Initial folder structure for the documentation. A draft of documentation change has been made in the BertModel class.
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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line.
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SPHINXOPTS =
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SPHINXBUILD = sphinx-build
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SOURCEDIR = source
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BUILDDIR = _build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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# Generating the documentation
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To generate the documentation, you first have to build it. Building it requires the package `sphinx` that you can
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install using:
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```bash
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pip install -U sphinx
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```
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You would also need the custom installed [theme](https://github.com/readthedocs/sphinx_rtd_theme) by
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[Read The Docs](https://readthedocs.org/). You can install it using the following command:
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```bash
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pip install sphinx_rtd_theme
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```
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Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:
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```bash
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make html
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```
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It should build the static app that will be available under `/docs/_build/html`
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CLI
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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`` <./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`` <./examples/run_bert_extract_features.py>`_\ , `\ ``run_bert_classifier.py`` <./examples/run_bert_classifier.py>`_ and `\ ``run_bert_squad.py`` <./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_pretrained_bert 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_pretrained_bert 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_pretrained_bert 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_pretrained_bert 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_pretrained_bert 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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# -*- coding: utf-8 -*-
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#
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# Configuration file for the Sphinx documentation builder.
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#
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# This file does only contain a selection of the most common options. For a
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# full list see the documentation:
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# http://www.sphinx-doc.org/en/master/config
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# -- Path setup --------------------------------------------------------------
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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#
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import os
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import sys
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sys.path.insert(0, os.path.abspath('../..'))
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# -- Project information -----------------------------------------------------
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project = u'pytorch-transformers'
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copyright = u'2019, huggingface'
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author = u'huggingface'
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# The short X.Y version
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version = u''
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# The full version, including alpha/beta/rc tags
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release = u'1.0.0'
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# -- General configuration ---------------------------------------------------
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# If your documentation needs a minimal Sphinx version, state it here.
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#
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# needs_sphinx = '1.0'
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.coverage',
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'sphinx.ext.napoleon'
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]
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ['_templates']
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# The suffix(es) of source filenames.
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# You can specify multiple suffix as a list of string:
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#
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source_suffix = ['.rst', '.md']
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# source_suffix = '.rst'
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# The master toctree document.
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master_doc = 'index'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.
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#
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# This is also used if you do content translation via gettext catalogs.
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# Usually you set "language" from the command line for these cases.
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language = None
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = None
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# -- Options for HTML output -------------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
|
||||
#
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html_theme = 'sphinx_rtd_theme'
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# Theme options are theme-specific and customize the look and feel of a theme
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||||
# further. For a list of options available for each theme, see the
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# documentation.
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#
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# html_theme_options = {}
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ['_static']
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# Custom sidebar templates, must be a dictionary that maps document names
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# to template names.
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#
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# The default sidebars (for documents that don't match any pattern) are
|
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# defined by theme itself. Builtin themes are using these templates by
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# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
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# 'searchbox.html']``.
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#
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# html_sidebars = {}
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# -- Options for HTMLHelp output ---------------------------------------------
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# Output file base name for HTML help builder.
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htmlhelp_basename = 'pytorch-transformersdoc'
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# -- Options for LaTeX output ------------------------------------------------
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latex_elements = {
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# The paper size ('letterpaper' or 'a4paper').
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#
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# 'papersize': 'letterpaper',
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# The font size ('10pt', '11pt' or '12pt').
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#
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# 'pointsize': '10pt',
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# Additional stuff for the LaTeX preamble.
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#
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# 'preamble': '',
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# Latex figure (float) alignment
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#
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# 'figure_align': 'htbp',
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}
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# Grouping the document tree into LaTeX files. List of tuples
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# (source start file, target name, title,
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# author, documentclass [howto, manual, or own class]).
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latex_documents = [
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(master_doc, 'pytorch-transformers.tex', u'pytorch-transformers Documentation',
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u'huggingface', 'manual'),
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]
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# -- Options for manual page output ------------------------------------------
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# One entry per manual page. List of tuples
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# (source start file, name, description, authors, manual section).
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man_pages = [
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(master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
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[author], 1)
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]
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# -- Options for Texinfo output ----------------------------------------------
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# Grouping the document tree into Texinfo files. List of tuples
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# (source start file, target name, title, author,
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# dir menu entry, description, category)
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texinfo_documents = [
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(master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
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author, 'pytorch-transformers', 'One line description of project.',
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'Miscellaneous'),
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]
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# -- Options for Epub output -------------------------------------------------
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# Bibliographic Dublin Core info.
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epub_title = project
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# The unique identifier of the text. This can be a ISBN number
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# or the project homepage.
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#
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# epub_identifier = ''
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# A unique identification for the text.
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#
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# epub_uid = ''
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# A list of files that should not be packed into the epub file.
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epub_exclude_files = ['search.html']
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# -- Extension configuration -------------------------------------------------
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Docs
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================================================
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Here is a detailed documentation of the classes in the package and how to use them:
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.. list-table::
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:header-rows: 1
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* - Sub-section
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- Description
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* - `Loading pre-trained weights <#loading-google-ai-or-openai-pre-trained-weights-or-pytorch-dump>`_
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- How to load Google AI/OpenAI's pre-trained weight or a PyTorch saved instance
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* - `Serialization best-practices <#serialization-best-practices>`_
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- How to save and reload a fine-tuned model
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* - `Configurations <#configurations>`_
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- API of the configuration classes for BERT, GPT, GPT-2 and Transformer-XL
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* - `Models <#models>`_
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- API of the PyTorch model classes for BERT, GPT, GPT-2 and Transformer-XL
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* - `Tokenizers <#tokenizers>`_
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- API of the tokenizers class for BERT, GPT, GPT-2 and Transformer-XL
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* - `Optimizers <#optimizers>`_
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- API of the optimizers
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Loading Google AI or OpenAI pre-trained weights or PyTorch dump
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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``from_pretrained()`` method
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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To load one of Google AI's, OpenAI's pre-trained models or a PyTorch saved model (an instance of ``BertForPreTraining`` saved with ``torch.save()``\ ), the PyTorch model classes and the tokenizer can be instantiated using the ``from_pretrained()`` method:
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.. code-block:: python
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model = BERT_CLASS.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None, from_tf=False, state_dict=None, *input, **kwargs)
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where
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* ``BERT_CLASS`` is either a tokenizer to load the vocabulary (\ ``BertTokenizer`` or ``OpenAIGPTTokenizer`` classes) or one of the eight BERT or three OpenAI GPT PyTorch model classes (to load the pre-trained weights): ``BertModel``\ , ``BertForMaskedLM``\ , ``BertForNextSentencePrediction``\ , ``BertForPreTraining``\ , ``BertForSequenceClassification``\ , ``BertForTokenClassification``\ , ``BertForMultipleChoice``\ , ``BertForQuestionAnswering``\ , ``OpenAIGPTModel``\ , ``OpenAIGPTLMHeadModel`` or ``OpenAIGPTDoubleHeadsModel``\ , and
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*
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``PRE_TRAINED_MODEL_NAME_OR_PATH`` is either:
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*
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the shortcut name of a Google AI's or OpenAI's pre-trained model selected in the list:
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* ``bert-base-uncased``: 12-layer, 768-hidden, 12-heads, 110M parameters
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* ``bert-large-uncased``: 24-layer, 1024-hidden, 16-heads, 340M parameters
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* ``bert-base-cased``: 12-layer, 768-hidden, 12-heads , 110M parameters
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* ``bert-large-cased``: 24-layer, 1024-hidden, 16-heads, 340M parameters
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* ``bert-base-multilingual-uncased``: (Orig, not recommended) 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
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* ``bert-base-multilingual-cased``: **(New, recommended)** 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
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* ``bert-base-chinese``: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters
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* ``bert-base-german-cased``: Trained on German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://deepset.ai/german-bert>`_
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* ``bert-large-uncased-whole-word-masking``: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
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* ``bert-large-cased-whole-word-masking``: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
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* ``bert-large-uncased-whole-word-masking-finetuned-squad``: The ``bert-large-uncased-whole-word-masking`` model finetuned on SQuAD (using the ``run_bert_squad.py`` examples). Results: *exact_match: 86.91579943235573, f1: 93.1532499015869*
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* ``openai-gpt``: OpenAI GPT English model, 12-layer, 768-hidden, 12-heads, 110M parameters
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* ``gpt2``: OpenAI GPT-2 English model, 12-layer, 768-hidden, 12-heads, 117M parameters
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* ``gpt2-medium``: OpenAI GPT-2 English model, 24-layer, 1024-hidden, 16-heads, 345M parameters
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* ``transfo-xl-wt103``: Transformer-XL English model trained on wikitext-103, 18-layer, 1024-hidden, 16-heads, 257M parameters
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*
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a path or url to a pretrained model archive containing:
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* ``bert_config.json`` or ``openai_gpt_config.json`` a configuration file for the model, and
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* ``pytorch_model.bin`` a PyTorch dump of a pre-trained instance of ``BertForPreTraining``\ , ``OpenAIGPTModel``\ , ``TransfoXLModel``\ , ``GPT2LMHeadModel`` (saved with the usual ``torch.save()``\ )
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If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/pytorch_pretrained_bert/modeling.py>`_\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ).
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*
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``cache_dir`` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example ``cache_dir='./pretrained_model_{}'.format(args.local_rank)`` (see the section on distributed training for more information).
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* ``from_tf``\ : should we load the weights from a locally saved TensorFlow checkpoint
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* ``state_dict``\ : an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
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* ``*inputs``\ , `**kwargs`: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification)
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``Uncased`` means that the text has been lowercased before WordPiece tokenization, e.g., ``John Smith`` becomes ``john smith``. The Uncased model also strips out any accent markers. ``Cased`` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the `Multilingual README <https://github.com/google-research/bert/blob/master/multilingual.md>`_ or the original TensorFlow repository.
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When using an ``uncased model``\ , make sure to pass ``--do_lower_case`` to the example training scripts (or pass ``do_lower_case=True`` to FullTokenizer if you're using your own script and loading the tokenizer your-self.).
