* added rag WIP
* path fix
* Formatting / renaming prior to actual work
* added rag WIP
* path fix
* Formatting / renaming prior to actual work
* added rag WIP
* path fix
* Formatting / renaming prior to actual work
* added rag WIP
* Formatting / renaming prior to actual work
* First commit
* improve comments
* Retrieval evaluation scripts
* refactor to include modeling outputs + MPI retriever
* Fix rag-token model + refactor
* Various fixes + finetuning logic
* use_bos fix
* Retrieval refactor
* Finetuning refactoring and cleanup
* Add documentation and cleanup
* Remove set_up_rag_env.sh file
* Fix retrieval wit HF index
* Fix import errors
* Fix quality errors
* Refactor as per suggestions in https://github.com/huggingface/transformers/pull/6813#issuecomment-687208867
* fix quality
* Fix RAG Sequence generation
* minor cleanup plus initial tests
* fix test
* fix tests 2
* Comments fix
* post-merge fixes
* Improve readme + post-rebase refactor
* Extra dependencied for tests
* Fix tests
* Fix tests 2
* Refactor test requirements
* Fix tests 3
* Post-rebase refactor
* rename nlp->datasets
* RAG integration tests
* add tokenizer to slow integration test and allow retriever to run on cpu
* add tests; fix position ids warning
* change structure
* change structure
* add from encoder generator
* save working solution
* make all integration tests pass
* add RagTokenizer.save/from_pretrained and RagRetriever.save/from_pretrained
* don't save paths
* delete unnecessary imports
* pass config to AutoTokenizer.from_pretrained for Rag tokenizers
* init wiki_dpr only once
* hardcode legacy index and passages paths (todo: add the right urls)
* finalize config
* finalize retriver api and config api
* LegacyIndex index download refactor
* add dpr to autotokenizer
* make from pretrained more flexible
* fix ragfortokengeneration
* small name changes in tokenizer
* add labels to models
* change default index name
* add retrieval tests
* finish token generate
* align test with previous version and make all tests pass
* add tests
* finalize tests
* implement thoms suggestions
* add first version of test
* make first tests work
* make retriever platform agnostic
* naming
* style
* add legacy index URL
* docstrings + simple retrieval test for distributed
* clean model api
* add doc_ids to retriever's outputs
* fix retrieval tests
* finish model outputs
* finalize model api
* fix generate problem for rag
* fix generate for other modles
* fix some tests
* save intermediate
* set generate to default
* big refactor generate
* delete rag_api
* correct pip faiss install
* fix auto tokenization test
* fix faiss install
* fix test
* move the distributed logic to examples
* model page
* docs
* finish tests
* fix dependencies
* fix import in __init__
* Refactor eval_rag and finetune scripts
* start docstring
* add psutil to test
* fix tf test
* move require torch to top
* fix retrieval test
* align naming
* finish automodel
* fix repo consistency
* test ragtokenizer save/load
* add rag model output docs
* fix ragtokenizer save/load from pretrained
* fix tokenizer dir
* remove torch in retrieval
* fix docs
* fixe finetune scripts
* finish model docs
* finish docs
* remove auto model for now
* add require torch
* remove solved todos
* integrate sylvains suggestions
* sams comments
* correct mistake on purpose
* improve README
* Add generation test cases
* fix rag token
* clean token generate
* fix test
* add note to test
* fix attention mask
* add t5 test for rag
* Fix handling prefix in finetune.py
* don't overwrite index_name
Co-authored-by: Patrick Lewis <plewis@fb.com>
Co-authored-by: Aleksandra Piktus <piktus@devfair0141.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5102.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5067.h2.fair>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>
my flake8 wasn't up-to-date enough `make quality` wasn't reporting the same things CI did - this PR adds the actual required version.
Thinking more about some of these minimal versions - CI will always install afresh and thus will always run the latest version. Is there a way to tell pip to always install the latest versions of certain dependencies on `pip install -i ".[dev]"`, rather than hardcoding the minimals which quickly become outdated?
unittest doesn't support pytest's super-handy `@pytest.mark.parametrize`, I researched and there are many proposed workarounds, most tedious at best. If we include https://pypi.org/project/parameterized/ in dev dependencies - it will provide a very easy to write parameterization in tests. Same as pytest's fixture, plus quite a few other ways.
