* initial design
* update all video processors
* add tests
* need to add qwen2-vl (not tested yet)
* add qwen2-vl in auto map
* fix copies
* isort
* resolve confilicts kinda
* nit:
* qwen2-vl is happy now
* qwen2-5 happy
* other models are happy
* fix copies
* fix tests
* add docs
* CI green now?
* add more tests
* even more changes + tests
* doc builder fail
* nit
* Update src/transformers/models/auto/processing_auto.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* small update
* imports correctly
* dump, otherwise this is getting unmanagebale T-T
* dump
* update
* another update
* update
* tests
* move
* modular
* docs
* test
* another update
* init
* remove flakiness in tests
* fixup
* clean up and remove commented lines
* docs
* skip this one!
* last fix after rebasing
* run fixup
* delete slow files
* remove unnecessary tests + clean up a bit
* small fixes
* fix tests
* more updates
* docs
* fix tests
* update
* style
* fix qwen2-5-vl
* fixup
* fixup
* unflatten batch when preparing
* dump, come back soon
* add docs and fix some tests
* how to guard this with new dummies?
* chat templates in qwen
* address some comments
* remove `Fast` suffix
* fixup
* oops should be imported from transforms
* typo in requires dummies
* new model added with video support
* fixup once more
* last fixup I hope
* revert image processor name + comments
* oh, this is why fetch test is failing
* fix tests
* fix more tests
* fixup
* add new models: internvl, smolvlm
* update docs
* imprt once
* fix failing tests
* do we need to guard it here again, why?
* new model was added, update it
* remove testcase from tester
* fix tests
* make style
* not related CI fail, lets' just fix here
* mark flaky for now, filas 15 out of 100
* style
* maybe we can do this way?
* don't download images in setup class
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* enable generation fsdp/utils test cases on XPU
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* fix style
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* xx
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* use backend_xx APIs
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* fix style
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
---------
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* update models
* why rename
* return attn weights when sdpa
* fixes
* fix attn implementation composite
* fix moshi
* add message
* add typings
* use explicitly all flags for each attn type
* fix some tests
* import what is needed
* kosmos on main has ew attention already, yay
* new models in main, run fixup
* won't fix kosmos yet
* fix-copies
* clean up after rebasing
* fix tests
* style
* dont cast attns to fp32
* did we update ruff? oke, let's just do what it asks
* fix pixtral after rebase
* Add fast image processor support for Swin2SR
* Add Swin2SR tests of fast image processing
* Update docs and remove unnecessary test func
* Fix docstring formatting
* Skip fast vs slow processing test
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* i guessreverted all CdGen classes
* style
* llava onevision
* fix copies
* fix some tests
* some more tests
* dump
* skip these
* nevermind, i am dumb
* revert fix not needed
* fixup
* fixup
* another fixup
* more fixup to make ci finally happy
* fixup after rebasing
* fix qwen tests
* add internVL + typos here and there
* image token index -> id
* style
* fix init weights
* revert blip-2 not supported
* address comments
* fix copies
* revert blip2 test file as well
* as discussed internally, revert back CdGen models
* fix some tests
* fix more tests for compile
* CI red
* fix copies
* enumerate explicitly allowed models
* address comments
* fix tests
* fixup
* style again
* add tests for new model class
* another fixup ( x _ x )
* [fixup] unused attributes can be removed post-deprecation
* Enable granite speech 3.3 tests
* skip sdpa test for granite speech
* Explicitly move model to device
* Use granite speech 2b in tests
---------
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* args keep_torch_compile=False in _save and _wwrap_method
* Fix FSDP execution on evaluation for torch_compile mode
* add test trainer FSDP + Torch Compile
* fix quality code
* make style
* Revert " make style"
This reverts commit 77e797f8829c50992cc21496be3d9a3e480e1c97.
