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87 lines
4.1 KiB
ReStructuredText
87 lines
4.1 KiB
ReStructuredText
Converting Tensorflow Checkpoints
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================================================
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A command-line interface is provided to convert a TensorFlow checkpoint in a PyTorch dump of the ``BertForPreTraining`` class (for BERT) or NumPy checkpoint in a PyTorch dump of the ``OpenAIGPTModel`` class (for OpenAI GPT).
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BERT
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^^^^
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You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py>`_ script.
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This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
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You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\ ``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.
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To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install tensorflow``\ ). The rest of the repository only requires PyTorch.
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Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:
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.. code-block:: shell
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export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
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pytorch_transformers bert \
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$BERT_BASE_DIR/bert_model.ckpt \
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$BERT_BASE_DIR/bert_config.json \
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$BERT_BASE_DIR/pytorch_model.bin
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You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.
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OpenAI GPT
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^^^^^^^^^^
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Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\ )
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.. code-block:: shell
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export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
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pytorch_transformers gpt \
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$OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
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$PYTORCH_DUMP_OUTPUT \
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[OPENAI_GPT_CONFIG]
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Transformer-XL
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^^^^^^^^^^^^^^
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Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here <https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
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.. code-block:: shell
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export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
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pytorch_transformers transfo_xl \
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$TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
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$PYTORCH_DUMP_OUTPUT \
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[TRANSFO_XL_CONFIG]
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GPT-2
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^^^^^
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Here is an example of the conversion process for a pre-trained OpenAI's GPT-2 model.
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.. code-block:: shell
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export GPT2_DIR=/path/to/gpt2/checkpoint
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pytorch_transformers gpt2 \
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$GPT2_DIR/model.ckpt \
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$PYTORCH_DUMP_OUTPUT \
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[GPT2_CONFIG]
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XLNet
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^^^^^
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Here is an example of the conversion process for a pre-trained XLNet model, fine-tuned on STS-B using the TensorFlow script:
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.. code-block:: shell
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export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
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export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
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pytorch_transformers xlnet \
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$TRANSFO_XL_CHECKPOINT_PATH \
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$TRANSFO_XL_CONFIG_PATH \
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$PYTORCH_DUMP_OUTPUT \
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STS-B \
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