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Original file line number | Diff line number | Diff line change |
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# Copyright © 2023 Apple Inc. | ||
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import argparse | ||
from itertools import starmap | ||
import collections | ||
import glob | ||
from pathlib import Path | ||
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import numpy as np | ||
import torch | ||
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SHARD_FIRST = ["wv", "wq", "wk", "w1", "w3", "output"] | ||
SHARD_SECOND = ["tok_embeddings", "wo", "w2"] | ||
SHARD_WEIGHTS = set(SHARD_FIRST + SHARD_SECOND) | ||
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def map_torch_to_mlx(key, value): | ||
if "tok_embedding" in key: | ||
key = "embedding.weight" | ||
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elif "norm" in key: | ||
key = key.replace("attention_norm", "norm1").replace("ffn_norm", "norm2") | ||
def shard_key(k): | ||
keys = k.split(".") | ||
if len(keys) < 2: | ||
return None | ||
return keys[-2] | ||
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elif "wq" in key or "wk" in key or "wv" in key or "wo" in key: | ||
key = key.replace("wq", "query_proj") | ||
key = key.replace("wk", "key_proj") | ||
key = key.replace("wv", "value_proj") | ||
key = key.replace("wo", "out_proj") | ||
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elif "w1" in key or "w2" in key or "w3" in key: | ||
# The FFN is a separate submodule in PyTorch | ||
key = key.replace("feed_forward.w1", "linear1") | ||
key = key.replace("feed_forward.w3", "linear2") | ||
key = key.replace("feed_forward.w2", "linear3") | ||
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elif "output" in key: | ||
key = key.replace("output", "out_proj") | ||
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elif "rope" in key: | ||
return None, None | ||
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return ( | ||
key, | ||
value.numpy() | ||
if value.dtype != torch.bfloat16 | ||
else value.to(torch.float32).numpy(), | ||
) | ||
def unshard(k, v): | ||
wn = shard_key(k) | ||
if wn not in SHARD_WEIGHTS: | ||
return v | ||
elif wn in SHARD_FIRST: | ||
axis = 0 | ||
elif wn in SHARD_SECOND: | ||
axis = 1 | ||
else: | ||
raise ValueError("Invalid weight name") | ||
return np.concatenate(v, axis=axis) | ||
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if __name__ == "__main__": | ||
parser = argparse.ArgumentParser(description="Convert Llama weights to MLX") | ||
parser.add_argument("torch_weights") | ||
parser.add_argument("output_file") | ||
parser.add_argument( | ||
"--model_path", | ||
help="Path to the Torch model. The MLX weights will also be saved there.", | ||
) | ||
args = parser.parse_args() | ||
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state = torch.load(args.torch_weights, map_location=torch.device('cpu')) | ||
np.savez( | ||
args.output_file, | ||
**{k: v for k, v in starmap(map_torch_to_mlx, state.items()) if k is not None} | ||
) | ||
model_path = Path(args.model_path) | ||
torch_files = glob.glob(str(model_path / "consolidated.*.pth")) | ||
weights = collections.defaultdict(list) | ||
for wf in torch_files: | ||
state = torch.load(wf, map_location=torch.device("cpu")) | ||
for k, v in state.items(): | ||
v = v.to(torch.float16).numpy() | ||
if shard_key(k) in SHARD_WEIGHTS: | ||
weights[k].append(v) | ||
else: | ||
weights[k] = v | ||
|
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out_file = str(model_path / "weights.npz") | ||
for k, v in weights.items(): | ||
weights[k] = unshard(k, v) | ||
np.savez(out_file, **weights) |
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