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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-generation
+tags:
+- ERNIE4.5
+library_name: transformers
+base_model:
+- baidu/ERNIE-4.5-21B-A3B-Thinking
+---
+
+<div align="center" style="line-height: 1;">
+  <a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
+    <img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
+    <img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
+    <img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="#license" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+# ERNIE-4.5-21B-A3B-Thinking
+
+## Model Highlights
+
+Over the past three months, we have continued to scale the **thinking capability** of ERNIE-4.5-21B-A3B, improving both the **quality and depth** of reasoning, thereby advancing the competitiveness of ERNIE **lightweight models** in complex reasoning tasks. We are pleased to introduce **ERNIE-4.5-21B-A3B-Thinking**, featuring the following key enhancements:
+
+* **Significantly improved performance** on reasoning tasks, including logical reasoning, mathematics, science, coding, text generation, and academic benchmarks that typically require human expertise.
+* **Efficient tool usage** capabilities.
+* **Enhanced 128K long-context understanding** capabilities.
+
+> [!NOTE]
+> Note: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
+
+![benchmark](./benchmark.png)
+
+## Model Overview
+
+ERNIE-4.5-21B-A3B-Thinking is a text MoE post-trained model, with 21B total parameters and 3B activated parameters for each token. The following are the model configuration details:
+
+|Key|Value|
+|-|-|
+|Modality|Text|
+|Training Stage|Posttraining|
+|Params(Total / Activated)|21B / 3B|
+|Layers|28|
+|Heads(Q/KV)|20 / 4|
+|Text Experts(Total / Activated)|64 / 6|
+|Vision Experts(Total / Activated)|64 / 6|
+|Shared Experts|2|
+|Context Length|131072|
+
+## Quickstart
+
+> [!NOTE]
+> To align with the wider community, this model releases Transformer-style weights. Both PyTorch and PaddlePaddle ecosystem tools, such as vLLM, transformers, and FastDeploy, are expected to be able to load and run this model.
+
+### FastDeploy Inference
+
+Quickly deploy services using FastDeploy as shown below. For more detailed usage, refer to the [FastDeploy GitHub Repository](https://github.com/PaddlePaddle/FastDeploy).
+
+**Note**: 80GB x 1 GPU resources are required. Deploying this model requires FastDeploy version 2.2.
+
+```bash
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model baidu/ERNIE-4.5-21B-A3B-Thinking \
+       --port 8180 \
+       --metrics-port 8181 \
+       --engine-worker-queue-port 8182 \
+       --load_choices "default_v1" \
+       --tensor-parallel-size 1 \
+       --max-model-len 131072 \
+       --reasoning-parser ernie_x1 \
+       --tool-call-parser ernie_x1 \
+       --max-num-seqs 32
+```
+
+The ERNIE-4.5-21B-A3B-Thinking model supports function call.
+
+```bash
+curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
+-H "Content-Type: application/json" \
+-d $'{
+  "messages": [
+    {
+      "role": "user",
+      "content": "How \'s the weather in Beijing today?"
+    }
+  ],
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "get_weather",
+        "description": "Determine weather in my location",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "location": {
+              "type": "string",
+              "description": "The city and state e.g. San Francisco, CA"
+            },
+            "unit": {
+              "type": "string",
+              "enum": [
+                "c",
+                "f"
+              ]
+            }
+          },
+          "additionalProperties": false,
+          "required": [
+            "location",
+            "unit"
+          ]
+        },
+        "strict": true
+      }
+    }]
+}'
+```
+
+### vLLM inference
+
+```bash
+vllm serve baidu/ERNIE-4.5-21B-A3B-Thinking
+```
+
+The `reasoning-parser` and `tool-call-parser` for vLLM Ernie are currently under development.
+
+### Using `transformers` library
+
+**Note**: You'll need the`transformers`library (version 4.54.0 or newer) installed to use this model.
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-Thinking"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+print("generate_text:", generate_text)
+```
+
+## License
+
+The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+
+## Citation
+
+If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
+
+```text
+@misc{ernie2025technicalreport,
+      title={ERNIE 4.5 Technical Report},
+      author={Baidu-ERNIE-Team},
+      year={2025},
+      primaryClass={cs.CL},
+      howpublished={\url{https://ernie.baidu.com/blog/publication/ERNIE_Technical_Report.pdf}}
+}
+```
+

chat_template.jinja

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+{{- '<|im_start|>system
+' }}{%- if messages[0].role != 'system' and not system_settings %}{{- '<global_setting>
+think_mode=True
+</global_setting>' }}{%- else%}{{- '<system_setting>
+' }}{{- system_settings + '
+' if system_settings else '' }}{{- (messages[0].content + '
+' if messages[0].role == 'system' else '') + '</system_setting>
+
+<global_setting>
+think_mode=True
+</global_setting>' }}{%- endif %}{%- if tools %}{{- "
+
+<tool_list>" }}{{- '
+' }}{{-'['}}{% for tool in tools %}{{'{"type": "function", "function": '}}{{-(tool.function | tojson)}}}{%-if not loop.last%},{%- endif %}{%endfor%}{{-']'}}{{- "
+</tool_list>" }}{%- endif %}{{-'<|im_end|>
+
+' }}{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_output>') and message.content.endswith('</tool_output>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '
+' + content + '<|im_end|>' + '
+
+' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.thoughts is string %}
+            {%- set reasoning_content = message.thoughts %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('
+').split('<think>')[-1].lstrip('
+') %}
+                {%- set content = content.split('</think>')[-1].lstrip('
+') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index and  (loop.last or (not loop.last and reasoning_content)) %} {{- '<|im_start|>' + message.role + '
+<think>
+' + reasoning_content.strip('
+') + '
+</think>
+' }} {%- else %} {{- '<|im_start|>' + message.role + '
+' }} {%- endif %}  {%- if content|length > 0 %}  {{- '<response>
+' + content + '
+</response>
+' }}  {%- endif %} {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '
+' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '
+<tool_call>
+{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}
+</tool_call>
+' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>
+
+' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>tool' }}
+        {%- endif %}
+        {{- '
+<tool_output>' }}
+        {{- message.content|tojson }}
+        {{- '</tool_output>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>
+
+' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+ {{- "<|im_start|>assistant
+<think>
+"}}

