File size: 6,639 Bytes
19ed98b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | #!/usr/bin/env python3
"""
Memory-efficient unary converter for Qwen3.
Processes one safetensors shard at a time to avoid OOM.
(c) 2026 OpenTransformers Ltd / Scott Bisset
"""
import numpy as np
import os, sys, json, time, gc
def quantize_unary(w_fp32, n_planes):
"""Quantize weight matrix to unary bitplane format"""
out_dim, in_dim = w_fp32.shape
max_val = n_planes
abs_max = np.abs(w_fp32).max(axis=1, keepdims=True)
abs_max = np.where(abs_max == 0, 1.0, abs_max)
scaled = w_fp32 / abs_max * max_val
rounded = np.clip(np.round(scaled), -max_val, max_val).astype(np.int32)
scales = (abs_max.flatten() / max_val).astype(np.float32)
signs = (rounded < 0)
magnitudes = np.abs(rounded)
chunks = (in_dim + 63) // 64
padded = chunks * 64
if padded > in_dim:
signs = np.pad(signs, ((0,0),(0,padded-in_dim)), constant_values=False)
magnitudes = np.pad(magnitudes, ((0,0),(0,padded-in_dim)), constant_values=0)
sign_bits = np.packbits(signs.astype(np.uint8), axis=1, bitorder='little')
sign_u64 = sign_bits.view(np.uint64)[:, :chunks]
plane_bits = np.zeros((n_planes, out_dim, chunks), dtype=np.uint64)
for p in range(n_planes):
mask = (magnitudes > p)
packed = np.packbits(mask.astype(np.uint8), axis=1, bitorder='little')
plane_bits[p] = packed.view(np.uint64)[:, :chunks]
return sign_u64, plane_bits, scales
def convert_model(model_dir, output_dir, n_planes=7):
os.makedirs(output_dir, exist_ok=True)
config = json.load(open(os.path.join(model_dir, "config.json")))
n_layers = config["num_hidden_layers"]
hidden = config["hidden_size"]
# Load index to know which keys are in which shard
index_file = os.path.join(model_dir, "model.safetensors.index.json")
if os.path.exists(index_file):
index = json.load(open(index_file))
weight_map = index["weight_map"]
shards = sorted(set(weight_map.values()))
else:
# Single shard
shards = [f for f in os.listdir(model_dir) if f.endswith('.safetensors')]
weight_map = None
print(f"Model: {n_layers} layers, hidden={hidden}, n_planes={n_planes}")
print(f"Shards: {len(shards)}")
manifest = {"unary": {}, "fp16": {}, "n_planes": n_planes, "n_layers": n_layers, "config": config}
total_converted = 0
total_linear = 0
# Count total linear layers
if weight_map:
total_linear = sum(1 for k in weight_map if k.endswith(".weight") and "proj" in k)
print(f"Total linear layers to convert: {total_linear}")
import torch
from safetensors import safe_open
for shard_idx, shard in enumerate(shards):
shard_path = os.path.join(model_dir, shard)
print(f"\n=== Shard {shard_idx+1}/{len(shards)}: {shard} ===")
with safe_open(shard_path, framework="pt") as f:
keys = list(f.keys())
print(f" {len(keys)} tensors in shard")
for key in sorted(keys):
fname = key.replace(".", "_")
is_linear = key.endswith(".weight") and "proj" in key and f.get_tensor(key).dim() == 2
if is_linear:
# Check if already converted
sign_path = os.path.join(output_dir, f"{fname}.sign")
if os.path.exists(sign_path):
w = f.get_tensor(key)
manifest["unary"][key] = list(w.shape)
total_converted += 1
print(f" [SKIP] {key} already converted")
continue
w = f.get_tensor(key).float().numpy()
t0 = time.time()
sign, planes, scales = quantize_unary(w, n_planes)
dt = time.time() - t0
np.array(sign).tofile(os.path.join(output_dir, f"{fname}.sign"))
np.array(planes).tofile(os.path.join(output_dir, f"{fname}.planes"))
np.array(scales).tofile(os.path.join(output_dir, f"{fname}.scales"))
orig_mb = w.nbytes / 1e6
comp_mb = (sign.nbytes + planes.nbytes + scales.nbytes) / 1e6
total_converted += 1
manifest["unary"][key] = list(w.shape)
print(f" [{total_converted}/{total_linear}] {key}: {list(w.shape)} -> {comp_mb:.1f}MB ({orig_mb/comp_mb:.1f}x) [{dt:.1f}s]")
del w, sign, planes, scales
else:
# FP16 weight (norms, embeddings, etc)
fp16_path = os.path.join(output_dir, f"{fname}.fp16")
if os.path.exists(fp16_path):
w = f.get_tensor(key)
manifest["fp16"][key] = list(w.shape)
print(f" [SKIP] {key} already saved")
continue
w = f.get_tensor(key).float().numpy()
w_fp16 = w.astype(np.float16)
w_fp16.view(np.uint16).tofile(fp16_path)
manifest["fp16"][key] = list(w.shape)
print(f" [FP16] {key}: {list(w.shape)} ({w_fp16.nbytes/1e6:.1f}MB)")
del w, w_fp16
# Force GC between shards
gc.collect()
print(f" Shard done, memory freed")
# Save manifest
with open(os.path.join(output_dir, "manifest.json"), "w") as f:
json.dump(manifest, f, indent=2)
# Copy config
import shutil
for cf in ["config.json", "tokenizer.json", "tokenizer_config.json", "special_tokens_map.json"]:
src = os.path.join(model_dir, cf)
if os.path.exists(src):
shutil.copy(src, os.path.join(output_dir, cf))
# Summary
total_unary = sum(os.path.getsize(os.path.join(output_dir, f))
for f in os.listdir(output_dir)
if f.endswith((".sign", ".planes", ".scales")))
total_fp16 = sum(os.path.getsize(os.path.join(output_dir, f))
for f in os.listdir(output_dir)
if f.endswith(".fp16"))
print(f"\n=== CONVERSION COMPLETE ===")
print(f"Unary linear: {total_unary/1e9:.2f} GB")
print(f"FP16 other: {total_fp16/1e9:.2f} GB")
print(f"Total: {(total_unary+total_fp16)/1e9:.2f} GB")
if __name__ == "__main__":
model_dir = sys.argv[1] if len(sys.argv) > 1 else "qwen3-4b-thinking-hf"
output_dir = sys.argv[2] if len(sys.argv) > 2 else "qwen3-4b-thinking-unary"
n_planes = int(sys.argv[3]) if len(sys.argv) > 3 else 7
convert_model(model_dir, output_dir, n_planes)
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