Example:
```
from parameterized import parameterized
@parameterized([
(2, 2, 4),
(2, 3, 8),
(1, 9, 1),
(0, 9, 0),
])
def test_pow(base, exponent, expected):
assert_equal(math.pow(base, exponent), expected)
```
(extra `self`var if inside a test class)
To remind the pytest style is slightly different:
```
@pytest.mark.parametrize("test_input,expected", [("3+5", 8), ("2+4", 6), ("6*9", 42)])
def test_eval(test_input, expected):
```
More examples here: https://pypi.org/project/parameterized
May I suggest that it will make it much easier to write some types of tests?
* Replace mecab-python3 with fugashi
This replaces mecab-python3 with fugashi for Japanese tokenization. I am
the maintainer of both projects.
Both projects are MeCab wrappers, so the underlying C++ code is the
same. fugashi is the newer wrapper and doesn't use SWIG, so for basic
use of the MeCab API it's easier to use.
This code insures the use of a version of ipadic installed via pip,
which should make versioning and tracking down issues easier.
fugashi has wheels for Windows, OSX, and Linux, which will help with
issues with installing old versions of mecab-python3 on Windows.
Compared to mecab-python3, because fugashi doesn't use SWIG, it doesn't
require a C++ runtime to be installed on Windows.
In adding this change I removed some code dealing with `cursor`,
`token_start`, and `token_end` variables. These variables didn't seem to
be used for anything, it is unclear to me why they were there.
I ran the tests and they passed, though I couldn't figure out how to run
the slow tests (`--runslow` gave an error) and didn't try testing with
Tensorflow.
* Style fix
* Remove unused variable
Forgot to delete this...
* Adapt doc with install instructions
* Fix typo
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add return lengths
* make pad a bit more flexible so it can be used as collate_fn
* check all kwargs sent to encoding method are known
* fixing kwargs in encodings
* New AddedToken class in python
This class let you specify specifique tokenization behaviors for some special tokens. Used in particular for GPT2 and Roberta, to control how white spaces are stripped around special tokens.
* style and quality
* switched to hugginface tokenizers library for AddedTokens
* up to tokenizer 0.8.0-rc3 - update API to use AddedToken state
* style and quality
* do not raise an error on additional or unused kwargs for tokenize() but only a warning
* transfo-xl pretrained model requires torch
* Update src/transformers/tokenization_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* add tpu and torchscipt for benchmark
* fix name in tests
* "fix email"
* make style
* better log message for tpu
* add more print and info for tpu
* allow possibility to print tpu metrics
* correct cpu usage
* fix test for non-install
* remove bugus file
* include psutil in testing
* run a couple of times before tracing in torchscript
* do not allow tpu memory tracing for now
* make style
* add torchscript to env
* better name for torch tpu
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* make transformers-cli cross-platform
Using "scripts" is a useful option in setup.py particularly when you want to get access to non-python scripts. However, in this case we want to have an entry point into some of our own Python scripts. To do this in a concise, cross-platfom way, we can use entry_points.console_scripts. This change is necessary to provide the CLI on different platforms, which "scripts" does not ensure. Usage remains the same, but the "transformers-cli" script has to be moved (be part of the library) and renamed (underscore + extension)
* make style & quality
* Added generic ONNX conversion script for PyTorch model.
* WIP initial TF support.
* TensorFlow/Keras ONNX export working.
* Print framework version info
* Add possibility to check the model is correctly loading on ONNX runtime.
* Remove quantization option.
* Specify ONNX opset version when exporting.
* Formatting.
* Remove unused imports.
* Make functions more generally reusable from other part of the code.
* isort happy.
* flake happy
* Export only feature-extraction for now
* Correctly check inputs order / filter before export.
* Removed task variable
* Fix invalid args call in load_graph_from_args.
* Fix invalid args call in convert.
* Fix invalid args call in infer_shapes.
* Raise exception and catch in caller function instead of exit.
* Add 04-onnx-export.ipynb notebook
* More WIP on the notebook
* Remove unused imports
* Simplify & remove unused constants.
* Export with constant_folding in PyTorch
* Let's try to put function args in the right order this time ...
* Disable external_data_format temporary
* ONNX notebook draft ready.
* Updated notebooks charts + wording
* Correct error while exporting last chart in notebook.
* Adressing @LysandreJik comment.
* Set ONNX opset to 11 as default value.
* Set opset param mandatory
* Added ONNX export unittests
* Quality.
* flake8 happy
* Add keras2onnx dependency on extras["tf"]
* Pin keras2onnx on github master to v1.6.5
* Second attempt.