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Examples:
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.. code-block:: python
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# BERT
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, do_basic_tokenize=True)
|
||||
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
|
||||
|
||||
# OpenAI GPT
|
||||
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
|
||||
model = OpenAIGPTModel.from_pretrained('openai-gpt')
|
||||
|
||||
# Transformer-XL
|
||||
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
|
||||
model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
|
||||
|
||||
# OpenAI GPT-2
|
||||
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
|
||||
model = GPT2Model.from_pretrained('gpt2')
|
||||
|
||||
Cache directory
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
``pytorch_pretrained_bert`` save the pretrained weights in a cache directory which is located at (in this order of priority):
|
||||
|
||||
|
||||
* ``cache_dir`` optional arguments to the ``from_pretrained()`` method (see above),
|
||||
* shell environment variable ``PYTORCH_PRETRAINED_BERT_CACHE``\ ,
|
||||
* PyTorch cache home + ``/pytorch_pretrained_bert/``
|
||||
where PyTorch cache home is defined by (in this order):
|
||||
|
||||
* shell environment variable ``ENV_TORCH_HOME``
|
||||
* shell environment variable ``ENV_XDG_CACHE_HOME`` + ``/torch/``\ )
|
||||
* default: ``~/.cache/torch/``
|
||||
|
||||
Usually, if you don't set any specific environment variable, ``pytorch_pretrained_bert`` cache will be at ``~/.cache/torch/pytorch_pretrained_bert/``.
|
||||
|
||||
You can alsways safely delete ``pytorch_pretrained_bert`` cache but the pretrained model weights and vocabulary files wil have to be re-downloaded from our S3.
|
||||
|
||||
Serialization best-practices
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
This section explain how you can save and re-load a fine-tuned model (BERT, GPT, GPT-2 and Transformer-XL).
|
||||
There are three types of files you need to save to be able to reload a fine-tuned model:
|
||||
|
||||
|
||||
* the model it-self which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`_\ ,
|
||||
* the configuration file of the model which is saved as a JSON file, and
|
||||
* the vocabulary (and the merges for the BPE-based models GPT and GPT-2).
|
||||
|
||||
The *default filenames* of these files are as follow:
|
||||
|
||||
|
||||
* the model weights file: ``pytorch_model.bin``\ ,
|
||||
* the configuration file: ``config.json``\ ,
|
||||
* the vocabulary file: ``vocab.txt`` for BERT and Transformer-XL, ``vocab.json`` for GPT/GPT-2 (BPE vocabulary),
|
||||
* for GPT/GPT-2 (BPE vocabulary) the additional merges file: ``merges.txt``.
|
||||
|
||||
**If you save a model using these *default filenames*\ , you can then re-load the model and tokenizer using the ``from_pretrained()`` method.**
|
||||
|
||||
Here is the recommended way of saving the model, configuration and vocabulary to an ``output_dir`` directory and reloading the model and tokenizer afterwards:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from pytorch_pretrained_bert import WEIGHTS_NAME, CONFIG_NAME
|
||||
|
||||
output_dir = "./models/"
|
||||
|
||||
# Step 1: Save a model, configuration and vocabulary that you have fine-tuned
|
||||
|
||||
# If we have a distributed model, save only the encapsulated model
|
||||
# (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
|
||||
model_to_save = model.module if hasattr(model, 'module') else model
|
||||
|
||||
# If we save using the predefined names, we can load using `from_pretrained`
|
||||
output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
|
||||
output_config_file = os.path.join(output_dir, CONFIG_NAME)
|
||||
|
||||
torch.save(model_to_save.state_dict(), output_model_file)
|
||||
model_to_save.config.to_json_file(output_config_file)
|
||||
tokenizer.save_vocabulary(output_dir)
|
||||
|
||||
# Step 2: Re-load the saved model and vocabulary
|
||||
|
||||
# Example for a Bert model
|
||||
model = BertForQuestionAnswering.from_pretrained(output_dir)
|
||||
tokenizer = BertTokenizer.from_pretrained(output_dir, do_lower_case=args.do_lower_case) # Add specific options if needed
|
||||
# Example for a GPT model
|
||||
model = OpenAIGPTDoubleHeadsModel.from_pretrained(output_dir)
|
||||
tokenizer = OpenAIGPTTokenizer.from_pretrained(output_dir)
|
||||
|
||||
Here is another way you can save and reload the model if you want to use specific paths for each type of files:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
output_model_file = "./models/my_own_model_file.bin"
|
||||
output_config_file = "./models/my_own_config_file.bin"
|
||||
output_vocab_file = "./models/my_own_vocab_file.bin"
|
||||
|
||||
# Step 1: Save a model, configuration and vocabulary that you have fine-tuned
|
||||
|
||||
# If we have a distributed model, save only the encapsulated model
|
||||
# (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
|
||||
model_to_save = model.module if hasattr(model, 'module') else model
|
||||
|
||||
torch.save(model_to_save.state_dict(), output_model_file)
|
||||
model_to_save.config.to_json_file(output_config_file)
|
||||
tokenizer.save_vocabulary(output_vocab_file)
|
||||
|
||||
# Step 2: Re-load the saved model and vocabulary
|
||||
|
||||
# We didn't save using the predefined WEIGHTS_NAME, CONFIG_NAME names, we cannot load using `from_pretrained`.
|
||||
# Here is how to do it in this situation:
|
||||
|
||||
# Example for a Bert model
|
||||
config = BertConfig.from_json_file(output_config_file)
|
||||
model = BertForQuestionAnswering(config)
|
||||
state_dict = torch.load(output_model_file)
|
||||
model.load_state_dict(state_dict)
|
||||
tokenizer = BertTokenizer(output_vocab_file, do_lower_case=args.do_lower_case)
|
||||
|
||||
# Example for a GPT model
|
||||
config = OpenAIGPTConfig.from_json_file(output_config_file)
|
||||
model = OpenAIGPTDoubleHeadsModel(config)
|
||||
state_dict = torch.load(output_model_file)
|
||||
model.load_state_dict(state_dict)
|
||||
tokenizer = OpenAIGPTTokenizer(output_vocab_file)
|
||||
|
||||
Configurations
|
||||
^^^^^^^^^^^^^^
|
||||
|
||||
Models (BERT, GPT, GPT-2 and Transformer-XL) are defined and build from configuration classes which containes the parameters of the models (number of layers, dimensionalities...) and a few utilities to read and write from JSON configuration files. The respective configuration classes are:
|
||||
|
||||
|
||||
* ``BertConfig`` for ``BertModel`` and BERT classes instances.
|
||||
* ``OpenAIGPTConfig`` for ``OpenAIGPTModel`` and OpenAI GPT classes instances.
|
||||
* ``GPT2Config`` for ``GPT2Model`` and OpenAI GPT-2 classes instances.
|
||||
* ``TransfoXLConfig`` for ``TransfoXLModel`` and Transformer-XL classes instances.
|
||||
|
||||
These configuration classes contains a few utilities to load and save configurations:
|
||||
|
||||
|
||||
* ``from_dict(cls, json_object)``\ : A class method to construct a configuration from a Python dictionary of parameters. Returns an instance of the configuration class.
|
||||
* ``from_json_file(cls, json_file)``\ : A class method to construct a configuration from a json file of parameters. Returns an instance of the configuration class.
|
||||
* ``to_dict()``\ : Serializes an instance to a Python dictionary. Returns a dictionary.
|
||||
* ``to_json_string()``\ : Serializes an instance to a JSON string. Returns a string.
|
||||
* ``to_json_file(json_file_path)``\ : Save an instance to a json file.
|
||||
|
||||
Models
|
||||
^^^^^^
|
||||
|
||||
1. ``BertModel``
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertModel
|
||||
:members:
|
||||
|
||||
|
||||
2. ``BertForPreTraining``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
3. ``BertForMaskedLM``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
4. ``BertForNextSentencePrediction``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForNextSentencePrediction
|
||||
:members:
|
||||
|
||||
|
||||
5. ``BertForSequenceClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
6. ``BertForMultipleChoice``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
7. ``BertForTokenClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
8. ``BertForQuestionAnswering``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.BertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
9. ``OpenAIGPTModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.OpenAIGPTModel
|
||||
:members:
|
||||
|
||||
|
||||
10. ``OpenAIGPTLMHeadModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.OpenAIGPTLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
11. ``OpenAIGPTDoubleHeadsModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.OpenAIGPTDoubleHeadsModel
|
||||
:members:
|
||||
|
||||
|
||||
12. ``TransfoXLModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.TransfoXLModel
|
||||
:members:
|
||||
|
||||
|
||||
13. ``TransfoXLLMHeadModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.TransfoXLLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
14. ``GPT2Model``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.GPT2Model
|
||||
:members:
|
||||
|
||||
|
||||
15. ``GPT2LMHeadModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.GPT2LMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
16. ``GPT2DoubleHeadsModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.GPT2DoubleHeadsModel
|
||||
:members:
|
||||
|
||||
|
||||
Tokenizers
|
||||
^^^^^^^^^^
|
||||
|
||||
``BertTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``BertTokenizer`` perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
|
||||
|
||||
This class has five arguments:
|
||||
|
||||
|
||||
* ``vocab_file``\ : path to a vocabulary file.
|
||||
* ``do_lower_case``\ : convert text to lower-case while tokenizing. **Default = True**.
|
||||
* ``max_len``\ : max length to filter the input of the Transformer. Default to pre-trained value for the model if ``None``. **Default = None**
|
||||
* ``do_basic_tokenize``\ : Do basic tokenization before wordpice tokenization. Set to false if text is pre-tokenized. **Default = True**.
|
||||
* ``never_split``\ : a list of tokens that should not be splitted during tokenization. **Default = ``["[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"]``\ **
|
||||
|
||||
and three methods:
|
||||
|
||||
|
||||
* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by (1) performing basic tokenization and (2) WordPiece tokenization.
|
||||
* ``convert_tokens_to_ids(tokens)``\ : convert a list of ``str`` tokens in a list of ``int`` indices in the vocabulary.
|
||||
* ``convert_ids_to_tokens(tokens)``\ : convert a list of ``int`` indices in a list of ``str`` tokens in the vocabulary.
|
||||
* `save_vocabulary(directory_path)`: save the vocabulary file to `directory_path`. Return the path to the saved vocabulary file: ``vocab_file_path``. The vocabulary can be reloaded with ``BertTokenizer.from_pretrained('vocab_file_path')`` or ``BertTokenizer.from_pretrained('directory_path')``.
|
||||
|
||||
Please refer to the doc strings and code in `\ ``tokenization.py`` <./pytorch_pretrained_bert/tokenization.py>`_ for the details of the ``BasicTokenizer`` and ``WordpieceTokenizer`` classes. In general it is recommended to use ``BertTokenizer`` unless you know what you are doing.
|
||||
|
||||
``OpenAIGPTTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``OpenAIGPTTokenizer`` perform Byte-Pair-Encoding (BPE) tokenization.
|
||||
|
||||
This class has four arguments:
|
||||
|
||||
|
||||
* ``vocab_file``\ : path to a vocabulary file.
|
||||
* ``merges_file``\ : path to a file containing the BPE merges.
|
||||
* ``max_len``\ : max length to filter the input of the Transformer. Default to pre-trained value for the model if ``None``. **Default = None**
|
||||
* ``special_tokens``\ : a list of tokens to add to the vocabulary for fine-tuning. If SpaCy is not installed and BERT's ``BasicTokenizer`` is used as the pre-BPE tokenizer, these tokens are not split. **Default= None**
|
||||
|
||||
and five methods:
|
||||
|
||||
|
||||
* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by performing BPE tokenization.