config.json

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+{
+  "architectures": [
+    "Ernie4_5_MoeForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_ernie4_5_moe.Ernie4_5_MoeConfig",
+    "AutoModel": "modeling_ernie4_5_moe.Ernie4_5_Model",
+    "AutoModelForCausalLM": "modeling_ernie4_5_moe.Ernie4_5_MoeForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 131072,
+  "model_type": "ernie4_5_moe",
+  "moe_capacity": [
+    64,
+    64,
+    64
+  ],
+  "moe_gate": "top2_fused",
+  "moe_intermediate_size": 1536,
+  "moe_k": 6,
+  "moe_layer_end_index": 27,
+  "moe_layer_interval": 1,
+  "moe_layer_start_index": 1,
+  "moe_norm_min": 1e-12,
+  "moe_num_experts": 64,
+  "moe_num_shared_experts": 2,
+  "moe_use_aux_free": true,
+  "num_attention_heads": 20,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "pad_token_id": 0,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 32,
+          "num_bits": 8,
+          "observer": "mse",
+          "observer_kwargs": {},
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int"
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "model.layers.0.self_attn.q_proj",
+      "model.layers.0.self_attn.k_proj",
+      "model.layers.0.self_attn.v_proj",
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+      "model.layers.27.mlp.shared_experts.up_proj",
+      "model.layers.27.mlp.shared_experts.down_proj",
+      "lm_head"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.10.3.dev47+ge463fe6"
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "router_aux_loss_coef": 0.001,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.1",
+  "use_bias": false,
+  "use_cache": true,
+  "vocab_size": 103424
+}

configuration_ernie4_5_moe.py

@@ -0,0 +1,187 @@
+# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers import PretrainedConfig
+
+
+class Ernie4_5_MoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Ernie4_5_Model`].
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (int): Size of the vocabulary (number of unique tokens)
+        hidden_size (int): Dimensionality of the encoder layers and the pooler layer
+        intermediate_size (int): Dimensionality of the "intermediate" (feed-forward) layer
+        max_position_embeddings (int): Maximum sequence length the model can handle
+        num_hidden_layers (int): Number of hidden layers in the Transformer encoder
+        num_attention_heads (int): Number of attention heads for each attention layer
+        rms_norm_eps (float): The epsilon used by the RMS normalization layers
+        use_cache (bool): Whether to use caching for faster generation (decoding)
+        use_flash_attention (bool): Whether to use FlashAttention for optimized attention computation
+        pad_token_id (int): Token ID used for padding sequences
+        bos_token_id (int): Token ID used for beginning-of-sequence
+        eos_token_id (int): Token ID used for end-of-sequence
+        use_bias (bool): Whether to use bias terms in linear layers
+        rope_theta (float): The base period of the RoPE embeddings
+        weight_share_add_bias (bool): Whether to share bias weights in certain layers
+        ignored_index (int): Target value that is ignored during loss computation
+        attention_probs_dropout_prob (float): Dropout probability for attention weights
+        hidden_dropout_prob (float): Dropout probability for hidden layers
+        num_key_value_heads (int): Number of key/value heads (for Grouped Query Attention)
+        max_sequence_length (int): Maximum sequence length for positional embeddings
+        moe_num_experts: Number of experts in MoE layers
+        moe_capacity: Capacity configuration for MoE layers
+        moe_layer_interval: Interval between MoE layers
+        moe_layer_start_index: Starting layer index for MoE
+        moe_layer_end_index: Ending layer index for MoE (-1 means last layer)
+        sinkhorn_2gate: Whether to use sinkhorn 2-gate routing
+        sinkhorn_temp: Temperature for sinkhorn routing
+        moe_dropout_prob: Dropout probability for MoE layers
+        moe_gate: Type of gating mechanism ('top2', etc.)
+        moe_intermediate_size: Intermediate size for MoE layers
+        moe_gate_act: Activation function for gating
+        moe_k: Number of experts to route to
+        **kwargs: Additional base model configuration parameters
+    """
+
+    model_type = "ernie4_5_moe"
+    use_keep_in_fp32_modules = True
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    attribute_map = {
+        "n_positions": "max_position_embeddings",
+        "n_embd": "hidden_size",
+        "n_layer": "num_hidden_layers",
+        "n_head": "num_attention_heads",
+        "n_inner": "intermediate_size",
+        "activation_function": "hidden_act",
+    }
+
+    # Default tensor parallel plan for base model `ernie_4_5_moe`
+    base_model_tp_plan = {
+        "model.layers.*.self_attn.q_proj": "colwise_rep",
+        "model.layers.*.self_attn.k_proj": "colwise_rep",
+        "model.layers.*.self_attn.v_proj": "colwise_rep",
+        "model.layers.*.self_attn.o_proj": "rowwise_rep",
+        "model.layers.*.mlp.experts.*.gate_proj": "colwise",
+        "model.layers.*.mlp.experts.*.up_proj": "colwise",
+        "model.layers.*.mlp.experts.*.down_proj": "rowwise",
+        "model.layers.*.mlp.gate_proj": "colwise",
+        "model.layers.*.mlp.up_proj": "colwise",
+        "model.layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        intermediate_size=11008,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        num_key_value_heads=None,
+        max_position_embeddings=32768,
+        rms_norm_eps=1e-6,
+        use_cache=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        attention_probs_dropout_prob=0.0,
+        hidden_dropout_prob=0.0,
+        rope_theta=10000.0,
+        use_flash_attention=False,
+        use_rmsnorm=True,
+        use_bias=False,
+        weight_share_add_bias=True,
+        max_sequence_length=None,
+        ignored_index=-100,
+        use_moe=True,
+        moe_num_experts=64,
+        moe_capacity=(64, 64, 64),
+        moe_layer_interval=2,
+        moe_layer_start_index=0,
+        moe_layer_end_index=-1,
+        sinkhorn_2gate=True,
+        sinkhorn_temp=3e-2,
+        moe_dropout_prob=0.0,
+        moe_gate="top2",
+        moe_intermediate_size=3584,
+        moe_k=2,
+        moe_gate_act: str = "softmax",
+        moe_use_aux_free=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.use_rmsnorm = use_rmsnorm
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.max_sequence_length = max_sequence_length
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.ignored_index = ignored_index
+        self.use_cache = use_cache
+        self.use_bias = use_bias
+        self.weight_share_add_bias = weight_share_add_bias
+        self.use_flash_attention = use_flash_attention
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.hidden_dropout_prob = hidden_dropout_prob
+
+        self.use_moe = moe_num_experts > 0 and use_moe
+        self.moe_num_experts = moe_num_experts
+        self.moe_capacity = moe_capacity
+        self.sinkhorn_2gate = sinkhorn_2gate
+        self.sinkhorn_temp = sinkhorn_temp
+        self.moe_layer_interval = moe_layer_interval
+        self.moe_dropout_prob = moe_dropout_prob
+        self.moe_gate = moe_gate
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_k = moe_k
+        self.moe_layer_start_index = moe_layer_start_index
+        self.moe_layer_end_index = (
+            self.num_hidden_layers - 1
+            if moe_layer_end_index == -1
+            else moe_layer_end_index
+        )
+        self.moe_gate_act = moe_gate_act
+        self.moe_use_aux_free = moe_use_aux_free
+
+        # Set default for tied embeddings if not specified.
+        if "tie_word_embeddings" not in kwargs:
+            kwargs["tie_word_embeddings"] = False
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.56.1"
+}

model-00001-of-00005.safetensors

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model-00002-of-00005.safetensors