* Third attempt.
* Use the right repo URL this time ...
* Do the same for onnxconverter-common
* Added keras2onnx and onnxconveter-common to 1.7.0 to supports TF2.2
* Correct commit hash.
* Addressing PR review: Optimization are enabled by default.
* Addressing PR review: small changes in the notebook
* setup.py comment about keras2onnx versioning.
* Allow BatchEncoding to be initialized empty.
This is required by recent changes introduced in TF 2.2.
* Attempt to unpin Tensorflow to 2.2 with the previous commit.
* Fix the issue to properly run the accumulator with TF 2.2
* Apply style
* Fix training_args_tf for TF 2.2
* Fix the TF training args when only one GPU is available
* Remove the fixed version of TF in setup.py
* First pass on utility classes and python tokenizers
* finishing cleanup pass
* style and quality
* Fix tests
* Updating following @mfuntowicz comment
* style and quality
* Fix Roberta
* fix batch_size/seq_length inBatchEncoding
* add alignement methods + tests
* Fix OpenAI and Transfo-XL tokenizers
* adding trim_offsets=True default for GPT2 et RoBERTa
* style and quality
* fix tests
* add_prefix_space in roberta
* bump up tokenizers to rc7
* style
* unfortunately tensorfow does like these - removing shape/seq_len for now
* Update src/transformers/tokenization_utils.py
Co-Authored-By: Stefan Schweter <stefan@schweter.it>
* Adding doc and docstrings
* making flake8 happy
Co-authored-by: Stefan Schweter <stefan@schweter.it>
* Renamed num_added_tokens to num_special_tokens_to_add
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Cherry-Pick: Partially fix space only input without special tokens added to the output #3091
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added property is_fast on PretrainedTokenizer and PretrainedTokenizerFast
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Make fast tokenizers unittests work on Windows.
* Entirely refactored unittest for tokenizers fast.
* Remove ABC class for CommonFastTokenizerTest
* Added embeded_special_tokens tests from allenai @dirkgr
* Make embeded_special_tokens tests from allenai more generic
* Uniformize vocab_size as a property for both Fast and normal tokenizers
* Move special tokens handling out of PretrainedTokenizer (SpecialTokensMixin)
* Ensure providing None input raise the same ValueError than Python tokenizer + tests.
* Fix invalid input for assert_padding when testing batch_encode_plus
* Move add_special_tokens from constructor to tokenize/encode/[batch_]encode_plus methods parameter.
* Ensure tokenize() correctly forward add_special_tokens to rust.
* Adding None checking on top on encode / encode_batch for TransfoXLTokenizerFast.
Avoid stripping on None values.
* unittests ensure tokenize() also throws a ValueError if provided None
* Added add_special_tokens unittest for all supported models.
* Style
* Make sure TransfoXL test run only if PyTorch is provided.
* Split up tokenizers tests for each model type.
* Fix invalid unittest with new tokenizers API.
* Filter out Roberta openai detector models from unittests.
* Introduce BatchEncoding on fast tokenizers path.
This new structure exposes all the mappings retrieved from Rust.
It also keeps the current behavior with model forward.
* Introduce BatchEncoding on slow tokenizers path.
Backward compatibility.
* Improve error message on BatchEncoding for slow path
* Make add_prefix_space True by default on Roberta fast to match Python in majority of cases.
* Style and format.
* Added typing on all methods for PretrainedTokenizerFast
* Style and format
* Added path for feeding pretokenized (List[str]) input to PretrainedTokenizerFast.
* Style and format
* encode_plus now supports pretokenized inputs.
* Remove user warning about add_special_tokens when working on pretokenized inputs.
* Always go through the post processor.
* Added support for pretokenized input pairs on encode_plus
* Added is_pretokenized flag on encode_plus for clarity and improved error message on input TypeError.
* Added pretokenized inputs support on batch_encode_plus
* Update BatchEncoding methods name to match Encoding.
* Bump setup.py tokenizers dependency to 0.7.0rc1
* Remove unused parameters in BertTokenizerFast
* Make sure Roberta returns token_type_ids for unittests.
* Added missing typings
* Update add_tokens prototype to match tokenizers side and allow AddedToken
* Bumping tokenizers to 0.7.0rc2
* Added documentation for BatchEncoding
* Added (unused) is_pretokenized parameter on PreTrainedTokenizer encode_plus/batch_encode_plus methods.
