* more precise name
* better docstrings
* Update src/transformers/cache_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add padding="max_length" to tokenizer kwargs and change crop_size to size for image_processor kwargs
* remove crop_size argument in align processor tests to be coherent with base tests
* Add pad_token when loading tokenizer if needed, change test override tokenizer kwargs, remove unnecessary test overwrites in grounding dino
* fix typo
* uniform kwargs
* make style
* add comments
* remove return_tensors
* remove common_kwargs from processor since it propagates
* make style
* return_token_type_ids to True
* revert the default imagekwargs since does not accept any value in the image processro
* revert processing_utils.py
* make style
* add molbap's commit
* fix typo
* fix common processor
* remain
* Revert "add molbap's commit"
This reverts commit a476c6ee88.
* add unsync PR
* revert
* make CI happy
* nit
* import annotationformat
* add new model like
* draft cuda forward - mismatched keys (sharding on conv1)
* match keys successfully
* fix split
* get generation/forward running (wrong gens, norm?)
* :update
* some refactoring
* fixes
* works up until copy to cache
* fix
* update
* NON WORKING VERSION
* version that work?
* nit
* fix config
* fix conversion script
* working cuda forward
* nit
* update
* simplifcation
* make mamba slow simple work
* no einops
* todo
* fix style
* no einops
* update fix no einsum
* nit
* remove einops
* bug: scan_output differs strongly
* add rms norm option
* fix fast + slow generation with and w/o cache ✔️
* draft integration tests
* remove a big chunk of the einsum
* fix slow, fast generations, without any einsum
* fix copies
* fix structure
* fix up modeling and tests
* fix tests
* clamping is indeed worse
* recover mamba2 cache test
* fix copies
* no cache position (yet)
* fix tf tests
* fix matmul for generate
* fixup
* skip cache tests for now
* [run-slow]mamba2
* tune out hidden states for padding
* test batched generation
* propagate attention mask changes
* fix past length
* fix integration test
* style
* address comments
* update readme
* add mamba2 version check
* fix tests
* [run-slow]mamba2
* skip edge tests
* [run-slow]mamba2
* last fixup
* [run-slow]mamba2
* update README
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
* Remove user-defined tokens which can be obtained through merges
* Remove debug line
* formatting
* Refactor spm slow -> fast converter
* revert unnecessary refactor
* set comprehension
* remove test files
* Use `vocab_scores`
* Always replace spiece underline with space in decode
* we no longer need token filtering
* Add save fast load slow unit test
* Remove tokenizers version check
* Remove duplicate code
* Make `<start_of_turn>` and `<end_of_turn>` special tokens
* Bias merge priority with length if score is the same
* Add unit test for merge priority
* CI
* mvp
* added test (a few models need fixes)
* fix a few test cases
* test nits
* harder test 😈
* revert changes in stablelm
* test with improved condition
* add todo
* tmp commit
* merged with main
* nits
* add todo
* final corrections
* add docs for generation compilation
* docs nits
* add tip
* PR suggestions
* add more details to the compilation docs
* fix cache positions
* cache is now init in generate; update docs
* tag test as flaky
* docs
* post rebase make fixup and other nits
* remove unintended changes
* whisper (encoder-decoder) not supported
* move token default updates to ; add tests for token defaults
* push changes
* manual rebase
* chameleon doesn't support this
* fix test_static_cache_mha_mqa_gqa (broken in another PR)
* docs: dynamic is better with end-to-end compilation
* No more default chat templates
* Add the template to the GPT-SW3 tests since it's not available by default now
* Fix GPT2 test
* Fix Bloom test
* Fix Bloom test
* Remove default templates again
* Updated ruff version and fixed the required code accorindg to the latest version.
* Updated ruff version and fixed the required code accorindg to the latest version.
* Added noqa directive to ignore 1 error shown by ruff
* Add YaRN and Dynamic-YaRN RoPE Scaling Methods
YaRN (Yet another RoPE extension method) combines the NTK-By-Parts
Interpolation and Attention Scaling methods, improving upon existing
RoPE interpolation methods for longer context window sizes.
Fine-tuned models maintain their original performance across benchmarks
while enabling efficient extrapolation and transfer learning for
quicker convergence, especially in compute-limited environments.
We implement YaRN and Dynamic-YaRN for the following list of models:
- LLaMA
- Falcon
- GPT-NeoX
- Olmo
- Persimmon
- Phi
- StableLM
- OpenLLaMA
New unit tests are added to assert YaRN's correct behavior on both
short and long sequence inputs.
For more details, please refer to https://arxiv.org/abs/2309.00071.
Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>
* Refactor YaRN implementation for LLaMA
Iterate on YaRN implementation for LLaMA and remove diff from remaining
models for increased PR modularity.
This commit includes the following changes:
- Merge 'yarn_rope_scaling' and 'rope_scaling' dictionaries
- Remove unnecessary attributes ('extrapolation_factor' and 'finetuned')
from YaRN classes
- Inherit 'forward' method in YaRN classes from superclass
- Rename 'yarn' method to 'compute_yarn_scaling'
- Extend YaRN tests with further assertions
- Fix style inconsistencies
Co-authored-by: Miguel Monte e Freitas <miguelmontefreitas@tecnico.ulisboa.pt>
* Refactor Tensor Building Logic for YaRN
- Comply with the the tensor building logic introduced in #30743
- Add referencing to the optimized Attention Factor equation
- Remove Dynamic YaRN for a more agile deployment
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
* remove unwanted file
---------
Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
* fix mask creation of gpt2 and gpt_neox caused by me
* forgot the reshape of masks when shape > 2
* add tests for gpt neox and gpt2
* nit on a comment
* 1,100%!
* Clean
* Don't touch DS
* Experiment with dtype allocation
* skip test_load_save_without_tied_weights test
* A little faster
* Include proper upscaling?
* Fixup tests
* Potentially skip?
* Let's see if this fixes git history
* Maintain new dtype
* Fin
* Rm hook idea for now
* New approach, see what breaks
* stage
* Clean
* Stash
* Should be fin now, just need to mark failing models
* Clean up
* Simplify
* Deal with weird models
* Enc/Dec
* Skip w/ reason
* Adjust test
* Fix test
* one more test
* Keep experimenting
* Fix ref
* TO REMOVE: testing feedback CI
* Right push
* Update tests/utils/test_modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* disable
* Add new func
* Test nits from Amy
* Update src/transformers/modeling_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Adjust comment
* Adjust comment on skip
* make private
* Fin
* Should be a not flag
* Clarify and rename test
---------
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add siglip loss function
* Update docs
* Enable training tests
[experimental] enable GC training tests as it has worked for my own data
* Remove test_training* overrides to enable training tests
[run_slow] siglip
* Skip training tests for Siglip text model and ImageClassificationModel
[run_slow] siglip
* Skip GC training tests for SiglipForImageClassification
* Explicitly skip training tests for SiglipVisionModel
Add skip reason for training tests for SiglipTextModel
* Remove copied from to fix CI
* Fix init for rt-detr heads
* Fixup
* Add separate prior_prob value to config for initialization
* Add bbox init
* Change to 1 / num_labels init
* Adjust weights init test
* Fix style for test
* squash into single commit
* run diff once more
* docstring
* tests
* minor chnages and ready to go
* Update src/transformers/models/llava_next_video/processing_llava_next_video.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/vipllava/test_modeling_vipllava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* [run-slow] llava-next-video
* [run-slow] llava-next-video
* [run-slow] llava_next_video
* fix two tests
* fix slow tests
* remove logit checks due to numeric errors
* run test once more
* [run-slow] llava_next_video
* final try to pass the test
* [run-slow] llava_next_video
* [run-slow] llava_next_video
* [run-slow] llava_next_video
* style
* fix
* style
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* starting support for sdpa in `gptneox` models
* small comment on tests
* fix dropout
* documentation and style
* clarify concrete paths for reference
* generalise attn projections and rope application
added head mask check to sdpa mask creation
handle sdpa memory backend bug via own version flag
* update docs and style
* move dtype casting outside of general attn_projection_and_rope function
fix flash_attn_2 stuff
* more generic attn warning if output_attns or head_mask
* simplify head mask check by moving head mask creation to a later point
* remove copied llama artifact
* remove padding_mask from attention function signature
* removing unnecessary comments, only "save" attn implementation once
* [run_slow] gpt_neox
* PR SPLIT: moving origina changes for adding user defined symbols
* adding gemma test and generalizing gemma converter
* ruff
* update common test
* update serialization test
* deberta v2 tests updates as rust version adds '.' as a user added token, so a space is not added
* removing commented lines
* applying feedback - user only added_tokens to add and check piece.type instead of trainer_spec for user_defined_symbols
* add comment referencing sentencepiece
* Pass datasets trust_remote_code
* Pass trust_remote_code in more tests
* Add trust_remote_dataset_code arg to some tests
* Revert "Temporarily pin datasets upper version to fix CI"
This reverts commit b7672826ca.