|
||||
* ``convert_tokens_to_ids(tokens)``\ : convert a list of ``str`` tokens in a list of ``int`` indices in the vocabulary.
|
||||
* ``convert_ids_to_tokens(tokens)``\ : convert a list of ``int`` indices in a list of ``str`` tokens in the vocabulary.
|
||||
* ``set_special_tokens(self, special_tokens)``\ : update the list of special tokens (see above arguments)
|
||||
* ``encode(text)``\ : convert a ``str`` in a list of ``int`` tokens by performing BPE encoding.
|
||||
* `decode(ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)`: decode a list of `int` indices in a string and do some post-processing if needed: (i) remove special tokens from the output and (ii) clean up tokenization spaces.
|
||||
* `save_vocabulary(directory_path)`: save the vocabulary, merge and special tokens files to `directory_path`. Return the path to the three files: ``vocab_file_path``\ , ``merge_file_path``\ , ``special_tokens_file_path``. The vocabulary can be reloaded with ``OpenAIGPTTokenizer.from_pretrained('directory_path')``.
|
||||
|
||||
Please refer to the doc strings and code in `\ ``tokenization_openai.py`` <./pytorch_pretrained_bert/tokenization_openai.py>`_ for the details of the ``OpenAIGPTTokenizer``.
|
||||
|
||||
``TransfoXLTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``TransfoXLTokenizer`` perform word tokenization. This tokenizer can be used for adaptive softmax and has utilities for counting tokens in a corpus to create a vocabulary ordered by toekn frequency (for adaptive softmax). See the adaptive softmax paper (\ `Efficient softmax approximation for GPUs <http://arxiv.org/abs/1609.04309>`_\ ) for more details.
|
||||
|
||||
The API is similar to the API of ``BertTokenizer`` (see above).
|
||||
|
||||
Please refer to the doc strings and code in `\ ``tokenization_transfo_xl.py`` <./pytorch_pretrained_bert/tokenization_transfo_xl.py>`_ for the details of these additional methods in ``TransfoXLTokenizer``.
|
||||
|
||||
``GPT2Tokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``GPT2Tokenizer`` perform byte-level Byte-Pair-Encoding (BPE) tokenization.
|
||||
|
||||
This class has three arguments:
|
||||
|
||||
|
||||
* ``vocab_file``\ : path to a vocabulary file.
|
||||
* ``merges_file``\ : path to a file containing the BPE merges.
|
||||
* ``errors``\ : How to handle unicode decoding errors. **Default = ``replace``\ **
|
||||
|
||||
and two methods:
|
||||
|
||||
|
||||
* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by performing byte-level BPE.
|
||||
* ``convert_tokens_to_ids(tokens)``\ : convert a list of ``str`` tokens in a list of ``int`` indices in the vocabulary.
|
||||
* ``convert_ids_to_tokens(tokens)``\ : convert a list of ``int`` indices in a list of ``str`` tokens in the vocabulary.
|
||||
* ``set_special_tokens(self, special_tokens)``\ : update the list of special tokens (see above arguments)
|
||||
* ``encode(text)``\ : convert a ``str`` in a list of ``int`` tokens by performing byte-level BPE.
|
||||
* ``decode(tokens)``\ : convert back a list of ``int`` tokens in a ``str``.
|
||||
* `save_vocabulary(directory_path)`: save the vocabulary, merge and special tokens files to `directory_path`. Return the path to the three files: ``vocab_file_path``\ , ``merge_file_path``\ , ``special_tokens_file_path``. The vocabulary can be reloaded with ``OpenAIGPTTokenizer.from_pretrained('directory_path')``.
|
||||
|
||||
Please refer to `\ ``tokenization_gpt2.py`` <./pytorch_pretrained_bert/tokenization_gpt2.py>`_ for more details on the ``GPT2Tokenizer``.
|
||||
|
||||
Optimizers
|
||||
^^^^^^^^^^
|
||||
|
||||
``BertAdam``
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
``BertAdam`` is a ``torch.optimizer`` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch Adam optimizer are the following:
|
||||
|
||||
|
||||
* BertAdam implements weight decay fix,
|
||||
* BertAdam doesn't compensate for bias as in the regular Adam optimizer.
|
||||
|
||||
The optimizer accepts the following arguments:
|
||||
|
||||
|
||||
* ``lr`` : learning rate
|
||||
* ``warmup`` : portion of ``t_total`` for the warmup, ``-1`` means no warmup. Default : ``-1``
|
||||
* ``t_total`` : total number of training steps for the learning
|
||||
rate schedule, ``-1`` means constant learning rate. Default : ``-1``
|
||||
* ``schedule`` : schedule to use for the warmup (see above).
|
||||
Can be ``'warmup_linear'``\ , ``'warmup_constant'``\ , ``'warmup_cosine'``\ , ``'none'``\ , ``None`` or a ``_LRSchedule`` object (see below).
|
||||
If ``None`` or ``'none'``\ , learning rate is always kept constant.
|
||||
Default : ``'warmup_linear'``
|
||||
* ``b1`` : Adams b1. Default : ``0.9``
|
||||
* ``b2`` : Adams b2. Default : ``0.999``
|
||||
* ``e`` : Adams epsilon. Default : ``1e-6``
|
||||
* ``weight_decay:`` Weight decay. Default : ``0.01``
|
||||
* ``max_grad_norm`` : Maximum norm for the gradients (\ ``-1`` means no clipping). Default : ``1.0``
|
||||
|
||||
``OpenAIAdam``
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``OpenAIAdam`` is similar to ``BertAdam``.
|
||||
The differences with ``BertAdam`` is that ``OpenAIAdam`` compensate for bias as in the regular Adam optimizer.
|
||||
|
||||
``OpenAIAdam`` accepts the same arguments as ``BertAdam``.
|
||||
|
||||
Learning Rate Schedules
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The ``.optimization`` module also provides additional schedules in the form of schedule objects that inherit from ``_LRSchedule``.
|
||||
All ``_LRSchedule`` subclasses accept ``warmup`` and ``t_total`` arguments at construction.
|
||||
When an ``_LRSchedule`` object is passed into ``BertAdam`` or ``OpenAIAdam``\ ,
|
||||
the ``warmup`` and ``t_total`` arguments on the optimizer are ignored and the ones in the ``_LRSchedule`` object are used.
|
||||
An overview of the implemented schedules:
|
||||
|
||||
|
||||
* ``ConstantLR``\ : always returns learning rate 1.
|
||||
* ``WarmupConstantSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
|
||||
Keeps learning rate equal to 1. after warmup.
|
||||
|
||||
.. image:: docs/imgs/warmup_constant_schedule.png
|
||||
:target: docs/imgs/warmup_constant_schedule.png
|
||||
:alt:
|
||||
|
||||
* ``WarmupLinearSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
|
||||
Linearly decreases learning rate from 1. to 0. over remaining ``1 - warmup`` steps.
|
||||
|
||||
.. image:: docs/imgs/warmup_linear_schedule.png
|
||||
:target: docs/imgs/warmup_linear_schedule.png
|
||||
:alt:
|
||||
|
||||
* ``WarmupCosineSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
|
||||
Decreases learning rate from 1. to 0. over remaining ``1 - warmup`` steps following a cosine curve.
|
||||
If ``cycles`` (default=0.5) is different from default, learning rate follows cosine function after warmup.
|
||||
|
||||
.. image:: docs/imgs/warmup_cosine_schedule.png
|
||||
:target: docs/imgs/warmup_cosine_schedule.png
|
||||
:alt:
|
||||
|
||||
* ``WarmupCosineWithHardRestartsSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
|
||||
If ``cycles`` (default=1.) is different from default, learning rate follows ``cycles`` times a cosine decaying learning rate (with hard restarts).
|
||||
|
||||
.. image:: docs/imgs/warmup_cosine_hard_restarts_schedule.png
|
||||
:target: docs/imgs/warmup_cosine_hard_restarts_schedule.png
|
||||
:alt:
|
||||
|
||||
* ``WarmupCosineWithWarmupRestartsSchedule``\ : All training progress is divided in ``cycles`` (default=1.) parts of equal length.
|
||||
Every part follows a schedule with the first ``warmup`` fraction of the training steps linearly increasing from 0. to 1.,
|
||||
followed by a learning rate decreasing from 1. to 0. following a cosine curve.
|
||||
Note that the total number of all warmup steps over all cycles together is equal to ``warmup`` * ``cycles``
|
||||
|
||||
.. image:: docs/imgs/warmup_cosine_warm_restarts_schedule.png
|
||||
:target: docs/imgs/warmup_cosine_warm_restarts_schedule.png
|
||||
:alt:
|
||||
|
|
@ -0,0 +1,593 @@
|
|||
Examples
|
||||
================================================
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Sub-section
|
||||
- Description
|
||||
* - `Training large models: introduction, tools and examples <#Training-large-models-introduction,-tools-and-examples>`_
|
||||
- How to use gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training to train Bert models
|
||||
* - `Fine-tuning with BERT: running the examples <#Fine-tuning-with-BERT-running-the-examples>`_
|
||||
- Running the examples in `\ ``./examples`` <./examples/>`_\ : ``extract_classif.py``\ , ``run_bert_classifier.py``\ , ``run_bert_squad.py`` and ``run_lm_finetuning.py``
|
||||
* - `Fine-tuning with OpenAI GPT, Transformer-XL and GPT-2 <#openai-gpt-transformer-xl-and-gpt-2-running-the-examples>`_
|
||||
- Running the examples in `\ ``./examples`` <./examples/>`_\ : ``run_openai_gpt.py``\ , ``run_transfo_xl.py`` and ``run_gpt2.py``
|
||||
* - `Fine-tuning BERT-large on GPUs <#Fine-tuning-BERT-large-on-GPUs>`_
|
||||
- How to fine tune ``BERT large``
|
||||
|
||||
|
||||
Training large models: introduction, tools and examples
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32).
|
||||
|
||||
To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts `\ ``run_bert_classifier.py`` <./examples/run_bert_classifier.py>`_ and `\ ``run_bert_squad.py`` <./examples/run_bert_squad.py>`_\ : gradient-accumulation, multi-gpu training, distributed training and 16-bits training . For more details on how to use these techniques you can read `the tips on training large batches in PyTorch <https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255>`_ that I published earlier this month.
|
||||
|
||||
Here is how to use these techniques in our scripts:
|
||||
|
||||
|
||||
* **Gradient Accumulation**\ : Gradient accumulation can be used by supplying a integer greater than 1 to the ``--gradient_accumulation_steps`` argument. The batch at each step will be divided by this integer and gradient will be accumulated over ``gradient_accumulation_steps`` steps.