* make style
* enable xpu in test_trainer
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* fix style
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* enhance _device_agnostic_dispatch to cover value
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* add default values for torch not available case
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
---------
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* [fix] one pixel should be added when length is odd
* [fix] add vision_aspect_ratio args & typo
* [fix] style
* [fix] do not fix fast file directly
* [fix] convert using modular
* remove duplicate codes
* match unpad logic with pad logic
* test odd-sized images for llava & aria
* test unpad odd-sized padding for llava family
* fix style
* add kwarg to onvision modular
* move vision_aspect_ratio from image_processor to processor
(llava_onevision)
* rm already deprecated padding max length
* truncate_strategy AS AN ARG is already deprecated for a few years
* fix
* rm test_padding_to_max_length
* rm pad_to_max_length=True in other tests
* rm from common
* missed fnet
* Support `AOPerModuleConfig` and include_embedding
Summary:
This PR adds support per module configuration for torchao
Also added per module quantization examples:
1. Quantizing different layers with different quantization configs
2. Skip quantization for certain layers
Test Plan:
python tests/quantization/torchao_integration/test_torchao.py -k test_include_embedding
python tests/quantization/torchao_integration/test_torchao.py -k test_per_module_config_skip
Reviewers:
Subscribers:
Tasks:
Tags:
* format
* format
* inlcude embedding remove input embedding from module not to convert
* more docs
* Update docs/source/en/quantization/torchao.md
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_torchao.py
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_torchao.py
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
---------
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
* enable internvl UTs on XPU
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* fix style
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* fix style per comments
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
---------
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
* copy the last changes from broken PR
* small format
* some fixes and refactoring after review
* format
* add config attr for loss
* some fixes and refactoring
* fix copies
* fix style
* add test for d-fine resnet
* fix decoder layer prop
* fix dummies
* format init
* remove extra print
* refactor modeling, move resnet into separate folder
* fix resnet config
* change resnet on hgnet_v2, add clamp into decoder
* fix init
* fix config doc
* fix init
* fix dummies
* fix config docs
* fix hgnet_v2 config typo
* format modular
* add image classification for hgnet, some refactoring
* format tests
* fix dummies
* fix init
* fix style
* fix init for hgnet v2
* fix index.md, add init rnage for hgnet
* fix conversion
* add missing attr to encoder
* add loss for d-fine, add additional output for rt-detr decoder
* tests and docs fixes
* fix rt_detr v2 conversion
* some fixes for loos and decoder output
* some fixes for loss
* small fix for converted modeling
* add n model config, some todo comments for modular
* convert script adjustments and fixes, small refact
* remove extra output for rt_detr
* make some outputs optionsl, fix conversion
* some posr merge fixes
* small fix
* last field fix
* fix not split for hgnet_v2
* disable parallelism test for hgnet_v2 image classification
* skip multi gpu for d-fine
* adjust after merge init
* remove extra comment
* fix repo name references
* small fixes for tests
* Fix checkpoint path
* Fix consistency
* Fixing docs
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* added fast image processor for VitMatte including updated and new tests, fixed a bug in the slow image processor that processed images incorrectly for input format ChannelDimension.FIRST in which case the trimaps were not added in the correct dimension, this bug was also reflected in the tests through incorretly shaped trimaps being passed
* final edits for fast vitmatte image processor and tests
* final edits for fast vitmatte image processor and tests
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* added the configuartion for sam_hq
* added the modeelling for sam_hq
* added the sam hq mask decoder with hq features
* added the code for the samhq
* added the code for the samhq
* added the code for the samhq
* Delete src/transformers/models/sam_hq/modelling_sam_hq.py
* added the code for the samhq
* added the code for the samhq
* added the chnages for the modeelling
* added the code for sam hq for image processing
* added code for the sam hq model
* added the required changes
* added the changes
* added the key mappings for the sam hq
* adding the working code of samhq
* added the required files
* adding the pt object
* added the push to hub account
* added the args for the sam maks decoder
* added the args for the sam hq vision config
* aded the some more documentation
* removed the unecessary spaces
* all required chnages
* removed the image processor
* added the required file
* added the changes for the checkcopies
* added the code for modular file
* added the changes for the __init file
* added the code for the interm embeds
* added the code for sam hq
* added the changes for modular file
* added the test file
* added the changes required
* added the changes required
* added the code for the
* added the cl errors
* added the changes
* added the required changes
* added the some code
* added the code for the removing image processor
* added the test dimensins
