* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* generate from config mvp
* fix failing tests
* max_time test
* Load default gen config at model load time; Update docs
* further documentation; add tests
* adapt rag to the new structure
* handle models not instantiated with from_pretained (like in tests)
* better default generation config
* add can_generate fn
* handle legacy use case of ad hoc model config changes
* initialize gen config from config in individual methods, if gen config is none
* fix _get_decoder_start_token_id when called outside GenerationMixin
* correct model config load order (set attr > model config > decoder config)
* update rag to match latest changes
* 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>
* load gen config from model config in model.from_pretrained
* fix can_generate fn
* handle generate calls without a previous from_pretrained (e.g. tests)
* add legacy behavior (and a warning)
* lower logger severity
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add templates for gpt-sw3
* Add templates for gpt-sw3
* Added sentencepiece tokenizer
* intermediate commit with many changes
* fixed conflicts
* Init commit for tokenization port
* Tokenization progress
* Remove fast tokenizer
* Clean up and rename spm.model -> spiece.model
* Remove TF -> PT conversion script template, Clean up Megatron -> PT script
* Optimize encode & decode performance
* added new attention
* added new attention
* attention for gpt-sw3 working
* attention good
* Cache is now working
* fixed attention mask so that it works with causal attention
* fixed badbmm bug for cpu and caching
* updated config with correct parameters
* Refactor and leave optimizations as separate functions to avoid breaking expected functionality
* Fix special tokens mapping for both tokenizers
* cleaning up of code and comments
* HF compatible attention outputs
* Tokenizer now passing tests, add documentation
* Update documentation
* reverted back to base implementation after checking that it is identical to pretrained model
* updated gpt-sw3 config
* updated conversion script
* aligned parameters with gpt-sw3 config
* changed default scale_attn_by_inverse_layer_idx to true
* removed flag from conversion script
* added temporary model path
* reverted back to functioning convert script
* small changes to default config
* updated tests for gpt-sw3
* make style, make quality, minor cleanup
* Change local paths to testing online repository
* Change name: GptSw3 -> GPTSw3
* Remove GPTSw3TokenizerFast references
* Use official model repository and add more model sizes
* Added reference to 6.7b model
* Add GPTSw3DoubleHeadsModel to IGNORE_NON_AUTO_CONFIGURED, like GPT2DoubleHeadsModel
* Remove pointers to non-existing TFGPTSw3
* Add GPTSw3 to docs/_toctree.yml
* Remove TF artifacts from GPTSw3 in __init__ files
* Update README:s with 'make fix-copies'
* Add 20b model to archive list
* Add documentation for GPT-Sw3
* Fix typo in documentation for GPT-Sw3
* Do 'make fix-copies' again after having updated docs
* Fix some typos in docs
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/models/gpt_sw3/test_tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Resolve comments from PR feedback
* Resolve more comments from PR feedback, also set use_cache=True in convert script
* Add '# Copied from' comments for GPTSw3 modeling
* Set 'is_parallelizable = False'
* Remove '# Copied from' where code was modified and add 'with x->y' when appropriate
* Remove parallelize in mdx
* make style, make quality
* Update GPTSw3Config default values and corresponding documentation
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean up and protect GPTSw3Tokenizer imports with is_sentencepiece_available
* Make style, make quality
* Add dummy object for GPTSw3Tokenizer via 'make fix-copies'
* make fix-copies
* Remove GPTSw3 modeling classes
* make style, make quality
* Add GPTSw3 auto-mappings for other GPT2 heads
* Update docs/source/en/model_doc/gpt-sw3.mdx
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove old TODO-comment
* Add example usage to GPTSw3Tokenizer docstring
* make style, make quality
* Add implementation details and example usage to gpt-sw3.mdx
Co-authored-by: JoeyOhman <joeyoh@kth.se>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* read to load
* base functionality
* revert init
* fix dummy data
* moving right along
* moving right along
* finally
* cleanup
* pull out comment
* add test
* update docstring for main class
* flake comments and rewriting copies from make repo-consistency`
* remove irrelevant differences/accidental spaces
* put copies back after space removals
* mid
* final test pass
* stray comment
* update test file
* update test file
* fixup
* black
* missed
* black missed one more
* sytle
* add doc update
* fix order of output class
* comment
* Revert "comment"
This reverts commit 03f86b6948.
* remove redundant function, and redundant reshape
* move change out of common
* style
* put common spaces back
* reorder kwargs in output
* doc style
* add `dpt-hybrid` support
* refactor
* final changes, all tests pass
* final cleanups
* final changes
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix docstring
* fix typo
* change `vit_hybrid` to `hybrid`
* replace dataclass
* add docstring
* move dataclasses
* fix test
* add `PretrainedConfig` support for `backbone_config`
* fix docstring
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* remove `embedding_type` and replace it by `is_hybrid`
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix cuda OOM by using single Prior
* only send to device when used
* use custom model
* Skip the big slow test
* Update tests/models/jukebox/test_modeling_jukebox.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* biogpt initial commit
* updated init
* fix faster decoding with use_cache
* 1. fix input_ids and input_embeds with correct device
2. added _keys_to_ignore_on_load_missing
3. updated prepare_inputs_for_generation
* add activation_dropout and scale_embedding
* replace fsmt attention with bart attention
* added test
* run make fix-copies
* doc init and fix build
* updated README with proper information
* 1. added tips to docs
2. updated BioGptTokenizer func
* 1. added tokenizer test
2. refactor tokenizer
* make fixup
* add biogpt fairseq to hf converter
* updated layer names more
similar to original checkpoints
* config update doc string and set defaults
* added "#copied" from bart model and
updated doc strings
* enable model_input_names in tokenizer
* 1. positionalembedding depending on attention_mask
2. added attention mask to prepare for generation
* added test to verify past and generation
* BioGptLMHeadModel -> BioGptForCausalLM
* fix typo
* tokenization and test
Copyright and updated assertion
* updated Copyright and
one func at time in line
* Copyright updates and
minor doc fix
* replace assertion with ValueError
* rm extra space
* added code syntax
* revert cmnt position change
* add tokenizer to auto
* updated doc string
* tokenizer doc string update
* biogpt hub model update to microsoft/biogpt
* make fixup
* rm cmnt to fix flake8 5.0.4 vs 6 error
* 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 minimal working gpt2 tokenizer
* graph mode and output equivalence tests working
* not today tensorflow. serialization test passing!