|
||||
* **Multi-GPU**\ : Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs.
|
||||
* **Distributed training**\ : Distributed training can be activated by supplying an integer greater or equal to 0 to the ``--local_rank`` argument (see below).
|
||||
* **16-bits training**\ : 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found `here <https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/>`_ and a full documentation is `here <https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html>`_. In our scripts, this option can be activated by setting the ``--fp16`` flag and you can play with loss scaling using the ``--loss_scale`` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjusted or a positive power of two in which case the scaling is static.
|
||||
|
||||
To use 16-bits training and distributed training, you need to install NVIDIA's apex extension `as detailed here <https://github.com/nvidia/apex>`_. You will find more information regarding the internals of ``apex`` and how to use ``apex`` in `the doc and the associated repository <https://github.com/nvidia/apex>`_. The results of the tests performed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in `the relevant PR of the present repository <https://github.com/huggingface/pytorch-pretrained-BERT/pull/116>`_.
|
||||
|
||||
Note: To use *Distributed Training*\ , you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see `the above mentioned blog post <(https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255>`_\ ) for more details):
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node=4 --nnodes=2 --node_rank=$THIS_MACHINE_INDEX --master_addr="192.168.1.1" --master_port=1234 run_bert_classifier.py (--arg1 --arg2 --arg3 and all other arguments of the run_classifier script)
|
||||
|
||||
Where ``$THIS_MACHINE_INDEX`` is an sequential index assigned to each of your machine (0, 1, 2...) and the machine with rank 0 has an IP address ``192.168.1.1`` and an open port ``1234``.
|
||||
|
||||
Fine-tuning with BERT: running the examples
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
We showcase several fine-tuning examples based on (and extended from) `the original implementation <https://github.com/google-research/bert/>`_\ :
|
||||
|
||||
|
||||
* a *sequence-level classifier* on nine different GLUE tasks,
|
||||
* a *token-level classifier* on the question answering dataset SQuAD, and
|
||||
* a *sequence-level multiple-choice classifier* on the SWAG classification corpus.
|
||||
* a *BERT language model* on another target corpus
|
||||
|
||||
GLUE results on dev set
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
We get the following results on the dev set of GLUE benchmark with an uncased BERT base
|
||||
model. All experiments were run on a P100 GPU with a batch size of 32.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Task
|
||||
- Metric
|
||||
- Result
|
||||
* - CoLA
|
||||
- Matthew's corr.
|
||||
- 57.29
|
||||
* - SST-2
|
||||
- accuracy
|
||||
- 93.00
|
||||
* - MRPC
|
||||
- F1/accuracy
|
||||
- 88.85/83.82
|
||||
* - STS-B
|
||||
- Pearson/Spearman corr.
|
||||
- 89.70/89.37
|
||||
* - QQP
|
||||
- accuracy/F1
|
||||
- 90.72/87.41
|
||||
* - MNLI
|
||||
- matched acc./mismatched acc.
|
||||
- 83.95/84.39
|
||||
* - QNLI
|
||||
- accuracy
|
||||
- 89.04
|
||||
* - RTE
|
||||
- accuracy
|
||||
- 61.01
|
||||
* - WNLI
|
||||
- accuracy
|
||||
- 53.52
|
||||
|
||||
|
||||
Some of these results are significantly different from the ones reported on the test set
|
||||
of GLUE benchmark on the website. For QQP and WNLI, please refer to `FAQ #12 <https://gluebenchmark.com/faq>`_ on the webite.
|
||||
|
||||
Before running anyone of these GLUE tasks you should download the
|
||||
`GLUE data <https://gluebenchmark.com/tasks>`_ by running
|
||||
`this script <https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e>`_
|
||||
and unpack it to some directory ``$GLUE_DIR``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export GLUE_DIR=/path/to/glue
|
||||
export TASK_NAME=MRPC
|
||||
|
||||
python run_bert_classifier.py \
|
||||
--task_name $TASK_NAME \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--do_lower_case \
|
||||
--data_dir $GLUE_DIR/$TASK_NAME \
|
||||
--bert_model bert-base-uncased \
|
||||
--max_seq_length 128 \
|
||||
--train_batch_size 32 \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--output_dir /tmp/$TASK_NAME/
|
||||
|
||||
where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
|
||||
|
||||
The dev set results will be present within the text file 'eval_results.txt' in the specified output_dir. In case of MNLI, since there are two separate dev sets, matched and mismatched, there will be a separate output folder called '/tmp/MNLI-MM/' in addition to '/tmp/MNLI/'.
|
||||
|
||||
The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI, CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being said, there shouldn't be any issues in running half-precision training with the remaining GLUE tasks as well, since the data processor for each task inherits from the base class DataProcessor.
|
||||
|
||||
MRPC
|
||||
~~~~
|
||||
|
||||
This example code fine-tunes BERT on the Microsoft Research Paraphrase
|
||||
Corpus (MRPC) corpus and runs in less than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
|
||||
|
||||
Before running this example you should download the
|
||||
`GLUE data <https://gluebenchmark.com/tasks>`_ by running
|
||||
`this script <https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e>`_
|
||||
and unpack it to some directory ``$GLUE_DIR``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export GLUE_DIR=/path/to/glue
|
||||
|
||||
python run_bert_classifier.py \
|
||||
--task_name MRPC \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--do_lower_case \
|
||||
--data_dir $GLUE_DIR/MRPC/ \
|
||||
--bert_model bert-base-uncased \
|
||||
--max_seq_length 128 \
|
||||
--train_batch_size 32 \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--output_dir /tmp/mrpc_output/
|
||||
|
||||
Our test ran on a few seeds with `the original implementation hyper-parameters <https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks>`_ gave evaluation results between 84% and 88%.
|
||||
|
||||
**Fast run with apex and 16 bit precision: fine-tuning on MRPC in 27 seconds!**
|
||||
First install apex as indicated `here <https://github.com/NVIDIA/apex>`_.
|
||||
Then run
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export GLUE_DIR=/path/to/glue
|
||||
|
||||
python run_bert_classifier.py \
|
||||
--task_name MRPC \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--do_lower_case \
|
||||
--data_dir $GLUE_DIR/MRPC/ \
|
||||
--bert_model bert-base-uncased \
|
||||
--max_seq_length 128 \
|
||||
--train_batch_size 32 \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--output_dir /tmp/mrpc_output/ \
|
||||
--fp16
|
||||
|
||||
**Distributed training**
|
||||
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking model to reach a F1 > 92 on MRPC:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name MRPC --do_train --do_eval --do_lower_case --data_dir $GLUE_DIR/MRPC/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir /tmp/mrpc_output/
|
||||
|
||||
Training with these hyper-parameters gave us the following results:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
acc = 0.8823529411764706
|
||||
acc_and_f1 = 0.901702786377709
|
||||
eval_loss = 0.3418912578906332
|
||||
f1 = 0.9210526315789473
|
||||
global_step = 174
|
||||
loss = 0.07231863956341798
|
||||
|
||||
Here is an example on MNLI:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name mnli --do_train --do_eval --do_lower_case --data_dir /datadrive/bert_data/glue_data//MNLI/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir ../models/wwm-uncased-finetuned-mnli/ --overwrite_output_dir
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
***** Eval results *****
|
||||
acc = 0.8679706601466992
|
||||
eval_loss = 0.4911287787382479
|
||||
global_step = 18408
|
||||
loss = 0.04755385363816904
|
||||
|
||||
***** Eval results *****
|
||||
acc = 0.8747965825874695
|
||||
eval_loss = 0.45516540421714036
|
||||
global_step = 18408
|
||||
loss = 0.04755385363816904
|
||||
|
||||
This is the example of the ``bert-large-uncased-whole-word-masking-finetuned-mnli`` model
|
||||
|
||||
SQuAD
|
||||
~~~~~
|
||||
|
||||
This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on a single tesla V100 16GB.
|
||||
|
||||
The data for SQuAD can be downloaded with the following links and should be saved in a ``$SQUAD_DIR`` directory.
|
||||
|
||||
|
||||
* `train-v1.1.json <https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json>`_
|
||||
* `dev-v1.1.json <https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json>`_
|
||||
* `evaluate-v1.1.py <https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py>`_
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export SQUAD_DIR=/path/to/SQUAD
|
||||
|
||||
python run_bert_squad.py \
|
||||
--bert_model bert-base-uncased \
|
||||
--do_train \
|
||||
--do_predict \
|
||||
--do_lower_case \
|
||||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||||
--train_batch_size 12 \
|
||||
--learning_rate 3e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--max_seq_length 384 \
|
||||
--doc_stride 128 \
|
||||
--output_dir /tmp/debug_squad/
|
||||
|
||||
Training with the previous hyper-parameters gave us the following results:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json /tmp/debug_squad/predictions.json
|
||||
{"f1": 88.52381567990474, "exact_match": 81.22043519394512}
|
||||
|
||||
**distributed training**
|
||||
|
||||
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node=8 \
|
||||
run_bert_squad.py \
|
||||
--bert_model bert-large-uncased-whole-word-masking \
|
||||
--do_train \
|
||||
--do_predict \
|
||||
--do_lower_case \
|
||||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||||
--learning_rate 3e-5 \
|
||||
--num_train_epochs 2 \
|
||||
--max_seq_length 384 \
|
||||
--doc_stride 128 \
|
||||
--output_dir ../models/wwm_uncased_finetuned_squad/ \
|
||||
--train_batch_size 24 \
|
||||
--gradient_accumulation_steps 12
|
||||
|
||||
Training with these hyper-parameters gave us the following results:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ../models/wwm_uncased_finetuned_squad/predictions.json
|
||||
{"exact_match": 86.91579943235573, "f1": 93.1532499015869}
|
||||
|
||||
This is the model provided as ``bert-large-uncased-whole-word-masking-finetuned-squad``.
