2023-12-01 03:08:53 +08:00
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# Copyright © 2023 Apple Inc.
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2023-11-30 02:38:20 +08:00
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import argparse
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from itertools import starmap
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import numpy as np
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import torch
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def map_torch_to_mlx(key, value):
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if "tok_embedding" in key:
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key = "embedding.weight"
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elif "norm" in key:
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key = key.replace("attention_norm", "norm1").replace("ffn_norm", "norm2")
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elif "wq" in key or "wk" in key or "wv" in key or "wo" in key:
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key = key.replace("wq", "query_proj")
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key = key.replace("wk", "key_proj")
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key = key.replace("wv", "value_proj")
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key = key.replace("wo", "out_proj")
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elif "w1" in key or "w2" in key or "w3" in key:
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# The FFN is a separate submodule in PyTorch
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key = key.replace("feed_forward.w1", "linear1")
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key = key.replace("feed_forward.w3", "linear2")
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key = key.replace("feed_forward.w2", "linear3")
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elif "output" in key:
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key = key.replace("output", "out_proj")
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elif "rope" in key:
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return None, None
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return key, value.numpy()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert Llama weights to MLX")
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parser.add_argument("torch_weights")
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parser.add_argument("output_file")
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args = parser.parse_args()
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state = torch.load(args.torch_weights)
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np.savez(
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args.output_file,
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**{k: v for k, v in starmap(map_torch_to_mlx, state.items()) if k is not None}
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)
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