* Pass trust_remote_code in librispeech_asr_dummy docstrings
* Revert "Pin datasets<2.20.0 for examples"
This reverts commit 833fc17a3e.
* Pass trust_remote_code to all examples
* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects
* Pass trust_remote_code to tests
* Pass trust_remote_code to docstrings
* Fix flax examples tests requirements
* Pass trust_remote_dataset_code arg to tests
* Replace trust_remote_dataset_code with trust_remote_code in one example
* Fix duplicate trust_remote_code
* Replace args.trust_remote_dataset_code with args.trust_remote_code
* Replace trust_remote_dataset_code with trust_remote_code in parser
* Replace trust_remote_dataset_code with trust_remote_code in dataclasses
* Replace trust_remote_dataset_code with trust_remote_code arg
* Draft fast image processors
* Draft working fast version
* py3.8 compatible cache
* Enable loading fast image processors through auto
* Tidy up; rescale behaviour based on input type
* Enable tests for fast image processors
* Smarter rescaling
* Don't default to Fast
* Safer imports
* Add necessary Pillow requirement
* Woops
* Add AutoImageProcessor test
* Fix up
* Fix test for imagegpt
* Fix test
* Review comments
* Add warning for TF and JAX input types
* Rearrange
* Return transforms
* NumpyToTensor transformation
* Rebase - include changes from upstream in ImageProcessingMixin
* Safe typing
* Fix up
* convert mean/std to tesnor to rescale
* Don't store transforms in state
* Fix up
* Update src/transformers/image_processing_utils_fast.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Warn if fast image processor available
* Update src/transformers/models/vit/image_processing_vit_fast.py
* Transpose incoming numpy images to be in CHW format
* Update mapping names based on packages, auto set fast to None
* Fix up
* Fix
* Add AutoImageProcessor.from_pretrained(checkpoint, use_fast=True) test
* Update src/transformers/models/vit/image_processing_vit_fast.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Add equivalence and speed tests
* Fix up
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Rename to test_model_common_attributes
The method name is misleading - it is testing being able to get and set embeddings, not common attributes to all models
* Explicitly skip
* Update TVP model to interpolate pre-trained image pad prompter encodings
* feat: Add 2D positional embeddings interpolation in TvpVisualInputEmbedding
* added required comments
* Update TVP model to interpolate pre-trained image pad prompter encodings
* feat: Add 2D positional embeddings interpolation in TvpVisualInputEmbedding
* added required comments
* docstring and argument fix
* doc fixes and test case fix suggested in review.
* varibale typo fix
* styling and name fixes for padding interpolation flag.
* Remove ConversationalPipeline and Conversation object, as they have been deprecated for some time and are due for removal
* Update not-doctested.txt
* Fix JA and ZH docs
* Fix JA and ZH docs some more
* Fix JA and ZH docs some more
* Initial attempt
* Updates: PR suggestions
* Interpolate the relative position bias when interpolate_pos_encoding is True
* Add slow tag for the added tests
* Add in DATA2VEC_VISION_INPUTS_DOCSTRING
* Added interpolate pos encoding feature and test to deit
* Added interpolate pos encoding feature and test for deit TF model
* readded accidentally delted test for multi_gpu
* storing only patch_size instead of entire config and removed commented code
* Update modeling_tf_deit.py to remove extra line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix the get_size_with_aspect_ratio in max_size situation
* make fix-up
* add more general solution
* consider when max_size is not defined
* fix typo
* fix typo
* simple fix
* fix error
* fix if else error
* fix error of size overwrite
* fix yolos image processing
* fix detr image processing
* make
* add longest related test script
* Update src/transformers/models/yolos/image_processing_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add more test
* add test script about longest size
* remove deprecated
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* seems like `split_special_tokens` is used here
* split special token
* add new line at end of file
* moving split special token test to common tests
* added assertions
* test
* fixup
* add co-author
* passing rest of args to gptsan_japanese, fixing tests
* removing direct comparison of fast and slow models
* adding test support for UDOP and LayoutXLM
* ruff fix
* readd check if slow tokenizer
* modify test to handle bos tokens
* removing commented function
* trigger build
* applying review feedback - updated docstrings, var names, and simplified tests
* ruff fixes
* Update tests/test_tokenization_common.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* applying feedback, comments
* shutil temp directory fix
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Ita Zaporozhets <itazaporozhets@Itas-MBP.localdomain>
Co-authored-by: itazap <itazap@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Ita Zaporozhets <itazaporozhets@Itas-MacBook-Pro.local>
* added interpolation for vitmae model in pytorch as well as tf.
* Update modeling_vit_mae.py
irreugalr import fixed
* small changes and proper formatting
* changes suggested in review.
* modified decoder interpolate_func
* arguments and docstring fix
* Apply suggestions from code review
doc fixes
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add test that currently fails
* test passed
* all perceiver passed
* fixup, style, quality, repo-consistency, all passed
* Apply suggestions from code review: default to False + compute sqrt once only
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix a minor bracket
* replace dim with self._num_channels
* add arguments to the rest preprocessors
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add prefix space ignored in llama #29625
* adding test with add_prefix_space=False
* ruff
---------
Co-authored-by: Ita Zaporozhets <itazaporozhets@Itas-MBP.localdomain>
* Add MistralForTokenClassification
* Add tests and docs
* Add token classification for Mixtral and Qwen2
* Save llma for token classification draft
* Add token classification support for Llama, Gemma, Persimmon, StableLm and StarCoder2
* Formatting
* Add token classification support for Qwen2Moe model
* Add dropout layer to each ForTokenClassification model
* Add copied from in tests
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Propagate suggested changes
* Style
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* add interpolation of positional encoding support to swin
* add style changes
* use default image processor and make size a dictionary
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove logits testing
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Refactor image size validation logic when interpolation is disabled
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove asserts in modeling
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add dynamic resolution input support to swinv2
* change size to ensure interpolation encoding path is triggered
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
* add dynamic resolution input to donut swin
* add dynamic resolution input to maskformer swin
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Support arbitrary processor
* fix
* nit
* update
* nit
* nit
* fix and revert
* add a small test
* better check
* fixup
* bug so let's just use class for now
* oups
* .
* Add support for mixing languages in a single batch
* Update docstring
* Enable different detected languages in batch
* Do not require input_features
* Test list of languages
* Fix comment
* Make init_tokens length-1 if possible, broadcast at the end
* Test for ValueError with language list of incorrect length
* Slow test for batched multilingual transcription
* fixup
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Address review, refactor
* Second attempt to move this line where it was originally
* Split test, fix a bug
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Initial commit
* Just a copy of modeling_idefics.py that will be ported to TF
* - Prepend TF to the name of all classes
- Convert pytorch ops to TF (not all operations are converted yet)
* Add TF imports
* Add autotranslated files
* Add TF classes to model_tf_auto.py
* Add the TF classes in model_doc
* include auto-translated code
* Adopted from auto-translated version
* Add a forgotten super().build
* Add test code for TF version.
* Fix indentation and load pytorch weights for now
* Some fixes. Many tests are still failing but some are passing now.
- I have added TODO's for some of the hacks I made to unblock me
and I will address them soon
- I have the processing_idefics.py hacked in my view to support TF temporarily
* Add ALL_LAYERNORM_LAYERS to match pytorch
* Revert "Add ALL_LAYERNORM_LAYERS to match pytorch"
This reverts commit 7e0a35119b4d7a6284d04d8c543fba1b29e573c9 as it
is not needed in the tf implementation.
* Fix freeze_relevant_params()
* Some more fixes
* Fix test_attention_outputs
* Add tf stuff to processing_idefics.py
processing_idefics.py supports both pytorch and tf now.
test_processor_idefics.py for pytorch is passing, so i didn't break anything
but still some issues with tf. I also need to add tf tests in
test_processor_idefics.py.