* added the code for the removing extra used variables
* added the code for modeluar file hf_mlp for a better name
* removed abbrevaation in core functionality
* removed abbrevaation in core functionality
* .contiguous() method is often used to ensure that the tensor is stored in a contiguous block of memory
* added the code which is after make fixup
* added some test for the intermediate embeddings test
* added the code for the torch support in sam hq
* added the code for the updated modular file
* added the changes for documentations as mentioned
* removed the heading
* add the changes for the code
* first mentioned issue resolved
* added the changes code to processor
* added the easy loading to init file
* added the changes to code
* added the code to changes
* added the code to work
* added the code for sam hq
* added the code for sam hq
* added the code for the point pad value
* added the small test for the image embeddings and intermediate embedding
* added the code
* added the code
* added the code for the tests
* added the code
* added ythe code for the processor file
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code for tests and some checks
* added some code
* added the code
* added the code
* added some code
* added some code
* added the changes for required
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added some changes
* added some changes
* removed spaces and quality checks
* added some code
* added some code
* added some code
* added code quality checks
* added the checks for quality checks
* addded some code which fixes test_inference_mask_generation_no_point
* added code for the test_inference_mask_generation_one_point_one_bb
* added code for the test_inference_mask_generation_one_point_one_bb_zero
* added code for the test_inference_mask_generation_one_box
* added some code in modelling for testing
* added some code which sort maks with high score
* added some code
* added some code
* added some code for the move KEYS_TO_MODIFY_MAPPING
* added some code for the unsqueeze removal
* added some code for the unsqueeze removal
* added some code
* added some code
* add some code
* added some code
* added some code
* added some testign values changed
* added changes to code in sam hq for readbility purpose
* added pre commit checks
* added the fix samvisionmodel for compatibilty
* added the changes made on sam by cyyever
* fixed the tests for samhq
* added some the code
* added some code related to init file issue during merge conflicts
* remobved the merge conflicts
* added changes mentioned by aruther and mobap
* added changes mentioned by aruther and mobap
* solving quality checks
* added the changes for input clearly
* added the changes
* added changes in mask generation file rgearding model inputs and sam hq quargs in processor file
* added changes in processor file
* added the Setup -> setupclass conversion
* added the code mentioned for processor
* added changes for the code
* added some code
* added some code
* added some code
---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Two PEFT tests are actually failing:
tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_delete_adapter
tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_pipeline_no_warning
This must have been going on for some time but was apparently never
noticed. The cause is that the tests themselves are faulty, the PEFT
integration is correct in these cases.
test_delete_adapter
The first faulty test was introduced by #34650. AFAICT, it should never
have passed in the first place, the PEFT integration logic was not
changed in the meantime. At this point, the logs for the PR CI are gone,
so I'm not sure if the test passed back then or not.
test_peft_pipeline_no_warning
This test was introduced in #36783 and should also never have passed, as
the self.assertNoLogs context manager only returns None, thus the assert
should never have worked (mea culpa for suggesting this code snippet).
Here too, the CI logs are deleted by now, so I can't check if the test
already failed back then.
* Fix qwen2_5 get_rope_index tensor device locations
* simpler fix
* edit right file for modular model
* add a test
* try normalizing type to fix non-video
* fix some imports
* add a video forward test with dummy input
* skip compilation on cpu offload
* add test
* better logic
* docstring
* boolean logic
* add disk offload check
* warn users if compilation options are set but compilation doesn happen
* fix test
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* update aria tests
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* add cuda tests
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* check outputs for cpu and cuda and xpu
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* check outputs for cpu and cuda and xpu
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* check outputs for cpu and cuda and xpu
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* check output for each device
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* fix style
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* fix style
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* fix xpu output
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* add comments and use assert list equal
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* rm pad token assign
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
---------
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
* fast image processor template for MobileNetV1 via transformers-cli
* Add fast image processors and unify tests for slow/fast image processor classes
* added loop over image_processor_list for all tests and removed boilerplate comments.