* fix style, documentation, docstrings and all that jazz
* passing consistency checks
* move keras nlp to tf dependencies
* fix tf modeling utils and gpt2 attention to enable compiling
* fix (I hope) keras nlp dependencies
* rever changes on generation
* remove debug prints
* remove redundant tf dummy objects
* add from config, get config and max length settings to address review
* let flake ignore the error on distillation you are welcome
* test from config
* add padding test
* address sgugger review
* Add Donut image processor
* Update src/transformers/image_transforms.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Fix docstrings
* Full var names in docstring
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* First draft
* Fix backwards compatibility
* More fixes
* More fixes
* Make backbone more general
* Improve backbone
* Improve test
* Fix config checkpoint
* Address comments
* Use model_type
* Address more comments
* Fix special model names
* Remove MaskFormerSwinModel and MaskFormerSwinPreTrainedModel from main init
* Fix typo
* Update backbone
* Apply suggestion
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add hidden states and attentions to backbone outputs
* Update ResNet
* Fix more tests
* Debug test
* Fix test_determinism
* Fix test_save_load
* Remove file
* Disable fx tests
* Test
* Add fx support for backbones
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* 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
* change the way sentinel tokens can retrived
* Fix line length for doc string
* Fix line length for doc string
* Add more stronger test for t5 tokenization
* Format file changes
* Make a stronger test for filtering sentinel tokens
* fix file format issues
* Optimizes DonutProcessor token2json method for speed
* Applies black formatting
* Updates Donut pretrained model name in test file
* remaining pytorch type hints (#20217)
* Update modeling_flava.py
* Update modeling_markuplm.py
* Update modeling_glpn.py
* Update modeling_roc_bert.py
* Update modeling_segformer.py
* Update modeling_tapas.py
* Update modeling_tapas.py
* Update modeling_tapas.py
* Update modeling_tapas.py
* Update modeling_trocr.py
* Update modeling_videomae.py
* Update modeling_videomae.py
* Update modeling_videomae.py
* Update modeling_yolos.py
* Update modeling_wav2vec2.py
* Update modeling_jukebox.py
* Update modeling_jukebox.py
* Update modeling_jukebox.py
* Update modeling_jukebox.py
* Data collator for token classification pads labels column when receives pytorch tensors (#20244)
* token cls data_collator pads labels column
* remove walrus operator for code quality
* remove redundat space
* remove comment that was fixed
* PR comments fix
Co-authored-by: Alexander Markov <amarkov.me@gmail.com>
* [Doctest] Add configuration_deformable_detr.py (#20273)
* Update configuration_deformable_detr.py comment
* Add DeformableDetrConfig to documentation_tests.txt
* Fix summarization script (#20286)
* [DOCTEST] Fix the documentation of RoCBert (#20142)
* update part of the doc
* add temp values, fix part of the doc
* add template outputs
* add correct models and outputss
* style
* fixup
* [bnb] Let's warn users when saving 8-bit models (#20282)
* add warning on 8-bit models
- added tests
- added wrapper
* move to a private attribute
- remove wrapper
- changed `save_pretrained` method
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding `zero-shot-object-detection` pipeline doctest. (#20274)
* Adding `zero-shot-object-detection` pipeline doctest.
* Remove nested_simplify.
* Adding doctest for `object-detection` pipeline. (#20258)
* Adding doctest for `object-detection` pipeline.
* Removed nested_simplify.
* Image transforms functionality used instead (#20278)
* Image transforms functionality used instead
* Import torch
* Import rather than copy
* Update src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py
* TF: add test for `PushToHubCallback` (#20231)
* test hub tf callback
* create repo before cloning it
* Generate: general TF XLA constrastive search are now slow tests (#20277)
* move contrastive search test to slow
* Fixing the doctests failures. (#20294)
* Fixing the doctests failures.
* Fixup.
* set the default cache_enable to True, aligned with the default value in pytorch cpu/cuda amp autocast (#20289)
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Add docstrings for canine model (#19457)
* Add docstrings for canine model
* Update CanineForTokenClassification
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add AutoBackbone + ResNetBackbone (#20229)
* Add ResNetBackbone
* Define channels and strides as property
* Remove file
* Add test for backbone
* Update BackboneOutput class
* Remove strides property
* Fix docstring
* Add backbones to SHOULD_HAVE_THEIR_OWN_PAGE
* Fix auto mapping name
* Add sanity check for out_features
* Set stage names based on depths
* Update to tuple
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add missing report button for Example test (#20293)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* refactor test (#20300)
- simplifies the devce checking test
* [Tiny model creation] deal with `ImageProcessor` (#20298)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix blender bot missleading doc (#20301)
* fix the doc to specify that add_prefix_space = False
* add correct expected output
* remove two tokens that should not be suppressed (#20302)
* [ASR Examples] Update README for Whisper (#20230)
* [ASR Examples] Update README for seq2seq
* add language info
* add training results
* re-word
* Add padding image transformation (#19838)
* Add padding transformation
* Add in upstream changes
* Update tests & docs
* Code formatting tuples in docstring
* Pin TensorFlow (#20313)
* Pin to the right version...