|
||||
|
||||
And here is the model provided as ``bert-large-cased-whole-word-masking-finetuned-squad``\ :
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node=8 run_bert_squad.py --bert_model bert-large-cased-whole-word-masking --do_train --do_predict --do_lower_case --train_file $SQUAD_DIR/train-v1.1.json --predict_file $SQUAD_DIR/dev-v1.1.json --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir ../models/wwm_cased_finetuned_squad/ --train_batch_size 24 --gradient_accumulation_steps 12
|
||||
|
||||
Training with these hyper-parameters gave us the following results:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ../models/wwm_uncased_finetuned_squad/predictions.json
|
||||
{"exact_match": 84.18164616840113, "f1": 91.58645594850135}
|
||||
|
||||
SWAG
|
||||
~~~~
|
||||
|
||||
The data for SWAG can be downloaded by cloning the following `repository <https://github.com/rowanz/swagaf>`_
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export SWAG_DIR=/path/to/SWAG
|
||||
|
||||
python run_bert_swag.py \
|
||||
--bert_model bert-base-uncased \
|
||||
--do_train \
|
||||
--do_lower_case \
|
||||
--do_eval \
|
||||
--data_dir $SWAG_DIR/data \
|
||||
--train_batch_size 16 \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_seq_length 80 \
|
||||
--output_dir /tmp/swag_output/ \
|
||||
--gradient_accumulation_steps 4
|
||||
|
||||
Training with the previous hyper-parameters on a single GPU gave us the following results:
|
||||
|
||||
.. code-block::
|
||||
|
||||
eval_accuracy = 0.8062081375587323
|
||||
eval_loss = 0.5966546792367169
|
||||
global_step = 13788
|
||||
loss = 0.06423990014260186
|
||||
|
||||
LM Fine-tuning
|
||||
~~~~~~~~~~~~~~
|
||||
|
||||
The data should be a text file in the same format as `sample_text.txt <./samples/sample_text.txt>`_ (one sentence per line, docs separated by empty line).
|
||||
You can download an `exemplary training corpus <https://ext-bert-sample.obs.eu-de.otc.t-systems.com/small_wiki_sentence_corpus.txt>`_ generated from wikipedia articles and splitted into ~500k sentences with spaCy.
|
||||
Training one epoch on this corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with ``train_batch_size=200`` and ``max_seq_length=128``\ :
|
||||
|
||||
Thank to the work of @Rocketknight1 and @tholor there are now **several scripts** that can be used to fine-tune BERT using the pretraining objective (combination of masked-language modeling and next sentence prediction loss). These scripts are detailed in the `\ ``README`` <./examples/lm_finetuning/README.md>`_ of the `\ ``examples/lm_finetuning/`` <./examples/lm_finetuning/>`_ folder.
|
||||
|
||||
OpenAI GPT, Transformer-XL and GPT-2: running the examples
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
We provide three examples of scripts for OpenAI GPT, Transformer-XL and OpenAI GPT-2 based on (and extended from) the respective original implementations:
|
||||
|
||||
|
||||
* fine-tuning OpenAI GPT on the ROCStories dataset
|
||||
* evaluating Transformer-XL on Wikitext 103
|
||||
* unconditional and conditional generation from a pre-trained OpenAI GPT-2 model
|
||||
|
||||
Fine-tuning OpenAI GPT on the RocStories dataset
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This example code fine-tunes OpenAI GPT on the RocStories dataset.
|
||||
|
||||
Before running this example you should download the
|
||||
`RocStories dataset <https://github.com/snigdhac/StoryComprehension_EMNLP/tree/master/Dataset/RoCStories>`_ and unpack it to some directory ``$ROC_STORIES_DIR``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export ROC_STORIES_DIR=/path/to/RocStories
|
||||
|
||||
python run_openai_gpt.py \
|
||||
--model_name openai-gpt \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
|
||||
--eval_dataset $ROC_STORIES_DIR/cloze_test_test__spring2016\ -\ cloze_test_ALL_test.csv \
|
||||
--output_dir ../log \
|
||||
--train_batch_size 16 \
|
||||
|
||||
This command runs in about 10 min on a single K-80 an gives an evaluation accuracy of about 87.7% (the authors report a median accuracy with the TensorFlow code of 85.8% and the OpenAI GPT paper reports a best single run accuracy of 86.5%).
|
||||
|
||||
Evaluating the pre-trained Transformer-XL on the WikiText 103 dataset
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This example code evaluate the pre-trained Transformer-XL on the WikiText 103 dataset.
|
||||
This command will download a pre-processed version of the WikiText 103 dataset in which the vocabulary has been computed.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
python run_transfo_xl.py --work_dir ../log
|
||||
|
||||
This command runs in about 1 min on a V100 and gives an evaluation perplexity of 18.22 on WikiText-103 (the authors report a perplexity of about 18.3 on this dataset with the TensorFlow code).
|
||||
|
||||
Unconditional and conditional generation from OpenAI's GPT-2 model
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This example code is identical to the original unconditional and conditional generation codes.
|
||||
|
||||
Conditional generation:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
python run_gpt2.py
|
||||
|
||||
Unconditional generation:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
python run_gpt2.py --unconditional
|
||||
|
||||
The same option as in the original scripts are provided, please refere to the code of the example and the original repository of OpenAI.
|
||||
|
||||
Fine-tuning BERT-large on GPUs
|
||||
------------------------------
|
||||
|
||||
The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation.
|
||||
|
||||
For example, fine-tuning BERT-large on SQuAD can be done on a server with 4 k-80 (these are pretty old now) in 18 hours. Our results are similar to the TensorFlow implementation results (actually slightly higher):
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
{"exact_match": 84.56953642384106, "f1": 91.04028647786927}
|
||||
|
||||
To get these results we used a combination of:
|
||||
|
||||
|
||||
* multi-GPU training (automatically activated on a multi-GPU server),
|
||||
* 2 steps of gradient accumulation and
|
||||
* perform the optimization step on CPU to store Adam's averages in RAM.
|
||||
|
||||
Here is the full list of hyper-parameters for this run:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
export SQUAD_DIR=/path/to/SQUAD
|
||||
|
||||
python ./run_bert_squad.py \
|
||||
--bert_model bert-large-uncased \
|
||||
--do_train \
|
||||
--do_predict \
|
||||
--do_lower_case \
|
||||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||||
--learning_rate 3e-5 \
|
||||
--num_train_epochs 2 \
|
||||
--max_seq_length 384 \
|
||||
--doc_stride 128 \
|
||||
--output_dir /tmp/debug_squad/ \
|
||||
--train_batch_size 24 \
|
||||
--gradient_accumulation_steps 2
|
||||
|
||||
If you have a recent GPU (starting from NVIDIA Volta series), you should try **16-bit fine-tuning** (FP16).
|
||||
|
||||
Here is an example of hyper-parameters for a FP16 run we tried:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
export SQUAD_DIR=/path/to/SQUAD
|
||||
|
||||
python ./run_bert_squad.py \
|
||||
--bert_model bert-large-uncased \
|
||||
--do_train \
|
||||
--do_predict \
|
||||
--do_lower_case \
|
||||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||||
--learning_rate 3e-5 \
|
||||
--num_train_epochs 2 \
|
||||
--max_seq_length 384 \
|
||||
--doc_stride 128 \
|
||||
--output_dir /tmp/debug_squad/ \
|
||||
--train_batch_size 24 \
|
||||
--fp16 \
|
||||
--loss_scale 128
|
||||
|
||||
The results were similar to the above FP32 results (actually slightly higher):
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
{"exact_match": 84.65468306527909, "f1": 91.238669287002}
|
||||
|
||||
Here is an example with the recent ``bert-large-uncased-whole-word-masking``\ :
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node=8 \
|
||||
run_bert_squad.py \
|
||||
--bert_model bert-large-uncased-whole-word-masking \
|
||||
--do_train \
|
||||
--do_predict \
|
||||
--do_lower_case \
|
||||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||||
--learning_rate 3e-5 \
|
||||
--num_train_epochs 2 \
|
||||
--max_seq_length 384 \
|
||||
--doc_stride 128 \
|
||||
--output_dir /tmp/debug_squad/ \
|
||||
--train_batch_size 24 \
|
||||
--gradient_accumulation_steps 2
|
||||
|
||||
Fine-tuning XLNet
|
||||
-----------------
|
||||
|
||||
STS-B
|
||||
~~~~~
|
||||
|
||||
This example code fine-tunes XLNet on the STS-B corpus.
|
||||
|
||||
Before running this example you should download the
|
||||
`GLUE data <https://gluebenchmark.com/tasks>`_ by running
|
||||
`this script <https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e>`_
|
||||
and unpack it to some directory ``$GLUE_DIR``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export GLUE_DIR=/path/to/glue
|
||||
|
||||
python run_xlnet_classifier.py \
|
||||
--task_name STS-B \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--data_dir $GLUE_DIR/STS-B/ \
|
||||
--max_seq_length 128 \
|
||||
--train_batch_size 8 \
|
||||
--gradient_accumulation_steps 1 \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--output_dir /tmp/mrpc_output/
|
||||
|
||||
Our test ran on a few seeds with `the original implementation hyper-parameters <https://github.com/zihangdai/xlnet#1-sts-b-sentence-pair-relevance-regression-with-gpus>`_ gave evaluation results between 84% and 88%.
|
||||
|
||||
**Distributed training**
|
||||
Here is an example using distributed training on 8 V100 GPUs to reach XXXX:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node 8 \
|
||||
run_xlnet_classifier.py \
|
||||
--task_name STS-B \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--data_dir $GLUE_DIR/STS-B/ \
|
||||
--max_seq_length 128 \
|
||||
--train_batch_size 8 \
|
||||
--gradient_accumulation_steps 1 \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--output_dir /tmp/mrpc_output/
|
||||
|
||||
Training with these hyper-parameters gave us the following results:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
acc = 0.8823529411764706
|
||||
acc_and_f1 = 0.901702786377709
|
||||
eval_loss = 0.3418912578906332
|
||||
f1 = 0.9210526315789473
|
||||
global_step = 174
|
||||
loss = 0.07231863956341798
|
||||
|
||||
Here is an example on MNLI:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py --bert_model bert-large-uncased-whole-word-masking --task_name mnli --do_train --do_eval --data_dir /datadrive/bert_data/glue_data//MNLI/ --max_seq_length 128 --train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir ../models/wwm-uncased-finetuned-mnli/ --overwrite_output_dir
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
***** Eval results *****
|
||||
acc = 0.8679706601466992
|
||||
eval_loss = 0.4911287787382479
|
||||
global_step = 18408
|
||||
loss = 0.04755385363816904
|
||||
|
||||
***** Eval results *****
|
||||
acc = 0.8747965825874695
|
||||
eval_loss = 0.45516540421714036
|
||||
global_step = 18408
|
||||
loss = 0.04755385363816904
|
||||
|
||||
This is the example of the ``bert-large-uncased-whole-word-masking-finetuned-mnli`` model
|
||||
|
||||
BERTology
|
||||
---------
|
||||
|
||||
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are:
|
||||
|
||||
|
||||
* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950
|
||||
* Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
|
||||
* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341
|
||||
|
||||
In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted from the great work of Paul Michel (https://arxiv.org/abs/1905.10650):
|
||||
|
||||
|
||||
* accessing all the hidden-states of BERT/GPT/GPT-2,
|
||||
* accessing all the attention weights for each head of BERT/GPT/GPT-2,
|
||||
* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
|
||||
|
||||
To help you understand and use these features, we have added a specific example script: `\ ``bertology.py`` <./examples/bertology.py>`_ while extract information and prune a model pre-trained on MRPC.
