File size: 6,869 Bytes
21d029f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | #!/usr/bin/env python3
"""Stream-preprocess Qwen3-Coder-30B-A3B-4bit."""
import os, sys, json, time, gc, shutil, glob
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))
import numpy as np
import mlx.core as mx
REPO = "mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit"
OUTPUT_DIR = os.path.expanduser("~/models/qwen3-coder-30b")
PAGE_SIZE = 16384
TENSOR_NAMES = [
"gate_proj.weight", "gate_proj.scales", "gate_proj.biases",
"up_proj.weight", "up_proj.scales", "up_proj.biases",
"down_proj.weight", "down_proj.scales", "down_proj.biases",
]
def convert_layer_to_bin(layer_data, layer_idx, num_experts, output_dir):
tensor_info = {}
expert_block_size = 0
for name in TENSOR_NAMES:
t = layer_data[name]
per_expert_shape = list(t.shape[1:])
elem_size = 4 if t.dtype == mx.uint32 else (2 if t.dtype in (mx.bfloat16, mx.float16) else 4)
nbytes = 1
for s in per_expert_shape:
nbytes *= s
nbytes *= elem_size
tensor_info[name] = {
"shape_per_expert": per_expert_shape,
"dtype": str(t.dtype).replace("mlx.core.", ""),
"nbytes": nbytes,
"inner_offset": expert_block_size,
}
expert_block_size += nbytes
header = {
"layer_idx": layer_idx, "num_experts": num_experts,
"layout": {"expert_block_size": expert_block_size, "data_start": PAGE_SIZE, "tensors": tensor_info}
}
header_bytes = json.dumps(header, indent=2).encode()
assert len(header_bytes) < PAGE_SIZE
header_bytes += b"\x00" * (PAGE_SIZE - len(header_bytes))
out_path = os.path.join(output_dir, "bin", f"moe_layer_{layer_idx:02d}.bin")
with open(out_path, "wb") as f:
f.write(header_bytes)
for expert_id in range(num_experts):
for name in TENSOR_NAMES:
t = layer_data[name][expert_id]
if t.dtype == mx.bfloat16:
raw = np.array(t.astype(mx.float16)).astype(np.float16).tobytes()
elif t.dtype == mx.uint32:
raw = np.array(t).astype(np.uint32).tobytes()
else:
raw = np.array(t).tobytes()
f.write(raw)
return os.path.getsize(out_path)
def main():
from huggingface_hub import hf_hub_download
print("=" * 55)
print(f" Stream Preprocess: {REPO.split('/')[-1]}")
print(f" Output: {OUTPUT_DIR}")
print("=" * 55)
os.makedirs(os.path.join(OUTPUT_DIR, "bin"), exist_ok=True)
for fname in ["config.json", "tokenizer.json", "tokenizer_config.json", "special_tokens_map.json"]:
try:
path = hf_hub_download(REPO, fname, local_dir="/tmp/sniper_dl_coder")
shutil.copy(path, os.path.join(OUTPUT_DIR, fname))
print(f" Downloaded {fname}")
except Exception as e:
print(f" Skipped {fname}: {e}")
with open(os.path.join(OUTPUT_DIR, "config.json")) as f:
config = json.load(f)
num_layers = config.get("num_hidden_layers", 48)
print(f" Layers: {num_layers}, Experts: {config.get('num_experts', 0)}")
idx_path = hf_hub_download(REPO, "model.safetensors.index.json", local_dir="/tmp/sniper_dl_coder")
with open(idx_path) as f:
idx = json.load(f)
shards = sorted(set(idx["weight_map"].values()))
print(f" {len(shards)} shards")
existing = set()
for f in os.listdir(os.path.join(OUTPUT_DIR, "bin")):
if f.startswith("moe_layer_") and f.endswith(".bin"):
existing.add(int(f.split("_")[2].split(".")[0]))
pinned = {}
layers_done = set(existing)
partial_layers = {}
for si, shard_name in enumerate(shards):
print(f"\n [{si+1}/{len(shards)}] Downloading {shard_name}...")
t0 = time.time()
shard_path = hf_hub_download(REPO, shard_name, local_dir="/tmp/sniper_dl_coder")
shard_size = os.path.getsize(shard_path) / 1e9
print(f" {shard_size:.1f} GB in {time.time()-t0:.0f}s")
data = mx.load(shard_path)
print(f" {len(data)} tensors")
layer_experts = {}
for key, tensor in data.items():
if "switch_mlp" in key:
layer = int(key.split(".layers.")[1].split(".")[0])
short = key.split(".switch_mlp.")[1]
layer_experts.setdefault(layer, {})[short] = tensor
else:
pinned[key] = tensor
for layer_idx, tensors in layer_experts.items():
if layer_idx in layers_done:
continue
if layer_idx in partial_layers:
partial_layers[layer_idx].update(tensors)
tensors = partial_layers[layer_idx]
if len(tensors) < 9:
partial_layers[layer_idx] = tensors
print(f" Layer {layer_idx}: partial ({len(tensors)}/9)")
continue
num_experts = tensors[list(tensors.keys())[0]].shape[0]
sz = convert_layer_to_bin(tensors, layer_idx, num_experts, OUTPUT_DIR)
layers_done.add(layer_idx)
if layer_idx in partial_layers:
del partial_layers[layer_idx]
print(f" Layer {layer_idx}: {sz/1e6:.0f} MB")
del data, layer_experts
gc.collect(); mx.clear_cache()
try:
os.remove(shard_path)
print(f" Deleted shard ({shard_size:.1f} GB freed)")
except:
pass
# Merge partials
for layer_idx, tensors in partial_layers.items():
if layer_idx in layers_done or len(tensors) < 9:
continue
num_experts = tensors[list(tensors.keys())[0]].shape[0]
sz = convert_layer_to_bin(tensors, layer_idx, num_experts, OUTPUT_DIR)
layers_done.add(layer_idx)
print(f" Layer {layer_idx}: {sz/1e6:.0f} MB (merged)")
if pinned:
print(f"\n Saving pinned ({len(pinned)} tensors)...")
mx.save_safetensors(os.path.join(OUTPUT_DIR, "pinned.safetensors"), pinned)
psz = os.path.getsize(os.path.join(OUTPUT_DIR, "pinned.safetensors")) / 1e9
print(f" Pinned: {psz:.2f} GB")
else:
psz = 0
shutil.rmtree("/tmp/sniper_dl_coder", ignore_errors=True)
bin_files = sorted(glob.glob(os.path.join(OUTPUT_DIR, "bin", "moe_layer_*.bin")))
total = sum(os.path.getsize(f) for f in bin_files)
missing = set(range(num_layers)) - layers_done
print(f"\n Expert layers: {len(bin_files)}/{num_layers}")
print(f" Expert total: {total/1e9:.2f} GB")
print(f" Pinned: {psz:.2f} GB")
if missing:
print(f" WARNING: Missing layers: {sorted(missing)}")
else:
print(f"\n All {num_layers} layers converted!")
print(f" Test: mlx-sniper run {OUTPUT_DIR} -p 'Write a Python quicksort' -v")
if __name__ == "__main__":
main()
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