* Also pin TensorFlow CPU
* Add AnyPrecisionAdamW optimizer (#18961)
* Add AnyPrecisionAdamW optimizer
* Add optim_args argument to TrainingArgs
* Add tests for AnyPrecisionOptimizer
* Change AnyPrecisionAdam default params to float32
* Move default_anyprecision_kwargs in trainer test
* Rename AnyPrecisionAdamW
* [Proposal] Breaking change `zero-shot-object-detection` for improved consistency. (#20280)
* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.
This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.
The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.
The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.
The pipeline is changed to `ChunkPipeline` to support batching cleanly.
This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.
**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.
**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.
**Breaking change**: The types is now simplified and flattened:
`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.
It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.
* Fixing the doctests.
* Behind is_torch_available.
* Fix flakey test with seed (#20318)
* Pin TF 2.10.1 for Push CI (#20319)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Remove double brackets (#20307)
* remove double brackets
* oops get other bracket
* TF: future proof our keras imports (#20317)
* future proof our tf code
* parse tf versions
* Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models (#20219)
* Add DiNAT
* Adds DiNAT + tests
* Minor fixes
* Added HF model
* Add natten to dependencies.
* Cleanup
* Minor fixup
* Reformat
* Optional NATTEN import.
* Reformat & add doc to _toctree
* Reformat (finally)
* Dummy objects for DiNAT
* Add NAT + minor changes
Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.
* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests
* Minor fixes.
* Fix READMEs.
* Requested changes to docs + minor fixes.
* Requested changes.
* Add NAT/DiNAT tests to layoutlm_job
* Correction to Dinat doc.
* Requested changes.
* organize pipelines by modality (#20306)
* Fix torch device issues (#20304)
* fix device issue
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Generate: add generation config class (#20218)
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* translate zh quicktour(#20095) (#20181)
* zh quicktour(#20095)
* add zh to doc workflow
* remove untranslation from toctree
Co-authored-by: BeifangSusu <BeifangSusu@bfss.com>
* Add Spanish translation of serialization.mdx (#20245)
* Update _toctree and clone original content
* Translate first three sections
* Add more translated chapters. Only 3 more left.
* Finish translation
* Run style from doc-builder
* Address recommended changes from reviewer
* Add LayerScale to NAT/DiNAT (#20325)
* Add LayerScale to NAT/DiNAT.
Completely dropped the ball on LayerScale in the original PR (#20219).
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
* Add LayerScale to NAT/DiNAT.
Minor error fixed.
Co-authored-by: Ali Hassani <ahassanijr@gmail.com>
* [Switch Transformers] Fix failing slow test (#20346)
* run slow test on GPU
* remove unnecessary device assignment
* use `torch_device` instead
* fix: "BigSicence" typo in docs (#20331)
* add MobileNetV1 model (#17799)
* add model files etc for MobileNetV2
rename files for MobileNetV1
initial implementation of MobileNetV1
fix conversion script
cleanup
write docs
tweaks
fix conversion script
extract hidden states
fix test cases
make fixup
fixup it all
remove main from doc link
fixes
fix tests
fix up
use google org
fix weird assert
* fixup
* use google organization for checkpoints
* Generate: `model_kwargs` can also be an input to `prepare_inputs_for_generation` (#20353)
* Update Special Language Tokens for PLBART (#19980)
* Update Special Language Tokens for PLBART
* fix format
* making mapping for language codes and updating tests:
* fix format
* fix consistency
* add assert to both tokenizer tests.
* fix format
* Update src/transformers/models/plbart/tokenization_plbart.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* improvin readability, setting self.tgt_lang
* fixing
* readability
Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add resources (#20296)
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Enhance HfArgumentParser functionality and ease of use (#20323)
* Enhance HfArgumentParser
* Fix type hints for older python versions
* Fix and add tests (+formatting)
* Add changes
* doc-builder formatting
* Remove unused import "Call"
* Add Audio Spectogram Transformer (#19981)
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add inference section to task guides (#18781)
* 📝 start adding inference section to task guides
* ✨ make style
* 📝 add multiple choice
* add rest of inference sections
* make style
* add compute_metric, push_to_hub, pipeline
* make style
* add updated sequence and token classification
* make style
* make edits in token classification
* add audio classification
* make style
* add asr
* make style
* add image classification
* make style
* add summarization
* make style
* add translation
* make style
* add multiple choice
* add language modeling
* add qa
* make style
* review and edits
* apply reviews
* make style
* fix call to processor
* apply audio reviews
* update to better asr model
* make style
* Fix toctree for Section 3 in Spanish Documentation (#20360)
* Order and group topics in the right section
* Translate "Computer Vision"
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: IMvision12 <88665786+IMvision12@users.noreply.github.com>
Co-authored-by: Alexander Markov <almarkv@yandex.ru>
Co-authored-by: Alexander Markov <amarkov.me@gmail.com>
Co-authored-by: Saad Mahmud <shuvro.mahmud79@gmail.com>
Co-authored-by: Zachary Mueller <muellerzr@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
Co-authored-by: raghavanone <115454562+raghavanone@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
Co-authored-by: atturaioe <76523524+atturaioe@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Ali Hassani <68103095+alihassanijr@users.noreply.github.com>
Co-authored-by: BFSS <31245245+bfss@users.noreply.github.com>
Co-authored-by: BeifangSusu <BeifangSusu@bfss.com>
Co-authored-by: Ian C <7807897+donelianc@users.noreply.github.com>
Co-authored-by: Ali Hassani <ahassanijr@gmail.com>
Co-authored-by: Raj Rajhans <me@rajrajhans.com>
Co-authored-by: Matthijs Hollemans <mail@hollance.com>
Co-authored-by: Jordan Clive <jordan.clive19@imperial.ac.uk>
Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
Co-authored-by: Konstantin Dobler <konstantin.j.dobler@gmail.com>
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update Special Language Tokens for PLBART
* fix format
* making mapping for language codes and updating tests:
* fix format
* fix consistency
* add assert to both tokenizer tests.
