* 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
* immutability fix for seq2seq as well as immutability tests for the collators
* ensure we don't act on none labels and formatting
* remove tf/pt in respective tests as they are not required
* more type error fixes tf/np
* remove todo
* apply suggestions from code review
* formatting / style
* Create CodeAgent and ReactAgent
* Fix formatting errors
* Update documentation for agents
* Add custom errors, improve logging
* Support variable usage in ReactAgent
* add messages
* Add message passing format
* Create React Code Agent
* Update
* Refactoring
* Fix errors
* Improve python interpreter
* Only non-tensor inputs should be sent to device
* Calculator tool slight refactor
* Improve docstrings
* Refactor
* Fix tests
* Fix more tests
* Fix even more tests
* Fix tests by replacing output and input types
* Fix operand type issue
* two small fixes
* EM TTS
* Fix agent running type errors
* Change text to speech tests to allow changed outputs
* Update doc with new agent types
* Improve code interpreter
* If max iterations reached, provide a real answer instead of an error
* Add edge case in interpreter
* Add safe imports to the interpreter
* Interpreter tweaks: tuples and listcomp
* Make style
* Make quality
* Add dictcomp to interpreter
* Rename ReactJSONAgent to ReactJsonAgent
* Misc changes
* ToolCollection
* Rename agent's logger to self.logger
* Add while loops to interpreter
* Update doc with new tools. still need to mention collections
* Add collections to the doc
* Small fixes on logs and interpretor
* Fix toolbox return type
* Docs + fixup
* Skip doctests
* Correct prompts with improved examples and formatting
* Update prompt
* Remove outdated docs
* Change agent to accept Toolbox object for tools
* Remove calculator tool
* Propagate removal of calculator in doc
* Fix 2 failing workflows
* Simplify additional argument passing
* AgentType audio
* Minor changes: function name, types
* Remove calculator tests
* Fix test
* Fix torch requirement
* Fix final answer tests
* Style fixes
* Fix tests
* Update docstrings with calculator removal
* Small type hint fixes
* Update tests/agents/test_translation.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_python_interpreter.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/default_tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_agents.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bert/configuration_bert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/speech_to_text.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_speech_to_text.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_tools_common.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* pygments
* Answer comments
* Cleaning up
* Simplifying init for all agents
* Improving prompts and making code nicer
* Style fixes
* Add multiple comparator test in interpreter
* Style fixes
* Improve BERT example in documentation
* Add examples to doc
* Fix python interpreter quality
* Logging improvements
* Change test flag to agents
* Quality fix
* Add example for HfEngine
* Improve conversation example for HfEngine
* typo fix
* Verify doc
* Update docs/source/en/agents.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/agents.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/prompts.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/python_interpreter.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/agents.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix style issues
* local s2t tool
---------
Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com>
Co-authored-by: Lysandre <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* 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
* Added cache clearing for GPU efficiency.
* Added cache clearing for GPU efficiency.
* Added batch_eval_metrics capability
* Ran make fixup
* Fixed bug
* Fixed whitespace issue
* Fixed outdated condition
* Updated docstrings with instructions for batch_eval_metrics. Updated end of dataloader logic
* Added first version of batch_eval_metrics Trainer test
* Fixed batch_eval_metrics Trainer tests for both eval and predict
* Fixed batch_eval_metrics behavior for new Trainer variables
* Fixed batch_eval_metrics Trainer tests
* Ran fixup
* Trainer: load checkpoint model with multiple adapters
* Trainer._load_from_checkpoint support multiple active adapters
* PeftModel.set_adapter does not support multiple adapters yet
* Trainer._load_from_checkpoint test multiple adapters
---------
Co-authored-by: Clara Luise Pohland <clara-luise.pohland@telekom.de>
* 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>
* fix seq2seq data collator to respect the given padding strategy
further added tests for the seq2seq data collator in the style of the `data_collator_for_token_classification` (pt, tf, np)
* formatting and change bool equals "==" to "is"
* add missed return types in tests
* update numpy test as it can handle unequal shapes, not like pt or tf
* 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)
* Introduce saveable callbacks
* Add note
* Test for non-present and flag
* Support early stopping and refusing to train further
* Update docstring
* More saving
* Import oopsie
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make it go through TrainerArguments
* Document
* Fix test
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Rework to allow for duplicates
* CLean
* Fix failing tests
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* 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
* [FEAT]: EETQ quantizer support
* Update quantization.md
* Update docs/source/en/main_classes/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/__init__.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/__init__.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* [FEAT]: EETQ quantizer support
* [FEAT]: EETQ quantizer support
* remove whitespaces
* update quantization.md
* style
* Update docs/source/en/quantization.md
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* add copyright
* Update quantization.md
* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Address the comments by amyeroberts
* style
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* stash commit (will discard all of this)
* stash commit
* First commit - needs a lot of testing!
