* tmp commit
* shorter
* nit
* explicit kwargs
* propagate changes
* mass propagation with a few manual touches (let's see how CI behaves)
* fix cacheless case
* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* make fixup
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add warning message for and parameters
* Fix when the warning is raised
* Formatting changes
* Improve testing and remove duplicated warning from _fix_key
* Add initial implementation of `spectrogram_batch`
* Format the initial implementation
* Add test suite for the `spectrogram_batch`
* Update `spectrogram_batch` to ensure compatibility with test suite
* Update `spectrogram_batch` to include pre and post-processing
* Add `amplitude_to_db_batch` function and associated tests
* Add `power_to_db_batch` function and associated tests
* Reimplement the test suite for `spectrogram_batch`
* Fix errors in `spectrogram_batch`
* Add the function annotation for `spectrogram_batch`
* Address code quality
* Re-add `test_chroma_equivalence` function
* Update src/transformers/audio_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/audio_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Pass datasets trust_remote_code
* Pass trust_remote_code in more tests
* Add trust_remote_dataset_code arg to some tests
* Revert "Temporarily pin datasets upper version to fix CI"
This reverts commit b7672826ca.
* Pass trust_remote_code in librispeech_asr_dummy docstrings
* Revert "Pin datasets<2.20.0 for examples"
This reverts commit 833fc17a3e.
* Pass trust_remote_code to all examples
* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects
* Pass trust_remote_code to tests
* Pass trust_remote_code to docstrings
* Fix flax examples tests requirements
* Pass trust_remote_dataset_code arg to tests
* Replace trust_remote_dataset_code with trust_remote_code in one example
* Fix duplicate trust_remote_code
* Replace args.trust_remote_dataset_code with args.trust_remote_code
* Replace trust_remote_dataset_code with trust_remote_code in parser
* Replace trust_remote_dataset_code with trust_remote_code in dataclasses
* Replace trust_remote_dataset_code with trust_remote_code arg
* First draft, still missing automatic function conversion
* First draft of the automatic schema generator
* Lots of small fixes
* the walrus has betrayed me
* please stop committing your debug breakpoints
* Lots of cleanup and edge cases, looking better now
* Comments and bugfixes for the type hint parser
* More cleanup
* Add tests, update schema generator
* Update tests, proper handling of return values
* Small docstring change
* More doc updates
* More doc updates
* Add json_schema decorator
* Clean up the TODOs and finish the docs
* self.maxDiff = None to see the whole diff for the nested list test
* add import for add_json_schema
* Quick test fix
* Fix something that was bugging me in the chat template docstring
* Less "anyOf" when unnecessary
* Support return types for the templates that need them
* Proper return type tests
* Switch to Google format docstrings
* Update chat templating docs to match new format
* Stop putting the return type in with the other parameters
* Add Tuple support
* No more decorator - we just do it implicitly!
* Add enum support to get_json_schema
* Update docstring
* Add copyright header
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/chat_templating.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/utils/chat_template_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/utils/chat_template_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add copyright header
* make fixup
* Fix indentation
* Reformat chat_template_utils
* Correct return value
* Make regexes module-level
* Support more complex, multi-line arg docstrings
* Update error message for ...
* Update ruff
* Add document type validation
* Refactor docs
* Refactor docs
* Refactor docs
* Clean up Tuple error
* Add an extra test for very complex defs and docstrings and clean everything up for it
* Document enum block
* Quick test fixes
* Stop supporting type hints in docstring to fix bugs and simplify the regex
* Update docs for the regex change
* Clean up enum regex
* Wrap functions in {"type": "function", "function": ...}
* Update src/transformers/utils/chat_template_utils.py
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* Temporary tool calling commit
* Add type hints to chat template utils, partially update docs (incomplete!)
* Code cleanup based on @molbap's suggestion
* Add comments to explain regexes
* Fix up type parsing for unions and lists
* Add custom exception types and adjust tests to look for them
* Update docs with a demo!
* Docs cleanup
* Pass content as string
* Update tool call formatting
* Update docs with new function format
* Update docs
* Update docs with a second tool to show the model choosing correctly
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* Fix has_file in offline mode
* harmonize env variable for offline mode
* Switch to HF_HUB_OFFLINE
* fix test
* revert test_offline to test TRANSFORMERS_OFFLINE
* Add new offline test
* merge conflicts
* docs
* Bookmark, initial impelemtation. Need to test
* Clean
* Working fully, woop woop
* I think working version now, testing
* Fin!
* rm cast, could keep None
* Fix typing issue
* rm typehint
* Add test
* Add tests and make more rigid
* Add test for parse_json_file
* Change Path to PathLike
* Fix `Import block is un-sorted or un-formatted`
* revert parse_json_file
* Fix ruff format
* Add parse_json_file test
* Port core files + ESM (because ESM code is odd)
* Search-replace in modelling code
* Fix up transfo_xl as well
* Fix other core files + tests (still need to add correct import to tests)
* Fix cookiecutter
* make fixup, fix imports in some more core files
* Auto-add imports to tests
* Cleanup, add imports to sagemaker tests
* Use correct exception for importing tf_keras
* Fixes in modeling_tf_utils
* make fixup
* Correct version parsing code
* Ensure the pipeline tests correctly revert to float32 after each test
* Ensure the pipeline tests correctly revert to float32 after each test
* More tf.keras -> keras
* Add dtype cast
* Better imports of tf_keras
* Add a cast for tf.assign, just in case
* Fix callback imports
* Enable instantiating model with pretrained backbone weights
* Update tests so backbone checkpoint isn't passed in
* Remove doc updates until changes made in modeling code
* Clarify pretrained import
* Update configs - docs and validation check
* Update src/transformers/utils/backbone_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Clarify exception message
* Update config init in tests
* Add test for when use_timm_backbone=True
* Small test updates
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Iteratre over out_features instead of stage_names
* Update for all backbones
* Add tests
* Fix
* Align timm backbone behaviour with other backbones
* Fix tests
* Stricter checks on set out_features and out_indices
* Revert back stage selection logic
* Remove out-of-order logic
* Document restriction in docstrings
* add sdpa
* wip
* cleaning
* add ref
* yet more cleaning
* and more :)
* wip llama
* working llama
* add output_attentions=True support
* bigcode sdpa support
* fixes
* gpt-bigcode support, require torch>=2.1.1
* add falcon support
* fix conflicts falcon
* style
* fix attention_mask definition
* remove output_attentions from attnmaskconverter
* support whisper without removing any Copied from statement
* fix mbart default to eager renaming
* fix typo in falcon
* fix is_causal in SDPA
* check is_flash_attn_2_available in the models init as well in case the model is not initialized through from_pretrained
* add warnings when falling back on the manual implementation
* precise doc
* wip replace _flash_attn_enabled by config.attn_implementation
* fix typo
* add tests
* style
* add a copy.deepcopy on the config in from_pretrained, as we do not want to modify it inplace
* obey to config.attn_implementation if a config is passed in from_pretrained
* fix is_torch_sdpa_available when torch is not installed
* remove dead code
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bart/modeling_bart.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* remove duplicate pretraining_tp code
* add dropout in llama
* precise comment on attn_mask
* add fmt: off for _unmask_unattended docstring
* precise num_masks comment
* nuke pretraining_tp in LlamaSDPAAttention following Arthur's suggestion
* cleanup modeling_utils
* backward compatibility
* fix style as requested
* style
* improve documentation
* test pass
* style
* add _unmask_unattended tests
* skip meaningless tests for idefics
* hard_check SDPA requirements when specifically requested
* standardize the use if XXX_ATTENTION_CLASSES
* fix SDPA bug with mem-efficient backend on CUDA when using fp32
* fix test
* rely on SDPA is_causal parameter to handle the causal mask in some cases
* fix FALCON_ATTENTION_CLASSES
* remove _flash_attn_2_enabled occurences
* fix test
* add OPT to the list of supported flash models
* improve test
* properly test on different SDPA backends, on different dtypes & properly handle separately the pad tokens in the test
* remove remaining _flash_attn_2_enabled occurence
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/perf_infer_gpu_one.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* remove use_attn_implementation
* fix docstring & slight bug
* make attn_implementation internal (_attn_implementation)
* typos
* fix tests
* deprecate use_flash_attention_2=True
* fix test
* add back llama that was removed by mistake
* fix tests
* remove _flash_attn_2_enabled occurences bis
* add check & test that passed attn_implementation is valid
* fix falcon torchscript export
* fix device of mask in tests
* add tip about torch.jit.trace and move bt doc below sdpa
* fix parameterized.expand order
* move tests from test_modeling_attn_mask_utils to test_modeling_utils as a relevant test class is already there
* update sdpaattention class with the new cache
* Update src/transformers/configuration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bark/modeling_bark.py
* address review comments
* WIP torch.jit.trace fix. left: test both eager & sdpa
* add test for torch.jit.trace for both eager/sdpa
* fix falcon with torch==2.0 that needs to use sdpa
* fix doc
* hopefully last fix
* fix key_value_length that has no default now in mask converter
* is it flacky?
