE 0.449901 +---------------------- -------------------------------------------------------- +Time: 0.675s Load: 0.010s, Pack+Encode: 0.262s, Decode+Unpack: 0.403s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 171, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 171, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4499 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample152-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample152-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample16-layer4-item1.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample16-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 185, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 185, 128) +Output shape: (1, 185, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.output: torch.Size([1, 185, 4096]) -> torch.Size([1, 1, 185, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,796B, BPFP=1.2160 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,120B, BPFP=2.4966 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,288B, BPFP=1.8703 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,756B, BPFP=2.6079 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,988B, BPFP=2.0688 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,392B, BPFP=2.6348 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,628B, BPFP=2.0958 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,880B, BPFP=2.6132 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,772B, BPFP=1.8907 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,304B, BPFP=2.6311 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 199,672B, BPFP=2.1080 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.output: torch.Size([1, 185, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.output: torch.Size([1, 185, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02889625 4.68586558 + layer.0.v_cache 0.00000026 0.00015028 + layer.1.k_cache 0.00305597 0.45105517 + layer.1.v_cache 0.00000081 0.00052306 + layer.2.k_cache 0.00119284 0.25677696 + layer.2.v_cache 0.00000119 0.00075948 + layer.3.k_cache 0.00128722 0.27194333 + layer.3.v_cache 0.00000243 0.00121507 + layer.4.k_cache 0.00400834 0.49051208 + layer.4.v_cache 0.00000313 0.00196589 + layer.4.output 0.00018370 0.04497905 + ------------------------------------------------------------------------------------- + TOTAL 0.00279880 0.45290594 + (elements=2,652,160) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2652160 +Total Bytes 723596 +BPFP 2.1827 bits/point +EBPFP 4.3653 equivalent bits/point +MSE 0.452906 +---------------------- -------------------------------------------------------- +Time: 0.673s Load: 0.010s, Pack+Encode: 0.259s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 185, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4529 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample16-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample16-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample162-layer4-item1.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample162-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 139, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 139, 128) +Output shape: (1, 139, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.0.v_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.1.k_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.1.v_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.2.k_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.2.v_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.3.k_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.3.v_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.4.k_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.4.v_cache: torch.Size([1, 8, 139, 128]) -> torch.Size([1, 1, 139, 1024]) + layer.4.output: torch.Size([1, 139, 4096]) -> torch.Size([1, 1, 139, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 22,168B, BPFP=1.2460 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 49,968B, BPFP=2.8085 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 36,856B, BPFP=2.0715 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 52,392B, BPFP=2.9447 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 41,156B, BPFP=2.3132 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 53,224B, BPFP=2.9915 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 42,160B, BPFP=2.3696 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 52,136B, BPFP=2.9303 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 37,084B, BPFP=2.0843 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 53,248B, BPFP=2.9928 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 165,308B, BPFP=2.3228 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 139, 128]) + layer.0.v_cache: torch.Size([1, 8, 139, 128]) + layer.1.k_cache: torch.Size([1, 8, 139, 128]) + layer.1.v_cache: torch.Size([1, 8, 139, 128]) + layer.2.k_cache: torch.Size([1, 8, 139, 128]) + layer.2.v_cache: torch.Size([1, 8, 139, 128]) + layer.3.k_cache: torch.Size([1, 8, 139, 128]) + layer.3.v_cache: torch.Size([1, 8, 139, 128]) + layer.4.k_cache: torch.Size([1, 8, 139, 128]) + layer.4.v_cache: torch.Size([1, 8, 139, 128]) + layer.4.output: torch.Size([1, 139, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 139, 128]) + layer.0.v_cache: torch.Size([1, 8, 139, 128]) + layer.1.k_cache: torch.Size([1, 8, 139, 128]) + layer.1.v_cache: torch.Size([1, 8, 139, 128]) + layer.2.k_cache: torch.Size([1, 8, 139, 128]) + layer.2.v_cache: torch.Size([1, 8, 139, 128]) + layer.3.k_cache: torch.Size([1, 8, 139, 128]) + layer.3.v_cache: torch.Size([1, 8, 139, 128]) + layer.4.k_cache: torch.Size([1, 8, 139, 128]) + layer.4.v_cache: torch.Size([1, 8, 139, 128]) + layer.4.output: torch.Size([1, 139, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03086843 4.70696846 + layer.0.v_cache 0.00000027 0.00015385 + layer.1.k_cache 0.00318250 0.46789210 + layer.1.v_cache 0.00000093 0.00054218 + layer.2.k_cache 0.00113687 0.25995677 + layer.2.v_cache 0.00000113 0.00078142 + layer.3.k_cache 0.00135365 0.28709697 + layer.3.v_cache 0.00000214 0.00118348 + layer.4.k_cache 0.00345519 0.52674350 + layer.4.v_cache 0.00000310 0.00198180 + layer.4.output 0.00019842 0.05476759 + ------------------------------------------------------------------------------------- + TOTAL 0.00291414 0.46231221 + (elements=1,992,704) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1992704 +Total Bytes 605700 +BPFP 2.4317 bits/point +EBPFP 4.8633 equivalent bits/point +MSE 0.462312 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.010s, Pack+Encode: 0.258s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 139, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 139, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4623 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample162-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample162-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample166-layer4-item1.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample166-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 172, 128) +Output shape: (1, 172, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.output: torch.Size([1, 172, 4096]) -> torch.Size([1, 1, 172, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,756B, BPFP=1.2153 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,004B, BPFP=2.5892 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,360B, BPFP=1.9241 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,724B, BPFP=2.7128 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,068B, BPFP=2.1379 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,368B, BPFP=2.7420 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,300B, BPFP=2.1939 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,712B, BPFP=2.7122 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,536B, BPFP=1.9320 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,456B, BPFP=2.7460 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,568B, BPFP=2.1640 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02678066 5.19833551 + layer.0.v_cache 0.00000028 0.00016026 + layer.1.k_cache 0.00311261 0.45663590 + layer.1.v_cache 0.00000082 0.00053803 + layer.2.k_cache 0.00118278 0.28040021 + layer.2.v_cache 0.00000114 0.00073129 + layer.3.k_cache 0.00135865 0.29884498 + layer.3.v_cache 0.00000220 0.00123969 + layer.4.k_cache 0.00351030 0.54111068 + layer.4.v_cache 0.00000304 0.00196210 + layer.4.output 0.00023867 0.05103150 + ------------------------------------------------------------------------------------- + TOTAL 0.00263622 0.49886319 + (elements=2,465,792) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2465792 +Total Bytes 694852 +BPFP 2.2544 bits/point +EBPFP 4.5087 equivalent bits/point +MSE 0.498863 +---------------------- -------------------------------------------------------- +Time: 0.674s Load: 0.011s, Pack+Encode: 0.259s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4989 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample166-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample166-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample175-layer4-item1.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample175-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 213, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 213, 128) +Output shape: (1, 213, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.0.v_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.1.k_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.1.v_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.2.k_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.2.v_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.3.k_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.3.v_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.4.k_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.4.v_cache: torch.Size([1, 8, 213, 128]) -> torch.Size([1, 1, 213, 1024]) + layer.4.output: torch.Size([1, 213, 4096]) -> torch.Size([1, 1, 213, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,240B, BPFP=1.0725 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 69,408B, BPFP=2.5458 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 51,620B, BPFP=1.8933 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 72,944B, BPFP=2.6755 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 57,656B, BPFP=2.1147 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 74,636B, BPFP=2.7375 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 59,832B, BPFP=2.1945 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 73,828B, BPFP=2.7079 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 51,876B, BPFP=1.9027 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 74,884B, BPFP=2.7466 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 226,224B, BPFP=2.0744 +⌛️ [2/4] FRONTEND: Frontend time: 0.309s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 213, 128]) + layer.0.v_cache: torch.Size([1, 8, 213, 128]) + layer.1.k_cache: torch.Size([1, 8, 213, 128]) + layer.1.v_cache: torch.Size([1, 8, 213, 128]) + layer.2.k_cache: torch.Size([1, 8, 213, 128]) + layer.2.v_cache: torch.Size([1, 8, 213, 128]) + layer.3.k_cache: torch.Size([1, 8, 213, 128]) + layer.3.v_cache: torch.Size([1, 8, 213, 128]) + layer.4.k_cache: torch.Size([1, 8, 213, 128]) + layer.4.v_cache: torch.Size([1, 8, 213, 128]) + layer.4.output: torch.Size([1, 213, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 213, 128]) + layer.0.v_cache: torch.Size([1, 8, 213, 128]) + layer.1.k_cache: torch.Size([1, 8, 213, 128]) + layer.1.v_cache: torch.Size([1, 8, 213, 128]) + layer.2.k_cache: torch.Size([1, 8, 213, 128]) + layer.2.v_cache: torch.Size([1, 8, 213, 128]) + layer.3.k_cache: torch.Size([1, 8, 213, 128]) + layer.3.v_cache: torch.Size([1, 8, 213, 128]) + layer.4.k_cache: torch.Size([1, 8, 213, 128]) + layer.4.v_cache: torch.Size([1, 8, 213, 128]) + layer.4.output: torch.Size([1, 213, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02775377 4.80331177 + layer.0.v_cache 0.00000028 0.00014474 + layer.1.k_cache 0.00299314 0.44431348 + layer.1.v_cache 0.00000076 0.00043151 + layer.2.k_cache 0.00116592 0.23904165 + layer.2.v_cache 0.00000113 0.00063404 + layer.3.k_cache 0.00131338 0.26917408 + layer.3.v_cache 0.00000208 0.00100639 + layer.4.k_cache 0.00376460 0.48698490 + layer.4.v_cache 0.00000302 0.00163607 + layer.4.output 0.00016925 0.04561985 + ------------------------------------------------------------------------------------- + TOTAL 0.00269108 0.45922557 + (elements=3,053,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3053568 +Total Bytes 842148 +BPFP 2.2063 bits/point +EBPFP 4.4127 equivalent bits/point +MSE 0.459226 +---------------------- -------------------------------------------------------- +Time: 0.827s Load: 0.011s, Pack+Encode: 0.309s, Decode+Unpack: 0.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 213, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 213, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4592 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample175-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample175-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample18-layer4-item1.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample18-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 179, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 179, 128) +Output shape: (1, 179, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.0.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.1.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.1.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.2.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.2.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.3.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.3.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.4.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.4.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.4.output: torch.Size([1, 179, 4096]) -> torch.Size([1, 1, 179, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,600B, BPFP=1.2483 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,876B, BPFP=2.5697 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,704B, BPFP=1.9075 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,616B, BPFP=2.6892 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,732B, BPFP=2.1269 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,060B, BPFP=2.7086 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,484B, BPFP=2.1597 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,364B, BPFP=2.6782 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,340B, BPFP=1.9352 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,296B, BPFP=2.7189 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 199,144B, BPFP=2.1729 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 179, 128]) + layer.0.v_cache: torch.Size([1, 8, 179, 128]) + layer.1.k_cache: torch.Size([1, 8, 179, 128]) + layer.1.v_cache: torch.Size([1, 8, 179, 128]) + layer.2.k_cache: torch.Size([1, 8, 179, 128]) + layer.2.v_cache: torch.Size([1, 8, 179, 128]) + layer.3.k_cache: torch.Size([1, 8, 179, 128]) + layer.3.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.k_cache: torch.Size([1, 8, 179, 128]) + layer.4.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.output: torch.Size([1, 179, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.403s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 179, 128]) + layer.0.v_cache: torch.Size([1, 8, 179, 128]) + layer.1.k_cache: torch.Size([1, 8, 179, 128]) + layer.1.v_cache: torch.Size([1, 8, 179, 128]) + layer.2.k_cache: torch.Size([1, 8, 179, 128]) + layer.2.v_cache: torch.Size([1, 8, 179, 128]) + layer.3.k_cache: torch.Size([1, 8, 179, 128]) + layer.3.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.k_cache: torch.Size([1, 8, 179, 128]) + layer.4.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.output: torch.Size([1, 179, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02728191 4.52553111 + layer.0.v_cache 0.00000029 0.00014575 + layer.1.k_cache 0.00306154 0.40888142 + layer.1.v_cache 0.00000084 0.00053237 + layer.2.k_cache 0.00116357 0.24587883 + layer.2.v_cache 0.00000127 0.00074433 + layer.3.k_cache 0.00133958 0.27282101 + layer.3.v_cache 0.00000230 0.00121160 + layer.4.k_cache 0.00349468 0.48299250 + layer.4.v_cache 0.00000328 0.00210808 + layer.4.output 0.00017175 0.04035069 + ------------------------------------------------------------------------------------- + TOTAL 0.00264545 0.43587498 + (elements=2,566,144) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2566144 +Total Bytes 720216 +BPFP 2.2453 bits/point +EBPFP 4.4906 equivalent bits/point +MSE 0.435875 +---------------------- -------------------------------------------------------- +Time: 0.673s Load: 0.011s, Pack+Encode: 0.260s, Decode+Unpack: 0.403s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 179, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4359 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample18-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample18-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample19-layer4-item1.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample19-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 180, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 180, 128) +Output shape: (1, 180, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.0.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.1.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.1.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.2.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.2.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.3.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.3.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.4.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.4.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.4.output: torch.Size([1, 180, 4096]) -> torch.Size([1, 1, 180, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,448B, BPFP=1.2347 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,616B, BPFP=2.5441 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,428B, BPFP=1.8849 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,232B, BPFP=2.6576 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,404B, BPFP=2.1009 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,888B, BPFP=2.6861 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,468B, BPFP=2.1470 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,104B, BPFP=2.6521 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,948B, BPFP=1.9075 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,820B, BPFP=2.6832 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 192,136B, BPFP=2.0848 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 180, 128]) + layer.0.v_cache: torch.Size([1, 8, 180, 128]) + layer.1.k_cache: torch.Size([1, 8, 180, 128]) + layer.1.v_cache: torch.Size([1, 8, 180, 128]) + layer.2.k_cache: torch.Size([1, 8, 180, 128]) + layer.2.v_cache: torch.Size([1, 8, 180, 128]) + layer.3.k_cache: torch.Size([1, 8, 180, 128]) + layer.3.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.k_cache: torch.Size([1, 8, 180, 128]) + layer.4.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.output: torch.Size([1, 180, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.408s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 180, 128]) + layer.0.v_cache: torch.Size([1, 8, 180, 128]) + layer.1.k_cache: torch.Size([1, 8, 180, 128]) + layer.1.v_cache: torch.Size([1, 8, 180, 128]) + layer.2.k_cache: torch.Size([1, 8, 180, 128]) + layer.2.v_cache: torch.Size([1, 8, 180, 128]) + layer.3.k_cache: torch.Size([1, 8, 180, 128]) + layer.3.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.k_cache: torch.Size([1, 8, 180, 128]) + layer.4.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.output: torch.Size([1, 180, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03239844 4.53579169 + layer.0.v_cache 0.00000026 0.00014894 + layer.1.k_cache 0.00307858 0.43850725 + layer.1.v_cache 0.00000079 0.00051138 + layer.2.k_cache 0.00118466 0.24995397 + layer.2.v_cache 0.00000116 0.00072956 + layer.3.k_cache 0.00130112 0.27768402 + layer.3.v_cache 0.00000222 0.00119785 + layer.4.k_cache 0.00350099 0.48769510 + layer.4.v_cache 0.00000308 0.00188754 + layer.4.output 0.00016632 0.03965093 + ------------------------------------------------------------------------------------- + TOTAL 0.00300976 0.43947936 + (elements=2,580,480) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2580480 +Total Bytes 710492 +BPFP 2.2027 bits/point +EBPFP 4.4053 equivalent bits/point +MSE 0.439479 +---------------------- -------------------------------------------------------- +Time: 0.679s Load: 0.009s, Pack+Encode: 0.262s, Decode+Unpack: 0.408s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 180, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4395 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample19-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample19-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample190-layer4-item1.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample190-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 145, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 145, 128) +Output shape: (1, 145, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.0.v_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.1.k_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.1.v_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.2.k_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.2.v_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.3.k_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.3.v_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.4.k_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.4.v_cache: torch.Size([1, 8, 145, 128]) -> torch.Size([1, 1, 145, 1024]) + layer.4.output: torch.Size([1, 145, 4096]) -> torch.Size([1, 1, 145, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 22,244B, BPFP=1.1985 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 50,676B, BPFP=2.7304 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 37,796B, BPFP=2.0364 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 53,836B, BPFP=2.9006 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 42,180B, BPFP=2.2726 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 54,340B, BPFP=2.9278 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 43,224B, BPFP=2.3289 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 53,456B, BPFP=2.8802 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 37,884B, BPFP=2.0412 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 54,212B, BPFP=2.9209 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 164,788B, BPFP=2.2197 +⌛️ [2/4] FRONTEND: Frontend time: 0.264s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 145, 128]) + layer.0.v_cache: torch.Size([1, 8, 145, 128]) + layer.1.k_cache: torch.Size([1, 8, 145, 128]) + layer.1.v_cache: torch.Size([1, 8, 145, 128]) + layer.2.k_cache: torch.Size([1, 8, 145, 128]) + layer.2.v_cache: torch.Size([1, 8, 145, 128]) + layer.3.k_cache: torch.Size([1, 8, 145, 128]) + layer.3.v_cache: torch.Size([1, 8, 145, 128]) + layer.4.k_cache: torch.Size([1, 8, 145, 128]) + layer.4.v_cache: torch.Size([1, 8, 145, 128]) + layer.4.output: torch.Size([1, 145, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.415s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 145, 128]) + layer.0.v_cache: torch.Size([1, 8, 145, 128]) + layer.1.k_cache: torch.Size([1, 8, 145, 128]) + layer.1.v_cache: torch.Size([1, 8, 145, 128]) + layer.2.k_cache: torch.Size([1, 8, 145, 128]) + layer.2.v_cache: torch.Size([1, 8, 145, 128]) + layer.3.k_cache: torch.Size([1, 8, 145, 128]) + layer.3.v_cache: torch.Size([1, 8, 145, 128]) + layer.4.k_cache: torch.Size([1, 8, 145, 128]) + layer.4.v_cache: torch.Size([1, 8, 145, 128]) + layer.4.output: torch.Size([1, 145, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02766713 5.11281233 + layer.0.v_cache 0.00000028 0.00015111 + layer.1.k_cache 0.00309559 0.46283233 + layer.1.v_cache 0.00000087 0.00053779 + layer.2.k_cache 0.00119714 0.25961354 + layer.2.v_cache 0.00000118 0.00077085 + layer.3.k_cache 0.00133867 0.28585773 + layer.3.v_cache 0.00000208 0.00114271 + layer.4.k_cache 0.00347171 0.50420864 + layer.4.v_cache 0.00000305 0.00184300 + layer.4.output 0.00016146 0.04413542 + ------------------------------------------------------------------------------------- + TOTAL 0.00267311 0.48616512 + (elements=2,078,720) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2078720 +Total Bytes 614636 +BPFP 2.3654 bits/point +EBPFP 4.7309 equivalent bits/point +MSE 0.486165 +---------------------- -------------------------------------------------------- +Time: 0.687s Load: 0.008s, Pack+Encode: 0.264s, Decode+Unpack: 0.415s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 145, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 145, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4862 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample190-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample190-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample20-layer4-item1.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample20-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 179, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 179, 128) +Output shape: (1, 179, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.0.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.1.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.1.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.2.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.2.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.3.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.3.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.4.k_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.4.v_cache: torch.Size([1, 8, 179, 128]) -> torch.Size([1, 1, 179, 1024]) + layer.4.output: torch.Size([1, 179, 4096]) -> torch.Size([1, 1, 179, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,944B, BPFP=1.1760 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,116B, BPFP=2.5365 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,756B, BPFP=1.9097 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,048B, BPFP=2.6645 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,436B, BPFP=2.1140 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,692B, BPFP=2.6926 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,520B, BPFP=2.1613 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,828B, BPFP=2.6549 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,836B, BPFP=1.9132 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,444B, BPFP=2.6817 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 193,496B, BPFP=2.1113 +⌛️ [2/4] FRONTEND: Frontend time: 0.268s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 179, 128]) + layer.0.v_cache: torch.Size([1, 8, 179, 128]) + layer.1.k_cache: torch.Size([1, 8, 179, 128]) + layer.1.v_cache: torch.Size([1, 8, 179, 128]) + layer.2.k_cache: torch.Size([1, 8, 179, 128]) + layer.2.v_cache: torch.Size([1, 8, 179, 128]) + layer.3.k_cache: torch.Size([1, 8, 179, 128]) + layer.3.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.k_cache: torch.Size([1, 8, 179, 128]) + layer.4.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.output: torch.Size([1, 179, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.416s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 179, 128]) + layer.0.v_cache: torch.Size([1, 8, 179, 128]) + layer.1.k_cache: torch.Size([1, 8, 179, 128]) + layer.1.v_cache: torch.Size([1, 8, 179, 128]) + layer.2.k_cache: torch.Size([1, 8, 179, 128]) + layer.2.v_cache: torch.Size([1, 8, 179, 128]) + layer.3.k_cache: torch.Size([1, 8, 179, 128]) + layer.3.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.k_cache: torch.Size([1, 8, 179, 128]) + layer.4.v_cache: torch.Size([1, 8, 179, 128]) + layer.4.output: torch.Size([1, 179, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02963084 4.68383721 + layer.0.v_cache 0.00000026 0.00014244 + layer.1.k_cache 0.00308074 0.42550983 + layer.1.v_cache 0.00000080 0.00050754 + layer.2.k_cache 0.00119595 0.26186627 + layer.2.v_cache 0.00000115 0.00073469 + layer.3.k_cache 0.00135258 0.28318706 + layer.3.v_cache 0.00000217 0.00112502 + layer.4.k_cache 0.00351189 0.52144499 + layer.4.v_cache 0.00000298 0.00184707 + layer.4.output 0.00021521 0.05279729 + ------------------------------------------------------------------------------------- + TOTAL 0.00283144 0.45652795 + (elements=2,566,144) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2566144 +Total Bytes 709116 +BPFP 2.2107 bits/point +EBPFP 4.4214 equivalent bits/point +MSE 0.456528 +---------------------- -------------------------------------------------------- +Time: 0.695s Load: 0.012s, Pack+Encode: 0.268s, Decode+Unpack: 0.416s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 179, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 179, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4565 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample20-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample20-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample21-layer4-item1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample21-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 157, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 157, 128) +Output shape: (1, 157, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.0.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.1.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.1.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.2.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.2.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.3.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.3.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.4.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.4.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.4.output: torch.Size([1, 157, 4096]) -> torch.Size([1, 1, 157, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,644B, BPFP=1.2761 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 53,812B, BPFP=2.6777 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,816B, BPFP=1.9813 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,960B, BPFP=2.8344 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,356B, BPFP=2.2072 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,380B, BPFP=2.8553 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,000B, BPFP=2.2393 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,308B, BPFP=2.8020 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,192B, BPFP=2.0000 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,404B, BPFP=2.8565 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 178,464B, BPFP=2.2201 +⌛️ [2/4] FRONTEND: Frontend time: 0.257s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 157, 128]) + layer.0.v_cache: torch.Size([1, 8, 157, 128]) + layer.1.k_cache: torch.Size([1, 8, 157, 128]) + layer.1.v_cache: torch.Size([1, 8, 157, 128]) + layer.2.k_cache: torch.Size([1, 8, 157, 128]) + layer.2.v_cache: torch.Size([1, 8, 157, 128]) + layer.3.k_cache: torch.Size([1, 8, 157, 128]) + layer.3.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.k_cache: torch.Size([1, 8, 157, 128]) + layer.4.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.output: torch.Size([1, 157, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 157, 128]) + layer.0.v_cache: torch.Size([1, 8, 157, 128]) + layer.1.k_cache: torch.Size([1, 8, 157, 128]) + layer.1.v_cache: torch.Size([1, 8, 157, 128]) + layer.2.k_cache: torch.Size([1, 8, 157, 128]) + layer.2.v_cache: torch.Size([1, 8, 157, 128]) + layer.3.k_cache: torch.Size([1, 8, 157, 128]) + layer.3.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.k_cache: torch.Size([1, 8, 157, 128]) + layer.4.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.output: torch.Size([1, 157, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02651285 4.68045374 + layer.0.v_cache 0.00000026 0.00014463 + layer.1.k_cache 0.00308002 0.47740149 + layer.1.v_cache 0.00000091 0.00056103 + layer.2.k_cache 0.00121857 0.27165387 + layer.2.v_cache 0.00000134 0.00078146 + layer.3.k_cache 0.00131702 0.28480005 + layer.3.v_cache 0.00000230 0.00116571 + layer.4.k_cache 0.00350090 0.50178372 + layer.4.v_cache 0.00000345 0.00204224 + layer.4.output 0.00019599 0.05685370 + ------------------------------------------------------------------------------------- + TOTAL 0.00260154 0.46058591 + (elements=2,250,752) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2250752 +Total Bytes 655336 +BPFP 2.3293 bits/point +EBPFP 4.6586 equivalent bits/point +MSE 0.460586 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.009s, Pack+Encode: 0.257s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 157, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4606 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample21-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample21-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample22-layer4-item1.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample22-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 175, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 175, 128) +Output shape: (1, 175, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.output: torch.Size([1, 175, 4096]) -> torch.Size([1, 1, 175, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,124B, BPFP=1.2555 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,424B, BPFP=2.6082 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,980B, BPFP=1.9187 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,960B, BPFP=2.7214 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,056B, BPFP=2.1454 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,476B, BPFP=2.7445 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,848B, BPFP=2.1807 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,832B, BPFP=2.7157 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,536B, BPFP=1.9436 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,708B, BPFP=2.7548 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 193,956B, BPFP=2.1647 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.output: torch.Size([1, 175, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.output: torch.Size([1, 175, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02748643 4.55369559 + layer.0.v_cache 0.00000027 0.00014753 + layer.1.k_cache 0.00298274 0.41747550 + layer.1.v_cache 0.00000083 0.00053103 + layer.2.k_cache 0.00117838 0.24914982 + layer.2.v_cache 0.00000115 0.00076787 + layer.3.k_cache 0.00127749 0.26865091 + layer.3.v_cache 0.00000229 0.00120175 + layer.4.k_cache 0.00388258 0.48992392 + layer.4.v_cache 0.00000318 0.00196533 + layer.4.output 0.00017171 0.03749671 + ------------------------------------------------------------------------------------- + TOTAL 0.00267873 0.43810686 + (elements=2,508,800) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2508800 +Total Bytes 708900 +BPFP 2.2605 bits/point +EBPFP 4.5210 equivalent bits/point +MSE 0.438107 +---------------------- -------------------------------------------------------- +Time: 0.671s Load: 0.010s, Pack+Encode: 0.259s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 175, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4381 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample22-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample22-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample23-layer4-item1.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample23-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 172, 128) +Output shape: (1, 172, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.output: torch.Size([1, 172, 4096]) -> torch.Size([1, 1, 172, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,896B, BPFP=1.2671 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,152B, BPFP=2.6414 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,612B, BPFP=1.9355 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,972B, BPFP=2.7694 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,784B, BPFP=2.1704 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,452B, BPFP=2.7912 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,516B, BPFP=2.2037 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,416B, BPFP=2.7442 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,144B, BPFP=1.9597 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,348B, BPFP=2.7865 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 192,940B, BPFP=2.1909 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02961682 4.67817511 + layer.0.v_cache 0.00000027 0.00015466 + layer.1.k_cache 0.00302783 0.42590093 + layer.1.v_cache 0.00000102 0.00056713 + layer.2.k_cache 0.00123476 0.25657002 + layer.2.v_cache 0.00000123 0.00079049 + layer.3.k_cache 0.00126690 0.27900898 + layer.3.v_cache 0.00000235 0.00123352 + layer.4.k_cache 0.00342846 0.48981307 + layer.4.v_cache 0.00000335 0.00203131 + layer.4.output 0.00019350 0.04539720 + ------------------------------------------------------------------------------------- + TOTAL 0.00281121 0.45113100 + (elements=2,465,792) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2465792 +Total Bytes 705232 +BPFP 2.2881 bits/point +EBPFP 4.5761 equivalent bits/point +MSE 0.451131 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.010s, Pack+Encode: 0.260s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4511 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample23-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample23-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample24-layer4-item1.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample24-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 178, 128) +Output shape: (1, 178, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.output: torch.Size([1, 178, 4096]) -> torch.Size([1, 1, 178, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,784B, BPFP=1.2195 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,168B, BPFP=2.5530 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,248B, BPFP=1.8982 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,992B, BPFP=2.6770 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,000B, BPFP=2.1067 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,392B, BPFP=2.6945 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,948B, BPFP=2.1483 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,656B, BPFP=2.6622 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,536B, BPFP=1.9108 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,620B, BPFP=2.7045 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,744B, BPFP=2.0930 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02741563 4.92971733 + layer.0.v_cache 0.00000026 0.00015158 + layer.1.k_cache 0.00307030 0.41847615 + layer.1.v_cache 0.00000077 0.00051161 + layer.2.k_cache 0.00116729 0.25834234 + layer.2.v_cache 0.00000113 0.00071045 + layer.3.k_cache 0.00134025 0.27271661 + layer.3.v_cache 0.00000215 0.00112125 + layer.4.k_cache 0.00345504 0.47091636 + layer.4.v_cache 0.00000307 0.00193145 + layer.4.output 0.00018601 0.03931277 + ------------------------------------------------------------------------------------- + TOTAL 0.00265714 0.46513187 + (elements=2,551,808) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2551808 +Total Bytes 705088 +BPFP 2.2105 bits/point +EBPFP 4.4209 equivalent bits/point +MSE 0.465132 +---------------------- -------------------------------------------------------- +Time: 0.670s Load: 0.010s, Pack+Encode: 0.258s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4651 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample24-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample24-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample25-layer4-item1.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample25-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 189, 128) +Output shape: (1, 189, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.output: torch.Size([1, 189, 4096]) -> torch.Size([1, 1, 189, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,240B, BPFP=1.2087 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,156B, BPFP=2.4453 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,424B, BPFP=1.8363 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,912B, BPFP=2.5592 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 49,560B, BPFP=2.0486 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,536B, BPFP=2.5850 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 50,356B, BPFP=2.0815 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,772B, BPFP=2.5534 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 45,044B, BPFP=1.8619 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,632B, BPFP=2.5890 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 198,632B, BPFP=2.0527 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.403s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02569163 4.69846372 + layer.0.v_cache 0.00000027 0.00015105 + layer.1.k_cache 0.00297363 0.42054167 + layer.1.v_cache 0.00000081 0.00052593 + layer.2.k_cache 0.00119805 0.25600183 + layer.2.v_cache 0.00000116 0.00072967 + layer.3.k_cache 0.00128923 0.27038760 + layer.3.v_cache 0.00000232 0.00123070 + layer.4.k_cache 0.00350442 0.48022441 + layer.4.v_cache 0.00000328 0.00200200 + layer.4.output 0.00017922 0.03950714 + ------------------------------------------------------------------------------------- + TOTAL 0.00252727 0.44916337 + (elements=2,709,504) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2709504 +Total Bytes 725264 +BPFP 2.1414 bits/point +EBPFP 4.2828 equivalent bits/point +MSE 0.449163 +---------------------- -------------------------------------------------------- +Time: 0.674s Load: 0.012s, Pack+Encode: 0.260s, Decode+Unpack: 0.403s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4492 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample25-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample25-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample26-layer4-item1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample26-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 178, 128) +Output shape: (1, 178, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) -> torch.Size([1, 1, 178, 1024]) + layer.4.output: torch.Size([1, 178, 4096]) -> torch.Size([1, 1, 178, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,680B, BPFP=1.2588 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,040B, BPFP=2.5913 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,412B, BPFP=1.9054 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,380B, BPFP=2.6940 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,472B, BPFP=2.1275 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,644B, BPFP=2.7056 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,408B, BPFP=2.1685 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,888B, BPFP=2.6724 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,816B, BPFP=1.9231 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,804B, BPFP=2.7126 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 193,972B, BPFP=2.1284 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 178, 128]) + layer.0.v_cache: torch.Size([1, 8, 178, 128]) + layer.1.k_cache: torch.Size([1, 8, 178, 128]) + layer.1.v_cache: torch.Size([1, 8, 178, 128]) + layer.2.k_cache: torch.Size([1, 8, 178, 128]) + layer.2.v_cache: torch.Size([1, 8, 178, 128]) + layer.3.k_cache: torch.Size([1, 8, 178, 128]) + layer.3.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.k_cache: torch.Size([1, 8, 178, 128]) + layer.4.v_cache: torch.Size([1, 8, 178, 128]) + layer.4.output: torch.Size([1, 178, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02863186 4.52232241 + layer.0.v_cache 0.00000027 0.00014569 + layer.1.k_cache 0.00302134 0.42568061 + layer.1.v_cache 0.00000082 0.00050822 + layer.2.k_cache 0.00115360 0.25086546 + layer.2.v_cache 0.00000119 0.00068840 + layer.3.k_cache 0.00131482 0.27433695 + layer.3.v_cache 0.00000222 0.00111892 + layer.4.k_cache 0.00345682 0.45887894 + layer.4.v_cache 0.00000324 0.00194879 + layer.4.output 0.00020381 0.04442757 + ------------------------------------------------------------------------------------- + TOTAL 0.00274296 0.43672890 + (elements=2,551,808) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2551808 +Total Bytes 712516 +BPFP 2.2338 bits/point +EBPFP 4.4675 equivalent bits/point +MSE 0.436729 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.010s, Pack+Encode: 0.260s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 178, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 178, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4367 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample26-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample26-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample27-layer4-item1.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample27-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 182, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 182, 128) +Output shape: (1, 182, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.0.v_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.1.k_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.1.v_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.2.k_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.2.v_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.3.k_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.3.v_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.4.k_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.4.v_cache: torch.Size([1, 8, 182, 128]) -> torch.Size([1, 1, 182, 1024]) + layer.4.output: torch.Size([1, 182, 4096]) -> torch.Size([1, 1, 182, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,256B, BPFP=1.2129 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,048B, BPFP=2.5347 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,812B, BPFP=1.8807 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,484B, BPFP=2.6393 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,700B, BPFP=2.0905 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,052B, BPFP=2.6636 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,584B, BPFP=2.1284 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,240B, BPFP=2.6288 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,188B, BPFP=1.8968 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,080B, BPFP=2.6648 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 194,868B, BPFP=2.0912 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 182, 128]) + layer.0.v_cache: torch.Size([1, 8, 182, 128]) + layer.1.k_cache: torch.Size([1, 8, 182, 128]) + layer.1.v_cache: torch.Size([1, 8, 182, 128]) + layer.2.k_cache: torch.Size([1, 8, 182, 128]) + layer.2.v_cache: torch.Size([1, 8, 182, 128]) + layer.3.k_cache: torch.Size([1, 8, 182, 128]) + layer.3.v_cache: torch.Size([1, 8, 182, 128]) + layer.4.k_cache: torch.Size([1, 8, 182, 128]) + layer.4.v_cache: torch.Size([1, 8, 182, 128]) + layer.4.output: torch.Size([1, 182, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.401s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 182, 128]) + layer.0.v_cache: torch.Size([1, 8, 182, 128]) + layer.1.k_cache: torch.Size([1, 8, 182, 128]) + layer.1.v_cache: torch.Size([1, 8, 182, 128]) + layer.2.k_cache: torch.Size([1, 8, 182, 128]) + layer.2.v_cache: torch.Size([1, 8, 182, 128]) + layer.3.k_cache: torch.Size([1, 8, 182, 128]) + layer.3.v_cache: torch.Size([1, 8, 182, 128]) + layer.4.k_cache: torch.Size([1, 8, 182, 128]) + layer.4.v_cache: torch.Size([1, 8, 182, 128]) + layer.4.output: torch.Size([1, 182, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02794281 4.80877686 + layer.0.v_cache 0.00000027 0.00015216 + layer.1.k_cache 0.00307012 0.44780383 + layer.1.v_cache 0.00000079 0.00049927 + layer.2.k_cache 0.00119021 0.24725539 + layer.2.v_cache 0.00000113 0.00070041 + layer.3.k_cache 0.00130892 0.28062588 + layer.3.v_cache 0.00000221 0.00111255 + layer.4.k_cache 0.00347814 0.48390269 + layer.4.v_cache 0.00000308 0.00187710 + layer.4.output 0.00018397 0.04764466 + ------------------------------------------------------------------------------------- + TOTAL 0.00269526 0.46166320 + (elements=2,609,152) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2609152 +Total Bytes 715312 +BPFP 2.1932 bits/point +EBPFP 4.3865 equivalent bits/point +MSE 0.461663 +---------------------- -------------------------------------------------------- +Time: 0.671s Load: 0.011s, Pack+Encode: 0.259s, Decode+Unpack: 0.401s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 182, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 182, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4617 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample27-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample27-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample3-layer4-item1.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample3-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 271, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.014s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 271, 128) +Output shape: (1, 271, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.0.v_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.1.k_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.1.v_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.2.k_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.2.v_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.3.k_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.3.v_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.4.k_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.4.v_cache: torch.Size([1, 8, 271, 128]) -> torch.Size([1, 1, 271, 1024]) + layer.4.output: torch.Size([1, 271, 4096]) -> torch.Size([1, 1, 271, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 39,656B, BPFP=1.1432 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 89,296B, BPFP=2.5743 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 65,916B, BPFP=1.9003 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 93,832B, BPFP=2.7050 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 72,952B, BPFP=2.1031 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 94,452B, BPFP=2.7229 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 74,532B, BPFP=2.1486 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 93,060B, BPFP=2.6828 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 66,036B, BPFP=1.9037 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 94,772B, BPFP=2.7321 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 279,916B, BPFP=2.0174 +⌛️ [2/4] FRONTEND: Frontend time: 0.368s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 271, 128]) + layer.0.v_cache: torch.Size([1, 8, 271, 128]) + layer.1.k_cache: torch.Size([1, 8, 271, 128]) + layer.1.v_cache: torch.Size([1, 8, 271, 128]) + layer.2.k_cache: torch.Size([1, 8, 271, 128]) + layer.2.v_cache: torch.Size([1, 8, 271, 128]) + layer.3.k_cache: torch.Size([1, 8, 271, 128]) + layer.3.v_cache: torch.Size([1, 8, 271, 128]) + layer.4.k_cache: torch.Size([1, 8, 271, 128]) + layer.4.v_cache: torch.Size([1, 8, 271, 128]) + layer.4.output: torch.Size([1, 271, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 271, 128]) + layer.0.v_cache: torch.Size([1, 8, 271, 128]) + layer.1.k_cache: torch.Size([1, 8, 271, 128]) + layer.1.v_cache: torch.Size([1, 8, 271, 128]) + layer.2.k_cache: torch.Size([1, 8, 271, 128]) + layer.2.v_cache: torch.Size([1, 8, 271, 128]) + layer.3.k_cache: torch.Size([1, 8, 271, 128]) + layer.3.v_cache: torch.Size([1, 8, 271, 128]) + layer.4.k_cache: torch.Size([1, 8, 271, 128]) + layer.4.v_cache: torch.Size([1, 8, 271, 128]) + layer.4.output: torch.Size([1, 271, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02401601 4.61824785 + layer.0.v_cache 0.00000027 0.00014345 + layer.1.k_cache 0.00298786 0.41295399 + layer.1.v_cache 0.00000080 0.00050163 + layer.2.k_cache 0.00117700 0.23795237 + layer.2.v_cache 0.00000117 0.00068383 + layer.3.k_cache 0.00127449 0.25786092 + layer.3.v_cache 0.00000214 0.00110121 + layer.4.k_cache 0.00368668 0.48006037 + layer.4.v_cache 0.00000306 0.00186629 + layer.4.output 0.00014564 0.03463746 + ------------------------------------------------------------------------------------- + TOTAL 0.00240943 0.43928012 + (elements=3,885,056) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3885056 +Total Bytes 1064420 +BPFP 2.1918 bits/point +EBPFP 4.3836 equivalent bits/point +MSE 0.439280 +---------------------- -------------------------------------------------------- +Time: 0.986s Load: 0.014s, Pack+Encode: 0.368s, Decode+Unpack: 0.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 271, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 271, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4393 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample3-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample3-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample30-layer4-item1.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample30-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 184, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 184, 128) +Output shape: (1, 184, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.0.v_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.1.k_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.1.v_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.2.k_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.2.v_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.3.k_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.3.v_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.4.k_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.4.v_cache: torch.Size([1, 8, 184, 128]) -> torch.Size([1, 1, 184, 1024]) + layer.4.output: torch.Size([1, 184, 4096]) -> torch.Size([1, 1, 184, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,348B, BPFP=1.2461 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,900B, BPFP=2.5008 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,944B, BPFP=1.8658 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,288B, BPFP=2.6022 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,704B, BPFP=2.0679 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,076B, BPFP=2.6357 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 50,104B, BPFP=2.1274 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,412B, BPFP=2.6075 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,556B, BPFP=1.8918 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,216B, BPFP=2.6416 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 205,608B, BPFP=2.1825 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 184, 128]) + layer.0.v_cache: torch.Size([1, 8, 184, 128]) + layer.1.k_cache: torch.Size([1, 8, 184, 128]) + layer.1.v_cache: torch.Size([1, 8, 184, 128]) + layer.2.k_cache: torch.Size([1, 8, 184, 128]) + layer.2.v_cache: torch.Size([1, 8, 184, 128]) + layer.3.k_cache: torch.Size([1, 8, 184, 128]) + layer.3.v_cache: torch.Size([1, 8, 184, 128]) + layer.4.k_cache: torch.Size([1, 8, 184, 128]) + layer.4.v_cache: torch.Size([1, 8, 184, 128]) + layer.4.output: torch.Size([1, 184, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.410s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 184, 128]) + layer.0.v_cache: torch.Size([1, 8, 184, 128]) + layer.1.k_cache: torch.Size([1, 8, 184, 128]) + layer.1.v_cache: torch.Size([1, 8, 184, 128]) + layer.2.k_cache: torch.Size([1, 8, 184, 128]) + layer.2.v_cache: torch.Size([1, 8, 184, 128]) + layer.3.k_cache: torch.Size([1, 8, 184, 128]) + layer.3.v_cache: torch.Size([1, 8, 184, 128]) + layer.4.k_cache: torch.Size([1, 8, 184, 128]) + layer.4.v_cache: torch.Size([1, 8, 184, 128]) + layer.4.output: torch.Size([1, 184, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02872292 4.99229298 + layer.0.v_cache 0.00000026 0.00014861 + layer.1.k_cache 0.00314513 0.44099857 + layer.1.v_cache 0.00000084 0.00051791 + layer.2.k_cache 0.00123314 0.26052436 + layer.2.v_cache 0.00000120 0.00074770 + layer.3.k_cache 0.00131532 0.27834366 + layer.3.v_cache 0.00000224 0.00117048 + layer.4.k_cache 0.00360741 0.51007868 + layer.4.v_cache 0.00000320 0.00193385 + layer.4.output 0.00023634 0.04817771 + ------------------------------------------------------------------------------------- + TOTAL 0.00278407 0.47710483 + (elements=2,637,824) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2637824 +Total Bytes 728156 +BPFP 2.2084 bits/point +EBPFP 4.4167 equivalent bits/point +MSE 0.477105 +---------------------- -------------------------------------------------------- +Time: 0.680s Load: 0.011s, Pack+Encode: 0.259s, Decode+Unpack: 0.410s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 184, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 184, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4771 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample30-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample30-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample31-layer4-item1.