* Add a test
* Fix imports and make the tests actually test something
* Tests pass!
* Rearrange test
* Add comments (but it's still a bit confusing)
* Stop storing the tokenizer
* Comment fixup
* Fix for input_ids with a single sequence
* Update tests to test single sequences
* make fixup
* Fix incorrect use of isin()
* Expand tests to catch more cases
* Expand tests to catch more cases
* make fixup
* Fix length calculation and update tests
* Handle Ġ as a space replacement too
* Update src/transformers/generation/stopping_criteria.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Add optimizations from Joao's suggestion
* Remove TODO
* Update src/transformers/generation/stopping_criteria.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/generation/test_stopping_criteria.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* make fixup
* Rename some variables and remove some debugging clauses for clarity
* Add tests for the sub-methods
* Clarify one test slightly
* Add stop_strings to GenerationConfig
* generate() supports stop_string arg, asks for tokenizer if not provided
* make fixup
* Cleanup code and rename variables for clarity
* Update tokenizer error
* Update tokenizer passing, handle generation on GPU
* Slightly more explanation cleanup
* More comment cleanup
* Factor out the token cleanup so it's more obvious what we're doing, and we can change it later
* Careful with that cleanup!
* Cleanup + optimizations to _get_matching_positions
* More minor performance tweaks
* Implement caching and eliminate some expensive ops (startup time: 200ms -> 9ms)
* Remove the pin_memory call
* Parallelize across all stop strings!
* Quick fix for tensor devices
* Update embeddings test for the new format
* Fix test imports
* Manual patching for BERT-like tokenizers
* Return a bool vector instead of a single True/False
* Better comment
* Better comment
* Add tests from @zucchini-nlp
* Amy's list creation nit
* tok_list -> token_list
* Push a big expanded docstring (should we put it somewhere else?)
* Expand docstrings
* Docstring fixups
* Rebase
* make fixup
* Make a properly general method for figuring out token strings
* Fix naming throughout the functions
* Move cache, refactor, fix tests
* Add comment
* Remove finished TODO
* Remove finished TODO
* make fixup
* Update src/transformers/generation/stopping_criteria.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update and shorten docstring
* Update tests to be shorter/clearer and test specific cases
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* 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>
* feat: multidevice for resnet
* feat: yes! resnet
* fix: compare all elements in tuple
* feat: support for regnet
* feat: support for convnextv2
* feat: support for bit
* feat: support for cvt
* feat: add support for focalnet
* feat: support for yolos
* feat: support for glpn
* feat: support for imagegpt
* feat: support for levit
* feat: support for mgp_str
* feat: support for mobilnet_v1
* feat: support for mobilnet_v2
* feat: support for mobilevit
* feat: support for mobilevitv2
* feat: support for poolformer
* fix: copies
* fix: code quality check
* update: upstream changes from main
* fix: consistency check
* feat: support for sam
* feat: support for switchformer
* feat: support for swin
* feat: support for swinv2
* feat: support for timesformer
* feat: suport for trocr
* feat: support for upernet
* fix: check copies
* update: rerun CI
* update: rerun again, maybe
* update: one more rerun
---------
Co-authored-by: Jacky Lee <jackylee328@gmail.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
* Bookmark, initial impelemtation. Need to test
* Clean
* Working fully, woop woop
* I think working version now, testing
* Fin!