* fix speculative decoding bug
* tests do pass
* fix following #27907
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Safetensors serialization by default
* First pass on the tests
* Second pass on the tests
* Third pass on the tests
* Fix TF weight loading from TF-format safetensors
* Specific encoder-decoder fixes for weight crossloading
* Add VisionEncoderDecoder fixes for TF too
* Change filename test for pt-to-tf
* One missing fix for TFVisionEncoderDecoder
* Fix the other crossload test
* Support for flax + updated tests
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Sanchit's comments
* Sanchit's comments 2
* Nico's comments
* Fix tests
* cleanup
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Register ModelOutput as supported torch pytree nodes
* Test ModelOutput as supported torch pytree nodes
* Update type hints for pytree unflatten functions
* add kaldi fbank
* make style
* add herz_to_mel_kaldi tests
* add mel to hertz kaldi test
* integration tests
* correct test and remove comment
* make style
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* change parameter name
* Apply suggestions from Arthur review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update remove_dc_offset description
* fix bug + make style
* fix error in using np.exp instead of np.power
* make style
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add @dataclass to MaskFormerPixelDecoderOutput
* Add dataclass check if subclass of ModelOutout
* Use unittest assertRaises rather than pytest per contribution doc
* Update src/transformers/utils/generic.py per suggested change
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Register ModelOutput subclasses as supported torch.utils._pytree nodes
Fixes#25357 where DDP with static_graph=True does not sync gradients when calling backward() over tensors contained in ModelOutput subclasses
* Add test for torch pytree ModelOutput serialization and deserialization
* Fix saved_model_creation_extended
* Skip the BLIP model creation test for now
* Fix TF SAM test
* Fix longformer tests
* Fix Wav2Vec2
* Add a skip for XLNet
* make fixup
* make fix-copies
* Add comments
* Add test for proper input signatures
* No more signature pruning
* Test the dummy inputs are valid too
* fine-tine -> fine-tune
* Fix indent in test_dataset_conversion
* Stop storing references to bound methods in tf.functions
* Remove the gc.collect calls now that we resolved the underlying problem
* Remove the default signature from model.serving entirely, big cleanup
* Remove _prune_signature as self.input_signature can prune itself
* Restore serving docstring
* Update int support test to check the input signature
* Make sure other tests also use model.input_signature and not serving.input_signature
* Restore _prune_signature
* Remove the doctest GC now it's no longer needed
* Correct core tests to use the pruned sig
* order lines correctly in core tests
* Add eager_serving back with a deprecation warning
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Don't forget the imports
* Add the imports to tests too
* make fixup
* Refactor tests that depended on get_type_hints
* Better test refactor
* Fix an old hidden bug in the test_keras_fit input creation code
* Fix for the Deit tests
* Adding Llama FastTokenizer support.
- Requires https://github.com/huggingface/tokenizers/pull/1183 version
- Only support byte_fallback for llama, raise otherwise (safety net).
- Lots of questions are special tokens
How to test:
```python
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers import AutoTokenizer
from tokenizers import Tokenizer
tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-7b")
if False:
new_tokenizer = Tokenizer.from_file("tok.json")
else:
new_tokenizer = convert_slow_tokenizer(tokenizer)
new_tokenizer.save("tok.json")
strings = [
"This is a test",
"生活的真谛是",
"生活的真谛是[MASK]。",
# XXX: This one is problematic because of special tokens
# "<s> Something something",
]
for string in strings:
encoded = tokenizer(string)["input_ids"]
encoded2 = new_tokenizer.encode(string).ids
assert encoded == encoded2, f"{encoded} != {encoded2}"
decoded = tokenizer.decode(encoded)
decoded2 = new_tokenizer.decode(encoded2)
assert decoded.strip() == decoded2, f"{repr(decoded)} != {repr(decoded2)}"
```
The converter + some test script.
The test script.
Tmp save.
Adding Fast tokenizer + tests.
Adding the tokenization tests.
Correct combination.
Small fix.
Fixing tests.
Fixing with latest update.
Rebased.
fix copies + normalized added tokens + copies.
Adding doc.
TMP.
Doc + split files.
Doc.
Versions + try import.
Fix Camembert + warnings -> Error.
Fix by ArthurZucker.
Not a decorator.
* Fixing comments.
* Adding more to docstring.
* Doc rewriting.
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* Fixed test_saved_model_extended
* Fix TFGPT2 tests
* make fixup
* Make sure keras-nlp utils are available for type hinting too
* Update src/transformers/testing_utils.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* make fixup
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* Add a test to ensure int dummy inputs are int64
* Move the test into the existing int64 test and update a lot of existing dummies
* Fix remaining dummies
* Fix remaining dummies
* Test for int64 serving sigs as well
* Update core tests to use tf.int64
* Add better messages to the assertions
* Update all serving sigs to int64
* More sneaky hiding tf.int32s
* Add an optional int32 signature in save_pretrained
* make fixup
* Add Amy's suggestions
* Switch all serving sigs back to tf.int32
* Switch all dummies to tf.int32
* Adjust tests to check for tf.int32 instead of tf.int64
* Fix base dummy_inputs dtype
* Start casting to tf.int32 in input_processing
* Change dtype for unpack_inputs test
* Add proper tf.int32 test
* Make the alternate serving signature int64
* Adapt FE methods to transforms library
* Mixin for saving the image processor
* Base processor skeleton
* BatchFeature for packaging image processor outputs
* Initial image processor for GLPN
* REmove accidental import
* Fixup and docs
* Mixin for saving the image processor
* Fixup and docs
* Import BatchFeature from feature_extraction_utils
* Fixup and docs
* Fixup and docs
* Fixup and docs
* Fixup and docs
* BatchFeature for packaging image processor outputs
* Import BatchFeature from feature_extraction_utils
* Import BatchFeature from feature_extraction_utils
* Fixup and docs
* Fixup and docs
* BatchFeature for packaging image processor outputs
* Import BatchFeature from feature_extraction_utils
* Fixup and docs
* Mixin for saving the image processor
* Fixup and docs
* Add rescale back and remove ImageType
* fix import mistake
* Fix enum var reference
* Can transform and specify image data format
* Remove redundant function
* Update reference
* Data format flag for rescale
* Fix typo
* Fix dimension check
* Fixes to make IP and FE outputs match
* Add tests for transforms
* Add test for utils
* Update some docstrings
* Make sure in channels last before converting to PIL
* Remove default to numpy batching
* Fix up
* Add docstring and model_input_types
* Use feature processor config from hub
* Alias GLPN feature extractor to image processor
* Alias feature extractor mixin
* Add return_numpy=False flag for resize
* Fix up
* Fix up
* Use different frameworks safely
* Safely import PIL
* Call function checking if PIL available
* Only import if vision available
* Address Sylvain PR comments
Co-authored-by: Sylvain.gugger@gmail.com
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/image_transforms.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Update src/transformers/models/glpn/feature_extraction_glpn.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add in docstrings
* Fix TFSwinSelfAttention to have relative position index as non-trainable weight (#18226)
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>
* Refactor `TFSwinLayer` to increase serving compatibility (#18352)
* Refactor `TFSwinLayer` to increase serving compatibility
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>
* Fix missed parameters while refactoring
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>
* Fix window_reverse to calculate batch size
Signed-off-by: Seunghwan Hong <harrydrippin@gmail.com>
Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add TF prefix to TF-Res test class (#18481)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Remove py.typed (#18485)
* Fix pipeline tests (#18487)
* Fix pipeline tests
* Make sure all pipelines tests run with init changes
* Use new huggingface_hub tools for download models (#18438)
* Draft new cached_file
* Initial draft for config and model
* Small fixes
* Fix first batch of tests
* Look in cache when internet is down
* Fix last tests
* Bad black, not fixing all quality errors
* Make diff less
* Implement change for TF and Flax models
* Add tokenizer and feature extractor
* For compatibility with main
* Add utils to move the cache and auto-do it at first use.