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model-00003-of-00005.safetensors

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model-00005-of-00005.safetensors

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+size 4683215280

modeling_ernie4_5_moe.py

@@ -0,0 +1,1516 @@
+# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from copy import deepcopy
+from dataclasses import dataclass
+from functools import partial
+from typing import Callable, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.modeling_outputs import ModelOutput, MoeCausalLMOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.processing_utils import Unpack
+from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging, is_torch_flex_attn_available
+
+from .configuration_ernie4_5_moe import Ernie4_5_MoeConfig
+
+
+if is_torch_flex_attn_available():
+    from torch.nn.attention.flex_attention import BlockMask
+
+    from transformers.integrations.flex_attention import make_flex_block_causal_mask
+
+logger = logging.get_logger(__name__)
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+@dataclass
+class Erine4_5_MoeModelOutputWithPast(ModelOutput):
+    last_hidden_state: Optional[torch.FloatTensor] = None
+    past_key_values: Optional[Cache] = None
+    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+    router_loss: Optional[torch.FloatTensor] = None
+    gate_logits: Optional[tuple[torch.FloatTensor, ...]] = None
+    mtp_outputs: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+class Ernie4_5_MoeCausalLMOutputWithPast(MoeCausalLMOutputWithPast):
+    router_loss: Optional[torch.FloatTensor] = None
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+
+    x1 = x[..., 0::2]
+    x2 = x[..., 1::2]
+    return torch.stack((-x2, x1), dim=-1).reshape(x.shape)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    orig_dtype = q.dtype
+    sin_pos = torch.stack([sin, sin], dim=-1).reshape(*sin.shape[:-1],-1)
+    cos_pos = torch.stack([cos, cos], dim=-1).reshape(*sin.shape[:-1],-1)
+    q_embed = (q.float() * cos_pos) + (rotate_half(q).float() * sin_pos)
+    k_embed = (k.float() * cos_pos) + (rotate_half(k).float() * sin_pos)
+    return q_embed.to(orig_dtype), k_embed.to(orig_dtype)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask.to(attn_weights.device)
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def topk_gate_func(
+    module: nn.Module,
+    hidden_states: torch.Tensor,
+):
+    capacity = module.get_capacity(hidden_states.shape[0])
+    with torch.autocast(device_type='cuda',dtype=torch.float32):
+        logits = module.gate(hidden_states.float())
+    router_loss = torch.zeros([1], dtype=torch.float32, device=hidden_states.device)
+    router_loss.detach()
+    return logits, capacity, router_loss
+
+
+class Ernie4_5_ResidualWithDropout(nn.Module):
+    """
+    Fused dropout implementation with residual connection support.
+
+    This layer combines dropout and residual addition in a single operation for better performance,
+    particularly on GPU devices. The dropout is conditionally applied based on the probability.
+
+    Args:
+        prob (float): Dropout probability (between 0 and 1)
+
+    Attributes:
+        prob (float): Stores the dropout probability
+        dropout (nn.Dropout): The actual dropout layer instance
+    """
+
+    def __init__(self, prob): 
+        """
+        Initialize the fused dropout layer.
+
+        Args:
+            prob (float): Dropout probability (0 means no dropout)
+        """
+        super().__init__()
+        self.prob = prob
+        self.dropout = nn.Dropout(p=prob)
+
+    def forward(self, x, y):
+        """
+        Forward pass of the fused dropout layer.
+
+        Args:
+            x (torch.Tensor): Input tensor to potentially apply dropout on
+            y (torch.Tensor): Residual tensor to add to the (possibly dropped out) x
+
+        Returns:
+            torch.Tensor: Result of x (with optional dropout) + y
+        """
+        if self.prob > 0:
+            x = self.dropout(x)
+        output = x + y
+
+        return output
+
+
+class Ernie4_5_Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config, layer_idx=0):
+        """
+        Args:
+            config (ErnieConfig): Model configuration.
+            layer_idx (int, optional): Index in transformer stack. Defaults to 0.
+        """
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads if config.num_key_value_heads is not None else self.nums_head
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.freq_allocation = config.freq_allocation if hasattr(config, "freq_allocation") else 0
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = getattr(config, "attention_probs_dropout_prob", 0.0)
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            self.hidden_size,
+            self.num_heads * self.head_dim,
+            bias=config.use_bias,
+        )
+
+        self.k_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.use_bias,
+        )
+
+        self.v_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.use_bias,
+        )
+
+        self.o_proj = nn.Linear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=config.use_bias,
+        )
+        
+        self.config = config
+
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        B, L = hidden_states.shape[:-1]
+
+        query_states = self.q_proj(hidden_states).view(B, L, self.num_heads, -1).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(B, L, self.num_key_value_heads, -1).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(B, L, self.num_key_value_heads, -1).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+        
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+        attn_output = attn_output.reshape(B, L, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class Ernie4_5_MLP(nn.Module):
+    """
+    Ernie4_5_MLP - Gated Multi-Layer Perceptron module used in Ernie model.
+    """
+
+    def __init__(self, config,intermediate_size=None):
+        """
+        Initialize the MLP module with configuration options.
+
+        Args:
+            config: Model configuration object with attributes:
+                - hidden_size: int
+                - intermediate_size: int
+                - use_bias: bool
+            layer_idx (int): Index of current layer (default: 0)
+        """
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
+        self.gate_proj =  nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias)
+        self.up_proj =  nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
+
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): shape [batch_size, seq_len, hidden_size]
+
+        Returns:
+            Tensor: shape [batch_size, seq_len, hidden_size]
+        """
+        down_proj = self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class Ernie4_5_MoeStatics(nn.Module):
+    """
+    Stores MoE (Mixture of Experts) statistics
+    and expert usage information.
+    """
+
+    def __init__(self, config):
+        """
+        Initialize MoE statistics tracking.
+
+        Args:
+            config: Model configuration containing MoE parameters
+        """
+        super().__init__()
+
+        num_experts = config.moe_num_experts
+        num_experts_groups = 1
+
+        self.e_score_correction_bias = nn.Parameter(
+            torch.zeros(num_experts_groups, num_experts, dtype=torch.float32),
+            requires_grad=False
+        )
+
+class Ernie4_5_MoeMLP(nn.Module):
+    """Mixture of Experts (MoE) variant of ERNIE's MLP layer."""
+
+    def __init__(self,config):
+        super().__init__()
+        self.config = config
+        self.k = config.moe_k
+        self.sinkhorn_2gate = config.sinkhorn_2gate
+        self.sinkhorn_temp = config.sinkhorn_temp
+
+        moe_intermediate_size = config.moe_intermediate_size if config.moe_intermediate_size else config.intermediate_size
+        self.gate = nn.Linear(config.hidden_size, config.moe_num_experts, bias=False, dtype=torch.float32)
+        if config.moe_gate_act == "softmax":
+            self.gate_act = partial(F.softmax, dim=-1)
+        elif config.moe_gate_act == "sigmoid":
+            self.gate_act = F.sigmoid
+        else:
+            raise ValueError(f"{config.moe_gate_act} is not supported.")
+        
+        self.experts = nn.ModuleList(
+            [Ernie4_5_MLP(config,moe_intermediate_size) for i in range(config.moe_num_experts)]
+        )
+        
+        if config.moe_use_aux_free:
+            self.moe_statics = Ernie4_5_MoeStatics(config)
+        
+        self.use_correction_bias = config.moe_use_aux_free