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample31-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 170, 128) +Output shape: (1, 170, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.output: torch.Size([1, 170, 4096]) -> torch.Size([1, 1, 170, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,588B, BPFP=1.3138 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,792B, BPFP=2.6559 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,416B, BPFP=1.9493 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,396B, BPFP=2.7756 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,232B, BPFP=2.1706 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,024B, BPFP=2.8044 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,352B, BPFP=2.2221 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,152B, BPFP=2.7643 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,992B, BPFP=1.9757 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,920B, BPFP=2.7996 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 191,244B, BPFP=2.1972 +⌛️ [2/4] FRONTEND: Frontend time: 0.261s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.401s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02895958 4.35918328 + layer.0.v_cache 0.00000027 0.00014885 + layer.1.k_cache 0.00314466 0.45672670 + layer.1.v_cache 0.00000087 0.00055582 + layer.2.k_cache 0.00118284 0.25925360 + layer.2.v_cache 0.00000133 0.00075865 + layer.3.k_cache 0.00130601 0.28246505 + layer.3.v_cache 0.00000238 0.00122667 + layer.4.k_cache 0.00330381 0.49921031 + layer.4.v_cache 0.00000327 0.00202592 + layer.4.output 0.00019227 0.04697103 + ------------------------------------------------------------------------------------- + TOTAL 0.00276244 0.43210278 + (elements=2,437,120) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2437120 +Total Bytes 701108 +BPFP 2.3014 bits/point +EBPFP 4.6029 equivalent bits/point +MSE 0.432103 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.010s, Pack+Encode: 0.261s, Decode+Unpack: 0.401s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4321 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample31-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample31-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample32-layer4-item1.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample32-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 206, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 206, 128) +Output shape: (1, 206, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.0.v_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.1.k_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.1.v_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.2.k_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.2.v_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.3.k_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.3.v_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.4.k_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.4.v_cache: torch.Size([1, 8, 206, 128]) -> torch.Size([1, 1, 206, 1024]) + layer.4.output: torch.Size([1, 206, 4096]) -> torch.Size([1, 1, 206, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 31,136B, BPFP=1.1808 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 70,288B, BPFP=2.6657 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 51,300B, BPFP=1.9455 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 73,708B, BPFP=2.7954 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 56,816B, BPFP=2.1547 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 74,456B, BPFP=2.8237 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 59,032B, BPFP=2.2388 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 73,664B, BPFP=2.7937 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 51,376B, BPFP=1.9484 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 74,444B, BPFP=2.8233 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 236,040B, BPFP=2.2379 +⌛️ [2/4] FRONTEND: Frontend time: 0.314s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 206, 128]) + layer.0.v_cache: torch.Size([1, 8, 206, 128]) + layer.1.k_cache: torch.Size([1, 8, 206, 128]) + layer.1.v_cache: torch.Size([1, 8, 206, 128]) + layer.2.k_cache: torch.Size([1, 8, 206, 128]) + layer.2.v_cache: torch.Size([1, 8, 206, 128]) + layer.3.k_cache: torch.Size([1, 8, 206, 128]) + layer.3.v_cache: torch.Size([1, 8, 206, 128]) + layer.4.k_cache: torch.Size([1, 8, 206, 128]) + layer.4.v_cache: torch.Size([1, 8, 206, 128]) + layer.4.output: torch.Size([1, 206, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 206, 128]) + layer.0.v_cache: torch.Size([1, 8, 206, 128]) + layer.1.k_cache: torch.Size([1, 8, 206, 128]) + layer.1.v_cache: torch.Size([1, 8, 206, 128]) + layer.2.k_cache: torch.Size([1, 8, 206, 128]) + layer.2.v_cache: torch.Size([1, 8, 206, 128]) + layer.3.k_cache: torch.Size([1, 8, 206, 128]) + layer.3.v_cache: torch.Size([1, 8, 206, 128]) + layer.4.k_cache: torch.Size([1, 8, 206, 128]) + layer.4.v_cache: torch.Size([1, 8, 206, 128]) + layer.4.output: torch.Size([1, 206, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02717657 5.01508754 + layer.0.v_cache 0.00000026 0.00015662 + layer.1.k_cache 0.00302264 0.45084811 + layer.1.v_cache 0.00000082 0.00051508 + layer.2.k_cache 0.00113561 0.23904356 + layer.2.v_cache 0.00000128 0.00072018 + layer.3.k_cache 0.00127405 0.28713806 + layer.3.v_cache 0.00000234 0.00120467 + layer.4.k_cache 0.00358677 0.49543929 + layer.4.v_cache 0.00000306 0.00180881 + layer.4.output 0.00018722 0.05755149 + ------------------------------------------------------------------------------------- + TOTAL 0.00263945 0.48015485 + (elements=2,953,216) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2953216 +Total Bytes 852260 +BPFP 2.3087 bits/point +EBPFP 4.6174 equivalent bits/point +MSE 0.480155 +---------------------- -------------------------------------------------------- +Time: 0.827s Load: 0.011s, Pack+Encode: 0.314s, Decode+Unpack: 0.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 206, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 206, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4802 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample32-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample32-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample33-layer4-item1.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample33-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 191, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 191, 128) +Output shape: (1, 191, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.0.v_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.1.k_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.1.v_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.2.k_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.2.v_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.3.k_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.3.v_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.4.k_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.4.v_cache: torch.Size([1, 8, 191, 128]) -> torch.Size([1, 1, 191, 1024]) + layer.4.output: torch.Size([1, 191, 4096]) -> torch.Size([1, 1, 191, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,448B, BPFP=1.1636 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,304B, BPFP=2.3848 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,540B, BPFP=1.7809 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,876B, BPFP=2.4900 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,312B, BPFP=1.9761 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,504B, BPFP=2.5157 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,284B, BPFP=2.0159 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,892B, BPFP=2.4907 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,008B, BPFP=1.8001 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,896B, BPFP=2.5317 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 194,352B, BPFP=1.9874 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 191, 128]) + layer.0.v_cache: torch.Size([1, 8, 191, 128]) + layer.1.k_cache: torch.Size([1, 8, 191, 128]) + layer.1.v_cache: torch.Size([1, 8, 191, 128]) + layer.2.k_cache: torch.Size([1, 8, 191, 128]) + layer.2.v_cache: torch.Size([1, 8, 191, 128]) + layer.3.k_cache: torch.Size([1, 8, 191, 128]) + layer.3.v_cache: torch.Size([1, 8, 191, 128]) + layer.4.k_cache: torch.Size([1, 8, 191, 128]) + layer.4.v_cache: torch.Size([1, 8, 191, 128]) + layer.4.output: torch.Size([1, 191, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.407s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 191, 128]) + layer.0.v_cache: torch.Size([1, 8, 191, 128]) + layer.1.k_cache: torch.Size([1, 8, 191, 128]) + layer.1.v_cache: torch.Size([1, 8, 191, 128]) + layer.2.k_cache: torch.Size([1, 8, 191, 128]) + layer.2.v_cache: torch.Size([1, 8, 191, 128]) + layer.3.k_cache: torch.Size([1, 8, 191, 128]) + layer.3.v_cache: torch.Size([1, 8, 191, 128]) + layer.4.k_cache: torch.Size([1, 8, 191, 128]) + layer.4.v_cache: torch.Size([1, 8, 191, 128]) + layer.4.output: torch.Size([1, 191, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02702075 4.32050264 + layer.0.v_cache 0.00000026 0.00014205 + layer.1.k_cache 0.00310674 0.41292967 + layer.1.v_cache 0.00000076 0.00048552 + layer.2.k_cache 0.00119798 0.25449226 + layer.2.v_cache 0.00000111 0.00069559 + layer.3.k_cache 0.00132973 0.26967880 + layer.3.v_cache 0.00000216 0.00112411 + layer.4.k_cache 0.00342601 0.48280318 + layer.4.v_cache 0.00000312 0.00192672 + layer.4.output 0.00019316 0.03794879 + ------------------------------------------------------------------------------------- + TOTAL 0.00263295 0.42118398 + (elements=2,738,176) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2738176 +Total Bytes 711416 +BPFP 2.0785 bits/point +EBPFP 4.1570 equivalent bits/point +MSE 0.421184 +---------------------- -------------------------------------------------------- +Time: 0.678s Load: 0.012s, Pack+Encode: 0.260s, Decode+Unpack: 0.407s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 191, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 191, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4212 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample33-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample33-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample35-layer4-item1.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample35-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 173, 128) +Output shape: (1, 173, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.output: torch.Size([1, 173, 4096]) -> torch.Size([1, 1, 173, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,720B, BPFP=1.2518 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,588B, BPFP=2.6006 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,716B, BPFP=1.9290 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,860B, BPFP=2.7484 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,264B, BPFP=2.1344 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,980B, BPFP=2.7538 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,324B, BPFP=2.1823 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,208B, BPFP=2.7189 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,836B, BPFP=1.9344 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,048B, BPFP=2.7569 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 191,580B, BPFP=2.1629 +⌛️ [2/4] FRONTEND: Frontend time: 0.261s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02805312 4.92451962 + layer.0.v_cache 0.00000027 0.00014904 + layer.1.k_cache 0.00341978 0.45581209 + layer.1.v_cache 0.00000082 0.00054403 + layer.2.k_cache 0.00114483 0.25287893 + layer.2.v_cache 0.00000117 0.00074471 + layer.3.k_cache 0.00131871 0.27890458 + layer.3.v_cache 0.00000219 0.00117491 + layer.4.k_cache 0.00345933 0.50037803 + layer.4.v_cache 0.00000316 0.00201835 + layer.4.output 0.00017521 0.04945060 + ------------------------------------------------------------------------------------- + TOTAL 0.00272173 0.47249476 + (elements=2,480,128) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2480128 +Total Bytes 701124 +BPFP 2.2616 bits/point +EBPFP 4.5231 equivalent bits/point +MSE 0.472495 +---------------------- -------------------------------------------------------- +Time: 0.720s Load: 0.012s, Pack+Encode: 0.261s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4725 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample35-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample35-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample36-layer4-item1.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample36-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 169, 128) +Output shape: (1, 169, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.output: torch.Size([1, 169, 4096]) -> torch.Size([1, 1, 169, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,756B, BPFP=1.2831 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,212B, BPFP=2.6448 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,912B, BPFP=1.9375 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,864B, BPFP=2.7674 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,816B, BPFP=2.1642 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,252B, BPFP=2.7853 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,940B, BPFP=2.2162 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,524B, BPFP=2.7517 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,376B, BPFP=1.9589 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,284B, BPFP=2.7868 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 189,976B, BPFP=2.1955 +⌛️ [2/4] FRONTEND: Frontend time: 0.261s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02802299 4.88321121 + layer.0.v_cache 0.00000027 0.00014817 + layer.1.k_cache 0.00309164 0.45216478 + layer.1.v_cache 0.00000083 0.00053298 + layer.2.k_cache 0.00116255 0.25324206 + layer.2.v_cache 0.00000115 0.00072169 + layer.3.k_cache 0.00130840 0.29665438 + layer.3.v_cache 0.00000220 0.00114688 + layer.4.k_cache 0.00347752 0.52204755 + layer.4.v_cache 0.00000302 0.00189430 + layer.4.output 0.00021363 0.05056013 + ------------------------------------------------------------------------------------- + TOTAL 0.00270893 0.47242890 + (elements=2,422,784) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2422784 +Total Bytes 693912 +BPFP 2.2913 bits/point +EBPFP 4.5826 equivalent bits/point +MSE 0.472429 +---------------------- -------------------------------------------------------- +Time: 0.674s Load: 0.009s, Pack+Encode: 0.261s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4724 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample36-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample36-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample37-layer4-item1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample37-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 189, 128) +Output shape: (1, 189, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.output: torch.Size([1, 189, 4096]) -> torch.Size([1, 1, 189, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,364B, BPFP=1.2138 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,168B, BPFP=2.4458 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,556B, BPFP=1.8418 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,716B, BPFP=2.5511 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 49,204B, BPFP=2.0339 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,344B, BPFP=2.5771 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 50,232B, BPFP=2.0764 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,716B, BPFP=2.5511 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,756B, BPFP=1.8500 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,636B, BPFP=2.5891 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 202,648B, BPFP=2.0942 +⌛️ [2/4] FRONTEND: Frontend time: 0.263s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.408s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02754041 4.62739167 + layer.0.v_cache 0.00000027 0.00015053 + layer.1.k_cache 0.00311113 0.42513425 + layer.1.v_cache 0.00000084 0.00050278 + layer.2.k_cache 0.00124070 0.26055234 + layer.2.v_cache 0.00000144 0.00073617 + layer.3.k_cache 0.00134127 0.28051554 + layer.3.v_cache 0.00000226 0.00115871 + layer.4.k_cache 0.00350879 0.48451665 + layer.4.v_cache 0.00000320 0.00191550 + layer.4.output 0.00022464 0.04850530 + ------------------------------------------------------------------------------------- + TOTAL 0.00268921 0.44832824 + (elements=2,709,504) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2709504 +Total Bytes 728340 +BPFP 2.1505 bits/point +EBPFP 4.3009 equivalent bits/point +MSE 0.448328 +---------------------- -------------------------------------------------------- +Time: 0.681s Load: 0.010s, Pack+Encode: 0.263s, Decode+Unpack: 0.408s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4483 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample37-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample37-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample38-layer4-item1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample38-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 177, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 177, 128) +Output shape: (1, 177, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.output: torch.Size([1, 177, 4096]) -> torch.Size([1, 1, 177, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,724B, BPFP=1.2237 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,992B, BPFP=2.5597 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,464B, BPFP=1.9184 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,832B, BPFP=2.6850 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,992B, BPFP=2.1183 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,288B, BPFP=2.7052 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,176B, BPFP=2.1706 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,484B, BPFP=2.6697 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,856B, BPFP=1.9357 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,464B, BPFP=2.7129 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 195,432B, BPFP=2.1565 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.output: torch.Size([1, 177, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.output: torch.Size([1, 177, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02865209 4.98231084 + layer.0.v_cache 0.00000026 0.00014354 + layer.1.k_cache 0.00315540 0.44758675 + layer.1.v_cache 0.00000078 0.00049593 + layer.2.k_cache 0.00119797 0.25844279 + layer.2.v_cache 0.00000110 0.00069410 + layer.3.k_cache 0.00133161 0.27788298 + layer.3.v_cache 0.00000204 0.00107326 + layer.4.k_cache 0.00358407 0.49744639 + layer.4.v_cache 0.00000307 0.00187196 + layer.4.output 0.00019353 0.04793973 + ------------------------------------------------------------------------------------- + TOTAL 0.00276446 0.47569339 + (elements=2,537,472) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2537472 +Total Bytes 709704 +BPFP 2.2375 bits/point +EBPFP 4.4750 equivalent bits/point +MSE 0.475693 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.009s, Pack+Encode: 0.259s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 177, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4757 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample38-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample38-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample39-layer4-item1.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample39-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 173, 128) +Output shape: (1, 173, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.output: torch.Size([1, 173, 4096]) -> torch.Size([1, 1, 173, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,532B, BPFP=1.2433 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,156B, BPFP=2.6263 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,488B, BPFP=1.9187 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,924B, BPFP=2.7513 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,308B, BPFP=2.1364 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,352B, BPFP=2.7706 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,476B, BPFP=2.1891 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,560B, BPFP=2.7348 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,096B, BPFP=1.9462 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,096B, BPFP=2.7590 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 195,376B, BPFP=2.2057 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02612204 4.58118311 + layer.0.v_cache 0.00000027 0.00014524 + layer.1.k_cache 0.00302657 0.46871181 + layer.1.v_cache 0.00000086 0.00053384 + layer.2.k_cache 0.00117788 0.25186567 + layer.2.v_cache 0.00000116 0.00073956 + layer.3.k_cache 0.00128963 0.27874105 + layer.3.v_cache 0.00000233 0.00118510 + layer.4.k_cache 0.00354125 0.48744100 + layer.4.v_cache 0.00000310 0.00193412 + layer.4.output 0.00019853 0.05484148 + ------------------------------------------------------------------------------------- + TOTAL 0.00256851 0.44941760 + (elements=2,480,128) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2480128 +Total Bytes 706364 +BPFP 2.2785 bits/point +EBPFP 4.5570 equivalent bits/point +MSE 0.449418 +---------------------- -------------------------------------------------------- +Time: 0.669s Load: 0.009s, Pack+Encode: 0.258s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4494 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample39-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample39-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample4-layer4-item1.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample4-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 243, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 243, 128) +Output shape: (1, 243, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.0.v_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.1.k_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.1.v_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.2.k_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.2.v_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.3.k_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.3.v_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.4.k_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.4.v_cache: torch.Size([1, 8, 243, 128]) -> torch.Size([1, 1, 243, 1024]) + layer.4.output: torch.Size([1, 243, 4096]) -> torch.Size([1, 1, 243, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 36,888B, BPFP=1.1860 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 78,128B, BPFP=2.5118 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 58,572B, BPFP=1.8831 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 81,980B, BPFP=2.6357 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 64,352B, BPFP=2.0689 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 82,756B, BPFP=2.6606 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 65,472B, BPFP=2.1049 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 81,784B, BPFP=2.6294 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 58,548B, BPFP=1.8823 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 82,548B, BPFP=2.6539 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 260,880B, BPFP=2.0968 +⌛️ [2/4] FRONTEND: Frontend time: 0.313s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 243, 128]) + layer.0.v_cache: torch.Size([1, 8, 243, 128]) + layer.1.k_cache: torch.Size([1, 8, 243, 128]) + layer.1.v_cache: torch.Size([1, 8, 243, 128]) + layer.2.k_cache: torch.Size([1, 8, 243, 128]) + layer.2.v_cache: torch.Size([1, 8, 243, 128]) + layer.3.k_cache: torch.Size([1, 8, 243, 128]) + layer.3.v_cache: torch.Size([1, 8, 243, 128]) + layer.4.k_cache: torch.Size([1, 8, 243, 128]) + layer.4.v_cache: torch.Size([1, 8, 243, 128]) + layer.4.output: torch.Size([1, 243, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 243, 128]) + layer.0.v_cache: torch.Size([1, 8, 243, 128]) + layer.1.k_cache: torch.Size([1, 8, 243, 128]) + layer.1.v_cache: torch.Size([1, 8, 243, 128]) + layer.2.k_cache: torch.Size([1, 8, 243, 128]) + layer.2.v_cache: torch.Size([1, 8, 243, 128]) + layer.3.k_cache: torch.Size([1, 8, 243, 128]) + layer.3.v_cache: torch.Size([1, 8, 243, 128]) + layer.4.k_cache: torch.Size([1, 8, 243, 128]) + layer.4.v_cache: torch.Size([1, 8, 243, 128]) + layer.4.output: torch.Size([1, 243, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02455453 4.46391541 + layer.0.v_cache 0.00000027 0.00014175 + layer.1.k_cache 0.00295349 0.41130427 + layer.1.v_cache 0.00000082 0.00052924 + layer.2.k_cache 0.00119501 0.24392810 + layer.2.v_cache 0.00000127 0.00077692 + layer.3.k_cache 0.00131209 0.26208429 + layer.3.v_cache 0.00000246 0.00121858 + layer.4.k_cache 0.00353116 0.46292623 + layer.4.v_cache 0.00000321 0.00191069 + layer.4.output 0.00017298 0.03858655 + ------------------------------------------------------------------------------------- + TOTAL 0.00244616 0.42879155 + (elements=3,483,648) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3483648 +Total Bytes 951908 +BPFP 2.1860 bits/point +EBPFP 4.3720 equivalent bits/point +MSE 0.428792 +---------------------- -------------------------------------------------------- +Time: 0.835s Load: 0.012s, Pack+Encode: 0.313s, Decode+Unpack: 0.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 243, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 243, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4288 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample4-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample4-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample40-layer4-item1.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample40-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 152, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 152, 128) +Output shape: (1, 152, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.0.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.1.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.1.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.2.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.2.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.3.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.3.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.4.k_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.4.v_cache: torch.Size([1, 8, 152, 128]) -> torch.Size([1, 1, 152, 1024]) + layer.4.output: torch.Size([1, 152, 4096]) -> torch.Size([1, 1, 152, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,304B, BPFP=1.2492 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,208B, BPFP=2.6834 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 38,640B, BPFP=1.9860 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 54,804B, BPFP=2.8168 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,468B, BPFP=2.2342 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 55,628B, BPFP=2.8592 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,160B, BPFP=2.2697 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 54,696B, BPFP=2.8113 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,060B, BPFP=2.0076 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 55,684B, BPFP=2.8620 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 174,040B, BPFP=2.2363 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 152, 128]) + layer.0.v_cache: torch.Size([1, 8, 152, 128]) + layer.1.k_cache: torch.Size([1, 8, 152, 128]) + layer.1.v_cache: torch.Size([1, 8, 152, 128]) + layer.2.k_cache: torch.Size([1, 8, 152, 128]) + layer.2.v_cache: torch.Size([1, 8, 152, 128]) + layer.3.k_cache: torch.Size([1, 8, 152, 128]) + layer.3.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.k_cache: torch.Size([1, 8, 152, 128]) + layer.4.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.output: torch.Size([1, 152, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.397s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 152, 128]) + layer.0.v_cache: torch.Size([1, 8, 152, 128]) + layer.1.k_cache: torch.Size([1, 8, 152, 128]) + layer.1.v_cache: torch.Size([1, 8, 152, 128]) + layer.2.k_cache: torch.Size([1, 8, 152, 128]) + layer.2.v_cache: torch.Size([1, 8, 152, 128]) + layer.3.k_cache: torch.Size([1, 8, 152, 128]) + layer.3.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.k_cache: torch.Size([1, 8, 152, 128]) + layer.4.v_cache: torch.Size([1, 8, 152, 128]) + layer.4.output: torch.Size([1, 152, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02793655 5.22918340 + layer.0.v_cache 0.00000027 0.00015307 + layer.1.k_cache 0.00308216 0.45698106 + layer.1.v_cache 0.00000096 0.00054763 + layer.2.k_cache 0.00119397 0.25776876 + layer.2.v_cache 0.00000115 0.00076018 + layer.3.k_cache 0.00132015 0.27873215 + layer.3.v_cache 0.00000229 0.00123680 + layer.4.k_cache 0.00355994 0.49801274 + layer.4.v_cache 0.00000322 0.00206596 + layer.4.output 0.00016979 0.04285995 + ------------------------------------------------------------------------------------- + TOTAL 0.00269856 0.49263440 + (elements=2,179,072) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2179072 +Total Bytes 636692 +BPFP 2.3375 bits/point +EBPFP 4.6750 equivalent bits/point +MSE 0.492634 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.008s, Pack+Encode: 0.262s, Decode+Unpack: 0.397s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 152, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 152, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4926 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample40-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample40-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample41-layer4-item1.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample41-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 185, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 185, 128) +Output shape: (1, 185, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.output: torch.Size([1, 185, 4096]) -> torch.Size([1, 1, 185, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,892B, BPFP=1.2201 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,488B, BPFP=2.5122 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,216B, BPFP=1.8672 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 62,080B, BPFP=2.6216 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 49,204B, BPFP=2.0779 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,280B, BPFP=2.6301 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,988B, BPFP=2.1110 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,692B, BPFP=2.6052 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,616B, BPFP=1.8841 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,400B, BPFP=2.6351 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 195,012B, BPFP=2.0588 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.output: torch.Size([1, 185, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.409s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.output: torch.Size([1, 185, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02732194 4.78909747 + layer.0.v_cache 0.00000026 0.00015573 + layer.1.k_cache 0.00316422 0.43678597 + layer.1.v_cache 0.00000081 0.00051736 + layer.2.k_cache 0.00119231 0.25874694 + layer.2.v_cache 0.00000116 0.00073075 + layer.3.k_cache 0.00128139 0.27446959 + layer.3.v_cache 0.00000230 0.00119481 + layer.4.k_cache 0.00354606 0.48844357 + layer.4.v_cache 0.00000329 0.00196562 + layer.4.output 0.00016551 0.04154919 + ------------------------------------------------------------------------------------- + TOTAL 0.00265541 0.45845033 + (elements=2,652,160) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2652160 +Total Bytes 719868 +BPFP 2.1714 bits/point +EBPFP 4.3428 equivalent bits/point +MSE 0.458450 +---------------------- -------------------------------------------------------- +Time: 0.680s Load: 0.009s, Pack+Encode: 0.262s, Decode+Unpack: 0.409s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 185, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4585 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample41-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample41-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample42-layer4-item1.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample42-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 169, 128) +Output shape: (1, 169, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.output: torch.Size([1, 169, 4096]) -> torch.Size([1, 1, 169, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,988B, BPFP=1.2476 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,012B, BPFP=2.6355 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,040B, BPFP=1.9434 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,992B, BPFP=2.7733 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,692B, BPFP=2.1585 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,576B, BPFP=2.8003 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,800B, BPFP=2.2097 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,744B, BPFP=2.7618 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,520B, BPFP=1.9656 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,652B, BPFP=2.8038 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,288B, BPFP=2.1991 +⌛️ [2/4] FRONTEND: Frontend time: 0.261s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02650603 5.12138881 + layer.0.v_cache 0.00000028 0.00015584 + layer.1.k_cache 0.00313577 0.46085227 + layer.1.v_cache 0.00000090 0.00052851 + layer.2.k_cache 0.00117709 0.26061452 + layer.2.v_cache 0.00000116 0.00074049 + layer.3.k_cache 0.00129027 0.27889942 + layer.3.v_cache 0.00000225 0.00117533 + layer.4.k_cache 0.00358651 0.49578559 + layer.4.v_cache 0.00000317 0.00196655 + layer.4.output 0.00019489 0.04408752 + ------------------------------------------------------------------------------------- + TOTAL 0.00260593 0.48560410 + (elements=2,422,784) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2422784 +Total Bytes 694304 +BPFP 2.2926 bits/point +EBPFP 4.5852 equivalent bits/point +MSE 0.485604 +---------------------- -------------------------------------------------------- +Time: 0.670s Load: 0.010s, Pack+Encode: 0.261s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4856 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample42-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample42-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample43-layer4-item1.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample43-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 170, 128) +Output shape: (1, 170, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.output: torch.Size([1, 170, 4096]) -> torch.Size([1, 1, 170, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,188B, BPFP=1.2494 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,368B, BPFP=2.6364 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,644B, BPFP=1.9597 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,320B, BPFP=2.7721 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,892B, BPFP=2.1550 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,512B, BPFP=2.7809 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,768B, BPFP=2.1952 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,852B, BPFP=2.7506 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,344B, BPFP=1.9460 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,608B, BPFP=2.7853 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 184,248B, BPFP=2.1168 +⌛️ [2/4] FRONTEND: Frontend time: 0.257s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.399s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02682906 4.77998514 + layer.0.v_cache 0.00000026 0.00015414 + layer.1.k_cache 0.00324658 0.45645312 + layer.1.v_cache 0.00000081 0.00052471 + layer.2.k_cache 0.00118449 0.25672722 + layer.2.v_cache 0.00000107 0.00070925 + layer.3.k_cache 0.00127207 0.26764827 + layer.3.v_cache 0.00000215 0.00111078 + layer.4.k_cache 0.00355165 0.50154361 + layer.4.v_cache 0.00000309 0.00196834 + layer.4.output 0.00015524 0.03900024 + ------------------------------------------------------------------------------------- + TOTAL 0.00262230 0.45877325 + (elements=2,437,120) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2437120 +Total Bytes 689744 +BPFP 2.2641 bits/point +EBPFP 4.5283 equivalent bits/point +MSE 0.458773 +---------------------- -------------------------------------------------------- +Time: 0.665s Load: 0.010s, Pack+Encode: 0.257s, Decode+Unpack: 0.399s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4588 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample43-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample43-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample44-layer4-item1.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample44-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 194, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 194, 128) +Output shape: (1, 194, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.0.v_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.1.k_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.1.v_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.2.k_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.2.v_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.3.k_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.3.v_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.4.k_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.4.v_cache: torch.Size([1, 8, 194, 128]) -> torch.Size([1, 1, 194, 1024]) + layer.4.output: torch.Size([1, 194, 4096]) -> torch.Size([1, 1, 194, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,228B, BPFP=1.1770 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 67,552B, BPFP=2.7204 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 49,172B, BPFP=1.9802 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 71,288B, BPFP=2.8708 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 55,208B, BPFP=2.2233 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 72,236B, BPFP=2.9090 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 56,612B, BPFP=2.2798 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 71,216B, BPFP=2.8679 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 49,288B, BPFP=1.9849 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 72,272B, BPFP=2.9104 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 219,252B, BPFP=2.2074 +⌛️ [2/4] FRONTEND: Frontend time: 0.316s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 194, 128]) + layer.0.v_cache: torch.Size([1, 8, 194, 128]) + layer.1.k_cache: torch.Size([1, 8, 194, 128]) + layer.1.v_cache: torch.Size([1, 8, 194, 128]) + layer.2.k_cache: torch.Size([1, 8, 194, 128]) + layer.2.v_cache: torch.Size([1, 8, 194, 128]) + layer.3.k_cache: torch.Size([1, 8, 194, 128]) + layer.3.v_cache: torch.Size([1, 8, 194, 128]) + layer.4.k_cache: torch.Size([1, 8, 194, 128]) + layer.4.v_cache: torch.Size([1, 8, 194, 128]) + layer.4.output: torch.Size([1, 194, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 194, 128]) + layer.0.v_cache: torch.Size([1, 8, 194, 128]) + layer.1.k_cache: torch.Size([1, 8, 194, 128]) + layer.1.v_cache: torch.Size([1, 8, 194, 128]) + layer.2.k_cache: torch.Size([1, 8, 194, 128]) + layer.2.v_cache: torch.Size([1, 8, 194, 128]) + layer.3.k_cache: torch.Size([1, 8, 194, 128]) + layer.3.v_cache: torch.Size([1, 8, 194, 128]) + layer.4.k_cache: torch.Size([1, 8, 194, 128]) + layer.4.v_cache: torch.Size([1, 8, 194, 128]) + layer.4.output: torch.Size([1, 194, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02710510 4.85019645 + layer.0.v_cache 0.00000027 0.00015041 + layer.1.k_cache 0.00315558 0.43879786 + layer.1.v_cache 0.00000085 0.00052360 + layer.2.k_cache 0.00115955 0.25480015 + layer.2.v_cache 0.00000114 0.00071008 + layer.3.k_cache 0.00127895 0.26720743 + layer.3.v_cache 0.00000244 0.00115892 + layer.4.k_cache 0.00355671 0.48406058 + layer.4.v_cache 0.00000315 0.00195249 + layer.4.output 0.00014532 0.04161530 + ------------------------------------------------------------------------------------- + TOTAL 0.00263179 0.46185851 + (elements=2,781,184) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2781184 +Total Bytes 813324 +BPFP 2.3395 bits/point +EBPFP 4.6790 equivalent bits/point +MSE 0.461859 +---------------------- -------------------------------------------------------- +Time: 0.837s Load: 0.011s, Pack+Encode: 0.316s, Decode+Unpack: 0.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 194, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 194, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4619 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample44-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample44-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample45-layer4-item1.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample45-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 170, 128) +Output shape: (1, 170, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.output: torch.Size([1, 170, 4096]) -> torch.Size([1, 1, 170, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,384B, BPFP=1.2585 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,000B, BPFP=2.6195 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,852B, BPFP=1.9233 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,632B, BPFP=2.7404 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,120B, BPFP=2.1195 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,212B, BPFP=2.7671 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,872B, BPFP=2.2000 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,388B, BPFP=2.7292 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,168B, BPFP=1.9379 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,960B, BPFP=2.7555 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 189,204B, BPFP=2.1738 +⌛️ [2/4] FRONTEND: Frontend time: 0.267s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.412s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02613039 5.03350974 + layer.0.v_cache 0.00000027 0.00016584 + layer.1.k_cache 0.00316806 0.48399676 + layer.1.v_cache 0.00000083 0.00056951 + layer.2.k_cache 0.00117426 0.26499021 + layer.2.v_cache 0.00000117 0.00078881 + layer.3.k_cache 0.00130856 0.29386904 + layer.3.v_cache 0.00000223 0.00130304 + layer.4.k_cache 0.00356532 0.53192471 + layer.4.v_cache 0.00000295 0.00187885 + layer.4.output 0.00018112 0.05035455 + ------------------------------------------------------------------------------------- + TOTAL 0.00257704 0.48674391 + (elements=2,437,120) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2437120 +Total Bytes 690792 +BPFP 2.2676 bits/point +EBPFP 4.5351 equivalent bits/point +MSE 0.486744 +---------------------- -------------------------------------------------------- +Time: 0.689s Load: 0.009s, Pack+Encode: 0.267s, Decode+Unpack: 0.412s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4867 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample45-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample45-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample46-layer4-item1.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample46-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 198, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 198, 128) +Output shape: (1, 198, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.0.v_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.1.k_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.1.v_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.2.k_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.2.v_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.3.k_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.3.v_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.4.k_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.4.v_cache: torch.Size([1, 8, 198, 128]) -> torch.Size([1, 1, 198, 1024]) + layer.4.output: torch.Size([1, 198, 4096]) -> torch.Size([1, 1, 198, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,672B, BPFP=1.0919 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 67,576B, BPFP=2.6664 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 49,860B, BPFP=1.9673 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 70,948B, BPFP=2.7994 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 55,780B, BPFP=2.2009 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 72,496B, BPFP=2.8605 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 57,508B, BPFP=2.2691 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 71,564B, BPFP=2.8237 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 49,624B, BPFP=1.9580 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 72,392B, BPFP=2.8564 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 211,448B, BPFP=2.0858 +⌛️ [2/4] FRONTEND: Frontend time: 0.312s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 198, 128]) + layer.0.v_cache: torch.Size([1, 8, 198, 128]) + layer.1.k_cache: torch.Size([1, 8, 198, 128]) + layer.1.v_cache: torch.Size([1, 8, 198, 128]) + layer.2.k_cache: torch.Size([1, 8, 198, 128]) + layer.2.v_cache: torch.Size([1, 8, 198, 128]) + layer.3.k_cache: torch.Size([1, 8, 198, 128]) + layer.3.v_cache: torch.Size([1, 8, 198, 128]) + layer.4.k_cache: torch.Size([1, 8, 198, 128]) + layer.4.v_cache: torch.Size([1, 8, 198, 128]) + layer.4.output: torch.Size([1, 198, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 198, 128]) + layer.0.v_cache: torch.Size([1, 8, 198, 128]) + layer.1.k_cache: torch.Size([1, 8, 198, 128]) + layer.1.v_cache: torch.Size([1, 8, 198, 128]) + layer.2.k_cache: torch.Size([1, 8, 198, 128]) + layer.2.v_cache: torch.Size([1, 8, 198, 128]) + layer.3.k_cache: torch.Size([1, 8, 198, 128]) + layer.3.v_cache: torch.Size([1, 8, 198, 128]) + layer.4.k_cache: torch.Size([1, 8, 198, 128]) + layer.4.v_cache: torch.Size([1, 8, 198, 128]) + layer.4.output: torch.Size([1, 198, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02819830 4.88161183 + layer.0.v_cache 0.00000029 0.00014199 + layer.1.k_cache 0.00303339 0.46026685 + layer.1.v_cache 0.00000075 0.00046087 + layer.2.k_cache 0.00117903 0.25540074 + layer.2.v_cache 0.00000119 0.00068812 + layer.3.k_cache 0.00132068 0.28089554 + layer.3.v_cache 0.00000222 0.00111382 + layer.4.k_cache 0.00360013 0.50654440 + layer.4.v_cache 0.00000329 0.00186528 + layer.4.output 0.00021245 0.04576167 + ------------------------------------------------------------------------------------- + TOTAL 0.00272779 0.46943115 + (elements=2,838,528) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2838528 +Total Bytes 806868 +BPFP 2.2740 bits/point +EBPFP 4.5481 equivalent bits/point +MSE 0.469431 +---------------------- -------------------------------------------------------- +Time: 0.834s Load: 0.010s, Pack+Encode: 0.312s, Decode+Unpack: 0.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 198, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 198, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4694 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample46-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample46-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample48-layer4-item1.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample48-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 173, 128) +Output shape: (1, 173, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.output: torch.Size([1, 173, 4096]) -> torch.Size([1, 1, 173, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,552B, BPFP=1.2442 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,756B, BPFP=2.6082 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,724B, BPFP=1.9294 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,664B, BPFP=2.7395 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,572B, BPFP=2.1483 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,228B, BPFP=2.7650 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,320B, BPFP=2.1821 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,464B, BPFP=2.7305 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,100B, BPFP=1.9464 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,420B, BPFP=2.7737 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 191,508B, BPFP=2.1621 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.418s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02631039 4.78027379 + layer.0.v_cache 0.00000026 0.00015000 + layer.1.k_cache 0.00315605 0.47010045 + layer.1.v_cache 0.00000083 0.00054624 + layer.2.k_cache 0.00121195 0.27439803 + layer.2.v_cache 0.00000119 0.00079098 + layer.3.k_cache 0.00131129 0.28601872 + layer.3.v_cache 0.00000228 0.00119738 + layer.4.k_cache 0.00352345 0.50604107 + layer.4.v_cache 0.00000333 0.00212399 + layer.4.output 0.00017266 0.05476269 + ------------------------------------------------------------------------------------- + TOTAL 0.00258655 0.46719224 + (elements=2,480,128) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2480128 +Total Bytes 702308 +BPFP 2.2654 bits/point +EBPFP 4.5308 equivalent bits/point +MSE 0.467192 +---------------------- -------------------------------------------------------- +Time: 0.714s Load: 0.010s, Pack+Encode: 0.285s, Decode+Unpack: 0.418s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4672 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample48-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample48-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample49-layer4-item1.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample49-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 175, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 175, 128) +Output shape: (1, 175, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) -> torch.Size([1, 1, 175, 1024]) + layer.4.output: torch.Size([1, 175, 4096]) -> torch.Size([1, 1, 175, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,988B, BPFP=1.2495 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,572B, BPFP=2.6148 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,452B, BPFP=1.9398 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,868B, BPFP=2.7173 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,016B, BPFP=2.1436 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,356B, BPFP=2.7391 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,092B, BPFP=2.1916 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,608B, BPFP=2.7057 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,492B, BPFP=1.9416 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,072B, BPFP=2.7264 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 193,820B, BPFP=2.1632 +⌛️ [2/4] FRONTEND: Frontend time: 0.264s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.output: torch.Size([1, 175, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 175, 128]) + layer.0.v_cache: torch.Size([1, 8, 175, 128]) + layer.1.k_cache: torch.Size([1, 8, 175, 128]) + layer.1.v_cache: torch.Size([1, 8, 175, 128]) + layer.2.k_cache: torch.Size([1, 8, 175, 128]) + layer.2.v_cache: torch.Size([1, 8, 175, 128]) + layer.3.k_cache: torch.Size([1, 8, 175, 128]) + layer.3.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.k_cache: torch.Size([1, 8, 175, 128]) + layer.4.v_cache: torch.Size([1, 8, 175, 128]) + layer.4.output: torch.Size([1, 175, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03100830 4.54168318 + layer.0.v_cache 0.00000027 0.00014398 + layer.1.k_cache 0.00306070 0.42908391 + layer.1.v_cache 0.00000080 0.00051727 + layer.2.k_cache 0.00119082 0.26691498 + layer.2.v_cache 0.00000118 0.00076660 + layer.3.k_cache 0.00134761 0.28561807 + layer.3.v_cache 0.00000229 0.00118386 + layer.4.k_cache 0.00348653 0.51195953 + layer.4.v_cache 0.00000313 0.00184152 + layer.4.output 0.00023428 0.05464003 + ------------------------------------------------------------------------------------- + TOTAL 0.00293134 0.44701950 + (elements=2,508,800) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2508800 +Total Bytes 708336 +BPFP 2.2587 bits/point +EBPFP 4.5174 equivalent bits/point +MSE 0.447020 +---------------------- -------------------------------------------------------- +Time: 0.686s Load: 0.011s, Pack+Encode: 0.264s, Decode+Unpack: 0.411s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 175, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 175, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4470 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample49-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample49-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample5-layer4-item1.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample5-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 240, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.013s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 240, 128) +Output shape: (1, 240, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.0.v_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.1.k_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.1.v_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.2.k_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.2.v_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.3.k_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.3.v_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.4.k_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.4.v_cache: torch.Size([1, 8, 240, 128]) -> torch.Size([1, 1, 240, 1024]) + layer.4.output: torch.Size([1, 240, 4096]) -> torch.Size([1, 1, 240, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 37,940B, BPFP=1.2350 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 78,272B, BPFP=2.5479 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 57,416B, BPFP=1.8690 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 81,464B, BPFP=2.6518 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 64,200B, BPFP=2.0898 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 81,908B, BPFP=2.6663 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 65,600B, BPFP=2.1354 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 81,092B, BPFP=2.6397 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 58,260B, BPFP=1.8965 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 82,076B, BPFP=2.6717 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 261,016B, BPFP=2.1242 +⌛️ [2/4] FRONTEND: Frontend time: 0.315s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 240, 128]) + layer.0.v_cache: torch.Size([1, 8, 240, 128]) + layer.1.k_cache: torch.Size([1, 8, 240, 128]) + layer.1.v_cache: torch.Size([1, 8, 240, 128]) + layer.2.k_cache: torch.Size([1, 8, 240, 128]) + layer.2.v_cache: torch.Size([1, 8, 240, 128]) + layer.3.k_cache: torch.Size([1, 8, 240, 128]) + layer.3.v_cache: torch.Size([1, 8, 240, 128]) + layer.4.k_cache: torch.Size([1, 8, 240, 128]) + layer.4.v_cache: torch.Size([1, 8, 240, 128]) + layer.4.output: torch.Size([1, 240, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 240, 128]) + layer.0.v_cache: torch.Size([1, 8, 240, 128]) + layer.1.k_cache: torch.Size([1, 8, 240, 128]) + layer.1.v_cache: torch.Size([1, 8, 240, 128]) + layer.2.k_cache: torch.Size([1, 8, 240, 128]) + layer.2.v_cache: torch.Size([1, 8, 240, 128]) + layer.3.k_cache: torch.Size([1, 8, 240, 128]) + layer.3.v_cache: torch.Size([1, 8, 240, 128]) + layer.4.k_cache: torch.Size([1, 8, 240, 128]) + layer.4.v_cache: torch.Size([1, 8, 240, 128]) + layer.4.output: torch.Size([1, 240, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02660088 4.22187449 + layer.0.v_cache 0.00000028 0.00015596 + layer.1.k_cache 0.00295962 0.42173643 + layer.1.v_cache 0.00000087 0.00052825 + layer.2.k_cache 0.00121828 0.24311722 + layer.2.v_cache 0.00000120 0.00072429 + layer.3.k_cache 0.00127180 0.26563317 + layer.3.v_cache 0.00000260 0.00117829 + layer.4.k_cache 0.00365680 0.47048518 + layer.4.v_cache 0.00000312 0.00191680 + layer.4.output 0.00015244 0.03918455 + ------------------------------------------------------------------------------------- + TOTAL 0.00259466 0.41314916 + (elements=3,440,640) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3440640 +Total Bytes 949244 +BPFP 2.2071 bits/point +EBPFP 4.4143 equivalent bits/point +MSE 0.413149 +---------------------- -------------------------------------------------------- +Time: 0.847s Load: 0.013s, Pack+Encode: 0.315s, Decode+Unpack: 0.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 240, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 240, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4131 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample5-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample5-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample50-layer4-item1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample50-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 189, 128) +Output shape: (1, 189, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.output: torch.Size([1, 189, 4096]) -> torch.Size([1, 1, 189, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,072B, BPFP=1.2017 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,972B, BPFP=2.4377 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,432B, BPFP=1.8366 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,960B, BPFP=2.5612 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 49,436B, BPFP=2.0435 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,516B, BPFP=2.5842 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 50,048B, BPFP=2.0688 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,652B, BPFP=2.5484 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,932B, BPFP=1.8573 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,584B, BPFP=2.5870 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 200,100B, BPFP=2.0678 +⌛️ [2/4] FRONTEND: Frontend time: 0.265s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.410s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02646759 4.67558329 + layer.0.v_cache 0.00000027 0.00015385 + layer.1.k_cache 0.00304090 0.41503390 + layer.1.v_cache 0.00000085 0.00056066 + layer.2.k_cache 0.00115999 0.25131355 + layer.2.v_cache 0.00000120 0.00075010 + layer.3.k_cache 0.00125869 0.27539515 + layer.3.v_cache 0.00000235 0.00121312 + layer.4.k_cache 0.00349331 0.47747980 + layer.4.v_cache 0.00000329 0.00203785 + layer.4.output 0.00016341 0.04019602 + ------------------------------------------------------------------------------------- + TOTAL 0.00257729 0.44716467 + (elements=2,709,504) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2709504 +Total Bytes 725704 +BPFP 2.1427 bits/point +EBPFP 4.2854 equivalent bits/point +MSE 0.447165 +---------------------- -------------------------------------------------------- +Time: 0.684s Load: 0.010s, Pack+Encode: 0.265s, Decode+Unpack: 0.410s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4472 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample50-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample50-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample51-layer4-item1.