* Quality
* Deal with empty commit shas
* Deal with empty etag
* Address review comments
* Fix `test_dbmdz_english` by updating expected values (#18482)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Move cache folder to huggingface/hub for consistency with hf_hub (#18492)
* Move cache folder to just huggingface
* Thank you VsCode for this needless import
* Move to hub
* Forgot one
* Update some expected values in `quicktour.mdx` for `resampy 0.3.0` (#18484)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Forgot one new_ for cache migration
* disable Onnx test for google/long-t5-tglobal-base (#18454)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Typo reported by Joel Grus on TWTR (#18493)
* Just re-reading the whole doc every couple of months 😬 (#18489)
* Delete valohai.yaml
* NLP => ML
* typo
* website supports https
* datasets
* 60k + modalities
* unrelated link fixing for accelerate
* Ok those links were actually broken
* Fix link
* Make `AutoTokenizer` auto-link
* wording tweak
* add at least one non-nlp task
* `transformers-cli login` => `huggingface-cli login` (#18490)
* zero chance anyone's using that constant no?
* `transformers-cli login` => `huggingface-cli login`
* `transformers-cli repo create` => `huggingface-cli repo create`
* `make style`
* Add seed setting to image classification example (#18519)
* [DX fix] Fixing QA pipeline streaming a dataset. (#18516)
* [DX fix] Fixing QA pipeline streaming a dataset.
QuestionAnsweringArgumentHandler would iterate over the whole dataset
effectively killing all properties of the pipeline.
This restores nice properties when using `Dataset` or `Generator` since
those are meant to be consumed lazily.
* Handling TF better.
* Clean up hub (#18497)
* Clean up utils.hub
* Remove imports
* More fixes
* Last fix
* update fsdp docs (#18521)
* updating fsdp documentation
* typo fix
* Fix compatibility with 1.12 (#17925)
* Fix compatibility with 1.12
* Remove pin from examples requirements
* Update torch scatter version
* Fix compatibility with 1.12
* Remove pin from examples requirements
* Update torch scatter version
* fix torch.onnx.symbolic_opset12 import
* Reject bad version
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Remove debug statement
* Specify en in doc-builder README example (#18526)
Co-authored-by: Ankur Goyal <ankur@impira.com>
* New cache fixes: add safeguard before looking in folders (#18522)
* unpin resampy (#18527)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* ✨ update to use interlibrary links instead of Markdown (#18500)
* Add example of multimodal usage to pipeline tutorial (#18498)
* 📝 add example of multimodal usage to pipeline tutorial
* 🖍 apply feedbacks
* 🖍 apply niels feedback
* [VideoMAE] Add model to doc tests (#18523)
* Add videomae to doc tests
* Add pip install decord
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update perf_train_gpu_one.mdx (#18532)
* Update no_trainer.py scripts to include accelerate gradient accumulation wrapper (#18473)
* Added accelerate gradient accumulation wrapper to run_image_classification_no_trainer.py example script
* make fixup changes
* PR comments
* changed input to Acceletor based on PR comment, ran make fixup
* Added comment explaining the sync_gradients statement
* Fixed lr scheduler max steps
* Changed run_clm_no_trainer.py script to use accelerate gradient accum wrapper
* Fixed all scripts except wav2vec2 pretraining to use accelerate gradient accum wrapper
* Added accelerate gradient accum wrapper for wav2vec2_pretraining_no_trainer.py script
* make fixup and lr_scheduler step inserted back into run_qa_beam_search_no_trainer.py
* removed changes to run_wav2vec2_pretraining_no_trainer.py script and fixed using wrong constant in qa_beam_search_no_trainer.py script
* Add Spanish translation of converting_tensorflow_models.mdx (#18512)
* Add file in spanish docs to be translated
* Finish translation to Spanish
* Improve Spanish wording
* Add suggested changes from review
* Spanish translation of summarization.mdx (#15947) (#18477)
* Add Spanish translation of summarization.mdx
* Apply suggestions from code review
Co-authored-by: Omar U. Espejel <espejelomar@gmail.com>
Co-authored-by: Omar U. Espejel <espejelomar@gmail.com>
* Let's not cast them all (#18471)
* add correct dtypes when checking for params dtype
* forward contrib credits
* Update src/transformers/modeling_utils.py
Co-authored-by: Thomas Wang <24695242+thomasw21@users.noreply.github.com>
* more comments
- added more comments on why we cast only floating point parameters
* Update src/transformers/modeling_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: sgugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Thomas Wang <24695242+thomasw21@users.noreply.github.com>
* fix: data2vec-vision Onnx ready-made configuration. (#18427)
* feat: add the data2vec conf that are missing https://huggingface.co/docs/transformers/serialization
* fix: wrong config
* Add mt5 onnx config (#18394)
* update features
* MT5OnnxConfig added with updated with tests and docs
* fix imports
* fix onnc_config_cls for mt5
Co-authored-by: Thomas Chaigneau <thomas.deeptools.ai>
* Minor update of `run_call_with_unpacked_inputs` (#18541)
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* BART - Fix attention mask device issue on copied models (#18540)
* attempt to fix attn mask device
* fix bart `_prepare_decoder_attention_mask`
- add correct device
- run `make fix-copies` to propagate the fix
* Adding a new `align_to_words` param to qa pipeline. (#18010)
* Adding a new `align_to_words` param to qa pipeline.
* Update src/transformers/pipelines/question_answering.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Import protection.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* 📝 update metric with evaluate (#18535)
* Restore _init_weights value in no_init_weights (#18504)
* Recover _init_weights value in no_init_weights
For potential nested use.
In addition, users might modify private no_init_weights as well.
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Remove private variable change check
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean up comment
* 📝 update documentation build section (#18548)
* `bitsandbytes` - `Linear8bitLt` integration into `transformers` models (#17901)
* first commit
* correct replace function
* add final changes
- works like charm!
- cannot implement tests yet
- tested
* clean up a bit
* add bitsandbytes dependencies
* working version
- added import function
- added bitsandbytes utils file
* small fix
* small fix
- fix import issue
* fix import issues
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit
- move bitsandbytes utils to utils
- change comments on functions
* reformat docstring
- reformat docstring on init_empty_weights_8bit
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert bad formatting
* change to bitsandbytes
* refactor a bit
- remove init8bit since it is useless
* more refactoring
- fixed init empty weights issue
- added threshold param
* small hack to make it work
* Update src/transformers/modeling_utils.py
* Update src/transformers/modeling_utils.py
* revmoe the small hack
* modify utils file
* make style + refactor a bit
* create correctly device map
* add correct dtype for device map creation
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply suggestions
- remove with torch.grad
- do not rely on Python bool magic!