* rm cast, could keep None
* Fix typing issue
* rm typehint
* Add test
* Add tests and make more rigid
* Add test for parse_json_file
* Change Path to PathLike
* Fix `Import block is un-sorted or un-formatted`
* revert parse_json_file
* Fix ruff format
* Add parse_json_file test
* 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>
* revert back to torch 2.1.1
* run test
* switch to torch 2.2.1
* udapte dockerfile
* fix awq tests
* fix test
* run quanto tests
* update tests
* split quantization tests
* fix
* fix again
* final fix
* fix report artifact
* build docker again
* Revert "build docker again"
This reverts commit 399a5f9d93.
* debug
* revert
* style
* new notification system
* testing notfication
* rebuild docker
* fix_prev_ci_results
* typo
* remove warning
* fix typo
* fix artifact name
* debug
* issue fixed
* debug again
* fix
* fix time
* test notif with faling test
* typo
* issues again
* final fix ?
* run all quantization tests again
* remove name to clear space
* revert modfiication done on workflow
* fix
* build docker
* build only quant docker
* fix quantization ci
* fix
* fix report
* better quantization_matrix
* add print
* revert to the basic one
* See if we can get tests to pass with the fixed weights
* See if we can get tests to pass with the fixed weights
* Replace the revisions now that we don't need them anymore
* 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>
* if output is tuple like facebook/hf-seamless-m4t-medium, waveform is the first element
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* add test and fix batch issue
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* add dict output support for seamless_m4t
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi <yi.a.wang@intel.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
* Hard error when ignoring tensors. (#27484)
* [WIP] Hard error when ignoring tensors.
* Better selection/error when saving a checkpoint.
- Find all names we should normally drop (those are in the transformers
config)
- Find all disjoint tensors (for those we can safely trigger a copy to
get rid of the sharing before saving)
- Clone those disjoint tensors getting rid of the issue
- Find all identical names (those should be declared in the config
but we try to find them all anyway.)
- For all identical names:
- If they are in the config, just ignore them everything is fine
- If they are not, warn about them.
- For all remainder tensors which are shared yet neither identical NOR
disjoint. raise a hard error.
* Adding a failing test on `main` that passes here.
* We don't need to keep the subfolder logic in this test.
* 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>
* Add small tests.
* Dead variable.
* Fixup.
* Fixing tied_Weights_keys on generic models.
* Fixup + T5 encoder/decoder tying (with different layers)
* Code quality.
* Dynamic member.
* trigger
* Fixing encoder name for other types of encoder/decoder combos.
* Fix scoping.
* Update .github/workflows/self-scheduled.yml
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fixing the tied_weights after the call.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* 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 issue with logit processor in beam search in Flax
* adding FlaxNoRepeatNGramLogitsProcessor class + unit test
* style correction and code verification
* add FlaxNoRepeatNGramLogitsProcessor to the test_processor_list and test_processor_list_jitted tests
* fix an issue where ngrams are banned only if they appear ==1 time + update description of get_previous_ngrams
* replace non-jit compatible masking of ngrams that are not yet generated with jittable version
* Revert "fix issue with logit processor in beam search in Flax"
This reverts commit 09b70d7e4d.
* add FlaxNoRepeatNGramLogitsProcessor to _get_logits_processor
* change the method of casting to boolean of banned tokens indices
* fix code style
* remove some useless operations + significantly faster computation of update indices using jax.lax.fori_loop
* remove useless loop iterations
* set some variables that were calculated and used multiple times
* fix format
* 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
* Start rework
* Fix failing test
* Include max
* Update src/transformers/trainer.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add functions to get number of params which require grad, get optimizer group for parameters and get learning rates of param groups to trainer.py
* add tests and raise ValueError when optimizer is None
* add second layer to test and freeze its weigths
* check if torch is available before running tests
* use decorator to check if torch is available
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix test indentation
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* Automatic safetensors conversion when lacking these files (#29390)
* Automatic safetensors conversion when lacking these files
* Remove debug
* Thread name
* Typo
* Ensure that raises do not affect the main thread
* Catch all errors
* 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>
* replace the 'decord' with 'av' in VideoClassificationPipeline
* fix the check of backend in VideoClassificationPipeline
* adjust the order of imports
* format 'video_classification.py'
* format 'video_classification.py' with ruff
---------
Co-authored-by: wanqiancheng <13541261013@163.com>