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample51-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 170, 128) +Output shape: (1, 170, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) -> torch.Size([1, 1, 170, 1024]) + layer.4.output: torch.Size([1, 170, 4096]) -> torch.Size([1, 1, 170, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,348B, BPFP=1.3028 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,628B, BPFP=2.6483 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,160B, BPFP=1.9375 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,028B, BPFP=2.7586 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,960B, BPFP=2.1581 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,584B, BPFP=2.7842 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,140B, BPFP=2.2123 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,832B, BPFP=2.7496 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,740B, BPFP=1.9642 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,796B, BPFP=2.7939 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 189,872B, BPFP=2.1814 +⌛️ [2/4] FRONTEND: Frontend time: 0.268s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.410s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 170, 128]) + layer.0.v_cache: torch.Size([1, 8, 170, 128]) + layer.1.k_cache: torch.Size([1, 8, 170, 128]) + layer.1.v_cache: torch.Size([1, 8, 170, 128]) + layer.2.k_cache: torch.Size([1, 8, 170, 128]) + layer.2.v_cache: torch.Size([1, 8, 170, 128]) + layer.3.k_cache: torch.Size([1, 8, 170, 128]) + layer.3.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.k_cache: torch.Size([1, 8, 170, 128]) + layer.4.v_cache: torch.Size([1, 8, 170, 128]) + layer.4.output: torch.Size([1, 170, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03233885 4.79332060 + layer.0.v_cache 0.00000026 0.00015043 + layer.1.k_cache 0.00307676 0.44463815 + layer.1.v_cache 0.00000080 0.00050930 + layer.2.k_cache 0.00113683 0.25458702 + layer.2.v_cache 0.00000124 0.00072199 + layer.3.k_cache 0.00130099 0.27617802 + layer.3.v_cache 0.00000223 0.00115529 + layer.4.k_cache 0.00352303 0.48887746 + layer.4.v_cache 0.00000334 0.00195379 + layer.4.output 0.00016991 0.04493849 + ------------------------------------------------------------------------------------- + TOTAL 0.00300457 0.46013186 + (elements=2,437,120) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2437120 +Total Bytes 697088 +BPFP 2.2882 bits/point +EBPFP 4.5765 equivalent bits/point +MSE 0.460132 +---------------------- -------------------------------------------------------- +Time: 0.687s Load: 0.009s, Pack+Encode: 0.268s, Decode+Unpack: 0.410s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 170, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 170, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4601 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample51-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample51-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample52-layer4-item1.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample52-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 158, 128) +Output shape: (1, 158, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.output: torch.Size([1, 158, 4096]) -> torch.Size([1, 1, 158, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,200B, BPFP=1.2460 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,144B, BPFP=2.6772 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,860B, BPFP=1.9709 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,860B, BPFP=2.8115 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,324B, BPFP=2.1917 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,408B, BPFP=2.8386 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,340B, BPFP=2.2419 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,536B, BPFP=2.7955 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,184B, BPFP=1.9869 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,368B, BPFP=2.8366 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 178,772B, BPFP=2.2099 +⌛️ [2/4] FRONTEND: Frontend time: 0.263s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.408s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02846188 4.74810250 + layer.0.v_cache 0.00000028 0.00015720 + layer.1.k_cache 0.00321952 0.49893266 + layer.1.v_cache 0.00000089 0.00054452 + layer.2.k_cache 0.00116433 0.26185465 + layer.2.v_cache 0.00000127 0.00075192 + layer.3.k_cache 0.00135431 0.30116888 + layer.3.v_cache 0.00000232 0.00119458 + layer.4.k_cache 0.00338441 0.49549750 + layer.4.v_cache 0.00000314 0.00200951 + layer.4.output 0.00020823 0.05273434 + ------------------------------------------------------------------------------------- + TOTAL 0.00274466 0.46579652 + (elements=2,265,088) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2265088 +Total Bytes 655996 +BPFP 2.3169 bits/point +EBPFP 4.6338 equivalent bits/point +MSE 0.465797 +---------------------- -------------------------------------------------------- +Time: 0.679s Load: 0.008s, Pack+Encode: 0.263s, Decode+Unpack: 0.408s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4658 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample52-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample52-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample53-layer4-item1.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample53-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 185, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 185, 128) +Output shape: (1, 185, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) -> torch.Size([1, 1, 185, 1024]) + layer.4.output: torch.Size([1, 185, 4096]) -> torch.Size([1, 1, 185, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,732B, BPFP=1.2133 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,976B, BPFP=2.4905 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,076B, BPFP=1.8613 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,532B, BPFP=2.5985 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,872B, BPFP=2.0639 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,960B, BPFP=2.6166 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,712B, BPFP=2.0993 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,368B, BPFP=2.5916 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,552B, BPFP=1.8814 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,404B, BPFP=2.6353 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 194,644B, BPFP=2.0549 +⌛️ [2/4] FRONTEND: Frontend time: 0.263s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.output: torch.Size([1, 185, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 185, 128]) + layer.0.v_cache: torch.Size([1, 8, 185, 128]) + layer.1.k_cache: torch.Size([1, 8, 185, 128]) + layer.1.v_cache: torch.Size([1, 8, 185, 128]) + layer.2.k_cache: torch.Size([1, 8, 185, 128]) + layer.2.v_cache: torch.Size([1, 8, 185, 128]) + layer.3.k_cache: torch.Size([1, 8, 185, 128]) + layer.3.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.k_cache: torch.Size([1, 8, 185, 128]) + layer.4.v_cache: torch.Size([1, 8, 185, 128]) + layer.4.output: torch.Size([1, 185, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02684415 4.40649117 + layer.0.v_cache 0.00000026 0.00015057 + layer.1.k_cache 0.00308337 0.43640062 + layer.1.v_cache 0.00000086 0.00050296 + layer.2.k_cache 0.00119263 0.26321846 + layer.2.v_cache 0.00000115 0.00070534 + layer.3.k_cache 0.00133167 0.27697575 + layer.3.v_cache 0.00000224 0.00118718 + layer.4.k_cache 0.00346133 0.50584890 + layer.4.v_cache 0.00000321 0.00201855 + layer.4.output 0.00019684 0.04420601 + ------------------------------------------------------------------------------------- + TOTAL 0.00262202 0.43359454 + (elements=2,652,160) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2652160 +Total Bytes 716828 +BPFP 2.1622 bits/point +EBPFP 4.3245 equivalent bits/point +MSE 0.433595 +---------------------- -------------------------------------------------------- +Time: 0.683s Load: 0.010s, Pack+Encode: 0.263s, Decode+Unpack: 0.411s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 185, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 185, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4336 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample53-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample53-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample54-layer4-item1.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample54-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 165, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 165, 128) +Output shape: (1, 165, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) -> torch.Size([1, 1, 165, 1024]) + layer.4.output: torch.Size([1, 165, 4096]) -> torch.Size([1, 1, 165, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,776B, BPFP=1.2678 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 56,328B, BPFP=2.6670 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,516B, BPFP=1.9657 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,196B, BPFP=2.8028 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,264B, BPFP=2.1905 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,720B, BPFP=2.8277 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,420B, BPFP=2.2453 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 58,716B, BPFP=2.7801 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 41,984B, BPFP=1.9879 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,668B, BPFP=2.8252 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 187,056B, BPFP=2.2142 +⌛️ [2/4] FRONTEND: Frontend time: 0.268s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.output: torch.Size([1, 165, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 165, 128]) + layer.0.v_cache: torch.Size([1, 8, 165, 128]) + layer.1.k_cache: torch.Size([1, 8, 165, 128]) + layer.1.v_cache: torch.Size([1, 8, 165, 128]) + layer.2.k_cache: torch.Size([1, 8, 165, 128]) + layer.2.v_cache: torch.Size([1, 8, 165, 128]) + layer.3.k_cache: torch.Size([1, 8, 165, 128]) + layer.3.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.k_cache: torch.Size([1, 8, 165, 128]) + layer.4.v_cache: torch.Size([1, 8, 165, 128]) + layer.4.output: torch.Size([1, 165, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02722176 4.63896003 + layer.0.v_cache 0.00000026 0.00013791 + layer.1.k_cache 0.00312822 0.46535154 + layer.1.v_cache 0.00000086 0.00050292 + layer.2.k_cache 0.00119859 0.25324656 + layer.2.v_cache 0.00000118 0.00072250 + layer.3.k_cache 0.00130010 0.28100789 + layer.3.v_cache 0.00000225 0.00110944 + layer.4.k_cache 0.00349983 0.48912649 + layer.4.v_cache 0.00000307 0.00187546 + layer.4.output 0.00018894 0.05392864 + ------------------------------------------------------------------------------------- + TOTAL 0.00265085 0.45341109 + (elements=2,365,440) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2365440 +Total Bytes 684644 +BPFP 2.3155 bits/point +EBPFP 4.6310 equivalent bits/point +MSE 0.453411 +---------------------- -------------------------------------------------------- +Time: 0.687s Load: 0.008s, Pack+Encode: 0.268s, Decode+Unpack: 0.411s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 165, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 165, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4534 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample54-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample54-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample56-layer4-item1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample56-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 210, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 210, 128) +Output shape: (1, 210, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.0.v_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.1.k_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.1.v_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.2.k_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.2.v_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.3.k_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.3.v_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.4.k_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.4.v_cache: torch.Size([1, 8, 210, 128]) -> torch.Size([1, 1, 210, 1024]) + layer.4.output: torch.Size([1, 210, 4096]) -> torch.Size([1, 1, 210, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,580B, BPFP=1.1004 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 69,688B, BPFP=2.5926 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 51,664B, BPFP=1.9220 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 73,344B, BPFP=2.7286 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 57,540B, BPFP=2.1406 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 74,348B, BPFP=2.7659 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 59,556B, BPFP=2.2156 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 73,532B, BPFP=2.7356 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 51,992B, BPFP=1.9342 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 74,668B, BPFP=2.7778 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 223,704B, BPFP=2.0806 +⌛️ [2/4] FRONTEND: Frontend time: 0.314s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 210, 128]) + layer.0.v_cache: torch.Size([1, 8, 210, 128]) + layer.1.k_cache: torch.Size([1, 8, 210, 128]) + layer.1.v_cache: torch.Size([1, 8, 210, 128]) + layer.2.k_cache: torch.Size([1, 8, 210, 128]) + layer.2.v_cache: torch.Size([1, 8, 210, 128]) + layer.3.k_cache: torch.Size([1, 8, 210, 128]) + layer.3.v_cache: torch.Size([1, 8, 210, 128]) + layer.4.k_cache: torch.Size([1, 8, 210, 128]) + layer.4.v_cache: torch.Size([1, 8, 210, 128]) + layer.4.output: torch.Size([1, 210, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 210, 128]) + layer.0.v_cache: torch.Size([1, 8, 210, 128]) + layer.1.k_cache: torch.Size([1, 8, 210, 128]) + layer.1.v_cache: torch.Size([1, 8, 210, 128]) + layer.2.k_cache: torch.Size([1, 8, 210, 128]) + layer.2.v_cache: torch.Size([1, 8, 210, 128]) + layer.3.k_cache: torch.Size([1, 8, 210, 128]) + layer.3.v_cache: torch.Size([1, 8, 210, 128]) + layer.4.k_cache: torch.Size([1, 8, 210, 128]) + layer.4.v_cache: torch.Size([1, 8, 210, 128]) + layer.4.output: torch.Size([1, 210, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02702481 4.95319766 + layer.0.v_cache 0.00000029 0.00014134 + layer.1.k_cache 0.00301925 0.40227254 + layer.1.v_cache 0.00000076 0.00045293 + layer.2.k_cache 0.00118472 0.24835696 + layer.2.v_cache 0.00000128 0.00064151 + layer.3.k_cache 0.00128382 0.25983017 + layer.3.v_cache 0.00000210 0.00102449 + layer.4.k_cache 0.00361221 0.47946788 + layer.4.v_cache 0.00000301 0.00170008 + layer.4.output 0.00015169 0.03755309 + ------------------------------------------------------------------------------------- + TOTAL 0.00262422 0.46409271 + (elements=3,010,560) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3010560 +Total Bytes 839616 +BPFP 2.2311 bits/point +EBPFP 4.4622 equivalent bits/point +MSE 0.464093 +---------------------- -------------------------------------------------------- +Time: 0.839s Load: 0.011s, Pack+Encode: 0.314s, Decode+Unpack: 0.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 210, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 210, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4641 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample56-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample56-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample57-layer4-item1.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample57-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 146, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 146, 128) +Output shape: (1, 146, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.0.v_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.1.k_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.1.v_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.2.k_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.2.v_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.3.k_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.3.v_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.4.k_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.4.v_cache: torch.Size([1, 8, 146, 128]) -> torch.Size([1, 1, 146, 1024]) + layer.4.output: torch.Size([1, 146, 4096]) -> torch.Size([1, 1, 146, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 23,408B, BPFP=1.2526 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 51,508B, BPFP=2.7562 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 37,648B, BPFP=2.0146 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 54,144B, BPFP=2.8973 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 42,508B, BPFP=2.2746 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 54,832B, BPFP=2.9341 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 43,292B, BPFP=2.3166 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 53,928B, BPFP=2.8857 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 38,476B, BPFP=2.0589 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 54,744B, BPFP=2.9294 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 169,272B, BPFP=2.2644 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 146, 128]) + layer.0.v_cache: torch.Size([1, 8, 146, 128]) + layer.1.k_cache: torch.Size([1, 8, 146, 128]) + layer.1.v_cache: torch.Size([1, 8, 146, 128]) + layer.2.k_cache: torch.Size([1, 8, 146, 128]) + layer.2.v_cache: torch.Size([1, 8, 146, 128]) + layer.3.k_cache: torch.Size([1, 8, 146, 128]) + layer.3.v_cache: torch.Size([1, 8, 146, 128]) + layer.4.k_cache: torch.Size([1, 8, 146, 128]) + layer.4.v_cache: torch.Size([1, 8, 146, 128]) + layer.4.output: torch.Size([1, 146, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.409s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 146, 128]) + layer.0.v_cache: torch.Size([1, 8, 146, 128]) + layer.1.k_cache: torch.Size([1, 8, 146, 128]) + layer.1.v_cache: torch.Size([1, 8, 146, 128]) + layer.2.k_cache: torch.Size([1, 8, 146, 128]) + layer.2.v_cache: torch.Size([1, 8, 146, 128]) + layer.3.k_cache: torch.Size([1, 8, 146, 128]) + layer.3.v_cache: torch.Size([1, 8, 146, 128]) + layer.4.k_cache: torch.Size([1, 8, 146, 128]) + layer.4.v_cache: torch.Size([1, 8, 146, 128]) + layer.4.output: torch.Size([1, 146, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02740652 4.88341909 + layer.0.v_cache 0.00000027 0.00014825 + layer.1.k_cache 0.00310827 0.43790263 + layer.1.v_cache 0.00000089 0.00053937 + layer.2.k_cache 0.00118257 0.26430073 + layer.2.v_cache 0.00000116 0.00077712 + layer.3.k_cache 0.00130374 0.28282194 + layer.3.v_cache 0.00000225 0.00123086 + layer.4.k_cache 0.00337717 0.50989731 + layer.4.v_cache 0.00000316 0.00211828 + layer.4.output 0.00015578 0.04990040 + ------------------------------------------------------------------------------------- + TOTAL 0.00264351 0.47019694 + (elements=2,093,056) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2093056 +Total Bytes 623760 +BPFP 2.3841 bits/point +EBPFP 4.7682 equivalent bits/point +MSE 0.470197 +---------------------- -------------------------------------------------------- +Time: 0.678s Load: 0.008s, Pack+Encode: 0.260s, Decode+Unpack: 0.409s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 146, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 146, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4702 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample57-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample57-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample6-layer4-item1.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample6-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 223, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 223, 128) +Output shape: (1, 223, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.0.v_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.1.k_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.1.v_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.2.k_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.2.v_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.3.k_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.3.v_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.4.k_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.4.v_cache: torch.Size([1, 8, 223, 128]) -> torch.Size([1, 1, 223, 1024]) + layer.4.output: torch.Size([1, 223, 4096]) -> torch.Size([1, 1, 223, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 34,700B, BPFP=1.2157 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 74,744B, BPFP=2.6186 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 54,084B, BPFP=1.8948 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 77,848B, BPFP=2.7273 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 60,788B, BPFP=2.1296 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 78,308B, BPFP=2.7434 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 62,012B, BPFP=2.1725 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 77,364B, BPFP=2.7103 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 54,752B, BPFP=1.9182 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 78,332B, BPFP=2.7443 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 244,940B, BPFP=2.1453 +⌛️ [2/4] FRONTEND: Frontend time: 0.315s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 223, 128]) + layer.0.v_cache: torch.Size([1, 8, 223, 128]) + layer.1.k_cache: torch.Size([1, 8, 223, 128]) + layer.1.v_cache: torch.Size([1, 8, 223, 128]) + layer.2.k_cache: torch.Size([1, 8, 223, 128]) + layer.2.v_cache: torch.Size([1, 8, 223, 128]) + layer.3.k_cache: torch.Size([1, 8, 223, 128]) + layer.3.v_cache: torch.Size([1, 8, 223, 128]) + layer.4.k_cache: torch.Size([1, 8, 223, 128]) + layer.4.v_cache: torch.Size([1, 8, 223, 128]) + layer.4.output: torch.Size([1, 223, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 223, 128]) + layer.0.v_cache: torch.Size([1, 8, 223, 128]) + layer.1.k_cache: torch.Size([1, 8, 223, 128]) + layer.1.v_cache: torch.Size([1, 8, 223, 128]) + layer.2.k_cache: torch.Size([1, 8, 223, 128]) + layer.2.v_cache: torch.Size([1, 8, 223, 128]) + layer.3.k_cache: torch.Size([1, 8, 223, 128]) + layer.3.v_cache: torch.Size([1, 8, 223, 128]) + layer.4.k_cache: torch.Size([1, 8, 223, 128]) + layer.4.v_cache: torch.Size([1, 8, 223, 128]) + layer.4.output: torch.Size([1, 223, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02710374 4.42564002 + layer.0.v_cache 0.00000027 0.00015381 + layer.1.k_cache 0.00293174 0.42191243 + layer.1.v_cache 0.00000088 0.00054210 + layer.2.k_cache 0.00135432 0.25431239 + layer.2.v_cache 0.00000122 0.00074329 + layer.3.k_cache 0.00128520 0.26549708 + layer.3.v_cache 0.00000265 0.00122342 + layer.4.k_cache 0.00356170 0.46320880 + layer.4.v_cache 0.00000337 0.00195098 + layer.4.output 0.00016975 0.03769874 + ------------------------------------------------------------------------------------- + TOTAL 0.00263743 0.42756995 + (elements=3,196,928) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3196928 +Total Bytes 897872 +BPFP 2.2468 bits/point +EBPFP 4.4937 equivalent bits/point +MSE 0.427570 +---------------------- -------------------------------------------------------- +Time: 0.844s Load: 0.012s, Pack+Encode: 0.315s, Decode+Unpack: 0.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 223, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 223, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4276 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample6-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample6-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample60-layer4-item1.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample60-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 167, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 167, 128) +Output shape: (1, 167, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.0.v_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.1.k_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.1.v_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.2.k_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.2.v_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.3.k_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.3.v_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.4.k_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.4.v_cache: torch.Size([1, 8, 167, 128]) -> torch.Size([1, 1, 167, 1024]) + layer.4.output: torch.Size([1, 167, 4096]) -> torch.Size([1, 1, 167, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,932B, BPFP=1.2599 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 56,744B, BPFP=2.6546 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,960B, BPFP=1.9629 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,456B, BPFP=2.7814 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,376B, BPFP=2.1695 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,860B, BPFP=2.8003 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,476B, BPFP=2.2210 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 58,988B, BPFP=2.7595 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 41,764B, BPFP=1.9538 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,736B, BPFP=2.7945 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 186,932B, BPFP=2.1862 +⌛️ [2/4] FRONTEND: Frontend time: 0.265s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 167, 128]) + layer.0.v_cache: torch.Size([1, 8, 167, 128]) + layer.1.k_cache: torch.Size([1, 8, 167, 128]) + layer.1.v_cache: torch.Size([1, 8, 167, 128]) + layer.2.k_cache: torch.Size([1, 8, 167, 128]) + layer.2.v_cache: torch.Size([1, 8, 167, 128]) + layer.3.k_cache: torch.Size([1, 8, 167, 128]) + layer.3.v_cache: torch.Size([1, 8, 167, 128]) + layer.4.k_cache: torch.Size([1, 8, 167, 128]) + layer.4.v_cache: torch.Size([1, 8, 167, 128]) + layer.4.output: torch.Size([1, 167, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.413s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 167, 128]) + layer.0.v_cache: torch.Size([1, 8, 167, 128]) + layer.1.k_cache: torch.Size([1, 8, 167, 128]) + layer.1.v_cache: torch.Size([1, 8, 167, 128]) + layer.2.k_cache: torch.Size([1, 8, 167, 128]) + layer.2.v_cache: torch.Size([1, 8, 167, 128]) + layer.3.k_cache: torch.Size([1, 8, 167, 128]) + layer.3.v_cache: torch.Size([1, 8, 167, 128]) + layer.4.k_cache: torch.Size([1, 8, 167, 128]) + layer.4.v_cache: torch.Size([1, 8, 167, 128]) + layer.4.output: torch.Size([1, 167, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02821272 4.59572176 + layer.0.v_cache 0.00000027 0.00015510 + layer.1.k_cache 0.00309555 0.48142494 + layer.1.v_cache 0.00000088 0.00055109 + layer.2.k_cache 0.00116749 0.25875208 + layer.2.v_cache 0.00000113 0.00074094 + layer.3.k_cache 0.00133544 0.29060147 + layer.3.v_cache 0.00000222 0.00119948 + layer.4.k_cache 0.00347765 0.50986266 + layer.4.v_cache 0.00000297 0.00188076 + layer.4.output 0.00020541 0.04231830 + ------------------------------------------------------------------------------------- + TOTAL 0.00272271 0.45072596 + (elements=2,394,112) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2394112 +Total Bytes 686224 +BPFP 2.2930 bits/point +EBPFP 4.5861 equivalent bits/point +MSE 0.450726 +---------------------- -------------------------------------------------------- +Time: 0.687s Load: 0.009s, Pack+Encode: 0.265s, Decode+Unpack: 0.413s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 167, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 167, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4507 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample60-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample60-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample61-layer4-item1.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample61-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 162, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 162, 128) +Output shape: (1, 162, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.output: torch.Size([1, 162, 4096]) -> torch.Size([1, 1, 162, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,220B, BPFP=1.2645 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 55,492B, BPFP=2.6761 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 40,260B, BPFP=1.9416 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 58,296B, BPFP=2.8113 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,884B, BPFP=2.1645 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 58,396B, BPFP=2.8162 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 46,032B, BPFP=2.2199 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 57,732B, BPFP=2.7841 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,788B, BPFP=1.9670 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 58,580B, BPFP=2.8250 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 182,496B, BPFP=2.2002 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.output: torch.Size([1, 162, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.411s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.output: torch.Size([1, 162, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02804119 4.64967478 + layer.0.v_cache 0.00000028 0.00015033 + layer.1.k_cache 0.00315224 0.44693106 + layer.1.v_cache 0.00000083 0.00053802 + layer.2.k_cache 0.00116199 0.25636000 + layer.2.v_cache 0.00000117 0.00073580 + layer.3.k_cache 0.00129529 0.27755325 + layer.3.v_cache 0.00000233 0.00122695 + layer.4.k_cache 0.00343653 0.48732296 + layer.4.v_cache 0.00000316 0.00205485 + layer.4.output 0.00017336 0.04707312 + ------------------------------------------------------------------------------------- + TOTAL 0.00269917 0.45077432 + (elements=2,322,432) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2322432 +Total Bytes 669176 +BPFP 2.3051 bits/point +EBPFP 4.6102 equivalent bits/point +MSE 0.450774 +---------------------- -------------------------------------------------------- +Time: 0.682s Load: 0.009s, Pack+Encode: 0.262s, Decode+Unpack: 0.411s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 162, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4508 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample61-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample61-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample62-layer4-item1.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample62-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 176, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 176, 128) +Output shape: (1, 176, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.output: torch.Size([1, 176, 4096]) -> torch.Size([1, 1, 176, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,316B, BPFP=1.2569 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,464B, BPFP=2.5952 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,092B, BPFP=1.9128 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,024B, BPFP=2.7088 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,300B, BPFP=2.1440 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,604B, BPFP=2.7346 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,996B, BPFP=2.1749 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,680B, BPFP=2.6935 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,560B, BPFP=1.9336 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,556B, BPFP=2.7324 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 197,208B, BPFP=2.1885 +⌛️ [2/4] FRONTEND: Frontend time: 0.263s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.output: torch.Size([1, 176, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.412s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.output: torch.Size([1, 176, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02849088 4.17225924 + layer.0.v_cache 0.00000028 0.00015370 + layer.1.k_cache 0.00305327 0.43708368 + layer.1.v_cache 0.00000091 0.00054271 + layer.2.k_cache 0.00120035 0.24817601 + layer.2.v_cache 0.00000116 0.00074359 + layer.3.k_cache 0.00128172 0.26995459 + layer.3.v_cache 0.00000269 0.00122177 + layer.4.k_cache 0.00351652 0.47930657 + layer.4.v_cache 0.00000314 0.00198399 + layer.4.output 0.00018003 0.04510850 + ------------------------------------------------------------------------------------- + TOTAL 0.00273365 0.41370427 + (elements=2,523,136) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2523136 +Total Bytes 712800 +BPFP 2.2600 bits/point +EBPFP 4.5201 equivalent bits/point +MSE 0.413704 +---------------------- -------------------------------------------------------- +Time: 0.684s Load: 0.009s, Pack+Encode: 0.263s, Decode+Unpack: 0.412s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 176, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4137 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample62-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample62-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample64-layer4-item1.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample64-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 168, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 168, 128) +Output shape: (1, 168, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.0.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.1.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.1.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.2.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.2.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.3.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.3.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.4.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.4.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.4.output: torch.Size([1, 168, 4096]) -> torch.Size([1, 1, 168, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,440B, BPFP=1.2760 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,428B, BPFP=2.6706 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,800B, BPFP=1.9438 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,784B, BPFP=2.7801 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,368B, BPFP=2.1562 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,984B, BPFP=2.7894 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,736B, BPFP=2.2199 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,096B, BPFP=2.7481 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,296B, BPFP=1.9669 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,960B, BPFP=2.7883 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 185,732B, BPFP=2.1593 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 168, 128]) + layer.0.v_cache: torch.Size([1, 8, 168, 128]) + layer.1.k_cache: torch.Size([1, 8, 168, 128]) + layer.1.v_cache: torch.Size([1, 8, 168, 128]) + layer.2.k_cache: torch.Size([1, 8, 168, 128]) + layer.2.v_cache: torch.Size([1, 8, 168, 128]) + layer.3.k_cache: torch.Size([1, 8, 168, 128]) + layer.3.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.k_cache: torch.Size([1, 8, 168, 128]) + layer.4.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.output: torch.Size([1, 168, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.401s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 168, 128]) + layer.0.v_cache: torch.Size([1, 8, 168, 128]) + layer.1.k_cache: torch.Size([1, 8, 168, 128]) + layer.1.v_cache: torch.Size([1, 8, 168, 128]) + layer.2.k_cache: torch.Size([1, 8, 168, 128]) + layer.2.v_cache: torch.Size([1, 8, 168, 128]) + layer.3.k_cache: torch.Size([1, 8, 168, 128]) + layer.3.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.k_cache: torch.Size([1, 8, 168, 128]) + layer.4.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.output: torch.Size([1, 168, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02847742 5.00198582 + layer.0.v_cache 0.00000027 0.00015365 + layer.1.k_cache 0.00308642 0.46450878 + layer.1.v_cache 0.00000083 0.00054168 + layer.2.k_cache 0.00117363 0.25212124 + layer.2.v_cache 0.00000114 0.00072552 + layer.3.k_cache 0.00131494 0.28124759 + layer.3.v_cache 0.00000212 0.00113731 + layer.4.k_cache 0.00375810 0.50455825 + layer.4.v_cache 0.00000313 0.00194657 + layer.4.output 0.00017449 0.03872147 + ------------------------------------------------------------------------------------- + TOTAL 0.00275114 0.47598659 + (elements=2,408,448) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2408448 +Total Bytes 687624 +BPFP 2.2840 bits/point +EBPFP 4.5681 equivalent bits/point +MSE 0.475987 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.009s, Pack+Encode: 0.262s, Decode+Unpack: 0.401s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 168, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4760 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample64-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample64-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample66-layer4-item1.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample66-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 160, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 160, 128) +Output shape: (1, 160, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.0.v_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.1.k_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.1.v_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.2.k_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.2.v_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.3.k_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.3.v_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.4.k_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.4.v_cache: torch.Size([1, 8, 160, 128]) -> torch.Size([1, 1, 160, 1024]) + layer.4.output: torch.Size([1, 160, 4096]) -> torch.Size([1, 1, 160, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,172B, BPFP=1.2291 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,268B, BPFP=2.6498 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,696B, BPFP=1.9383 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,692B, BPFP=2.7682 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,920B, BPFP=2.1445 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,256B, BPFP=2.7957 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,240B, BPFP=2.2090 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,220B, BPFP=2.7451 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,960B, BPFP=1.9512 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,128B, BPFP=2.7895 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 177,236B, BPFP=2.1635 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 160, 128]) + layer.0.v_cache: torch.Size([1, 8, 160, 128]) + layer.1.k_cache: torch.Size([1, 8, 160, 128]) + layer.1.v_cache: torch.Size([1, 8, 160, 128]) + layer.2.k_cache: torch.Size([1, 8, 160, 128]) + layer.2.v_cache: torch.Size([1, 8, 160, 128]) + layer.3.k_cache: torch.Size([1, 8, 160, 128]) + layer.3.v_cache: torch.Size([1, 8, 160, 128]) + layer.4.k_cache: torch.Size([1, 8, 160, 128]) + layer.4.v_cache: torch.Size([1, 8, 160, 128]) + layer.4.output: torch.Size([1, 160, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.399s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 160, 128]) + layer.0.v_cache: torch.Size([1, 8, 160, 128]) + layer.1.k_cache: torch.Size([1, 8, 160, 128]) + layer.1.v_cache: torch.Size([1, 8, 160, 128]) + layer.2.k_cache: torch.Size([1, 8, 160, 128]) + layer.2.v_cache: torch.Size([1, 8, 160, 128]) + layer.3.k_cache: torch.Size([1, 8, 160, 128]) + layer.3.v_cache: torch.Size([1, 8, 160, 128]) + layer.4.k_cache: torch.Size([1, 8, 160, 128]) + layer.4.v_cache: torch.Size([1, 8, 160, 128]) + layer.4.output: torch.Size([1, 160, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02762303 4.76045456 + layer.0.v_cache 0.00000027 0.00015680 + layer.1.k_cache 0.00317047 0.46671958 + layer.1.v_cache 0.00000088 0.00054240 + layer.2.k_cache 0.00116413 0.26428504 + layer.2.v_cache 0.00000117 0.00077031 + layer.3.k_cache 0.00133371 0.29100800 + layer.3.v_cache 0.00000211 0.00115843 + layer.4.k_cache 0.00351672 0.52017179 + layer.4.v_cache 0.00000299 0.00189312 + layer.4.output 0.00019762 0.03810806 + ------------------------------------------------------------------------------------- + TOTAL 0.00268614 0.46139945 + (elements=2,293,760) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2293760 +Total Bytes 652788 +BPFP 2.2767 bits/point +EBPFP 4.5535 equivalent bits/point +MSE 0.461399 +---------------------- -------------------------------------------------------- +Time: 0.668s Load: 0.009s, Pack+Encode: 0.259s, Decode+Unpack: 0.399s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 160, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 160, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4614 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample66-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample66-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample67-layer4-item1.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample67-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 168, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 168, 128) +Output shape: (1, 168, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.0.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.1.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.1.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.2.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.2.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.3.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.3.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.4.k_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.4.v_cache: torch.Size([1, 8, 168, 128]) -> torch.Size([1, 1, 168, 1024]) + layer.4.output: torch.Size([1, 168, 4096]) -> torch.Size([1, 1, 168, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,308B, BPFP=1.2699 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 56,896B, BPFP=2.6458 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,620B, BPFP=1.9355 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,584B, BPFP=2.7708 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,712B, BPFP=2.1722 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,208B, BPFP=2.7999 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,408B, BPFP=2.2046 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,160B, BPFP=2.7511 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,088B, BPFP=1.9572 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,180B, BPFP=2.7985 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 186,968B, BPFP=2.1736 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 168, 128]) + layer.0.v_cache: torch.Size([1, 8, 168, 128]) + layer.1.k_cache: torch.Size([1, 8, 168, 128]) + layer.1.v_cache: torch.Size([1, 8, 168, 128]) + layer.2.k_cache: torch.Size([1, 8, 168, 128]) + layer.2.v_cache: torch.Size([1, 8, 168, 128]) + layer.3.k_cache: torch.Size([1, 8, 168, 128]) + layer.3.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.k_cache: torch.Size([1, 8, 168, 128]) + layer.4.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.output: torch.Size([1, 168, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.403s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 168, 128]) + layer.0.v_cache: torch.Size([1, 8, 168, 128]) + layer.1.k_cache: torch.Size([1, 8, 168, 128]) + layer.1.v_cache: torch.Size([1, 8, 168, 128]) + layer.2.k_cache: torch.Size([1, 8, 168, 128]) + layer.2.v_cache: torch.Size([1, 8, 168, 128]) + layer.3.k_cache: torch.Size([1, 8, 168, 128]) + layer.3.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.k_cache: torch.Size([1, 8, 168, 128]) + layer.4.v_cache: torch.Size([1, 8, 168, 128]) + layer.4.output: torch.Size([1, 168, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03000965 5.43415142 + layer.0.v_cache 0.00000027 0.00015648 + layer.1.k_cache 0.00306367 0.45699079 + layer.1.v_cache 0.00000085 0.00054257 + layer.2.k_cache 0.00121342 0.25824751 + layer.2.v_cache 0.00000122 0.00075219 + layer.3.k_cache 0.00130310 0.27602514 + layer.3.v_cache 0.00000229 0.00121573 + layer.4.k_cache 0.00355794 0.50375407 + layer.4.v_cache 0.00000315 0.00202513 + layer.4.output 0.00017029 0.03750645 + ------------------------------------------------------------------------------------- + TOTAL 0.00284548 0.50599192 + (elements=2,408,448) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2408448 +Total Bytes 688132 +BPFP 2.2857 bits/point +EBPFP 4.5715 equivalent bits/point +MSE 0.505992 +---------------------- -------------------------------------------------------- +Time: 0.670s Load: 0.009s, Pack+Encode: 0.258s, Decode+Unpack: 0.403s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 168, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 168, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5060 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample67-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample67-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample68-layer4-item1.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample68-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 176, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 176, 128) +Output shape: (1, 176, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.output: torch.Size([1, 176, 4096]) -> torch.Size([1, 1, 176, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,116B, BPFP=1.2480 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,904B, BPFP=2.5703 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,960B, BPFP=1.9070 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,300B, BPFP=2.6767 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,780B, BPFP=2.1209 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,836B, BPFP=2.7005 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,576B, BPFP=2.1562 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,200B, BPFP=2.6722 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,448B, BPFP=1.9286 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,312B, BPFP=2.7216 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 189,916B, BPFP=2.1076 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.output: torch.Size([1, 176, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.405s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.output: torch.Size([1, 176, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02702593 4.14096173 + layer.0.v_cache 0.00000026 0.00014385 + layer.1.k_cache 0.00320345 0.44892667 + layer.1.v_cache 0.00000081 0.00050288 + layer.2.k_cache 0.00117342 0.25131143 + layer.2.v_cache 0.00000112 0.00070154 + layer.3.k_cache 0.00133382 0.27069530 + layer.3.v_cache 0.00000216 0.00113841 + layer.4.k_cache 0.00342137 0.50866799 + layer.4.v_cache 0.00000324 0.00203165 + layer.4.output 0.00018686 0.04302459 + ------------------------------------------------------------------------------------- + TOTAL 0.00263664 0.41408427 + (elements=2,523,136) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2523136 +Total Bytes 701348 +BPFP 2.2237 bits/point +EBPFP 4.4475 equivalent bits/point +MSE 0.414084 +---------------------- -------------------------------------------------------- +Time: 0.674s Load: 0.009s, Pack+Encode: 0.260s, Decode+Unpack: 0.405s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 176, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4141 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample68-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample68-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample69-layer4-item1.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample69-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 157, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 157, 128) +Output shape: (1, 157, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.0.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.1.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.1.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.2.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.2.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.3.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.3.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.4.k_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.4.v_cache: torch.Size([1, 8, 157, 128]) -> torch.Size([1, 1, 157, 1024]) + layer.4.output: torch.Size([1, 157, 4096]) -> torch.Size([1, 1, 157, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,744B, BPFP=1.2811 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 53,816B, BPFP=2.6779 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,856B, BPFP=1.9833 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,820B, BPFP=2.8274 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,216B, BPFP=2.2002 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,120B, BPFP=2.8424 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,044B, BPFP=2.2414 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,212B, BPFP=2.7972 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,104B, BPFP=1.9956 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,268B, BPFP=2.8497 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 177,688B, BPFP=2.2105 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 157, 128]) + layer.0.v_cache: torch.Size([1, 8, 157, 128]) + layer.1.k_cache: torch.Size([1, 8, 157, 128]) + layer.1.v_cache: torch.Size([1, 8, 157, 128]) + layer.2.k_cache: torch.Size([1, 8, 157, 128]) + layer.2.v_cache: torch.Size([1, 8, 157, 128]) + layer.3.k_cache: torch.Size([1, 8, 157, 128]) + layer.3.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.k_cache: torch.Size([1, 8, 157, 128]) + layer.4.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.output: torch.Size([1, 157, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 157, 128]) + layer.0.v_cache: torch.Size([1, 8, 157, 128]) + layer.1.k_cache: torch.Size([1, 8, 157, 128]) + layer.1.v_cache: torch.Size([1, 8, 157, 128]) + layer.2.k_cache: torch.Size([1, 8, 157, 128]) + layer.2.v_cache: torch.Size([1, 8, 157, 128]) + layer.3.k_cache: torch.Size([1, 8, 157, 128]) + layer.3.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.k_cache: torch.Size([1, 8, 157, 128]) + layer.4.v_cache: torch.Size([1, 8, 157, 128]) + layer.4.output: torch.Size([1, 157, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02778443 5.19048023 + layer.0.v_cache 0.00000027 0.00015552 + layer.1.k_cache 0.00313050 0.47950395 + layer.1.v_cache 0.00000087 0.00053100 + layer.2.k_cache 0.00120658 0.26263474 + layer.2.v_cache 0.00000114 0.00074696 + layer.3.k_cache 0.00130588 0.29004718 + layer.3.v_cache 0.00000220 0.00116012 + layer.4.k_cache 0.00347338 0.50974730 + layer.4.v_cache 0.00000322 0.00206786 + layer.4.output 0.00013960 0.05102182 + ------------------------------------------------------------------------------------- + TOTAL 0.00267620 0.49579729 + (elements=2,250,752) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2250752 +Total Bytes 653888 +BPFP 2.3242 bits/point +EBPFP 4.6483 equivalent bits/point +MSE 0.495797 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.008s, Pack+Encode: 0.259s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 157, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 157, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4958 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample69-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample69-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample7-layer4-item1.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample7-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 221, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 221, 128) +Output shape: (1, 221, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.0.v_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.1.k_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.1.v_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.2.k_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.2.v_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.3.k_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.3.v_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.4.k_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.4.v_cache: torch.Size([1, 8, 221, 128]) -> torch.Size([1, 1, 221, 1024]) + layer.4.output: torch.Size([1, 221, 4096]) -> torch.Size([1, 1, 221, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 33,516B, BPFP=1.1848 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 72,844B, BPFP=2.5751 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 53,824B, BPFP=1.9027 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 76,444B, BPFP=2.7023 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 59,764B, BPFP=2.1127 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 77,056B, BPFP=2.7240 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 61,624B, BPFP=2.1785 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 76,248B, BPFP=2.6954 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 54,516B, BPFP=1.9272 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 77,396B, BPFP=2.7360 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 236,540B, BPFP=2.0905 +⌛️ [2/4] FRONTEND: Frontend time: 0.311s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 221, 128]) + layer.0.v_cache: torch.Size([1, 8, 221, 128]) + layer.1.k_cache: torch.Size([1, 8, 221, 128]) + layer.1.v_cache: torch.Size([1, 8, 221, 128]) + layer.2.k_cache: torch.Size([1, 8, 221, 128]) + layer.2.v_cache: torch.Size([1, 8, 221, 128]) + layer.3.k_cache: torch.Size([1, 8, 221, 128]) + layer.3.v_cache: torch.Size([1, 8, 221, 128]) + layer.4.k_cache: torch.Size([1, 8, 221, 128]) + layer.4.v_cache: torch.Size([1, 8, 221, 128]) + layer.4.output: torch.Size([1, 221, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 221, 128]) + layer.0.v_cache: torch.Size([1, 8, 221, 128]) + layer.1.k_cache: torch.Size([1, 8, 221, 128]) + layer.1.v_cache: torch.Size([1, 8, 221, 128]) + layer.2.k_cache: torch.Size([1, 8, 221, 128]) + layer.2.v_cache: torch.Size([1, 8, 221, 128]) + layer.3.k_cache: torch.Size([1, 8, 221, 128]) + layer.3.v_cache: torch.Size([1, 8, 221, 128]) + layer.4.k_cache: torch.Size([1, 8, 221, 128]) + layer.4.v_cache: torch.Size([1, 8, 221, 128]) + layer.4.output: torch.Size([1, 221, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02443308 4.82793830 + layer.0.v_cache 0.00000027 0.00015452 + layer.1.k_cache 0.00299519 0.45365277 + layer.1.v_cache 0.00000096 0.00049241 + layer.2.k_cache 0.00117758 0.25759639 + layer.2.v_cache 0.00000111 0.00068242 + layer.3.k_cache 0.00134364 0.28940789 + layer.3.v_cache 0.00000231 0.00118215 + layer.4.k_cache 0.00352016 0.51525506 + layer.4.v_cache 0.00000295 0.00180285 + layer.4.output 0.00021856 0.04890080 + ------------------------------------------------------------------------------------- + TOTAL 0.00245368 0.46741200 + (elements=3,168,256) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3168256 +Total Bytes 879772 +BPFP 2.2215 bits/point +EBPFP 4.4429 equivalent bits/point +MSE 0.467412 +---------------------- -------------------------------------------------------- +Time: 0.829s Load: 0.011s, Pack+Encode: 0.311s, Decode+Unpack: 0.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 221, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 221, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4674 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample7-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample7-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample70-layer4-item1.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample70-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 172, 128) +Output shape: (1, 172, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.output: torch.Size([1, 172, 4096]) -> torch.Size([1, 1, 172, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,964B, BPFP=1.2702 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,356B, BPFP=2.6052 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,480B, BPFP=1.9295 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,356B, BPFP=2.7415 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,376B, BPFP=2.1519 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,856B, BPFP=2.7642 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,332B, BPFP=2.1953 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,036B, BPFP=2.7269 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,028B, BPFP=1.9544 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,012B, BPFP=2.7713 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,680B, BPFP=2.1652 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02652868 4.73339666 + layer.0.v_cache 0.00000028 0.00015919 + layer.1.k_cache 0.00309189 0.44078255 + layer.1.v_cache 0.00000082 0.00055532 + layer.2.k_cache 0.00116742 0.25722504 + layer.2.v_cache 0.00000113 0.00073259 + layer.3.k_cache 0.00134765 0.28104385 + layer.3.v_cache 0.00000211 0.00116059 + layer.4.k_cache 0.00352837 0.51521780 + layer.4.v_cache 0.00000329 0.00212401 + layer.4.output 0.00017786 0.04232029 + ------------------------------------------------------------------------------------- + TOTAL 0.00259879 0.45726277 + (elements=2,465,792) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2465792 +Total Bytes 699476 +BPFP 2.2694 bits/point +EBPFP 4.5388 equivalent bits/point +MSE 0.457263 +---------------------- -------------------------------------------------------- +Time: 0.671s Load: 0.010s, Pack+Encode: 0.259s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4573 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample70-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample70-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample72-layer4-item1.