* add docstring
- add docstring for new kwargs
* add docstring
- comment `replace_8bit_linear` function
- fix weird formatting
* - added more documentation
- added new utility function for memory footprint tracking
- colab demo to add
* few modifs
- typo doc
- force cast into float16 when load_in_8bit is enabled
* added colab link
* add test architecture + docstring a bit
* refactor a bit testing class
* make style + refactor a bit
* enhance checks
- add more checks
- start writing saving test
* clean up a bit
* male style
* add more details on doc
* add more tests
- still needs to fix 2 tests
* replace by "or"
- could not fix it from GitHub GUI
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* refactor a bit testing code + add readme
* make style
* fix import issue
* Update src/transformers/modeling_utils.py
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* add few comments
* add more doctring + make style
* more docstring
* raise error when loaded in 8bit
* make style
* add warning if loaded on CPU
* add small sanity check
* fix small comment
* add bitsandbytes on dockerfile
* Improve documentation
- improve documentation from comments
* add few comments
* slow tests pass on the VM but not on the CI VM
* Fix merge conflict
* make style
* another test should pass on a multi gpu setup
* fix bad import in testing file
* Fix slow tests
- remove dummy batches
- no more CUDA illegal memory errors
* odify dockerfile
* Update docs/source/en/main_classes/model.mdx
* Update Dockerfile
* Update model.mdx
* Update Dockerfile
* Apply suggestions from code review
* few modifications
- lm head can stay on disk/cpu
- change model name so that test pass
* change test value
- change test value to the correct output
- torch bmm changed to baddmm in bloom modeling when merging
* modify installation guidelines
* Apply suggestions from code review
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* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* replace `n`by `name`
* merge `load_in_8bit` and `low_cpu_mem_usage`
* first try - keep the lm head in full precision
* better check
- check the attribute `base_model_prefix` instead of computing the number of parameters
* added more tests
* Update src/transformers/utils/bitsandbytes.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Merge branch 'integration-8bit' of https://github.com/younesbelkada/transformers into integration-8bit
* improve documentation
- fix typos for installation
- change title in the documentation
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* TF: XLA-trainable DeBERTa v2 (#18546)
* fix deberta issues
* add different code paths for gpu and tpu
* shorter gpu take along axis
* Stable Dropout without tf cond
* variable must be float
* Preserve hub-related kwargs in AutoModel.from_pretrained (#18545)
* Preserve hub-related kwargs in AutoModel.from_pretrained
* Fix tests
* Remove debug statement
* TF Examples Rewrite (#18451)
* Finished QA example
* Dodge a merge conflict
* Update text classification and LM examples
* Update NER example
* New Keras metrics WIP, fix NER example
* Update NER example
* Update MC, summarization and translation examples
* Add XLA warnings when shapes are variable
* Make sure batch_size is consistently scaled by num_replicas
* Add PushToHubCallback to all models
* Add docs links for KerasMetricCallback
* Add docs links for prepare_tf_dataset and jit_compile
* Correct inferred model names
* Don't assume the dataset has 'lang'
* Don't assume the dataset has 'lang'
* Write metrics in text classification
* Add 'framework' to TrainingArguments and TFTrainingArguments
* Export metrics in all examples and add tests
* Fix training args for Flax
* Update command line args for translation test
* make fixup
* Fix accidentally running other tests in fp16
* Remove do_train/do_eval from run_clm.py
* Remove do_train/do_eval from run_mlm.py
* Add tensorflow tests to circleci
* Fix circleci
* Update examples/tensorflow/language-modeling/run_mlm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/test_tensorflow_examples.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/translation/run_translation.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/token-classification/run_ner.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Fix save path for tests
* Fix some model card kwargs
* Explain the magical -1000
* Actually enable tests this time
* Skip text classification PR until we fix shape inference
* make fixup
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Use commit hash to look in cache instead of calling head (#18534)
* Use commit hash to look in cache instead of calling head
* Add tests
* Add attr for local configs too
* Stupid typos
* Fix tests
* Update src/transformers/utils/hub.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Address Julien's comments
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* `pipeline` support for `device="mps"` (or any other string) (#18494)
* `pipeline` support for `device="mps"` (or any other string)
* Simplify `if` nesting
* Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix? @sgugger
* passing `attr=None` is not the same as not passing `attr` 🤯
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update philosophy to include other preprocessing classes (#18550)
* 📝 update philosophy to include other preprocessing classes
* 🖍 apply feedbacks
* Properly move cache when it is not in default path (#18563)
* Adds CLIP to models exportable with ONNX (#18515)
* onnx config for clip
* default opset as 14
* changes from the original repo
* input values order fix
* outputs fix
* remove unused import
* ran make fix-copies
* black format
* review comments: forward ref, import fix, model change revert, .to cleanup
* make style
* formatting fixes
* revert groupvit
* comment for cast to int32
* comment fix
* make .T as .t() for onnx conversion
* ran make fix-copies
* remove unneeded comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix copies
* remove comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* raise atol for MT5OnnxConfig (#18560)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* fix string (#18568)
* Segformer TF: fix output size in documentation (#18572)
* Segformer TF: fix output size in doc
* Segformer pytorch: fix output size in doc
Co-authored-by: Maxime Gardoni <maxime.gardoni@ecorobotix.com>
* Fix resizing bug in OWL-ViT (#18573)
* Fixes resizing bug in OWL-ViT
* Defaults to square resize if size is set to an int
* Sets do_center_crop default value to False
* Fix LayoutLMv3 documentation (#17932)
* fix typos
* fix sequence_length docs of LayoutLMv3Model
* delete trailing white spaces
* fix layoutlmv3 docs more
* apply make fixup & quality
* change to two versions of input docstring
* apply make fixup & quality
* Skip broken tests
* Change BartLearnedPositionalEmbedding's forward method signature to support Opacus training (#18486)
* changing BartLearnedPositionalEmbedding forward signature and references to it
* removing debugging dead code (thanks style checker)
* blackened modeling_bart file
* removing copy inconsistencies via make fix-copies
* changing references to copied signatures in Bart variants
* make fix-copies once more
* using expand over repeat (thanks @michaelbenayoun)
* expand instead of repeat for all model copies
Co-authored-by: Daniel Jones <jonesdaniel@microsoft.com>
* german docs translation (#18544)
* Create _config.py
* Create _toctree.yml
* Create index.mdx
not sure about "du / ihr" oder "sie"
* Create quicktour.mdx
* Update _toctree.yml
* Update build_documentation.yml
* Update build_pr_documentation.yml
* fix build
* Update index.mdx
* Update quicktour.mdx
* Create installation.mdx
* Update _toctree.yml
* Deberta V2: Fix critical trace warnings to allow ONNX export (#18272)
* Fix critical trace warnings to allow ONNX export
* Force input to `sqrt` to be float type
* Cleanup code
* Remove unused import statement
* Update model sew
* Small refactor
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Use broadcasting instead of repeat
* Implement suggestion
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Match deberta v2 changes in sew_d
* Improve code quality
* Update code quality
* Consistency of small refactor
* Match changes in sew_d
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* [FX] _generate_dummy_input supports audio-classification models for labels (#18580)
* Support audio classification architectures for labels generation, as well as provides a flag to print warnings or not
* Use ENV_VARS_TRUE_VALUES
* Fix docstrings with last version of hf-doc-builder styler (#18581)
* Fix docstrings with last version of hf-doc-builder styler
* Remove empty Parameter block
* Bump nbconvert from 6.0.1 to 6.3.0 in /examples/research_projects/lxmert (#18565)
Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 6.0.1 to 6.3.0.