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* support poolformer fast image processor
* support test for crop_pct=None
* run make style
* Apply suggestions from code review
* rename test
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* tokenize inputs directly in apply_chat_template
* refactor processing
* revert changes processing llava
* Update docs
* fix issue with str being iterable
* add test chat text only
* change function name
* enable blip2 and emu3 modeling cases on XPU
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* fix style
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* remove extra new line
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* update
---------
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* enable 6 granite cases on XPU
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* make them all pass on A100
Signed-off-by: N <matrix.yao@intel.com>
* fix style
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* update
---------
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Signed-off-by: N <matrix.yao@intel.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* enable mllama testing on xpu
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* more mllama cases enabling
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* make cases pass on A100
Signed-off-by: N <matrix.yao@intel.com>
---------
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Signed-off-by: N <matrix.yao@intel.com>
* fix: RecurrentGemma crashes during inference for inputs longer than sliding window width
* fix recurrentgemma tests; add long test bigger than context window
* [fix] make legacy bnb code work
* [fix] use get with default instead of getter
* add test for bnb 8bit optim skip embed
* [fix] style
* add require annotation of bnb
---------
Co-authored-by: jaycha <jaycha@ncsoft.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix: qwen2.5 omni modular get_rope_index
* test: add test for qwen2.5 omni rope index (video with audio input)
* style
* expected_position_ids readability
* fix: use spatial_merge_size = 1 in unit test
* initial commit
* add convert internvl
* add first end-to-end working internvl
* nit prompt and image proc
* add working chat template
* add conversion llama-based models
* add tests
* pass all tests
* fix isort
* fix modular after main merge
* add video processing for internvl
* add support for interlaced images and videos
* Remove processing and config from modular, add more tests
* add llama model tests
* Modify processor for compatibility with refactored got ocr image processor
* add comments in processor
* Add docs and nits
* change video processing to use custom sample_indices_fn
* rebase and fix tests
* add processor tests
* Add changes Raushan review
* Use the new attention interface for the vision model
* nits
* add support for custom video_load_backend
* remove mention to InternVLTokenizer
* refactor vision model to simplify logic
* refactor processor for better readibility
* fix copies
* fix require av processor test
* refactor internVL vision
* Update processor and fix processing tests
* fix docstring
* update convert_weights for internvl3
* change image processor to fast by default
* remove do_center_crop=True in convert_weights
* force use_cache to True
* push_to_hub before reloading
* fix internVLVision for larger models
* update convert weight for qk norm
* fix convert_weights
* fix eos_token_id in convert
* update docs and integration tests
* make modifs after review
* fix wrong k_norm and reduce modular
* change image_token_index to image_token_id
* change checkpoint to OpenGVLab org
* last nits
* explicitely del self.num_key_value_groups
* add extra special tokens
* use only `xxx_token_id` for multimodal tokens
* update modeling files as well
* fixup
* why fixup doesn't fix modular docstring first?
* janus, need to update configs in the hub still
* last fixup
* Iterative generation using input embeds
* Add Janus model
* discard changes
* Janus imports
* Refactor config and processor
* Added Vision tower of Janus
* Import Janus Image processor
* Vision tower fixes
* Refactor code
* Added VQ Model
* Complete model integration
* temp conversion script
* processor refactor
* Adding files to facilitate pulling
* Fixes after debugging
* Skip test for these models
* Add Janus Model
* discard changes
* Janus imports
* Refactor config and processor
* Added Vision tower of Janus
* Import Janus Image processor
* Vision tower fixes
* Refactor code
* Added VQ Model
* Complete model integration
* temp conversion script
* processor refactor
* Adding files to facilitate pulling
* Fixes after debugging
* Refactor to Text config
* ✨ Added generate function
* Saving intermediate convert file. Still need to read configs from the hub and convert them to our format.
* Adding version that reads from the JSON files. Still have to tweak some parameters manually.
* relative imports
* Initial tests
* Refactor image processor
* Seemingly working version of the conversion script, will need to test further.
* Adding command message
* Fixing conflicting JanusTextConfig class
* Incorporating some of the discussed changes.
* Small fix to create dir.
* Removing system from JINJA template
* Adding draft processor tests
* style fixes
* Minor fixes and enhancement
* added generation config
* Initial tests
* Small modifications, tests are now passing.
* Small changes I noticed while reading code.
* more fixes
* Added JanusModel class
* Small merge adaptations
* Small merge adaptations
* Image processing tests passing
* More tests and fixes
* Convert script updated and refactored
* Tests and cleanup
* make style
* Postprocessing for image generation
* generate refactor
* fixes
* - Passing tests that write a part of the model to cpu (e.g. test_cpu_offload)
- Passing tests of dispatching SDPA
- Only gradient checkpointing tests are left.
