474 lines
16 KiB
Python
474 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2018 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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""" Convert pytorch checkpoints to TensorFlow """
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from __future__ import absolute_import, division, print_function
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import argparse
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import logging
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import os
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from transformers import (
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ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
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OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
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T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
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TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
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XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
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XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
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AlbertConfig,
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BertConfig,
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CTRLConfig,
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DistilBertConfig,
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GPT2Config,
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OpenAIGPTConfig,
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RobertaConfig,
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T5Config,
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TFAlbertForMaskedLM,
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TFBertForPreTraining,
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TFBertForQuestionAnswering,
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TFBertForSequenceClassification,
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TFCTRLLMHeadModel,
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TFDistilBertForMaskedLM,
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TFDistilBertForQuestionAnswering,
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TFGPT2LMHeadModel,
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TFOpenAIGPTLMHeadModel,
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TFRobertaForMaskedLM,
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TFRobertaForSequenceClassification,
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TFT5WithLMHeadModel,
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TFTransfoXLLMHeadModel,
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TFXLMWithLMHeadModel,
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TFXLNetLMHeadModel,
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TransfoXLConfig,
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XLMConfig,
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XLNetConfig,
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cached_path,
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is_torch_available,
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load_pytorch_checkpoint_in_tf2_model,
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)
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if is_torch_available():
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import torch
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import numpy as np
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from transformers import (
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BertForPreTraining,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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GPT2LMHeadModel,
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GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
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XLNetLMHeadModel,
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XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
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XLMWithLMHeadModel,
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
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TransfoXLLMHeadModel,
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TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
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OpenAIGPTLMHeadModel,
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
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RobertaForMaskedLM,
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RobertaForSequenceClassification,
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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DistilBertForMaskedLM,
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DistilBertForQuestionAnswering,
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DistilBertForSequenceClassification,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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CTRLLMHeadModel,
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CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
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AlbertForMaskedLM,
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ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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T5WithLMHeadModel,
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T5_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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else:
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(
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BertForPreTraining,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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GPT2LMHeadModel,
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GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
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XLNetLMHeadModel,
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XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
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XLMWithLMHeadModel,
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
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TransfoXLLMHeadModel,
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TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
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OpenAIGPTLMHeadModel,
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
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RobertaForMaskedLM,
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RobertaForSequenceClassification,
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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DistilBertForMaskedLM,
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DistilBertForSequenceClassification,
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DistilBertForQuestionAnswering,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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CTRLLMHeadModel,
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CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
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AlbertForMaskedLM,
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ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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T5WithLMHeadModel,
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T5_PRETRAINED_MODEL_ARCHIVE_MAP,
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) = (
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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None,
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)
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logging.basicConfig(level=logging.INFO)
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MODEL_CLASSES = {
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"bert": (
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BertConfig,
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TFBertForPreTraining,
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BertForPreTraining,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"bert-large-uncased-whole-word-masking-finetuned-squad": (
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BertConfig,
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TFBertForQuestionAnswering,
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BertForQuestionAnswering,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"bert-large-cased-whole-word-masking-finetuned-squad": (
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BertConfig,
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TFBertForQuestionAnswering,
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BertForQuestionAnswering,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"bert-base-cased-finetuned-mrpc": (
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BertConfig,
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TFBertForSequenceClassification,
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BertForSequenceClassification,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"gpt2": (
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GPT2Config,
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TFGPT2LMHeadModel,
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GPT2LMHeadModel,
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GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
