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4680ef4413
* Update convert.py * Update model.py * Update test.py * Update model.py * Update convert.py * Add files via upload * Update convert.py * format * nit * nit --------- Co-authored-by: Awni Hannun <awni@apple.com>
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
import argparse
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import numpy
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from transformers import AutoModel
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def replace_key(key: str) -> str:
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key = key.replace(".layer.", ".layers.")
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key = key.replace(".self.key.", ".key_proj.")
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key = key.replace(".self.query.", ".query_proj.")
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key = key.replace(".self.value.", ".value_proj.")
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key = key.replace(".attention.output.dense.", ".attention.out_proj.")
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key = key.replace(".attention.output.LayerNorm.", ".ln1.")
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key = key.replace(".output.LayerNorm.", ".ln2.")
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key = key.replace(".intermediate.dense.", ".linear1.")
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key = key.replace(".output.dense.", ".linear2.")
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key = key.replace(".LayerNorm.", ".norm.")
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key = key.replace("pooler.dense.", "pooler.")
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return key
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def convert(bert_model: str, mlx_model: str) -> None:
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model = AutoModel.from_pretrained(bert_model)
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# save the tensors
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tensors = {
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replace_key(key): tensor.numpy() for key, tensor in model.state_dict().items()
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}
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numpy.savez(mlx_model, **tensors)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert BERT weights to MLX.")
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parser.add_argument(
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"--bert-model",
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type=str,
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default="bert-base-uncased",
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help="The huggingface name of the BERT model to save. Any BERT-like model can be specified.",
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)
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parser.add_argument(
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"--mlx-model",
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type=str,
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default="weights/bert-base-uncased.npz",
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help="The output path for the MLX BERT weights.",
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)
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args = parser.parse_args()
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convert(args.bert_model, args.mlx_model)
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