* Removing temporary code
* Changes
* Writing change to modular
* Added JanusVisionModel. SDPA dispatch tests pass more robustly. Gradient checkpoint tests are next
* Gradient checkpoint tests passing
* Removing debug code
* Major generate refactor 😮💨
* Temp changes for testing
* Green quality CI
* 2 out of 4 integration tests passing
* breadcrumbs
* Usage Examples
* Regenerate modeling after merge
* dirty code
* JanusIntegrationTest are passing
* breadcrumbs
* happy CI
* fixes
* Changing template
* nits
* Text generation logits matching original codebase at 100% precision
* Remove ./tmp from git tracking
* Remove ./tmp from git tracking
* Checkpointing changes after reviewing
* Fixing code in docstrings
* CHanging comments and small bug in convert file
* Fixing bug in image_token_id for 7B version
* Removing line that was added by both of us
* Pushing changes after discussion. Only one left is to change the key mapping for convert file.
* Updating module file
* New convert file using dict. Tested that it is equivalent to the old one by:
- comparing keys in a script
- comparing checksums of the output files between version generated with the current convert script and those generated with the old script. This is a more reliable test.
* revert changes
* mistake
* consistency change for CI
* make style
* doc fixes
* more fixes
* experimenting with masking out pad token
* checkpoint
* Batched generation with multi-images working for 1B models. Will test 7B next.
* Device fix.
* Writing changes to modular, previous ones were written to modeling just for quick testing.
* Using passed processor attention mask (only in modeling for now)
* Matching performance done in the non-standard way
* Working version of batched generation. Will change how some args are passed to make it more similar to language case
* More compliant version of the code
* Removed duplicated `_prepare_4d_causal_attention_mask_with_cache_position`
* Updating modular file, making masked filling with paddings more efficient
* Slightly more efficient version
* Modifying JanusVisionModel to be a wrapper
* Fixing test to comply with new names
* Modular overhaul
* More refactoring
* - Changing JanusVisionModel back
- Changing forward pass
- Adding boi token to the comparison
* - Removing whole context model_ids
- Using inherited implementation of prepare_inputs_for_generation
* Moving the way boi token is passed to the model
* Fixing sdpa test
* Minor changes
* testing changes
* Minor fix
* - Adding postprocessing test
- checking values of generated image on integration test
* changes
* Removing pooled attention vision module, fixing convert script as a consequence
* More changes
* Fixes
* Draft after merge
* Bug fixes
* More bug fix
* Fixing docs
* Nits
* Refactor return dict
* Moving image post processing test to main processor post process
* Passing guidance_scale as kwarg
* make style
* 🔥 refactor
* make style
* Update and green CI
* Nits and tests update
* up
* Added MID block
* fix
* Dead code
* update testcase
* update
* model_id change
* init_weight changes
---------
Co-authored-by: hsilva664 <metallic-silver@hotmail.com>
* add support for fast tokenizer
* make style
* fix according to reviews
* make style
* relax slow_fast_equivalence mean diff
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
* Fix mamba2 grouped support in bamba torch path
* patch zamba2 and mamba2
* Add a unit test for grouped SSD
* add comment for the new unit test
* add output_size arg value to repeat_interleave calls
* Add comment
* added efficientnet image preprocessor but tests fail
* ruff checks pass
* ruff formatted
* properly pass rescale_offset through the functions
* - corrected indentation, ordering of methods
- reshape test passes when casted to float64
- equivalence test doesn't pass
* all tests now pass
- changes order of rescale, normalize acc to slow
- rescale_offset defaults to False acc to slow
- resample was causing difference in fast and slow. Changing test to bilinear resolves this difference
* ruff reformat
* F.InterpolationMode.NEAREST_EXACT gives TypeError: Object of type InterpolationMode is not JSON serializable
* fixes offset not being applied when do_rescale and do_normalization are both true
* - using nearest_exact sampling
- added tests for rescale + normalize
* resolving reviews
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* update
* apply suggestion
* fix tests for main branch
* remove unused logger
* add special tokens in tests
* nit
* fix more tests
* fix test
* pg also
* initial documentation
* rename mask to attention_mask
* smaller tests
* fixup
* fix copies
* move to time series section
* sort docs
* isort fix
* batch_size is not a configuration
* rename to TimesFMModelForPrediction
* initial script
* add check_outputs
* remove dropout_rate
* works with torch.Tensor inputs
* rename script
* fix docstrings
* fix freq when window_size is given
* add loss
* fix _quantile_loss
* formatting
* fix isort
* add weight init
* add support for sdpa and flash_attention_2
* fixes for flash_attention
* formatting
* remove flash_attention
* fix tests
* fix file name
* fix quantile loss
* added initial TimesFMModelIntegrationTests
* fix formatting
* fix import order
* fix _quantile_loss
* add doc for SDPA
* use timesfm 2.0
* bug fix in timesfm decode function.