* Pass return_tensors to image processing code and fix test
* Pass return_tensors to the image processor __init__
* Fix several test cases
- Make input to some of the forward pass of type `TFModelInputType`
- Decorate main layer forward pass with `@unpack_inputs`
- Decorate main layer with `@keras_serializable`
- Pass `inputs` to TFIdeficsModel
* Some more fixes forgotten in last commit
* Fix processing code and vision_tf.py
* Fix perceiver bug
* Import from
* Auto-add build() methods + style pass
* Fix build() errors due to `None` being passed as shape to some layers
* Change name in TFIdeficsForVisionText2Text to attribute in IdeficsForVisionText2Text
* Fix pytorch weights load for tf2
There were a lot of `name=` missing in weight initialization code.
* Attempt to fix CI
* Add back accidently removed line
* Remove torch-specific stuff from the TF test file
* make fix-copies, make style, remove autotranslated files
* Fixes to imports/docstrings
* Let's try the from future import in desperation
* Fix the core random_attention_mask fn to match the torch/flax behaviour
* Clean random_attention_mask up correctly
* Remove torch-only test
* Fix loss shape, couple of nits
* make style
* Don't test for OOB embeddings because IDEFICS uses those deliberately
* Fix loss computation to handle masking
* Fix test failures when flattening
* Fix some test failures
- Add cross attention gate which was missing and wasn't being passed arround
- Fix overwriting of image_attention_mask due to hack I had for dummy inputs
* Add a proper stateless scaled_dot_product_attention
* make style
* Adding missing attribute from the PyTorch version
* Small cleanups to decoupledlinearlayer in case that helps
* Pass epsilon to LayerNormalization
* Attemp to fix pytorch weight cross-loading for TFIdeficsEmbedding
* Fix a bug in TFIdeficsGatedCrossAttentionLayer
* Patching up build() methods
* Constant self.inv_freq
* Constant self.inv_freq
* First working version
The TF implementation works now, there was a bug in the TFIdeficsDecoupledLinear
where the weights were mis-intialized (in_features,out_features)
when it should be: (out_features, in_features)
I have tested this so far with tiny-random and idefics-9b-instruct
and gives correct output.
I also dumped the final outputs for both pytorch and TF
and they are identical.
* Fix some test failures
* remove print statement
* Fix return_tensors
* Fix CI test failure check_code_quality
* Attempt to fix CI failures by running `make fixup`
The hardcoded IDs in test_modeling_tf_idefics.py are for the integration
test and makes that file unreadable and should probably be moved to a seperate file.
* Attempt to fix tests_pr_documentation_tests
* Fix a test failure in test_image_processing_idefics.py
* Fix test test_pt_tf_model_equivalence
* Fix a few failures
* Tiny fix
* Some minor fixes
* Remove a duplicate test
* Override a few test failures for IDEFICS
- `test_keras_save_load` is passing now
- `test_compile_tf_model` is still failing
* Fix processing_idefics.py after rebase
* Guard import keras with is_tf_available
* fix check code quality
* fix check code quality
* Minor fixes
* Skip test_save_load temporarily
This test passed on my local box but fails on the CI, skipping
for now to see if there are other remaining failures on the CI.
* Run `ruff format tests src utils`
* Fix last failing test, `test_compile_tf_model`
* Add fixes for vision_tf.py
I forgot to add this file in last commit.
* Minor fixes
* Replace "<<<" with "<<" for doc tests
IDEFICS-9B is too big for doctest runner, so don't run it there
* Make code more readable
* Fix bug after code review
I added a layer_norm_eps to IdeficsConfig but I don't even need it
since the vision config has a layer_norm_eps.
* Fix after code review
Use original code tokenizer.convert_tokens_to_ids
* Keep PyTorch as the default return_tensors
* Fixes to modeling_tf after code review
* Fixes from code review
- Remove all references of `TF_IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST`
- Pass 1e-5 to LayerNormalization in perceiver
* Run ruff
* Undo a change
* Refactor processing code after Matt's suggestion
* Remove TODO's that aren't needed anymore
* For pytorch, Use original pytorch processing code from main
Since this PR is a TF port it shouldn't make any modifications
to pytorch IDEFICS code. This changes undo's the pytorch processing
modifications I made and uses original code from main.
* Update tests/models/idefics/test_modeling_idefics.py
* Update tests/models/idefics/test_modeling_tf_idefics.py
* Add missing imports for is_pt_tf_cross_test
* [DO NOT MERGE]: This is a commit for debugging and will be reverted
The cross test `test_pt_tf_model_equivalence` passes locally but
fails when running on the CI. This commit is to help debug that
and will be reverted.
* Revert "[DO NOT MERGE]: This is a commit for debugging and will be reverted"
This reverts commit 8f0d709ec5bd46685fb0b4259d914ffee794875b.
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 998cc38b8c3d313bf5e5eb55a7f5b7b881897b89.
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 1c695ac4219c4ae4d39b330b01744dc27deb7dd4.
* Don't skip test_save_load
IIRC test_save_load was also failing on the CI but not on my local
box, it might be easier to debug that on the CI first than the cross tests
* Debugging commit, will be reverted
* Revert "Debugging commit, will be reverted"
This reverts commit 8eafc8e41e20c4e95a3a90834f06a6e9f445e2d5.
* Override `test_save_load` and push model to save
Maybe this will help me repro this weird bug
* pass my repo_id
* add endpoint
* Pass a temp (write) token just for this CI
* Undo last few commits, still pushing to hub for model debugging
The issue seems to be with save_pretrained(), when I looked at the model saved
from the CI test failure it is basically empty and has no weights.
`self.save_weights(..)` seems to be failing in save_pretrained but needs
more debugging
* Add logging to modeling tf utils, will be reverted just for debugging
* Debugging, will revert
* Revert "Debugging, will revert"
This reverts commit 9d0d3075fb7c82d8cde3a5c76bc8f3876c5c55d3.
* Revert "Add logging to modeling tf utils, will be reverted just for debugging"
This reverts commit 774b6b7b1c17b3ce5d7634ade768f2f686cee617.
* Remove `test_save_load`
The CI failures are gone after my latest rebase, no idea why
but I was still saving the model to my hub on HF and the tf_model.h5
file now has everything.
* Run make fix-copies
* Run ruff format tests src utils
* Debugging commit, will be reverted
* Run ruff, also trigger CI run
* Run ruff again
* Undo debugging commit
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* blip with interpolated pos encoding
* feat: Add interpolate_pos_encoding option to other models from `BLIP` family.
* include check for textual generated content in tests
* Adding _tie_weights() to prediction heads to support low_cpu_mem_usage=True
* Testing for the non-safe-tensors case, since the default is safe-tensors already
* Running fixup/fix-copies
* Adding accelerate annotations to tests
* change cis
* nits
* update
* minor updates
* [push-ci-image]
* nit [push-ci-image]
* nitsssss
* [build-ci-image]
* [push-ci-image]
* [push-ci-image]
* both
* [push-ci-image]
* this?
* [push-ci-image]
* pypi-kenlm needs g++
* [push-ci-image]
* nit
* more nits [push-ci-image]
* nits [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* add vision
* [push-ci-image]
* [push-ci-image]
* add new dummy file but will need to update them [push-ci-image]
* [push-ci-image]
* show package size as well
* [push-ci-image]
* potentially ignore failures
* workflow updates
* nits [push-ci-image]
* [push-ci-image]
* fix consistency
* clean nciida triton
* also show big packages [push-ci-image]
* nit
* update
* another one
* line escape?
* add accelerate [push-ci-image]
* updates [push-ci-image]
* nits to run tests, no push-ci
* try to parse skip reason to make sure nothing is skipped that should no be skippped
* nit?
* always show skipped reasons
* nits
* better parsing of the test outputs
* action="store_true",
* failure on failed
* show matched
* debug
* update short summary with skipped, failed and errors
* nits
* nits
* coolu pdates
* remove docbuilder
* fix
* always run checks
* oups
* nits
* don't error out on library printing
* non zero exi codes
* no warning
* nit
* WAT?
* format nit
* [push-ci-image]
* fail if fail is needed
* [push-ci-image]
* sound file for torch light?
* [push-ci-image]
* order is important [push-ci-image]
* [push-ci-image] reduce even further
* [push-ci-image]
* use pytest rich !
* yes [push-ci-image]
* oupsy
* bring back the full traceback, but pytest rich should help
* nit
* [push-ci-image]
* re run
* nit
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* empty push to trigger
* [push-ci-image]
* nit? [push-ci-image]
* empty
* try to install timm with no deps
* [push-ci-image]
* oups [push-ci-image]
* [push-ci-image]
* [push-ci-image] ?