+        self.num_local_experts = len(self.experts)
+
+        self.shared_experts = self._init_shared_experts()
+
+    def _init_shared_experts(self):
+        """
+        Initialize the shared expert module.
+
+        Returns:
+            shared_experts: Shared expert module, returns None if no shared experts are needed.
+
+        """
+        cfg = deepcopy(self.config)
+        if getattr(cfg, 'moe_num_shared_experts', 0) > 0:
+            if getattr(cfg, 'moe_intermediate_size', None):
+                cfg.intermediate_size = cfg.moe_intermediate_size * cfg.moe_num_shared_experts
+            else:
+                cfg.intermediate_size = cfg.intermediate_size * cfg.moe_num_shared_experts
+            shared_experts = Ernie4_5_MLP(cfg, cfg.intermediate_size)
+        else:
+            shared_experts = None
+        return shared_experts
+
+    def forward(
+        self,
+        input: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Forward pass through MoE layer.
+
+        Args:
+            input (Tensor): Input tensor of shape [s, d].
+            token_type_ids: Optional tensor for token types.
+
+        Returns:
+            tuple: (output, combine_weights, router_loss, gate_logits)
+        """
+
+        if input.dim() == 3:
+            orig_shape = input.shape
+            input = input.reshape(-1, input.shape[-1])
+        else:
+            orig_shape = None
+        assert input.dim() == 2, f"input Tensor must have dimensions: (s)equence, (d)im, got:{input.shape}"
+
+        assert self.gate is not None
+
+        gate_input = input
+
+        (
+            dispatched_input,
+            combine_weights,
+            dispatch_mask,
+            scatter_index,
+            router_loss,
+            gate_logits,
+            gate_prob
+        ) = self.gate_and_dispatch(gate_input)
+
+        expert_out = self.forward_experts(dispatched_input)
+
+        combined_output = self.combine_expert_output(expert_out, combine_weights, scatter_index)
+
+        if self.shared_experts is not None:
+            shared_expert_out = self.shared_experts(gate_input)
+            combined_output += shared_expert_out
+
+        if orig_shape:
+            combined_output = combined_output.reshape(orig_shape[:-1] + (combined_output.shape[-1],))
+
+        return combined_output, combine_weights, router_loss, gate_logits
+
+    def forward_experts(self, dispatched_input: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass through experts sequentially.
+
+        Args:
+            dispatched_input (Tensor): Input tensor of shape [num_experts, capacity, dim].
+
+        Returns:
+            Tensor: Expert outputs of shape [num_experts, capacity, dim].
+        """
+        true_experts = self.experts
+        dispatched_input = dispatched_input.reshape(
+            1, self.num_local_experts, -1, dispatched_input.shape[-1]
+        )
+        expert_outputs = []
+        if isinstance(self.experts, nn.ModuleList):
+            chunks = dispatched_input.permute(1, 0, 2, 3).contiguous().unbind(0)
+            assert len(chunks) == len(true_experts), f"{len(chunks)}, {len(true_experts)}"
+            for chunk, expert in zip(chunks, true_experts):
+                expert_outputs.append(expert(chunk))
+        else:
+            dispatched_input = dispatched_input.permute(1, 0, 2, 3).contiguous()
+            orig_shape = dispatched_input.shape
+            chunks = dispatched_input.reshape(orig_shape[0], -1, orig_shape[-1])
+            chunks = self.experts(chunks)
+            chunks = chunks.reshape(orig_shape[:-1] + (chunks.shape[-1],)).unbind(0)
+            expert_outputs.extend(chunks)
+            
+        expert_output = torch.stack(expert_outputs, dim=1)
+        return expert_output
+
+    def moe_gate_dispatch(
+        self,
+        x: torch.Tensor,                
+        gate_logits: torch.Tensor,
+        k: int,
+        capacity: Optional[int],
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor,
+               torch.Tensor, torch.Tensor]:
+
+        S, H = x.shape
+        E = gate_logits.shape[1]
+        device = x.device
+        topk_prob, topk_idx = torch.topk(gate_logits, k, dim=-1)               
+        combine_weights = topk_prob                                     
+        expert_id = topk_idx                                            
+        y = x.new_zeros((E, capacity, H))                           
+        scatter_index = x.new_full((k, S), -1, dtype=torch.int32)
+        
+        # per-expert slot counters
+        slot_counter = torch.zeros(E, dtype=torch.int32, device=device)
+
+        for tok in range(S):
+            for route in range(k):
+                e = expert_id[tok, route].item()
+                slot = slot_counter[e].item()
+                if slot >= capacity:
+                    combine_weights[tok, route] = 0.0
+                    continue
+
+                # record mapping & dispatch activation
+                scatter_index[route, tok] = e * capacity + slot
+                y[e, slot] = x[tok]
+                slot_counter[e] += 1
+
+        expert_offset = torch.cumsum(slot_counter, 0, dtype=torch.int64)
+
+        return y, combine_weights, scatter_index, expert_offset, expert_id
+    
+    def combine_expert_output(self, expert_output: torch.Tensor, combine_weights: torch.Tensor, scatter_index: torch.Tensor) -> torch.Tensor:
+        """
+        Combine expert outputs using combination weights.
+
+        Args:
+            expert_output (Tensor): Expert outputs [num_experts, capacity, dim].
+            combine_weights (Tensor): Combination weights.
+            scatter_index (Tensor): Scatter indices.
+
+        Returns:
+            Tensor: Combined output [seqlen, dim].
+        """
+        expert_output = expert_output.reshape(-1, expert_output.shape[-1])
+        combined_output = self.combining(expert_output, combine_weights, scatter_index)
+        return combined_output
+
+    def combining(self, x, combine_weights, scatter_index):
+        """
+        Combines and aggregates input matrix using combination weights.
+
+        Args:
+            x (Tensor): Input tensor of shape [num_experts * capacity, dim]
+            combine_weights (Tensor): Combination weights of shape [seq, 2]
+            scatter_index (Tensor): Scatter indices of shape [seq, 2]
+
+        Returns:
+            Tensor: Combined output tensor of shape [seq, dim]
+        """
+        dim = x.shape[-1]
+
+        scatter_index = scatter_index.reshape([-1])
+        num_k = combine_weights.shape[-1]
+        
+        combine_weights = combine_weights.unsqueeze(1)
+
+        x = x[scatter_index].reshape([-1, num_k, dim])
+
+        return torch.matmul(combine_weights, x).squeeze(1)
+
+    def gate_and_dispatch(self, input):
+        """
+        Calculate gate and dispatch inputs.
+
+        Args:
+            input: Input tensor of shape [seq, dim]
+
+        Returns:
+            tuple: (dispatched_input, combine_weights, dispatch_mask,
+            scatter_index, router_loss, gate_logits, gate_prob)
+        """
+        gate_logits, capacity, router_loss = topk_gate_func(
+            self,
+            input,
+        )
+
+        # capacity no use
+        prob = self.gate_act(gate_logits)
+        (
+            dispatched_input,
+            combine_weights_unnorm,
+            scatter_index,
+            dispatch_mask,
+            _,
+        ) = self.moe_gate_dispatch(input, prob,  k=self.k, capacity=capacity)
+        dispatch_mask = torch.diff(F.pad(dispatch_mask, (1, 0)))
+
+        scatter_index.detach()
+        dispatch_mask.detach()
+
+        scatter_index = scatter_index.transpose(0, 1)  # [k, s] -> [s, k]
+        combine_weights = combine_weights_unnorm / torch.clamp(
+            combine_weights_unnorm.sum(dim=-1, keepdim=True), min=1e-12
+        )
+        combine_weights = combine_weights.to(dtype=dispatched_input.dtype)
+
+        return dispatched_input, combine_weights, dispatch_mask, scatter_index, router_loss, gate_logits, prob
+
+    def get_capacity(self, num_tokens, cap_factor=None):
+        """
+        Calculate capacity based on number of tokens.
+
+        Args:
+            num_tokens: Number of input tokens
+            cap_factor: Optional capacity factor override
+
+        Returns:
+            int: Calculated capacity
+        """
+        num_experts = self.config.moe_num_experts
+        if cap_factor is not None:
+            cap = cap_factor
+        else:
+            if self.training:
+                cap = self.config.moe_capacity[0]
+            elif num_tokens < num_experts:
+                cap = self.config.moe_capacity[2]
+            else:
+                cap = self.config.moe_capacity[1]
+
+        capacity = int(cap * num_tokens // num_experts)
+        assert capacity > 0, f"requires capacity to >= 0. cap={cap}, num_tokens={num_tokens}"
+        return capacity
+
+
+class Ernie4_5_RMSNorm(nn.Module):
+    """
+    Ernie Root Mean Square Layer Normalization (Ernie4_5_RMSNorm) implementation.
+
+    Ernie4_5_RMSNorm is a simplified version of LayerNorm that focuses on the root mean square of inputs,
+    omitting the mean-centering operation. This provides computational efficiency while maintaining
+    good performance.
+
+    """
+
+    def __init__(self, config):
+        """
+        Initialize RMSNorm layer.
+
+        Args:
+            config (ErnieConfig): Model configuration.
+        """
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.weight = nn.Parameter(torch.ones(config.hidden_size))
+        self.variance_epsilon = config.rms_norm_eps
+
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to input hidden states.
+
+        Args:
+            hidden_states (Tensor): Input tensor of shape [batch_size, seq_len, hidden_size]
+
+        Returns:
+            Tensor: Normalized output tensor of same shape as input
+        """
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(dim=-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class Ernie4_5_RopeEmbedding(nn.Module):
+    def __init__(self, config: Ernie4_5_MoeConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None,None,:].float()
+        position_ids_expanded = position_ids[...,None].float()
+        freqs = (inv_freq_expanded.float() * position_ids_expanded.float())
+        cos = torch.cos(freqs) * self.attention_scaling
+        sin = torch.sin(freqs) * self.attention_scaling
+        return cos, sin
+        # return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Ernie4_5_DecoderLayer(nn.Module):
+    """A single transformer decoder layer in ERNIE-MoE model.
+
+    Contains self-attention and feed-forward components with optional MoE (Mixture of Experts)
+    support, residual connections, and layer normalization.
+    """
+
+    def __init__(self, config, layer_idx):
+        """Initialize the decoder layer.
+
+        Args:
+            config (ErnieMoEConfig): Model configuration.
+            layer_idx (int): Index of this layer in the transformer stack
+        """
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.layer_idx = layer_idx
+        self.config = config
+        self.use_moe = config.use_moe
+        self.self_attn = Ernie4_5_Attention(config, layer_idx)
+
+        moe_layer_start_index = (
+            min(config.moe_layer_start_index)
+            if isinstance(config.moe_layer_start_index, (tuple, list))
+            else config.moe_layer_start_index
+        )
+        moe_layer_end_index = (
+            max(config.moe_layer_end_index)
+            if isinstance(config.moe_layer_end_index, (tuple, list))
+            else config.moe_layer_end_index
+        )
+
+        if (
+            self.use_moe
+            and ((layer_idx + 1) % config.moe_layer_interval == 0)
+            and layer_idx >= moe_layer_start_index
+            and layer_idx <= moe_layer_end_index
+        ):
+            self.mlp = Ernie4_5_MoeMLP(config)
+        else:
+            self.mlp = Ernie4_5_MLP(config)
+
+        self.input_layernorm = Ernie4_5_RMSNorm(config)
+        self.post_attention_layernorm = Ernie4_5_RMSNorm(config)
+
+        self.residual_add1 = Ernie4_5_ResidualWithDropout(config.hidden_dropout_prob)
+        self.residual_add2 = Ernie4_5_ResidualWithDropout(config.hidden_dropout_prob)
+    
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        output_router_loss: bool = True,
+        output_gate_logits: bool = True,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """Forward pass through the decoder layer.
+
+        Args:
+            hidden_states (torch.Tensor): Input tensor [batch_size, seq_len, hidden_size]
+            attention_mask (Optional[torch.Tensor]): Attention mask tensor
+            position_ids (Optional[torch.Tensor]): Position indices for rotary embeddings
+            past_key_value (Optional[Tuple[torch.Tensor]]): Cached key/value states
+            output_attentions (Optional[bool]): Whether to return attention weights
+            use_cache (Optional[bool]): Whether to cache key/value states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            output_router_loss (bool): Whether to return MoE router loss
+            output_gate_logits (bool): Whether to return MoE gate logits
+
+        Returns:
+            Union: Various output combinations depending on arguments:
+                - Base case: Hidden states tensor
+                - With attention: Tuple of (hidden_states, attention_weights)
+                - With router loss: May include gate logits in output tuple
+                - With MoE gate logits: May include gate logits in output tuple
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_value=past_key_value,
+            position_ids=position_ids,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+
+        hidden_states = self.residual_add1(hidden_states, residual)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        router_loss = None
+        gate_logits = None
+
+        if isinstance(self.mlp, Ernie4_5_MoeMLP):
+            hidden_states, _, router_loss, gate_logits = self.mlp(hidden_states)
+        else:
+            hidden_states = self.mlp(hidden_states)
+
+        hidden_states = self.residual_add2(hidden_states, residual)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if output_router_loss:
+            outputs += (router_loss,)
+
+        if output_gate_logits:
+            outputs += (gate_logits,)
+
+        return outputs
+
+
+@auto_docstring
+class Ernie4_5_PretrainedModel(PreTrainedModel):
+    """Base class for ERNIE pretrained models."""
+    config_class = Ernie4_5_MoeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Ernie4_5_DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+
+
+def subbatch(f, arg_idx, axis, bs, out_idx, same_arg_idx={}):
+    """
+    Converts a function to one that applies to subbatch of an input dimension.
+    Useful for processing large tensors in smaller chunks to reduce memory usage.
+
+    Args:
+        f (Callable): Function to be subbatched.
+        arg_idx ([int]): Indices of the inputs to be subbatched.
+        axis ([int]): Indices of the dimensions to be subbatched for each input.
+        bs (int): Subbatch size.
+        out_idx (int): Dimension to concatenate outputs along.
+        same_arg_idx (dict): Mapping of argument indices that share the same tensor.
+
+    Returns:
+        Callable: New function that processes inputs in subbatches.
+    """
+
+    @functools.wraps(f)
+    def wrapper(*args, **kwargs):
+
+        assert len(arg_idx) == len(axis), "Number of batching args and number of batching dims should match."
+
+        inps = [args[i] for i in arg_idx]
+        axis_width = [inp.shape[d] for inp, d in zip(inps, axis)]
+        assert len(set(axis_width)) == 1, "Batch sizes should be kept equal."
+
+        inp_axis = {idx: d for idx, d in zip(arg_idx, axis)}
+
+        axis_width = axis_width[0]
+        if axis_width < bs:
+            return f(*args, **kwargs)
+
+        outs = []
+        for slice_at in range(0, axis_width, bs):
+            _args = []
+            for i, inp in enumerate(args):
+                if i in same_arg_idx:
+                    assert (
+                        i > same_arg_idx[i]
+                    ), f"expect i > same_arg_idx[i], but got i: {i} and same_arg_idx[i]: {same_arg_idx[i]}"
+                    _args.append(_args[same_arg_idx[i]])
+                elif i in arg_idx:
+                    d = inp_axis[i]
+                    start = slice_at
+                    end = min(inp.shape[d], slice_at + bs)
+                    # Build slice for all dims, only slice along axis d
+                    slices = [slice(None)] * inp.ndim
+                    slices[d] = slice(start, end)
+                    _args.append(inp[tuple(slices)])
+                else:
+                    _args.append(inp)
+
+            out = f(*_args, **kwargs)
+            outs.append(out)
+
+        return torch.cat(outs, dim=out_idx)
+
+    return wrapper
+
+
+class ErniePretrainingCriterion(nn.Module):
+    """Criterion for ERNIE pretraining task."""
+
+    def __init__(self, config, return_tuple=True):
+        """Initialize the pretraining criterion.
+
+        Args:
+            config (ErnieConfig): Model configuration.