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample72-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 161, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 161, 128) +Output shape: (1, 161, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.0.v_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.1.k_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.1.v_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.2.k_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.2.v_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.3.k_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.3.v_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.4.k_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.4.v_cache: torch.Size([1, 8, 161, 128]) -> torch.Size([1, 1, 161, 1024]) + layer.4.output: torch.Size([1, 161, 4096]) -> torch.Size([1, 1, 161, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,292B, BPFP=1.2273 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,352B, BPFP=2.6374 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,432B, BPFP=1.9134 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 57,012B, BPFP=2.7665 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,864B, BPFP=2.1285 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,380B, BPFP=2.7844 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,032B, BPFP=2.1852 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,308B, BPFP=2.7323 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,748B, BPFP=1.9288 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,336B, BPFP=2.7822 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 176,916B, BPFP=2.1462 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 161, 128]) + layer.0.v_cache: torch.Size([1, 8, 161, 128]) + layer.1.k_cache: torch.Size([1, 8, 161, 128]) + layer.1.v_cache: torch.Size([1, 8, 161, 128]) + layer.2.k_cache: torch.Size([1, 8, 161, 128]) + layer.2.v_cache: torch.Size([1, 8, 161, 128]) + layer.3.k_cache: torch.Size([1, 8, 161, 128]) + layer.3.v_cache: torch.Size([1, 8, 161, 128]) + layer.4.k_cache: torch.Size([1, 8, 161, 128]) + layer.4.v_cache: torch.Size([1, 8, 161, 128]) + layer.4.output: torch.Size([1, 161, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 161, 128]) + layer.0.v_cache: torch.Size([1, 8, 161, 128]) + layer.1.k_cache: torch.Size([1, 8, 161, 128]) + layer.1.v_cache: torch.Size([1, 8, 161, 128]) + layer.2.k_cache: torch.Size([1, 8, 161, 128]) + layer.2.v_cache: torch.Size([1, 8, 161, 128]) + layer.3.k_cache: torch.Size([1, 8, 161, 128]) + layer.3.v_cache: torch.Size([1, 8, 161, 128]) + layer.4.k_cache: torch.Size([1, 8, 161, 128]) + layer.4.v_cache: torch.Size([1, 8, 161, 128]) + layer.4.output: torch.Size([1, 161, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02852683 4.47441490 + layer.0.v_cache 0.00000027 0.00014960 + layer.1.k_cache 0.00307027 0.45841155 + layer.1.v_cache 0.00000080 0.00051654 + layer.2.k_cache 0.00118547 0.26104841 + layer.2.v_cache 0.00000112 0.00073167 + layer.3.k_cache 0.00132345 0.27936099 + layer.3.v_cache 0.00000221 0.00113769 + layer.4.k_cache 0.00350348 0.50251941 + layer.4.v_cache 0.00000302 0.00187879 + layer.4.output 0.00018782 0.05145920 + ------------------------------------------------------------------------------------- + TOTAL 0.00274058 0.44185760 + (elements=2,308,096) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2308096 +Total Bytes 652672 +BPFP 2.2622 bits/point +EBPFP 4.5244 equivalent bits/point +MSE 0.441858 +---------------------- -------------------------------------------------------- +Time: 0.666s Load: 0.008s, Pack+Encode: 0.258s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 161, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 161, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4419 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample72-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample72-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample73-layer4-item1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample73-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 176, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 176, 128) +Output shape: (1, 176, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) -> torch.Size([1, 1, 176, 1024]) + layer.4.output: torch.Size([1, 176, 4096]) -> torch.Size([1, 1, 176, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,884B, BPFP=1.2377 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,224B, BPFP=2.5845 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,316B, BPFP=1.9228 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,940B, BPFP=2.7051 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,116B, BPFP=2.1358 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,264B, BPFP=2.7195 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,268B, BPFP=2.1870 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,736B, BPFP=2.6960 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,000B, BPFP=1.9531 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,700B, BPFP=2.7388 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 194,768B, BPFP=2.1614 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.output: torch.Size([1, 176, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 176, 128]) + layer.0.v_cache: torch.Size([1, 8, 176, 128]) + layer.1.k_cache: torch.Size([1, 8, 176, 128]) + layer.1.v_cache: torch.Size([1, 8, 176, 128]) + layer.2.k_cache: torch.Size([1, 8, 176, 128]) + layer.2.v_cache: torch.Size([1, 8, 176, 128]) + layer.3.k_cache: torch.Size([1, 8, 176, 128]) + layer.3.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.k_cache: torch.Size([1, 8, 176, 128]) + layer.4.v_cache: torch.Size([1, 8, 176, 128]) + layer.4.output: torch.Size([1, 176, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02614397 4.27820448 + layer.0.v_cache 0.00000027 0.00015075 + layer.1.k_cache 0.00303479 0.44605524 + layer.1.v_cache 0.00000100 0.00056936 + layer.2.k_cache 0.00119315 0.24708254 + layer.2.v_cache 0.00000111 0.00071995 + layer.3.k_cache 0.00127363 0.26901965 + layer.3.v_cache 0.00000250 0.00126241 + layer.4.k_cache 0.00361948 0.49760641 + layer.4.v_cache 0.00000340 0.00205586 + layer.4.output 0.00017471 0.04460516 + ------------------------------------------------------------------------------------- + TOTAL 0.00256944 0.42293909 + (elements=2,523,136) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2523136 +Total Bytes 710216 +BPFP 2.2519 bits/point +EBPFP 4.5037 equivalent bits/point +MSE 0.422939 +---------------------- -------------------------------------------------------- +Time: 0.673s Load: 0.009s, Pack+Encode: 0.260s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 176, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 176, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4229 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample73-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample73-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample75-layer4-item1.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample75-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 153, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 153, 128) +Output shape: (1, 153, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.0.v_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.1.k_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.1.v_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.2.k_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.2.v_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.3.k_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.3.v_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.4.k_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.4.v_cache: torch.Size([1, 8, 153, 128]) -> torch.Size([1, 1, 153, 1024]) + layer.4.output: torch.Size([1, 153, 4096]) -> torch.Size([1, 1, 153, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,324B, BPFP=1.2931 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 53,068B, BPFP=2.7098 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,064B, BPFP=1.9947 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 55,564B, BPFP=2.8372 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,416B, BPFP=2.2169 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 56,040B, BPFP=2.8615 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,428B, BPFP=2.2686 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 54,992B, BPFP=2.8080 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,380B, BPFP=2.0108 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 56,012B, BPFP=2.8601 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 175,852B, BPFP=2.2448 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 153, 128]) + layer.0.v_cache: torch.Size([1, 8, 153, 128]) + layer.1.k_cache: torch.Size([1, 8, 153, 128]) + layer.1.v_cache: torch.Size([1, 8, 153, 128]) + layer.2.k_cache: torch.Size([1, 8, 153, 128]) + layer.2.v_cache: torch.Size([1, 8, 153, 128]) + layer.3.k_cache: torch.Size([1, 8, 153, 128]) + layer.3.v_cache: torch.Size([1, 8, 153, 128]) + layer.4.k_cache: torch.Size([1, 8, 153, 128]) + layer.4.v_cache: torch.Size([1, 8, 153, 128]) + layer.4.output: torch.Size([1, 153, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.399s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 153, 128]) + layer.0.v_cache: torch.Size([1, 8, 153, 128]) + layer.1.k_cache: torch.Size([1, 8, 153, 128]) + layer.1.v_cache: torch.Size([1, 8, 153, 128]) + layer.2.k_cache: torch.Size([1, 8, 153, 128]) + layer.2.v_cache: torch.Size([1, 8, 153, 128]) + layer.3.k_cache: torch.Size([1, 8, 153, 128]) + layer.3.v_cache: torch.Size([1, 8, 153, 128]) + layer.4.k_cache: torch.Size([1, 8, 153, 128]) + layer.4.v_cache: torch.Size([1, 8, 153, 128]) + layer.4.output: torch.Size([1, 153, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02796515 4.57487697 + layer.0.v_cache 0.00000026 0.00015239 + layer.1.k_cache 0.00317772 0.47204550 + layer.1.v_cache 0.00000088 0.00053747 + layer.2.k_cache 0.00119895 0.25502792 + layer.2.v_cache 0.00000122 0.00078372 + layer.3.k_cache 0.00130332 0.27712170 + layer.3.v_cache 0.00000226 0.00123025 + layer.4.k_cache 0.00344657 0.49278170 + layer.4.v_cache 0.00000314 0.00194069 + layer.4.output 0.00015128 0.05329566 + ------------------------------------------------------------------------------------- + TOTAL 0.00269319 0.44926293 + (elements=2,193,408) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2193408 +Total Bytes 643140 +BPFP 2.3457 bits/point +EBPFP 4.6914 equivalent bits/point +MSE 0.449263 +---------------------- -------------------------------------------------------- +Time: 0.665s Load: 0.009s, Pack+Encode: 0.258s, Decode+Unpack: 0.399s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 153, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 153, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4493 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample75-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample75-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample76-layer4-item1.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample76-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 154, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 154, 128) +Output shape: (1, 154, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) -> torch.Size([1, 1, 154, 1024]) + layer.4.output: torch.Size([1, 154, 4096]) -> torch.Size([1, 1, 154, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,800B, BPFP=1.2581 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 52,988B, BPFP=2.6881 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,088B, BPFP=1.9830 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 55,860B, BPFP=2.8338 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 43,904B, BPFP=2.2273 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 56,396B, BPFP=2.8610 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 44,568B, BPFP=2.2610 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 55,456B, BPFP=2.8133 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 39,608B, BPFP=2.0093 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 56,496B, BPFP=2.8661 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 174,584B, BPFP=2.2142 +⌛️ [2/4] FRONTEND: Frontend time: 0.256s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.output: torch.Size([1, 154, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 154, 128]) + layer.0.v_cache: torch.Size([1, 8, 154, 128]) + layer.1.k_cache: torch.Size([1, 8, 154, 128]) + layer.1.v_cache: torch.Size([1, 8, 154, 128]) + layer.2.k_cache: torch.Size([1, 8, 154, 128]) + layer.2.v_cache: torch.Size([1, 8, 154, 128]) + layer.3.k_cache: torch.Size([1, 8, 154, 128]) + layer.3.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.k_cache: torch.Size([1, 8, 154, 128]) + layer.4.v_cache: torch.Size([1, 8, 154, 128]) + layer.4.output: torch.Size([1, 154, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02770888 4.72666436 + layer.0.v_cache 0.00000027 0.00015458 + layer.1.k_cache 0.00299767 0.44360381 + layer.1.v_cache 0.00000089 0.00053944 + layer.2.k_cache 0.00118955 0.26049052 + layer.2.v_cache 0.00000124 0.00076696 + layer.3.k_cache 0.00128217 0.27618284 + layer.3.v_cache 0.00000233 0.00121261 + layer.4.k_cache 0.00329405 0.46904720 + layer.4.v_cache 0.00000340 0.00213965 + layer.4.output 0.00014754 0.05141606 + ------------------------------------------------------------------------------------- + TOTAL 0.00264790 0.45617616 + (elements=2,207,744) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2207744 +Total Bytes 643748 +BPFP 2.3327 bits/point +EBPFP 4.6654 equivalent bits/point +MSE 0.456176 +---------------------- -------------------------------------------------------- +Time: 0.663s Load: 0.008s, Pack+Encode: 0.256s, Decode+Unpack: 0.398s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 154, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 154, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4562 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample76-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample76-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample78-layer4-item1.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample78-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 169, 128) +Output shape: (1, 169, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.output: torch.Size([1, 169, 4096]) -> torch.Size([1, 1, 169, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,584B, BPFP=1.2751 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,224B, BPFP=2.6453 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,880B, BPFP=1.9360 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,164B, BPFP=2.7812 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,716B, BPFP=2.1596 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,648B, BPFP=2.8036 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,052B, BPFP=2.2213 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,056B, BPFP=2.7763 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,460B, BPFP=1.9628 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,692B, BPFP=2.8057 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 196,100B, BPFP=2.2663 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.401s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02814460 5.22423955 + layer.0.v_cache 0.00000027 0.00015570 + layer.1.k_cache 0.00305883 0.46547252 + layer.1.v_cache 0.00000085 0.00054587 + layer.2.k_cache 0.00120620 0.25815892 + layer.2.v_cache 0.00000155 0.00079196 + layer.3.k_cache 0.00127672 0.28381521 + layer.3.v_cache 0.00000231 0.00118363 + layer.4.k_cache 0.00421318 0.49104169 + layer.4.v_cache 0.00000320 0.00191681 + layer.4.output 0.00020059 0.05077414 + ------------------------------------------------------------------------------------- + TOTAL 0.00276501 0.49502989 + (elements=2,422,784) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2422784 +Total Bytes 701576 +BPFP 2.3166 bits/point +EBPFP 4.6332 equivalent bits/point +MSE 0.495030 +---------------------- -------------------------------------------------------- +Time: 0.669s Load: 0.009s, Pack+Encode: 0.259s, Decode+Unpack: 0.401s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4950 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample78-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample78-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample79-layer4-item1.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample79-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 180, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 180, 128) +Output shape: (1, 180, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.0.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.1.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.1.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.2.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.2.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.3.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.3.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.4.k_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.4.v_cache: torch.Size([1, 8, 180, 128]) -> torch.Size([1, 1, 180, 1024]) + layer.4.output: torch.Size([1, 180, 4096]) -> torch.Size([1, 1, 180, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,792B, BPFP=1.2063 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,776B, BPFP=2.5510 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,396B, BPFP=1.8835 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,520B, BPFP=2.6701 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,936B, BPFP=2.1240 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,332B, BPFP=2.7054 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,676B, BPFP=2.1561 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,776B, BPFP=2.6812 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 44,056B, BPFP=1.9122 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,236B, BPFP=2.7012 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 197,072B, BPFP=2.1384 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 180, 128]) + layer.0.v_cache: torch.Size([1, 8, 180, 128]) + layer.1.k_cache: torch.Size([1, 8, 180, 128]) + layer.1.v_cache: torch.Size([1, 8, 180, 128]) + layer.2.k_cache: torch.Size([1, 8, 180, 128]) + layer.2.v_cache: torch.Size([1, 8, 180, 128]) + layer.3.k_cache: torch.Size([1, 8, 180, 128]) + layer.3.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.k_cache: torch.Size([1, 8, 180, 128]) + layer.4.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.output: torch.Size([1, 180, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.404s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 180, 128]) + layer.0.v_cache: torch.Size([1, 8, 180, 128]) + layer.1.k_cache: torch.Size([1, 8, 180, 128]) + layer.1.v_cache: torch.Size([1, 8, 180, 128]) + layer.2.k_cache: torch.Size([1, 8, 180, 128]) + layer.2.v_cache: torch.Size([1, 8, 180, 128]) + layer.3.k_cache: torch.Size([1, 8, 180, 128]) + layer.3.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.k_cache: torch.Size([1, 8, 180, 128]) + layer.4.v_cache: torch.Size([1, 8, 180, 128]) + layer.4.output: torch.Size([1, 180, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02888792 4.83693983 + layer.0.v_cache 0.00000029 0.00015757 + layer.1.k_cache 0.00301893 0.41226095 + layer.1.v_cache 0.00000086 0.00052812 + layer.2.k_cache 0.00125228 0.25601412 + layer.2.v_cache 0.00000118 0.00074674 + layer.3.k_cache 0.00126354 0.26809936 + layer.3.v_cache 0.00000246 0.00128300 + layer.4.k_cache 0.00353636 0.47944162 + layer.4.v_cache 0.00000320 0.00194932 + layer.4.output 0.00017167 0.04206178 + ------------------------------------------------------------------------------------- + TOTAL 0.00276098 0.45897627 + (elements=2,580,480) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2580480 +Total Bytes 717568 +BPFP 2.2246 bits/point +EBPFP 4.4492 equivalent bits/point +MSE 0.458976 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.009s, Pack+Encode: 0.259s, Decode+Unpack: 0.404s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 180, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 180, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4590 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample79-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample79-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample8-layer4-item1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample8-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 212, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 212, 128) +Output shape: (1, 212, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.0.v_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.1.k_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.1.v_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.2.k_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.2.v_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.3.k_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.3.v_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.4.k_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.4.v_cache: torch.Size([1, 8, 212, 128]) -> torch.Size([1, 1, 212, 1024]) + layer.4.output: torch.Size([1, 212, 4096]) -> torch.Size([1, 1, 212, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 32,172B, BPFP=1.1856 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 71,300B, BPFP=2.6275 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 52,588B, BPFP=1.9379 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 74,508B, BPFP=2.7457 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 58,524B, BPFP=2.1567 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 75,344B, BPFP=2.7765 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 60,048B, BPFP=2.2129 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 74,248B, BPFP=2.7361 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 52,704B, BPFP=1.9422 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 75,540B, BPFP=2.7838 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 233,756B, BPFP=2.1536 +⌛️ [2/4] FRONTEND: Frontend time: 0.309s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 212, 128]) + layer.0.v_cache: torch.Size([1, 8, 212, 128]) + layer.1.k_cache: torch.Size([1, 8, 212, 128]) + layer.1.v_cache: torch.Size([1, 8, 212, 128]) + layer.2.k_cache: torch.Size([1, 8, 212, 128]) + layer.2.v_cache: torch.Size([1, 8, 212, 128]) + layer.3.k_cache: torch.Size([1, 8, 212, 128]) + layer.3.v_cache: torch.Size([1, 8, 212, 128]) + layer.4.k_cache: torch.Size([1, 8, 212, 128]) + layer.4.v_cache: torch.Size([1, 8, 212, 128]) + layer.4.output: torch.Size([1, 212, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 212, 128]) + layer.0.v_cache: torch.Size([1, 8, 212, 128]) + layer.1.k_cache: torch.Size([1, 8, 212, 128]) + layer.1.v_cache: torch.Size([1, 8, 212, 128]) + layer.2.k_cache: torch.Size([1, 8, 212, 128]) + layer.2.v_cache: torch.Size([1, 8, 212, 128]) + layer.3.k_cache: torch.Size([1, 8, 212, 128]) + layer.3.v_cache: torch.Size([1, 8, 212, 128]) + layer.4.k_cache: torch.Size([1, 8, 212, 128]) + layer.4.v_cache: torch.Size([1, 8, 212, 128]) + layer.4.output: torch.Size([1, 212, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02614697 4.46573509 + layer.0.v_cache 0.00000027 0.00014179 + layer.1.k_cache 0.00297715 0.41524916 + layer.1.v_cache 0.00000079 0.00049994 + layer.2.k_cache 0.00116049 0.25310067 + layer.2.v_cache 0.00000116 0.00072661 + layer.3.k_cache 0.00132944 0.28764707 + layer.3.v_cache 0.00000234 0.00119102 + layer.4.k_cache 0.00407070 0.52465514 + layer.4.v_cache 0.00000311 0.00185669 + layer.4.output 0.00021146 0.04658026 + ------------------------------------------------------------------------------------- + TOTAL 0.00260988 0.43836601 + (elements=3,039,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 3039232 +Total Bytes 860732 +BPFP 2.2657 bits/point +EBPFP 4.5313 equivalent bits/point +MSE 0.438366 +---------------------- -------------------------------------------------------- +Time: 0.827s Load: 0.011s, Pack+Encode: 0.309s, Decode+Unpack: 0.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 212, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 212, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4384 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample8-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample8-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample80-layer4-item1.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample80-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 172, 128) +Output shape: (1, 172, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) -> torch.Size([1, 1, 172, 1024]) + layer.4.output: torch.Size([1, 172, 4096]) -> torch.Size([1, 1, 172, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,568B, BPFP=1.2522 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,564B, BPFP=2.6146 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,580B, BPFP=1.9340 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,488B, BPFP=2.7475 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,556B, BPFP=2.1601 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,948B, BPFP=2.7684 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,292B, BPFP=2.1935 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,968B, BPFP=2.7238 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,848B, BPFP=1.9462 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,916B, BPFP=2.7669 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,288B, BPFP=2.1608 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.401s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 172, 128]) + layer.0.v_cache: torch.Size([1, 8, 172, 128]) + layer.1.k_cache: torch.Size([1, 8, 172, 128]) + layer.1.v_cache: torch.Size([1, 8, 172, 128]) + layer.2.k_cache: torch.Size([1, 8, 172, 128]) + layer.2.v_cache: torch.Size([1, 8, 172, 128]) + layer.3.k_cache: torch.Size([1, 8, 172, 128]) + layer.3.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.k_cache: torch.Size([1, 8, 172, 128]) + layer.4.v_cache: torch.Size([1, 8, 172, 128]) + layer.4.output: torch.Size([1, 172, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02862963 5.43747516 + layer.0.v_cache 0.00000027 0.00014716 + layer.1.k_cache 0.00312686 0.44416175 + layer.1.v_cache 0.00000083 0.00054962 + layer.2.k_cache 0.00115513 0.25832316 + layer.2.v_cache 0.00000117 0.00074451 + layer.3.k_cache 0.00132852 0.27942484 + layer.3.v_cache 0.00000218 0.00112780 + layer.4.k_cache 0.00345250 0.49109973 + layer.4.v_cache 0.00000308 0.00192793 + layer.4.output 0.00017775 0.04532305 + ------------------------------------------------------------------------------------- + TOTAL 0.00274366 0.50687670 + (elements=2,465,792) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2465792 +Total Bytes 699016 +BPFP 2.2679 bits/point +EBPFP 4.5358 equivalent bits/point +MSE 0.506877 +---------------------- -------------------------------------------------------- +Time: 0.670s Load: 0.009s, Pack+Encode: 0.260s, Decode+Unpack: 0.401s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 172, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 172, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5069 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample80-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample80-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample81-layer4-item1.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample81-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 158, 128) +Output shape: (1, 158, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.output: torch.Size([1, 158, 4096]) -> torch.Size([1, 1, 158, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,700B, BPFP=1.2213 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,040B, BPFP=2.6721 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,896B, BPFP=1.9727 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,880B, BPFP=2.8125 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,312B, BPFP=2.1911 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,332B, BPFP=2.8348 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,104B, BPFP=2.2302 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,400B, BPFP=2.7888 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,200B, BPFP=1.9877 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,340B, BPFP=2.8352 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 175,852B, BPFP=2.1738 +⌛️ [2/4] FRONTEND: Frontend time: 0.257s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02915672 5.05800001 + layer.0.v_cache 0.00000028 0.00015295 + layer.1.k_cache 0.00318231 0.47092022 + layer.1.v_cache 0.00000085 0.00052934 + layer.2.k_cache 0.00121414 0.25264571 + layer.2.v_cache 0.00000113 0.00071382 + layer.3.k_cache 0.00130024 0.28826868 + layer.3.v_cache 0.00000226 0.00117402 + layer.4.k_cache 0.00343194 0.49762156 + layer.4.v_cache 0.00000318 0.00203845 + layer.4.output 0.00014314 0.04516040 + ------------------------------------------------------------------------------------- + TOTAL 0.00277611 0.48233617 + (elements=2,265,088) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2265088 +Total Bytes 652056 +BPFP 2.3030 bits/point +EBPFP 4.6060 equivalent bits/point +MSE 0.482336 +---------------------- -------------------------------------------------------- +Time: 0.663s Load: 0.008s, Pack+Encode: 0.257s, Decode+Unpack: 0.398s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4823 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample81-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample81-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample88-layer4-item1.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample88-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 169, 128) +Output shape: (1, 169, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.output: torch.Size([1, 169, 4096]) -> torch.Size([1, 1, 169, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,656B, BPFP=1.2322 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 56,940B, BPFP=2.6322 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,072B, BPFP=1.9449 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,712B, BPFP=2.7604 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 46,832B, BPFP=2.1649 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,288B, BPFP=2.7870 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,856B, BPFP=2.2123 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 59,464B, BPFP=2.7489 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,436B, BPFP=1.9617 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,140B, BPFP=2.7801 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 187,548B, BPFP=2.1675 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02741317 5.07086723 + layer.0.v_cache 0.00000027 0.00015355 + layer.1.k_cache 0.00313704 0.48583104 + layer.1.v_cache 0.00000078 0.00049860 + layer.2.k_cache 0.00120918 0.26726570 + layer.2.v_cache 0.00000116 0.00071240 + layer.3.k_cache 0.00136700 0.29131579 + layer.3.v_cache 0.00000220 0.00112950 + layer.4.k_cache 0.00352943 0.52023695 + layer.4.v_cache 0.00000294 0.00181920 + layer.4.output 0.00020829 0.04923220 + ------------------------------------------------------------------------------------- + TOTAL 0.00267831 0.48833991 + (elements=2,422,784) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2422784 +Total Bytes 689944 +BPFP 2.2782 bits/point +EBPFP 4.5564 equivalent bits/point +MSE 0.488340 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.009s, Pack+Encode: 0.258s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4883 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample88-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample88-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample89-layer4-item1.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample89-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 162, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 162, 128) +Output shape: (1, 162, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.output: torch.Size([1, 162, 4096]) -> torch.Size([1, 1, 162, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,592B, BPFP=1.2342 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,792B, BPFP=2.6424 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 40,140B, BPFP=1.9358 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 57,412B, BPFP=2.7687 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,304B, BPFP=2.1366 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,748B, BPFP=2.7849 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,584B, BPFP=2.1983 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,772B, BPFP=2.7378 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,008B, BPFP=1.9294 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,648B, BPFP=2.7801 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 179,580B, BPFP=2.1651 +⌛️ [2/4] FRONTEND: Frontend time: 0.257s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.output: torch.Size([1, 162, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.output: torch.Size([1, 162, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02771055 4.85236462 + layer.0.v_cache 0.00000026 0.00016002 + layer.1.k_cache 0.00323549 0.48286367 + layer.1.v_cache 0.00000081 0.00053948 + layer.2.k_cache 0.00117871 0.27365871 + layer.2.v_cache 0.00000115 0.00074447 + layer.3.k_cache 0.00135828 0.29378477 + layer.3.v_cache 0.00000214 0.00117978 + layer.4.k_cache 0.00384025 0.52853422 + layer.4.v_cache 0.00000294 0.00186079 + layer.4.output 0.00023325 0.05485117 + ------------------------------------------------------------------------------------- + TOTAL 0.00273311 0.47536394 + (elements=2,322,432) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2322432 +Total Bytes 659580 +BPFP 2.2720 bits/point +EBPFP 4.5441 equivalent bits/point +MSE 0.475364 +---------------------- -------------------------------------------------------- +Time: 0.665s Load: 0.008s, Pack+Encode: 0.257s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 162, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4754 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample89-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample89-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample9-layer4-item1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample9-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 193, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 193, 128) +Output shape: (1, 193, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.0.v_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.1.k_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.1.v_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.2.k_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.2.v_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.3.k_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.3.v_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.4.k_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.4.v_cache: torch.Size([1, 8, 193, 128]) -> torch.Size([1, 1, 193, 1024]) + layer.4.output: torch.Size([1, 193, 4096]) -> torch.Size([1, 1, 193, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,872B, BPFP=1.1687 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 67,684B, BPFP=2.7398 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 48,500B, BPFP=1.9632 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 71,652B, BPFP=2.9004 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 54,956B, BPFP=2.2246 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 72,588B, BPFP=2.9383 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 56,016B, BPFP=2.2675 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 70,872B, BPFP=2.8688 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 48,908B, BPFP=1.9798 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 72,428B, BPFP=2.9318 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 218,356B, BPFP=2.2097 +⌛️ [2/4] FRONTEND: Frontend time: 0.307s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 193, 128]) + layer.0.v_cache: torch.Size([1, 8, 193, 128]) + layer.1.k_cache: torch.Size([1, 8, 193, 128]) + layer.1.v_cache: torch.Size([1, 8, 193, 128]) + layer.2.k_cache: torch.Size([1, 8, 193, 128]) + layer.2.v_cache: torch.Size([1, 8, 193, 128]) + layer.3.k_cache: torch.Size([1, 8, 193, 128]) + layer.3.v_cache: torch.Size([1, 8, 193, 128]) + layer.4.k_cache: torch.Size([1, 8, 193, 128]) + layer.4.v_cache: torch.Size([1, 8, 193, 128]) + layer.4.output: torch.Size([1, 193, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 193, 128]) + layer.0.v_cache: torch.Size([1, 8, 193, 128]) + layer.1.k_cache: torch.Size([1, 8, 193, 128]) + layer.1.v_cache: torch.Size([1, 8, 193, 128]) + layer.2.k_cache: torch.Size([1, 8, 193, 128]) + layer.2.v_cache: torch.Size([1, 8, 193, 128]) + layer.3.k_cache: torch.Size([1, 8, 193, 128]) + layer.3.v_cache: torch.Size([1, 8, 193, 128]) + layer.4.k_cache: torch.Size([1, 8, 193, 128]) + layer.4.v_cache: torch.Size([1, 8, 193, 128]) + layer.4.output: torch.Size([1, 193, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02858143 4.22425581 + layer.0.v_cache 0.00000027 0.00015461 + layer.1.k_cache 0.00303266 0.45257952 + layer.1.v_cache 0.00000097 0.00055799 + layer.2.k_cache 0.00124314 0.25830043 + layer.2.v_cache 0.00000124 0.00078505 + layer.3.k_cache 0.00125873 0.27314746 + layer.3.v_cache 0.00000241 0.00126066 + layer.4.k_cache 0.00343145 0.46056773 + layer.4.v_cache 0.00000350 0.00205098 + layer.4.output 0.00017011 0.04181006 + ------------------------------------------------------------------------------------- + TOTAL 0.00273116 0.41720718 + (elements=2,766,848) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2766848 +Total Bytes 810832 +BPFP 2.3444 bits/point +EBPFP 4.6888 equivalent bits/point +MSE 0.417207 +---------------------- -------------------------------------------------------- +Time: 0.816s Load: 0.010s, Pack+Encode: 0.307s, Decode+Unpack: 0.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 193, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 193, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4172 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample9-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample9-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample90-layer4-item1.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample90-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.012s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 189, 128) +Output shape: (1, 189, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.output: torch.Size([1, 189, 4096]) -> torch.Size([1, 1, 189, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,196B, BPFP=1.2068 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,212B, BPFP=2.4476 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,364B, BPFP=1.8338 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 62,168B, BPFP=2.5698 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 49,408B, BPFP=2.0423 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,484B, BPFP=2.5828 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 50,104B, BPFP=2.0711 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 61,972B, BPFP=2.5617 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 45,028B, BPFP=1.8613 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,820B, BPFP=2.5967 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 199,880B, BPFP=2.0656 +⌛️ [2/4] FRONTEND: Frontend time: 0.259s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.402s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02726509 4.55340382 + layer.0.v_cache 0.00000027 0.00015413 + layer.1.k_cache 0.00303300 0.41162061 + layer.1.v_cache 0.00000085 0.00054691 + layer.2.k_cache 0.00116837 0.24693302 + layer.2.v_cache 0.00000118 0.00072683 + layer.3.k_cache 0.00127025 0.26817834 + layer.3.v_cache 0.00000234 0.00121024 + layer.4.k_cache 0.00356333 0.46350913 + layer.4.v_cache 0.00000334 0.00201661 + layer.4.output 0.00017162 0.04123904 + ------------------------------------------------------------------------------------- + TOTAL 0.00264246 0.43666113 + (elements=2,709,504) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2709504 +Total Bytes 726636 +BPFP 2.1454 bits/point +EBPFP 4.2909 equivalent bits/point +MSE 0.436661 +---------------------- -------------------------------------------------------- +Time: 0.672s Load: 0.012s, Pack+Encode: 0.259s, Decode+Unpack: 0.402s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4367 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample90-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample90-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample91-layer4-item1.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample91-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 189, 128) +Output shape: (1, 189, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) -> torch.Size([1, 1, 189, 1024]) + layer.4.output: torch.Size([1, 189, 4096]) -> torch.Size([1, 1, 189, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 29,268B, BPFP=1.2098 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 59,304B, BPFP=2.4514 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 44,480B, BPFP=1.8386 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 62,316B, BPFP=2.5759 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 49,480B, BPFP=2.0453 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 62,568B, BPFP=2.5863 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 50,188B, BPFP=2.0746 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 62,100B, BPFP=2.5670 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 45,144B, BPFP=1.8661 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 62,988B, BPFP=2.6037 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 200,176B, BPFP=2.0686 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.430s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 189, 128]) + layer.0.v_cache: torch.Size([1, 8, 189, 128]) + layer.1.k_cache: torch.Size([1, 8, 189, 128]) + layer.1.v_cache: torch.Size([1, 8, 189, 128]) + layer.2.k_cache: torch.Size([1, 8, 189, 128]) + layer.2.v_cache: torch.Size([1, 8, 189, 128]) + layer.3.k_cache: torch.Size([1, 8, 189, 128]) + layer.3.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.k_cache: torch.Size([1, 8, 189, 128]) + layer.4.v_cache: torch.Size([1, 8, 189, 128]) + layer.4.output: torch.Size([1, 189, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02698849 4.59276698 + layer.0.v_cache 0.00000027 0.00015219 + layer.1.k_cache 0.00302237 0.41656325 + layer.1.v_cache 0.00000087 0.00053487 + layer.2.k_cache 0.00117769 0.24493731 + layer.2.v_cache 0.00000119 0.00074286 + layer.3.k_cache 0.00126503 0.26740067 + layer.3.v_cache 0.00000236 0.00118367 + layer.4.k_cache 0.00350408 0.46496970 + layer.4.v_cache 0.00000327 0.00194350 + layer.4.output 0.00016083 0.03888980 + ------------------------------------------------------------------------------------- + TOTAL 0.00261492 0.43905387 + (elements=2,709,504) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2709504 +Total Bytes 728012 +BPFP 2.1495 bits/point +EBPFP 4.2990 equivalent bits/point +MSE 0.439054 +---------------------- -------------------------------------------------------- +Time: 0.699s Load: 0.010s, Pack+Encode: 0.258s, Decode+Unpack: 0.430s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 189, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 189, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4391 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample91-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample91-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample93-layer4-item1.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample93-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 158, 128) +Output shape: (1, 158, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) -> torch.Size([1, 1, 158, 1024]) + layer.4.output: torch.Size([1, 158, 4096]) -> torch.Size([1, 1, 158, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 24,776B, BPFP=1.2251 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,164B, BPFP=2.6782 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 39,524B, BPFP=1.9543 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 56,796B, BPFP=2.8083 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,068B, BPFP=2.1790 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 57,428B, BPFP=2.8396 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,264B, BPFP=2.2381 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 56,464B, BPFP=2.7919 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,104B, BPFP=1.9830 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 57,368B, BPFP=2.8366 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 177,624B, BPFP=2.1957 +⌛️ [2/4] FRONTEND: Frontend time: 0.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.399s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 158, 128]) + layer.0.v_cache: torch.Size([1, 8, 158, 128]) + layer.1.k_cache: torch.Size([1, 8, 158, 128]) + layer.1.v_cache: torch.Size([1, 8, 158, 128]) + layer.2.k_cache: torch.Size([1, 8, 158, 128]) + layer.2.v_cache: torch.Size([1, 8, 158, 128]) + layer.3.k_cache: torch.Size([1, 8, 158, 128]) + layer.3.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.k_cache: torch.Size([1, 8, 158, 128]) + layer.4.v_cache: torch.Size([1, 8, 158, 128]) + layer.4.output: torch.Size([1, 158, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02813752 5.03544520 + layer.0.v_cache 0.00000027 0.00015967 + layer.1.k_cache 0.00306488 0.46804172 + layer.1.v_cache 0.00000089 0.00053873 + layer.2.k_cache 0.00118156 0.25778232 + layer.2.v_cache 0.00000118 0.00075444 + layer.3.k_cache 0.00130882 0.29373497 + layer.3.v_cache 0.00000213 0.00113416 + layer.4.k_cache 0.00347855 0.51990499 + layer.4.v_cache 0.00000307 0.00191513 + layer.4.output 0.00015371 0.04995477 + ------------------------------------------------------------------------------------- + TOTAL 0.00269955 0.48423074 + (elements=2,265,088) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2265088 +Total Bytes 653580 +BPFP 2.3084 bits/point +EBPFP 4.6167 equivalent bits/point +MSE 0.484231 +---------------------- -------------------------------------------------------- +Time: 0.667s Load: 0.010s, Pack+Encode: 0.258s, Decode+Unpack: 0.399s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 158, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 158, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4842 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample93-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample93-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample94-layer4-item1.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample94-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 177, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 177, 128) +Output shape: (1, 177, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.output: torch.Size([1, 177, 4096]) -> torch.Size([1, 1, 177, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 27,744B, BPFP=1.2246 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,916B, BPFP=2.5563 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,804B, BPFP=1.9334 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 61,048B, BPFP=2.6946 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,484B, BPFP=2.1400 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,580B, BPFP=2.7180 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 49,132B, BPFP=2.1686 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,608B, BPFP=2.6751 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,852B, BPFP=1.9356 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,588B, BPFP=2.7184 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 195,060B, BPFP=2.1524 +⌛️ [2/4] FRONTEND: Frontend time: 0.260s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.output: torch.Size([1, 177, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.400s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.output: torch.Size([1, 177, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02696669 4.65660725 + layer.0.v_cache 0.00000028 0.00014232 + layer.1.k_cache 0.00303349 0.43080687 + layer.1.v_cache 0.00000089 0.00052455 + layer.2.k_cache 0.00118683 0.26029151 + layer.2.v_cache 0.00000112 0.00072060 + layer.3.k_cache 0.00129356 0.26994994 + layer.3.v_cache 0.00000212 0.00111193 + layer.4.k_cache 0.00349999 0.47709582 + layer.4.v_cache 0.00000320 0.00189684 + layer.4.output 0.00017955 0.04198869 + ------------------------------------------------------------------------------------- + TOTAL 0.00262188 0.44765017 + (elements=2,537,472) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2537472 +Total Bytes 710816 +BPFP 2.2410 bits/point +EBPFP 4.4820 equivalent bits/point +MSE 0.447650 +---------------------- -------------------------------------------------------- +Time: 0.669s Load: 0.009s, Pack+Encode: 0.260s, Decode+Unpack: 0.400s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 177, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4477 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample94-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample94-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample95-layer4-item1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample95-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 169, 128) +Output shape: (1, 169, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) -> torch.Size([1, 1, 169, 1024]) + layer.4.output: torch.Size([1, 169, 4096]) -> torch.Size([1, 1, 169, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,664B, BPFP=1.2326 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 56,336B, BPFP=2.6043 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 41,536B, BPFP=1.9201 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 59,176B, BPFP=2.7356 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 45,924B, BPFP=2.1230 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 59,656B, BPFP=2.7578 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 47,300B, BPFP=2.1866 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 58,964B, BPFP=2.7258 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 41,884B, BPFP=1.9362 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 59,768B, BPFP=2.7629 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 183,172B, BPFP=2.1169 +⌛️ [2/4] FRONTEND: Frontend time: 0.268s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.409s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 169, 128]) + layer.0.v_cache: torch.Size([1, 8, 169, 128]) + layer.1.k_cache: torch.Size([1, 8, 169, 128]) + layer.1.v_cache: torch.Size([1, 8, 169, 128]) + layer.2.k_cache: torch.Size([1, 8, 169, 128]) + layer.2.v_cache: torch.Size([1, 8, 169, 128]) + layer.3.k_cache: torch.Size([1, 8, 169, 128]) + layer.3.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.k_cache: torch.Size([1, 8, 169, 128]) + layer.4.v_cache: torch.Size([1, 8, 169, 128]) + layer.4.output: torch.Size([1, 169, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02729142 5.16322462 + layer.0.v_cache 0.00000026 0.00015224 + layer.1.k_cache 0.00319685 0.49308122 + layer.1.v_cache 0.00000078 0.00050196 + layer.2.k_cache 0.00117458 0.26485592 + layer.2.v_cache 0.00000113 0.00071756 + layer.3.k_cache 0.00133789 0.28982147 + layer.3.v_cache 0.00000210 0.00115017 + layer.4.k_cache 0.00349792 0.51616154 + layer.4.v_cache 0.00000290 0.00186198 + layer.4.output 0.00017352 0.04231785 + ------------------------------------------------------------------------------------- + TOTAL 0.00265714 0.49291429 + (elements=2,422,784) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2422784 +Total Bytes 680380 +BPFP 2.2466 bits/point +EBPFP 4.4932 equivalent bits/point +MSE 0.492914 +---------------------- -------------------------------------------------------- +Time: 0.686s Load: 0.009s, Pack+Encode: 0.268s, Decode+Unpack: 0.409s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 169, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 169, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4929 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample95-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample95-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample96-layer4-item1.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample96-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 162, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 162, 128) +Output shape: (1, 162, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) -> torch.Size([1, 1, 162, 1024]) + layer.4.output: torch.Size([1, 162, 4096]) -> torch.Size([1, 1, 162, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 25,944B, BPFP=1.2512 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 54,932B, BPFP=2.6491 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 40,160B, BPFP=1.9367 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 57,836B, BPFP=2.7892 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 44,792B, BPFP=2.1601 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 58,404B, BPFP=2.8166 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 45,872B, BPFP=2.2122 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 57,288B, BPFP=2.7627 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 40,680B, BPFP=1.9618 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 58,264B, BPFP=2.8098 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 179,700B, BPFP=2.1665 +⌛️ [2/4] FRONTEND: Frontend time: 0.262s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.output: torch.Size([1, 162, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.407s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 162, 128]) + layer.0.v_cache: torch.Size([1, 8, 162, 128]) + layer.1.k_cache: torch.Size([1, 8, 162, 128]) + layer.1.v_cache: torch.Size([1, 8, 162, 128]) + layer.2.k_cache: torch.Size([1, 8, 162, 128]) + layer.2.v_cache: torch.Size([1, 8, 162, 128]) + layer.3.k_cache: torch.Size([1, 8, 162, 128]) + layer.3.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.k_cache: torch.Size([1, 8, 162, 128]) + layer.4.v_cache: torch.Size([1, 8, 162, 128]) + layer.4.output: torch.Size([1, 162, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02640657 4.52388057 + layer.0.v_cache 0.00000026 0.00014596 + layer.1.k_cache 0.00309680 0.44886921 + layer.1.v_cache 0.00000084 0.00053442 + layer.2.k_cache 0.00116892 0.26593361 + layer.2.v_cache 0.00000121 0.00076889 + layer.3.k_cache 0.00130413 0.27283068 + layer.3.v_cache 0.00000229 0.00118110 + layer.4.k_cache 0.00341342 0.50275600 + layer.4.v_cache 0.00000318 0.00201244 + layer.4.output 0.00016848 0.04625363 + ------------------------------------------------------------------------------------- + TOTAL 0.00257654 0.44313767 + (elements=2,322,432) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2322432 +Total Bytes 663872 +BPFP 2.2868 bits/point +EBPFP 4.5736 equivalent bits/point +MSE 0.443138 +---------------------- -------------------------------------------------------- +Time: 0.678s Load: 0.009s, Pack+Encode: 0.262s, Decode+Unpack: 0.407s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 162, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 162, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4431 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample96-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample96-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample98-layer4-item1.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample98-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 173, 128) +Output shape: (1, 173, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) -> torch.Size([1, 1, 173, 1024]) + layer.4.output: torch.Size([1, 173, 4096]) -> torch.Size([1, 1, 173, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 26,876B, BPFP=1.2137 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 57,564B, BPFP=2.5995 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 42,796B, BPFP=1.9326 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,636B, BPFP=2.7383 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 47,236B, BPFP=2.1331 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 60,856B, BPFP=2.7482 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,360B, BPFP=2.1839 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,140B, BPFP=2.7159 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 42,940B, BPFP=1.9391 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 60,876B, BPFP=2.7491 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 189,756B, BPFP=2.1423 +⌛️ [2/4] FRONTEND: Frontend time: 0.261s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.413s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 173, 128]) + layer.0.v_cache: torch.Size([1, 8, 173, 128]) + layer.1.k_cache: torch.Size([1, 8, 173, 128]) + layer.1.v_cache: torch.Size([1, 8, 173, 128]) + layer.2.k_cache: torch.Size([1, 8, 173, 128]) + layer.2.v_cache: torch.Size([1, 8, 173, 128]) + layer.3.k_cache: torch.Size([1, 8, 173, 128]) + layer.3.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.k_cache: torch.Size([1, 8, 173, 128]) + layer.4.v_cache: torch.Size([1, 8, 173, 128]) + layer.4.output: torch.Size([1, 173, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02785747 5.16491629 + layer.0.v_cache 0.00000027 0.00016461 + layer.1.k_cache 0.00305214 0.46627261 + layer.1.v_cache 0.00000084 0.00055329 + layer.2.k_cache 0.00114171 0.25926063 + layer.2.v_cache 0.00000110 0.00072824 + layer.3.k_cache 0.00128082 0.28476151 + layer.3.v_cache 0.00000218 0.00116462 + layer.4.k_cache 0.00367794 0.51980882 + layer.4.v_cache 0.00000299 0.00193097 + layer.4.output 0.00016652 0.05017943 + ------------------------------------------------------------------------------------- + TOTAL 0.00269168 0.49287709 + (elements=2,480,128) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2480128 +Total Bytes 698036 +BPFP 2.2516 bits/point +EBPFP 4.5032 equivalent bits/point +MSE 0.492877 +---------------------- -------------------------------------------------------- +Time: 0.684s Load: 0.009s, Pack+Encode: 0.261s, Decode+Unpack: 0.413s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 173, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 173, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4929 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample98-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample98-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample99-layer4-item1.