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https://github.com/ml-explore/mlx-examples.git
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* 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>
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import argparse
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from typing import List
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import model
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import numpy as np
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from transformers import AutoModel, AutoTokenizer
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def run_torch(bert_model: str, batch: List[str]):
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tokenizer = AutoTokenizer.from_pretrained(bert_model)
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torch_model = AutoModel.from_pretrained(bert_model)
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torch_tokens = tokenizer(batch, return_tensors="pt", padding=True)
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torch_forward = torch_model(**torch_tokens)
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torch_output = torch_forward.last_hidden_state.detach().numpy()
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torch_pooled = torch_forward.pooler_output.detach().numpy()
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return torch_output, torch_pooled
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Run a BERT-like model for a batch of text."
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)
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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 model identifier for a BERT-like model from Hugging Face Transformers.",
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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 path of the stored MLX BERT weights (npz file).",
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)
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parser.add_argument(
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"--text",
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nargs="+",
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default=["This is an example of BERT working in MLX."],
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help="A batch of texts to process. Multiple texts should be separated by spaces.",
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)
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args = parser.parse_args()
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torch_output, torch_pooled = run_torch(args.bert_model, args.text)
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mlx_output, mlx_pooled = model.run(args.bert_model, args.mlx_model, args.text)
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if torch_pooled is not None and mlx_pooled is not None:
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assert np.allclose(
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torch_output, mlx_output, rtol=1e-4, atol=1e-5
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), "Model output is different"
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assert np.allclose(
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torch_pooled, mlx_pooled, rtol=1e-4, atol=1e-5
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), "Model pooled output is different"
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print("Tests pass :)")
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else:
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print("Pooled outputs were not compared due to one or both being None.")
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