- [Release notes](https://github.com/jupyter/nbconvert/releases)
- [Commits](https://github.com/jupyter/nbconvert/compare/6.0.1...6.3.0)
---
updated-dependencies:
- dependency-name: nbconvert
dependency-type: direct:production
...
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* Bump nbconvert in /examples/research_projects/visual_bert (#18566)
Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 6.0.1 to 6.3.0.
- [Release notes](https://github.com/jupyter/nbconvert/releases)
- [Commits](https://github.com/jupyter/nbconvert/compare/6.0.1...6.3.0)
---
updated-dependencies:
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* fix owlvit tests, update docstring examples (#18586)
* Return the permuted hidden states if return_dict=True (#18578)
* Load sharded pt to flax (#18419)
* initial commit
* add small test
* add cross pt tf flag to test
* fix quality
* style
* update test with new repo
* fix failing test
* update
* fix wrong param ordering
* style
* update based on review
* update related to recent new caching mechanism
* quality
* Update based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* quality and style
* Update src/transformers/modeling_flax_utils.py
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add type hints for ViLT models (#18577)
* Add type hints for Vilt models
* Add missing return type for TokenClassification class
* update doc for perf_train_cpu_many, add intel mpi introduction (#18576)
* update doc for perf_train_cpu_many, add mpi introduction
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Update docs/source/en/perf_train_cpu_many.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/en/perf_train_cpu_many.mdx
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* typos (#18594)
* FSDP bug fix for `load_state_dict` (#18596)
* Add `TFAutoModelForSemanticSegmentation` to the main `__init__.py` (#18600)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Generate: validate `model_kwargs` (and catch typos in generate arguments) (#18261)
* validate generate model_kwargs
* generate tests -- not all models have an attn mask
* Supporting seq2seq models for `bitsandbytes` integration (#18579)
* Supporting seq2seq models for `bitsandbytes` integration
- `bitsandbytes` integration supports now seq2seq models
- check if a model has tied weights as an additional check
* small modification
- tie the weights before looking at tied weights!
* Add Donut (#18488)
* First draft
* Improve script
* Update script
* Make conversion work
* Add final_layer_norm attribute to Swin's config
* Add DonutProcessor
* Convert more models
* Improve feature extractor and convert base models
* Fix bug
* Improve integration tests
* Improve integration tests and add model to README
* Add doc test
* Add feature extractor to docs
* Fix integration tests
* Remove register_buffer
* Fix toctree and add missing attribute
* Add DonutSwin
* Make conversion script work
* Improve conversion script
* Address comment
* Fix bug
* Fix another bug
* Remove deprecated method from docs
* Make Swin and Swinv2 untouched
* Fix code examples
* Fix processor
* Update model_type to donut-swin
* Add feature extractor tests, add token2json method, improve feature extractor
* Fix failing tests, remove integration test
* Add do_thumbnail for consistency
* Improve code examples
* Add code example for document parsing
* Add DonutSwin to MODEL_NAMES_MAPPING
* Add model to appropriate place in toctree
* Update namespace to appropriate organization
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Fix URLs (#18604)
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update BLOOM parameter counts (#18531)
* Update BLOOM parameter counts
* Update BLOOM parameter counts
* [doc] fix anchors (#18591)
the manual anchors end up being duplicated with automatically added anchors and no longer work.
* [fsmt] deal with -100 indices in decoder ids (#18592)
* [fsmt] deal with -100 indices in decoder ids
Fixes: https://github.com/huggingface/transformers/issues/17945
decoder ids get the default index -100, which breaks the model - like t5 and many other models add a fix to replace -100 with the correct pad index.
For some reason this use case hasn't been used with this model until recently - so this issue was there since the beginning it seems.
Any suggestions to how to add a simple test here? or perhaps we have something similar already? user's script is quite massive.
* style
* small change (#18584)
* Flax Remat for LongT5 (#17994)
* [Flax] Add remat (gradient checkpointing)
* fix variable naming in test
* flip: checkpoint using a method
* fix naming
* fix class naming
* apply PVP's suggestions from code review
* add gradient_checkpointing to examples
* Add gradient_checkpointing to run_mlm_flax
* Add remat to longt5
* Add gradient checkpointing test longt5
* Fix args errors
* Fix remaining tests
* Make fixup & quality fixes
* replace kwargs
* remove unecessary kwargs
* Make fixup changes
* revert long_t5_flax changes
* Remove return_dict and copy to LongT5
* Remove test_gradient_checkpointing
Co-authored-by: sanchit-gandhi <sanchit@huggingface.co>
* mac m1 `mps` integration (#18598)
* mac m1 `mps` integration
* Update docs/source/en/main_classes/trainer.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* addressing comments
* Apply suggestions from code review
Co-authored-by: Dan Saattrup Nielsen <47701536+saattrupdan@users.noreply.github.com>
* resolve comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Dan Saattrup Nielsen <47701536+saattrupdan@users.noreply.github.com>
* Change scheduled CIs to use torch 1.12.1 (#18644)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add checks for some workflow jobs (#18583)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* TF: Fix generation repetition penalty with XLA (#18648)
* Update longt5.mdx (#18634)
* Update run_translation_no_trainer.py (#18637)
* Update run_translation_no_trainer.py
found an error in selecting `no_decay` parameters and some small modifications when the user continues to train from a checkpoint
* fixs `no_decay` and `resume_step` issue
1. change `no_decay` list
2. if use continue to train their model from provided checkpoint, the `resume_step` will not be initialized properly if `args.gradient_accumulation_steps != 1`
* [bnb] Minor modifications (#18631)
* bnb minor modifications
- refactor documentation
- add troubleshooting README
- add PyPi library on DockerFile
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Apply suggestions from code review
* Apply suggestions from code review
* Apply suggestions from code review
* put in one block
- put bash instructions in one block
* update readme
- refactor a bit hardware requirements
* change text a bit
* Apply suggestions from code review
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* apply suggestions
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* add link to paper
* Apply suggestions from code review
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* Update tests/mixed_int8/README.md
* Apply suggestions from code review
* refactor a bit
* add instructions Turing & Amperer
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* add A6000
* clarify a bit
* remove small part
* Update tests/mixed_int8/README.md
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
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* Examples: add Bloom support for token classification (#18632)
* examples: add Bloom support for token classification (FLAX, PyTorch and TensorFlow)
* examples: remove support for Bloom in token classication (FLAX and TensorFlow currently have no support for it)
* Fix Yolos ONNX export test (#18606)
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fixup
* Fix up
* Move PIL default arguments inside function for safe imports
* Add image utils to toctree
* Update `rescale` method to reflect changes in #18677
* Update docs/source/en/internal/image_processing_utils.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Address Niels PR comments
* Add normalize method to transforms library
* Apply suggestions from code review - remove defaults to None
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix docstrings and revert to PIL.Image.XXX resampling
Use PIL.Image.XXX resampling values instead of PIL.Image.Resampling.XXX enum as it's only in the recent version >= 9.10 and version is not yet pinned and older version support deprecated
* Some more docstrings and PIL.Image tidy up
* Reorganise arguments so flags by modifiers
* Few last docstring fixes
* Add normalize to docs
* Accept PIL.Image inputs with deprecation warning
* Update src/transformers/image_transforms.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update warning to include version
* Trigger CI - hash clash on doc build
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Amy Roberts <amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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* Adapt FE methods to transforms library
* Mixin for saving the image processor
* Base processor skeleton
* BatchFeature for packaging image processor outputs
* Initial image processor for GLPN
* REmove accidental import
* Fixup and docs
* Mixin for saving the image processor
* Fixup and docs
* Import BatchFeature from feature_extraction_utils
* Fixup and docs
* Fixup and docs
* Fixup and docs
* Fixup and docs
* BatchFeature for packaging image processor outputs
* Import BatchFeature from feature_extraction_utils
* Import BatchFeature from feature_extraction_utils
* Fixup and docs
* Fixup and docs
* BatchFeature for packaging image processor outputs
* Import BatchFeature from feature_extraction_utils
* Fixup and docs
* Mixin for saving the image processor
* Fixup and docs
* Add rescale back and remove ImageType
* fix import mistake
* Fix enum var reference
* Can transform and specify image data format
* Remove redundant function
* Update reference
* Data format flag for rescale
* Fix typo
* Fix dimension check
* Fixes to make IP and FE outputs match
* Add tests for transforms
* Add test for utils
* Update some docstrings
* Make sure in channels last before converting to PIL
* Remove default to numpy batching
* Fix up
* Add docstring and model_input_types
* Use feature processor config from hub
* Alias GLPN feature extractor to image processor
* Alias feature extractor mixin
* Add return_numpy=False flag for resize
* Fix up
* Fix up
* Use different frameworks safely
* Safely import PIL
* Call function checking if PIL available
* Only import if vision available
* Address Sylvain PR comments
Co-authored-by: Sylvain.gugger@gmail.com
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/image_transforms.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Update src/transformers/models/glpn/feature_extraction_glpn.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add in docstrings
* Fix TFSwinSelfAttention to have relative position index as non-trainable weight (#18226)
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>
* Refactor `TFSwinLayer` to increase serving compatibility (#18352)
* Refactor `TFSwinLayer` to increase serving compatibility
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>
* Fix missed parameters while refactoring
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>
* Fix window_reverse to calculate batch size
Signed-off-by: Seunghwan Hong <harrydrippin@gmail.com>
Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add TF prefix to TF-Res test class (#18481)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Remove py.typed (#18485)
* Fix pipeline tests (#18487)
* Fix pipeline tests
* Make sure all pipelines tests run with init changes
* Use new huggingface_hub tools for download models (#18438)
* Draft new cached_file
* Initial draft for config and model
* Small fixes
* Fix first batch of tests
* Look in cache when internet is down
* Fix last tests
* Bad black, not fixing all quality errors
* Make diff less
* Implement change for TF and Flax models
* Add tokenizer and feature extractor
* For compatibility with main
* Add utils to move the cache and auto-do it at first use.