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample99-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 177, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 177, 128) +Output shape: (1, 177, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) -> torch.Size([1, 1, 177, 1024]) + layer.4.output: torch.Size([1, 177, 4096]) -> torch.Size([1, 1, 177, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 28,136B, BPFP=1.2419 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 58,116B, BPFP=2.5651 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 43,288B, BPFP=1.9107 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 60,696B, BPFP=2.6790 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 48,012B, BPFP=2.1192 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 61,156B, BPFP=2.6993 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 48,844B, BPFP=2.1559 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 60,456B, BPFP=2.6684 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 43,640B, BPFP=1.9262 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 61,528B, BPFP=2.7157 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 190,676B, BPFP=2.1040 +⌛️ [2/4] FRONTEND: Frontend time: 0.263s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.output: torch.Size([1, 177, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.410s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 177, 128]) + layer.0.v_cache: torch.Size([1, 8, 177, 128]) + layer.1.k_cache: torch.Size([1, 8, 177, 128]) + layer.1.v_cache: torch.Size([1, 8, 177, 128]) + layer.2.k_cache: torch.Size([1, 8, 177, 128]) + layer.2.v_cache: torch.Size([1, 8, 177, 128]) + layer.3.k_cache: torch.Size([1, 8, 177, 128]) + layer.3.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.k_cache: torch.Size([1, 8, 177, 128]) + layer.4.v_cache: torch.Size([1, 8, 177, 128]) + layer.4.output: torch.Size([1, 177, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02698118 4.46585066 + layer.0.v_cache 0.00000026 0.00014908 + layer.1.k_cache 0.00304540 0.44289605 + layer.1.v_cache 0.00000079 0.00050860 + layer.2.k_cache 0.00119876 0.25423735 + layer.2.v_cache 0.00000114 0.00069950 + layer.3.k_cache 0.00131734 0.27491881 + layer.3.v_cache 0.00000220 0.00113907 + layer.4.k_cache 0.00342134 0.50060449 + layer.4.v_cache 0.00000318 0.00199208 + layer.4.output 0.00016969 0.04538364 + ------------------------------------------------------------------------------------- + TOTAL 0.00261788 0.43746645 + (elements=2,537,472) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2537472 +Total Bytes 704548 +BPFP 2.2213 bits/point +EBPFP 4.4425 equivalent bits/point +MSE 0.437466 +---------------------- -------------------------------------------------------- +Time: 0.683s Load: 0.009s, Pack+Encode: 0.263s, Decode+Unpack: 0.410s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 177, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 177, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4375 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_truthfulqa_mc1/sample99-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_truthfulqa_mc1/sample99-layer4-item1.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 2.2608 bits/point +Avg EBPFP 4.5216 equivalent bits/point +Avg MSE 0.458210 +Avg Time 0.712s +------------------------ ---------------------------- diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/dtufc_hyperprior-featurecoding_qwen_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/dtufc_hyperprior-featurecoding_qwen_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..d46c7789ab0be4684c7a6b6eaeff365e1f2a012c --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/dtufc_hyperprior-featurecoding_qwen_individual.log @@ -0,0 +1,16958 @@ +Experiment: dtufc_hyperprior-featurecoding_qwen_individual +Log file: output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/dtufc_hyperprior-featurecoding_qwen_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: qwen + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.0.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.0.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_0_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.1.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.1.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_1_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.2.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.2.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_2_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.3.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.3.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_3_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_k.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.4.k_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_k.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_v.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.4.v_cache' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_layer_4_v.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_feature.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.4.output' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/qwen3/arc_fewshot-8bit_feature.json +Loaded per-key mappings: model=qwen + Keys: ['layer.0.k_cache', 'layer.0.v_cache', 'layer.1.k_cache', 'layer.1.v_cache', 'layer.2.k_cache', 'layer.2.v_cache', 'layer.3.k_cache', 'layer.3.v_cache', 'layer.4.k_cache', 'layer.4.v_cache', 'layer.4.output'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande +Output output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample0-layer4-item1.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample0-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 100, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 100, 128) +Output shape: (1, 100, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.0.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.1.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.1.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.2.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.2.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.3.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.3.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.4.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.4.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.4.output: torch.Size([1, 100, 4096]) -> torch.Size([1, 1, 100, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 17,200B, BPFP=1.3438 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 36,176B, BPFP=2.8262 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 26,980B, BPFP=2.1078 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 37,872B, BPFP=2.9588 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 29,712B, BPFP=2.3213 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 38,360B, BPFP=2.9969 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 30,472B, BPFP=2.3806 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 37,612B, BPFP=2.9384 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 26,772B, BPFP=2.0916 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 38,412B, BPFP=3.0009 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 121,316B, BPFP=2.3695 +⌛️ [2/4] FRONTEND: Frontend time: 0.448s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 100, 128]) + layer.0.v_cache: torch.Size([1, 8, 100, 128]) + layer.1.k_cache: torch.Size([1, 8, 100, 128]) + layer.1.v_cache: torch.Size([1, 8, 100, 128]) + layer.2.k_cache: torch.Size([1, 8, 100, 128]) + layer.2.v_cache: torch.Size([1, 8, 100, 128]) + layer.3.k_cache: torch.Size([1, 8, 100, 128]) + layer.3.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.k_cache: torch.Size([1, 8, 100, 128]) + layer.4.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.output: torch.Size([1, 100, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.330s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 100, 128]) + layer.0.v_cache: torch.Size([1, 8, 100, 128]) + layer.1.k_cache: torch.Size([1, 8, 100, 128]) + layer.1.v_cache: torch.Size([1, 8, 100, 128]) + layer.2.k_cache: torch.Size([1, 8, 100, 128]) + layer.2.v_cache: torch.Size([1, 8, 100, 128]) + layer.3.k_cache: torch.Size([1, 8, 100, 128]) + layer.3.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.k_cache: torch.Size([1, 8, 100, 128]) + layer.4.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.output: torch.Size([1, 100, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03019863 5.51007202 + layer.0.v_cache 0.00000027 0.00014425 + layer.1.k_cache 0.00347471 0.48838028 + layer.1.v_cache 0.00000091 0.00049521 + layer.2.k_cache 0.00113692 0.26139378 + layer.2.v_cache 0.00000108 0.00070758 + layer.3.k_cache 0.00132974 0.28876791 + layer.3.v_cache 0.00000211 0.00114417 + layer.4.k_cache 0.00328898 0.51178825 + layer.4.v_cache 0.00000306 0.00197506 + layer.4.output 0.00017021 0.05554049 + ------------------------------------------------------------------------------------- + TOTAL 0.00286552 0.52050218 + (elements=1,433,600) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1433600 +Total Bytes 440884 +BPFP 2.4603 bits/point +EBPFP 4.9206 equivalent bits/point +MSE 0.520502 +---------------------- -------------------------------------------------------- +Time: 0.784s Load: 0.006s, Pack+Encode: 0.448s, Decode+Unpack: 0.330s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 100, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5205 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample0-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample0-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample1-layer4-item1.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample1-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 98, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 98, 128) +Output shape: (1, 98, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.output: torch.Size([1, 98, 4096]) -> torch.Size([1, 1, 98, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,984B, BPFP=1.3540 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 35,092B, BPFP=2.7975 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,852B, BPFP=2.0609 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 36,592B, BPFP=2.9171 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,680B, BPFP=2.2864 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,996B, BPFP=2.9493 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 29,264B, BPFP=2.3329 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 36,292B, BPFP=2.8932 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,972B, BPFP=2.0705 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 37,240B, BPFP=2.9688 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 116,380B, BPFP=2.3194 +⌛️ [2/4] FRONTEND: Frontend time: 0.215s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.output: torch.Size([1, 98, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.output: torch.Size([1, 98, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03033472 5.61538883 + layer.0.v_cache 0.00000026 0.00014020 + layer.1.k_cache 0.00340990 0.49732886 + layer.1.v_cache 0.00000094 0.00051780 + layer.2.k_cache 0.00111132 0.26452162 + layer.2.v_cache 0.00000109 0.00072442 + layer.3.k_cache 0.00132423 0.30891041 + layer.3.v_cache 0.00000205 0.00119369 + layer.4.k_cache 0.00330658 0.52819135 + layer.4.v_cache 0.00000307 0.00204087 + layer.4.output 0.00020590 0.05642752 + ------------------------------------------------------------------------------------- + TOTAL 0.00287984 0.53176201 + (elements=1,404,928) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1404928 +Total Bytes 425344 +BPFP 2.4220 bits/point +EBPFP 4.8440 equivalent bits/point +MSE 0.531762 +---------------------- -------------------------------------------------------- +Time: 0.523s Load: 0.007s, Pack+Encode: 0.215s, Decode+Unpack: 0.301s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 98, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5318 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample1-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample1-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample10-layer4-item1.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample10-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,832B, BPFP=1.3158 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,092B, BPFP=2.8334 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,572B, BPFP=2.1253 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,656B, BPFP=2.9634 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,936B, BPFP=2.3218 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,188B, BPFP=3.0076 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,648B, BPFP=2.3810 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,620B, BPFP=2.9604 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,412B, BPFP=2.1120 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,124B, BPFP=3.0023 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 114,528B, BPFP=2.3797 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.304s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03043811 5.70796106 + layer.0.v_cache 0.00000028 0.00014560 + layer.1.k_cache 0.00344812 0.50479211 + layer.1.v_cache 0.00000082 0.00051681 + layer.2.k_cache 0.00115027 0.26306696 + layer.2.v_cache 0.00000109 0.00072399 + layer.3.k_cache 0.00130609 0.30090937 + layer.3.v_cache 0.00000216 0.00120445 + layer.4.k_cache 0.00321842 0.53504814 + layer.4.v_cache 0.00000315 0.00198404 + layer.4.output 0.00017058 0.06597255 + ------------------------------------------------------------------------------------- + TOTAL 0.00287506 0.54144591 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 415608 +BPFP 2.4673 bits/point +EBPFP 4.9346 equivalent bits/point +MSE 0.541446 +---------------------- -------------------------------------------------------- +Time: 0.521s Load: 0.006s, Pack+Encode: 0.211s, Decode+Unpack: 0.304s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5414 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample10-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample10-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample100-layer4-item1.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample100-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 88, 128) +Output shape: (1, 88, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.output: torch.Size([1, 88, 4096]) -> torch.Size([1, 1, 88, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,696B, BPFP=1.3047 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,384B, BPFP=2.7862 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,932B, BPFP=2.1246 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,984B, BPFP=3.0170 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,664B, BPFP=2.3672 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,432B, BPFP=3.0568 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,064B, BPFP=2.4027 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,784B, BPFP=2.9993 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,580B, BPFP=2.0934 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,540B, BPFP=3.0664 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 102,820B, BPFP=2.2820 +⌛️ [2/4] FRONTEND: Frontend time: 0.214s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03339012 5.53751893 + layer.0.v_cache 0.00000028 0.00014683 + layer.1.k_cache 0.00350260 0.52694364 + layer.1.v_cache 0.00000079 0.00050164 + layer.2.k_cache 0.00114403 0.26638148 + layer.2.v_cache 0.00000109 0.00071649 + layer.3.k_cache 0.00131544 0.29897909 + layer.3.v_cache 0.00000207 0.00113182 + layer.4.k_cache 0.00326494 0.51450344 + layer.4.v_cache 0.00000300 0.00196032 + layer.4.output 0.00017308 0.05693448 + ------------------------------------------------------------------------------------- + TOTAL 0.00309405 0.52689440 + (elements=1,261,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1261568 +Total Bytes 386880 +BPFP 2.4533 bits/point +EBPFP 4.9067 equivalent bits/point +MSE 0.526894 +---------------------- -------------------------------------------------------- +Time: 0.520s Load: 0.007s, Pack+Encode: 0.214s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5269 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample100-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample100-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample103-layer4-item1.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample103-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,044B, BPFP=1.3624 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,536B, BPFP=2.8478 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,188B, BPFP=2.1389 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,100B, BPFP=2.9806 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,736B, BPFP=2.3553 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,648B, BPFP=3.0272 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,388B, BPFP=2.4107 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,948B, BPFP=2.9677 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,868B, BPFP=2.1118 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,684B, BPFP=3.0302 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,112B, BPFP=2.3801 +⌛️ [2/4] FRONTEND: Frontend time: 0.215s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03121759 5.27895388 + layer.0.v_cache 0.00000027 0.00014994 + layer.1.k_cache 0.00337054 0.51921446 + layer.1.v_cache 0.00000081 0.00051729 + layer.2.k_cache 0.00113856 0.27105783 + layer.2.v_cache 0.00000109 0.00073690 + layer.3.k_cache 0.00130416 0.30541470 + layer.3.v_cache 0.00000204 0.00117221 + layer.4.k_cache 0.00326587 0.51385092 + layer.4.v_cache 0.00000304 0.00206666 + layer.4.output 0.00017424 0.04492521 + ------------------------------------------------------------------------------------- + TOTAL 0.00292864 0.50520254 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 409252 +BPFP 2.4824 bits/point +EBPFP 4.9647 equivalent bits/point +MSE 0.505203 +---------------------- -------------------------------------------------------- +Time: 0.521s Load: 0.006s, Pack+Encode: 0.215s, Decode+Unpack: 0.301s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5052 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample103-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample103-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample105-layer4-item1.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample105-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 91, 128) +Output shape: (1, 91, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.output: torch.Size([1, 91, 4096]) -> torch.Size([1, 1, 91, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,820B, BPFP=1.3582 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,120B, BPFP=2.8434 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,972B, BPFP=2.1439 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,632B, BPFP=2.9732 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,372B, BPFP=2.3499 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,092B, BPFP=3.0127 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,232B, BPFP=2.4238 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,520B, BPFP=2.9636 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,672B, BPFP=2.1181 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,176B, BPFP=3.0199 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,696B, BPFP=2.3329 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03135960 5.33222576 + layer.0.v_cache 0.00000027 0.00014265 + layer.1.k_cache 0.00345943 0.51783270 + layer.1.v_cache 0.00000081 0.00050741 + layer.2.k_cache 0.00114090 0.26110400 + layer.2.v_cache 0.00000105 0.00069653 + layer.3.k_cache 0.00133425 0.30237841 + layer.3.v_cache 0.00000201 0.00112636 + layer.4.k_cache 0.00327424 0.52359051 + layer.4.v_cache 0.00000301 0.00193321 + layer.4.output 0.00017460 0.04608485 + ------------------------------------------------------------------------------------- + TOTAL 0.00294814 0.50899121 + (elements=1,304,576) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1304576 +Total Bytes 402304 +BPFP 2.4670 bits/point +EBPFP 4.9341 equivalent bits/point +MSE 0.508991 +---------------------- -------------------------------------------------------- +Time: 0.519s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.301s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5090 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample105-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample105-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample107-layer4-item1.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample107-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,536B, BPFP=1.3486 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,492B, BPFP=2.8205 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,376B, BPFP=2.1160 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,232B, BPFP=2.9715 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,956B, BPFP=2.3399 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,724B, BPFP=3.0142 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,792B, BPFP=2.4125 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,048B, BPFP=2.9556 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,136B, BPFP=2.0951 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,768B, BPFP=3.0181 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,832B, BPFP=2.2750 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03118788 5.14291517 + layer.0.v_cache 0.00000027 0.00014724 + layer.1.k_cache 0.00346696 0.50451101 + layer.1.v_cache 0.00000079 0.00050274 + layer.2.k_cache 0.00116280 0.26982511 + layer.2.v_cache 0.00000105 0.00071651 + layer.3.k_cache 0.00130692 0.30420443 + layer.3.v_cache 0.00000207 0.00112407 + layer.4.k_cache 0.00320606 0.52114923 + layer.4.v_cache 0.00000305 0.00198544 + layer.4.output 0.00017695 0.06010651 + ------------------------------------------------------------------------------------- + TOTAL 0.00293183 0.49910764 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 393892 +BPFP 2.4423 bits/point +EBPFP 4.8846 equivalent bits/point +MSE 0.499108 +---------------------- -------------------------------------------------------- +Time: 0.518s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4991 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample107-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample107-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample111-layer4-item1.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample111-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,800B, BPFP=1.3417 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,480B, BPFP=2.8431 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,180B, BPFP=2.1382 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,096B, BPFP=2.9803 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,700B, BPFP=2.3522 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,552B, BPFP=3.0190 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,484B, BPFP=2.4188 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,980B, BPFP=2.9704 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,832B, BPFP=2.1087 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,516B, BPFP=3.0160 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,884B, BPFP=2.3540 +⌛️ [2/4] FRONTEND: Frontend time: 0.217s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.310s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03125828 5.41399549 + layer.0.v_cache 0.00000028 0.00014757 + layer.1.k_cache 0.00363872 0.51578211 + layer.1.v_cache 0.00000085 0.00052415 + layer.2.k_cache 0.00115386 0.26815361 + layer.2.v_cache 0.00000106 0.00071442 + layer.3.k_cache 0.00130545 0.30062078 + layer.3.v_cache 0.00000205 0.00112951 + layer.4.k_cache 0.00323476 0.52059373 + layer.4.v_cache 0.00000303 0.00199799 + layer.4.output 0.00019770 0.05137013 + ------------------------------------------------------------------------------------- + TOTAL 0.00295637 0.51636714 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407504 +BPFP 2.4718 bits/point +EBPFP 4.9435 equivalent bits/point +MSE 0.516367 +---------------------- -------------------------------------------------------- +Time: 0.533s Load: 0.006s, Pack+Encode: 0.217s, Decode+Unpack: 0.310s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5164 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample111-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample111-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample112-layer4-item1.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample112-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,456B, BPFP=1.2981 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,992B, BPFP=2.7830 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,728B, BPFP=2.1307 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,200B, BPFP=2.9813 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,512B, BPFP=2.3807 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,064B, BPFP=3.0589 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,060B, BPFP=2.4300 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,320B, BPFP=2.9921 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,420B, BPFP=2.1031 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,128B, BPFP=3.0647 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 100,668B, BPFP=2.2600 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.314s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03173990 5.09001984 + layer.0.v_cache 0.00000027 0.00014573 + layer.1.k_cache 0.00353239 0.51224829 + layer.1.v_cache 0.00000080 0.00050700 + layer.2.k_cache 0.00117039 0.25945598 + layer.2.v_cache 0.00000104 0.00069507 + layer.3.k_cache 0.00134488 0.31077115 + layer.3.v_cache 0.00000203 0.00115840 + layer.4.k_cache 0.00324251 0.50725555 + layer.4.v_cache 0.00000295 0.00199212 + layer.4.output 0.00019562 0.05860188 + ------------------------------------------------------------------------------------- + TOTAL 0.00298712 0.49418976 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 381548 +BPFP 2.4473 bits/point +EBPFP 4.8947 equivalent bits/point +MSE 0.494190 +---------------------- -------------------------------------------------------- +Time: 0.525s Load: 0.006s, Pack+Encode: 0.205s, Decode+Unpack: 0.314s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4942 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample112-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample112-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample113-layer4-item1.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample113-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,472B, BPFP=1.2996 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,636B, BPFP=2.7511 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,756B, BPFP=2.1333 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,232B, BPFP=2.9842 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,436B, BPFP=2.3739 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,068B, BPFP=3.0593 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,980B, BPFP=2.4228 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,416B, BPFP=3.0007 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,424B, BPFP=2.1034 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,060B, BPFP=3.0585 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,472B, BPFP=2.2331 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.299s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03084559 5.03639590 + layer.0.v_cache 0.00000028 0.00014585 + layer.1.k_cache 0.00352810 0.50860070 + layer.1.v_cache 0.00000080 0.00051841 + layer.2.k_cache 0.00113435 0.25422195 + layer.2.v_cache 0.00000106 0.00070462 + layer.3.k_cache 0.00133457 0.30612737 + layer.3.v_cache 0.00000203 0.00114561 + layer.4.k_cache 0.00324423 0.49822051 + layer.4.v_cache 0.00000296 0.00192942 + layer.4.output 0.00017424 0.05890939 + ------------------------------------------------------------------------------------- + TOTAL 0.00291364 0.48883199 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 379952 +BPFP 2.4371 bits/point +EBPFP 4.8742 equivalent bits/point +MSE 0.488832 +---------------------- -------------------------------------------------------- +Time: 0.518s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.299s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4888 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample113-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample113-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample117-layer4-item1.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample117-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 85, 128) +Output shape: (1, 85, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.output: torch.Size([1, 85, 4096]) -> torch.Size([1, 1, 85, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,268B, BPFP=1.3114 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,124B, BPFP=2.7687 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,516B, BPFP=2.1614 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 32,844B, BPFP=3.0187 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 25,876B, BPFP=2.3783 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,784B, BPFP=3.1051 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,508B, BPFP=2.4364 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,120B, BPFP=3.0441 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,128B, BPFP=2.1257 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,656B, BPFP=3.0934 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,880B, BPFP=2.2950 +⌛️ [2/4] FRONTEND: Frontend time: 0.223s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.311s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03172885 5.16959121 + layer.0.v_cache 0.00000027 0.00014527 + layer.1.k_cache 0.00352961 0.54882633 + layer.1.v_cache 0.00000080 0.00051444 + layer.2.k_cache 0.00112280 0.26601688 + layer.2.v_cache 0.00000110 0.00074142 + layer.3.k_cache 0.00133082 0.30438331 + layer.3.v_cache 0.00000207 0.00113794 + layer.4.k_cache 0.00329730 0.52098007 + layer.4.v_cache 0.00000296 0.00201583 + layer.4.output 0.00017366 0.06453425 + ------------------------------------------------------------------------------------- + TOTAL 0.00297937 0.50517784 + (elements=1,218,560) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1218560 +Total Bytes 376704 +BPFP 2.4731 bits/point +EBPFP 4.9462 equivalent bits/point +MSE 0.505178 +---------------------- -------------------------------------------------------- +Time: 0.540s Load: 0.006s, Pack+Encode: 0.223s, Decode+Unpack: 0.311s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5052 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample117-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample117-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample12-layer4-item1.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample12-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,012B, BPFP=1.3451 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,720B, BPFP=2.8327 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,388B, BPFP=2.1327 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,416B, BPFP=2.9751 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,896B, BPFP=2.3434 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,720B, BPFP=3.0007 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,468B, BPFP=2.3915 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,088B, BPFP=2.9476 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,148B, BPFP=2.1126 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,868B, BPFP=3.0131 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,392B, BPFP=2.3394 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03136649 5.41562415 + layer.0.v_cache 0.00000028 0.00014703 + layer.1.k_cache 0.00359034 0.54016466 + layer.1.v_cache 0.00000079 0.00050741 + layer.2.k_cache 0.00112294 0.28484685 + layer.2.v_cache 0.00000105 0.00069754 + layer.3.k_cache 0.00134461 0.30778397 + layer.3.v_cache 0.00000211 0.00114115 + layer.4.k_cache 0.00331549 0.53872065 + layer.4.v_cache 0.00000311 0.00199569 + layer.4.output 0.00017890 0.06688079 + ------------------------------------------------------------------------------------- + TOTAL 0.00296163 0.52565373 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 410116 +BPFP 2.4609 bits/point +EBPFP 4.9217 equivalent bits/point +MSE 0.525654 +---------------------- -------------------------------------------------------- +Time: 0.509s Load: 0.008s, Pack+Encode: 0.211s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5257 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample12-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample12-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample121-layer4-item1.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample121-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 84, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 84, 128) +Output shape: (1, 84, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.0.v_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.1.k_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.1.v_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.2.k_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.2.v_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.3.k_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.3.v_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.4.k_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.4.v_cache: torch.Size([1, 8, 84, 128]) -> torch.Size([1, 1, 84, 1024]) + layer.4.output: torch.Size([1, 84, 4096]) -> torch.Size([1, 1, 84, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,172B, BPFP=1.3181 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,192B, BPFP=2.8080 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,268B, BPFP=2.1641 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 32,700B, BPFP=3.0413 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 25,596B, BPFP=2.3806 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,304B, BPFP=3.0975 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,344B, BPFP=2.4501 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 32,828B, BPFP=3.0532 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,108B, BPFP=2.1492 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,380B, BPFP=3.1045 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 98,612B, BPFP=2.2929 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 84, 128]) + layer.0.v_cache: torch.Size([1, 8, 84, 128]) + layer.1.k_cache: torch.Size([1, 8, 84, 128]) + layer.1.v_cache: torch.Size([1, 8, 84, 128]) + layer.2.k_cache: torch.Size([1, 8, 84, 128]) + layer.2.v_cache: torch.Size([1, 8, 84, 128]) + layer.3.k_cache: torch.Size([1, 8, 84, 128]) + layer.3.v_cache: torch.Size([1, 8, 84, 128]) + layer.4.k_cache: torch.Size([1, 8, 84, 128]) + layer.4.v_cache: torch.Size([1, 8, 84, 128]) + layer.4.output: torch.Size([1, 84, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 84, 128]) + layer.0.v_cache: torch.Size([1, 8, 84, 128]) + layer.1.k_cache: torch.Size([1, 8, 84, 128]) + layer.1.v_cache: torch.Size([1, 8, 84, 128]) + layer.2.k_cache: torch.Size([1, 8, 84, 128]) + layer.2.v_cache: torch.Size([1, 8, 84, 128]) + layer.3.k_cache: torch.Size([1, 8, 84, 128]) + layer.3.v_cache: torch.Size([1, 8, 84, 128]) + layer.4.k_cache: torch.Size([1, 8, 84, 128]) + layer.4.v_cache: torch.Size([1, 8, 84, 128]) + layer.4.output: torch.Size([1, 84, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03306588 5.24468595 + layer.0.v_cache 0.00000027 0.00014442 + layer.1.k_cache 0.00337037 0.49367323 + layer.1.v_cache 0.00000088 0.00049184 + layer.2.k_cache 0.00113180 0.26657495 + layer.2.v_cache 0.00000103 0.00067736 + layer.3.k_cache 0.00132828 0.30812763 + layer.3.v_cache 0.00000204 0.00107646 + layer.4.k_cache 0.00320824 0.52255294 + layer.4.v_cache 0.00000300 0.00193717 + layer.4.output 0.00017823 0.05021188 + ------------------------------------------------------------------------------------- + TOTAL 0.00305891 0.50291353 + (elements=1,204,224) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1204224 +Total Bytes 373504 +BPFP 2.4813 bits/point +EBPFP 4.9626 equivalent bits/point +MSE 0.502914 +---------------------- -------------------------------------------------------- +Time: 0.502s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 84, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 84, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5029 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample121-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample121-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample123-layer4-item1.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample123-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,584B, BPFP=1.3528 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,160B, BPFP=2.7917 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,364B, BPFP=2.1149 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,308B, BPFP=2.9781 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,140B, BPFP=2.3559 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,716B, BPFP=3.0135 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,596B, BPFP=2.3955 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,036B, BPFP=2.9545 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,112B, BPFP=2.0931 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,948B, BPFP=3.0337 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 105,276B, BPFP=2.2846 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03166741 5.48450114 + layer.0.v_cache 0.00000027 0.00015132 + layer.1.k_cache 0.00338869 0.48890169 + layer.1.v_cache 0.00000080 0.00051086 + layer.2.k_cache 0.00112306 0.26532366 + layer.2.v_cache 0.00000109 0.00071781 + layer.3.k_cache 0.00129226 0.29729214 + layer.3.v_cache 0.00000208 0.00114119 + layer.4.k_cache 0.00325011 0.50592083 + layer.4.v_cache 0.00000309 0.00200901 + layer.4.output 0.00016397 0.05400611 + ------------------------------------------------------------------------------------- + TOTAL 0.00295605 0.51874958 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 394240 +BPFP 2.4444 bits/point +EBPFP 4.8889 equivalent bits/point +MSE 0.518750 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5187 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample123-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample123-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample124-layer4-item1.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample124-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 88, 128) +Output shape: (1, 88, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.output: torch.Size([1, 88, 4096]) -> torch.Size([1, 1, 88, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,700B, BPFP=1.3050 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,180B, BPFP=2.7681 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,976B, BPFP=2.1286 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,644B, BPFP=2.9869 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,516B, BPFP=2.3540 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,376B, BPFP=3.0518 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,256B, BPFP=2.4197 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,680B, BPFP=2.9901 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,572B, BPFP=2.0927 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,424B, BPFP=3.0561 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,280B, BPFP=2.2923 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03171545 5.66029289 + layer.0.v_cache 0.00000027 0.00014774 + layer.1.k_cache 0.00364625 0.52449222 + layer.1.v_cache 0.00000082 0.00051142 + layer.2.k_cache 0.00114710 0.27500120 + layer.2.v_cache 0.00000106 0.00072214 + layer.3.k_cache 0.00132811 0.30944894 + layer.3.v_cache 0.00000203 0.00115622 + layer.4.k_cache 0.00332194 0.50094110 + layer.4.v_cache 0.00000300 0.00195569 + layer.4.output 0.00016865 0.05409358 + ------------------------------------------------------------------------------------- + TOTAL 0.00298862 0.53507456 + (elements=1,261,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1261568 +Total Bytes 386604 +BPFP 2.4516 bits/point +EBPFP 4.9032 equivalent bits/point +MSE 0.535075 +---------------------- -------------------------------------------------------- +Time: 0.501s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5351 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample124-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample124-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample126-layer4-item1.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample126-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,936B, BPFP=1.3111 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,744B, BPFP=2.7865 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,072B, BPFP=2.1131 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,044B, BPFP=2.9884 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,724B, BPFP=2.3459 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,556B, BPFP=3.0334 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,428B, BPFP=2.4077 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,880B, BPFP=2.9740 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,728B, BPFP=2.0829 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,652B, BPFP=3.0418 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,632B, BPFP=2.2742 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03218093 5.52434960 + layer.0.v_cache 0.00000027 0.00015159 + layer.1.k_cache 0.00364370 0.52277743 + layer.1.v_cache 0.00000081 0.00052506 + layer.2.k_cache 0.00115129 0.28078242 + layer.2.v_cache 0.00000108 0.00074565 + layer.3.k_cache 0.00133425 0.30838928 + layer.3.v_cache 0.00000206 0.00116390 + layer.4.k_cache 0.00326870 0.53219922 + layer.4.v_cache 0.00000299 0.00193054 + layer.4.output 0.00018569 0.06005349 + ------------------------------------------------------------------------------------- + TOTAL 0.00302349 0.52951633 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 389396 +BPFP 2.4415 bits/point +EBPFP 4.8831 equivalent bits/point +MSE 0.529516 +---------------------- -------------------------------------------------------- +Time: 0.504s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5295 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample126-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample126-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample13-layer4-item1.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample13-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,724B, BPFP=1.3209 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,724B, BPFP=2.8330 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,384B, BPFP=2.1324 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,396B, BPFP=2.9735 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,888B, BPFP=2.3427 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,696B, BPFP=2.9987 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,420B, BPFP=2.3874 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,064B, BPFP=2.9456 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,168B, BPFP=2.1142 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,872B, BPFP=3.0134 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,952B, BPFP=2.3301 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03082583 5.42470427 + layer.0.v_cache 0.00000028 0.00014482 + layer.1.k_cache 0.00340863 0.54180592 + layer.1.v_cache 0.00000080 0.00050946 + layer.2.k_cache 0.00115869 0.28209733 + layer.2.v_cache 0.00000105 0.00069896 + layer.3.k_cache 0.00132054 0.30798873 + layer.3.v_cache 0.00000208 0.00113367 + layer.4.k_cache 0.00325612 0.54809833 + layer.4.v_cache 0.00000315 0.00199309 + layer.4.output 0.00020065 0.07181573 + ------------------------------------------------------------------------------------- + TOTAL 0.00291284 0.52831696 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 409288 +BPFP 2.4559 bits/point +EBPFP 4.9118 equivalent bits/point +MSE 0.528317 +---------------------- -------------------------------------------------------- +Time: 0.504s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5283 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample13-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample13-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample132-layer4-item1.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample132-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,596B, BPFP=1.3538 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,120B, BPFP=2.7882 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,396B, BPFP=2.1177 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,260B, BPFP=2.9740 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,924B, BPFP=2.3372 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,712B, BPFP=3.0132 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,688B, BPFP=2.4035 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,012B, BPFP=2.9524 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,980B, BPFP=2.0816 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,752B, BPFP=3.0167 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,060B, BPFP=2.2582 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03096869 5.03679877 + layer.0.v_cache 0.00000027 0.00015129 + layer.1.k_cache 0.00338461 0.51198010 + layer.1.v_cache 0.00000079 0.00051952 + layer.2.k_cache 0.00111556 0.26257774 + layer.2.v_cache 0.00000105 0.00069946 + layer.3.k_cache 0.00129967 0.30334439 + layer.3.v_cache 0.00000204 0.00113598 + layer.4.k_cache 0.00324687 0.51222170 + layer.4.v_cache 0.00000301 0.00195380 + layer.4.output 0.00020513 0.05525064 + ------------------------------------------------------------------------------------- + TOTAL 0.00291736 0.48945609 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 392500 +BPFP 2.4337 bits/point +EBPFP 4.8673 equivalent bits/point +MSE 0.489456 +---------------------- -------------------------------------------------------- +Time: 0.502s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4895 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample132-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample132-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample134-layer4-item1.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample134-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,632B, BPFP=1.2844 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,560B, BPFP=2.7704 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,048B, BPFP=2.1110 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,968B, BPFP=2.9817 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,624B, BPFP=2.3371 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,572B, BPFP=3.0348 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,424B, BPFP=2.4073 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,044B, BPFP=2.9884 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,804B, BPFP=2.0895 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,756B, BPFP=3.0509 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,752B, BPFP=2.2769 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03072730 5.81373442 + layer.0.v_cache 0.00000028 0.00015018 + layer.1.k_cache 0.00346408 0.50584240 + layer.1.v_cache 0.00000080 0.00050659 + layer.2.k_cache 0.00114708 0.27581594 + layer.2.v_cache 0.00000106 0.00072506 + layer.3.k_cache 0.00132643 0.30372716 + layer.3.v_cache 0.00000205 0.00115968 + layer.4.k_cache 0.00323342 0.54274450 + layer.4.v_cache 0.00000308 0.00194579 + layer.4.output 0.00016383 0.05083359 + ------------------------------------------------------------------------------------- + TOTAL 0.00289721 0.54640615 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 389184 +BPFP 2.4402 bits/point +EBPFP 4.8804 equivalent bits/point +MSE 0.546406 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.007s, Pack+Encode: 0.206s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5464 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample134-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample134-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample135-layer4-item1.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample135-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,712B, BPFP=1.3342 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,472B, BPFP=2.8424 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,180B, BPFP=2.1382 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,100B, BPFP=2.9806 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,648B, BPFP=2.3478 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,584B, BPFP=3.0217 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,360B, BPFP=2.4083 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,904B, BPFP=2.9640 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,764B, BPFP=2.1029 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,560B, BPFP=3.0197 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,704B, BPFP=2.3077 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03070580 5.47889179 + layer.0.v_cache 0.00000027 0.00014511 + layer.1.k_cache 0.00347185 0.50396720 + layer.1.v_cache 0.00000080 0.00050441 + layer.2.k_cache 0.00112340 0.26499918 + layer.2.v_cache 0.00000106 0.00070544 + layer.3.k_cache 0.00133882 0.29538899 + layer.3.v_cache 0.00000200 0.00108302 + layer.4.k_cache 0.00333409 0.51751402 + layer.4.v_cache 0.00000307 0.00192455 + layer.4.output 0.00016236 0.04526757 + ------------------------------------------------------------------------------------- + TOTAL 0.00290219 0.51758528 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 404988 +BPFP 2.4565 bits/point +EBPFP 4.9130 equivalent bits/point +MSE 0.517585 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5176 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample135-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample135-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample136-layer4-item1.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample136-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,276B, BPFP=1.2820 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,864B, BPFP=2.7716 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,716B, BPFP=2.1297 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,264B, BPFP=2.9871 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,432B, BPFP=2.3736 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,992B, BPFP=3.0524 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,004B, BPFP=2.4249 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,436B, BPFP=3.0025 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,332B, BPFP=2.0952 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,104B, BPFP=3.0625 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,732B, BPFP=2.2390 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03132415 5.01531000 + layer.0.v_cache 0.00000028 0.00013999 + layer.1.k_cache 0.00348164 0.50976238 + layer.1.v_cache 0.00000079 0.00050112 + layer.2.k_cache 0.00113511 0.25617481 + layer.2.v_cache 0.00000106 0.00069543 + layer.3.k_cache 0.00131762 0.30587085 + layer.3.v_cache 0.00000207 0.00114029 + layer.4.k_cache 0.00320374 0.49829001 + layer.4.v_cache 0.00000303 0.00197301 + layer.4.output 0.00016514 0.05736897 + ------------------------------------------------------------------------------------- + TOTAL 0.00293786 0.48709527 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 380152 +BPFP 2.4384 bits/point +EBPFP 4.8767 equivalent bits/point +MSE 0.487095 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.007s, Pack+Encode: 0.206s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4871 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample136-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample136-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample137-layer4-item1.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample137-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 88, 128) +Output shape: (1, 88, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.output: torch.Size([1, 88, 4096]) -> torch.Size([1, 1, 88, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,808B, BPFP=1.3146 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,340B, BPFP=2.7823 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,952B, BPFP=2.1264 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,660B, BPFP=2.9883 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,604B, BPFP=2.3619 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,288B, BPFP=3.0440 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,184B, BPFP=2.4134 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,632B, BPFP=2.9858 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,548B, BPFP=2.0906 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,556B, BPFP=3.0678 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 102,348B, BPFP=2.2716 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03137242 5.58775191 + layer.0.v_cache 0.00000027 0.00015097 + layer.1.k_cache 0.00337550 0.52265332 + layer.1.v_cache 0.00000078 0.00048498 + layer.2.k_cache 0.00112765 0.27321161 + layer.2.v_cache 0.00000106 0.00069375 + layer.3.k_cache 0.00130259 0.31253992 + layer.3.v_cache 0.00000201 0.00110099 + layer.4.k_cache 0.00311145 0.50420505 + layer.4.v_cache 0.00000304 0.00193347 + layer.4.output 0.00020755 0.07119580 + ------------------------------------------------------------------------------------- + TOTAL 0.00293764 0.53496494 + (elements=1,261,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1261568 +Total Bytes 385920 +BPFP 2.4472 bits/point +EBPFP 4.8945 equivalent bits/point +MSE 0.534965 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5350 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample137-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample137-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample138-layer4-item1.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample138-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,364B, BPFP=1.2769 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,908B, BPFP=2.8182 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,572B, BPFP=2.1253 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,552B, BPFP=2.9548 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,940B, BPFP=2.3221 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,952B, BPFP=2.9880 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,624B, BPFP=2.3790 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,368B, BPFP=2.9395 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,252B, BPFP=2.0987 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,972B, BPFP=2.9897 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,688B, BPFP=2.3206 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.315s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03043337 5.62349685 + layer.0.v_cache 0.00000027 0.00014698 + layer.1.k_cache 0.00337587 0.53791265 + layer.1.v_cache 0.00000080 0.00052126 + layer.2.k_cache 0.00115990 0.26917827 + layer.2.v_cache 0.00000105 0.00070939 + layer.3.k_cache 0.00132055 0.31207576 + layer.3.v_cache 0.00000202 0.00112775 + layer.4.k_cache 0.00326599 0.53104360 + layer.4.v_cache 0.00000301 0.00199500 + layer.4.output 0.00017344 0.06370693 + ------------------------------------------------------------------------------------- + TOTAL 0.00287547 0.53807394 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 411192 +BPFP 2.4411 bits/point +EBPFP 4.8821 equivalent bits/point +MSE 0.538074 +---------------------- -------------------------------------------------------- +Time: 0.532s Load: 0.007s, Pack+Encode: 0.210s, Decode+Unpack: 0.315s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5381 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample138-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample138-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample139-layer4-item1.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample139-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 88, 128) +Output shape: (1, 88, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.output: torch.Size([1, 88, 4096]) -> torch.Size([1, 1, 88, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,676B, BPFP=1.3029 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,920B, BPFP=2.7450 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,000B, BPFP=2.1307 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,748B, BPFP=2.9961 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,508B, BPFP=2.3533 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,228B, BPFP=3.0387 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,276B, BPFP=2.4215 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,444B, BPFP=2.9691 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,436B, BPFP=2.0806 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,268B, BPFP=3.0423 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 101,880B, BPFP=2.2612 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03171148 5.57154638 + layer.0.v_cache 0.00000028 0.00014904 + layer.1.k_cache 0.00359844 0.55580274 + layer.1.v_cache 0.00000084 0.00051626 + layer.2.k_cache 0.00114211 0.27209601 + layer.2.v_cache 0.00000108 0.00072910 + layer.3.k_cache 0.00132976 0.31176558 + layer.3.v_cache 0.00000213 0.00117675 + layer.4.k_cache 0.00324865 0.51942145 + layer.4.v_cache 0.00000293 0.00191963 + layer.4.output 0.00016674 0.05201143 + ------------------------------------------------------------------------------------- + TOTAL 0.00297890 0.53165490 + (elements=1,261,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1261568 +Total Bytes 384384 +BPFP 2.4375 bits/point +EBPFP 4.8750 equivalent bits/point +MSE 0.531655 +---------------------- -------------------------------------------------------- +Time: 0.519s Load: 0.008s, Pack+Encode: 0.211s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5317 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample139-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample139-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample140-layer4-item1.