+            return_tuple (bool): Whether to return loss as tuple (loss, loss_sum). Defaults to True.
+        """
+        super().__init__()
+        self.ignored_index = getattr(config, "ignored_index", -100)
+        self.config = config
+        self.return_tuple = return_tuple
+    
+        self.loss_func = nn.CrossEntropyLoss(reduction="none")
+
+    def forward(self, prediction_scores, masked_lm_labels, loss_mask, router_loss=None, mtp_logits=None):
+        """Compute the combined pretraining loss.
+
+        Args:
+            prediction_scores: Prediction scores tensor, [batch_size, seq_len, vocab_size]
+            masked_lm_labels: Target labels tensor [batch_size, seq_len]
+            loss_mask: Optional mask for valid tokens
+            router_loss: Optional MoE router loss tensor
+
+        Returns:
+            Union:
+                - If return_tuple=True: Tuple of (combined_loss, mlm_loss_sum)
+                - If return_tuple=False: Combined loss tensor
+        """
+        if self.config.num_nextn_predict_layers > 0 and self.training:
+            masked_lm_labels_ori = masked_lm_labels
+            masked_lm_labels = masked_lm_labels[:, : -self.config.num_nextn_predict_layers]
+            loss_mask = loss_mask[:, : -self.config.num_nextn_predict_layers]
+            seq_length = masked_lm_labels.shape[1]
+
+        res = self.forward_impl(prediction_scores, masked_lm_labels, loss_mask)
+
+        if self.config.num_nextn_predict_layers > 0 and self.training:
+            mtp_loss_res = []
+            for depth in range(self.config.num_nextn_predict_layers):
+                prediction_scores_cur_depth = mtp_logits[depth]
+                masked_lm_labels_cur_depth = masked_lm_labels_ori[:, (depth + 1) : (depth + 1 + seq_length)]
+                res_cur_depth = super().forward(
+                    prediction_scores_cur_depth,
+                    masked_lm_labels_cur_depth,
+                )
+                mtp_loss_res.append(res_cur_depth)
+
+        def add_loss(main_loss, loss):
+            return main_loss + loss - loss.detach()
+        
+
+        if self.return_tuple:
+            loss, loss_sum = res
+            if self.config.num_nextn_predict_layers > 0 and self.training:
+                loss = add_loss(
+                    loss, self.config.multi_token_pred_lambda * sum([x[0] for x in mtp_loss_res]) / len(mtp_loss_res)
+                )
+                loss_sum = loss_sum + self.config.multi_token_pred_lambda * sum(
+                    [x[1].detach() for x in mtp_loss_res]
+                ) / len(mtp_loss_res)
+        else:
+            loss, loss_sum = res, None
+            if self.config.num_nextn_predict_layers > 0 and self.training:
+                loss = add_loss(
+                    loss, self.config.multi_token_pred_lambda * sum([x[0] for x in mtp_loss_res]) / len(mtp_loss_res)
+                )
+
+        if router_loss is not None and isinstance(router_loss, torch.Tensor):
+            loss = loss + router_loss - router_loss.detach()
+
+        return loss, loss_sum
+
+
+    def loss_impl(self, prediction_scores: torch.Tensor, masked_lm_labels: torch.Tensor) -> torch.Tensor:
+        """
+        Core loss computation without reduction (but per-token).
+
+        Args:
+            prediction_scores (torch.Tensor): Logits tensor [batch_size, seq_len, vocab_size].
+            masked_lm_labels (torch.Tensor): Target labels tensor [batch_size, seq_len].
+
+        Returns:
+            torch.Tensor: Unreduced loss tensor of shape [batch_size, seq_len].
+                          Losses are calculated in float32.
+        """
+        scores_float32 = prediction_scores.to(torch.float32)
+        # prediction_scores: [batch_size, seq_len, vocab_size]
+        # masked_lm_labels: [batch_size, seq_len]
+        # Transpose prediction_scores to [batch_size, vocab_size, seq_len]
+        unreduced_loss = self.loss_func(
+            scores_float32.transpose(1, 2),  # Shape: [batch_size, vocab_size, seq_len]
+            masked_lm_labels.long()          # Shape: [batch_size, seq_len], ensure long type
+        )
+        # unreduced_loss will be of shape [batch_size, seq_len] and dtype float32
+        return unreduced_loss
+
+    def forward_impl(self, prediction_scores, masked_lm_labels, loss_mask=None):
+        prediction_scores_dims = len(prediction_scores.shape)
+        
+        loss_subbatch_seqlen_config_key = "loss_subbatch_seqlen"
+        default_loss_subbatch_seqlen = 32768
+
+        current_loss_subbatch_seqlen = self.config.get(
+            loss_subbatch_seqlen_config_key, default_loss_subbatch_seqlen
+        )
+
+        if prediction_scores_dims == 2 and prediction_scores.shape[0] > current_loss_subbatch_seqlen:
+            sb_loss_func = subbatch(
+                self.loss_impl, [0, 1], [0, 0], current_loss_subbatch_seqlen, 0
+            )
+            masked_lm_loss = sb_loss_func(prediction_scores, masked_lm_labels)
+        elif prediction_scores_dims == 3 and prediction_scores.shape[1] > current_loss_subbatch_seqlen:
+            sb_loss_func = subbatch(
+                self.loss_impl, [0, 1], [1, 1], current_loss_subbatch_seqlen, 1
+            )
+            masked_lm_loss = sb_loss_func(prediction_scores, masked_lm_labels)
+        else:
+            masked_lm_loss = self.loss_impl(prediction_scores, masked_lm_labels)
+
+        if loss_mask is None:
+            loss_mask = masked_lm_labels != self.ignored_index
+
+        loss_mask = loss_mask.reshape(-1).to(torch.float32)
+
+        masked_lm_loss = torch.sum(masked_lm_loss.to(torch.float32).reshape(-1) * loss_mask)
+        
+        # The division will be in float32
+        loss = masked_lm_loss / loss_mask.sum()
+        
+        loss_sum = masked_lm_loss.sum().detach()
+
+        if not self.return_tuple:
+            if self.training:
+                return loss
+            return loss_sum
+        return loss, loss_sum
+
+@auto_docstring
+class Ernie4_5_Model(Ernie4_5_PretrainedModel):
+    """The core ERNIE transformer model with MoE (Mixture of Experts) support."""
+    _keep_in_fp32_modules = ['gate']
+    def __init__(self, config: Ernie4_5_MoeConfig):
+        """Initialize the ERNIE model architecture."""
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.hidden_size = config.hidden_size
+        self.config = config
+
+        self.embed_tokens = nn.Embedding(
+            self.vocab_size,
+            self.hidden_size,
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                Ernie4_5_DecoderLayer(config, i)
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = Ernie4_5_RMSNorm(config)
+        self.rotary_emb = Ernie4_5_RopeEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+
+        if config.num_nextn_predict_layers > 0 and self.training:
+            self.mtp_block = nn.ModuleList(
+                [Ernie4_5_DecoderLayer(config, layer_idx) for layer_idx in range(config.num_nextn_predict_layers)]
+            )
+            self.mtp_emb_norm = nn.ModuleList(
+                [Ernie4_5_RMSNorm(config) for _ in range(config.num_nextn_predict_layers)]
+            )
+            self.mtp_hidden_norm = nn.ModuleList(
+                [Ernie4_5_RMSNorm(config) for _ in range(config.num_nextn_predict_layers)]
+            )
+            self.mtp_linear_proj = nn.ModuleList(
+                [nn.Linear(config.hidden_size * 2, config.hidden_size, bias=config.use_bias) for _ in range(config.num_nextn_predict_layers)]
+            )
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        """Get the input embedding layer."""
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        """Set new input embeddings."""
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ):
+        """Forward pass through the ERNIE model."""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        
+        inputs_embeds = inputs_embeds.to(self.embed_tokens.weight.dtype)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+        
+        seq_length = inputs_embeds.size(1)
+        if self.config.num_nextn_predict_layers > 0 and self.training:
+            seq_length -= self.config.num_nextn_predict_layers
+            seq_length_with_past = seq_length
+            if position_ids is not None:
+                position_ids = position_ids[:, :seq_length]
+            inputs_embeds_extra = inputs_embeds[:, -self.config.num_nextn_predict_layers :, :]
+            inputs_embeds = inputs_embeds[:, : -self.config.num_nextn_predict_layers, :]
+            inputs_embeds_ori = inputs_embeds