* [push-ci-image] open ssh client for git checkout fast
* empty for torch light
* updates [push-ci-image]
* nit
* @v4 for checkout
* [push-ci-image]
* [push-ci-image]
* fix fetch tests with parallelism
* [push-ci-image]
* more parallelism
* nit
* more nits
* empty to re-trigger
* empty to re-trigger
* split by timing
* did not work with previous commit
* junit.xml
* no path?
* mmm this?
* junitxml format
* split by timing
* nit
* fix junit family
* now we can test if the xunit1 is compatible!
* this?
* fully list tests
* update
* update
* oups
* finally
* use classname
* remove working directory to make sure the path does not interfere
* okay no juni should have the correct path
* name split?
* sort by classname is what make most sense
* some testing
* naem
* oups
* test something fun
* autodetect
* 18?
* nit
* file size?
* uip
* 4 is best
* update to see versions
* better print
* [push-ci-image]
* [push-ci-image]
* please install the correct keras version
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* uv is fucking me up
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* nits
* [push-ci-image]
* [push-ci-image]
* install issues an pins
* tapas as well
* nits
* more paralellism
* short tb
* soundfile
* soundfile
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* oups
* [push-ci-image]
* fix some things
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* use torch-light for hub
* small git lfs for hub job
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* fix tf tapas
* [push-ci-image]
* nits
* [push-ci-image]
* don't update the test
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* no use them
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* update tf proba
* [push-ci-image]
* [push-ci-image]
* woops
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* test with built dockers
* [push-ci-image]
* skip annoying tests
* revert fix copy
* update test values
* update
* last skip and fixup
* nit
* ALL GOOOD
* quality
* Update tests/models/layoutlmv2/test_image_processing_layoutlmv2.py
* Update docker/quality.dockerfile
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Update src/transformers/models/tapas/modeling_tf_tapas.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* use torch-speed
* updates
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* fuck ken-lm [push-ci-image]
* [push-ci-image]
* [push-ci-image]
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* move scaling to nn.Module
* let the test be here for now (need to fix)
* failing tests
* last failing models
* Revert commit 4c14817f38
* clean-up
* oops forgot
* codestyle
* raise NotImplemented when possible
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* skip tests in respective modeling files
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Enable instantiating model with pretrained backbone weights
* Clarify pretrained import
* Use load_backbone instead
* Add backbone_kwargs to config
* Fix up
* Add tests
* Tidy up
* Enable instantiating model with pretrained backbone weights
* Update tests so backbone checkpoint isn't passed in
* Clarify pretrained import
* Update configs - docs and validation check
* Update src/transformers/utils/backbone_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Clarify exception message
* Update config init in tests
* Add test for when use_timm_backbone=True
* Use load_backbone instead
* Add use_timm_backbone to the model configs
* Add backbone_kwargs to config
* Pass kwargs to constructors
* Draft
* Fix tests
* Add back timm - weight naming
* More tidying up
* Whoops
* Tidy up
* Handle when kwargs are none
* Update tests
* Revert test changes
* Deformable detr test - don't use default
* Don't mutate; correct model attributes
* Add some clarifying comments
* nit - grammar is hard
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Adding SDPA support for BERT
* Using the proper input name for testing model input in inference()
* Adding documentation for SDPA in BERT model page
* Use the stable link for the documentation
* Adding a gate to only call .contiguous() for torch < 2.2.0
* Additions and fixes to the documentation
* Minor updates to documentation
* Adding extra requirements needed for the contiguous() bug
* Adding "Adapted from" in plcae of the "Copied from"
* Add benchmark speedup tables to the documentation
* Minor fixes to the documentation
* Use ClapText as a replacemenet for Bert in the Copied-From
* Some more fixes for the fix-copies references
* Overriding the test_eager_matches_sdpa_generate in bert tests to not load with low_cpu_mem_usage
[test all]
* Undo changes to separate test
* Refactored SDPA self attention code for KV projections
* Change use_sdpa to attn_implementation
* Fix test_sdpa_can_dispatch_on_flash by preparing input (required for MultipleChoice models)
* first modeling code
* make repository
* still WIP
* update model
* add tests
* add latest change
* clean docstrings and copied from
* update docstrings md and readme
* correct chroma function
* correct copied from and remove unreleated test
* add doc to toctree
* correct imports
* add convert script to notdoctested
* Add suggestion from Sanchit
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* correct get_uncoditional_inputs docstrings
* modify README according to SANCHIT feedback
* add chroma to audio utils
* clean librosa and torchaudio hard dependencies
* fix FE
* refactor audio decoder -> audio encoder for consistency with previous musicgen
* refactor conditional -> encoder
* modify sampling rate logics
* modify license at the beginning
* refactor all_self_attns->all_attentions
* remove ignore copy from causallm generate
* add copied from for from_sub_models
* fix make copies
* add warning if audio is truncated
* add copied from where relevant
* remove artefact
* fix convert script
* fix torchaudio and FE
* modify chroma method according to feedback-> better naming
* refactor input_values->input_features
* refactor input_values->input_features and fix import fe
* add input_features to docstrigs
* correct inputs_embeds logics
* remove dtype conversion
* refactor _prepare_conditional_hidden_states_kwargs_for_generation ->_prepare_encoder_hidden_states_kwargs_for_generation
* change warning for chroma length
* Update src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* change way to save wav, using soundfile
* correct docs and change to soundfile
* fix import
* fix init proj layers
* add draft training
* fix cross entropy
* clean loss computation
* fix labels
* remove line breaks from md
* fix issue with docstrings
* add FE suggestions
* improve is in logics and remove useless imports
* remove custom from_pretrained
* simplify docstring code
* add suggestions for modeling tests
* make style
* update converting script with sanity check
* remove encoder attention mask from conditional generation
* replace musicgen melody checkpoints with official orga
* rename ylacombe->facebook in checkpoints
* fix copies
* remove unecessary warning
* add shape in code docstrings
* add files to slow doc tests
* fix md bug and add md to not_tested
* make fix-copies
* fix hidden states test and batching
* update training code
* add training tests for melody
* add training for o.g musicgen
* fix copied from
* remove final todos
* make style
* fix style
* add suggestions from review
* add ref to the original loss computation code
* rename method + fix labels in tests
* make style
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* chore(root): Initial commit of Phi-3 files.
* fix(root): Fixes Phi-3 missing on readme.
* fix(root): Ensures files are consistent.
* fix(phi3): Fixes unit tests.
* fix(tests): Fixes style of phi-3 test file.
* chore(tests): Adds integration tests for Phi-3.
* fix(phi3): Removes additional flash-attention usage, .e.g, swiglu and rmsnorm.
* fix(phi3): Fixes incorrect docstrings.
* fix(phi3): Fixes docstring typos.
* fix(phi3): Adds support for Su and Yarn embeddings.
* fix(phi3): Improves according first batch of reviews.
* fix(phi3): Uses up_states instead of y in Phi3MLP.
* fix(phi3): Uses gemma rotary embedding to support torch.compile.
* fix(phi3): Improves how rotary embedding classes are defined.
* fix(phi3): Fixes inv_freq not being re-computed for extended RoPE.
* fix(phi3): Adds last suggestions to modeling file.
* fix(phi3): Splits inv_freq calculation in two lines.
