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c807c0c 7ca597c c807c0c 7ca597c c807c0c 7ca597c c807c0c 7ca597c c807c0c 7ca597c c807c0c 7ca597c c807c0c | 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 | #!/usr/bin/env python3
"""Convert Hugging Face Whisper weights to compact inference safetensors.
Usage:
uv run --with numpy --with safetensors --with ml-dtypes python convert.py \
model.safetensors model-f16.safetensors
"""
import argparse
import json
from pathlib import Path
import numpy as np
from safetensors import safe_open
from safetensors.numpy import save_file
def remap_key(key: str) -> str:
key = key.removeprefix("model.")
key = {
"encoder.embed_positions.weight": "encoder.positional_embedding",
"decoder.embed_positions.weight": "decoder.positional_embedding",
}.get(key, key)
key = key.replace("decoder.embed_tokens", "decoder.token_embedding", 1)
key = key.replace("encoder.layer_norm", "encoder.ln_post", 1)
key = key.replace("encoder.layers.", "encoder.blocks.", 1)
key = key.replace("decoder.layer_norm", "decoder.ln", 1)
key = key.replace("decoder.layers.", "decoder.blocks.", 1)
key = key.replace("self_attn_layer_norm", "attn_ln")
key = key.replace("encoder_attn_layer_norm", "cross_attn_ln")
key = key.replace("encoder_attn", "cross_attn")
key = key.replace("self_attn", "attn")
key = key.replace("q_proj", "query")
key = key.replace("k_proj", "key")
key = key.replace("v_proj", "value")
key = key.replace("out_proj", "out")
key = key.replace("fc1", "mlp.0")
key = key.replace("fc2", "mlp.2")
return key.replace("final_layer_norm", "mlp_ln")
def keeps_float32(key: str) -> bool:
return (
key in {"encoder.positional_embedding", "decoder.positional_embedding"}
or key.startswith("encoder.ln_post.")
or key.startswith("decoder.ln.")
or any(part in key for part in (".attn_ln.", ".cross_attn_ln.", ".mlp_ln."))
)
def quantizes_fp8(key: str, tensor: np.ndarray, compute_dtype: str) -> bool:
return (
compute_dtype == "float8_e4m3fn"
and tensor.ndim == 2
and key != "decoder.token_embedding.weight"
and not keeps_float32(key)
)
def target_dtype(key: str, tensor: np.ndarray, compute_dtype: str):
if keeps_float32(key):
return np.float32
if quantizes_fp8(key, tensor, compute_dtype):
import ml_dtypes
return ml_dtypes.float8_e4m3fn
return np.float16
def quantize_fp8(tensor: np.ndarray):
import ml_dtypes
value = tensor.astype(np.float32)
axes = tuple(range(1, value.ndim))
scale = np.max(np.abs(value), axis=axes, keepdims=True) / 448.0
scale = np.where(scale == 0, 1.0, scale).astype(np.float16)
quantized = (value / scale.astype(np.float32)).astype(ml_dtypes.float8_e4m3fn)
return quantized, scale
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("input", type=Path, help="Source model.safetensors")
parser.add_argument("output", type=Path, help="Converted model.safetensors")
parser.add_argument("--source", help="Source repository and revision for metadata")
parser.add_argument("--compute-dtype", choices=("float16", "float8_e4m3fn"), default="float16")
args = parser.parse_args()
tensors = {}
with safe_open(args.input, framework="numpy") as checkpoint:
source_metadata = checkpoint.metadata() or {}
for source_key in checkpoint.keys():
key = remap_key(source_key)
if key in tensors:
raise ValueError(f"duplicate normalized key: {key}")
tensor = checkpoint.get_tensor(source_key)
if np.issubdtype(tensor.dtype, np.floating):
dtype = target_dtype(key, tensor, args.compute_dtype)
if quantizes_fp8(key, tensor, args.compute_dtype):
tensor, scale = quantize_fp8(tensor)
tensors[f"{key}.weight_scale"] = np.ascontiguousarray(scale)
else:
tensor = tensor.astype(dtype)
tensors[key] = np.ascontiguousarray(tensor)
transform = "HF keys normalized; compute weights FP16; positional embeddings and LayerNorm FP32"
if args.compute_dtype == "float8_e4m3fn":
transform = (
"HF keys normalized; linear weights float8_e4m3fn with per-output-channel scales; "
"token/positional embeddings, convolutions, biases, and scales FP16 except LayerNorm/positional FP32"
)
metadata = {
**source_metadata,
"format": "pt",
"precision": f"mixed-{args.compute_dtype}-f16-f32",
"transform": transform,
}
if args.source:
metadata["source"] = args.source
args.output.parent.mkdir(parents=True, exist_ok=True)
save_file(tensors, args.output, metadata=metadata)
counts = {str(dtype): sum(t.dtype == dtype for t in tensors.values()) for dtype in {t.dtype for t in tensors.values()}}
size = args.output.stat().st_size / 2**30
print(json.dumps({"output": str(args.output), "size_gib": round(size, 3), "tensors": len(tensors), "dtypes": counts}))
if __name__ == "__main__":
main()
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