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+        
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_router_loss = torch.tensor(0.0, device=inputs_embeds.device) if self.config.use_moe else None
+        all_gate_logits = ()
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    partial(decoder_layer.__call__, **flash_attn_kwargs),
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                    **flash_attn_kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+            
+            if self.config.use_moe:
+                layer_outputs, gate_logits = layer_outputs[:-1], layer_outputs[-1]
+                all_gate_logits = all_gate_logits + (gate_logits,)
+        
+        mtp_outputs = []
+        if self.config.num_nextn_predict_layers > 0 and self.training:
+            mtp_outputs.append(hidden_states)
+            for depth in range(self.config.num_nextn_predict_layers):
+                inputs_embeds_cur_depth = torch.concat(
+                    [inputs_embeds_ori[:, (depth + 1) :, :], inputs_embeds_extra[:, : (depth + 1), :]], axis=1
+                )
+                inputs_embeds_cur_depth_norm = self.mtp_emb_norm[depth](inputs_embeds_cur_depth)
+                hidden_states_norm = self.mtp_hidden_norm[depth](hidden_states)
+                
+                inputs_embeds_cur_depth = self.mtp_linear_proj[depth](
+                    torch.concat([inputs_embeds_cur_depth_norm, hidden_states_norm], axis=-1)
+                )
+
+                decoder_layer = self.mtp_block[depth]
+                layer_outputs = decoder_layer(
+                    inputs_embeds_cur_depth,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                    **flash_attn_kwargs,
+                )
+                if isinstance(layer_outputs, (tuple, list)):
+                    hidden_states = layer_outputs[0]
+                else:
+                    hidden_states = layer_outputs
+
+                if self.config.use_moe:
+                    layer_outputs, gate_logits = layer_outputs[:-1], layer_outputs[-1]
+                    all_gate_logits = all_gate_logits + (gate_logits,)
+
+                mtp_outputs.append(hidden_states)
+            mtp_outputs = [self.norm(hidden_states) for depth, hidden_states in enumerate(mtp_outputs)]
+            hidden_states, mtp_outputs = mtp_outputs[0], mtp_outputs[1:]
+        else:
+            hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        # assert all_router_loss is None, f'moe not support `return-dict`'
+        return Erine4_5_MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            router_loss=all_router_loss,
+            gate_logits=all_gate_logits,
+            mtp_outputs=mtp_outputs,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: Union[torch.Tensor, "BlockMask"],
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool = False,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and past_key_values is not None:
+                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
+                if is_padding_right:
+                    raise ValueError(
+                        "You are attempting to perform batched generation with padding_side='right'"
+                        " this may lead to unexpected behaviour for Flash Attention version of Qwen3. Make sure to "
+                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                    )
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+        if self.config._attn_implementation == "flex_attention":
+            if isinstance(attention_mask, torch.Tensor):
+                attention_mask = make_flex_block_causal_mask(attention_mask)
+            return attention_mask
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if (
+            self.config._attn_implementation == "sdpa"
+            and not (using_static_cache or using_sliding_window_cache)
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                sliding_window=self.config.sliding_window,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype = input_tensor.dtype
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        # SlidingWindowCache or StaticCache
+        if using_sliding_window_cache or using_static_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        # DynamicCache or no cache
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+            config=self.config,
+            past_key_values=past_key_values,
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type in ["cuda", "xpu", "npu"]
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        config: Ernie4_5_MoeConfig,
+        past_key_values: Cache,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+            config (`Ernie4_5_MoeConfig`):
+                The model's configuration class
+            past_key_values (`Cache`):
+                The cache class that is being used currently to generate
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+            )
+            diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
+                -1, 1
+            )
+            text_config = config.get_text_config()
+            if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
+                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
+                # the check is needed to verify is current checkpoint was trained with sliding window or not
+                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
+                    sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
+                        cache_position.reshape(-1, 1) - text_config.sliding_window
+                    )
+                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
+            causal_mask *= diagonal_attend_mask
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                if attention_mask.shape[-1] > target_length:
+                    attention_mask = attention_mask[:, :target_length]
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+                    causal_mask.device
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+        return causal_mask
+
+@auto_docstring
+class Ernie4_5_MoeForCausalLM(Ernie4_5_PretrainedModel,GenerationMixin):
+    """ERNIE Mixture of Experts (MoE) model for causal language modeling."""
+
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        """
+        Initializes the ERNIE MoE model for causal language modeling.
+
+        Args:
+            config (dict): Model configuration.
+        """
+        super().__init__(config)
+        self.config = config
+        self.model = Ernie4_5_Model(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size,bias=config.weight_share_add_bias and config.use_bias) # TODO
+        self.loss_function = ErniePretrainingCriterion(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        """Returns the input embeddings layer."""
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        """Sets the input embeddings layer."""
+        self.ernie.embed_tokens = value
+
+    def get_output_embeddings(self):
+        """Returns the output embeddings (LM head)."""
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        """Sets the output embeddings layer."""
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        """Sets the ERNIE decoder model."""
+        self.model = decoder
+
+    def get_decoder(self):
+        """Get the transformer decoder."""
+        return self.model
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values: Optional[list[torch.FloatTensor]] = None,
+        inputs_embeds=None,
+        labels=None,
+        loss_mask=None,
+        use_cache=False,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ):
+        """
+        Forward pass for causal language modeling.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        outputs = self.model(
+            input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        mtp_outputs = outputs.mtp_outputs
+
+        logits = self.lm_head(hidden_states)
+        mtp_logits = []
+        if len(mtp_outputs) > 0:
+            mtp_logits = [self.lm_head(_hidden_states) for _hidden_states in mtp_outputs]
+        loss, router_loss = None, None
+        if getattr(self.config, "use_moe", False):
+            router_loss = outputs.router_loss
+
+        if labels is not None:
+            loss, _ = self.loss_function(logits, labels, loss_mask, router_loss, mtp_logits)
+
+        return Ernie4_5_MoeCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_loss=router_loss,
+        )
+
+
+
+__all__ = [
+    "Ernie4_5_Model",
+    "Ernie4_5_MoeForCausalLM",
+    "Ernie4_5_PretrainedModel"
+]