* fix format
* Update src/transformers/models/plbart/tokenization_plbart.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* improvin readability, setting self.tgt_lang
* fixing
* readability
Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add model files etc for MobileNetV2
rename files for MobileNetV1
initial implementation of MobileNetV1
fix conversion script
cleanup
write docs
tweaks
fix conversion script
extract hidden states
fix test cases
make fixup
fixup it all
remove main from doc link
fixes
fix tests
fix up
use google org
fix weird assert
* fixup
* use google organization for checkpoints
* Add DiNAT
* Adds DiNAT + tests
* Minor fixes
* Added HF model
* Add natten to dependencies.
* Cleanup
* Minor fixup
* Reformat
* Optional NATTEN import.
* Reformat & add doc to _toctree
* Reformat (finally)
* Dummy objects for DiNAT
* Add NAT + minor changes
Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.
* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests
* Minor fixes.
* Fix READMEs.
* Requested changes to docs + minor fixes.
* Requested changes.
* Add NAT/DiNAT tests to layoutlm_job
* Correction to Dinat doc.
* Requested changes.
* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.
This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.
The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.
The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.
The pipeline is changed to `ChunkPipeline` to support batching cleanly.
This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.
**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.
**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.
**Breaking change**: The types is now simplified and flattened:
`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.
It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.
* Fixing the doctests.
* Behind is_torch_available.
* Add ResNetBackbone
* Define channels and strides as property
* Remove file
* Add test for backbone
* Update BackboneOutput class
* Remove strides property
* Fix docstring
* Add backbones to SHOULD_HAVE_THEIR_OWN_PAGE
* Fix auto mapping name
* Add sanity check for out_features
* Set stage names based on depths
* Update to tuple
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Adds image-guided object detection method to OwlViTForObjectDetection class as described in the original paper. One-shot/ image-guided object detection enables users to use a query image to search for similar objects in the input image.
Co-Authored-By: Dhruv Karan k4r4n.dhruv@gmail.com
* Slightly alter Keras dummy loss
* Slightly alter Keras dummy loss
* Add sample weight to test_keras_fit
* Fix test_keras_fit for datasets
* Skip the sample_weight stuff for models where the model tester has no batch_size
* allow loading projection in text and vision model
* begin tests
* finish test for CLIPTextModelTest
* style
* add slow tests
* add new classes for projection heads
* remove with_projection
* add in init
* add in doc
* fix tests
* fix some more tests
* fix copies
* fix docs
* remove leftover from fix-copies
* add the head models in IGNORE_NON_AUTO_CONFIGURED
* fix docstr
* fix tests
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add docstr for models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Try PT1.13 by removing torch scatter
* Skip failing tests
* Style
* Remvoe testing extras for repo utils
* Try with all decorators
* Try to wipe the cache
* Fix all tests?
* Try this way
* Fix comma
* Update to main
* Try with less deps
* Quality
* add `accelerate` support for `ViT` family
- add `_no_split_modules`
- manually cast to the right `dtype`: to change
* enable `float16` for `deit`
* fix `make fixup`
* add `slow` test for `fp16` inference
* another safety check
* Update src/transformers/models/deit/modeling_deit.py
* update relative positional embedding
* make fix copies
* add `use_cache` to list of arguments
* fixup
* 1line fucntion
* add `test_decoder_model_past_with_large_inputs_relative_pos_emb`
* add relative pos embedding test for more models
* style
* Fix ImageSegmentationPipelineTests
* Use 0.9
* no zip
* links to show images
* links to show images
* rebase
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* add model files etc for MobileNetV2
* rename files for MobileNetV1
* initial implementation of MobileNetV1
* fix conversion script
* cleanup
* write docs
* tweaks
* fix conversion script
* extract hidden states
* fix test cases
* make fixup
* fixup it all
* rename V1 to V2
* fix checkpoints
* fixup
* implement first block + weight conversion
* add remaining layers
* add output stride and dilation
* fixup
* add tests
* add deeplabv3+ head
* a bit of fixup
* finish deeplab conversion
* add link to doc
* fix issue with JIT trace
in_height and in_width would be Tensor objects during JIT trace, which caused Core ML conversion to fail on the remainder op. By making them ints, the result of the padding calculation becomes a constant value.
* cleanup
* fix order of models
* fix rebase error
* remove main from doc link
* add image processor
* remove old feature extractor
* fix converter + other issues
* fixup
* fix unit test
* add to onnx tests (but these appear broken now)
* add post_process_semantic_segmentation
* use google org
* remove unused imports
* move args
* replace weird assert
* Apply fix
* Fix test
* Remove another argument which is not used
* Fix pipeline test
* Add argument back, add deprecation warning
* Add warning add other location
* Use warnings instead
* Add num_channels to config
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
* Adding support for LayoutLMvX variants for `object-detection`.
