* add test for glue
* add tests for clm
* fix clm test
* add summrization tests
* more tests
* fix few tests
* add test for t5 mlm
* fix t5 mlm test
* fix tests for multi device
* cleanup
* ci job
* fix metric file name
* make t5 more robust
* started bf16 integration
* minor changes
* code now runs
* style
* lay foundation for bf16 testing
* lay foundation for bf16 testing
* start the tests
* better bf16 check
* style
* 2 separate checkers - one for bf16 support, another for bf16+autocast
* Update src/transformers/training_args.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* a couple of comment resolutions
* more comment resolutions
* resolved a small bug
* just some print statemtns
* added todo marking
* added a todo
* adjust for API change s/fast_dtype/dtype/
* fix style
* merge 2 bf16 util functions
* bf16 now does scaling too
* Add support for bfloat16
* Revert T5 layernorm to float32
This is based on the comment at https://github.com/huggingface/transformers/pull/14448/files#r752660929 and the PyTorch PR https://github.com/pytorch/pytorch/pull/66920 .
* Add comment about conversion to float32 before returning the numpy data
* Add comment about AMP-bfloat16 incompatibility
* Fix formatting
* typo
* reformer / bf16
* cleanup
* require at least pt-1.10
* fix
* will deal with deepspeed separately
* cleanup
* revert
* cleanup
* fp16_full_eval and bf16_full_eval are separate modes
* proper deprecation
* cleanup
* test and fixes
* spelling
* cleanup
* add a note that this API is experimental
Co-authored-by: jamie <jamie@cortx.com>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: suriya <suriya@cortx.com>
Co-authored-by: Manuel R. Ciosici <manuelrciosici@gmail.com>
* Init Flax implementation for Blenderbot
* Add a majority of stuff except for tests
* make style quality
* Add tests and fix some bugs
* Add tests
* Clean source code and fix some bugs
* Fix copies and docs
* Fix jax device condition for tests
* Fix layer norm in the encoder
* Fix a few typos in the test file
* make fix-copies
* make fix-copies
* fix layer norm
* Fix Flax params dtype (#13090)
* Fix PR reference (#13098)
* make fix-copies
* Update tests/test_modeling_flax_blenderbot.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* TF Tapas first commit
* updated docs
* updated logger message
* updated pytorch weight conversion
script to support scalar array
* added use_cache to tapas model config to
work properly with tf input_processing
* 1. rm embeddings_sum
2. added # Copied
3. + TFTapasMLMHead
4. and lot other small fixes
* updated docs
* + test for tapas
* updated testing_utils to check
is_tensorflow_probability_available
* converted model logits post processing using
numpy to work with both PT and TF models
* + TFAutoModelForTableQuestionAnswering
* added TF support
* added test for
TFAutoModelForTableQuestionAnswering
* added test for
TFAutoModelForTableQuestionAnswering pipeline
* updated auto model docs
* fixed typo in import
* added tensorflow_probability to run tests
* updated MLM head
* updated tapas.rst with TF model docs
* fixed optimizer import in docs
* updated convert to np
data from pt model is not
`transformers.tokenization_utils_base.BatchEncoding`
after pipeline upgrade
* updated pipeline:
1. with torch.no_gard removed, pipeline forward handles
2. token_type_ids converted to numpy
* updated docs.
* removed `use_cache` from config
* removed floats_tensor
* updated code comment
* updated Copyright Year and
logits_aggregation Optional
* updated docs and comments
* updated docstring
* fixed model weight loading
* make fixup
* fix indentation
* added tf slow pipeline test
* pip upgrade
* upgrade python to 3.7
* removed from_pt from tests
* revert commit f18cfa9
* add sigopt hpo to transformers.
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* extend sigopt changes to test code and others..
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* Style.
* fix style for sigopt integration.
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* Add necessary information to run unittests on SigOpt.
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Enabling dataset iteration on pipelines.