recipe.yaml

@@ -0,0 +1,34 @@
+quant_stage:
+  quant_modifiers:
+    AWQModifier:
+      config_groups:
+        group_0:
+          targets: [Linear]
+          weights:
+            num_bits: 8
+            type: int
+            symmetric: true
+            group_size: 32
+            strategy: group
+            block_structure: null
+            dynamic: false
+            actorder: null
+            observer: mse
+            observer_kwargs: {}
+          input_activations: null
+          output_activations: null
+          format: null
+      targets: [Linear]
+      ignore: [lm_head, 're:model.layers.0[.].*', 're:.*shared_experts.*', 're:.*moe_statics.*',
+        're:.*mlp.gate$', model.embed_tokens, 're:.*input_layernorm$', 're:.*post_attention_layernorm$',
+        model.norm]
+      mappings:
+      - smooth_layer: re:.*input_layernorm$
+        balance_layers: ['re:.*q_proj$', 're:.*k_proj$', 're:.*v_proj$']
+      - smooth_layer: re:.*v_proj$
+        balance_layers: ['re:.*o_proj$']
+      - smooth_layer: re:.*post_attention_layernorm$
+        balance_layers: ['re:.*gate_proj$', 're:.*up_proj$']
+      - smooth_layer: re:.*up_proj$
+        balance_layers: ['re:.*down_proj$']
+      duo_scaling: true

special_tokens_map.json

@@ -0,0 +1,33 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": "<|begin_of_sentence|>",
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": "<mask:1>",
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:335bb0e7d5f2ce8ed19f2305324ccaeae5925f95e6ee784d86569346ae389bc9
+size 10999640

tokenizer.model

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+oid sha256:098d756440604e3829c6c2375f835a82a1968c044b74e561f4b0084e53befd2e
+size 1614702

tokenizer_config.json

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+}