transformers/docs/source/converting_tensorflow_models.rst

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Converting Tensorflow Checkpoints
=======================================================================================================================

A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints to models
that can 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
:prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
<src/transformers/models/bert/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 ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
<examples/pytorch/text-classification/run_glue.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
:prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
<src/transformers/models/albert/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:

.. 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]


T5
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Here is an example of the conversion process for a pre-trained T5 model:

.. code-block:: shell

    export T5=/path/to/t5/uncased_L-12_H-768_A-12

    transformers-cli convert --model_type t5 \
      --tf_checkpoint $T5/t5_model.ckpt \
      --config $T5/t5_config.json \
      --pytorch_dump_output $T5/pytorch_model.bin