* compare mean forecasts
* refactor type hints, use CamelCase
* consolidate decode func
* more readable code for weight conversion
* fix-copies
* simpler init
* renaem TimesFmMLP
* use T5LayerNorm
* fix tests
* use initializer_range
* TimesFmModel instead of TimesFmDecoder
* TimesFmPositionalEmbedding takes config for its init
* 2.0-500m-pytorch default configs
* use TimesFmModel
* fix formatting
* ignore TimesFmModel for testing
* fix docstring
* override generate as its not needed
* add doc strings
* fix logging
* add docstrings to output data classes
* initial copy from t5
* added config and attention layers
* add TimesFMPositionalEmbedding
* calcuate scale_factor once
* add more configs and TimesFMResidualBlock
* fix input_dims
* standardize code format with black
* remove unneeded modules
* TimesFM Model
* order of imports
* copy from Google official implementation
* remove covariate forecasting
* Adapting TimesFM to HF format
* restructing in progress
* adapted to HF convention
* timesfm test
* the model runs
* fixing unit tests
* fixing unit tests in progress
* add post_init
* do not change TimesFMOutput
* fixing unit tests
* all unit tests passed
* remove timesfm_layers
* add intermediate_size and initialize with config
* initial documentation
* rename mask to attention_mask
* smaller tests
* fixup
* fix copies
* move to time series section
* sort docs
* isort fix
* batch_size is not a configuration
* rename to TimesFMModelForPrediction
* initial script
* add check_outputs
* remove dropout_rate
* works with torch.Tensor inputs
* rename script
* fix docstrings
* fix freq when window_size is given
* add loss
* fix _quantile_loss
* formatting
* fix isort
* add weight init
* add support for sdpa and flash_attention_2
* fixes for flash_attention
* formatting
* remove flash_attention
* fix tests
* fix file name
* fix quantile loss
* added initial TimesFMModelIntegrationTests
* fix formatting
* fix import order
* fix _quantile_loss
* add doc for SDPA
* use timesfm 2.0
* bug fix in timesfm decode function.
* compare mean forecasts
* refactor type hints, use CamelCase
* consolidate decode func
* more readable code for weight conversion
* fix-copies
* simpler init
* renaem TimesFmMLP
* use T5LayerNorm
* fix tests
* use initializer_range
* TimesFmModel instead of TimesFmDecoder
* TimesFmPositionalEmbedding takes config for its init
* 2.0-500m-pytorch default configs
* use TimesFmModel
* fix formatting
* ignore TimesFmModel for testing
* fix docstring
* override generate as its not needed
* add doc strings
* fix logging
* add docstrings to output data classes
* add _CHECKPOINT_FOR_DOC
* fix comments
* Revert "fix comments"
This reverts commit 8deeb3e191.
* add _prepare_4d_attention_mask
* we do not have generative model classes
* use Cache
* return past_key_values
* modules initialized with config only
* update year
* Update docs/source/en/model_doc/timesfm.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* add layer_idx to cache
* modular timesfm
* fix test
* unwrap sequential class
* fix toctree
* remove TimesFmOnnxConfig
* fix modular
* remove TimesFmStackedDecoder
* split qkv layer into individual layers
* rename projection layers
* use ALL_ATTENTION_FUNCTIONS
* is_causal is True
* rename config
* does not support flash_attn_2
* formatting
* fix typo in docsstring
* rename inputs
* add time series mapping
* Update src/transformers/models/olmo2/modeling_olmo2.py
* Update src/transformers/models/moonshine/modeling_moonshine.py
* use updated arguments
* fix class name
* add MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING
* isort
* consolidate _preprocess into forward
* fix a typo
* fix a typo
* fix toc
* fix modular
* remove aaserts
* use self.config._attn_implementation
* move to _postprocess_output
* remove timesfm_get_large_negative_number
* use view unstead of multiple unsqueeze
* make helpers static methods of the Model
* use to_tuple
* use to_tuple if not return_dict
* remove unused intitialization block as its incorporated in nn.Linear
* remove unused num_key_value_groups
* use the same convention as the masking method
* update modular
* do not use unsqueeze
* use view instead of unsqueeze
* use buffer for inv_timescales
* formatting
* modular conversion
* remove unneeded intialization
* add missing docstrings
* remove cache
* use simple_eager_attention_forward
* support tp_plan
* support for flex and flash attention masks
* Revert "support for flex and flash attention masks"
This reverts commit def36c4fcf.