* Fixed main train issues
* Added loss test
* Update src/transformers/models/seggpt/modeling_seggpt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Added missing labels arg in SegGptModel forward
* Fixed typo
* Added slow test to test loss calculation
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* push legacy to fast as well
* super strange
* Update src/transformers/convert_slow_tokenizer.py
* make sure we are BC
* fix Llama test
* nit
* revert
* more test
* style
* update
* small update w.r.t tokenizers
* nit
* don't split
* lol
* add a test for `add_prefix_space=False`
* fix gemma tokenizer as well
* update
* fix gemma
* nicer failures
* fixup
* update
* fix the example for legacy = False
* use `huggyllama/llama-7b` for the PR doctest
* nit
* use from_slow
* fix llama
* Duplicate swiftformer
* Convert SwiftFormerPatchEmbedding
* Convert SwiftFormerEmbeddings
* Convert TFSwiftFormerMlp
* Convert TFSwiftFormerConvEncoder
* Convert TFSwiftFormerLocalRepresentation
* convert TFSwiftFormerEncoderBlock
* Convert SwiftFormerStage
* Convert SwiftFormerEncoder
* Add TFSWiftFormerPreTrainedModel
* Convert SwiftFormerForImageClassification
* Add kwargs and start drop path
* Fix syntax
* Change Model class name
* Add TFSwiftFormer to __init__
* Duplicate test_modeling_swiftformer
* First test conversions
* Change require_torch to require_tf
* Add exports to swiftformer __init__
* Add TFSwiftFormerModel wrapper
* Fix __init__ and run black
* Remove docstring from MainLayer, fix padding
* Use keras.layers.Activation on keras.Sequential
* Fix swiftformer exports
* Fix activation layer from config
* Remove post_inits
* Use tf.keras.layers.ZeroPadding2D
* Convert torch normalize
* Change tf test input shape
* Fix softmax and reduce_sum
* Convert expand_dims and repeat
* Add missing reshape and tranpose
* Simplify TFSwiftFormerEncoderBlock.call
* Fix mismatch in patch embeddings
* Fix expected output shape to match channels last
* Fix swiftformer typo
* Disable test_onnx
* Fix TFSwiftFormerForImageClassification call
* Add unpack inputs
* Convert flatten(2).mean(-1)
* Change vision dummy inputs (to be reviewed)
* Change test_forward_signature to use .call
* Fix @unpack_inputs
* Set return_tensors="tf" and rename class
* Rename wrongly named patch_embeddings layer
* Add serving_output and change dummy_input shape
* Make dimensions BCHW and transpose inside embedding layer
* Change SwiftFormerEncoderBlock
* Fix ruff problems
* Add image size to swiftformer config
* Change tranpose to MainLayer and use -1 for reshape
* Remove serving_outputs and dummy_inputs
* Remove test_initialization test from tf model
* Make Sequential component a separate layer
* Fix layers' names
* Tranpose encoder outputs
* Fix tests and check if hidden states is not None
* Fix TFSwiftFormerForImageClassification
* Run make fixup
* Run make fix-copies
* Update modeling_tf_auto
* Update docs
* Fix modeling auto mapping
* Update modelint_tf_swiftformer docs
* Fill image_size doc and type
* Add reduction=None to loss computation
* Update docs
* make style
* Debug: Delete the tip to see if that changes anything
* Re-add tip
* Remove add_code_sample_docstrings
* Remove unused import
* Get the debug to actually tell us the problem it has with the docs
* Try a substitution to match the PyTorch file?
* Add swiftformer to ignore list
* Add build() methods
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove FIXME comment
* Remove from_pt
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Rename one-letter variables
* Remove FIXMEs related to momentum
* Remove old TODO comment
* Remove outstanding FIXME comments
* Get dropout rate from config
* Add specific dropout config for MLP
* Add convencoder dropout to config
* Pass config to SwiftFormerDropPath layer
* Fix drop_path variable name and add Adapted from comment
* Run ruff
* Removed copied from comment
* Run fix copies
* Change drop_path to identity to match pt
* Cleanup build() methods and move to new keras imports
* Update docs/source/en/model_doc/swiftformer.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Raise error if drop_path_rate > 0.0
* Apply suggestions from code review
Replace (self.dim), with self.dim,
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Remove drop_path function
* Add training to TFSwiftFormerEncoder
* Set self.built = True last
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Should have been added to previous commit
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Change default_feature_extractor to default_image_processor
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Import Keras from modeling_tf_utils
* Remove relative import
* Run ruff --fix
* Move import keras to tf_available
* Add copied from comment to test_forward_signature
* Reduce batch size and num_labels
* Extract loss logic to hf_compute_loss
* Run ruff format
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* wip
* fix __init__.py
* add docs
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* address comments 1
* work on make fixup
* pass configs down
* add sdpa attention
* remove DbrxBlock
* add to configuration_auto
* docstring now passes formatting test
* fix style
* update READMEs
* add dbrx to modeling_auto
* make fix-copies generated this
* add DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* config docstring passes formatting test
* rename moe_loss_weight to router_aux_loss_coef
* add to flash-attn documentation
* fix model-path in tests
* Explicitly make `"suli"` the default `ffn_act_fn`
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* default to using router_aux_loss_coef over ffn_config[moe_loss_weight]
* fix _flash_attn_uses_top_left_mask and is_causal
* fix tests path
* don't use token type IDs
* follow Llama and remove token_type_ids from test
* init ConfigTester differently so tests pass
* remove multiple choice test
* remove question + answer test
* remove sequence classification test
* remove token classification test
* copy Llama tests and remove token_type_ids from test inputs
* do not test pruning or headmasking; style code
* add _tied_weights_keys parameter to pass test
* add type hints
* fix type check
* update config tester
* remove masked_lm test
* remove encoder tests
* initialize DbrxModelTester with correct params
* style
* torch_dtype does not rely on torch
* run make fixup, fix-copies
* use https://huggingface.co/v2ray/dbrx-base-fixed/blob/main/modeling_dbrx.py
* add copyright info
* fix imports and DbrxRotaryEmbedding
* update DbrxModel docstring
* use copies
* change model path in docstring
* use config in DbrxFFN
* fix flashattention2, sdpaattention
* input config to DbrXAttention, DbrxNormAttentionNorm
* more fixes
* fix
* fix again!
* add informative comment
* fix ruff?
* remove print statement + style
* change doc-test
* fix doc-test
* fix docstring
* delete commented out text
* make defaults match dbrx-instruct
* replace `router_aux_loss_coef` with `moe_loss_weight`
* is_decoder=True
* remove is_decoder from configtester
* implement sdpa properly
* make is_decoder pass tests
* start on the GenerationTesterMixin tests
* add dbrx to sdpa documentation
* skip weight typing test
* style
* initialize smaller model
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Add DBRX to toctree
* skip test_new_cache_format
* make config defaults smaller again
* add pad_token_id
* remove pad_token_id from config
* Remove all references to DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* Update src/transformers/models/dbrx/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix typo
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update docs, fix configuration_auto.py
* address pr comments
* remove is_decoder flag
* slice
* fix requires grad
* remove grad
* disconnect differently
* remove grad
* enable grads
* patch
* detach expert
* nissan al ghaib
* Update modeling_dbrx.py
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* replace "Gemma" with "Dbrx"
* remove # type: ignore
* don't hardcode vocab_size
* remove ToDo
* Re-add removed idefics2 line
* Update test to use tiny-random!
* Remove TODO
* Remove one more case of loading the entire dbrx-instruct in the tests
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* address some comments
* small model
* add dbrx to tokenization_auto
* More docstrings with add_start_docstrings
* Dbrx for now
* add PipelineTesterMixin
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove flash-attn2 import error
* fix docstring
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add useage example
* put on one line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix ffn_act_fn
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* change "dbrx" to "DBRX" for display purposes.
* fix __init__.py?
* fix __init__.py
* fix README
* return the aux_loss
* remove extra spaces
* fix configuration_auto.py
* fix format in tokenization_auto
* remove new line
* add more useage examples
---------
Co-authored-by: Abhi Venigalla <abhi.venigalla@databricks.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Eitan Turok <eitan.turok@databricks.com>
Co-authored-by: Eitan Turok <150733043+eitanturok@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Eitan Turok <eitanturok@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add jamba arch
* apply "make fix-copies" changes
* fix link to model in JambaConfig docstring
* Add n_ctx in modeling file because repo-consistency wants that
* Add jamba to flash attention and sdpa documentation
* mamba dt_proj quant fix now works for LoRA as well
* override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers
* add jamba to tokenization auto
* fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24)
* simple PR fixes
* remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer
* remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530)
* Add copied comment on JambaMLP (it's the same as MixtralMLP)
* remove padding_mask warnings. It's not supported anymore
* fix docstring. Float instead of int
* A few more minor PR fixes
* (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass
* Return None attention weights from mamba layers. Append to all attentions only if not None.