* Quality
* Deal with empty commit shas
* Deal with empty etag
* Address review comments
* Fix `test_dbmdz_english` by updating expected values (#18482)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Move cache folder to huggingface/hub for consistency with hf_hub (#18492)
* Move cache folder to just huggingface
* Thank you VsCode for this needless import
* Move to hub
* Forgot one
* Update some expected values in `quicktour.mdx` for `resampy 0.3.0` (#18484)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Forgot one new_ for cache migration
* disable Onnx test for google/long-t5-tglobal-base (#18454)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Typo reported by Joel Grus on TWTR (#18493)
* Just re-reading the whole doc every couple of months 😬 (#18489)
* Delete valohai.yaml
* NLP => ML
* typo
* website supports https
* datasets
* 60k + modalities
* unrelated link fixing for accelerate
* Ok those links were actually broken
* Fix link
* Make `AutoTokenizer` auto-link
* wording tweak
* add at least one non-nlp task
* `transformers-cli login` => `huggingface-cli login` (#18490)
* zero chance anyone's using that constant no?
* `transformers-cli login` => `huggingface-cli login`
* `transformers-cli repo create` => `huggingface-cli repo create`
* `make style`
* Add seed setting to image classification example (#18519)
* [DX fix] Fixing QA pipeline streaming a dataset. (#18516)
* [DX fix] Fixing QA pipeline streaming a dataset.
QuestionAnsweringArgumentHandler would iterate over the whole dataset
effectively killing all properties of the pipeline.
This restores nice properties when using `Dataset` or `Generator` since
those are meant to be consumed lazily.
* Handling TF better.
* Clean up hub (#18497)
* Clean up utils.hub
* Remove imports
* More fixes
* Last fix
* update fsdp docs (#18521)
* updating fsdp documentation
* typo fix
* Fix compatibility with 1.12 (#17925)
* Fix compatibility with 1.12
* Remove pin from examples requirements
* Update torch scatter version
* Fix compatibility with 1.12
* Remove pin from examples requirements
* Update torch scatter version
* fix torch.onnx.symbolic_opset12 import
* Reject bad version
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Remove debug statement
* Specify en in doc-builder README example (#18526)
Co-authored-by: Ankur Goyal <ankur@impira.com>
* New cache fixes: add safeguard before looking in folders (#18522)
* unpin resampy (#18527)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* ✨ update to use interlibrary links instead of Markdown (#18500)
* Add example of multimodal usage to pipeline tutorial (#18498)
* 📝 add example of multimodal usage to pipeline tutorial
* 🖍 apply feedbacks
* 🖍 apply niels feedback
* [VideoMAE] Add model to doc tests (#18523)
* Add videomae to doc tests
* Add pip install decord
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update perf_train_gpu_one.mdx (#18532)
* Update no_trainer.py scripts to include accelerate gradient accumulation wrapper (#18473)
* Added accelerate gradient accumulation wrapper to run_image_classification_no_trainer.py example script
* make fixup changes
* PR comments
* changed input to Acceletor based on PR comment, ran make fixup
* Added comment explaining the sync_gradients statement
* Fixed lr scheduler max steps
* Changed run_clm_no_trainer.py script to use accelerate gradient accum wrapper
* Fixed all scripts except wav2vec2 pretraining to use accelerate gradient accum wrapper
* Added accelerate gradient accum wrapper for wav2vec2_pretraining_no_trainer.py script
* make fixup and lr_scheduler step inserted back into run_qa_beam_search_no_trainer.py
* removed changes to run_wav2vec2_pretraining_no_trainer.py script and fixed using wrong constant in qa_beam_search_no_trainer.py script
* Add Spanish translation of converting_tensorflow_models.mdx (#18512)
* Add file in spanish docs to be translated
* Finish translation to Spanish
* Improve Spanish wording
* Add suggested changes from review
* Spanish translation of summarization.mdx (#15947) (#18477)
* Add Spanish translation of summarization.mdx
* Apply suggestions from code review
Co-authored-by: Omar U. Espejel <espejelomar@gmail.com>
Co-authored-by: Omar U. Espejel <espejelomar@gmail.com>
* Let's not cast them all (#18471)
* add correct dtypes when checking for params dtype
* forward contrib credits
* Update src/transformers/modeling_utils.py
Co-authored-by: Thomas Wang <24695242+thomasw21@users.noreply.github.com>
* more comments
- added more comments on why we cast only floating point parameters
* Update src/transformers/modeling_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: sgugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Thomas Wang <24695242+thomasw21@users.noreply.github.com>
* fix: data2vec-vision Onnx ready-made configuration. (#18427)
* feat: add the data2vec conf that are missing https://huggingface.co/docs/transformers/serialization
* fix: wrong config
* Add mt5 onnx config (#18394)
* update features
* MT5OnnxConfig added with updated with tests and docs
* fix imports
* fix onnc_config_cls for mt5
Co-authored-by: Thomas Chaigneau <thomas.deeptools.ai>
* Minor update of `run_call_with_unpacked_inputs` (#18541)
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* BART - Fix attention mask device issue on copied models (#18540)
* attempt to fix attn mask device
* fix bart `_prepare_decoder_attention_mask`
- add correct device
- run `make fix-copies` to propagate the fix
* Adding a new `align_to_words` param to qa pipeline. (#18010)
* Adding a new `align_to_words` param to qa pipeline.
* Update src/transformers/pipelines/question_answering.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Import protection.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* 📝 update metric with evaluate (#18535)
* Restore _init_weights value in no_init_weights (#18504)
* Recover _init_weights value in no_init_weights
For potential nested use.
In addition, users might modify private no_init_weights as well.