* Revert bogs `layoutlm` feature extractor which does not exist (it was a
V2 model) .
* Updated condition.
* Handling the comments.
* move generation_*.py src files into generation/*.py
* populate generation.__init__ with lazy loading
* move imports and references from generation.xxx.object to generation.object
* Attempting to test automatically the `_keys_to_ignore`.
* Style.
* First fix pass.
* Moving test on its own.
* Another batch.
* Second round removing BatchNorm
* Fixing layoutlmv{2,3} + support older Python.
* Disable miss missing warning.
* Removing dodgy additions.
* Big pass.
* mbart.
* More corrections.
* Fixup.
* Updating test_correct_missing_keys
* Add escape hatch for when the head has no extra params so doesn't need
the missing keys check.
* Fixing test.
* Greener.
* Green ! (except for weird splinter bug).
* Adding a test about `named_parameters` usage.
* Shorten message.
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* After rebase modifications.
* More explicit condition checking.
* Fixing slow tests issues.
* Remove extra pdb.
* Remove print.
* Attempt to make failure consistent + fixing roc_bert.
* Removing the seed (all tests passing with it).
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add first draft
* Update conversion script
* Improve conversion script
* Improve conversion script some more
* Add conditional embeddings
* Add initial decoder
* Fix activation function of decoder
* Make decoder outputs match original implementation
* Make decoder outputs match original implementation
* Add more copied from statements
* Improve model outputs
* Fix auto tokenizer file
* Fix more tests
* Add test
* Improve README and docs, improve conditional embeddings
* Fix more tests
* Remove print statements
* Remove initial embeddings
* Improve conversion script
* Add interpolation of position embeddings
* Finish addition of interpolation of position embeddings
* Add support for refined checkpoint
* Fix refined checkpoint
* Remove unused parameter
* Improve conversion script
* Add support for training
* Fix conversion script
* Add CLIPSegFeatureExtractor
* Fix processor
* Fix CLIPSegProcessor
* Fix conversion script
* Fix most tests
* Fix equivalence test
* Fix README
* Add model to doc tests
* Use better variable name
* Convert other checkpoint as well
* Update config, add link to paper
* Add docs
* Update organization
* Replace base_model_prefix with clip
* Fix base_model_prefix
* Fix checkpoint of config
* Fix config checkpoint
* Remove file
* Use logits for output
* Fix tests
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add test for SentencePiece not adding special tokens to strings
* Add SentencePieceStringConversionMixin to fix issue 15003
* Fix conversion from tokens to string for most SentencePiece tokenizers
Tokenizers fixed:
- AlbertTokenizer
- BarthezTokenizer
- CamembertTokenizer
- FNetTokenizer
- M2M100Tokenizer
- MBart50Tokenizer
- PegasusTokenizer
- Speech2TextTokenizer
* Fix MarianTokenizer, adjust SentencePiece test to accomodate vocab
* Fix DebertaV2Tokenizer
* Ignore LayoutXLMTokenizer in SentencePiece string conversion test
* Run 'make style' and 'make quality'
* Clean convert_tokens_to_string test
Instead of explicitly ignoring LayoutXLMTokenizer in the test,
override the test in LayoutLMTokenizationTest and do nothing in it.
* Remove commented out code
* Improve robustness of convert_tokens_to_string test
Instead of comparing lengths of re-tokenized text and input_ids,
check that converting all special tokens to string yields a string
with all special tokens.
* Inline and remove SentencePieceStringConversionMixin
The convert_tokens_to_string method is now implemented
in each relevant SentencePiece tokenizer.
* Run 'make style' and 'make quality'
* Revert removal of space in convert_tokens_to_string
* Remove redundant import
* Revert test text to original
* Uncomment the lowercasing of the reverse_text variable
* Mimic Rust tokenizer behavior for tokenizers
- Albert
- Barthez
- Camembert
- MBart50
- T5
* Fix accidentally skipping test in wrong tokenizer
* Add test for equivalent Rust and slow tokenizer behavior
* Override _decode in BigBirdTokenizer to mimic Rust behavior
* Override _decode in FNetTokenizer to mimic Rust behavior
* Override _decode in XLNetTokenizer to mimic Rust behavior
* Remove unused 're' import
* Update DebertaV2Tokenizer to mimic Rust tokenizer
* Deberta tokenizer now behaves like Albert and its `convert_tokens_to_string` is not tested.
* Ignore problematic tests in Deberta V2
* Add comment on why the Deberta V2 tests are skipped
* initial commit
* First draft that gets outputs without crashing!
* Add all the ported openfold dependencies
* testing
* Restructure config files for ESMFold
* Debugging to find output discrepancies
* Mainly style
* Make model runnable without extra deps
* Remove utils and merge them to the modeling file
* Use correct gelu and remove some debug prints
* More cleanup
* Update esm docs
* Update conversion script to support ESMFold properly
* Port some top-level changes from ESMFold repo
* Expand EsmFold docstrings
* Make attention_mask optional (default to all 1s)
* Add inference test for ESMFold
* Use config and not n kwargs
* Add modeling output class
* Remove einops
* Remove chunking in ESM FFN
* Update tests for ESMFold
* Quality
* REpo consistency
* Remove tree dependency from ESMFold
* make fixup
* Add an error in case my structure map function breaks later
* Remove needless code
* Stop auto-casting the LM to float16 so CPU tests pass
* Stop auto-casting the LM to float16 so CPU tests pass
* Final test updates
* Split test file
* Copyright and quality
* Unpin PyTorch to see built doc
* Fix config file to_dict() method
* Add some docstrings to the output
* Skip TF checkpoint tests for ESM until we reupload those
* make fixup
* More docstrings
* Unpin to get even with main
* Flag example to write
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Support segformer fx
* Add fx_compatible attribute to test_modeling_segformer.py
* Update glpn model (fx support)
glpn model was copied from segformer.
