Converting Tensorflow Checkpoints¶
A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models
than be loaded using the from_pretrained
methods of the library.
Note
Since 2.3.0 the conversion script is now part of the transformers CLI (transformers-cli) available in any transformers >= 2.3.0 installation.
The documentation below reflects the transformers-cli convert command format.
BERT¶
You can convert any TensorFlow checkpoint for BERT (in particular the pre-trained models released by Google) in a PyTorch save file by using the convert_bert_original_tf_checkpoint_to_pytorch.py script.
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,
run_bert_classifier.py and
run_bert_squad.py).
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.
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.
Here is an example of the conversion process for a pre-trained BERT-Base Uncased
model:
export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
transformers-cli convert --model_type bert \
--tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
--config $BERT_BASE_DIR/bert_config.json \
--pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
You can download Google’s pre-trained models for the conversion here.
ALBERT¶
Convert TensorFlow model checkpoints of ALBERT to PyTorch using the convert_albert_original_tf_checkpoint_to_pytorch.py script.
The CLI takes as input a TensorFlow checkpoint (three files starting with model.ckpt-best
) and the accompanying
configuration file (albert_config.json
), then creates and saves a PyTorch model. To run this conversion you
will need to have TensorFlow and PyTorch installed.
Here is an example of the conversion process for the pre-trained ALBERT Base
model:
export ALBERT_BASE_DIR=/path/to/albert/albert_base
transformers-cli convert --model_type albert \
--tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
--config $ALBERT_BASE_DIR/albert_config.json \
--pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
You can download Google’s pre-trained models for the conversion here.
OpenAI GPT¶
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)
export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
transformers-cli convert --model_type gpt \
--tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--config OPENAI_GPT_CONFIG] \
[--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
OpenAI GPT-2¶
Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see here)
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
transformers-cli convert --model_type gpt2 \
--tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--config OPENAI_GPT2_CONFIG] \
[--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
Transformer-XL¶
Here is an example of the conversion process for a pre-trained Transformer-XL model (see here)
export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
transformers-cli convert --model_type transfo_xl \
--tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--config TRANSFO_XL_CONFIG] \
[--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
XLNet¶
Here is an example of the conversion process for a pre-trained XLNet model:
export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
transformers-cli convert --model_type xlnet \
--tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
--config $TRANSFO_XL_CONFIG_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--finetuning_task_name XLNET_FINETUNED_TASK] \
XLM¶
Here is an example of the conversion process for a pre-trained XLM model:
export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
transformers-cli convert --model_type xlm \
--tf_checkpoint $XLM_CHECKPOINT_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT
[--config XML_CONFIG] \
[--finetuning_task_name XML_FINETUNED_TASK]