Enabling dataset iteration on pipelines.
Unifying parameters under `set_parameters` function.
Small fix.
Last fixes after rebase
Remove print.
Fixing text2text `generate_kwargs`
No more `self.max_length`.
Fixing tf only conversational.
Consistency in start/stop index over TF/PT.
Speeding up drastically on TF (nasty bug where max_length would increase
a ton.)
Adding test for support for non fast tokenizers.
Fixign GPU usage on zero-shot.
Fix working on Tf.
Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Small cleanup.
Remove all asserts + simple format.
* Fixing audio-classification for large PR.
* Overly explicity null checking.
* Encapsulating GPU/CPU pytorch manipulation directly within `base.py`.
* Removed internal state for parameters of the pipeline.
Instead of overriding implicitly internal state, we moved
to real named arguments on every `preprocess`, `_forward`,
`postprocess` function.
Instead `_sanitize_parameters` will be used to split all kwargs
of both __init__ and __call__ into the 3 kinds of named parameters.
* Move import warnings.
* Small fixes.
* Quality.
* Another small fix, using the CI to debug faster.
* Last fixes.
* Last fix.
* Small cleanup of tensor moving.
* is not None.
* Adding a bunch of docs + a iteration test.
* Fixing doc style.
* KeyDataset = None guard.
* RRemoving the Cuda test for pipelines (was testing).
* Even more simple iteration test.
* Correct import .
* Long day.
* Fixes in docs.
* [WIP] migrating object detection.
* Fixed the target_size bug.
* Fixup.
* Bad variable name.
* Fixing `ensure_on_device` respects original ModelOutput.
* First commit
* Make style
* Fix dummy objects
* Add Detectron2 config
* Add LayoutLMv2 pooler
* More improvements, add documentation
* More improvements
* Add model tests
* Add clarification regarding image input
* Improve integration test
* Fix bug
* Fix another bug
* Fix another bug
* Fix another bug
* More improvements
* Make more tests pass
* Make more tests pass
* Improve integration test
* Remove gradient checkpointing and add head masking
* Add integration test
* Add LayoutLMv2ForSequenceClassification to the tests
* Add LayoutLMv2ForQuestionAnswering
* More improvements
* More improvements
* Small improvements
* Fix _LazyModule
* Fix fast tokenizer
* Move sync_batch_norm to a separate method
* Replace dummies by requires_backends
* Move calculation of visual bounding boxes to separate method + update README
* Add models to main init
* First draft
* More improvements
* More improvements
* More improvements
* More improvements
* More improvements
* Remove is_split_into_words
* More improvements
* Simply tesseract - no use of pandas anymore
* Add LayoutLMv2Processor
* Update is_pytesseract_available
* Fix bugs
* Improve feature extractor
* Fix bug
* Add print statement
* Add truncation of bounding boxes
* Add tests for LayoutLMv2FeatureExtractor and LayoutLMv2Tokenizer
* Improve tokenizer tests
* Make more tokenizer tests pass
* Make more tests pass, add integration tests
* Finish integration tests
* More improvements
* More improvements - update API of the tokenizer
* More improvements
* Remove support for VQA training
* Remove some files
* Improve feature extractor
* Improve documentation and one more tokenizer test
* Make quality and small docs improvements
* Add batched tests for LayoutLMv2Processor, remove fast tokenizer
* Add truncation of labels
* Apply suggestions from code review
* Improve processor tests
* Fix failing tests and add suggestion from code review
* Fix tokenizer test
* Add detectron2 CI job
* Simplify CI job
* Comment out non-detectron2 jobs and specify number of processes
* Add pip install torchvision
* Add durations to see which tests are slow
* Fix tokenizer test and make model tests smaller
* Frist draft
* Use setattr
* Possible fix
* Proposal with configuration
* First draft of fast tokenizer
* More improvements
* Enable fast tokenizer tests
* Make more tests pass
* Make more tests pass
* More improvements
* Addd padding to fast tokenizer
* Mkae more tests pass
* Make more tests pass
* Make all tests pass for fast tokenizer
* Make fast tokenizer support overflowing boxes and labels
* Add support for overflowing_labels to slow tokenizer
* Add support for fast tokenizer to the processor
* Update processor tests for both slow and fast tokenizers
* Add head models to model mappings
* Make style & quality
* Remove Detectron2 config file
* Add configurable option to label all subwords
* Fix test
* Skip visual segment embeddings in test
* Use ResNet-18 backbone in tests instead of ResNet-101
* Proposal
* Re-enable all jobs on CI
* Fix installation of tesseract
* Fix failing test
* Fix index table
* Add LayoutXLM doc page, first draft of code examples
* Improve documentation a lot
* Update expected boxes for Tesseract 4.0.0 beta
* Use offsets to create labels instead of checking if they start with ##
* Update expected boxes for Tesseract 4.1.1
* Fix conflict
* Make variable names cleaner, add docstring, add link to notebooks
* Revert "Fix conflict"
This reverts commit a9b46ce9afe47ebfcfe7b45e6a121d49e74ef2c5.