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample140-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,748B, BPFP=1.2946 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,696B, BPFP=2.7823 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,052B, BPFP=2.1113 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,948B, BPFP=2.9800 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,796B, BPFP=2.3522 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,476B, BPFP=3.0263 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,472B, BPFP=2.4115 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,012B, BPFP=2.9856 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,756B, BPFP=2.0853 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,656B, BPFP=3.0421 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,844B, BPFP=2.2789 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.302s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03094440 5.42671152 + layer.0.v_cache 0.00000027 0.00014947 + layer.1.k_cache 0.00351605 0.50202029 + layer.1.v_cache 0.00000081 0.00050889 + layer.2.k_cache 0.00112935 0.27843156 + layer.2.v_cache 0.00000105 0.00071094 + layer.3.k_cache 0.00132761 0.30621034 + layer.3.v_cache 0.00000216 0.00113274 + layer.4.k_cache 0.00319280 0.54183188 + layer.4.v_cache 0.00000300 0.00194795 + layer.4.output 0.00020683 0.05816100 + ------------------------------------------------------------------------------------- + TOTAL 0.00292463 0.52087854 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 389456 +BPFP 2.4419 bits/point +EBPFP 4.8838 equivalent bits/point +MSE 0.520879 +---------------------- -------------------------------------------------------- +Time: 0.519s Load: 0.007s, Pack+Encode: 0.210s, Decode+Unpack: 0.302s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5209 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample140-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample140-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample141-layer4-item1.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample141-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,944B, BPFP=1.3539 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,428B, BPFP=2.8387 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,172B, BPFP=2.1376 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,140B, BPFP=2.9840 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,592B, BPFP=2.3431 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,584B, BPFP=3.0217 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,440B, BPFP=2.4151 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,924B, BPFP=2.9657 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,856B, BPFP=2.1107 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,520B, BPFP=3.0163 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,592B, BPFP=2.3478 +⌛️ [2/4] FRONTEND: Frontend time: 0.218s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.310s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03142291 5.65763523 + layer.0.v_cache 0.00000027 0.00015120 + layer.1.k_cache 0.00332372 0.50011179 + layer.1.v_cache 0.00000083 0.00050940 + layer.2.k_cache 0.00117076 0.26410074 + layer.2.v_cache 0.00000109 0.00070668 + layer.3.k_cache 0.00132340 0.30262410 + layer.3.v_cache 0.00000203 0.00111171 + layer.4.k_cache 0.00333891 0.52015727 + layer.4.v_cache 0.00000309 0.00205346 + layer.4.output 0.00017306 0.05040984 + ------------------------------------------------------------------------------------- + TOTAL 0.00294852 0.53220007 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407192 +BPFP 2.4699 bits/point +EBPFP 4.9397 equivalent bits/point +MSE 0.532200 +---------------------- -------------------------------------------------------- +Time: 0.534s Load: 0.006s, Pack+Encode: 0.218s, Decode+Unpack: 0.310s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5322 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample141-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample141-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample142-layer4-item1.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample142-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,888B, BPFP=1.3347 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,784B, BPFP=2.8380 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,404B, BPFP=2.1341 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,560B, BPFP=2.9872 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,820B, BPFP=2.3370 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,948B, BPFP=3.0198 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,484B, BPFP=2.3928 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,300B, BPFP=2.9654 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,108B, BPFP=2.1092 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,960B, BPFP=3.0208 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,140B, BPFP=2.3551 +⌛️ [2/4] FRONTEND: Frontend time: 0.217s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.310s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03274377 5.45967414 + layer.0.v_cache 0.00000028 0.00015469 + layer.1.k_cache 0.00350561 0.51483138 + layer.1.v_cache 0.00000082 0.00052525 + layer.2.k_cache 0.00114290 0.26861187 + layer.2.v_cache 0.00000109 0.00074726 + layer.3.k_cache 0.00132787 0.30702259 + layer.3.v_cache 0.00000209 0.00120484 + layer.4.k_cache 0.00329855 0.52924355 + layer.4.v_cache 0.00000319 0.00203075 + layer.4.output 0.00017541 0.06190946 + ------------------------------------------------------------------------------------- + TOTAL 0.00305199 0.52369173 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 411396 +BPFP 2.4685 bits/point +EBPFP 4.9371 equivalent bits/point +MSE 0.523692 +---------------------- -------------------------------------------------------- +Time: 0.534s Load: 0.007s, Pack+Encode: 0.217s, Decode+Unpack: 0.310s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5237 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample142-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample142-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample145-layer4-item1.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample145-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 82, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.005s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 82, 128) +Output shape: (1, 82, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.0.v_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.1.k_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.1.v_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.2.k_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.2.v_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.3.k_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.3.v_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.4.k_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.4.v_cache: torch.Size([1, 8, 82, 128]) -> torch.Size([1, 1, 82, 1024]) + layer.4.output: torch.Size([1, 82, 4096]) -> torch.Size([1, 1, 82, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 13,956B, BPFP=1.3296 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 29,832B, BPFP=2.8422 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 22,828B, BPFP=2.1749 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 32,288B, BPFP=3.0762 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 25,444B, BPFP=2.4242 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,152B, BPFP=3.1585 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,160B, BPFP=2.4924 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 32,512B, BPFP=3.0976 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 22,872B, BPFP=2.1791 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,084B, BPFP=3.1521 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 98,528B, BPFP=2.3468 +⌛️ [2/4] FRONTEND: Frontend time: 0.214s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 82, 128]) + layer.0.v_cache: torch.Size([1, 8, 82, 128]) + layer.1.k_cache: torch.Size([1, 8, 82, 128]) + layer.1.v_cache: torch.Size([1, 8, 82, 128]) + layer.2.k_cache: torch.Size([1, 8, 82, 128]) + layer.2.v_cache: torch.Size([1, 8, 82, 128]) + layer.3.k_cache: torch.Size([1, 8, 82, 128]) + layer.3.v_cache: torch.Size([1, 8, 82, 128]) + layer.4.k_cache: torch.Size([1, 8, 82, 128]) + layer.4.v_cache: torch.Size([1, 8, 82, 128]) + layer.4.output: torch.Size([1, 82, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 82, 128]) + layer.0.v_cache: torch.Size([1, 8, 82, 128]) + layer.1.k_cache: torch.Size([1, 8, 82, 128]) + layer.1.v_cache: torch.Size([1, 8, 82, 128]) + layer.2.k_cache: torch.Size([1, 8, 82, 128]) + layer.2.v_cache: torch.Size([1, 8, 82, 128]) + layer.3.k_cache: torch.Size([1, 8, 82, 128]) + layer.3.v_cache: torch.Size([1, 8, 82, 128]) + layer.4.k_cache: torch.Size([1, 8, 82, 128]) + layer.4.v_cache: torch.Size([1, 8, 82, 128]) + layer.4.output: torch.Size([1, 82, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03240362 5.71631008 + layer.0.v_cache 0.00000028 0.00014442 + layer.1.k_cache 0.00358562 0.49771588 + layer.1.v_cache 0.00000082 0.00052191 + layer.2.k_cache 0.00116773 0.27100689 + layer.2.v_cache 0.00000108 0.00074674 + layer.3.k_cache 0.00133166 0.30844895 + layer.3.v_cache 0.00000211 0.00117299 + layer.4.k_cache 0.00321141 0.51014923 + layer.4.v_cache 0.00000303 0.00200010 + layer.4.output 0.00019620 0.06323452 + ------------------------------------------------------------------------------------- + TOTAL 0.00303515 0.54008252 + (elements=1,175,552) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1175552 +Total Bytes 370656 +BPFP 2.5224 bits/point +EBPFP 5.0449 equivalent bits/point +MSE 0.540083 +---------------------- -------------------------------------------------------- +Time: 0.520s Load: 0.005s, Pack+Encode: 0.214s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 82, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 82, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5401 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample145-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample145-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample147-layer4-item1.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample147-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,520B, BPFP=1.3039 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,764B, BPFP=2.7626 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,704B, BPFP=2.1286 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,472B, BPFP=3.0057 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,392B, BPFP=2.3700 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,316B, BPFP=3.0815 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,072B, BPFP=2.4310 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,580B, BPFP=3.0154 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,492B, BPFP=2.1096 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,260B, BPFP=3.0765 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 101,672B, BPFP=2.2825 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03155136 4.89733466 + layer.0.v_cache 0.00000028 0.00014715 + layer.1.k_cache 0.00336733 0.50761440 + layer.1.v_cache 0.00000080 0.00050669 + layer.2.k_cache 0.00113514 0.25602229 + layer.2.v_cache 0.00000108 0.00070027 + layer.3.k_cache 0.00133108 0.30596183 + layer.3.v_cache 0.00000214 0.00116503 + layer.4.k_cache 0.00321453 0.51459442 + layer.4.v_cache 0.00000314 0.00196674 + layer.4.output 0.00017293 0.05916477 + ------------------------------------------------------------------------------------- + TOTAL 0.00294990 0.48019089 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 383244 +BPFP 2.4582 bits/point +EBPFP 4.9164 equivalent bits/point +MSE 0.480191 +---------------------- -------------------------------------------------------- +Time: 0.504s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4802 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample147-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample147-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample149-layer4-item1.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample149-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,536B, BPFP=1.3193 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,476B, BPFP=2.8427 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,040B, BPFP=2.1264 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,112B, BPFP=2.9817 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,668B, BPFP=2.3495 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,488B, BPFP=3.0136 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,344B, BPFP=2.4069 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,920B, BPFP=2.9654 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,688B, BPFP=2.0965 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,572B, BPFP=3.0207 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 109,244B, BPFP=2.3192 +⌛️ [2/4] FRONTEND: Frontend time: 0.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03082907 5.38763992 + layer.0.v_cache 0.00000028 0.00015047 + layer.1.k_cache 0.00336151 0.49703449 + layer.1.v_cache 0.00000078 0.00050881 + layer.2.k_cache 0.00113870 0.27001111 + layer.2.v_cache 0.00000105 0.00070178 + layer.3.k_cache 0.00130133 0.29692049 + layer.3.v_cache 0.00000208 0.00114567 + layer.4.k_cache 0.00328680 0.51256719 + layer.4.v_cache 0.00000308 0.00200191 + layer.4.output 0.00016380 0.04227982 + ------------------------------------------------------------------------------------- + TOTAL 0.00289856 0.50984294 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 405088 +BPFP 2.4571 bits/point +EBPFP 4.9142 equivalent bits/point +MSE 0.509843 +---------------------- -------------------------------------------------------- +Time: 0.509s Load: 0.007s, Pack+Encode: 0.209s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5098 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample149-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample149-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample15-layer4-item1.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample15-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,420B, BPFP=1.3385 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,184B, BPFP=2.7938 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,400B, BPFP=2.1181 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,188B, BPFP=2.9677 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,012B, BPFP=2.3448 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,752B, BPFP=3.0167 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,740B, BPFP=2.4080 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,080B, BPFP=2.9583 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,116B, BPFP=2.0934 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,848B, BPFP=3.0250 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,756B, BPFP=2.2734 +⌛️ [2/4] FRONTEND: Frontend time: 0.219s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.302s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03195141 5.23033583 + layer.0.v_cache 0.00000027 0.00014309 + layer.1.k_cache 0.00349494 0.50622236 + layer.1.v_cache 0.00000082 0.00050156 + layer.2.k_cache 0.00113744 0.26483133 + layer.2.v_cache 0.00000112 0.00074330 + layer.3.k_cache 0.00129971 0.31795497 + layer.3.v_cache 0.00000221 0.00118009 + layer.4.k_cache 0.00331958 0.50473404 + layer.4.v_cache 0.00000310 0.00199697 + layer.4.output 0.00017227 0.05425424 + ------------------------------------------------------------------------------------- + TOTAL 0.00299284 0.50326146 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 393496 +BPFP 2.4398 bits/point +EBPFP 4.8797 equivalent bits/point +MSE 0.503261 +---------------------- -------------------------------------------------------- +Time: 0.528s Load: 0.007s, Pack+Encode: 0.219s, Decode+Unpack: 0.302s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5033 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample15-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample15-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample153-layer4-item1.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample153-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,436B, BPFP=1.2829 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,864B, BPFP=2.8145 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,592B, BPFP=2.1270 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,512B, BPFP=2.9515 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,908B, BPFP=2.3195 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,976B, BPFP=2.9900 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,648B, BPFP=2.3810 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,352B, BPFP=2.9382 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,276B, BPFP=2.1007 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,968B, BPFP=2.9894 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,716B, BPFP=2.3212 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.303s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03049540 5.56213314 + layer.0.v_cache 0.00000028 0.00014776 + layer.1.k_cache 0.00333180 0.53335880 + layer.1.v_cache 0.00000080 0.00052158 + layer.2.k_cache 0.00116407 0.26576499 + layer.2.v_cache 0.00000105 0.00070892 + layer.3.k_cache 0.00132929 0.31319038 + layer.3.v_cache 0.00000200 0.00112482 + layer.4.k_cache 0.00323017 0.53463729 + layer.4.v_cache 0.00000303 0.00201676 + layer.4.output 0.00017160 0.06128253 + ------------------------------------------------------------------------------------- + TOTAL 0.00287459 0.53276675 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 411248 +BPFP 2.4414 bits/point +EBPFP 4.8828 equivalent bits/point +MSE 0.532767 +---------------------- -------------------------------------------------------- +Time: 0.521s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.303s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5328 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample153-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample153-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample154-layer4-item1.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample154-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 85, 128) +Output shape: (1, 85, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.output: torch.Size([1, 85, 4096]) -> torch.Size([1, 1, 85, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,216B, BPFP=1.3066 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,156B, BPFP=2.7717 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,372B, BPFP=2.1482 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 32,668B, BPFP=3.0026 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 25,884B, BPFP=2.3790 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,672B, BPFP=3.0949 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,388B, BPFP=2.4254 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,008B, BPFP=3.0338 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,136B, BPFP=2.1265 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,672B, BPFP=3.0949 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,008B, BPFP=2.2750 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.299s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03223823 5.45577321 + layer.0.v_cache 0.00000028 0.00014881 + layer.1.k_cache 0.00365975 0.52737826 + layer.1.v_cache 0.00000080 0.00050853 + layer.2.k_cache 0.00113736 0.26369696 + layer.2.v_cache 0.00000107 0.00072413 + layer.3.k_cache 0.00130376 0.29969999 + layer.3.v_cache 0.00000210 0.00113568 + layer.4.k_cache 0.00330933 0.53323234 + layer.4.v_cache 0.00000299 0.00200323 + layer.4.output 0.00016397 0.06553788 + ------------------------------------------------------------------------------------- + TOTAL 0.00302225 0.52474662 + (elements=1,218,560) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1218560 +Total Bytes 375180 +BPFP 2.4631 bits/point +EBPFP 4.9262 equivalent bits/point +MSE 0.524747 +---------------------- -------------------------------------------------------- +Time: 0.516s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.299s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5247 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample154-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample154-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample155-layer4-item1.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample155-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,456B, BPFP=1.2981 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,568B, BPFP=2.7450 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,832B, BPFP=2.1401 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,388B, BPFP=2.9982 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,536B, BPFP=2.3829 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,160B, BPFP=3.0675 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,004B, BPFP=2.4249 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,436B, BPFP=3.0025 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,324B, BPFP=2.0945 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,044B, BPFP=3.0571 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 100,128B, BPFP=2.2478 +⌛️ [2/4] FRONTEND: Frontend time: 0.213s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.299s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03126283 5.01580319 + layer.0.v_cache 0.00000028 0.00015084 + layer.1.k_cache 0.00362225 0.52401865 + layer.1.v_cache 0.00000080 0.00053312 + layer.2.k_cache 0.00113278 0.26370700 + layer.2.v_cache 0.00000108 0.00073704 + layer.3.k_cache 0.00132193 0.29720398 + layer.3.v_cache 0.00000209 0.00120013 + layer.4.k_cache 0.00322631 0.51058635 + layer.4.v_cache 0.00000306 0.00201452 + layer.4.output 0.00017090 0.06426347 + ------------------------------------------------------------------------------------- + TOTAL 0.00294693 0.49092919 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 380876 +BPFP 2.4430 bits/point +EBPFP 4.8860 equivalent bits/point +MSE 0.490929 +---------------------- -------------------------------------------------------- +Time: 0.517s Load: 0.006s, Pack+Encode: 0.213s, Decode+Unpack: 0.299s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4909 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample155-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample155-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample156-layer4-item1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample156-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 88, 128) +Output shape: (1, 88, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.output: torch.Size([1, 88, 4096]) -> torch.Size([1, 1, 88, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,692B, BPFP=1.3043 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,072B, BPFP=2.7585 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,880B, BPFP=2.1200 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,840B, BPFP=3.0043 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,616B, BPFP=2.3629 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,352B, BPFP=3.0497 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,204B, BPFP=2.4151 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,720B, BPFP=2.9936 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,536B, BPFP=2.0895 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,436B, BPFP=3.0572 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,004B, BPFP=2.2861 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03162268 5.46629750 + layer.0.v_cache 0.00000028 0.00014506 + layer.1.k_cache 0.00348150 0.52012075 + layer.1.v_cache 0.00000080 0.00050661 + layer.2.k_cache 0.00116795 0.26977496 + layer.2.v_cache 0.00000108 0.00074722 + layer.3.k_cache 0.00133447 0.30366187 + layer.3.v_cache 0.00000211 0.00117445 + layer.4.k_cache 0.00331899 0.51649692 + layer.4.v_cache 0.00000308 0.00197476 + layer.4.output 0.00016829 0.05784011 + ------------------------------------------------------------------------------------- + TOTAL 0.00297187 0.52230432 + (elements=1,261,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1261568 +Total Bytes 386352 +BPFP 2.4500 bits/point +EBPFP 4.9000 equivalent bits/point +MSE 0.522304 +---------------------- -------------------------------------------------------- +Time: 0.518s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5223 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample156-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample156-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample157-layer4-item1.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample157-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,488B, BPFP=1.3444 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,328B, BPFP=2.8062 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,268B, BPFP=2.1066 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,128B, BPFP=2.9625 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,068B, BPFP=2.3497 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,636B, BPFP=3.0066 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,660B, BPFP=2.4010 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,024B, BPFP=2.9535 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,048B, BPFP=2.0875 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,748B, BPFP=3.0163 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,228B, BPFP=2.2619 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.295s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03130752 5.10271064 + layer.0.v_cache 0.00000027 0.00014908 + layer.1.k_cache 0.00339397 0.50818965 + layer.1.v_cache 0.00000079 0.00051004 + layer.2.k_cache 0.00115273 0.26827543 + layer.2.v_cache 0.00000105 0.00070722 + layer.3.k_cache 0.00133725 0.30269561 + layer.3.v_cache 0.00000203 0.00114796 + layer.4.k_cache 0.00329241 0.51782405 + layer.4.v_cache 0.00000297 0.00200706 + layer.4.output 0.00017286 0.05999724 + ------------------------------------------------------------------------------------- + TOTAL 0.00294160 0.49601469 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 392624 +BPFP 2.4344 bits/point +EBPFP 4.8688 equivalent bits/point +MSE 0.496015 +---------------------- -------------------------------------------------------- +Time: 0.515s Load: 0.007s, Pack+Encode: 0.212s, Decode+Unpack: 0.295s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4960 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample157-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample157-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample16-layer4-item1.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample16-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,520B, BPFP=1.3472 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,616B, BPFP=2.8312 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,424B, BPFP=2.1201 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,320B, BPFP=2.9792 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,056B, BPFP=2.3486 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,716B, BPFP=3.0135 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,776B, BPFP=2.4111 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,120B, BPFP=2.9618 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,144B, BPFP=2.0958 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,860B, BPFP=3.0260 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,564B, BPFP=2.2692 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03118540 4.95452406 + layer.0.v_cache 0.00000028 0.00014412 + layer.1.k_cache 0.00362682 0.50675604 + layer.1.v_cache 0.00000082 0.00051424 + layer.2.k_cache 0.00111630 0.27766609 + layer.2.v_cache 0.00000105 0.00071272 + layer.3.k_cache 0.00134410 0.30554894 + layer.3.v_cache 0.00000205 0.00110973 + layer.4.k_cache 0.00329490 0.52578918 + layer.4.v_cache 0.00000297 0.00188361 + layer.4.output 0.00020247 0.06522916 + ------------------------------------------------------------------------------------- + TOTAL 0.00295604 0.48825467 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 394116 +BPFP 2.4437 bits/point +EBPFP 4.8874 equivalent bits/point +MSE 0.488255 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4883 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample16-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample16-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample17-layer4-item1.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample17-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,984B, BPFP=1.3573 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,428B, BPFP=2.8387 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,096B, BPFP=2.1311 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,132B, BPFP=2.9834 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,788B, BPFP=2.3597 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,596B, BPFP=3.0228 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,392B, BPFP=2.4110 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,936B, BPFP=2.9667 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,884B, BPFP=2.1131 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,608B, BPFP=3.0238 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,708B, BPFP=2.3503 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03142573 5.64843020 + layer.0.v_cache 0.00000027 0.00014595 + layer.1.k_cache 0.00360029 0.49847537 + layer.1.v_cache 0.00000081 0.00051432 + layer.2.k_cache 0.00114206 0.27260934 + layer.2.v_cache 0.00000107 0.00072077 + layer.3.k_cache 0.00131314 0.29414815 + layer.3.v_cache 0.00000205 0.00115141 + layer.4.k_cache 0.00327277 0.52164874 + layer.4.v_cache 0.00000301 0.00200690 + layer.4.output 0.00017705 0.04181620 + ------------------------------------------------------------------------------------- + TOTAL 0.00296210 0.52907971 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407552 +BPFP 2.4720 bits/point +EBPFP 4.9441 equivalent bits/point +MSE 0.529080 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5291 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample17-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample17-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample18-layer4-item1.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample18-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,624B, BPFP=1.3268 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,488B, BPFP=2.8438 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,980B, BPFP=2.1213 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,092B, BPFP=2.9800 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,568B, BPFP=2.3410 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,416B, BPFP=3.0075 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,396B, BPFP=2.4113 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,844B, BPFP=2.9589 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,844B, BPFP=2.1097 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,456B, BPFP=3.0109 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 109,912B, BPFP=2.3334 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03108079 5.49570432 + layer.0.v_cache 0.00000027 0.00014237 + layer.1.k_cache 0.00341615 0.51821717 + layer.1.v_cache 0.00000080 0.00050215 + layer.2.k_cache 0.00113779 0.27147797 + layer.2.v_cache 0.00000105 0.00069242 + layer.3.k_cache 0.00132123 0.29393493 + layer.3.v_cache 0.00000201 0.00110626 + layer.4.k_cache 0.00323701 0.52441253 + layer.4.v_cache 0.00000300 0.00197211 + layer.4.output 0.00016915 0.04621761 + ------------------------------------------------------------------------------------- + TOTAL 0.00291977 0.52093090 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 405620 +BPFP 2.4603 bits/point +EBPFP 4.9207 equivalent bits/point +MSE 0.520931 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5209 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample18-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample18-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample19-layer4-item1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample19-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,576B, BPFP=1.3521 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,652B, BPFP=2.8344 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,420B, BPFP=2.1198 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,344B, BPFP=2.9813 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,100B, BPFP=2.3524 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,764B, BPFP=3.0177 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,764B, BPFP=2.4101 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,132B, BPFP=2.9628 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,164B, BPFP=2.0976 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,832B, BPFP=3.0236 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,644B, BPFP=2.2709 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03108226 4.84883965 + layer.0.v_cache 0.00000028 0.00014534 + layer.1.k_cache 0.00355800 0.51694010 + layer.1.v_cache 0.00000082 0.00051841 + layer.2.k_cache 0.00111789 0.27501873 + layer.2.v_cache 0.00000106 0.00071484 + layer.3.k_cache 0.00134609 0.31301354 + layer.3.v_cache 0.00000204 0.00111586 + layer.4.k_cache 0.00329523 0.53794729 + layer.4.v_cache 0.00000295 0.00187415 + layer.4.output 0.00029330 0.06371268 + ------------------------------------------------------------------------------------- + TOTAL 0.00296998 0.48221276 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 394392 +BPFP 2.4454 bits/point +EBPFP 4.8908 equivalent bits/point +MSE 0.482213 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4822 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample19-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample19-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample2-layer4-item1.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample2-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 98, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 98, 128) +Output shape: (1, 98, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.output: torch.Size([1, 98, 4096]) -> torch.Size([1, 1, 98, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,996B, BPFP=1.3549 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 35,000B, BPFP=2.7902 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,728B, BPFP=2.0510 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 36,496B, BPFP=2.9094 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,544B, BPFP=2.2755 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,948B, BPFP=2.9455 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 29,220B, BPFP=2.3294 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 36,228B, BPFP=2.8881 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,788B, BPFP=2.0558 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 37,144B, BPFP=2.9611 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 117,224B, BPFP=2.3363 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.output: torch.Size([1, 98, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.295s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.output: torch.Size([1, 98, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03050847 5.47367392 + layer.0.v_cache 0.00000027 0.00014985 + layer.1.k_cache 0.00347784 0.50798412 + layer.1.v_cache 0.00000091 0.00051918 + layer.2.k_cache 0.00115394 0.26451522 + layer.2.v_cache 0.00000107 0.00072771 + layer.3.k_cache 0.00132167 0.30086537 + layer.3.v_cache 0.00000207 0.00115690 + layer.4.k_cache 0.00336398 0.50973721 + layer.4.v_cache 0.00000299 0.00192870 + layer.4.output 0.00016871 0.05046632 + ------------------------------------------------------------------------------------- + TOTAL 0.00289343 0.51879453 + (elements=1,404,928) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1404928 +Total Bytes 425316 +BPFP 2.4219 bits/point +EBPFP 4.8437 equivalent bits/point +MSE 0.518795 +---------------------- -------------------------------------------------------- +Time: 0.510s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.295s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 98, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5188 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample2-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample2-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample20-layer4-item1.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample20-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,208B, BPFP=1.3616 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,876B, BPFP=2.8458 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,484B, BPFP=2.1408 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,436B, BPFP=2.9768 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,788B, BPFP=2.3343 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,820B, BPFP=3.0091 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,416B, BPFP=2.3871 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,076B, BPFP=2.9466 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,944B, BPFP=2.0954 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,888B, BPFP=3.0148 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,696B, BPFP=2.3458 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03217121 5.38322465 + layer.0.v_cache 0.00000028 0.00014979 + layer.1.k_cache 0.00347451 0.52965435 + layer.1.v_cache 0.00000083 0.00051513 + layer.2.k_cache 0.00115003 0.27548480 + layer.2.v_cache 0.00000108 0.00072717 + layer.3.k_cache 0.00134333 0.30033926 + layer.3.v_cache 0.00000208 0.00117083 + layer.4.k_cache 0.00325638 0.53447485 + layer.4.v_cache 0.00000311 0.00202491 + layer.4.output 0.00016214 0.06159414 + ------------------------------------------------------------------------------------- + TOTAL 0.00300367 0.51958159 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 410632 +BPFP 2.4639 bits/point +EBPFP 4.9279 equivalent bits/point +MSE 0.519582 +---------------------- -------------------------------------------------------- +Time: 0.507s Load: 0.006s, Pack+Encode: 0.208s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5196 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample20-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample20-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample21-layer4-item1.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample21-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,608B, BPFP=1.2972 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,816B, BPFP=2.8105 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,368B, BPFP=2.1084 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,308B, BPFP=2.9345 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,812B, BPFP=2.3115 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,840B, BPFP=2.9787 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,528B, BPFP=2.3710 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,248B, BPFP=2.9295 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,156B, BPFP=2.0908 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,804B, BPFP=2.9757 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,028B, BPFP=2.3069 +⌛️ [2/4] FRONTEND: Frontend time: 0.213s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03064084 6.13150674 + layer.0.v_cache 0.00000027 0.00014896 + layer.1.k_cache 0.00361138 0.52495948 + layer.1.v_cache 0.00000084 0.00050057 + layer.2.k_cache 0.00114431 0.27241220 + layer.2.v_cache 0.00000106 0.00069546 + layer.3.k_cache 0.00136507 0.31688560 + layer.3.v_cache 0.00000197 0.00107956 + layer.4.k_cache 0.00326917 0.54878190 + layer.4.v_cache 0.00000291 0.00188352 + layer.4.output 0.00019732 0.06952528 + ------------------------------------------------------------------------------------- + TOTAL 0.00291622 0.57692536 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 409516 +BPFP 2.4311 bits/point +EBPFP 4.8622 equivalent bits/point +MSE 0.576925 +---------------------- -------------------------------------------------------- +Time: 0.514s Load: 0.008s, Pack+Encode: 0.213s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5769 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample21-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample21-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample23-layer4-item1.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample23-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 96, 128) +Output shape: (1, 96, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.output: torch.Size([1, 96, 4096]) -> torch.Size([1, 1, 96, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,764B, BPFP=1.3643 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,468B, BPFP=2.8050 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,352B, BPFP=2.0632 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 36,108B, BPFP=2.9385 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,828B, BPFP=2.2646 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,312B, BPFP=2.9551 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,608B, BPFP=2.3281 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,772B, BPFP=2.9111 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,236B, BPFP=2.0537 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,368B, BPFP=2.9596 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 113,952B, BPFP=2.3184 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.299s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03181871 4.81690089 + layer.0.v_cache 0.00000028 0.00015421 + layer.1.k_cache 0.00341635 0.50001335 + layer.1.v_cache 0.00000081 0.00050859 + layer.2.k_cache 0.00112215 0.26735862 + layer.2.v_cache 0.00000111 0.00072601 + layer.3.k_cache 0.00131706 0.30292801 + layer.3.v_cache 0.00000210 0.00113902 + layer.4.k_cache 0.00330853 0.51777768 + layer.4.v_cache 0.00000300 0.00194642 + layer.4.output 0.00017506 0.06106939 + ------------------------------------------------------------------------------------- + TOTAL 0.00297788 0.47526645 + (elements=1,376,256) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1376256 +Total Bytes 416768 +BPFP 2.4226 bits/point +EBPFP 4.8452 equivalent bits/point +MSE 0.475266 +---------------------- -------------------------------------------------------- +Time: 0.512s Load: 0.007s, Pack+Encode: 0.206s, Decode+Unpack: 0.299s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4753 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample23-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample23-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample24-layer4-item1.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample24-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,764B, BPFP=1.3387 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,412B, BPFP=2.8373 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,024B, BPFP=2.1250 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,092B, BPFP=2.9800 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,604B, BPFP=2.3441 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,424B, BPFP=3.0082 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,420B, BPFP=2.4134 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,864B, BPFP=2.9606 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,828B, BPFP=2.1084 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,484B, BPFP=3.0132 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,184B, BPFP=2.3392 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.307s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03098850 5.40429057 + layer.0.v_cache 0.00000027 0.00014177 + layer.1.k_cache 0.00332311 0.51657009 + layer.1.v_cache 0.00000079 0.00049684 + layer.2.k_cache 0.00114598 0.26907237 + layer.2.v_cache 0.00000105 0.00069099 + layer.3.k_cache 0.00130880 0.29537711 + layer.3.v_cache 0.00000204 0.00111263 + layer.4.k_cache 0.00318646 0.52487809 + layer.4.v_cache 0.00000304 0.00198481 + layer.4.output 0.00017235 0.04715638 + ------------------------------------------------------------------------------------- + TOTAL 0.00290353 0.51451720 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 406100 +BPFP 2.4632 bits/point +EBPFP 4.9265 equivalent bits/point +MSE 0.514517 +---------------------- -------------------------------------------------------- +Time: 0.522s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.307s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5145 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample24-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample24-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample27-layer4-item1.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample27-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 95, 128) +Output shape: (1, 95, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.output: torch.Size([1, 95, 4096]) -> torch.Size([1, 1, 95, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,208B, BPFP=1.3329 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,376B, BPFP=2.8270 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,424B, BPFP=2.0908 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,844B, BPFP=2.9477 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,020B, BPFP=2.3043 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,236B, BPFP=2.9799 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,744B, BPFP=2.3638 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,628B, BPFP=2.9299 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,444B, BPFP=2.0924 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,424B, BPFP=2.9954 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 113,392B, BPFP=2.3312 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.326s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03093621 5.17996473 + layer.0.v_cache 0.00000027 0.00015098 + layer.1.k_cache 0.00341424 0.48586615 + layer.1.v_cache 0.00000077 0.00048109 + layer.2.k_cache 0.00114464 0.25792527 + layer.2.v_cache 0.00000103 0.00068851 + layer.3.k_cache 0.00132643 0.29475058 + layer.3.v_cache 0.00000197 0.00109078 + layer.4.k_cache 0.00330159 0.51338035 + layer.4.v_cache 0.00000302 0.00194557 + layer.4.output 0.00016139 0.05681156 + ------------------------------------------------------------------------------------- + TOTAL 0.00291255 0.49739216 + (elements=1,361,920) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1361920 +Total Bytes 415740 +BPFP 2.4421 bits/point +EBPFP 4.8842 equivalent bits/point +MSE 0.497392 +---------------------- -------------------------------------------------------- +Time: 0.542s Load: 0.008s, Pack+Encode: 0.207s, Decode+Unpack: 0.326s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4974 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample27-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample27-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample28-layer4-item1.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample28-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 95, 128) +Output shape: (1, 95, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.output: torch.Size([1, 95, 4096]) -> torch.Size([1, 1, 95, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,136B, BPFP=1.3270 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,372B, BPFP=2.8266 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,404B, BPFP=2.0891 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,848B, BPFP=2.9480 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,988B, BPFP=2.3016 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,216B, BPFP=2.9783 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,728B, BPFP=2.3625 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,620B, BPFP=2.9293 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,448B, BPFP=2.0928 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,416B, BPFP=2.9947 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 113,356B, BPFP=2.3305 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03089984 5.19263434 + layer.0.v_cache 0.00000027 0.00015134 + layer.1.k_cache 0.00345231 0.48728192 + layer.1.v_cache 0.00000077 0.00048327 + layer.2.k_cache 0.00115012 0.25823955 + layer.2.v_cache 0.00000103 0.00068695 + layer.3.k_cache 0.00131461 0.29662823 + layer.3.v_cache 0.00000196 0.00108637 + layer.4.k_cache 0.00336511 0.50785924 + layer.4.v_cache 0.00000303 0.00194504 + layer.4.output 0.00016181 0.05576176 + ------------------------------------------------------------------------------------- + TOTAL 0.00291688 0.49786023 + (elements=1,361,920) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1361920 +Total Bytes 415532 +BPFP 2.4409 bits/point +EBPFP 4.8817 equivalent bits/point +MSE 0.497860 +---------------------- -------------------------------------------------------- +Time: 0.502s Load: 0.006s, Pack+Encode: 0.206s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4979 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample28-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample28-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample29-layer4-item1.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample29-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,776B, BPFP=1.3397 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,540B, BPFP=2.8482 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,088B, BPFP=2.1304 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,168B, BPFP=2.9864 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,772B, BPFP=2.3584 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,580B, BPFP=3.0214 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,336B, BPFP=2.4062 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,968B, BPFP=2.9694 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,720B, BPFP=2.0992 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,452B, BPFP=3.0105 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,164B, BPFP=2.2963 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.295s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03058780 5.68870080 + layer.0.v_cache 0.00000028 0.00014942 + layer.1.k_cache 0.00355631 0.51813466 + layer.1.v_cache 0.00000081 0.00052057 + layer.2.k_cache 0.00116497 0.27320883 + layer.2.v_cache 0.00000107 0.00070862 + layer.3.k_cache 0.00134281 0.30073995 + layer.3.v_cache 0.00000206 0.00113380 + layer.4.k_cache 0.00330740 0.51427966 + layer.4.v_cache 0.00000300 0.00194936 + layer.4.output 0.00016528 0.05345826 + ------------------------------------------------------------------------------------- + TOTAL 0.00290197 0.53666848 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 404564 +BPFP 2.4539 bits/point +EBPFP 4.9079 equivalent bits/point +MSE 0.536668 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.006s, Pack+Encode: 0.206s, Decode+Unpack: 0.295s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5367 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample29-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample29-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample30-layer4-item1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample30-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 98, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 98, 128) +Output shape: (1, 98, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) -> torch.Size([1, 1, 98, 1024]) + layer.4.output: torch.Size([1, 98, 4096]) -> torch.Size([1, 1, 98, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,708B, BPFP=1.3320 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,996B, BPFP=2.7899 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,828B, BPFP=2.0590 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 36,508B, BPFP=2.9104 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,584B, BPFP=2.2787 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,828B, BPFP=2.9359 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 29,272B, BPFP=2.3335 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 36,200B, BPFP=2.8858 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,608B, BPFP=2.0415 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 37,188B, BPFP=2.9646 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 115,836B, BPFP=2.3086 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.output: torch.Size([1, 98, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.294s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 98, 128]) + layer.0.v_cache: torch.Size([1, 8, 98, 128]) + layer.1.k_cache: torch.Size([1, 8, 98, 128]) + layer.1.v_cache: torch.Size([1, 8, 98, 128]) + layer.2.k_cache: torch.Size([1, 8, 98, 128]) + layer.2.v_cache: torch.Size([1, 8, 98, 128]) + layer.3.k_cache: torch.Size([1, 8, 98, 128]) + layer.3.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.k_cache: torch.Size([1, 8, 98, 128]) + layer.4.v_cache: torch.Size([1, 8, 98, 128]) + layer.4.output: torch.Size([1, 98, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02931073 5.57548056 + layer.0.v_cache 0.00000028 0.00015417 + layer.1.k_cache 0.00346088 0.48949977 + layer.1.v_cache 0.00000093 0.00050235 + layer.2.k_cache 0.00114617 0.26547934 + layer.2.v_cache 0.00000108 0.00072115 + layer.3.k_cache 0.00130775 0.29962584 + layer.3.v_cache 0.00000199 0.00112133 + layer.4.k_cache 0.00329454 0.50034585 + layer.4.v_cache 0.00000306 0.00196603 + layer.4.output 0.00016599 0.04810109 + ------------------------------------------------------------------------------------- + TOTAL 0.00279938 0.52337863 + (elements=1,404,928) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1404928 +Total Bytes 423556 +BPFP 2.4118 bits/point +EBPFP 4.8237 equivalent bits/point +MSE 0.523379 +---------------------- -------------------------------------------------------- +Time: 0.513s Load: 0.009s, Pack+Encode: 0.210s, Decode+Unpack: 0.294s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 98, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 98, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5234 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample30-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample30-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample31-layer4-item1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample31-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 96, 128) +Output shape: (1, 96, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.output: torch.Size([1, 96, 4096]) -> torch.Size([1, 1, 96, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,252B, BPFP=1.3226 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,236B, BPFP=2.7861 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,256B, BPFP=2.0553 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,684B, BPFP=2.9040 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,736B, BPFP=2.2572 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,052B, BPFP=2.9339 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,548B, BPFP=2.3232 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,484B, BPFP=2.8877 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,236B, BPFP=2.0537 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,368B, BPFP=2.9596 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,400B, BPFP=2.2868 +⌛️ [2/4] FRONTEND: Frontend time: 0.213s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.306s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03011865 4.88915348 + layer.0.v_cache 0.00000028 0.00014810 + layer.1.k_cache 0.00341880 0.49001340 + layer.1.v_cache 0.00000077 0.00048020 + layer.2.k_cache 0.00113755 0.26609186 + layer.2.v_cache 0.00000103 0.00069932 + layer.3.k_cache 0.00137011 0.30618040 + layer.3.v_cache 0.00000200 0.00110839 + layer.4.k_cache 0.00324136 0.53843896 + layer.4.v_cache 0.00000292 0.00189609 + layer.4.output 0.00022292 0.06127308 + ------------------------------------------------------------------------------------- + TOTAL 0.00287037 0.48137875 + (elements=1,376,256) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1376256 +Total Bytes 413252 +BPFP 2.4022 bits/point +EBPFP 4.8044 equivalent bits/point +MSE 0.481379 +---------------------- -------------------------------------------------------- +Time: 0.526s Load: 0.006s, Pack+Encode: 0.213s, Decode+Unpack: 0.306s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4814 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample31-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample31-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample32-layer4-item1.