* remove some leftover jamba archive lists
* Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel
* no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers
* Add Jamba paper on READMEs
* (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes)
* Add copied from comment
* remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms
* clearer docstring for _convert_to_standard_cache
* style fixes
* Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs
* rename test so it still overrides what its meant to override
* draft
* oups
* nit
* remove more complexe logic
* fix names used in config
* fix fix fix
* style
* fix some more failing tests
* generate did not init the cache 🙃
* more small nits
* typo
* config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes
* fix init of pkv with torch.tensor()
* empty tensor
* fix some init issues
* stupid changes required by generate because it does not even support it's own DynamicCache class
* more fixes
* fix general assisted gen cache_position bug
* tests passing
* Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py
* fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache
* no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore
* fix docstrings and typehints for past_key_values
* style fixes
* fix docs
* change typehint due to copy from Mixtral
* forgot import
* import order
* Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward)
* Add integration test with tiny tandom Jamba model on hub
* fix flash attention cache shapes
* bring back forgotten hidden states
* rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model
* align integration test after modeling fixes
* bugfix - mamba can use precomputed states only of forward pass is on a single token
* bugfix - mamba can use precomputed states only if they match the batch size
* typo
* remove making _prepare_4d_causal_attention_mask a leaf function
* stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
* Add OLMo using add-new-model-like with Llama
* Fix incorrect tokenizer for OLMo
* Copy-paste relevant OLMo methods and their imports
* Add OLMo config
* Modify OLMo config to follow HF conventions
* Remove unneeded Llama code from OLMo model
* Add ability for OLMo model to output attentions
* Add OLMoPreTrainedModel and OLMoModel
* Add OLMoForCausalLM
* Minor fixes to OLMo model for style and missing functions
* Implement OLMo tokenizer
* Implement OLMo to HF conversion script
* Add tests for OLMo model
* Add tests for OLMo fast tokenizer
* Add auto-generated dummy objects
* Remove unimplemented OLMo classes from auto and init classes and re-format
* Add README and associated auto-generated files
* Use OLMo names for common properties
* Run make fixup
* Remove `|` from OLMo typing
* Remove unneeded tokenization_olmo.py
* Revert model, config and converter to add-new-model-like Llama
* Move logic for adding bos/eos token into GPTNeoxTokenizerFast
* Change OLMoConfig defaults to match OLMo-7B
* Use GPTNeoXToknizerFast in OLMo tokenizer tests
* Modify auto-generated OLMoModelTests to work for OLMo
* Add non-parametric layer norm OLMoLayerNorm
* Update weight conversion script for OLMo
* Fix __init__ and auto structure for OLMo
* Fix errors from make fixup
* Remove OLMoTokenizerFast from documentation
* Add missing 'Copied from' for OLMoModel._update_causal_mask
* Run make fix-copies
* Rearrange string replacements in OLMoForCausalLM Copied from
* Move OLMo and Llama CausalLM.forward example into global constants
* Fix OLMO_GENERATION_EXAMPLE doc string typo
* Add option for qkv clipping to OLMo
* Rearrange OLMoConfig kwargs in convert_olmo_weights_to_hf
* Add clip_qkv to OLMoConfig in convert_olmo_weights_to_hf
* Fix OLMo tokenization bug using conversion script
* Keep model in full precision after conversion
* Do not add eos token automatically
* Update references to OLMo model in HF Hub
* Do not add eos token during encoding by default
* Fix Llama generation example
* Run make fixup
* OLMo 7B integration test fix
* Remove unneeded special case for OLMoConfig
* OLMo 7B Twin 2T integration test fix
* Fix test_model_7b_greedy_generation
* Remove test_compile_static_cache
* Fix OLMo and Llama generation example
* Run make fixup
* Revert "OLMo 7B integration test fix"
This reverts commit 4df56a4b15.
* Revert "OLMo 7B Twin 2T integration test fix"
This reverts commit 9ff65a4a29.
* Ungate 7B integration tests and fix greedy generation test
* Add retries for flaky test_eager_matches_sdpa_generate
* Fix output of doc example for OLMoForCausalLM.forward
* Downsize OLMo doc test for OLMoForCausalLM.forward to 1B model
* Try fix incorrect characters in OLMoForCausalLM.forward doct test
* Try fix incorrect characters in OLMoForCausalLM.forward doc test using end quotes
* Remove pretraining_tp from OLMo config and model
* Add missing 'Copied from' instances
* Remove unneeded causal_mask from OLMoModel
* Revert Llama changes
* Ignore copy for OLMoForCausalLM.forward
* Change 'OLMo' to 'Olmo' in classes
* Move minimal OLMo tokenization tests to model tests
* Add missed 'Copied from' for repeat_kv
* Add create token type ids to CodeGenTokenizer
* Fix inconsistent length of token type ids
* Format source codes
* Fix inconsistent order of methods
* Update docstring
* add test_tokenizer_integration test
* Format source codes
* Add `copied from` comment to CodeGenTokenizerFast
* Add doc of create_token_type_ids_from_sequences
* Make return_token_type_ids False by default
* Make test_tokenizer_integration as slow test
* Add return_token_type_ids to tokenizer init arg
* Add test for tokenizer's init return_token_type_ids
* Format source codes
* Remove auto class
* Update ImagePointDescriptionOutput
* Update model outputs
* Rename output class
* Revert "Remove auto class"
This reverts commit ed4a8f549d.
* Address comments
* Fork.
* RecurrentGemma initial commit.
* Updating __init__.py.
* Minor modification to how we initialize the cache.
Changing how the config specifies the architecture.
* Reformat code to 4 spaces.
Fixed a few typos.
* Fixed the forward pass.
Still unclear on the cache?
* Fixed the RecurrentGemmaForCausalLM
* Minor comment that we might not need attention_mask and output_attention arguments.
* Now cache should work as well.
* Adding a temporary example to check whether the model generation works.
* Adding the tests and updating imports.
* Adding the example file missing in the previous commit.
* First working example.
* Removing .gitignore and reverting parts of __init__.
* Re-add .gitignore.
* Addressing comments for configuration.
* Move mask creation to `_prepare_inputs_for_generation`.
* First try at integration tests:
1. AttributeError: 'GriffinCausalLMOutput' object has no attribute 'attentions'.
2. `cache_position` not passed
* Transfoering between machines.
* Running normal tests.
* Minor fix.
* More fixes.
* Addressing more comments.
* Minor fixes.
* first stab at cleanup
* more refactoring
* fix copies and else
* renaming and get init to work
* fix causal mask creation
* update
* nit
* fix a hell lot of things
* updates
* update conversion script
* make all keys importable
* nits
* add auto mappings
* properly convert ffw_up and down
* add scaling
* fix generations
* for recurrent dtype
* update
* fix going beyong window
* fixup
* add missing files
* current updates to remove last einops
* finish modeling refactor
* TADA
* fix compile
* fix most failing testt ? ?
* update tests
* refactor and update
* update
* nits, fixup and update tests
* more fixup
* nits
* fix imports
* test format
* fixups
* nits
* tuple typing
* fix code quality
* add model card
* fix doc
* skip most generation tests
* nits
* style
* doc fixes
* fix pr and check_copies?
* last nit
* oupsy
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* update
* Update src/transformers/models/recurrent_gemma/convert_recurrent_gemma_to_hf.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* update based on review
* doc nit
* fix quality
* quality
* fix slow test model path
* update default dype
* ignore attributes that can be safely ignored in check config attributes
* 0lallalala come on
* save nit
* style
* remove to dict update
* make sure we can also run in float16
* style
---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: Aleksandar Botev <botev@google.com>
Co-authored-by: Leonard Berrada <lberrada@users.noreply.github.com>
Co-authored-by: anushanf <anushanf@google.com>
Co-authored-by: botev <botevmg@gmail.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* init: add StableLm 2 support
* add integration test for parallel residual and qk layernorm
* update(modeling): match qk norm naming for consistency with phi/persimmon
* fix(tests): run fwd/bwd on random init test model to jitter norm weights off identity
* `use_parallel_residual`: add copy pointer to `GPTNeoXLayer.forward`
* refactor: rename head states var in `StableLmLayerNormPerHead`
* tests: update test model and add generate check
* add _torch_extract_fbank_features_batch function in feature_extractor_whisper
* reformat feature_extraction_whisper.py file
* handle batching in single function
* add gpu test & doc
* add batch test & device in each __call__
* add device arg in doc string
---------
Co-authored-by: vaibhav.aggarwal <vaibhav.aggarwal@sprinklr.com>
* Defaulted IdeficsProcessor padding to 'longest', removed manual padding
* make fixup
* Defaulted processor call to padding=False
* Add padding to processor call in IdeficsModelIntegrationTest as well
* Defaulted IdeficsProcessor padding to 'longest', removed manual padding
* make fixup
* Defaulted processor call to padding=False
* Add padding to processor call in IdeficsModelIntegrationTest as well
* redefaulted padding=longest again
* fixup/doc
* Fix generate_with_fallback **kwargs
* Change pop to get
* Delete keys from kwargs to prevent overriding generation_config
* Revert to passing kwargs by reference, but make a (shallow) copy
* dict -> copy.copy
* Add test_whisper_longform_multi_batch_beam
* Fix skip_special_tokens process for Wav2Vec2CTCTokenizer._decode
* Fix skip_special_tokens for Wav2Vec2CTCTokenizer._decode
* Exclude pad_token filtering since it is used as CTC-blank token
* Add small test for skip_special_tokens
* Update decoding test for added new token
* add FA2 to o.g Musicgen
* make style
* add FA2 support to Musicgen Melody
* add generation FA2 tests to o.g Musicgen
* make style and fix copies
* add Musicgen to FA2 docs + deprecate list
* add sdpa supports to Musicgen's
* make style and fix copies
* refactor attention implementation arguments
* add Copied from to sdpa tests
* add copied form in sdpa tests melody
* add copied for FA2 generation tests
* add FA2 inference copied from
* make style
* Fix sinusoidal_embeddings in FlaubertModel
* Fix for Informer
* Fix for XLM
* Move sinusoidal emb for XLM
* Move sinusoidal emb for Flaubert
* Small cleanup
* Add comments on tests code copied from
* Add with Distilbert->
* fix bug and add tests
* nit
* otherway to get the cur len instead of attention mask
* more places where this might have been broken
* nit
* oups
* inputs_embeds vs input_embeds
* test generated outptus
* style
* nit
* fix
* skip failing biogpt
* Check for requires_grad when initing weights
* Add unit test
* Move sinusoidal positional encoding generation after post_init()
* Add modules to skip init list
* Move create_sinusoidal_embeddings to _init_weights
* add support for qwen2 MoE models
* update docs
* add support for qwen2 MoE models
* update docs
* update model name & test
* update readme
* update class names & readme & model_doc of Qwen2MoE.