* Revert to make integration test pass
* Apply suggestions from @LysandreJik's review
* Address @patrickvonplaten's comments
* Remove fixtures DocVQA in favor of dataset on the hub
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Laying down building stone for more flexible ONNX export capabilities
* Ability to provide a map of config key to override before exporting.
* Makes it possible to export BART with/without past keys.
* Supports simple mathematical syntax for OnnxVariable.repeated
* Effectively apply value override from onnx config for model
* Supports export with additional features such as with-past for seq2seq
* Store the output path directly in the args for uniform usage across.
* Make BART_ONNX_CONFIG_* constants and fix imports.
* Support BERT model.
* Use tokenizer for more flexibility in defining the inputs of a model.
* Add TODO as remainder to provide the batch/sequence_length as CLI args
* Enable optimizations to be done on the model.
* Enable GPT2 + past
* Improve model validation with outputs containing nested structures
* Enable Roberta
* Enable Albert
* Albert requires opset >= 12
* BERT-like models requires opset >= 12
* Remove double printing.
* Enable XLM-Roberta
* Enable DistilBERT
* Disable optimization by default
* Fix missing setattr when applying optimizer_features
* Add value field to OnnxVariable to define constant input (not from tokenizers)
* Add T5 support.
* Simplify model type retrieval
* Example exporting token_classification pipeline for DistilBERT.
* Refactoring to package `transformers.onnx`
* Solve circular dependency & __main__
* Remove unnecessary imports in `__init__`
* Licences
* Use @Narsil's suggestion to forward the model's configuration to the ONNXConfig to avoid interpolation.
* Onnx export v2 fixes (#12388)
* Tiny fixes
Remove `convert_pytorch` from onnxruntime-less runtimes
Correct reference to model
* Style
* Fix Copied from
* LongFormer ONNX config.
* Removed optimizations
* Remvoe bad merge relicas.
* Remove unused constants.
* Remove some deleted constants from imports.
* Fix unittest to remove usage of PyTorch model for onnx.utils.
* Fix distilbert export
* Enable ONNX export test for supported model.
* Style.
* Fix lint.
* Enable all supported default models.
* GPT2 only has one output
* Fix bad property name when overriding config.
* Added unittests and docstrings.
* Disable with_past tests for now.
* Enable outputs validation for default export.
* Remove graph opt lvls.
* Last commit with on-going past commented.
* Style.
* Disabled `with_past` for now
* Remove unused imports.
* Remove framework argument
* Remove TFPreTrainedModel reference
* Add documentation
* Add onnxruntime tests to CircleCI
* Add test
* Rename `convert_pytorch` to `export`
* Use OrderedDict for dummy inputs
* WIP Wav2Vec2
* Revert "WIP Wav2Vec2"
This reverts commit f665efb04c92525c3530e589029f0ae7afdf603e.