|
||||
|
|
@ -0,0 +1,207 @@
|
|||
Pytorch-Transformers: The Big & Extending Repository of pretrained Transformers
|
||||
================================================================================================================================================
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
installation
|
||||
usage
|
||||
doc
|
||||
examples
|
||||
notebooks
|
||||
tpu
|
||||
cli
|
||||
|
||||
|
||||
.. image:: https://circleci.com/gh/huggingface/pytorch-pretrained-BERT.svg?style=svg
|
||||
:target: https://circleci.com/gh/huggingface/pytorch-pretrained-BERT
|
||||
:alt: CircleCI
|
||||
|
||||
|
||||
This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for:
|
||||
|
||||
|
||||
* `Google's BERT model <https://github.com/google-research/bert>`_\ ,
|
||||
* `OpenAI's GPT model <https://github.com/openai/finetune-transformer-lm>`_\ ,
|
||||
* `Google/CMU's Transformer-XL model <https://github.com/kimiyoung/transformer-xl>`_\ , and
|
||||
* `OpenAI's GPT-2 model <https://blog.openai.com/better-language-models/>`_.
|
||||
|
||||
These implementations have been tested on several datasets (see the examples) and should match the performances of the associated TensorFlow implementations (e.g. ~91 F1 on SQuAD for BERT, ~88 F1 on RocStories for OpenAI GPT and ~18.3 perplexity on WikiText 103 for the Transformer-XL). You can find more details in the `Examples <#examples>`_ section below.
|
||||
|
||||
Here are some information on these models:
|
||||
|
||||
**BERT** was released together with the paper `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
This PyTorch implementation of BERT is provided with `Google's pre-trained models <https://github.com/google-research/bert>`_\ , examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided.
|
||||
|
||||
**OpenAI GPT** was released together with the paper `Improving Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised/>`_ by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
This PyTorch implementation of OpenAI GPT is an adaptation of the `PyTorch implementation by HuggingFace <https://github.com/huggingface/pytorch-openai-transformer-lm>`_ and is provided with `OpenAI's pre-trained model <https://github.com/openai/finetune-transformer-lm>`_ and a command-line interface that was used to convert the pre-trained NumPy checkpoint in PyTorch.
|
||||
|
||||
**Google/CMU's Transformer-XL** was released together with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <http://arxiv.org/abs/1901.02860>`_ by Zihang Dai\ *, Zhilin Yang*\ , Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
This PyTorch implementation of Transformer-XL is an adaptation of the original `PyTorch implementation <https://github.com/kimiyoung/transformer-xl>`_ which has been slightly modified to match the performances of the TensorFlow implementation and allow to re-use the pretrained weights. A command-line interface is provided to convert TensorFlow checkpoints in PyTorch models.
|
||||
|
||||
**OpenAI GPT-2** was released together with the paper `Language Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models/>`_ by Alec Radford\ *, Jeffrey Wu*\ , Rewon Child, David Luan, Dario Amodei\ ** and Ilya Sutskever**.
|
||||
This PyTorch implementation of OpenAI GPT-2 is an adaptation of the `OpenAI's implementation <https://github.com/openai/gpt-2>`_ and is provided with `OpenAI's pre-trained model <https://github.com/openai/gpt-2>`_ and a command-line interface that was used to convert the TensorFlow checkpoint in PyTorch.
|
||||
|
||||
Content
|
||||
-------
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Section
|
||||
- Description
|
||||
* - `Installation <#installation>`_
|
||||
- How to install the package
|
||||
* - `Overview <#overview>`_
|
||||
- Overview of the package
|
||||
* - `Usage <#usage>`_
|
||||
- Quickstart examples
|
||||
* - `Doc <#doc>`_
|
||||
- Detailed documentation
|
||||
* - `Examples <#examples>`_
|
||||
- Detailed examples on how to fine-tune Bert
|
||||
* - `Notebooks <#notebooks>`_
|
||||
- Introduction on the provided Jupyter Notebooks
|
||||
* - `TPU <#tpu>`_
|
||||
- Notes on TPU support and pretraining scripts
|
||||
* - `Command-line interface <#Command-line-interface>`_
|
||||
- Convert a TensorFlow checkpoint in a PyTorch dump
|
||||
|
||||
Overview
|
||||
--------
|
||||
|
||||
This package comprises the following classes that can be imported in Python and are detailed in the `Doc <#doc>`_ section of this readme:
|
||||
|
||||
|
||||
*
|
||||
Eight **Bert** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py>`_ file):
|
||||
|
||||
|
||||
* `BertModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L639>`_ - raw BERT Transformer model (\ **fully pre-trained**\ ),
|
||||
* `BertForMaskedLM <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L793>`_ - BERT Transformer with the pre-trained masked language modeling head on top (\ **fully pre-trained**\ ),
|
||||
* `BertForNextSentencePrediction <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L854>`_ - BERT Transformer with the pre-trained next sentence prediction classifier on top (\ **fully pre-trained**\ ),
|
||||
* `BertForPreTraining <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L722>`_ - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (\ **fully pre-trained**\ ),
|
||||
* `BertForSequenceClassification <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L916>`_ - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**\ , the sequence classification head **is only initialized and has to be trained**\ ),
|
||||
* `BertForMultipleChoice <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L982>`_ - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**\ , the multiple choice classification head **is only initialized and has to be trained**\ ),
|
||||
* `BertForTokenClassification <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L1051>`_ - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**\ , the token classification head **is only initialized and has to be trained**\ ),
|
||||
* `BertForQuestionAnswering <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L1124>`_ - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**\ , the token classification head **is only initialized and has to be trained**\ ).
|
||||
|
||||
*
|
||||
Three **OpenAI GPT** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_openai.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_openai.py>`_ file):
|
||||
|
||||
|
||||
* `OpenAIGPTModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_openai.py#L536>`_ - raw OpenAI GPT Transformer model (\ **fully pre-trained**\ ),
|
||||
* `OpenAIGPTLMHeadModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_openai.py#L643>`_ - OpenAI GPT Transformer with the tied language modeling head on top (\ **fully pre-trained**\ ),
|
||||
* `OpenAIGPTDoubleHeadsModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_openai.py#L722>`_ - OpenAI GPT Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT Transformer is **pre-trained**\ , the multiple choice classification head **is only initialized and has to be trained**\ ),
|
||||
|
||||
*
|
||||
Two **Transformer-XL** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_transfo_xl.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_transfo_xl.py>`_ file):
|
||||
|
||||
|
||||
* `TransfoXLModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_transfo_xl.py#L983>`_ - Transformer-XL model which outputs the last hidden state and memory cells (\ **fully pre-trained**\ ),
|
||||
* `TransfoXLLMHeadModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_transfo_xl.py#L1260>`_ - Transformer-XL with the tied adaptive softmax head on top for language modeling which outputs the logits/loss and memory cells (\ **fully pre-trained**\ ),
|
||||
|
||||
*
|
||||
Three **OpenAI GPT-2** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_gpt2.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_gpt2.py>`_ file):
|
||||
|
||||
|
||||
* `GPT2Model <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_gpt2.py#L479>`_ - raw OpenAI GPT-2 Transformer model (\ **fully pre-trained**\ ),
|
||||
* `GPT2LMHeadModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_gpt2.py#L559>`_ - OpenAI GPT-2 Transformer with the tied language modeling head on top (\ **fully pre-trained**\ ),
|
||||
* `GPT2DoubleHeadsModel <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_gpt2.py#L624>`_ - OpenAI GPT-2 Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT-2 Transformer is **pre-trained**\ , the multiple choice classification head **is only initialized and has to be trained**\ ),
|
||||
|
||||
*
|
||||
Tokenizers for **BERT** (using word-piece) (in the `tokenization.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization.py>`_ file):
|
||||
|
||||
|
||||
* ``BasicTokenizer`` - basic tokenization (punctuation splitting, lower casing, etc.),
|
||||
* ``WordpieceTokenizer`` - WordPiece tokenization,
|
||||
* ``BertTokenizer`` - perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
|
||||
|
||||
*
|
||||
Tokenizer for **OpenAI GPT** (using Byte-Pair-Encoding) (in the `tokenization_openai.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_openai.py>`_ file):
|
||||
|
||||
|
||||
* ``OpenAIGPTTokenizer`` - perform Byte-Pair-Encoding (BPE) tokenization.
|
||||
|
||||
*
|
||||
Tokenizer for **Transformer-XL** (word tokens ordered by frequency for adaptive softmax) (in the `tokenization_transfo_xl.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_transfo_xl.py>`_ file):
|
||||
|
||||
|
||||
* ``OpenAIGPTTokenizer`` - perform word tokenization and can order words by frequency in a corpus for use in an adaptive softmax.
|
||||
|
||||
*
|
||||
Tokenizer for **OpenAI GPT-2** (using byte-level Byte-Pair-Encoding) (in the `tokenization_gpt2.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_gpt2.py>`_ file):
|
||||
|
||||
|
||||
* ``GPT2Tokenizer`` - perform byte-level Byte-Pair-Encoding (BPE) tokenization.
|
||||
|
||||
*
|
||||
Optimizer for **BERT** (in the `optimization.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/pytorch_pretrained_bert/optimization.py>`_ file):
|
||||
|
||||
|
||||
* ``BertAdam`` - Bert version of Adam algorithm with weight decay fix, warmup and linear decay of the learning rate.
|
||||
|
||||
*
|
||||
Optimizer for **OpenAI GPT** (in the `optimization_openai.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/optimization_openai.py>`_ file):
|
||||
|
||||
|
||||
* ``OpenAIAdam`` - OpenAI GPT version of Adam algorithm with weight decay fix, warmup and linear decay of the learning rate.
|
||||
|
||||
*
|
||||
Configuration classes for BERT, OpenAI GPT and Transformer-XL (in the respective `modeling.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py>`_\ , `modeling_openai.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_openai.py>`_\ , `modeling_transfo_xl.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_transfo_xl.py>`_ files):
|
||||
|
||||
|
||||
* ``BertConfig`` - Configuration class to store the configuration of a ``BertModel`` with utilities to read and write from JSON configuration files.
|
||||
* ``OpenAIGPTConfig`` - Configuration class to store the configuration of a ``OpenAIGPTModel`` with utilities to read and write from JSON configuration files.
|
||||
* ``GPT2Config`` - Configuration class to store the configuration of a ``GPT2Model`` with utilities to read and write from JSON configuration files.
|
||||
* ``TransfoXLConfig`` - Configuration class to store the configuration of a ``TransfoXLModel`` with utilities to read and write from JSON configuration files.