* Added higher-level typing for tokenize / encode_plus / batch_encode_plus.
* Fix unittests failing because add_special_tokens was defined as a constructor parameter on Rust Tokenizers.
* Fix text-classification pipeline using the wrong tokenizer
* Make pipelines works with BatchEncoding
* Turn off add_special_tokens on tokenize by default.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove add_prefix_space from tokenize call in unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Style and quality
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Correct message for batch_encode_plus none input exception.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix invalid list comprehension for offset_mapping overriding content every iteration.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* TransfoXL uses Strip normalizer.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizers dependency to 0.7.0rc3
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Support AddedTokens for special_tokens and use left stripping on mask for Roberta.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* SpecilaTokenMixin can use slots to faster access to underlying attributes.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove update_special_tokens from fast tokenizers.
* Ensure TransfoXL unittests are run only when torch is available.
* Style.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Style
* Style 🙏🙏
* Remove slots on SpecialTokensMixin, need deep dive into pickle protocol.
* Remove Roberta warning on __init__.
* Move documentation to Google style.
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
* [ci] Also run test_examples in py37
(will revert at the end of the experiment)
* InputExample: use immutable dataclass
* [deps] Install dataclasses for Py<3.7
* [skip ci] Revert "[ci] Also run test_examples in py37"
This reverts commit d29afd9959.
The CONTRIBUTING file pins to a specific version of isort, so we might as well install that in `dev` . This makes it easier for contributors so they don't have to manually install the specific commit.
* memory benchmark rss
* have both forward pass and line-by-line mem tracing
* cleaned up tracing
* refactored and cleaning up API
* no f-strings yet...
* add GPU mem logging
* fix GPU memory monitoring
* style and quality
* clean up and doc
* update with comments
* Switching to python 3.6+
* fix quality
* Renamed file generate by tokenizers when calling save_pretrained to match python.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added save_vocabulary tests.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove python quick and dirty fix for clean Rust impl.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizers dependency to 0.5.1
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* TransfoXLTokenizerFast uses a json vocabulary file + warning about incompatibility between Python and Rust
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added some save_pretrained / from_pretrained unittests.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Update tokenizers to 0.5.2
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Quality and format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* flake8
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Making sure there is really a bug in unittest
* Fix TransfoXL constructor vocab_file / pretrained_vocab_file mixin.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Implemented fast version of tokenizers
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bumped tokenizers version requirements to latest 0.2.1
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added matching tests
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Matching OpenAI GPT tokenization !
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Matching GPT2 on tokenizers
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Expose add_prefix_space as constructor parameter for GPT2
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Matching Roberta tokenization !
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Removed fast implementation of CTRL.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Binding TransformerXL tokenizers to Rust.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Updating tests accordingly.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added tokenizers as top-level modules.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Black & isort.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Rename LookupTable to WordLevel to match Rust side.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Black.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use "fast" suffix instead of "ru" for rust tokenizers implementations.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Introduce tokenize() method on fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* encode_plus dispatchs to batch_encode_plus
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* batch_encode_plus now dispatchs to encode if there is only one input element.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bind all the encode_plus parameter to the forwarded batch_encode_plus call.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizers dependency to 0.3.0
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Formatting.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix tokenization_auto with support for new (python, fast) mapping schema.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Give correct fixtures path in test_tokenization_fast.py for the CLI.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Expose max_len_ properties on BertTokenizerFast
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Move max_len_ properties to PreTrainedTokenizerFast and override in specific subclasses.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* _convert_encoding should keep the batch axis tensor if only one sample in the batch.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Add warning message for RobertaTokenizerFast if used for MLM.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added use_fast (bool) parameter on AutoTokenizer.from_pretrained().
This allows to easily enable/disable Rust-based tokenizer instantiation.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Let's tokenizers handle all the truncation and padding stuff.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Allow to provide tokenizer arguments during pipeline creation.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Update test_fill_mask pipeline to not use fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix too much parameters for convert_encoding.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* When enabling padding, max_length should be set to None.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Avoid returning nested tensors of length 1 when calling encode_plus
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure output is padded when return_tensor is not None.
Tensor creation requires the inital list input to be of the exact same size.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Disable transfoxl unittest if pytorch is not available (required to load the model)
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* encode_plus should not remove the leading batch axis if return_tensor is set
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Temporary disable fast tokenizers on QA pipelines.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix formatting issues.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Update tokenizers to 0.4.0
* Update style
* Enable truncation + stride unit test on fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Add unittest ensuring special_tokens set match between Python and Rust.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure special_tokens are correctly set during construction.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Give more warning feedback to the user in case of padding without pad_token.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* quality & format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added possibility to add a single token as str
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added unittest for add_tokens and add_special_tokens on fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix rebase mismatch on pipelines qa default model.