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample32-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,692B, BPFP=1.3042 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,012B, BPFP=2.8268 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,468B, BPFP=2.1167 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,468B, BPFP=2.9478 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,936B, BPFP=2.3218 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,916B, BPFP=2.9850 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,588B, BPFP=2.3760 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,276B, BPFP=2.9318 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,312B, BPFP=2.1037 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,976B, BPFP=2.9900 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,636B, BPFP=2.3196 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03206433 5.42344114 + layer.0.v_cache 0.00000027 0.00014971 + layer.1.k_cache 0.00350816 0.50969842 + layer.1.v_cache 0.00000080 0.00051650 + layer.2.k_cache 0.00113886 0.27095624 + layer.2.v_cache 0.00000104 0.00069122 + layer.3.k_cache 0.00133101 0.31545517 + layer.3.v_cache 0.00000202 0.00114915 + layer.4.k_cache 0.00327566 0.53986927 + layer.4.v_cache 0.00000298 0.00199985 + layer.4.output 0.00018563 0.07018709 + ------------------------------------------------------------------------------------- + TOTAL 0.00300483 0.52461964 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 411280 +BPFP 2.4416 bits/point +EBPFP 4.8832 equivalent bits/point +MSE 0.524620 +---------------------- -------------------------------------------------------- +Time: 0.507s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5246 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample32-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample32-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample33-layer4-item1.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample33-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 97, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 97, 128) +Output shape: (1, 97, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.output: torch.Size([1, 97, 4096]) -> torch.Size([1, 1, 97, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,228B, BPFP=1.3070 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,272B, BPFP=2.7603 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,124B, BPFP=2.0235 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,880B, BPFP=2.8898 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,736B, BPFP=2.2339 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,260B, BPFP=2.9204 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,432B, BPFP=2.2899 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,660B, BPFP=2.8721 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,148B, BPFP=2.0255 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,540B, BPFP=2.9430 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 114,272B, BPFP=2.3009 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.output: torch.Size([1, 97, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.output: torch.Size([1, 97, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.02997011 4.96506650 + layer.0.v_cache 0.00000027 0.00014851 + layer.1.k_cache 0.00344926 0.48487685 + layer.1.v_cache 0.00000085 0.00049857 + layer.2.k_cache 0.00114066 0.27340751 + layer.2.v_cache 0.00000106 0.00072377 + layer.3.k_cache 0.00133063 0.29965770 + layer.3.v_cache 0.00000201 0.00111470 + layer.4.k_cache 0.00322564 0.49001788 + layer.4.v_cache 0.00000314 0.00195622 + layer.4.output 0.00016478 0.04824508 + ------------------------------------------------------------------------------------- + TOTAL 0.00284162 0.47931775 + (elements=1,390,592) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1390592 +Total Bytes 415552 +BPFP 2.3906 bits/point +EBPFP 4.7813 equivalent bits/point +MSE 0.479318 +---------------------- -------------------------------------------------------- +Time: 0.504s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 97, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4793 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample33-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample33-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample35-layer4-item1.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample35-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 95, 128) +Output shape: (1, 95, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.output: torch.Size([1, 95, 4096]) -> torch.Size([1, 1, 95, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,680B, BPFP=1.3717 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,516B, BPFP=2.8385 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,560B, BPFP=2.1020 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,940B, BPFP=2.9556 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,052B, BPFP=2.3069 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,332B, BPFP=2.9878 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,764B, BPFP=2.3655 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,616B, BPFP=2.9289 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,404B, BPFP=2.0891 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,284B, BPFP=2.9839 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,552B, BPFP=2.3140 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03212489 5.18508622 + layer.0.v_cache 0.00000028 0.00015146 + layer.1.k_cache 0.00341497 0.49530375 + layer.1.v_cache 0.00000078 0.00051247 + layer.2.k_cache 0.00114913 0.25829468 + layer.2.v_cache 0.00000106 0.00071103 + layer.3.k_cache 0.00131997 0.30889067 + layer.3.v_cache 0.00000202 0.00111888 + layer.4.k_cache 0.00329406 0.53183160 + layer.4.v_cache 0.00000310 0.00206076 + layer.4.output 0.00015936 0.06162382 + ------------------------------------------------------------------------------------- + TOTAL 0.00299627 0.50217549 + (elements=1,361,920) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1361920 +Total Bytes 415700 +BPFP 2.4418 bits/point +EBPFP 4.8837 equivalent bits/point +MSE 0.502175 +---------------------- -------------------------------------------------------- +Time: 0.507s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5022 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample35-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample35-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample36-layer4-item1.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample36-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,584B, BPFP=1.3234 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,448B, BPFP=2.8404 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,068B, BPFP=2.1287 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,060B, BPFP=2.9772 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,596B, BPFP=2.3434 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,636B, BPFP=3.0262 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,444B, BPFP=2.4154 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,852B, BPFP=2.9596 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,828B, BPFP=2.1084 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,560B, BPFP=3.0197 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 109,648B, BPFP=2.3278 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.294s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03130991 5.64583953 + layer.0.v_cache 0.00000027 0.00014731 + layer.1.k_cache 0.00358715 0.50181903 + layer.1.v_cache 0.00000080 0.00049319 + layer.2.k_cache 0.00113844 0.27097034 + layer.2.v_cache 0.00000105 0.00069464 + layer.3.k_cache 0.00133437 0.30355934 + layer.3.v_cache 0.00000205 0.00112841 + layer.4.k_cache 0.00328807 0.53213779 + layer.4.v_cache 0.00000299 0.00193022 + layer.4.output 0.00019541 0.04513971 + ------------------------------------------------------------------------------------- + TOTAL 0.00296048 0.53137705 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 405724 +BPFP 2.4610 bits/point +EBPFP 4.9219 equivalent bits/point +MSE 0.531377 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.294s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5314 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample36-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample36-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample37-layer4-item1.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample37-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 95, 128) +Output shape: (1, 95, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.output: torch.Size([1, 95, 4096]) -> torch.Size([1, 1, 95, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,024B, BPFP=1.3178 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,372B, BPFP=2.8266 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,556B, BPFP=2.1016 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,908B, BPFP=2.9530 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,080B, BPFP=2.3092 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,368B, BPFP=2.9908 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,848B, BPFP=2.3724 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,596B, BPFP=2.9273 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,572B, BPFP=2.1030 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,264B, BPFP=2.9822 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,992B, BPFP=2.3230 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03212384 5.11992252 + layer.0.v_cache 0.00000028 0.00014851 + layer.1.k_cache 0.00332589 0.49023233 + layer.1.v_cache 0.00000080 0.00051118 + layer.2.k_cache 0.00116734 0.26546181 + layer.2.v_cache 0.00000106 0.00069650 + layer.3.k_cache 0.00135603 0.30374386 + layer.3.v_cache 0.00000201 0.00113485 + layer.4.k_cache 0.00321713 0.53923605 + layer.4.v_cache 0.00000315 0.00202153 + layer.4.output 0.00019059 0.06423429 + ------------------------------------------------------------------------------------- + TOTAL 0.00299713 0.49857473 + (elements=1,361,920) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1361920 +Total Bytes 415580 +BPFP 2.4411 bits/point +EBPFP 4.8823 equivalent bits/point +MSE 0.498575 +---------------------- -------------------------------------------------------- +Time: 0.502s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4986 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample37-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample37-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample39-layer4-item1.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample39-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,840B, BPFP=1.3306 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,716B, BPFP=2.8323 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,352B, BPFP=2.1297 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,280B, BPFP=2.9637 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,596B, BPFP=2.3182 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,788B, BPFP=3.0064 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,512B, BPFP=2.3952 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,088B, BPFP=2.9476 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,088B, BPFP=2.1075 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,880B, BPFP=3.0141 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,116B, BPFP=2.3546 +⌛️ [2/4] FRONTEND: Frontend time: 0.204s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03063477 5.39975665 + layer.0.v_cache 0.00000027 0.00014668 + layer.1.k_cache 0.00346550 0.52420241 + layer.1.v_cache 0.00000078 0.00051851 + layer.2.k_cache 0.00113041 0.26505645 + layer.2.v_cache 0.00000112 0.00071944 + layer.3.k_cache 0.00132028 0.30415775 + layer.3.v_cache 0.00000203 0.00114042 + layer.4.k_cache 0.00322248 0.53712767 + layer.4.v_cache 0.00000299 0.00198510 + layer.4.output 0.00017253 0.06553204 + ------------------------------------------------------------------------------------- + TOTAL 0.00289077 0.52120994 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 410256 +BPFP 2.4617 bits/point +EBPFP 4.9234 equivalent bits/point +MSE 0.521210 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.006s, Pack+Encode: 0.204s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5212 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample39-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample39-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample4-layer4-item1.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample4-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 100, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 100, 128) +Output shape: (1, 100, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.0.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.1.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.1.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.2.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.2.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.3.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.3.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.4.k_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.4.v_cache: torch.Size([1, 8, 100, 128]) -> torch.Size([1, 1, 100, 1024]) + layer.4.output: torch.Size([1, 100, 4096]) -> torch.Size([1, 1, 100, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 17,424B, BPFP=1.3613 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 36,320B, BPFP=2.8375 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 26,944B, BPFP=2.1050 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 37,996B, BPFP=2.9684 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 29,608B, BPFP=2.3131 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 38,336B, BPFP=2.9950 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 30,392B, BPFP=2.3744 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 37,580B, BPFP=2.9359 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 26,856B, BPFP=2.0981 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 38,432B, BPFP=3.0025 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 121,532B, BPFP=2.3737 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 100, 128]) + layer.0.v_cache: torch.Size([1, 8, 100, 128]) + layer.1.k_cache: torch.Size([1, 8, 100, 128]) + layer.1.v_cache: torch.Size([1, 8, 100, 128]) + layer.2.k_cache: torch.Size([1, 8, 100, 128]) + layer.2.v_cache: torch.Size([1, 8, 100, 128]) + layer.3.k_cache: torch.Size([1, 8, 100, 128]) + layer.3.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.k_cache: torch.Size([1, 8, 100, 128]) + layer.4.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.output: torch.Size([1, 100, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 100, 128]) + layer.0.v_cache: torch.Size([1, 8, 100, 128]) + layer.1.k_cache: torch.Size([1, 8, 100, 128]) + layer.1.v_cache: torch.Size([1, 8, 100, 128]) + layer.2.k_cache: torch.Size([1, 8, 100, 128]) + layer.2.v_cache: torch.Size([1, 8, 100, 128]) + layer.3.k_cache: torch.Size([1, 8, 100, 128]) + layer.3.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.k_cache: torch.Size([1, 8, 100, 128]) + layer.4.v_cache: torch.Size([1, 8, 100, 128]) + layer.4.output: torch.Size([1, 100, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03019520 5.61023438 + layer.0.v_cache 0.00000027 0.00014524 + layer.1.k_cache 0.00345483 0.48316597 + layer.1.v_cache 0.00000092 0.00051195 + layer.2.k_cache 0.00115188 0.26222998 + layer.2.v_cache 0.00000108 0.00068380 + layer.3.k_cache 0.00131981 0.29774672 + layer.3.v_cache 0.00000202 0.00111895 + layer.4.k_cache 0.00326509 0.52769585 + layer.4.v_cache 0.00000302 0.00202843 + layer.4.output 0.00016666 0.04580616 + ------------------------------------------------------------------------------------- + TOTAL 0.00286148 0.52634185 + (elements=1,433,600) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1433600 +Total Bytes 441420 +BPFP 2.4633 bits/point +EBPFP 4.9266 equivalent bits/point +MSE 0.526342 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 100, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 100, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5263 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample4-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample4-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample40-layer4-item1.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample40-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,080B, BPFP=1.3237 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,644B, BPFP=2.7777 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,076B, BPFP=2.1134 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,100B, BPFP=2.9933 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,640B, BPFP=2.3385 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,632B, BPFP=3.0400 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,472B, BPFP=2.4115 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,040B, BPFP=2.9881 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,872B, BPFP=2.0955 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,880B, BPFP=3.0618 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,876B, BPFP=2.3015 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03166650 5.44715110 + layer.0.v_cache 0.00000027 0.00015034 + layer.1.k_cache 0.00343321 0.50830841 + layer.1.v_cache 0.00000094 0.00053871 + layer.2.k_cache 0.00113193 0.27659725 + layer.2.v_cache 0.00000110 0.00075639 + layer.3.k_cache 0.00132786 0.30811901 + layer.3.v_cache 0.00000214 0.00120989 + layer.4.k_cache 0.00323286 0.52926473 + layer.4.v_cache 0.00000312 0.00204100 + layer.4.output 0.00018081 0.06015292 + ------------------------------------------------------------------------------------- + TOTAL 0.00296594 0.52248204 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 391312 +BPFP 2.4536 bits/point +EBPFP 4.9071 equivalent bits/point +MSE 0.522482 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5225 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample40-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample40-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample41-layer4-item1.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample41-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,084B, BPFP=1.3511 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,888B, BPFP=2.8468 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,348B, BPFP=2.1294 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,432B, BPFP=2.9765 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,744B, BPFP=2.3306 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,820B, BPFP=3.0091 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,376B, BPFP=2.3837 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,180B, BPFP=2.9553 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,224B, BPFP=2.1190 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,888B, BPFP=3.0148 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,464B, BPFP=2.3619 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03068307 5.53351552 + layer.0.v_cache 0.00000028 0.00015407 + layer.1.k_cache 0.00346304 0.52861203 + layer.1.v_cache 0.00000079 0.00052353 + layer.2.k_cache 0.00114171 0.27047096 + layer.2.v_cache 0.00000107 0.00071971 + layer.3.k_cache 0.00131636 0.30903256 + layer.3.v_cache 0.00000209 0.00113352 + layer.4.k_cache 0.00323274 0.55161839 + layer.4.v_cache 0.00000311 0.00204084 + layer.4.output 0.00016873 0.06469496 + ------------------------------------------------------------------------------------- + TOTAL 0.00289423 0.53261436 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 411448 +BPFP 2.4688 bits/point +EBPFP 4.9377 equivalent bits/point +MSE 0.532614 +---------------------- -------------------------------------------------------- +Time: 0.504s Load: 0.007s, Pack+Encode: 0.206s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5326 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample41-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample41-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample43-layer4-item1.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample43-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,476B, BPFP=1.3434 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,372B, BPFP=2.8101 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,348B, BPFP=2.1135 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,148B, BPFP=2.9642 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,064B, BPFP=2.3493 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,600B, BPFP=3.0035 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,728B, BPFP=2.4069 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,112B, BPFP=2.9611 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,132B, BPFP=2.0948 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,832B, BPFP=3.0236 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,936B, BPFP=2.2773 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03151998 5.04041748 + layer.0.v_cache 0.00000027 0.00014409 + layer.1.k_cache 0.00334378 0.48969510 + layer.1.v_cache 0.00000080 0.00050332 + layer.2.k_cache 0.00114309 0.26260855 + layer.2.v_cache 0.00000106 0.00070111 + layer.3.k_cache 0.00133556 0.31418521 + layer.3.v_cache 0.00000205 0.00114262 + layer.4.k_cache 0.00317137 0.50981488 + layer.4.v_cache 0.00000297 0.00202243 + layer.4.output 0.00023356 0.06003586 + ------------------------------------------------------------------------------------- + TOTAL 0.00296108 0.49009845 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 393748 +BPFP 2.4414 bits/point +EBPFP 4.8828 equivalent bits/point +MSE 0.490098 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4901 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample43-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample43-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample44-layer4-item1.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample44-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,416B, BPFP=1.2945 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,980B, BPFP=2.7820 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,748B, BPFP=2.1325 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,320B, BPFP=2.9921 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,464B, BPFP=2.3764 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,208B, BPFP=3.0718 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,060B, BPFP=2.4300 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,420B, BPFP=3.0011 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,388B, BPFP=2.1002 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,952B, BPFP=3.0489 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 100,244B, BPFP=2.2504 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03168404 5.11318303 + layer.0.v_cache 0.00000027 0.00014568 + layer.1.k_cache 0.00359645 0.52162679 + layer.1.v_cache 0.00000085 0.00050967 + layer.2.k_cache 0.00115081 0.26016075 + layer.2.v_cache 0.00000106 0.00070013 + layer.3.k_cache 0.00133256 0.29846424 + layer.3.v_cache 0.00000205 0.00115321 + layer.4.k_cache 0.00340755 0.51692901 + layer.4.v_cache 0.00000299 0.00197262 + layer.4.output 0.00018399 0.06601901 + ------------------------------------------------------------------------------------- + TOTAL 0.00299390 0.49849437 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 381200 +BPFP 2.4451 bits/point +EBPFP 4.8902 equivalent bits/point +MSE 0.498494 +---------------------- -------------------------------------------------------- +Time: 0.502s Load: 0.006s, Pack+Encode: 0.207s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4985 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample44-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample44-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample46-layer4-item1.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample46-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 96, 128) +Output shape: (1, 96, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.output: torch.Size([1, 96, 4096]) -> torch.Size([1, 1, 96, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,068B, BPFP=1.3076 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,040B, BPFP=2.7702 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,192B, BPFP=2.0501 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,620B, BPFP=2.8988 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,704B, BPFP=2.2546 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,824B, BPFP=2.9154 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,572B, BPFP=2.3252 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,336B, BPFP=2.8757 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,100B, BPFP=2.0426 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,236B, BPFP=2.9489 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,996B, BPFP=2.2786 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03074032 5.04444345 + layer.0.v_cache 0.00000027 0.00015256 + layer.1.k_cache 0.00337797 0.50100621 + layer.1.v_cache 0.00000078 0.00049843 + layer.2.k_cache 0.00113475 0.26106648 + layer.2.v_cache 0.00000103 0.00070266 + layer.3.k_cache 0.00138182 0.30219960 + layer.3.v_cache 0.00000203 0.00113665 + layer.4.k_cache 0.00323877 0.52749745 + layer.4.v_cache 0.00000295 0.00196808 + layer.4.output 0.00017303 0.05846676 + ------------------------------------------------------------------------------------- + TOTAL 0.00289806 0.49103847 + (elements=1,376,256) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1376256 +Total Bytes 411688 +BPFP 2.3931 bits/point +EBPFP 4.7862 equivalent bits/point +MSE 0.491038 +---------------------- -------------------------------------------------------- +Time: 0.500s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4910 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample46-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample46-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample47-layer4-item1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample47-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.005s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,508B, BPFP=1.3028 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,828B, BPFP=2.7683 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,752B, BPFP=2.1329 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,244B, BPFP=2.9853 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,456B, BPFP=2.3757 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,216B, BPFP=3.0726 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,032B, BPFP=2.4274 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,408B, BPFP=3.0000 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,444B, BPFP=2.1052 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,004B, BPFP=3.0535 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 100,452B, BPFP=2.2551 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.294s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03178630 5.07472352 + layer.0.v_cache 0.00000027 0.00014477 + layer.1.k_cache 0.00361903 0.52198454 + layer.1.v_cache 0.00000083 0.00051003 + layer.2.k_cache 0.00113732 0.25931992 + layer.2.v_cache 0.00000106 0.00070312 + layer.3.k_cache 0.00132704 0.30306222 + layer.3.v_cache 0.00000206 0.00116409 + layer.4.k_cache 0.00332258 0.51626745 + layer.4.v_cache 0.00000303 0.00196352 + layer.4.output 0.00018372 0.06923834 + ------------------------------------------------------------------------------------- + TOTAL 0.00299531 0.49691404 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 381344 +BPFP 2.4460 bits/point +EBPFP 4.8920 equivalent bits/point +MSE 0.496914 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.005s, Pack+Encode: 0.206s, Decode+Unpack: 0.294s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4969 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample47-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample47-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample48-layer4-item1.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample48-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 97, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 97, 128) +Output shape: (1, 97, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.output: torch.Size([1, 97, 4096]) -> torch.Size([1, 1, 97, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,076B, BPFP=1.2948 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,356B, BPFP=2.7671 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,204B, BPFP=2.0300 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,812B, BPFP=2.8843 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,804B, BPFP=2.2394 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,140B, BPFP=2.9108 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,580B, BPFP=2.3019 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,544B, BPFP=2.8628 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,228B, BPFP=2.0319 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,504B, BPFP=2.9401 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 114,476B, BPFP=2.3050 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.output: torch.Size([1, 97, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.output: torch.Size([1, 97, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03078698 4.85950431 + layer.0.v_cache 0.00000027 0.00015449 + layer.1.k_cache 0.00338495 0.50889501 + layer.1.v_cache 0.00000090 0.00050729 + layer.2.k_cache 0.00114245 0.27001871 + layer.2.v_cache 0.00000106 0.00071973 + layer.3.k_cache 0.00133426 0.29486648 + layer.3.v_cache 0.00000206 0.00119591 + layer.4.k_cache 0.00327800 0.49323402 + layer.4.v_cache 0.00000314 0.00203858 + layer.4.output 0.00016719 0.05121754 + ------------------------------------------------------------------------------------- + TOTAL 0.00290020 0.47400033 + (elements=1,390,592) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1390592 +Total Bytes 415724 +BPFP 2.3916 bits/point +EBPFP 4.7833 equivalent bits/point +MSE 0.474000 +---------------------- -------------------------------------------------------- +Time: 0.500s Load: 0.006s, Pack+Encode: 0.205s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 97, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4740 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample48-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample48-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample50-layer4-item1.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample50-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,996B, BPFP=1.3584 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,492B, BPFP=2.8441 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,180B, BPFP=2.1382 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,148B, BPFP=2.9847 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,752B, BPFP=2.3567 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,460B, BPFP=3.0112 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,352B, BPFP=2.4076 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,788B, BPFP=2.9541 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,828B, BPFP=2.1084 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,492B, BPFP=3.0139 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,932B, BPFP=2.3126 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.303s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03091334 5.43172621 + layer.0.v_cache 0.00000027 0.00014743 + layer.1.k_cache 0.00334237 0.50690203 + layer.1.v_cache 0.00000084 0.00050088 + layer.2.k_cache 0.00114123 0.26557815 + layer.2.v_cache 0.00000105 0.00068026 + layer.3.k_cache 0.00132733 0.30013920 + layer.3.v_cache 0.00000206 0.00110902 + layer.4.k_cache 0.00332026 0.52166520 + layer.4.v_cache 0.00000301 0.00196349 + layer.4.output 0.00018747 0.04403872 + ------------------------------------------------------------------------------------- + TOTAL 0.00291440 0.51475477 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 405420 +BPFP 2.4591 bits/point +EBPFP 4.9182 equivalent bits/point +MSE 0.514755 +---------------------- -------------------------------------------------------- +Time: 0.519s Load: 0.006s, Pack+Encode: 0.210s, Decode+Unpack: 0.303s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5148 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample50-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample50-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample51-layer4-item1.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample51-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,964B, BPFP=1.3268 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,032B, BPFP=2.8285 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,564B, BPFP=2.1247 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,460B, BPFP=2.9471 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,884B, BPFP=2.3175 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,084B, BPFP=2.9990 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,644B, BPFP=2.3807 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,488B, BPFP=2.9495 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,432B, BPFP=2.1137 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,056B, BPFP=2.9967 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,976B, BPFP=2.3474 +⌛️ [2/4] FRONTEND: Frontend time: 0.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03107318 5.52799631 + layer.0.v_cache 0.00000027 0.00014507 + layer.1.k_cache 0.00348481 0.50954056 + layer.1.v_cache 0.00000081 0.00051484 + layer.2.k_cache 0.00116411 0.26132458 + layer.2.v_cache 0.00000106 0.00070257 + layer.3.k_cache 0.00133018 0.30037454 + layer.3.v_cache 0.00000204 0.00115862 + layer.4.k_cache 0.00337179 0.54226628 + layer.4.v_cache 0.00000293 0.00191950 + layer.4.output 0.00016660 0.05804213 + ------------------------------------------------------------------------------------- + TOTAL 0.00293554 0.52700795 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 413584 +BPFP 2.4553 bits/point +EBPFP 4.9105 equivalent bits/point +MSE 0.527008 +---------------------- -------------------------------------------------------- +Time: 0.516s Load: 0.006s, Pack+Encode: 0.209s, Decode+Unpack: 0.301s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5270 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample51-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample51-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample53-layer4-item1.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample53-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,072B, BPFP=1.2636 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,168B, BPFP=2.7091 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,696B, BPFP=2.1279 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 32,704B, BPFP=2.9368 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,304B, BPFP=2.3621 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,900B, BPFP=3.0442 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,872B, BPFP=2.4131 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,164B, BPFP=2.9781 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,208B, BPFP=2.0841 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,984B, BPFP=3.0517 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,260B, BPFP=2.2284 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03151683 5.15934736 + layer.0.v_cache 0.00000028 0.00015102 + layer.1.k_cache 0.00361716 0.51453058 + layer.1.v_cache 0.00000078 0.00050242 + layer.2.k_cache 0.00116662 0.26067045 + layer.2.v_cache 0.00000103 0.00068879 + layer.3.k_cache 0.00135197 0.30811490 + layer.3.v_cache 0.00000203 0.00110426 + layer.4.k_cache 0.00327724 0.50779496 + layer.4.v_cache 0.00000296 0.00197788 + layer.4.output 0.00017041 0.06256778 + ------------------------------------------------------------------------------------- + TOTAL 0.00297275 0.50036813 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 377332 +BPFP 2.4203 bits/point +EBPFP 4.8406 equivalent bits/point +MSE 0.500368 +---------------------- -------------------------------------------------------- +Time: 0.517s Load: 0.006s, Pack+Encode: 0.211s, Decode+Unpack: 0.301s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5004 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample53-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample53-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample54-layer4-item1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample54-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 95, 128) +Output shape: (1, 95, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) -> torch.Size([1, 1, 95, 1024]) + layer.4.output: torch.Size([1, 95, 4096]) -> torch.Size([1, 1, 95, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,216B, BPFP=1.3336 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,336B, BPFP=2.8237 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,592B, BPFP=2.1046 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,928B, BPFP=2.9546 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,988B, BPFP=2.3016 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,364B, BPFP=2.9905 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,724B, BPFP=2.3622 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,620B, BPFP=2.9293 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,428B, BPFP=2.0911 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,416B, BPFP=2.9947 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,952B, BPFP=2.3222 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.304s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 95, 128]) + layer.0.v_cache: torch.Size([1, 8, 95, 128]) + layer.1.k_cache: torch.Size([1, 8, 95, 128]) + layer.1.v_cache: torch.Size([1, 8, 95, 128]) + layer.2.k_cache: torch.Size([1, 8, 95, 128]) + layer.2.v_cache: torch.Size([1, 8, 95, 128]) + layer.3.k_cache: torch.Size([1, 8, 95, 128]) + layer.3.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.k_cache: torch.Size([1, 8, 95, 128]) + layer.4.v_cache: torch.Size([1, 8, 95, 128]) + layer.4.output: torch.Size([1, 95, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03080931 5.07030415 + layer.0.v_cache 0.00000028 0.00014551 + layer.1.k_cache 0.00333058 0.48711186 + layer.1.v_cache 0.00000087 0.00049622 + layer.2.k_cache 0.00113656 0.26076921 + layer.2.v_cache 0.00000104 0.00069629 + layer.3.k_cache 0.00135899 0.29530025 + layer.3.v_cache 0.00000196 0.00108459 + layer.4.k_cache 0.00332312 0.51835825 + layer.4.v_cache 0.00000307 0.00200728 + layer.4.output 0.00019584 0.05953471 + ------------------------------------------------------------------------------------- + TOTAL 0.00291065 0.49102946 + (elements=1,361,920) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1361920 +Total Bytes 415564 +BPFP 2.4410 bits/point +EBPFP 4.8821 equivalent bits/point +MSE 0.491029 +---------------------- -------------------------------------------------------- +Time: 0.520s Load: 0.006s, Pack+Encode: 0.210s, Decode+Unpack: 0.304s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 95, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 95, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4910 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample54-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample54-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample55-layer4-item1.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample55-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,320B, BPFP=1.2859 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,628B, BPFP=2.7504 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,700B, BPFP=2.1282 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,224B, BPFP=2.9835 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,288B, BPFP=2.3606 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,088B, BPFP=3.0611 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,972B, BPFP=2.4221 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,228B, BPFP=2.9838 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,268B, BPFP=2.0894 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,028B, BPFP=3.0557 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 101,068B, BPFP=2.2689 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03144652 5.29868852 + layer.0.v_cache 0.00000028 0.00014873 + layer.1.k_cache 0.00348072 0.50255962 + layer.1.v_cache 0.00000083 0.00052878 + layer.2.k_cache 0.00116226 0.25610207 + layer.2.v_cache 0.00000106 0.00072976 + layer.3.k_cache 0.00133044 0.31234982 + layer.3.v_cache 0.00000208 0.00115600 + layer.4.k_cache 0.00328583 0.51090644 + layer.4.v_cache 0.00000293 0.00195525 + layer.4.output 0.00019032 0.06525937 + ------------------------------------------------------------------------------------- + TOTAL 0.00296244 0.51044018 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 380812 +BPFP 2.4426 bits/point +EBPFP 4.8852 equivalent bits/point +MSE 0.510440 +---------------------- -------------------------------------------------------- +Time: 0.509s Load: 0.006s, Pack+Encode: 0.211s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5104 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample55-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample55-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample59-layer4-item1.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample59-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 91, 128) +Output shape: (1, 91, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.output: torch.Size([1, 91, 4096]) -> torch.Size([1, 1, 91, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,672B, BPFP=1.3455 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,988B, BPFP=2.8321 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,008B, BPFP=2.1470 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,896B, BPFP=2.9959 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,520B, BPFP=2.3626 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,180B, BPFP=3.0203 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,232B, BPFP=2.4238 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,604B, BPFP=2.9708 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,712B, BPFP=2.1216 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,176B, BPFP=3.0199 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,024B, BPFP=2.3185 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.302s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03082078 5.07530967 + layer.0.v_cache 0.00000027 0.00014585 + layer.1.k_cache 0.00345050 0.48992291 + layer.1.v_cache 0.00000081 0.00050531 + layer.2.k_cache 0.00112876 0.25747368 + layer.2.v_cache 0.00000108 0.00071085 + layer.3.k_cache 0.00131171 0.30279558 + layer.3.v_cache 0.00000203 0.00112468 + layer.4.k_cache 0.00324214 0.49883283 + layer.4.v_cache 0.00000302 0.00192385 + layer.4.output 0.00017376 0.03858828 + ------------------------------------------------------------------------------------- + TOTAL 0.00290401 0.48450702 + (elements=1,304,576) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1304576 +Total Bytes 402012 +BPFP 2.4652 bits/point +EBPFP 4.9305 equivalent bits/point +MSE 0.484507 +---------------------- -------------------------------------------------------- +Time: 0.520s Load: 0.007s, Pack+Encode: 0.212s, Decode+Unpack: 0.302s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4845 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample59-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample59-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample60-layer4-item1.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample60-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,416B, BPFP=1.2945 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,712B, BPFP=2.7579 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,736B, BPFP=2.1315 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,396B, BPFP=2.9989 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,432B, BPFP=2.3736 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,184B, BPFP=3.0697 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,900B, BPFP=2.4156 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,448B, BPFP=3.0036 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,324B, BPFP=2.0945 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,112B, BPFP=3.0632 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,824B, BPFP=2.2410 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.303s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03212153 5.18604568 + layer.0.v_cache 0.00000027 0.00014063 + layer.1.k_cache 0.00343529 0.49517765 + layer.1.v_cache 0.00000080 0.00049749 + layer.2.k_cache 0.00113462 0.25596943 + layer.2.v_cache 0.00000107 0.00069709 + layer.3.k_cache 0.00129755 0.30152544 + layer.3.v_cache 0.00000202 0.00111513 + layer.4.k_cache 0.00322320 0.50405520 + layer.4.v_cache 0.00000301 0.00196083 + layer.4.output 0.00017088 0.05699875 + ------------------------------------------------------------------------------------- + TOTAL 0.00299306 0.49822711 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 380484 +BPFP 2.4405 bits/point +EBPFP 4.8810 equivalent bits/point +MSE 0.498227 +---------------------- -------------------------------------------------------- +Time: 0.522s Load: 0.007s, Pack+Encode: 0.212s, Decode+Unpack: 0.303s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4982 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample60-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample60-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample62-layer4-item1.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample62-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,392B, BPFP=1.3361 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,328B, BPFP=2.8062 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,364B, BPFP=2.1149 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,148B, BPFP=2.9642 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,040B, BPFP=2.3472 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,604B, BPFP=3.0038 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,656B, BPFP=2.4007 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,964B, BPFP=2.9483 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,080B, BPFP=2.0903 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,844B, BPFP=3.0247 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,292B, BPFP=2.2633 +⌛️ [2/4] FRONTEND: Frontend time: 0.213s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.303s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03068415 5.00083110 + layer.0.v_cache 0.00000027 0.00014700 + layer.1.k_cache 0.00335417 0.50318798 + layer.1.v_cache 0.00000084 0.00050864 + layer.2.k_cache 0.00111816 0.26141438 + layer.2.v_cache 0.00000105 0.00072404 + layer.3.k_cache 0.00134433 0.30318192 + layer.3.v_cache 0.00000196 0.00112722 + layer.4.k_cache 0.00329790 0.51794311 + layer.4.v_cache 0.00000303 0.00198473 + layer.4.output 0.00016086 0.05257593 + ------------------------------------------------------------------------------------- + TOTAL 0.00288924 0.48581099 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 392712 +BPFP 2.4350 bits/point +EBPFP 4.8699 equivalent bits/point +MSE 0.485811 +---------------------- -------------------------------------------------------- +Time: 0.522s Load: 0.006s, Pack+Encode: 0.213s, Decode+Unpack: 0.303s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4858 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample62-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample62-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample63-layer4-item1.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample63-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,400B, BPFP=1.3368 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,368B, BPFP=2.8097 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,392B, BPFP=2.1174 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,292B, BPFP=2.9767 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,092B, BPFP=2.3517 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,768B, BPFP=3.0181 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,792B, BPFP=2.4125 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,164B, BPFP=2.9656 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,216B, BPFP=2.1021 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,840B, BPFP=3.0243 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,804B, BPFP=2.2744 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.302s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03145831 5.22076687 + layer.0.v_cache 0.00000027 0.00014579 + layer.1.k_cache 0.00344612 0.49569405 + layer.1.v_cache 0.00000080 0.00051544 + layer.2.k_cache 0.00114283 0.26157689 + layer.2.v_cache 0.00000108 0.00070980 + layer.3.k_cache 0.00133751 0.30352580 + layer.3.v_cache 0.00000207 0.00113871 + layer.4.k_cache 0.00330099 0.51240362 + layer.4.v_cache 0.00000295 0.00198000 + layer.4.output 0.00016962 0.05502242 + ------------------------------------------------------------------------------------- + TOTAL 0.00295510 0.50132476 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 394128 +BPFP 2.4438 bits/point +EBPFP 4.8875 equivalent bits/point +MSE 0.501325 +---------------------- -------------------------------------------------------- +Time: 0.520s Load: 0.007s, Pack+Encode: 0.211s, Decode+Unpack: 0.302s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5013 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample63-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample63-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample65-layer4-item1.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample65-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 91, 128) +Output shape: (1, 91, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.output: torch.Size([1, 91, 4096]) -> torch.Size([1, 1, 91, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,848B, BPFP=1.3606 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,104B, BPFP=2.8420 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,060B, BPFP=2.1514 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,832B, BPFP=2.9904 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,532B, BPFP=2.3637 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,128B, BPFP=3.0158 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,272B, BPFP=2.4272 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,628B, BPFP=2.9729 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,820B, BPFP=2.1308 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,228B, BPFP=3.0244 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,488B, BPFP=2.3285 +⌛️ [2/4] FRONTEND: Frontend time: 0.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.301s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03132481 5.22510839 + layer.0.v_cache 0.00000027 0.00014270 + layer.1.k_cache 0.00345143 0.49079270 + layer.1.v_cache 0.00000080 0.00049798 + layer.2.k_cache 0.00115294 0.26678098 + layer.2.v_cache 0.00000107 0.00069973 + layer.3.k_cache 0.00135241 0.30382882 + layer.3.v_cache 0.00000203 0.00110879 + layer.4.k_cache 0.00326810 0.51039480 + layer.4.v_cache 0.00000305 0.00193405 + layer.4.output 0.00025628 0.05158337 + ------------------------------------------------------------------------------------- + TOTAL 0.00297015 0.50054446 + (elements=1,304,576) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1304576 +Total Bytes 402940 +BPFP 2.4709 bits/point +EBPFP 4.9419 equivalent bits/point +MSE 0.500544 +---------------------- -------------------------------------------------------- +Time: 0.518s Load: 0.006s, Pack+Encode: 0.211s, Decode+Unpack: 0.301s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5005 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample65-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample65-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample66-layer4-item1.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample66-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,200B, BPFP=1.3609 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,712B, BPFP=2.8320 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,328B, BPFP=2.1277 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,340B, BPFP=2.9688 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,756B, BPFP=2.3317 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,784B, BPFP=3.0060 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,492B, BPFP=2.3935 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,148B, BPFP=2.9526 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,200B, BPFP=2.1169 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,860B, BPFP=3.0124 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,896B, BPFP=2.3500 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03759774 5.50301238 + layer.0.v_cache 0.00000028 0.00014926 + layer.1.k_cache 0.00332062 0.52950394 + layer.1.v_cache 0.00000078 0.00051349 + layer.2.k_cache 0.00112828 0.26891700 + layer.2.v_cache 0.00000108 0.00073757 + layer.3.k_cache 0.00133686 0.30556014 + layer.3.v_cache 0.00000204 0.00115826 + layer.4.k_cache 0.00329063 0.53504985 + layer.4.v_cache 0.00000307 0.00202378 + layer.4.output 0.00016515 0.06447913 + ------------------------------------------------------------------------------------- + TOTAL 0.00338157 0.52889587 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 410716 +BPFP 2.4645 bits/point +EBPFP 4.9289 equivalent bits/point +MSE 0.528896 +---------------------- -------------------------------------------------------- +Time: 0.516s Load: 0.006s, Pack+Encode: 0.210s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5289 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample66-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample66-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample67-layer4-item1.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample67-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,532B, BPFP=1.2909 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,088B, BPFP=2.8331 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,432B, BPFP=2.1137 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,416B, BPFP=2.9435 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,956B, BPFP=2.3235 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,864B, BPFP=2.9807 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,684B, BPFP=2.3840 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,364B, BPFP=2.9392 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,388B, BPFP=2.1100 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,020B, BPFP=2.9937 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,612B, BPFP=2.3398 +⌛️ [2/4] FRONTEND: Frontend time: 0.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.304s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03150173 5.50676257 + layer.0.v_cache 0.00000027 0.00014866 + layer.1.k_cache 0.00348105 0.53574030 + layer.1.v_cache 0.00000083 0.00049441 + layer.2.k_cache 0.00114206 0.26874128 + layer.2.v_cache 0.00000103 0.00067642 + layer.3.k_cache 0.00133122 0.31357857 + layer.3.v_cache 0.00000211 0.00115185 + layer.4.k_cache 0.00332532 0.53145449 + layer.4.v_cache 0.00000306 0.00187253 + layer.4.output 0.00018383 0.06403113 + ------------------------------------------------------------------------------------- + TOTAL 0.00296600 0.52976755 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 412356 +BPFP 2.4480 bits/point +EBPFP 4.8959 equivalent bits/point +MSE 0.529768 +---------------------- -------------------------------------------------------- +Time: 0.519s Load: 0.006s, Pack+Encode: 0.209s, Decode+Unpack: 0.304s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5298 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample67-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample67-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample68-layer4-item1.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample68-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 91, 128) +Output shape: (1, 91, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.output: torch.Size([1, 91, 4096]) -> torch.Size([1, 1, 91, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,944B, BPFP=1.3688 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,212B, BPFP=2.8513 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,892B, BPFP=2.1370 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,720B, BPFP=2.9808 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,480B, BPFP=2.3592 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,112B, BPFP=3.0144 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,248B, BPFP=2.4251 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,568B, BPFP=2.9677 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,844B, BPFP=2.1329 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,240B, BPFP=3.0254 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 108,316B, BPFP=2.3248 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.302s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03172423 5.39691196 + layer.0.v_cache 0.00000027 0.00014674 + layer.1.k_cache 0.00338285 0.49741997 + layer.1.v_cache 0.00000085 0.00050216 + layer.2.k_cache 0.00114373 0.25892769 + layer.2.v_cache 0.00000106 0.00067579 + layer.3.k_cache 0.00134976 0.29818147 + layer.3.v_cache 0.00000205 0.00111082 + layer.4.k_cache 0.00321980 0.51482291 + layer.4.v_cache 0.00000306 0.00192484 + layer.4.output 0.00016531 0.04816774 + ------------------------------------------------------------------------------------- + TOTAL 0.00296349 0.51166395 + (elements=1,304,576) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1304576 +Total Bytes 402576 +BPFP 2.4687 bits/point +EBPFP 4.9374 equivalent bits/point +MSE 0.511664 +---------------------- -------------------------------------------------------- +Time: 0.520s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.302s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5117 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample68-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample68-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample7-layer4-item1.