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GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"xlnet": (
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XLNetConfig,
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TFXLNetLMHeadModel,
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XLNetLMHeadModel,
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XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
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XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"xlm": (
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XLMConfig,
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TFXLMWithLMHeadModel,
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XLMWithLMHeadModel,
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
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XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"transfo-xl": (
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TransfoXLConfig,
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TFTransfoXLLMHeadModel,
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TransfoXLLMHeadModel,
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TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
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TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"openai-gpt": (
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OpenAIGPTConfig,
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TFOpenAIGPTLMHeadModel,
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OpenAIGPTLMHeadModel,
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
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OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"roberta": (
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RobertaConfig,
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TFRobertaForMaskedLM,
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RobertaForMaskedLM,
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"roberta-large-mnli": (
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RobertaConfig,
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TFRobertaForSequenceClassification,
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RobertaForSequenceClassification,
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"distilbert": (
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DistilBertConfig,
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TFDistilBertForMaskedLM,
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DistilBertForMaskedLM,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"distilbert-base-uncased-distilled-squad": (
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DistilBertConfig,
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TFDistilBertForQuestionAnswering,
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DistilBertForQuestionAnswering,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"distilbert-base-uncased-distilled-squad": (
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DistilBertConfig,
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TFDistilBertForQuestionAnswering,
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DistilBertForQuestionAnswering,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"ctrl": (
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CTRLConfig,
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TFCTRLLMHeadModel,
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CTRLLMHeadModel,
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CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
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CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"albert": (
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AlbertConfig,
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TFAlbertForMaskedLM,
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AlbertForMaskedLM,
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ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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"t5": (
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T5Config,
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TFT5WithLMHeadModel,
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T5WithLMHeadModel,
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T5_PRETRAINED_MODEL_ARCHIVE_MAP,
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T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
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),
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}
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def convert_pt_checkpoint_to_tf(
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model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True
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):
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if model_type not in MODEL_CLASSES:
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raise ValueError("Unrecognized model type, should be one of {}.".format(list(MODEL_CLASSES.keys())))
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config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]
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# Initialise TF model
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if config_file in aws_config_map:
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config_file = cached_path(aws_config_map[config_file], force_download=not use_cached_models)
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config = config_class.from_json_file(config_file)
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config.output_hidden_states = True
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config.output_attentions = True
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print("Building TensorFlow model from configuration: {}".format(str(config)))
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tf_model = model_class(config)
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# Load weights from tf checkpoint
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if pytorch_checkpoint_path in aws_model_maps:
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pytorch_checkpoint_path = cached_path(
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aws_model_maps[pytorch_checkpoint_path], force_download=not use_cached_models
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)
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# Load PyTorch checkpoint in tf2 model:
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tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
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if compare_with_pt_model:
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tfo = tf_model(tf_model.dummy_inputs, training=False) # build the network
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state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu")
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pt_model = pt_model_class.from_pretrained(
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pretrained_model_name_or_path=None, config=config, state_dict=state_dict
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)
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with torch.no_grad():
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pto = pt_model(**pt_model.dummy_inputs)
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np_pt = pto[0].numpy()
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np_tf = tfo[0].numpy()
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diff = np.amax(np.abs(np_pt - np_tf))
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print("Max absolute difference between models outputs {}".format(diff))
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assert diff <= 2e-2, "Error, model absolute difference is >2e-2: {}".format(diff)
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# Save pytorch-model
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print("Save TensorFlow model to {}".format(tf_dump_path))
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tf_model.save_weights(tf_dump_path, save_format="h5")
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def convert_all_pt_checkpoints_to_tf(
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args_model_type,
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tf_dump_path,
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model_shortcut_names_or_path=None,
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config_shortcut_names_or_path=None,
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compare_with_pt_model=False,
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use_cached_models=False,
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remove_cached_files=False,
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only_convert_finetuned_models=False,
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):
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assert os.path.isdir(args.tf_dump_path), "--tf_dump_path should be a directory"
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if args_model_type is None:
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model_types = list(MODEL_CLASSES.keys())
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else:
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model_types = [args_model_type]
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for j, model_type in enumerate(model_types, start=1):
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print("=" * 100)
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print(" Converting model type {}/{}: {}".format(j, len(model_types), model_type))
