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fa84ae26d6
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.
100 lines
4.4 KiB
Python
100 lines
4.4 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocessing script before training the distilled model.
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Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2.
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"""
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from transformers import BertForMaskedLM, RobertaForMaskedLM, GPT2LMHeadModel
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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(
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description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation"
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)
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parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
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parser.add_argument("--model_name", default="roberta-large", type=str)
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parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.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 == "roberta":
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model = RobertaForMaskedLM.from_pretrained(args.model_name)
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prefix = "roberta"
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elif args.model_type == "gpt2":
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model = GPT2LMHeadModel.from_pretrained(args.model_name)
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prefix = "transformer"
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state_dict = model.state_dict()
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compressed_sd = {}
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### Embeddings ###
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if args.model_type == "gpt2":
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for param_name in ["wte.weight", "wpe.weight"]:
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compressed_sd[f"{prefix}.{param_name}"] = state_dict[f"{prefix}.{param_name}"]
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else:
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for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
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param_name = f"{prefix}.embeddings.{w}.weight"
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compressed_sd[param_name] = state_dict[param_name]
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for w in ["weight", "bias"]:
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param_name = f"{prefix}.embeddings.LayerNorm.{w}"
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compressed_sd[param_name] = state_dict[param_name]
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### Transformer Blocks ###
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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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if args.model_type == "gpt2":
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for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
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for w in ["weight", "bias"]:
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compressed_sd[f"{prefix}.h.{std_idx}.{layer}.{w}"] = state_dict[
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f"{prefix}.h.{teacher_idx}.{layer}.{w}"
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]
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compressed_sd[f"{prefix}.h.{std_idx}.attn.bias"] = state_dict[f"{prefix}.h.{teacher_idx}.attn.bias"]
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else:
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for layer in [
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"attention.self.query",
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"attention.self.key",
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"attention.self.value",
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"attention.output.dense",
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"attention.output.LayerNorm",
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"intermediate.dense",
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"output.dense",
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"output.LayerNorm",
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]:
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for w in ["weight", "bias"]:
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compressed_sd[f"{prefix}.encoder.layer.{std_idx}.{layer}.{w}"] = state_dict[
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f"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
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]
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std_idx += 1
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### Language Modeling Head ###s
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if args.model_type == "roberta":
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for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
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compressed_sd[f"{layer}"] = state_dict[f"{layer}"]
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if args.vocab_transform:
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for w in ["weight", "bias"]:
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compressed_sd[f"lm_head.dense.{w}"] = state_dict[f"lm_head.dense.{w}"]
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compressed_sd[f"lm_head.layer_norm.{w}"] = state_dict[f"lm_head.layer_norm.{w}"]
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elif args.model_type == "gpt2":
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for w in ["weight", "bias"]:
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compressed_sd[f"{prefix}.ln_f.{w}"] = state_dict[f"{prefix}.ln_f.{w}"]
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compressed_sd[f"lm_head.weight"] = state_dict[f"lm_head.weight"]
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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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torch.save(compressed_sd, args.dump_checkpoint)
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