transformers/docs/source/converting_tensorflow_models.rst

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 <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_bert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/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 <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>`_\ ).

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:

.. code-block:: shell

   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 <https://github.com/google-research/bert#pre-trained-models>`__.

ALBERT
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Convert TensorFlow model checkpoints of ALBERT to PyTorch using the `convert_albert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_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:

.. code-block:: shell

   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 <https://github.com/google-research/albert#pre-trained-models>`__.

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 <https://github.com/openai/finetune-transformer-lm>`__\ )

.. code-block:: shell

   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 <https://github.com/openai/gpt-2>`__\ )

.. code-block:: shell

   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 <https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )

.. code-block:: shell

   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:

.. code-block:: shell

   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:

.. code-block:: shell

   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]