* 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