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Remove private variable change check
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean up comment
* 📝 update documentation build section (#18548)
* `bitsandbytes` - `Linear8bitLt` integration into `transformers` models (#17901)
* first commit
* correct replace function
* add final changes
- works like charm!
- cannot implement tests yet
- tested
* clean up a bit
* add bitsandbytes dependencies
* working version
- added import function
- added bitsandbytes utils file
* small fix
* small fix
- fix import issue
* fix import issues
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit
- move bitsandbytes utils to utils
- change comments on functions
* reformat docstring
- reformat docstring on init_empty_weights_8bit
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert bad formatting
* change to bitsandbytes
* refactor a bit
- remove init8bit since it is useless
* more refactoring
- fixed init empty weights issue
- added threshold param
* small hack to make it work
* Update src/transformers/modeling_utils.py
* Update src/transformers/modeling_utils.py
* revmoe the small hack
* modify utils file
* make style + refactor a bit
* create correctly device map
* add correct dtype for device map creation
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply suggestions
- remove with torch.grad
- do not rely on Python bool magic!
* add docstring
- add docstring for new kwargs
* add docstring
- comment `replace_8bit_linear` function
- fix weird formatting
* - added more documentation
- added new utility function for memory footprint tracking
- colab demo to add
* few modifs
- typo doc
- force cast into float16 when load_in_8bit is enabled
* added colab link
* add test architecture + docstring a bit
* refactor a bit testing class
* make style + refactor a bit
* enhance checks
- add more checks
- start writing saving test
* clean up a bit
* male style
* add more details on doc
* add more tests
- still needs to fix 2 tests
* replace by "or"
- could not fix it from GitHub GUI
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit testing code + add readme
* make style
* fix import issue
* Update src/transformers/modeling_utils.py
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* add few comments
* add more doctring + make style
* more docstring
* raise error when loaded in 8bit
* make style
* add warning if loaded on CPU
* add small sanity check
* fix small comment
* add bitsandbytes on dockerfile
* Improve documentation
- improve documentation from comments
* add few comments
* slow tests pass on the VM but not on the CI VM
* Fix merge conflict
* make style
* another test should pass on a multi gpu setup
* fix bad import in testing file
* Fix slow tests
- remove dummy batches
- no more CUDA illegal memory errors
* odify dockerfile
* Update docs/source/en/main_classes/model.mdx
* Update Dockerfile
* Update model.mdx
* Update Dockerfile
* Apply suggestions from code review
* few modifications
- lm head can stay on disk/cpu
- change model name so that test pass
* change test value
- change test value to the correct output
- torch bmm changed to baddmm in bloom modeling when merging
* modify installation guidelines
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* replace `n`by `name`
* merge `load_in_8bit` and `low_cpu_mem_usage`
* first try - keep the lm head in full precision
* better check
- check the attribute `base_model_prefix` instead of computing the number of parameters
* added more tests
* Update src/transformers/utils/bitsandbytes.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Merge branch 'integration-8bit' of https://github.com/younesbelkada/transformers into integration-8bit
* improve documentation
- fix typos for installation
- change title in the documentation
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* TF: XLA-trainable DeBERTa v2 (#18546)
* fix deberta issues
* add different code paths for gpu and tpu
* shorter gpu take along axis
* Stable Dropout without tf cond
* variable must be float
* Preserve hub-related kwargs in AutoModel.from_pretrained (#18545)
* Preserve hub-related kwargs in AutoModel.from_pretrained
* Fix tests
* Remove debug statement
* TF Examples Rewrite (#18451)
* Finished QA example
* Dodge a merge conflict
* Update text classification and LM examples
* Update NER example
* New Keras metrics WIP, fix NER example
* Update NER example
* Update MC, summarization and translation examples
* Add XLA warnings when shapes are variable
* Make sure batch_size is consistently scaled by num_replicas
* Add PushToHubCallback to all models
* Add docs links for KerasMetricCallback
* Add docs links for prepare_tf_dataset and jit_compile
* Correct inferred model names
* Don't assume the dataset has 'lang'
* Don't assume the dataset has 'lang'
* Write metrics in text classification
* Add 'framework' to TrainingArguments and TFTrainingArguments
* Export metrics in all examples and add tests
* Fix training args for Flax
* Update command line args for translation test
* make fixup
* Fix accidentally running other tests in fp16
* Remove do_train/do_eval from run_clm.py
* Remove do_train/do_eval from run_mlm.py
* Add tensorflow tests to circleci
* Fix circleci
* Update examples/tensorflow/language-modeling/run_mlm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/test_tensorflow_examples.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/translation/run_translation.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/token-classification/run_ner.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Fix save path for tests
* Fix some model card kwargs
* Explain the magical -1000
* Actually enable tests this time
* Skip text classification PR until we fix shape inference
* make fixup
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Use commit hash to look in cache instead of calling head (#18534)
* Use commit hash to look in cache instead of calling head
* Add tests
* Add attr for local configs too
* Stupid typos
* Fix tests
* Update src/transformers/utils/hub.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Address Julien's comments
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* `pipeline` support for `device="mps"` (or any other string) (#18494)
* `pipeline` support for `device="mps"` (or any other string)
* Simplify `if` nesting
* Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix? @sgugger
* passing `attr=None` is not the same as not passing `attr` 🤯
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update philosophy to include other preprocessing classes (#18550)
* 📝 update philosophy to include other preprocessing classes
* 🖍 apply feedbacks
* Properly move cache when it is not in default path (#18563)
* Adds CLIP to models exportable with ONNX (#18515)
* onnx config for clip
* default opset as 14
* changes from the original repo
* input values order fix
* outputs fix
* remove unused import
* ran make fix-copies
* black format
* review comments: forward ref, import fix, model change revert, .to cleanup
* make style
* formatting fixes
* revert groupvit
* comment for cast to int32
* comment fix
* make .T as .t() for onnx conversion
* ran make fix-copies
* remove unneeded comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix copies
* remove comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* raise atol for MT5OnnxConfig (#18560)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* fix string (#18568)
* Segformer TF: fix output size in documentation (#18572)
* Segformer TF: fix output size in doc
* Segformer pytorch: fix output size in doc
Co-authored-by: Maxime Gardoni <maxime.gardoni@ecorobotix.com>
* Fix resizing bug in OWL-ViT (#18573)
* Fixes resizing bug in OWL-ViT
* Defaults to square resize if size is set to an int
* Sets do_center_crop default value to False
* Fix LayoutLMv3 documentation (#17932)
* fix typos
* fix sequence_length docs of LayoutLMv3Model
* delete trailing white spaces
* fix layoutlmv3 docs more
* apply make fixup & quality
* change to two versions of input docstring
* apply make fixup & quality
* Skip broken tests
* Change BartLearnedPositionalEmbedding's forward method signature to support Opacus training (#18486)
* changing BartLearnedPositionalEmbedding forward signature and references to it
* removing debugging dead code (thanks style checker)
* blackened modeling_bart file
* removing copy inconsistencies via make fix-copies
* changing references to copied signatures in Bart variants
* make fix-copies once more
* using expand over repeat (thanks @michaelbenayoun)
* expand instead of repeat for all model copies
Co-authored-by: Daniel Jones <jonesdaniel@microsoft.com>
* german docs translation (#18544)
* Create _config.py
* Create _toctree.yml
* Create index.mdx
not sure about "du / ihr" oder "sie"
* Create quicktour.mdx
* Update _toctree.yml
* Update build_documentation.yml
* Update build_pr_documentation.yml
* fix build
* Update index.mdx
* Update quicktour.mdx
* Create installation.mdx
* Update _toctree.yml
* Deberta V2: Fix critical trace warnings to allow ONNX export (#18272)
* Fix critical trace warnings to allow ONNX export
* Force input to `sqrt` to be float type
* Cleanup code
* Remove unused import statement
* Update model sew
* Small refactor
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Use broadcasting instead of repeat
* Implement suggestion
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Match deberta v2 changes in sew_d
* Improve code quality
* Update code quality
* Consistency of small refactor
* Match changes in sew_d
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* [FX] _generate_dummy_input supports audio-classification models for labels (#18580)
* Support audio classification architectures for labels generation, as well as provides a flag to print warnings or not
* Use ENV_VARS_TRUE_VALUES
* Fix docstrings with last version of hf-doc-builder styler (#18581)
* Fix docstrings with last version of hf-doc-builder styler
* Remove empty Parameter block
* Bump nbconvert from 6.0.1 to 6.3.0 in /examples/research_projects/lxmert (#18565)
Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 6.0.1 to 6.3.0.