* Update utils/fx.py | add semantic-segmentation
for SegformerForSemanticSegmentation model
* Fix minor import order(isort)
* Add random input generation for segformer fx
Co-authored-by: noelbird <lduldu00228@gmail.com>
* Wip
* Add safetensors support for TensorFlow
* First tests
* Add final test for now
* Retrigger CI like this
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Factored out some code in the image-segmentation pipeline
Re-enable `small_model_pt`.
Re-enable `small_model_pt`.
Enabling the current test with the current values.
Debugging the values on the CI.
More logs ? Printing doesn't work ?
Using the CI values instead. Seems to be a Pillow sensitivity.
Added a test showcasing that models not supporting some tasks get a
clear error.
Factored out code.
Further factor out.
Fixup.
Bad rebase.
Put `panoptic` before `instance` as it should be a superset.
* Fixing tests.
* Adding subtasks tests
+ Fixes `instance` segmentation which was broken due to default and
non kwargs arguments.
* Fix bad replace.
* support sentencepiece for bertjapanesetokenizer
* add test vocab file for sentencepiece, bertjapanesetokenizer
* make BasicTokenizer be identical to transformers.models.bert.tokenization_bert.BasicTokenizer
* fix missing of \n in comment
* fix init argument missing in tests
* make spm_file be optional, exclude spiece.model from tests/fixtures, and add description comments
* make comment length less than 119
* apply doc style check
* First step of PT->TF for composite models
* Update the tests
* For VisionEncoderDecoderModel
* Fix
* Fix
* Add comment
* Fix
* clean up import
* Save memory
* For (TF)EncoderDecoderModel
* For (TF)EncoderDecoderModel
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Re-enable `small_model_pt`.
Re-enable `small_model_pt`.
Enabling the current test with the current values.
Debugging the values on the CI.
More logs ? Printing doesn't work ?
Using the CI values instead. Seems to be a Pillow sensitivity.
* Update src/transformers/pipelines/image_segmentation.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* add: the contrastive search for generaton_utils
* add: testing scripts for contrastive search under examples/text-generation
* update the quality of codes
* revise the docstring; make the generation_contrastive_search.py scripts;
* revise the examples/pytorch/text-generation/run_generation_contrastive_search.py to the auto-APIs format
* revise the necessary documents
* fix: revise the docstring of generation_contrastive_search.py
* Fix the code indentation
* fix: revise the nits and examples in contrastive_search docstring.
* fix the copyright
* delete generation_contrastive_search.py
* revise the logic in contrastive_search
* update the intergration test and the docstring
* run the tests over
* add the slow decorate to the contrastive_search intergrate test
* add more test
* do the style, quality, consistency checks
* Clean up deprecation warnings
Notes:
Changed some strings in tests to raw strings, which will change the literal content of the strings as they are fed into whatever machine handles them.
Test cases for past in the past/past_key_values switch changed/removed due to warning of impending removal
* Add PILImageResampling abstraction for PIL.Image.Resampling
This PR (https://github.com/huggingface/transformers/pull/19367) introduced a few breaking changes:
- Removed an argument `mask_threshold`.
- Broke the default behavior (instance vs panoptic in the function call)
https://github.com/huggingface/transformers/pull/19367/files#diff-60f846b86fb6a21d4caf60f5b3d593a04accb8f248de3029cccae2ff898c5bc3R119-R120
- Broke the actual masks: https://github.com/huggingface/transformers/pull/1961
This PR is the start of a handful that will aim at bringing back the old
behavior(s).
- tests should not have to specify `task` by default, unless we want to
modify the behavior and have a lower form of segmentation running)
- `test_small_model_pt` should be working.
This specific PR starts with adding more information to the masks hash
because missing the actual mask was actual easy to miss (the hashes do
change, but it was easy to miss that one code path wasn't properly
updated).
So we go from a simple `hash` to
```
{"hash": #smaller hash, "shape": (h, w), "white_pixels": n}
```
The `shape` should help make sure the interpolation of the mask works
correctly, the `white_pixels` hopefully helps detect big regressions in
their amount when the hash gets modified.
* add return_tensors parameter for feature_extraction w/ test
add return_tensor parameter for feature extraction
Revert "Merge branch 'feature-extraction-return-tensor' of https://github.com/ajsanjoaquin/transformers into feature-extraction-return-tensor"
This reverts commit d559da743b87914e111a84a98ba6dbb70d08ad88, reversing
changes made to bbef89278650c04c090beb65637a8e9572dba222.
call parameter directly
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Fixup.
Update src/transformers/pipelines/feature_extraction.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix the imports.
* Fixing the test by not overflowing the model capacity.
Co-authored-by: AJ San Joaquin <ajsanjoaquin@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* 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
...
Signed-off-by: dependabot[bot] <support@github.com>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
* 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
...
Signed-off-by: dependabot[bot] <support@github.com>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
* 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
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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
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* add A6000
* clarify a bit
* remove small part
* Update tests/mixed_int8/README.md
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* 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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* Partial TF port for ESM model
* Add ESM-TF tests
* Add the various imports for TF-ESM
* TF weight conversion almost ready
* Stop ignoring the decoder weights in PT
* Add tests and lots of fixes
* fix-copies
* Fix imports, add model docs
* Add get_vocab() to tokenizer
* Fix vocab links for pretrained files
* Allow multiple inputs with a sep
* Use EOS as SEP token because ESM vocab lacks SEP
* Correctly return special tokens mask from ESM tokenizer
* make fixup
* Stop testing unsupported embedding resizing
* Handle TF bias correctly
* Skip all models with slow tokenizers in the token classification test
* Fixing the batch/unbatcher of pipelines to accomodate the `None` being
passed around.
