Reformat source code with black.
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
This commit is contained in:
Aymeric Augustin
@@ -20,63 +20,70 @@ from transformers import BertForMaskedLM, RobertaForMaskedLM
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import torch
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import argparse
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation"
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)
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parser.add_argument("--model_type", default="bert", choices=["bert"])
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parser.add_argument("--model_name", default='bert-base-uncased', type=str)
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parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
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parser.add_argument("--vocab_transform", action='store_true')
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parser.add_argument("--model_name", default="bert-base-uncased", type=str)
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parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
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parser.add_argument("--vocab_transform", action="store_true")
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args = parser.parse_args()
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if args.model_type == 'bert':
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if args.model_type == "bert":
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model = BertForMaskedLM.from_pretrained(args.model_name)
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prefix = 'bert'
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prefix = "bert"
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else:
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raise ValueError(f'args.model_type should be "bert".')
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state_dict = model.state_dict()
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compressed_sd = {}
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for w in ['word_embeddings', 'position_embeddings']:
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compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
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state_dict[f'{prefix}.embeddings.{w}.weight']
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for w in ['weight', 'bias']:
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compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
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state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
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for w in ["word_embeddings", "position_embeddings"]:
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compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"]
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for w in ["weight", "bias"]:
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compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
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std_idx = 0
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for teacher_idx in [0, 2, 4, 7, 9, 11]:
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for w in ['weight', 'bias']:
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}']
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}']
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}']
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for w in ["weight", "bias"]:
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
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]
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
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]
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
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]
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
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]
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
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]
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}']
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
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]
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
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]
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compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
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]
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std_idx += 1
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compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight']
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compressed_sd[f'vocab_projector.bias'] = state_dict[f'cls.predictions.bias']
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compressed_sd[f"vocab_projector.weight"] = state_dict[f"cls.predictions.decoder.weight"]
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compressed_sd[f"vocab_projector.bias"] = state_dict[f"cls.predictions.bias"]
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if args.vocab_transform:
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for w in ['weight', 'bias']:
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compressed_sd[f'vocab_transform.{w}'] = state_dict[f'cls.predictions.transform.dense.{w}']
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compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
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for w in ["weight", "bias"]:
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compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]
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compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
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print(f'N layers selected for distillation: {std_idx}')
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print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
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print(f"N layers selected for distillation: {std_idx}")
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print(f"Number of params transfered for distillation: {len(compressed_sd.keys())}")
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print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
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print(f"Save transfered checkpoint to {args.dump_checkpoint}.")
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torch.save(compressed_sd, args.dump_checkpoint)
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