* Style
* Use OrderedDict for I/O
* Style.
* Specify OrderedDict documentation.
* Style :)
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Squash all commits of modeling_detr_v7 branch into one
* Improve docs
* Fix tests
* Style
* Improve docs some more and fix most tests
* Fix slow tests of ViT, DeiT and DETR
* Improve replacement of batch norm
* Restructure timm backbone forward
* Make DetrForSegmentation support any timm backbone
* Fix name of output
* Address most comments by @LysandreJik
* Give better names for variables
* Conditional imports + timm in setup.py
* Address additional comments by @sgugger
* Make style, add require_timm and require_vision to testsé
* Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone
* Add png files to fixtures
* Fix type hint
* Add timm to workflows
* Add `BatchNorm2d` to the weight initialization
* Fix retain_grad test
* Replace model checkpoints by Facebook namespace
* Fix name of checkpoint in test
* Add user-friendly message when scipy is not available
* Address most comments by @patrickvonplaten
* Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner
* Better initialization
* Scipy is necessary to get sklearn metrics
* Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel
* Make style
* Improve docs and add 2 community notebooks
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* [TokenClassification] Label realignment for subword aggregation
Tentative to replace https://github.com/huggingface/transformers/pull/11622/files
- Added `AggregationStrategy`
- `ignore_subwords` and `grouped_entities` arguments are now fused
into `aggregation_strategy`. It makes more sense anyway because
`ignore_subwords=True` with `grouped_entities=False` did not have a
meaning anyway.
- Added 2 new ways to aggregate which are MAX, and AVERAGE
- AVERAGE requires a bit more information than the others, for now this
case is slightly specific, we should keep that in mind for future
changes.
- Testing has been modified to reflect new argument, and to check the
correct deprecation and the new aggregation_strategy.
- Put the testing argument and testing results for aggregation_strategy,
close together, so that readers can understand what is supposed to
happen.
- `aggregate` is now only tested on a small model as it does not mean
anything to test it globally for all models.
- Previous tests are unchanged in desired output.
- Added a new test case that showcases better the difference between the
FIRST, MAX and AVERAGE strategies.
* Wrong framework.
* Addressing three issues.
1- Tags might not follow B-, I- convention, so any tag should work now
(assumed as B-TAG)
2- Fixed an issue with average that leads to a substantial code change.
3- The testing suite was not checking for the "index" key for "none"
strategy. This is now fixed.
The issue is that "O" could not be chosen by AVERAGE strategy because
those tokens were filtered out beforehand, so their relative scores were
not counted in the average. Now filtering on
ignore_labels will happen at the very end of the pipeline fixing
that issue.
It's a bit hard to make sure this stays like that because we do
not have a end-to-end test for that behavior
* Formatting.
* Adding formatting to code + cleaner handling of B-, I- tags.
Co-authored-by: Francesco Rubbo <rubbo.francesco@gmail.com>
Co-authored-by: elk-cloner <rezakakhki.rk@gmail.com>
* Typo.
Co-authored-by: Francesco Rubbo <rubbo.francesco@gmail.com>
Co-authored-by: elk-cloner <rezakakhki.rk@gmail.com>
* Initial support for upload to hub
* push -> upload
* Fixes + examples
* Fix torchhub test
* Torchhub test I hate you
* push_model_to_hub -> push_to_hub
* Apply mixin to other pretrained models
* Remove ABC inheritance
* Add tests
* Typo
* Run tests
* Install git-lfs
* Change approach
* Add push_to_hub to all
* Staging test suite
* Typo
* Maybe like this?
* More deps
* Cache
* Adapt name
* Quality
* MOAR tests
* Put it in testing_utils
* Docs + torchhub last hope
* Styling
* Wrong method
* Typos
* Update src/transformers/file_utils.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Address review comments
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [WIP] Enabling multilingual models for translation pipelines.