|
||||
|
||||
The repository further comprises:
|
||||
|
||||
|
||||
*
|
||||
Five examples on how to use **BERT** (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`_\ ):
|
||||
|
||||
|
||||
* `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_bert_extract_features.py>`_ - Show how to extract hidden states from an instance of ``BertModel``\ ,
|
||||
* `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_bert_classifier.py>`_ - Show how to fine-tune an instance of ``BertForSequenceClassification`` on GLUE's MRPC task,
|
||||
* `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_bert_squad.py>`_ - Show how to fine-tune an instance of ``BertForQuestionAnswering`` on SQuAD v1.0 and SQuAD v2.0 tasks.
|
||||
* `run_swag.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_swag.py>`_ - Show how to fine-tune an instance of ``BertForMultipleChoice`` on Swag task.
|
||||
* `simple_lm_finetuning.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/lm_finetuning/simple_lm_finetuning.py>`_ - Show how to fine-tune an instance of ``BertForPretraining`` on a target text corpus.
|
||||
|
||||
*
|
||||
One example on how to use **OpenAI GPT** (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`_\ ):
|
||||
|
||||
|
||||
* `run_openai_gpt.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_openai_gpt.py>`_ - Show how to fine-tune an instance of ``OpenGPTDoubleHeadsModel`` on the RocStories task.
|
||||
|
||||
*
|
||||
One example on how to use **Transformer-XL** (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`_\ ):
|
||||
|
||||
|
||||
* `run_transfo_xl.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_transfo_xl.py>`_ - Show how to load and evaluate a pre-trained model of ``TransfoXLLMHeadModel`` on WikiText 103.
|
||||
|
||||
*
|
||||
One example on how to use **OpenAI GPT-2** in the unconditional and interactive mode (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`_\ ):
|
||||
|
||||
|
||||
* `run_gpt2.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py>`_ - Show how to use OpenAI GPT-2 an instance of ``GPT2LMHeadModel`` to generate text (same as the original OpenAI GPT-2 examples).
|
||||
|
||||
These examples are detailed in the `Examples <#examples>`_ section of this readme.
|
||||
|
||||
*
|
||||
Three notebooks that were used to check that the TensorFlow and PyTorch models behave identically (in the `notebooks folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks>`_\ ):
|
||||
|
||||
|
||||
* `Comparing-TF-and-PT-models.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing-TF-and-PT-models.ipynb>`_ - Compare the hidden states predicted by ``BertModel``\ ,
|
||||
* `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_ - Compare the spans predicted by ``BertForQuestionAnswering`` instances,
|
||||
* `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_ - Compare the predictions of the ``BertForPretraining`` instances.
|
||||
|
||||
These notebooks are detailed in the `Notebooks <#notebooks>`_ section of this readme.
|
||||
|
||||
|
||||
*
|
||||
A command-line interface to convert TensorFlow checkpoints (BERT, Transformer-XL) or NumPy checkpoint (OpenAI) in a PyTorch save of the associated PyTorch model:
|
||||
|
||||
This CLI is detailed in the `Command-line interface <#Command-line-interface>`_ section of this readme.
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
Installation
|
||||
================================================
|
||||
|
||||
This repo was tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 0.4.1/1.0.0
|
||||
|
||||
With pip
|
||||
^^^^^^^^
|
||||
|
||||
PyTorch pretrained bert can be installed by pip as follows:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install pytorch-pretrained-bert
|
||||
|
||||
If you want to reproduce the original tokenization process of the ``OpenAI GPT`` paper, you will need to install ``ftfy`` (limit to version 4.4.3 if you are using Python 2) and ``SpaCy`` :
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install spacy ftfy==4.4.3
|
||||
python -m spacy download en
|
||||
|
||||
If you don't install ``ftfy`` and ``SpaCy``\ , the ``OpenAI GPT`` tokenizer will default to tokenize using BERT's ``BasicTokenizer`` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
|
||||
|
||||
From source
|
||||
^^^^^^^^^^^
|
||||
|
||||
Clone the repository and run:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install [--editable] .
|
||||
|
||||
Here also, if you want to reproduce the original tokenization process of the ``OpenAI GPT`` model, you will need to install ``ftfy`` (limit to version 4.4.3 if you are using Python 2) and ``SpaCy`` :
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install spacy ftfy==4.4.3
|
||||
python -m spacy download en
|
||||
|
||||
Again, if you don't install ``ftfy`` and ``SpaCy``\ , the ``OpenAI GPT`` tokenizer will default to tokenize using BERT's ``BasicTokenizer`` followed by Byte-Pair Encoding (which should be fine for most usage).
|
||||
|
||||
A series of tests is included in the `tests folder <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/tests>`_ and can be run using ``pytest`` (install pytest if needed: ``pip install pytest``\ ).
|
||||
|
||||
You can run the tests with the command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m pytest -sv tests/
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
Notebooks
|
||||
================================================
|
||||
|
||||
We include `three Jupyter Notebooks <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/notebooks>`_ that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
|
||||
|
||||
|
||||
*
|
||||
The first NoteBook (\ `Comparing-TF-and-PT-models.ipynb <./notebooks/Comparing-TF-and-PT-models.ipynb>`_\ ) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
|
||||
|
||||
*
|
||||
The second NoteBook (\ `Comparing-TF-and-PT-models-SQuAD.ipynb <./notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_\ ) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the ``BertForQuestionAnswering`` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
|
||||
|
||||
*
|
||||
The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <./notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
|
||||
|
||||
Please follow the instructions given in the notebooks to run and modify them.
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
TPU
|
||||
================================================
|
||||
|
||||
TPU support and pretraining scripts
|
||||
------------------------------------------------
|
||||
|
||||
TPU are not supported by the current stable release of PyTorch (0.4.1). However, the next version of PyTorch (v1.0) should support training on TPU and is expected to be released soon (see the recent `official announcement <https://cloud.google.com/blog/products/ai-machine-learning/introducing-pytorch-across-google-cloud>`_\ ).
|
||||
|
||||
We will add TPU support when this next release is published.
|
||||
|
||||
The original TensorFlow code further comprises two scripts for pre-training BERT: `create_pretraining_data.py <https://github.com/google-research/bert/blob/master/create_pretraining_data.py>`_ and `run_pretraining.py <https://github.com/google-research/bert/blob/master/run_pretraining.py>`_.
|
||||
|
||||
Since, pre-training BERT is a particularly expensive operation that basically requires one or several TPUs to be completed in a reasonable amout of time (see details `here <https://github.com/google-research/bert#pre-training-with-bert>`_\ ) we have decided to wait for the inclusion of TPU support in PyTorch to convert these pre-training scripts.
|
||||
|
|
@ -0,0 +1,339 @@
|
|||
Usage
|
||||
================================================
|
||||
|
||||
BERT
|
||||
^^^^
|
||||
|
||||
Here is a quick-start example using ``BertTokenizer``\ , ``BertModel`` and ``BertForMaskedLM`` class with Google AI's pre-trained ``Bert base uncased`` model. See the `doc section <#doc>`_ below for all the details on these classes.
|
||||
|
||||
First let's prepare a tokenized input with ``BertTokenizer``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import torch
|
||||
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
|
||||
|
||||
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
|
||||
import logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
# Load pre-trained model tokenizer (vocabulary)
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
|
||||
# Tokenized input
|
||||
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
|
||||
tokenized_text = tokenizer.tokenize(text)
|
||||
|
||||
# Mask a token that we will try to predict back with `BertForMaskedLM`
|
||||
masked_index = 8
|
||||
tokenized_text[masked_index] = '[MASK]'
|
||||
assert tokenized_text == ['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer', '[SEP]']
|
||||
|
||||
# Convert token to vocabulary indices
|
||||
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
|
||||
# Define sentence A and B indices associated to 1st and 2nd sentences (see paper)
|
||||
segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
|
||||
|
||||
# Convert inputs to PyTorch tensors
|
||||
tokens_tensor = torch.tensor([indexed_tokens])
|
||||
segments_tensors = torch.tensor([segments_ids])
|
||||
|
||||
Let's see how to use ``BertModel`` to get hidden states
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = BertModel.from_pretrained('bert-base-uncased')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor = tokens_tensor.to('cuda')
|
||||
segments_tensors = segments_tensors.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
# Predict hidden states features for each layer
|
||||
with torch.no_grad():
|
||||
encoded_layers, _ = model(tokens_tensor, segments_tensors)
|
||||
# We have a hidden states for each of the 12 layers in model bert-base-uncased
|
||||
assert len(encoded_layers) == 12
|
||||
|
||||
And how to use ``BertForMaskedLM``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor = tokens_tensor.to('cuda')
|
||||
segments_tensors = segments_tensors.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
# Predict all tokens
|
||||
with torch.no_grad():
|
||||
predictions = model(tokens_tensor, segments_tensors)
|
||||
|
||||
# confirm we were able to predict 'henson'
|
||||
predicted_index = torch.argmax(predictions[0, masked_index]).item()
|
||||
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
|
||||
assert predicted_token == 'henson'
|
||||
|
||||
OpenAI GPT
|
||||
^^^^^^^^^^
|
||||
|
||||
Here is a quick-start example using ``OpenAIGPTTokenizer``\ , ``OpenAIGPTModel`` and ``OpenAIGPTLMHeadModel`` class with OpenAI's pre-trained model. See the `doc section <#doc>`_ below for all the details on these classes.
|
||||
|
||||
First let's prepare a tokenized input with ``OpenAIGPTTokenizer``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import torch
|
||||
from pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTModel, OpenAIGPTLMHeadModel
|
||||
|
||||
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
|
||||
import logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
# Load pre-trained model tokenizer (vocabulary)
|
||||
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
|
||||
|
||||
# Tokenized input
|
||||
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
|
||||
tokenized_text = tokenizer.tokenize(text)
|
||||
|
||||
# Convert token to vocabulary indices
|
||||
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
|
||||
|
||||
# Convert inputs to PyTorch tensors
|
||||
tokens_tensor = torch.tensor([indexed_tokens])
|
||||
|
||||
Let's see how to use ``OpenAIGPTModel`` to get hidden states
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = OpenAIGPTModel.from_pretrained('openai-gpt')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor = tokens_tensor.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
# Predict hidden states features for each layer
|
||||
with torch.no_grad():
|
||||
hidden_states = model(tokens_tensor)
|
||||
|
||||
And how to use ``OpenAIGPTLMHeadModel``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor = tokens_tensor.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
# Predict all tokens
|
||||
with torch.no_grad():
|
||||
predictions = model(tokens_tensor)
|
||||
|
||||
# get the predicted last token
|
||||
predicted_index = torch.argmax(predictions[0, -1, :]).item()
|
||||
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
|
||||
assert predicted_token == '.</w>'
|
||||
|
||||
And how to use ``OpenAIGPTDoubleHeadsModel``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = OpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')
|
||||
model.eval()
|
||||
|
||||
# Prepare tokenized input
|
||||
text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
|
||||
text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
|
||||
tokenized_text1 = tokenizer.tokenize(text1)
|
||||
tokenized_text2 = tokenizer.tokenize(text2)
|
||||
indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
|
||||
indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
|
||||
tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
|
||||
mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
|
||||
|
||||
# Predict hidden states features for each layer
|
||||
with torch.no_grad():
|
||||
lm_logits, multiple_choice_logits = model(tokens_tensor, mc_token_ids)
|
||||
|
||||
Transformer-XL
|
||||
^^^^^^^^^^^^^^
|
||||
|
||||
Here is a quick-start example using ``TransfoXLTokenizer``\ , ``TransfoXLModel`` and ``TransfoXLModelLMHeadModel`` class with the Transformer-XL model pre-trained on WikiText-103. See the `doc section <#doc>`_ below for all the details on these classes.