- [Release notes](https://github.com/jupyter/nbconvert/releases)
- [Commits](https://github.com/jupyter/nbconvert/compare/6.0.1...6.3.0)
---
updated-dependencies:
- dependency-name: nbconvert
dependency-type: direct:production
...
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* Bump nbconvert in /examples/research_projects/visual_bert (#18566)
Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 6.0.1 to 6.3.0.
- [Release notes](https://github.com/jupyter/nbconvert/releases)
- [Commits](https://github.com/jupyter/nbconvert/compare/6.0.1...6.3.0)
---
updated-dependencies:
- dependency-name: nbconvert
dependency-type: direct:production
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* fix owlvit tests, update docstring examples (#18586)
* Return the permuted hidden states if return_dict=True (#18578)
* Load sharded pt to flax (#18419)
* initial commit
* add small test
* add cross pt tf flag to test
* fix quality
* style
* update test with new repo
* fix failing test
* update
* fix wrong param ordering
* style
* update based on review
* update related to recent new caching mechanism
* quality
* Update based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* quality and style
* Update src/transformers/modeling_flax_utils.py
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
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* Add type hints for ViLT models (#18577)
* Add type hints for Vilt models
* Add missing return type for TokenClassification class
* update doc for perf_train_cpu_many, add intel mpi introduction (#18576)
* update doc for perf_train_cpu_many, add mpi introduction
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* Update docs/source/en/perf_train_cpu_many.mdx
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* Update docs/source/en/perf_train_cpu_many.mdx
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* typos (#18594)
* FSDP bug fix for `load_state_dict` (#18596)
* Add `TFAutoModelForSemanticSegmentation` to the main `__init__.py` (#18600)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Generate: validate `model_kwargs` (and catch typos in generate arguments) (#18261)
* validate generate model_kwargs
* generate tests -- not all models have an attn mask
* Supporting seq2seq models for `bitsandbytes` integration (#18579)
* Supporting seq2seq models for `bitsandbytes` integration
- `bitsandbytes` integration supports now seq2seq models
- check if a model has tied weights as an additional check
* small modification
- tie the weights before looking at tied weights!
* Add Donut (#18488)
* First draft
* Improve script
* Update script
* Make conversion work
* Add final_layer_norm attribute to Swin's config
* Add DonutProcessor
* Convert more models
* Improve feature extractor and convert base models
* Fix bug
* Improve integration tests
* Improve integration tests and add model to README
* Add doc test
* Add feature extractor to docs
* Fix integration tests
* Remove register_buffer
* Fix toctree and add missing attribute
* Add DonutSwin
* Make conversion script work
* Improve conversion script
* Address comment
* Fix bug
* Fix another bug
* Remove deprecated method from docs
* Make Swin and Swinv2 untouched
* Fix code examples
* Fix processor
* Update model_type to donut-swin
* Add feature extractor tests, add token2json method, improve feature extractor
* Fix failing tests, remove integration test
* Add do_thumbnail for consistency
* Improve code examples
* Add code example for document parsing
* Add DonutSwin to MODEL_NAMES_MAPPING
* Add model to appropriate place in toctree
* Update namespace to appropriate organization
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Fix URLs (#18604)
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update BLOOM parameter counts (#18531)
* Update BLOOM parameter counts
* Update BLOOM parameter counts
* [doc] fix anchors (#18591)
the manual anchors end up being duplicated with automatically added anchors and no longer work.
* [fsmt] deal with -100 indices in decoder ids (#18592)
* [fsmt] deal with -100 indices in decoder ids
Fixes: https://github.com/huggingface/transformers/issues/17945
decoder ids get the default index -100, which breaks the model - like t5 and many other models add a fix to replace -100 with the correct pad index.
For some reason this use case hasn't been used with this model until recently - so this issue was there since the beginning it seems.
Any suggestions to how to add a simple test here? or perhaps we have something similar already? user's script is quite massive.
* style
* small change (#18584)
* Flax Remat for LongT5 (#17994)
* [Flax] Add remat (gradient checkpointing)
* fix variable naming in test
* flip: checkpoint using a method
* fix naming
* fix class naming
* apply PVP's suggestions from code review
* add gradient_checkpointing to examples
* Add gradient_checkpointing to run_mlm_flax
* Add remat to longt5
* Add gradient checkpointing test longt5
* Fix args errors
* Fix remaining tests
* Make fixup & quality fixes
* replace kwargs
* remove unecessary kwargs
* Make fixup changes
* revert long_t5_flax changes
* Remove return_dict and copy to LongT5
* Remove test_gradient_checkpointing
Co-authored-by: sanchit-gandhi <sanchit@huggingface.co>
* mac m1 `mps` integration (#18598)
* mac m1 `mps` integration
* Update docs/source/en/main_classes/trainer.mdx
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* addressing comments
* Apply suggestions from code review
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* resolve comment
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* Change scheduled CIs to use torch 1.12.1 (#18644)
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* Add checks for some workflow jobs (#18583)
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* TF: Fix generation repetition penalty with XLA (#18648)
* Update longt5.mdx (#18634)
* Update run_translation_no_trainer.py (#18637)
* Update run_translation_no_trainer.py
found an error in selecting `no_decay` parameters and some small modifications when the user continues to train from a checkpoint
* fixs `no_decay` and `resume_step` issue
1. change `no_decay` list
2. if use continue to train their model from provided checkpoint, the `resume_step` will not be initialized properly if `args.gradient_accumulation_steps != 1`
* [bnb] Minor modifications (#18631)
* bnb minor modifications
- refactor documentation
- add troubleshooting README
- add PyPi library on DockerFile
* Apply suggestions from code review
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* Apply suggestions from code review
* Apply suggestions from code review
* Apply suggestions from code review
* put in one block
- put bash instructions in one block
* update readme
- refactor a bit hardware requirements
* change text a bit
* Apply suggestions from code review
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* apply suggestions
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* add link to paper
* Apply suggestions from code review
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* Update tests/mixed_int8/README.md
* Apply suggestions from code review
* refactor a bit
* add instructions Turing & Amperer
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* add A6000
* clarify a bit
* remove small part
* Update tests/mixed_int8/README.md
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* Examples: add Bloom support for token classification (#18632)
* examples: add Bloom support for token classification (FLAX, PyTorch and TensorFlow)
* examples: remove support for Bloom in token classication (FLAX and TensorFlow currently have no support for it)
* Fix Yolos ONNX export test (#18606)
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* Fixup
* Fix up
* Move PIL default arguments inside function for safe imports
* Add image utils to toctree
* Update `rescale` method to reflect changes in #18677
* Update docs/source/en/internal/image_processing_utils.mdx
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* Address Niels PR comments
* Apply suggestions from code review - remove defaults to None
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* Fix docstrings and revert to PIL.Image.XXX resampling
Use PIL.Image.XXX resampling values instead of PIL.Image.Resampling.XXX enum as it's only in the recent version >= 9.10 and version is not yet pinned and older version support deprecated
* Some more docstrings and PIL.Image tidy up
* Reorganise arguments so flags by modifiers
* Few last docstring fixes
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