* Fixing pipeline bug caused by slow tokenizer being different.
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update set_input_embeddings and the copyright notices
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Remove key word argument X from pipeline predict and transform methods
As __call__ of pipeline clasees require one positional argument, passing
the input as a keyword argument inside predict, transform methods, causing
__call__ to fail. Hence in this commit the keyword argument is modified
into positional argument.
* Implement basic tests for scikitcompat pipeline interface
* Seperate tests instead of running with parameterized based on framework as both frameworks will not be active at the same time
* Improve error messaging for ASR pipeline.
- Raise error early (in `_sanitize`) so users don't waste time trying to
run queries with invalid params.
- Fix the error was after using `config.inputs_to_logits_ratio` so our
check was masked by the failing property does not exist.
- Added some manual check on s2t for the error message.
No non ctc model seems to be used by the default runner (they are all
skipped).
* Removing pdb.
* Stop the early error it doesn't really work :(.
* add suport for non fast tf bert tokenizer
* add tests for non fast tf bert tokenizer
* fix fast bert tf tokenizer flag
* double tokenizers list on tf tokenizers test to aovid breaking zip on test output equivalence
* reformat code with black to comply with code quality checks
* trigger ci
* return None to avoid recursive call
* Give error
* Give error
* Add test
* More tests
* Quality
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* 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
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* fix copies
* remove comment
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* 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>
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
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* Update docs/source/en/perf_train_cpu_many.mdx
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
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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
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* 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)
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
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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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* Add initial files for depth estimation pipelines
* Add test file for depth estimation pipeline
* Update model mapping names
* Add updates for depth estimation output
* Add generic test
* Hopefully fixing the tests.
* Check if test passes
* Add make fixup and make fix-copies changes after rebase with main
* Rebase with main
* Fixing up depth pipeline.
* This is not used anymore.
* Fixing the test. `Image` is a module `Image.Image` is the type.
* Update docs/source/en/main_classes/pipelines.mdx
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* First draft
* Fix more things
* Improve more things
* Remove some head models
* Fix more things
* Add missing layers
* Remove tokenizer
* Fix more things
* Fix copied from statements
* Make all tests pass
* Remove print statements
* Remove files
* Fix README and docs
* Add integration test and fix organization
* Add tips
* Apply suggestions from code review
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* Make tests faster, improve docs
* Fix doc tests
* Add model to toctree
* Add docs
* Add note about creating new checkpoint
* Remove is_decoder
* Make tests smaller, add docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* implemented TFCvtModel and TFCvtForImageClassification and modified relevant files, added an exception in convert_tf_weight_name_to_pt_weight_name, added quick testing file to compare with pytorch model
* added docstring + testing file in transformers testing suite
* added test in testing file, modified docs to pass repo-consistency, passed formatting test
* refactoring + passing all test
* small refacto, removing unwanted comments
* improved testing config
* corrected import error
* modified acces to pretrained model archive list, to pass tf_test
* corrected import structure in init files
* modified testing for keras_fit with cpu
* correcting PR issues + Refactoring
* Refactoring : improving readability and reducing the number of permutations
* corrected momentum value + cls_token initialization
* removed from_pt as weights were added to the hub
* Update tests/models/cvt/test_modeling_tf_cvt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* added test
* correct embedding init
* some changes in blenderbot (incomplete)
* update blenderbot (diff to be used as reference)
* update blenderbot_small
* update LED
* update marian
* update T5 and remove TFWrappedEmbeddings
* nullcontext() -> ContextManagers()
* fix embedding init
* update feature extractor params
* update attention mask handling
* fix doc and pipeline test
* add warning when skipping test
* add whisper translation and transcription test
* fix build doc test
* Add `OPTForQuestionAnswering`
- added `OPTForQuestionAnswering` class based on `BloomForQuestionAnswering`
- added `OPTForQuestionAnswering` in common tests
- all common tests pass
- make fixup done
* added docstrings for OPTForQuestionAnswering
* Fix docstrings for OPTForQuestionAnswering
* Rework pipeline tests
* Try to fix Flax tests
* Try to put it before
* Use a new decorator instead
* Remove ignore marker since it doesn't work
* Filter pipeline tests
* Woopsie
* Use the fitlered list
* Clean up and fake modif
* Remove init
* Revert fake modif
- Fixes the image segmentation pipeline test failures caused by changes to the postprocessing methods of supported models
- Updates the ImageSegmentationPipeline tests
- Improves docs, adds 'task' argument to optionally perform semantic, instance or panoptic segmentation
* validate onnx models with a different input geometry than saved with
* only test working features for now
* simpler test skipping
* rm TODO
* expose batch_size/seq_length on vit
* skip certain name, feature, framework parameterizations known to fail validation
* Trigger CI
* Trigger CI
* Add ZeroShotObjectDetectionPipeline (#18445)
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add batching for ZeroShotObjectDetectionPipeline
* Fix doc-string ZeroShotObjectDetectionPipeline
* Fix output format: ZeroShotObjectDetectionPipeline
Ensures post_process_instance_segmentation and post_process_panoptic_segmentation methods return a tensor of shape (target_height, target_width) filled with -1 values if no segment with score > threshold is found.