* update architecture name
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fix style
* fix test when there are sparse and non sparse layers
* fixup
* Update README.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup
* fixup
* add archive back
* add support for qwen2 MoE models
* update docs
* update model name & test
* update readme
* update class names & readme & model_doc of Qwen2MoE.
* update architecture name
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fixup
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* fix style
* fix test when there are sparse and non sparse layers
* fixup
* add archive back
* fix integration test
* fixup
---------
Co-authored-by: bozheng-hit <dsoul0621@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* attempt to fix
* the actual fix that works with compilation!
* this?
* temporary update
* nit?
* dispatcg to memory efficient?
* update both models that have static cache support
* fix copies fix compile
* make sure fix
* fix cohere and gemma
* fix beams?
* nit
* slipped through the cracks
* nit
* nits
* update
* fix-copies
* skip failing tests
* nits
* Added SuperPoint docs
* Added tests
* Removed commented part
* Commit to create and fix add_superpoint branch with a new branch
* Fixed dummy_pt_objects
* Committed missing files
* Fixed README.md
* Apply suggestions from code review
Fixed small changes
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Moved ImagePointDescriptionOutput from modeling_outputs.py to modeling_superpoint.py
* Removed AutoModelForKeypointDetection and related stuff
* Fixed inconsistencies in image_processing_superpoint.py
* Moved infer_on_model logic simply in test_inference
* Fixed bugs, added labels to forward method with checks whether it is properly a None value, also added tests about this logic in test_modeling_superpoint.py
* Added tests to SuperPointImageProcessor to ensure that images are properly converted to grayscale
* Removed remaining mentions of MODEL_FOR_KEYPOINT_DETECTION_MAPPING
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fixed from (w, h) to (h, w) as input for tests
* Removed unnecessary condition
* Moved last_hidden_state to be the first returned
* Moved last_hidden_state to be the first returned (bis)
* Moved last_hidden_state to be the first returned (ter)
* Switched image_width and image_height in tests to match recent changes
* Added config as first SuperPointConvBlock init argument
* Reordered README's after merge
* Added missing first config argument to SuperPointConvBlock instantiations
* Removed formatting error
* Added SuperPoint to README's de, pt-br, ru, te and vi
* Checked out README_fr.md
* Fixed README_fr.md
* Test fix README_fr.md
* Test fix README_fr.md
* Last make fix-copies !
* Updated checkpoint path
* Removed unused SuperPoint doc
* Added missing image
* Update src/transformers/models/superpoint/modeling_superpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Removed unnecessary import
* Update src/transformers/models/superpoint/modeling_superpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Added SuperPoint to _toctree.yml
---------
Co-authored-by: steven <steven.bucaillle@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
* use user_defined_symbols
* fixup
* nit
* add a very robust test
* make sure all models are tested with the `pretrained_tokenizer_to_test`
* should we make sure we test all of them?
* merge
* remove the id
* fix test
* update
* ousies
* oups
* fixup
* fix copies check
* remove `pretrained_tokenizer_to_test`
* Cohere Model Release (#1)
Cohere Model Release
* Remove unnecessary files and code (#2)
Some cleanup
* Delete cohere-model directory (#3)
* Make Fix (#5)
* Pr fixes (#6)
* fixes for pr
* pr fixes for the format
* pr fixes for the format
* src/transformers/models/auto/tokenization_auto.py
* Tokenizer test (#8)
* tokenizer test
* format fix
* Adding Docs and other minor changes (#7)
* Add modeling tests (#9)
* Smol Fix (#11)
* tokenization tests are fixed
* format fixes
* fix pr doc tests
* fix pr doc tests
* fix pr doc tests
* fix pr style check
* small changes in cohere.md
* FIX: Address final comments for transformers integration (#13)
* fix modeling final nits and add proper test file
* for now leave empty tests
* add integration test
* push new test
* fix modeling cohere (#14)
* Update chat templates to use the new API (#15)
---------
Co-authored-by: ahmetustun <ahmetustun89@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Added pytests for pvt-v2, all passed
* Added pvt_v2 to docs/source/end/model_doc
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat. Added additional type support for image size in config
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Reverted batch eval changes for PR
* Updated index.md
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat
* Ran fix-copies
* Fixed PvtV2Backbone tests
* Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py
* Fixed backbone stuff and fixed tests: all passing
* Ran make fixup
* Made modifications for code checks
* Remove ONNX config from configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use explicit image size dict in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make image_size optional in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove _ntuple use in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove reference to fp16_enabled
* Model modules now take config as first argument even when not used
* Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling"
* All LayerNorm now instantiates with config.layer_norm_eps
* Added docstring for depth-wise conv layer
* PvtV2Config now only takes Union[int, Tuple[int, int]] for image size
* Refactored PVTv2 in prep for gradient checkpointing
* Gradient checkpointing ready to test
* Removed override of _set_gradient_checkpointing
* Cleaned out old code
* Applied code fixup
* Applied code fixup
* Began debug of pvt_v2 tests
* Leave handling of num_labels to base pretrained config class
* Deactivated gradient checkpointing tests until it is fixed
* Removed PvtV2ImageProcessor which duped PvtImageProcessor
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Added pvt_v2 to docs/source/end/model_doc
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat. Added additional type support for image size in config
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat
* Ran fix-copies
* Fixed PvtV2Backbone tests
* Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py
* Fixed backbone stuff and fixed tests: all passing
* Ran make fixup
* Made modifications for code checks
* Remove ONNX config from configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use explicit image size dict in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make image_size optional in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove _ntuple use in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove reference to fp16_enabled
* Model modules now take config as first argument even when not used
* Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling"
* All LayerNorm now instantiates with config.layer_norm_eps
* Added docstring for depth-wise conv layer
* PvtV2Config now only takes Union[int, Tuple[int, int]] for image size
* Refactored PVTv2 in prep for gradient checkpointing
* Gradient checkpointing ready to test
* Removed override of _set_gradient_checkpointing
* Cleaned out old code
* Applied code fixup
* Applied code fixup
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Ran fix-copies and fixup. All checks passed
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Fixed config docstring. Added channels property
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Ran fix-copies and fixup. All checks passed
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Fixed config backbone compat
* Ran fix-copies
* Began debug of pvt_v2 tests
* Leave handling of num_labels to base pretrained config class
* Deactivated gradient checkpointing tests until it is fixed
* Removed PvtV2ImageProcessor which duped PvtImageProcessor
* Fixed issue from rebase
* Fixed issue from rebase
* Set tests for gradient checkpointing to skip those using reentrant since it isn't supported
* Fixed issue from rebase
* Fixed issue from rebase
* Changed model name in docs
* Removed duplicate PvtV2Backbone
* Work around type switching issue in tests
* Fix model name in config comments
* Update docs/source/en/model_doc/pvt_v2.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Changed name of variable from 'attn_reduce' to 'sr_type'
* Changed name of variable from 'attn_reduce' to 'sr_type'
* Changed from using 'sr_type' to 'linear_attention' for clarity
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Removed old code
* Changed from using 'sr_type' to 'linear_attention' for clarity
* Fixed Class names to be more descriptive
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Removed outdated code
* Moved paper abstract to single line in pvt_v2.md
* Added usage tips to pvt_v2.md
* Simplified module inits by passing layer_idx
* Fixed typing for hidden_act in PvtV2Config
* Removed unusued import
* Add pvt_v2 to docs/source/en/_toctree.yml
* Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive.
* Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive.
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Move function parameters to single line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Update year of copyright to 2024
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Make code more explicit
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Updated sr_ratio to be more explicit spatial_reduction_ratio
* Removed excess type hints in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Move params to single line in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Removed needless comment in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update copyright date in pvt_v2.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Moved params to single line in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Updated copyright date in configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Cleaned comments in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Renamed spatial_reduction Conv2D operation
* Revert "Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
"
This reverts commit c4a04416dd.
* Updated conversion script to reflect module name change
* Deprecated reshape_last_stage option in config
* Removed unused imports
* Code formatting
* Fixed outdated decorators on test_inference_fp16
* Added "Copied from" comments in test_modeling_pvt_v2.py
* Fixed import listing
* Updated model name
* Force empty commit for PR refresh
* Fixed linting issue
* Removed # Copied from comments
* Added PVTv2 to README_fr.md
* Ran make fix-copies
* Replace all FoamoftheSea hub references with OpenGVLab
* Fixed out_indices and out_features logic in configuration_pvt_v2.py
* Made ImageNet weight conversion verification optional in convert_pvt_v2_to_pytorch.py
* Ran code fixup
* Fixed order of parent classes in PvtV2Config to fix the to_dict method override
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial implementation of flash attention for gptj
* modify flash attention and overwrite test_flash_attn_2_generate_padding_right
* update flash attention support list
* remove the copy line in the `CodeGenBlock`
* address copy mechanism
* Update src/transformers/models/gptj/modeling_gptj.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add GPTJ attention classes
* add expected outputs in the gptj test
* Ensure repo consistency with 'make fix-copies'
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add tests for batching support
* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* fixes and comments
* use cosine distance for conv models
* skip mra model testing
* Update tests/models/vilt/test_modeling_vilt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* finzalize and make style
* check model type by input names
* Update tests/models/vilt/test_modeling_vilt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fixed batch size for all testers
* Revert "fixed batch size for all testers"
This reverts commit 525f3a0a05.
* add batch_size for all testers
* dict from model output
* do not skip layoutlm
* bring back some code from git revert
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* clean-up
* where did minus go in tolerance
* make whisper happy
* deal with consequences of losing minus
* deal with consequences of losing minus
* maskformer needs its own test for happiness
* fix more models
* tag flaky CV models from Amy's approval
* make codestyle
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* left-padding test revisited
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial-commit
* start cleaning
* small nits
* small nits
* current updates
* add kernels
* small refactoring little step
* add comments
* styling
* nit
* nits
* Style
* Small changes
* Push dummy mambda simple slow
* nit
* Use original names
* Use original names and remove norm
* Updates for inference params
* Style nd updates
* nits
* Match logits
* Add a test
* Add expected generated text
* nits doc, imports and styling
* style
* oups
* dont install kernels, invite users to install the required kernels
* let use use the original packages
* styling
* nits
* fix some copieds
* update doc
* fix-copies
* styling done
* nits
* fix import check
* run but wrong cuda ress
* mamba CUDA works :)
* fix the fast path
* config naming nits
* conversion script is not required at this stage
* finish fixing the fast path: generation make sense now!
* nit
* Let's start working on the CIs
* style
* better style
* more nits
* test nit
* quick fix for now
* nits
* nit
* nit
* nit
* nits
* update test rest
* fixup
* update test
* nit
* some fixes
* nits
* update test values
* fix styling
* nit
* support peft
* integrations tests require torchg
* also add slow markers
* styling
* chose forward wisely
* nits
* update tests
* fix gradient checkpointing
* fixup
* nit
* fix doc
* check copies
* fix the docstring
* fix some more tests
* style
* fix beam search
* add init schene
* update
* nit
* fix
* fixup the doc
* fix the doc
* fixup
* tentative update but slow is no longer good
* nit
* should we always use float32?
* nits
* revert wrong changes
* res in float32
* cleanup
* skip fmt for now
* update generation values
* update test values running original model
* fixup
* update tests + rename inference_params to cache_params + make sure training does not use cache_params
* small nits
* more nits
* fix final CIs
* style
* nit doc
* I hope final doc nits
* nit
* 🫠
* final touch!
* fix torch import
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Apply suggestions from code review
* fix fix and fix
* fix base model prefix!
* nit
* Update src/transformers/models/mamba/__init__.py
* Update docs/source/en/model_doc/mamba.md
Co-authored-by: Lysandre Debut <hi@lysand.re>
* nit
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* First draft
* More improvements
* More improvements
* More fixes
* Fix copies
* More improvements
* More fixes
* More improvements
* Convert checkpoint
* More improvements, set up tests
* Fix more tests
* Add UdopModel
* More improvements
* Fix equivalence test
* More fixes
* Redesign model
* Extend conversion script
* Use real inputs for conversion script
* Add image processor
* Improve conversion script
* Add UdopTokenizer
* Add fast tokenizer
* Add converter
* Update README's
* Add processor
* Add fully fledged tokenizer
* Add fast tokenizer
* Use processor in conversion script
* Add tokenizer tests
* Fix one more test
* Fix more tests
* Fix tokenizer tests
* Enable fast tokenizer tests
* Fix more tests
* Fix additional_special_tokens of fast tokenizer
* Fix tokenizer tests
* Fix more tests
* Fix equivalence test
* Rename image to pixel_values
* Rename seg_data to bbox
* More renamings
* Remove vis_special_token
* More improvements
* Add docs
* Fix copied from
* Update slow tokenizer
* Update fast tokenizer design
* Make text input optional
* Add first draft of processor tests
* Fix more processor tests
* Fix decoder_start_token_id
* Fix test_initialization
* Add integration test
* More improvements
* Improve processor, add test
* Add more copied from
* Add more copied from
* Add more copied from
* Add more copied from
* Remove print statement
* Update README and auto mapping
* Delete files
* Delete another file
* Remove code
* Fix test
* Fix docs
* Remove asserts
* Add doc tests
* Include UDOP in exotic model tests
* Add expected tesseract decodings
* Add sentencepiece
* Use same design as T5
* Add UdopEncoderModel
* Add UdopEncoderModel to tests
* More fixes
* Fix fast tokenizer
* Fix one more test
* Remove parallelisable attribute
* Fix copies
* Remove legacy file
* Copy from T5Tokenizer
* Fix rebase
* More fixes, copy from T5
* More fixes
* Fix init
* Use ArthurZ/udop for tests
* Make all model tests pass
* Remove UdopForConditionalGeneration from auto mapping
* Fix more tests
* fixups
* more fixups
* fix the tokenizers
* remove un-necessary changes
* nits
* nits
* replace truncate_sequences_boxes with truncate_sequences for fix-copies
* nit current path
* add a test for input ids
* ids that we should get taken from c9f7a32f57
* nits converting
* nits
* apply ruff
* nits
* nits
* style
* fix slow order of addition
* fix udop fast range as well
* fixup
* nits
* Add docstrings
* Fix gradient checkpointing
* Update code examples
* Skip tests
* Update integration test
* Address comment
* Make fixup
* Remove extra ids from tokenizer
* Skip test
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update year
* Address comment
* Address more comments
* Address comments
* Add copied from
* Update CI
* Rename script
* Update model id
* Add AddedToken, skip tests
* Update CI
* Fix doc tests
* Do not use Tesseract for the doc tests
* Remove kwargs
* Add original inputs
* Update casting
* Fix doc test
* Update question
* Update question
* Use LayoutLMv3ImageProcessor
* Update organization
* Improve docs
* Update forward signature
* Make images optional
* Remove deprecated device argument
* Add comment, add add_prefix_space
* More improvements
* Remove kwargs
---------
Co-authored-by: ArthurZucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* 🐛 Fix oneformer instance post processing when using panoptic task type
* ✅ Add unit test for oneformer instance post processing panoptic bug
---------
Co-authored-by: Nick DeGroot <1966472+nickthegroot@users.noreply.github.com>