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample7-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,772B, BPFP=1.3249 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,664B, BPFP=2.8280 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,108B, BPFP=2.1092 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,248B, BPFP=2.9610 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,628B, BPFP=2.3209 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,616B, BPFP=2.9919 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,412B, BPFP=2.3868 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,952B, BPFP=2.9362 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,040B, BPFP=2.1035 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,744B, BPFP=3.0027 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,012B, BPFP=2.3314 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03162601 5.64671933 + layer.0.v_cache 0.00000026 0.00014815 + layer.1.k_cache 0.00335281 0.52915950 + layer.1.v_cache 0.00000078 0.00050979 + layer.2.k_cache 0.00111993 0.27336986 + layer.2.v_cache 0.00000106 0.00070858 + layer.3.k_cache 0.00131014 0.30689703 + layer.3.v_cache 0.00000197 0.00113399 + layer.4.k_cache 0.00327871 0.53046212 + layer.4.v_cache 0.00000299 0.00196676 + layer.4.output 0.00016230 0.06312782 + ------------------------------------------------------------------------------------- + TOTAL 0.00295313 0.53882760 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 408196 +BPFP 2.4493 bits/point +EBPFP 4.8987 equivalent bits/point +MSE 0.538828 +---------------------- -------------------------------------------------------- +Time: 0.509s Load: 0.007s, Pack+Encode: 0.210s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5388 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample7-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample7-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample70-layer4-item1.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample70-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 94, 128) +Output shape: (1, 94, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) -> torch.Size([1, 1, 94, 1024]) + layer.4.output: torch.Size([1, 94, 4096]) -> torch.Size([1, 1, 94, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,116B, BPFP=1.3394 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,236B, BPFP=2.8454 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,568B, BPFP=2.1250 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,720B, BPFP=2.9688 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 28,064B, BPFP=2.3324 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,184B, BPFP=3.0073 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,728B, BPFP=2.3876 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,532B, BPFP=2.9531 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,576B, BPFP=2.1257 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,148B, BPFP=3.0043 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 114,104B, BPFP=2.3708 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 94, 128]) + layer.0.v_cache: torch.Size([1, 8, 94, 128]) + layer.1.k_cache: torch.Size([1, 8, 94, 128]) + layer.1.v_cache: torch.Size([1, 8, 94, 128]) + layer.2.k_cache: torch.Size([1, 8, 94, 128]) + layer.2.v_cache: torch.Size([1, 8, 94, 128]) + layer.3.k_cache: torch.Size([1, 8, 94, 128]) + layer.3.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.k_cache: torch.Size([1, 8, 94, 128]) + layer.4.v_cache: torch.Size([1, 8, 94, 128]) + layer.4.output: torch.Size([1, 94, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03152096 5.49023243 + layer.0.v_cache 0.00000028 0.00015406 + layer.1.k_cache 0.00374224 0.51974236 + layer.1.v_cache 0.00000083 0.00052948 + layer.2.k_cache 0.00112812 0.26464909 + layer.2.v_cache 0.00000115 0.00076349 + layer.3.k_cache 0.00131826 0.30792135 + layer.3.v_cache 0.00000212 0.00118685 + layer.4.k_cache 0.00331939 0.54468289 + layer.4.v_cache 0.00000319 0.00203600 + layer.4.output 0.00017462 0.06517588 + ------------------------------------------------------------------------------------- + TOTAL 0.00298107 0.52804297 + (elements=1,347,584) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1347584 +Total Bytes 415976 +BPFP 2.4695 bits/point +EBPFP 4.9389 equivalent bits/point +MSE 0.528043 +---------------------- -------------------------------------------------------- +Time: 0.507s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 94, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 94, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5280 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample70-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample70-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample71-layer4-item1.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample71-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,804B, BPFP=1.3421 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,532B, BPFP=2.8475 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,176B, BPFP=2.1379 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,172B, BPFP=2.9868 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,744B, BPFP=2.3560 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,676B, BPFP=3.0296 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,412B, BPFP=2.4127 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,964B, BPFP=2.9691 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,820B, BPFP=2.1077 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,552B, BPFP=3.0190 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,200B, BPFP=2.3395 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.288s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03010495 5.37427421 + layer.0.v_cache 0.00000027 0.00015307 + layer.1.k_cache 0.00345006 0.51000106 + layer.1.v_cache 0.00000091 0.00051983 + layer.2.k_cache 0.00113922 0.27150712 + layer.2.v_cache 0.00000110 0.00073772 + layer.3.k_cache 0.00129336 0.30862663 + layer.3.v_cache 0.00000204 0.00115697 + layer.4.k_cache 0.00332675 0.51700617 + layer.4.v_cache 0.00000304 0.00200292 + layer.4.output 0.00015992 0.04713718 + ------------------------------------------------------------------------------------- + TOTAL 0.00285438 0.51246674 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407052 +BPFP 2.4690 bits/point +EBPFP 4.9380 equivalent bits/point +MSE 0.512467 +---------------------- -------------------------------------------------------- +Time: 0.501s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.288s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5125 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample71-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample71-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample72-layer4-item1.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample72-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,000B, BPFP=1.3441 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,640B, BPFP=2.8259 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,432B, BPFP=2.1364 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,332B, BPFP=2.9681 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,804B, BPFP=2.3357 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,724B, BPFP=3.0010 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,452B, BPFP=2.3901 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,968B, BPFP=2.9375 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,076B, BPFP=2.1065 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,736B, BPFP=3.0020 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,444B, BPFP=2.3405 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03144996 5.42421731 + layer.0.v_cache 0.00000027 0.00014700 + layer.1.k_cache 0.00343081 0.53696819 + layer.1.v_cache 0.00000081 0.00053118 + layer.2.k_cache 0.00115410 0.27461749 + layer.2.v_cache 0.00000107 0.00071307 + layer.3.k_cache 0.00131217 0.30582633 + layer.3.v_cache 0.00000205 0.00113419 + layer.4.k_cache 0.00325876 0.55914553 + layer.4.v_cache 0.00000310 0.00195864 + layer.4.output 0.00018378 0.06830560 + ------------------------------------------------------------------------------------- + TOTAL 0.00295345 0.52703438 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 409608 +BPFP 2.4578 bits/point +EBPFP 4.9156 equivalent bits/point +MSE 0.527034 +---------------------- -------------------------------------------------------- +Time: 0.508s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5270 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample72-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample72-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample73-layer4-item1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample73-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 85, 128) +Output shape: (1, 85, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.output: torch.Size([1, 85, 4096]) -> torch.Size([1, 1, 85, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,432B, BPFP=1.3265 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,324B, BPFP=2.7871 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,456B, BPFP=2.1559 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,016B, BPFP=3.0346 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 25,900B, BPFP=2.3805 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,644B, BPFP=3.0923 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,584B, BPFP=2.4434 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,280B, BPFP=3.0588 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,316B, BPFP=2.1430 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,780B, BPFP=3.1048 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 100,332B, BPFP=2.3054 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03191245 5.12470847 + layer.0.v_cache 0.00000028 0.00014456 + layer.1.k_cache 0.00355479 0.52920810 + layer.1.v_cache 0.00000082 0.00051938 + layer.2.k_cache 0.00115713 0.27164690 + layer.2.v_cache 0.00000109 0.00073222 + layer.3.k_cache 0.00128442 0.30442877 + layer.3.v_cache 0.00000206 0.00114697 + layer.4.k_cache 0.00320041 0.51525726 + layer.4.v_cache 0.00000307 0.00201292 + layer.4.output 0.00016954 0.06339417 + ------------------------------------------------------------------------------------- + TOTAL 0.00298533 0.50024159 + (elements=1,218,560) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1218560 +Total Bytes 378064 +BPFP 2.4820 bits/point +EBPFP 4.9641 equivalent bits/point +MSE 0.500242 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.006s, Pack+Encode: 0.208s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5002 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample73-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample73-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample74-layer4-item1.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample74-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,716B, BPFP=1.3642 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,660B, BPFP=2.8351 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,372B, BPFP=2.1156 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,280B, BPFP=2.9757 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,168B, BPFP=2.3583 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,824B, BPFP=3.0229 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,700B, BPFP=2.4045 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,300B, BPFP=2.9774 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,160B, BPFP=2.0972 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,984B, BPFP=3.0368 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 106,912B, BPFP=2.3201 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03149115 4.78711853 + layer.0.v_cache 0.00000027 0.00014813 + layer.1.k_cache 0.00351934 0.51002731 + layer.1.v_cache 0.00000089 0.00053019 + layer.2.k_cache 0.00114079 0.26529020 + layer.2.v_cache 0.00000110 0.00073088 + layer.3.k_cache 0.00129789 0.31419644 + layer.3.v_cache 0.00000226 0.00121367 + layer.4.k_cache 0.00328524 0.51788847 + layer.4.v_cache 0.00000318 0.00206741 + layer.4.output 0.00020520 0.06361622 + ------------------------------------------------------------------------------------- + TOTAL 0.00296878 0.47526258 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 397076 +BPFP 2.4620 bits/point +EBPFP 4.9241 equivalent bits/point +MSE 0.475263 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.208s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4753 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample74-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample74-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample75-layer4-item1.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample75-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,184B, BPFP=1.3743 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,576B, BPFP=2.8512 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,232B, BPFP=2.1427 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,208B, BPFP=2.9898 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,756B, BPFP=2.3570 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,624B, BPFP=3.0251 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,344B, BPFP=2.4069 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,992B, BPFP=2.9715 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,008B, BPFP=2.1236 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,664B, BPFP=3.0285 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,344B, BPFP=2.3426 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03262251 5.73068237 + layer.0.v_cache 0.00000028 0.00014588 + layer.1.k_cache 0.00349940 0.50377274 + layer.1.v_cache 0.00000082 0.00050867 + layer.2.k_cache 0.00113634 0.26962081 + layer.2.v_cache 0.00000107 0.00071110 + layer.3.k_cache 0.00132950 0.30216955 + layer.3.v_cache 0.00000207 0.00113992 + layer.4.k_cache 0.00323306 0.51622789 + layer.4.v_cache 0.00000312 0.00202921 + layer.4.output 0.00016774 0.03794916 + ------------------------------------------------------------------------------------- + TOTAL 0.00303565 0.53420034 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407932 +BPFP 2.4744 bits/point +EBPFP 4.9487 equivalent bits/point +MSE 0.534200 +---------------------- -------------------------------------------------------- +Time: 0.505s Load: 0.008s, Pack+Encode: 0.207s, Decode+Unpack: 0.290s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5342 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample75-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample75-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample78-layer4-item1.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample78-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 91, 128) +Output shape: (1, 91, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.output: torch.Size([1, 91, 4096]) -> torch.Size([1, 1, 91, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,576B, BPFP=1.3372 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,128B, BPFP=2.8441 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,788B, BPFP=2.1281 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,760B, BPFP=2.9842 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,496B, BPFP=2.3606 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,048B, BPFP=3.0089 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,196B, BPFP=2.4207 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,552B, BPFP=2.9663 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,712B, BPFP=2.1216 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,292B, BPFP=3.0299 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 107,928B, BPFP=2.3164 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03087558 5.32065993 + layer.0.v_cache 0.00000028 0.00014375 + layer.1.k_cache 0.00335485 0.50273979 + layer.1.v_cache 0.00000080 0.00051129 + layer.2.k_cache 0.00115595 0.26241399 + layer.2.v_cache 0.00000105 0.00070695 + layer.3.k_cache 0.00133087 0.30127098 + layer.3.v_cache 0.00000204 0.00111732 + layer.4.k_cache 0.00324117 0.51211376 + layer.4.v_cache 0.00000319 0.00207940 + layer.4.output 0.00016760 0.04519479 + ------------------------------------------------------------------------------------- + TOTAL 0.00290258 0.50603831 + (elements=1,304,576) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1304576 +Total Bytes 401476 +BPFP 2.4620 bits/point +EBPFP 4.9239 equivalent bits/point +MSE 0.506038 +---------------------- -------------------------------------------------------- +Time: 0.508s Load: 0.008s, Pack+Encode: 0.208s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5060 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample78-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample78-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample8-layer4-item1.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample8-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 97, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 97, 128) +Output shape: (1, 97, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) -> torch.Size([1, 1, 97, 1024]) + layer.4.output: torch.Size([1, 97, 4096]) -> torch.Size([1, 1, 97, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,284B, BPFP=1.3115 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,424B, BPFP=2.7726 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,140B, BPFP=2.0248 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 36,056B, BPFP=2.9040 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,784B, BPFP=2.2378 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,252B, BPFP=2.9198 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,652B, BPFP=2.3077 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,672B, BPFP=2.8731 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,136B, BPFP=2.0245 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,596B, BPFP=2.9475 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 114,592B, BPFP=2.3073 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.output: torch.Size([1, 97, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 97, 128]) + layer.0.v_cache: torch.Size([1, 8, 97, 128]) + layer.1.k_cache: torch.Size([1, 8, 97, 128]) + layer.1.v_cache: torch.Size([1, 8, 97, 128]) + layer.2.k_cache: torch.Size([1, 8, 97, 128]) + layer.2.v_cache: torch.Size([1, 8, 97, 128]) + layer.3.k_cache: torch.Size([1, 8, 97, 128]) + layer.3.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.k_cache: torch.Size([1, 8, 97, 128]) + layer.4.v_cache: torch.Size([1, 8, 97, 128]) + layer.4.output: torch.Size([1, 97, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03000178 4.96472640 + layer.0.v_cache 0.00000027 0.00015523 + layer.1.k_cache 0.00327340 0.50433888 + layer.1.v_cache 0.00000092 0.00051906 + layer.2.k_cache 0.00113100 0.26585876 + layer.2.v_cache 0.00000104 0.00071181 + layer.3.k_cache 0.00131735 0.30067876 + layer.3.v_cache 0.00000202 0.00114236 + layer.4.k_cache 0.00333574 0.50066738 + layer.4.v_cache 0.00000307 0.00198625 + layer.4.output 0.00017322 0.04811123 + ------------------------------------------------------------------------------------- + TOTAL 0.00283996 0.48094499 + (elements=1,390,592) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1390592 +Total Bytes 416588 +BPFP 2.3966 bits/point +EBPFP 4.7932 equivalent bits/point +MSE 0.480945 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 97, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 97, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4809 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample8-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample8-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample80-layer4-item1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample80-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 91, 128) +Output shape: (1, 91, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) -> torch.Size([1, 1, 91, 1024]) + layer.4.output: torch.Size([1, 91, 4096]) -> torch.Size([1, 1, 91, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,724B, BPFP=1.3499 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,884B, BPFP=2.8231 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,780B, BPFP=2.1274 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,808B, BPFP=2.9883 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,432B, BPFP=2.3551 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,200B, BPFP=3.0220 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,156B, BPFP=2.4172 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,604B, BPFP=2.9708 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,848B, BPFP=2.1332 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,196B, BPFP=3.0216 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 107,420B, BPFP=2.3055 +⌛️ [2/4] FRONTEND: Frontend time: 0.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 91, 128]) + layer.0.v_cache: torch.Size([1, 8, 91, 128]) + layer.1.k_cache: torch.Size([1, 8, 91, 128]) + layer.1.v_cache: torch.Size([1, 8, 91, 128]) + layer.2.k_cache: torch.Size([1, 8, 91, 128]) + layer.2.v_cache: torch.Size([1, 8, 91, 128]) + layer.3.k_cache: torch.Size([1, 8, 91, 128]) + layer.3.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.k_cache: torch.Size([1, 8, 91, 128]) + layer.4.v_cache: torch.Size([1, 8, 91, 128]) + layer.4.output: torch.Size([1, 91, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03041337 5.35884044 + layer.0.v_cache 0.00000027 0.00014616 + layer.1.k_cache 0.00341539 0.49989445 + layer.1.v_cache 0.00000079 0.00049879 + layer.2.k_cache 0.00115498 0.26415961 + layer.2.v_cache 0.00000106 0.00070155 + layer.3.k_cache 0.00132454 0.29615901 + layer.3.v_cache 0.00000206 0.00109952 + layer.4.k_cache 0.00329878 0.51668477 + layer.4.v_cache 0.00000306 0.00196372 + layer.4.output 0.00018052 0.04843149 + ------------------------------------------------------------------------------------- + TOTAL 0.00288117 0.50956243 + (elements=1,304,576) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1304576 +Total Bytes 401052 +BPFP 2.4594 bits/point +EBPFP 4.9187 equivalent bits/point +MSE 0.509562 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.209s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 91, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 91, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5096 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample80-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample80-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample81-layer4-item1.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample81-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.005s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 88, 128) +Output shape: (1, 88, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) -> torch.Size([1, 1, 88, 1024]) + layer.4.output: torch.Size([1, 88, 4096]) -> torch.Size([1, 1, 88, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,456B, BPFP=1.2834 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,736B, BPFP=2.7287 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,000B, BPFP=2.1307 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,828B, BPFP=3.0032 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,436B, BPFP=2.3469 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,404B, BPFP=3.0543 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,140B, BPFP=2.4094 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,744B, BPFP=2.9957 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,572B, BPFP=2.0927 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,516B, BPFP=3.0643 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,160B, BPFP=2.2896 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 88, 128]) + layer.0.v_cache: torch.Size([1, 8, 88, 128]) + layer.1.k_cache: torch.Size([1, 8, 88, 128]) + layer.1.v_cache: torch.Size([1, 8, 88, 128]) + layer.2.k_cache: torch.Size([1, 8, 88, 128]) + layer.2.v_cache: torch.Size([1, 8, 88, 128]) + layer.3.k_cache: torch.Size([1, 8, 88, 128]) + layer.3.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.k_cache: torch.Size([1, 8, 88, 128]) + layer.4.v_cache: torch.Size([1, 8, 88, 128]) + layer.4.output: torch.Size([1, 88, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03108732 5.69960577 + layer.0.v_cache 0.00000028 0.00014815 + layer.1.k_cache 0.00347999 0.53066046 + layer.1.v_cache 0.00000081 0.00051102 + layer.2.k_cache 0.00113734 0.26763268 + layer.2.v_cache 0.00000108 0.00072620 + layer.3.k_cache 0.00129639 0.30916296 + layer.3.v_cache 0.00000204 0.00111980 + layer.4.k_cache 0.00326982 0.52505129 + layer.4.v_cache 0.00000302 0.00199892 + layer.4.output 0.00017394 0.06080239 + ------------------------------------------------------------------------------------- + TOTAL 0.00292670 0.54141620 + (elements=1,261,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1261568 +Total Bytes 385992 +BPFP 2.4477 bits/point +EBPFP 4.8954 equivalent bits/point +MSE 0.541416 +---------------------- -------------------------------------------------------- +Time: 0.499s Load: 0.005s, Pack+Encode: 0.205s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 88, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 88, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5414 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample81-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample81-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample82-layer4-item1.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample82-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,880B, BPFP=1.3485 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,496B, BPFP=2.8444 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,080B, BPFP=2.1298 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,052B, BPFP=2.9766 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,760B, BPFP=2.3573 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,508B, BPFP=3.0153 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,468B, BPFP=2.4175 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,924B, BPFP=2.9657 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,884B, BPFP=2.1131 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,560B, BPFP=3.0197 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,992B, BPFP=2.3563 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03045331 5.54472849 + layer.0.v_cache 0.00000027 0.00014772 + layer.1.k_cache 0.00344094 0.51638031 + layer.1.v_cache 0.00000081 0.00052163 + layer.2.k_cache 0.00113827 0.27140464 + layer.2.v_cache 0.00000106 0.00072149 + layer.3.k_cache 0.00131047 0.29961654 + layer.3.v_cache 0.00000202 0.00114275 + layer.4.k_cache 0.00338451 0.51513875 + layer.4.v_cache 0.00000307 0.00202690 + layer.4.output 0.00016907 0.05226636 + ------------------------------------------------------------------------------------- + TOTAL 0.00288650 0.52577819 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407604 +BPFP 2.4724 bits/point +EBPFP 4.9447 equivalent bits/point +MSE 0.525778 +---------------------- -------------------------------------------------------- +Time: 0.506s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5258 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample82-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample82-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample83-layer4-item1.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample83-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,860B, BPFP=1.3468 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,444B, BPFP=2.8400 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,100B, BPFP=2.1315 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,096B, BPFP=2.9803 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,788B, BPFP=2.3597 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,476B, BPFP=3.0126 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,428B, BPFP=2.4141 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,916B, BPFP=2.9650 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,800B, BPFP=2.1060 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,548B, BPFP=3.0187 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,972B, BPFP=2.3559 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03019064 5.56619860 + layer.0.v_cache 0.00000027 0.00014689 + layer.1.k_cache 0.00347154 0.51625994 + layer.1.v_cache 0.00000080 0.00052357 + layer.2.k_cache 0.00112864 0.27026235 + layer.2.v_cache 0.00000106 0.00072029 + layer.3.k_cache 0.00131097 0.29782876 + layer.3.v_cache 0.00000203 0.00115085 + layer.4.k_cache 0.00338659 0.51416322 + layer.4.v_cache 0.00000308 0.00202363 + layer.4.output 0.00016864 0.05071552 + ------------------------------------------------------------------------------------- + TOTAL 0.00286930 0.52658144 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 407428 +BPFP 2.4713 bits/point +EBPFP 4.9426 equivalent bits/point +MSE 0.526581 +---------------------- -------------------------------------------------------- +Time: 0.504s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.293s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5266 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample83-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample83-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample85-layer4-item1.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample85-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 96, 128) +Output shape: (1, 96, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) -> torch.Size([1, 1, 96, 1024]) + layer.4.output: torch.Size([1, 96, 4096]) -> torch.Size([1, 1, 96, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,300B, BPFP=1.3265 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 34,220B, BPFP=2.7848 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,308B, BPFP=2.0596 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,864B, BPFP=2.9186 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,836B, BPFP=2.2653 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 36,084B, BPFP=2.9365 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,548B, BPFP=2.3232 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,456B, BPFP=2.8854 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,256B, BPFP=2.0553 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 36,228B, BPFP=2.9482 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 113,128B, BPFP=2.3016 +⌛️ [2/4] FRONTEND: Frontend time: 0.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 96, 128]) + layer.0.v_cache: torch.Size([1, 8, 96, 128]) + layer.1.k_cache: torch.Size([1, 8, 96, 128]) + layer.1.v_cache: torch.Size([1, 8, 96, 128]) + layer.2.k_cache: torch.Size([1, 8, 96, 128]) + layer.2.v_cache: torch.Size([1, 8, 96, 128]) + layer.3.k_cache: torch.Size([1, 8, 96, 128]) + layer.3.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.k_cache: torch.Size([1, 8, 96, 128]) + layer.4.v_cache: torch.Size([1, 8, 96, 128]) + layer.4.output: torch.Size([1, 96, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03109797 4.76365185 + layer.0.v_cache 0.00000027 0.00014886 + layer.1.k_cache 0.00337694 0.48448936 + layer.1.v_cache 0.00000077 0.00050402 + layer.2.k_cache 0.00112303 0.26758222 + layer.2.v_cache 0.00000110 0.00072084 + layer.3.k_cache 0.00132768 0.29931438 + layer.3.v_cache 0.00000201 0.00111272 + layer.4.k_cache 0.00329028 0.53123307 + layer.4.v_cache 0.00000306 0.00194669 + layer.4.output 0.00016342 0.06379989 + ------------------------------------------------------------------------------------- + TOTAL 0.00291977 0.47185025 + (elements=1,376,256) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1376256 +Total Bytes 414228 +BPFP 2.4079 bits/point +EBPFP 4.8157 equivalent bits/point +MSE 0.471850 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.007s, Pack+Encode: 0.207s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 96, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 96, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4719 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample85-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample85-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample88-layer4-item1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample88-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,436B, BPFP=1.3807 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,828B, BPFP=2.8417 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,384B, BPFP=2.1324 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,376B, BPFP=2.9718 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,636B, BPFP=2.3216 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,768B, BPFP=3.0047 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,404B, BPFP=2.3861 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 35,064B, BPFP=2.9456 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,156B, BPFP=2.1132 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,828B, BPFP=3.0097 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 111,316B, BPFP=2.3378 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.292s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03084236 5.51220178 + layer.0.v_cache 0.00000028 0.00014839 + layer.1.k_cache 0.00343721 0.54492384 + layer.1.v_cache 0.00000078 0.00051561 + layer.2.k_cache 0.00115435 0.27682114 + layer.2.v_cache 0.00000107 0.00071539 + layer.3.k_cache 0.00133139 0.30695027 + layer.3.v_cache 0.00000204 0.00113351 + layer.4.k_cache 0.00330969 0.54138229 + layer.4.v_cache 0.00000309 0.00192972 + layer.4.output 0.00020550 0.06582546 + ------------------------------------------------------------------------------------- + TOTAL 0.00292173 0.53214456 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 410196 +BPFP 2.4613 bits/point +EBPFP 4.9227 equivalent bits/point +MSE 0.532145 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.007s, Pack+Encode: 0.205s, Decode+Unpack: 0.292s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5321 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample88-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample88-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample90-layer4-item1.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample90-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,736B, BPFP=1.2935 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,252B, BPFP=2.7433 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,108B, BPFP=2.1162 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,944B, BPFP=2.9796 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,716B, BPFP=2.3452 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,508B, BPFP=3.0291 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,612B, BPFP=2.4238 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,944B, BPFP=2.9796 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,788B, BPFP=2.0881 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,724B, BPFP=3.0481 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,768B, BPFP=2.2772 +⌛️ [2/4] FRONTEND: Frontend time: 0.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.288s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03125147 5.54315048 + layer.0.v_cache 0.00000028 0.00015448 + layer.1.k_cache 0.00348025 0.52171981 + layer.1.v_cache 0.00000083 0.00053293 + layer.2.k_cache 0.00111536 0.27056653 + layer.2.v_cache 0.00000108 0.00073511 + layer.3.k_cache 0.00131582 0.30739426 + layer.3.v_cache 0.00000206 0.00115746 + layer.4.k_cache 0.00329313 0.53450685 + layer.4.v_cache 0.00000300 0.00202799 + layer.4.output 0.00017131 0.05407647 + ------------------------------------------------------------------------------------- + TOTAL 0.00293918 0.52844656 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 389100 +BPFP 2.4397 bits/point +EBPFP 4.8794 equivalent bits/point +MSE 0.528447 +---------------------- -------------------------------------------------------- +Time: 0.499s Load: 0.006s, Pack+Encode: 0.205s, Decode+Unpack: 0.288s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5284 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample90-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample90-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample92-layer4-item1.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample92-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 93, 128) +Output shape: (1, 93, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) -> torch.Size([1, 1, 93, 1024]) + layer.4.output: torch.Size([1, 93, 4096]) -> torch.Size([1, 1, 93, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 16,392B, BPFP=1.3770 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,608B, BPFP=2.8233 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,340B, BPFP=2.1287 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,252B, BPFP=2.9614 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,628B, BPFP=2.3209 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,652B, BPFP=2.9950 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,344B, BPFP=2.3810 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,980B, BPFP=2.9385 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 25,108B, BPFP=2.1092 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,816B, BPFP=3.0087 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 112,236B, BPFP=2.3571 +⌛️ [2/4] FRONTEND: Frontend time: 0.206s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 93, 128]) + layer.0.v_cache: torch.Size([1, 8, 93, 128]) + layer.1.k_cache: torch.Size([1, 8, 93, 128]) + layer.1.v_cache: torch.Size([1, 8, 93, 128]) + layer.2.k_cache: torch.Size([1, 8, 93, 128]) + layer.2.v_cache: torch.Size([1, 8, 93, 128]) + layer.3.k_cache: torch.Size([1, 8, 93, 128]) + layer.3.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.k_cache: torch.Size([1, 8, 93, 128]) + layer.4.v_cache: torch.Size([1, 8, 93, 128]) + layer.4.output: torch.Size([1, 93, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03101371 5.42443946 + layer.0.v_cache 0.00000028 0.00015035 + layer.1.k_cache 0.00364020 0.53153770 + layer.1.v_cache 0.00000086 0.00050617 + layer.2.k_cache 0.00114853 0.27269171 + layer.2.v_cache 0.00000108 0.00069486 + layer.3.k_cache 0.00131058 0.30571841 + layer.3.v_cache 0.00000204 0.00112013 + layer.4.k_cache 0.00330942 0.55193419 + layer.4.v_cache 0.00000298 0.00194677 + layer.4.output 0.00017313 0.06875956 + ------------------------------------------------------------------------------------- + TOTAL 0.00293730 0.52612700 + (elements=1,333,248) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1333248 +Total Bytes 410356 +BPFP 2.4623 bits/point +EBPFP 4.9246 equivalent bits/point +MSE 0.526127 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.008s, Pack+Encode: 0.206s, Decode+Unpack: 0.289s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 93, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 93, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5261 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample92-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample92-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample93-layer4-item1.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample93-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 90, 128) +Output shape: (1, 90, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) -> torch.Size([1, 1, 90, 1024]) + layer.4.output: torch.Size([1, 90, 4096]) -> torch.Size([1, 1, 90, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,324B, BPFP=1.3302 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 32,232B, BPFP=2.7979 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,232B, BPFP=2.1035 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,976B, BPFP=2.9493 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,988B, BPFP=2.3427 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,524B, BPFP=2.9969 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,648B, BPFP=2.4000 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,912B, BPFP=2.9438 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,024B, BPFP=2.0854 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,704B, BPFP=3.0125 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 104,128B, BPFP=2.2597 +⌛️ [2/4] FRONTEND: Frontend time: 0.204s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.291s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 90, 128]) + layer.0.v_cache: torch.Size([1, 8, 90, 128]) + layer.1.k_cache: torch.Size([1, 8, 90, 128]) + layer.1.v_cache: torch.Size([1, 8, 90, 128]) + layer.2.k_cache: torch.Size([1, 8, 90, 128]) + layer.2.v_cache: torch.Size([1, 8, 90, 128]) + layer.3.k_cache: torch.Size([1, 8, 90, 128]) + layer.3.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.k_cache: torch.Size([1, 8, 90, 128]) + layer.4.v_cache: torch.Size([1, 8, 90, 128]) + layer.4.output: torch.Size([1, 90, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03189978 5.25387777 + layer.0.v_cache 0.00000027 0.00015020 + layer.1.k_cache 0.00346617 0.51625849 + layer.1.v_cache 0.00000078 0.00050030 + layer.2.k_cache 0.00115535 0.26615785 + layer.2.v_cache 0.00000103 0.00068577 + layer.3.k_cache 0.00134436 0.30866472 + layer.3.v_cache 0.00000198 0.00112533 + layer.4.k_cache 0.00332606 0.53030501 + layer.4.v_cache 0.00000301 0.00202153 + layer.4.output 0.00018488 0.05779863 + ------------------------------------------------------------------------------------- + TOTAL 0.00299559 0.50792439 + (elements=1,290,240) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1290240 +Total Bytes 391692 +BPFP 2.4286 bits/point +EBPFP 4.8573 equivalent bits/point +MSE 0.507924 +---------------------- -------------------------------------------------------- +Time: 0.503s Load: 0.008s, Pack+Encode: 0.204s, Decode+Unpack: 0.291s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 90, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 90, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5079 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample93-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample93-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample94-layer4-item1.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample94-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,684B, BPFP=1.2890 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,920B, BPFP=2.8020 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,068B, BPFP=2.1127 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,056B, BPFP=2.9895 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,724B, BPFP=2.3459 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,552B, BPFP=3.0330 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,500B, BPFP=2.4140 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,924B, BPFP=2.9779 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,880B, BPFP=2.0962 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,684B, BPFP=3.0446 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,272B, BPFP=2.2663 +⌛️ [2/4] FRONTEND: Frontend time: 0.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.298s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03210033 5.50140861 + layer.0.v_cache 0.00000027 0.00015555 + layer.1.k_cache 0.00338447 0.51360120 + layer.1.v_cache 0.00000081 0.00052227 + layer.2.k_cache 0.00116617 0.28064085 + layer.2.v_cache 0.00000108 0.00072639 + layer.3.k_cache 0.00134809 0.30567293 + layer.3.v_cache 0.00000208 0.00117049 + layer.4.k_cache 0.00324151 0.53203201 + layer.4.v_cache 0.00000296 0.00194315 + layer.4.output 0.00019744 0.05769552 + ------------------------------------------------------------------------------------- + TOTAL 0.00300268 0.52633254 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 389264 +BPFP 2.4407 bits/point +EBPFP 4.8814 equivalent bits/point +MSE 0.526333 +---------------------- -------------------------------------------------------- +Time: 0.514s Load: 0.006s, Pack+Encode: 0.209s, Decode+Unpack: 0.298s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5263 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample94-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample94-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample95-layer4-item1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample95-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 89, 128) +Output shape: (1, 89, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) -> torch.Size([1, 1, 89, 1024]) + layer.4.output: torch.Size([1, 89, 4096]) -> torch.Size([1, 1, 89, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,656B, BPFP=1.2865 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 31,756B, BPFP=2.7876 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 24,044B, BPFP=2.1106 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 34,000B, BPFP=2.9846 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,704B, BPFP=2.3441 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,544B, BPFP=3.0323 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 27,452B, BPFP=2.4098 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,956B, BPFP=2.9807 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,852B, BPFP=2.0938 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,676B, BPFP=3.0439 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 103,060B, BPFP=2.2617 +⌛️ [2/4] FRONTEND: Frontend time: 0.208s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.298s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 89, 128]) + layer.0.v_cache: torch.Size([1, 8, 89, 128]) + layer.1.k_cache: torch.Size([1, 8, 89, 128]) + layer.1.v_cache: torch.Size([1, 8, 89, 128]) + layer.2.k_cache: torch.Size([1, 8, 89, 128]) + layer.2.v_cache: torch.Size([1, 8, 89, 128]) + layer.3.k_cache: torch.Size([1, 8, 89, 128]) + layer.3.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.k_cache: torch.Size([1, 8, 89, 128]) + layer.4.v_cache: torch.Size([1, 8, 89, 128]) + layer.4.output: torch.Size([1, 89, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03204785 5.53915611 + layer.0.v_cache 0.00000027 0.00015400 + layer.1.k_cache 0.00337303 0.51817999 + layer.1.v_cache 0.00000080 0.00052232 + layer.2.k_cache 0.00115601 0.28101362 + layer.2.v_cache 0.00000108 0.00073252 + layer.3.k_cache 0.00135783 0.31291419 + layer.3.v_cache 0.00000209 0.00117681 + layer.4.k_cache 0.00324176 0.53707526 + layer.4.v_cache 0.00000298 0.00193820 + layer.4.output 0.00019437 0.05895286 + ------------------------------------------------------------------------------------- + TOTAL 0.00299723 0.53061960 + (elements=1,275,904) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1275904 +Total Bytes 388700 +BPFP 2.4372 bits/point +EBPFP 4.8743 equivalent bits/point +MSE 0.530620 +---------------------- -------------------------------------------------------- +Time: 0.512s Load: 0.006s, Pack+Encode: 0.208s, Decode+Unpack: 0.298s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 89, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 89, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5306 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample95-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample95-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample96-layer4-item1.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample96-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 85, 128) +Output shape: (1, 85, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) -> torch.Size([1, 1, 85, 1024]) + layer.4.output: torch.Size([1, 85, 4096]) -> torch.Size([1, 1, 85, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,332B, BPFP=1.3173 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,104B, BPFP=2.7669 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,424B, BPFP=2.1529 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 32,792B, BPFP=3.0140 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 25,872B, BPFP=2.3779 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 33,624B, BPFP=3.0904 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,428B, BPFP=2.4290 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 32,964B, BPFP=3.0298 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,152B, BPFP=2.1279 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 33,652B, BPFP=3.0930 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 99,352B, BPFP=2.2829 +⌛️ [2/4] FRONTEND: Frontend time: 0.210s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.298s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 85, 128]) + layer.0.v_cache: torch.Size([1, 8, 85, 128]) + layer.1.k_cache: torch.Size([1, 8, 85, 128]) + layer.1.v_cache: torch.Size([1, 8, 85, 128]) + layer.2.k_cache: torch.Size([1, 8, 85, 128]) + layer.2.v_cache: torch.Size([1, 8, 85, 128]) + layer.3.k_cache: torch.Size([1, 8, 85, 128]) + layer.3.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.k_cache: torch.Size([1, 8, 85, 128]) + layer.4.v_cache: torch.Size([1, 8, 85, 128]) + layer.4.output: torch.Size([1, 85, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03332241 5.36908031 + layer.0.v_cache 0.00000028 0.00015245 + layer.1.k_cache 0.00345674 0.51550971 + layer.1.v_cache 0.00000080 0.00051165 + layer.2.k_cache 0.00115406 0.26855038 + layer.2.v_cache 0.00000108 0.00072525 + layer.3.k_cache 0.00129640 0.30012081 + layer.3.v_cache 0.00000208 0.00114300 + layer.4.k_cache 0.00325461 0.53470351 + layer.4.v_cache 0.00000299 0.00201061 + layer.4.output 0.00016656 0.06629852 + ------------------------------------------------------------------------------------- + TOTAL 0.00308269 0.51840727 + (elements=1,218,560) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1218560 +Total Bytes 375696 +BPFP 2.4665 bits/point +EBPFP 4.9330 equivalent bits/point +MSE 0.518407 +---------------------- -------------------------------------------------------- +Time: 0.513s Load: 0.006s, Pack+Encode: 0.210s, Decode+Unpack: 0.298s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 85, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 85, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5184 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample96-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample96-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample98-layer4-item1.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample98-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.006s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 92, 128) +Output shape: (1, 92, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) -> torch.Size([1, 1, 92, 1024]) + layer.4.output: torch.Size([1, 92, 4096]) -> torch.Size([1, 1, 92, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 15,792B, BPFP=1.3410 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 33,524B, BPFP=2.8468 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 25,100B, BPFP=2.1315 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 35,208B, BPFP=2.9898 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 27,728B, BPFP=2.3546 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 35,516B, BPFP=3.0160 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 28,420B, BPFP=2.4134 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 34,876B, BPFP=2.9616 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 24,928B, BPFP=2.1168 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 35,496B, BPFP=3.0143 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 110,384B, BPFP=2.3434 +⌛️ [2/4] FRONTEND: Frontend time: 0.212s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 92, 128]) + layer.0.v_cache: torch.Size([1, 8, 92, 128]) + layer.1.k_cache: torch.Size([1, 8, 92, 128]) + layer.1.v_cache: torch.Size([1, 8, 92, 128]) + layer.2.k_cache: torch.Size([1, 8, 92, 128]) + layer.2.v_cache: torch.Size([1, 8, 92, 128]) + layer.3.k_cache: torch.Size([1, 8, 92, 128]) + layer.3.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.k_cache: torch.Size([1, 8, 92, 128]) + layer.4.v_cache: torch.Size([1, 8, 92, 128]) + layer.4.output: torch.Size([1, 92, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03086128 5.64184040 + layer.0.v_cache 0.00000027 0.00014878 + layer.1.k_cache 0.00344194 0.50567689 + layer.1.v_cache 0.00000079 0.00052039 + layer.2.k_cache 0.00114743 0.26784590 + layer.2.v_cache 0.00000107 0.00071637 + layer.3.k_cache 0.00130642 0.30276336 + layer.3.v_cache 0.00000205 0.00114023 + layer.4.k_cache 0.00331827 0.51543551 + layer.4.v_cache 0.00000300 0.00203472 + layer.4.output 0.00017802 0.04071314 + ------------------------------------------------------------------------------------- + TOTAL 0.00291390 0.52864108 + (elements=1,318,912) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1318912 +Total Bytes 406972 +BPFP 2.4685 bits/point +EBPFP 4.9371 equivalent bits/point +MSE 0.528641 +---------------------- -------------------------------------------------------- +Time: 0.518s Load: 0.006s, Pack+Encode: 0.212s, Decode+Unpack: 0.300s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 92, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 92, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.5286 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample98-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample98-layer4-item1.zst + + 💪 Processing: ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample99-layer4-item1.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample99-layer4-item1.zst... + +Original data structure: +root: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.005s + +------------------------------------------------------------ +Qwen Features Summary +------------------------------------------------------------ +Number of layers: 5 +Layer indices: [0, 1, 2, 3, 4] +Last layer index: 4 +Cache shape: (1, 8, 87, 128) +Output shape: (1, 87, 4096) +Data type: torch.bfloat16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) -> torch.Size([1, 1, 87, 1024]) + layer.4.output: torch.Size([1, 87, 4096]) -> torch.Size([1, 1, 87, 4096]) + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + layer.0.k_cache: 14,752B, BPFP=1.3247 + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + layer.0.v_cache: 30,880B, BPFP=2.7730 + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + layer.1.k_cache: 23,772B, BPFP=2.1347 + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + layer.1.v_cache: 33,540B, BPFP=3.0119 + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + layer.2.k_cache: 26,512B, BPFP=2.3807 + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + layer.2.v_cache: 34,196B, BPFP=3.0708 + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + layer.3.k_cache: 26,980B, BPFP=2.4228 + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + layer.3.v_cache: 33,492B, BPFP=3.0075 + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + layer.4.k_cache: 23,444B, BPFP=2.1052 + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + layer.4.v_cache: 34,196B, BPFP=3.0708 + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + layer.4.output: 101,168B, BPFP=2.2712 +⌛️ [2/4] FRONTEND: Frontend time: 0.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.298s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.0.k_cache: torch.Size([256])) for layer.0.k_cache + Using per-key quantization points (layer.0.v_cache: torch.Size([256])) for layer.0.v_cache + Using per-key quantization points (layer.1.k_cache: torch.Size([256])) for layer.1.k_cache + Using per-key quantization points (layer.1.v_cache: torch.Size([256])) for layer.1.v_cache + Using per-key quantization points (layer.2.k_cache: torch.Size([256])) for layer.2.k_cache + Using per-key quantization points (layer.2.v_cache: torch.Size([256])) for layer.2.v_cache + Using per-key quantization points (layer.3.k_cache: torch.Size([256])) for layer.3.k_cache + Using per-key quantization points (layer.3.v_cache: torch.Size([256])) for layer.3.v_cache + Using per-key quantization points (layer.4.k_cache: torch.Size([256])) for layer.4.k_cache + Using per-key quantization points (layer.4.v_cache: torch.Size([256])) for layer.4.v_cache + Using per-key quantization points (layer.4.output: torch.Size([256])) for layer.4.output + IndividualUnPacker: + layer.0.k_cache: torch.Size([1, 8, 87, 128]) + layer.0.v_cache: torch.Size([1, 8, 87, 128]) + layer.1.k_cache: torch.Size([1, 8, 87, 128]) + layer.1.v_cache: torch.Size([1, 8, 87, 128]) + layer.2.k_cache: torch.Size([1, 8, 87, 128]) + layer.2.v_cache: torch.Size([1, 8, 87, 128]) + layer.3.k_cache: torch.Size([1, 8, 87, 128]) + layer.3.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.k_cache: torch.Size([1, 8, 87, 128]) + layer.4.v_cache: torch.Size([1, 8, 87, 128]) + layer.4.output: torch.Size([1, 87, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.0.k_cache 0.03104359 4.82537877 + layer.0.v_cache 0.00000028 0.00014538 + layer.1.k_cache 0.00347075 0.51701048 + layer.1.v_cache 0.00000085 0.00053126 + layer.2.k_cache 0.00119839 0.26259466 + layer.2.v_cache 0.00000112 0.00073750 + layer.3.k_cache 0.00133526 0.30344827 + layer.3.v_cache 0.00000211 0.00117766 + layer.4.k_cache 0.00323497 0.49354027 + layer.4.v_cache 0.00000315 0.00207833 + layer.4.output 0.00017081 0.06385425 + ------------------------------------------------------------------------------------- + TOTAL 0.00292669 0.47586140 + (elements=1,247,232) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler qwen +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 1247232 +Total Bytes 382932 +BPFP 2.4562 bits/point +EBPFP 4.9124 equivalent bits/point +MSE 0.475861 +---------------------- -------------------------------------------------------- +Time: 0.513s Load: 0.005s, Pack+Encode: 0.209s, Decode+Unpack: 0.298s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 3 keys + key['output']: [Tensor] shape=torch.Size([1, 87, 4096]), dtype=torch.bfloat16, device=cpu + key['key']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + key['value']: [list] with 5 items + item[0]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[1]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[2]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[3]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu + item[4]: [Tensor] shape=torch.Size([1, 8, 87, 128]), dtype=torch.bfloat16, device=cpu +💾 Converting with 0.4759 MSE: + from ../datasets/Qwen3-500features-L5wCache/Qwen3-500features-L5wCache/qwen/qwen3-8b/fc_winogrande/sample99-layer4-item1.zst + to output-fixed/qwen/lambda0.02/hyperprior-featurecoding-8bit-individual/fc_winogrande/sample99-layer4-item1.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 2.4496 bits/point +Avg EBPFP 4.8991 equivalent bits/point +Avg MSE 0.512894 +Avg Time 0.515s +------------------------ ----------------------------