* add sudachipy and jumanpp tokenizers for bert_japanese
* use ImportError instead of ModuleNotFoundError in SudachiTokenizer and JumanppTokenizer
* put test cases of test_tokenization_bert_japanese in one line
* add require_sudachi and require_jumanpp decorator for testing
* add sudachi and pyknp(jumanpp) to dependencies
* remove sudachi_dict_small and sudachi_dict_full from dependencies
* empty commit for ci
- Improves MaskFormer docs, corrects minor typos
- Restructures MaskFormerFeatureExtractor.post_process_panoptic_segmentation for better readability, adds target_sizes argument for optional resizing
- Adds post_process_semantic_segmentation and post_process_instance_segmentation methods.
- Adds a deprecation warning to post_process_segmentation method in favour of post_process_instance_segmentation
* add bloom for question answering
- attempt to add Bloom for question answering
- adapted from `GPTJForQuestionAnswering`
- Fixed `num_labels` to `2` for common tests
- Added a bit of docstring
- All common tests pass
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert changes related to `num_labels`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Poc to use safetensors
* Typo
* Final version
* Add tests
* Save with the right name!
* Update tests/test_modeling_common.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Support for sharded checkpoints
* Test from Hub part 1
* Test from hub part 2
* Fix regular checkpoint sharding
* Bump for fixes
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Rebase ESM PR and update all file formats
* Fix test relative imports
* Add __init__.py to the test dir
* Disable gradient checkpointing
* Remove references to TFESM... FOR NOW >:|
* Remove completed TODOs from tests
* Convert docstrings to mdx, fix-copies from BERT
* fix-copies for the README and index
* Update ESM's __init__.py to the modern format
* Add to _toctree.yml
* Ensure we correctly copy the pad_token_id from the original ESM model
* Ensure we correctly copy the pad_token_id from the original ESM model
* Tiny grammar nitpicks
* Make the layer norm after embeddings an optional flag
* Make the layer norm after embeddings an optional flag
* Update the conversion script to handle other model classes
* Remove token_type_ids entirely, fix attention_masking and add checks to convert_esm.py
* Break the copied from link from BertModel.forward to remove token_type_ids
* Remove debug array saves
* Begin ESM-2 porting
* Add a hacky workaround for the precision issue in original repo
* Code cleanup
* Remove unused checkpoint conversion code
* Remove unused checkpoint conversion code
* Fix copyright notices
* Get rid of all references to the TF weights conversion
* Remove token_type_ids from the tests
* Fix test code
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add credit
* Remove _ args and __ kwargs in rotary embedding
* Assertively remove asserts
* Replace einsum with torch.outer()
* Fix docstring formatting
* Remove assertions in tokenization
* Add paper citation to ESMModel docstring
* Move vocab list to single line
* Remove ESMLayer from init
* Add Facebook copyrights
* Clean up RotaryEmbedding docstring
* Fix docstring formatting
* Fix docstring for config object
* Add explanation for new config methods
* make fix-copies
* Rename all the ESM- classes to Esm-
* Update conversion script to allow pushing to hub
* Update tests to point at my repo for now
* Set config properly for tests
* Remove the gross hack that forced loss of precision in inv_freq and instead copy the data from the model being converted
* make fixup
* Update expected values for slow tests
* make fixup
* Remove EsmForCausalLM for now
* Remove EsmForCausalLM for now
* Fix padding idx test
* Updated README and docs with ESM-1b and ESM-2 separately (#19221)
* Updated README and docs with ESM-1b and ESM-2 separately
* Update READMEs, longer entry with 3 citations
* make fix-copies
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Tom Sercu <tsercu@fb.com>
Co-authored-by: Your Name <you@example.com>
* chore: initial commit
* chore: adding util methods
yet to work on the nn.functional.interpolate port with align_corener=True
* chore: refactor the utils
* used tf.compat.v1.image.resize to align the F.interpolate function
* added type hints to the method signatures
* added references to the gists where one 2 one alignment of torch and tf has been shown
* chore: adding the layers
* chore: porting all the layers from torch to tf
This is the initial draft, nothing is tested yet.
* chore: aligning the layers with reference to tf clip
* chore: aligning the modules
* added demaraction comments
* added copied and adapted from comments
* chore: aligning with CLIP
* chore: wrangling the layers to keep it tf compatible
* chore: aligning the names of the layers for porting
* chore: style changes
* chore: adding docs and inits
* chore: adding tfp dependencis
the code is taken from TAPAS
* chore: initial commit for testing
* chore: aligning the vision embeddings with the vit implementatino
* chore: changing model prefix
* chore: fixing the name of the model and the layer normalization test case
* chore: every test passes but the slow ones
* chore: fix style and integration test
* chore: moving comments below decorators
* chore: make fixup and fix-copies changes
* chore: adding the Vision and Text Model to check_repo
* chore: modifying the prefix name to align it with the torch implementation
* chore: fix typo in configuration
* choer: changing the name of the model variable
* chore: adding segmentation flag
* chore: gante's review
* chore: style refactor
* chore: amy review
* chore: adding shape_list to parts that have been copied from other snippets
* chore: init batchnorm with torch defaults
* chore: adding shape_list to pass the tests
* test fix: adding seed as 0
* set seed
* chore: changing the straight through trick to fix -ve dimensinos
* chore: adding a dimension to the loss
* chore: adding reviewers and contributors names to the docs
* chore: added changes after review
* chore: code quality fixup
* chore: fixing the segmentation snippet
* chore: adding to the layer calls
* chore: changing int32 to int64 for inputs of serving
* chore: review changes
* chore: style changes
* chore: remove from_pt=True
* fix: repo consistency
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