* decoder_input_ids -> forced_bos_token_id
* Improve docstring.
* Rebase
* Fixing 2 bugs
- Type token_ids coming from `_parse_and_tokenize`
- Wrong index from tgt_lang.
* Fixing black version.
* Adding tests for _build_translation_inputs and add them for all
tokenizers.
* Mbart actually puts the lang code at the end.
* Fixing m2m100.
* Adding TF support to `deep_round`.
* Update src/transformers/pipelines/text2text_generation.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding one line comment.
* Fixing M2M100 `_build_translation_input_ids`, and fix the call site.
* Fixing tests + deep_round -> nested_simplify
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* pass hf optimizer and scheduler to deepspeed if not specified in ds config
* pass hf optimizer and scheduler to deepspeed if not specified in ds config
* update
* make init_deepspeed support config dict
* fix docstring formatting
* clean up trainer's comments
* add new tests
* fix type
* composit argparse doesn't work
* style
* add a new test, rename others
* document new functionality
* complete tests, add docs
* style
* correct level
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add new methods to the doc
* must tell DS we are using a non-native optimizer
* add protection against cpu_offload + HF optimizer combo
* fix the cli overrides
* sync docs + tests
* restore AdamW
* better docs
* need new version
* no longer needed
* remove outdate information
* refactor duplicated code
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Tests run on Docker
Co-authored-by: Morgan <funtowiczmo@gmail.com>
* Comments from code review
* Reply to itself
* Dependencies
Co-authored-by: Morgan <funtowiczmo@gmail.com>
* Add check-ops script
* Finish to implement check_tf_ops and start the test
* Make the test mandatory only for BERT
* Update tf_ops folder
* Remove useless classes
* Add the ONNX test for GPT2 and BART
* Add a onnxruntime slow test + better opset flexibility
* Fix test + apply style
* fix tests
* Switch min opset from 12 to 10
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Fix GPT2
* Remove extra shape_list usage
* Fix GPT2
* Address Morgan's comments
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Don't import libs to check they are available
* Don't import integrations at init
* Add importlib_metdata to deps
* Remove old vars references
* Avoid syntax error
* Adapt testing utils
* Try to appease torchhub
* Add dependency
* Remove more private variables
* Fix typo
* Another typo
* Refine the tf availability test
* First commit: adding all files from tapas_v3
* Fix multiple bugs including soft dependency and new structure of the library
* Improve testing by adding torch_device to inputs and adding dependency on scatter
* Use Python 3 inheritance rather than Python 2
* First draft model cards of base sized models
* Remove model cards as they are already on the hub
* Fix multiple bugs with integration tests
* All model integration tests pass
* Remove print statement
* Add test for convert_logits_to_predictions method of TapasTokenizer
* Incorporate suggestions by Google authors
* Fix remaining tests
* Change position embeddings sizes to 512 instead of 1024
* Comment out positional embedding sizes
* Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
* Added more model names
* Fix truncation when no max length is specified
* Disable torchscript test
* Make style & make quality
* Quality
* Address CI needs
* Test the Masked LM model
* Fix the masked LM model
* Truncate when overflowing
* More much needed docs improvements
* Fix some URLs
* Some more docs improvements
* Test PyTorch scatter
* Set to slow + minify
* Calm flake8 down
* First commit: adding all files from tapas_v3
* Fix multiple bugs including soft dependency and new structure of the library
* Improve testing by adding torch_device to inputs and adding dependency on scatter
* Use Python 3 inheritance rather than Python 2
* First draft model cards of base sized models
* Remove model cards as they are already on the hub
* Fix multiple bugs with integration tests
* All model integration tests pass
* Remove print statement
* Add test for convert_logits_to_predictions method of TapasTokenizer
* Incorporate suggestions by Google authors
* Fix remaining tests
* Change position embeddings sizes to 512 instead of 1024
* Comment out positional embedding sizes
* Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
* Added more model names
* Fix truncation when no max length is specified
* Disable torchscript test
* Make style & make quality
* Quality
* Address CI needs
* Test the Masked LM model
* Fix the masked LM model
* Truncate when overflowing
* More much needed docs improvements
* Fix some URLs
* Some more docs improvements
* Add add_pooling_layer argument to TapasModel
Fix comments by @sgugger and @patrickvonplaten
* Fix issue in docs + fix style and quality
* Clean up conversion script and add task parameter to TapasConfig
* Revert the task parameter of TapasConfig
Some minor fixes
* Improve conversion script and add test for absolute position embeddings
* Improve conversion script and add test for absolute position embeddings
* Fix bug with reset_position_index_per_cell arg of the conversion cli
* Add notebooks to the examples directory and fix style and quality
* Apply suggestions from code review
* Move from `nielsr/` to `google/` namespace
* Apply Sylvain's comments
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Rogge Niels <niels.rogge@howest.be>
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* initial commit
* [cli] lfs commands
* Fix FileSlice
* Tweak to FileSlice
* [hf_api] Backport filetype arg from `datasets`
cc @lhoestq
* Silm down the CI while i'm working
* Ok let's try this in CI
* Update config.yml
* Do not try this at home
* one more try
* Update lfs.py
* Revert "Tweak to FileSlice"
This reverts commit d7e32c4b3500400486411e85a2b74e57fb6b52f5.
* Update test_hf_api.py
* Update test_hf_api.py
* Update test_hf_api.py
* CI still green?
* make CI green again?
* Update test_hf_api.py
* make CI red again?
* Update test_hf_api.py
* add CI style back
* Fix CI?
* oh my
* doc + switch back to real staging endpoint
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Pierric Cistac <Pierrci@users.noreply.github.com>
* Fix docblock + f-strings
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Pierric Cistac <Pierrci@users.noreply.github.com>
* [testing utils] get_auto_remove_tmp_dir default change
Now that I have been using `get_auto_remove_tmp_dir default change` for a while, I realized that the defaults aren't most optimal.
99% of the time we want the tmp dir to be empty at the beginning of the test - so changing the default to `before=True` - this shouldn't impact any tests since this feature is used only during debug.
* simplify things
* update docs
* fix doc layout
* style
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* better 3-state doc
* style
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* s/tmp/temporary/ + style
* correct the statement
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add a multi-gpu job for all example tests
* run only ported tests
* rename
* explain why env is re-activated on each step
* mark all unported/checked tests with @require_torch_non_multigpu_but_fix_me
* style
* Apply suggestions from code review
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* make it possible to invoke testconf.py in both test suites without crashing on having the same option added
* perl -pi -e 's|--make_reports|--make-reports|' to be consistent with other opts
* add `pytest --make-reports` to all CIs (and artifacts)
* fix
* first draft
* show design proposition for new generate method
* up
* make better readable
* make first version
* gpt2 tests pass
* make beam search for gpt2 work
* add first encoder-decoder code
* delete typo
* make t5 work
* save indermediate
* make bart work with beam search
* finish beam search bart / t5
* add default kwargs
* make more tests pass
* fix no bad words sampler
* some fixes and tests for all distribution processors
* fix test
* fix rag slow tests
* merge to master
* add nograd to generate
* make all slow tests pass
* speed up generate
* fix edge case bug
* small fix
* correct typo
* add type hints and docstrings
* fix typos in tests
* add beam search tests
* add tests for beam scorer
* fix test rag
* finish beam search tests
* move generation tests in seperate file
* fix generation tests
* more tests
* add aggressive generation tests
* fix tests
* add gpt2 sample test
* add more docstring
* add more docs
* finish doc strings
* apply some more of sylvains and sams comments
* fix some typos
* make fix copies
* apply lysandres and sylvains comments
* final corrections on examples
* small fix for reformer