|
||||
|
||||
First let's prepare a tokenized input with ``TransfoXLTokenizer``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import torch
|
||||
from pytorch_pretrained_bert import TransfoXLTokenizer, TransfoXLModel, TransfoXLLMHeadModel
|
||||
|
||||
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
|
||||
import logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
# Load pre-trained model tokenizer (vocabulary from wikitext 103)
|
||||
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
|
||||
|
||||
# Tokenized input
|
||||
text_1 = "Who was Jim Henson ?"
|
||||
text_2 = "Jim Henson was a puppeteer"
|
||||
tokenized_text_1 = tokenizer.tokenize(text_1)
|
||||
tokenized_text_2 = tokenizer.tokenize(text_2)
|
||||
|
||||
# Convert token to vocabulary indices
|
||||
indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
|
||||
indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
|
||||
|
||||
# Convert inputs to PyTorch tensors
|
||||
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
|
||||
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
|
||||
|
||||
Let's see how to use ``TransfoXLModel`` to get hidden states
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor_1 = tokens_tensor_1.to('cuda')
|
||||
tokens_tensor_2 = tokens_tensor_2.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
with torch.no_grad():
|
||||
# Predict hidden states features for each layer
|
||||
hidden_states_1, mems_1 = model(tokens_tensor_1)
|
||||
# We can re-use the memory cells in a subsequent call to attend a longer context
|
||||
hidden_states_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
|
||||
|
||||
And how to use ``TransfoXLLMHeadModel``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor_1 = tokens_tensor_1.to('cuda')
|
||||
tokens_tensor_2 = tokens_tensor_2.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
with torch.no_grad():
|
||||
# Predict all tokens
|
||||
predictions_1, mems_1 = model(tokens_tensor_1)
|
||||
# We can re-use the memory cells in a subsequent call to attend a longer context
|
||||
predictions_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
|
||||
|
||||
# get the predicted last token
|
||||
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
|
||||
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
|
||||
assert predicted_token == 'who'
|
||||
|
||||
OpenAI GPT-2
|
||||
^^^^^^^^^^^^
|
||||
|
||||
Here is a quick-start example using ``GPT2Tokenizer``\ , ``GPT2Model`` and ``GPT2LMHeadModel`` class with OpenAI's pre-trained model. See the `doc section <#doc>`_ below for all the details on these classes.
|
||||
|
||||
First let's prepare a tokenized input with ``GPT2Tokenizer``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import torch
|
||||
from pytorch_pretrained_bert import GPT2Tokenizer, GPT2Model, GPT2LMHeadModel
|
||||
|
||||
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
|
||||
import logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
# Load pre-trained model tokenizer (vocabulary)
|
||||
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
|
||||
|
||||
# Encode some inputs
|
||||
text_1 = "Who was Jim Henson ?"
|
||||
text_2 = "Jim Henson was a puppeteer"
|
||||
indexed_tokens_1 = tokenizer.encode(text_1)
|
||||
indexed_tokens_2 = tokenizer.encode(text_2)
|
||||
|
||||
# Convert inputs to PyTorch tensors
|
||||
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
|
||||
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
|
||||
|
||||
Let's see how to use ``GPT2Model`` to get hidden states
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = GPT2Model.from_pretrained('gpt2')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor_1 = tokens_tensor_1.to('cuda')
|
||||
tokens_tensor_2 = tokens_tensor_2.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
# Predict hidden states features for each layer
|
||||
with torch.no_grad():
|
||||
hidden_states_1, past = model(tokens_tensor_1)
|
||||
# past can be used to reuse precomputed hidden state in a subsequent predictions
|
||||
# (see beam-search examples in the run_gpt2.py example).
|
||||
hidden_states_2, past = model(tokens_tensor_2, past=past)
|
||||
|
||||
And how to use ``GPT2LMHeadModel``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = GPT2LMHeadModel.from_pretrained('gpt2')
|
||||
model.eval()
|
||||
|
||||
# If you have a GPU, put everything on cuda
|
||||
tokens_tensor_1 = tokens_tensor_1.to('cuda')
|
||||
tokens_tensor_2 = tokens_tensor_2.to('cuda')
|
||||
model.to('cuda')
|
||||
|
||||
# Predict all tokens
|
||||
with torch.no_grad():
|
||||
predictions_1, past = model(tokens_tensor_1)
|
||||
# past can be used to reuse precomputed hidden state in a subsequent predictions
|
||||
# (see beam-search examples in the run_gpt2.py example).
|
||||
predictions_2, past = model(tokens_tensor_2, past=past)
|
||||
|
||||
# get the predicted last token
|
||||
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
|
||||
predicted_token = tokenizer.decode([predicted_index])
|
||||
|
||||
And how to use ``GPT2DoubleHeadsModel``
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# Load pre-trained model (weights)
|
||||
model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
|
||||
model.eval()
|
||||
|
||||
# Prepare tokenized input
|
||||
text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
|
||||
text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
|
||||
tokenized_text1 = tokenizer.tokenize(text1)
|
||||
tokenized_text2 = tokenizer.tokenize(text2)
|
||||
indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
|
||||
indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
|
||||
tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
|
||||
mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
|
||||
|
||||
# Predict hidden states features for each layer
|
||||
with torch.no_grad():
|
||||
lm_logits, multiple_choice_logits, past = model(tokens_tensor, mc_token_ids)
|
||||
|
|
@ -565,53 +565,25 @@ class BertPreTrainedModel(PreTrainedModel):
|
|||
|
||||
|
||||
class BertModel(BertPreTrainedModel):
|
||||
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
|
||||
r"""BERT model ("Bidirectional Embedding Representations from a Transformer").
|
||||
|
||||
Params:
|
||||
`config`: a BertConfig class instance with the configuration to build a new model
|
||||
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
||||
`output_hidden_states`: If True, also output hidden states computed by the model at each layer. Default: False
|
||||
:class:`~pytorch_pretrained_bert.BertModel` is the basic BERT Transformer model with a layer of summed token, \
|
||||
position and sequence embeddings followed by a series of identical self-attention blocks (12 for BERT-base, 24 \
|
||||
for BERT-large). The model is instantiated with the following parameters.
|
||||
|
||||
Inputs:
|
||||
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
||||
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
||||
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
|
||||
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
||||
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
||||
a `sentence B` token (see BERT paper for more details).
|
||||
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
||||
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
||||
input sequence length in the current batch. It's the mask that we typically use for attention when
|
||||
a batch has varying length sentences.
|
||||
`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
|
||||
`head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
||||
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
||||
Arguments:
|
||||
config: a BertConfig class instance with the configuration to build a new model
|
||||
output_attentions: If True, also output attentions weights computed by the model at each layer. Default: False
|
||||
output_hidden_states: If True, also output hidden states computed by the model at each layer. Default: Fals
|
||||
|
||||
|
||||
Outputs: Tuple of (encoded_layers, pooled_output)
|
||||
`encoded_layers`: controled by `output_all_encoded_layers` argument:
|
||||
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
|
||||
of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
|
||||
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
|
||||
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
|
||||
to the last attention block of shape [batch_size, sequence_length, hidden_size],
|
||||
`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
|
||||
classifier pretrained on top of the hidden state associated to the first character of the
|
||||
input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
|
||||
Example::
|
||||
|
||||
Example usage:
|
||||
```python
|
||||
# Already been converted into WordPiece token ids
|
||||
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
||||
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
||||
config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
||||
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
||||
|
||||
config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
||||
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
||||
model = modeling.BertModel(config=config)
|
||||
|
||||
model = modeling.BertModel(config=config)
|
||||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
||||
```
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(BertModel, self).__init__(config)
|
||||
|
|
@ -631,6 +603,58 @@ class BertModel(BertPreTrainedModel):
|
|||
self.encoder.layer[layer].attention.prune_heads(heads)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, head_mask=None):
|
||||
"""
|
||||
Performs a model forward pass. Can be called by calling the class directly, once it has been instantiated.
|
||||
|
||||
|
||||
Arguments:
|
||||
input_ids: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the \
|
||||
vocabulary(see the tokens pre-processing logic in the scripts `run_bert_extract_features.py`, \
|
||||
`run_bert_classifier.py` and `run_bert_squad.py`)
|
||||
token_type_ids: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token \
|
||||
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to \
|
||||
a `sentence B` token (see BERT paper for more details).
|
||||
attention_mask: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices \
|
||||
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max \
|
||||
input sequence length in the current batch. It's the mask that we typically use for attention when \
|
||||
a batch has varying length sentences.
|
||||
output_all_encoded_layers: boolean which controls the content of the `encoded_layers` output as described \
|
||||
below. Default: `True`.
|
||||
head_mask: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 \
|
||||
and 1. It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 \
|
||||
=> head is not masked.
|
||||
|
||||
|
||||
Returns:
|
||||
A tuple composed of (encoded_layers, pooled_output). Encoded layers are controlled by the \
|
||||
``output_all_encoded_layers`` argument.
|
||||
|
||||
If ``output_all_encoded_layers`` is set to True, outputs a list of the full sequences of \
|
||||
encoded-hidden-states at the end of each attention \
|
||||
block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a\
|
||||
torch.FloatTensor of size [batch_size, sequence_length, hidden_size].
|
||||
|
||||
If set to False, outputs only the full sequence of hidden-states corresponding \
|
||||
to the last attention block of shape [batch_size, sequence_length, hidden_size].
|
||||
|
||||
``pooled_output`` is a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a \
|
||||
classifier pretrained on top of the hidden state associated to the first character of the \
|
||||
input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
|
||||
|
||||
Example::
|
||||
|
||||
# Already been converted into WordPiece token ids
|
||||
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
||||
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
||||
|
||||
|
||||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
||||
# or
|
||||
all_encoder_layers, pooled_output = model.forward(input_ids, token_type_ids, input_mask)
|
||||
|
||||
|
||||
"""
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones_like(input_ids)
|
||||
if token_type_ids is None:
|
||||
|
|
|
|||
Loading…
Reference in New Issue