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print("=" * 100)
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if model_type not in MODEL_CLASSES:
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raise ValueError(
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"Unrecognized model type {}, should be one of {}.".format(model_type, list(MODEL_CLASSES.keys()))
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)
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config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]
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if model_shortcut_names_or_path is None:
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model_shortcut_names_or_path = list(aws_model_maps.keys())
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if config_shortcut_names_or_path is None:
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config_shortcut_names_or_path = model_shortcut_names_or_path
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for i, (model_shortcut_name, config_shortcut_name) in enumerate(
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zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1
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):
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print("-" * 100)
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if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
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if not only_convert_finetuned_models:
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print(" Skipping finetuned checkpoint {}".format(model_shortcut_name))
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continue
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model_type = model_shortcut_name
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elif only_convert_finetuned_models:
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print(" Skipping not finetuned checkpoint {}".format(model_shortcut_name))
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continue
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print(
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" Converting checkpoint {}/{}: {} - model_type {}".format(
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i, len(aws_config_map), model_shortcut_name, model_type
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)
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)
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print("-" * 100)
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if config_shortcut_name in aws_config_map:
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config_file = cached_path(aws_config_map[config_shortcut_name], force_download=not use_cached_models)
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else:
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config_file = cached_path(config_shortcut_name, force_download=not use_cached_models)
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if model_shortcut_name in aws_model_maps:
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model_file = cached_path(aws_model_maps[model_shortcut_name], force_download=not use_cached_models)
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else:
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model_file = cached_path(model_shortcut_name, force_download=not use_cached_models)
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if os.path.isfile(model_shortcut_name):
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model_shortcut_name = "converted_model"
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convert_pt_checkpoint_to_tf(
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model_type=model_type,
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pytorch_checkpoint_path=model_file,
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config_file=config_file,
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tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"),
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compare_with_pt_model=compare_with_pt_model,
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)
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if remove_cached_files:
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os.remove(config_file)
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os.remove(model_file)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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# Required parameters
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parser.add_argument(
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"--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
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)
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parser.add_argument(
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"--model_type",
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default=None,
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type=str,
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help="Model type selected in the list of {}. If not given, will download and convert all the models from AWS.".format(
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list(MODEL_CLASSES.keys())
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),
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)
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parser.add_argument(
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"--pytorch_checkpoint_path",
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default=None,
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type=str,
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help="Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
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"If not given, will download and convert all the checkpoints from AWS.",
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)
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parser.add_argument(
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"--config_file",
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default=None,
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type=str,
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help="The config json file corresponding to the pre-trained model. \n"
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"This specifies the model architecture. If not given and "
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"--pytorch_checkpoint_path is not given or is a shortcut name"
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"use the configuration associated to the shortcut name on the AWS",
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)
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parser.add_argument(
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"--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
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)
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parser.add_argument(
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"--use_cached_models",
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action="store_true",
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help="Use cached models if possible instead of updating to latest checkpoint versions.",
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)
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parser.add_argument(
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"--remove_cached_files",
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action="store_true",
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help="Remove pytorch models after conversion (save memory when converting in batches).",
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)
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parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
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args = parser.parse_args()
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# if args.pytorch_checkpoint_path is not None:
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# convert_pt_checkpoint_to_tf(args.model_type.lower(),
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# args.pytorch_checkpoint_path,
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# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
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# args.tf_dump_path,
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# compare_with_pt_model=args.compare_with_pt_model,
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# use_cached_models=args.use_cached_models)
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# else:
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convert_all_pt_checkpoints_to_tf(
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args.model_type.lower() if args.model_type is not None else None,
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args.tf_dump_path,
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model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
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if args.pytorch_checkpoint_path is not None
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else None,
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config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
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compare_with_pt_model=args.compare_with_pt_model,
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use_cached_models=args.use_cached_models,
|
|
remove_cached_files=args.remove_cached_files,
|
|
only_convert_finetuned_models=args.only_convert_finetuned_models,
|
|
)
|