QA requires cased input while the tokenizers would be uncased.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Using offset mapping relative to the original string + unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: save_vocabulary requires folder and file name
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Simplify import for Bert.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: truncate_and_pad disables padding according to the same heuristic than the one enabling padding.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Remove private member access in tokenize()
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Bump tokenizers dependency to 0.4.2
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* format & quality.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Use named arguments when applicable.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Add Github link to Roberta/GPT2 space issue on masked input.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Move max_len_single_sentence / max_len_sentences_pair to PreTrainedTokenizerFast + tests.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Relax type checking to include tuple and list object.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Document the truncate_and_pad manager behavior.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Raise an exception if return_offsets_mapping is not available with the current tokenizer.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure padding is set on the tokenizers before setting any padding strategy + unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* On pytorch we need to stack tensor to get proper new axis.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Generalize tests to different framework removing hard written return_tensors="..."
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizer dependency for num_special_tokens_to_add
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Overflowing tokens in batch_encode_plus are now stacked over the batch axis.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Improved error message for padding strategy without pad token.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bumping tokenizers dependency to 0.5.0 for release.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Optimizing convert_encoding around 4x improvement. 🚀
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* expose pad_to_max_length in encode_plus to avoid duplicating the parameters in kwargs
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Generate a proper overflow_to_sampling_mapping when return_overflowing_tokens is True.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix unittests for overflow_to_sampling_mapping not being returned as tensor.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Format & quality.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove perfect alignment constraint for Roberta (allowing 1% difference max)
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Triggering final CI
Co-authored-by: MOI Anthony <xn1t0x@gmail.com>
Tensorflow 2.1.0 introduce a new dependency model where pip install tensorflow would install tf with GPU support.
Before it would just install with CPU support, thus CircleCI is looking for NVidia driver version at initialization of the
tensorflow related tests but fails as their is no NVidia Driver running.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
- It appears that `tqdm` only introduced `tqdm.auto` in 4.27.
- See https://github.com/tqdm/tqdm/releases/tag/v4.27.0.
- Without the lower bound I received the following stack trace in an environment where I already had tqdm installed:
```
File "/home/brendanr/anaconda3/envs/allennlp/lib/python3.6/site-packages/transformers/__init__.py", line 20, in <module>
from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
File "/home/brendanr/anaconda3/envs/allennlp/lib/python3.6/site-packages/transformers/file_utils.py", line 24, in <module>
from tqdm.auto import tqdm
ModuleNotFoundError: No module named 'tqdm.auto'
```
If a user or contributor ran `pip install -e .` on transformers < 3.0,
pip created a transformers.egg-info directory next to the transformers
directory at the root of the repository.
In transformers 3.0, the source is in a `src` subdirectory.
`pip install -e .` creates a transformers.egg-info directory there.
However, pip will still pick transformers.egg-info from the previous
location. This is a bug: https://github.com/pypa/pip/issues/5466
Users and contributors are likely to hit this problem because the
documentation for transformers 3.0 relies heavily on extra_requires
which didn't exist in earlier versions, so aren't defined in a stale
transformers.egg-info directory.
If such a directory exists, remove it. It's autogenerated, gitignored
and not supposed to contain anything of value.
This prevents transformers from being importable simply because the CWD
is the root of the git repository, while not being importable from other
directories. That led to inconsistent behavior, especially in examples.
Once you fetch this commit, in your dev environment, you must run:
$ pip uninstall transformers
$ pip install -e .
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
Since the file is written to the filesystem, a filesystem lock is the
way to go here. Add a dependency on the third-party filelock library to
get cross-platform functionality.
* Switch to plain unittest for skipping slow tests.
Add a RUN_SLOW environment variable for running them.
* Switch to plain unittest for PyTorch dependency.
* Switch to plain unittest for TensorFlow dependency.
* Avoid leaking open files in the test suite.
This prevents spurious warnings when running tests.
* Fix unicode warning on Python 2 when running tests.
The warning was:
UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
* Support running PyTorch tests on a GPU.
Reverts 27e015bd.
* Tests no longer require pytest.
* Make tests pass on cuda