* fix device
* fix tests on gpu
* remove unsued large model test
* removed unneeded comments
* add example usage
* fix style
* add import
* Update docs/source/en/model_doc/timesfm.md
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
* inherit from LlamaRMSNorm
* use can_return_tuple decorator
* remvoe return_dict
* fix year
* Update docs/source/en/model_doc/timesfm.md
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
* pretrained does not inherit from GenerationMixin
* use model for integration test
---------
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
Co-authored-by: Rajat Sen <rsen91@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
* Add MLCD model
* Update codes for auto-mapping
* Add test scripts for MLCD
* Update doc for MLCD model
* Fix import error
* Fix import error
* Fix CI error for attention_outputs
* Fix code style for CI
* Fix code style for CI
* Fix code style for CI
* Fix code style for CI
* Fix code style for CI
* Fix CI error for initialization
* Fix code style for CI
* Fix code style for CI
* Reformat codes and docs for CI test
* Reformat codes and docs for CI test
* Remove unused attributes for CI test
* Fix style for CI test
* List MLCD in flash_attn doc
* Fix: typos, modulars, refactors from suggestions
* Refactoring convert_mlcd_weights_to_hf.py from suggestions
* Fix: docs conflicts
* Fix error for CI test
* Fix style for CI test
* Add integration test for MLCD
* Refactoring by class inheritance
* Fix: refactor attention interface, adjust codes
* Fix: merging conflicts
* Fix: merging conflicts
* Fix: style for CI test
* Fix: style for CI test
* Fix: set test_resize_embeddings to be False
* Fix: initializer for CI test
* Fix: conflicts, CI test, warning and refactoring
* Fix: merging conflicts
* Refactor
* Update docs
* Fix mistakes
* Remove unused args and fix multi-gpu error
* Revert position_embeddings
* Solve conflicts
* Solve conflicts
* Remove dummy
* Update _init_weights
* Update _init_weights
* Update _init_weights for CI test
* Add ImageProcessorFast to BiT processor
* propose a fast processor and add tests
* all tests pass except one
* run make
* remove useless print
* use same test as clip
* apply make
* Update src/transformers/models/bit/image_processing_bit_fast.py
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* Update setup.py
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* Update src/transformers/models/bit/image_processing_bit_fast.py
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* apply review comment
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* support fast image processor layoutlmv3
* make style
* add warning and update test
* make style
* Update src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py
* Update image_processing_auto.py
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* support flava fast image processor
* run style and quality
* update test
* update according to reviews
* make style
* update comment on BICUBIC
* make style
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* add test and fast image processor
* make style
* Update src/transformers/models/perceiver/image_processing_perceiver_fast.py
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* make style
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* First pass at speech granite
Add encoder / projector, rename things
* Combine into one model file with causal lm outputs for forward
* Add loss calc
* Fix config loading
Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com>
* Split new / old loading logic
* Use transformers integration for loading peft adapters
* Add generation wrapper for selective lora enablement
* Add note for qformer encoder automodel
* Guard torch/audio imports in feature extractor
* Handle granite speech autoclasses
* Handle optional deps in package structure for granite speech
* Add granite pretrained model def for init
* Add dummy objects for torch/torchaudio
* Add tests for granite speech processor
* Minor formatting fixes and refactoring
* Add options for falling back to config in forward
* Tentative model docstrings for granite speech
* Fix config type
* Remove legacy load
* Allow non-lora variants for granite speech
* Override weight tying for llm
* Use text config instead of llm config
* Add output embeddings getter to fix weight tying
* Fix relative imports
* computing the number of audio features, based on the raw audio sequence.
* collating audio inputs, and keeping the original lengths.
* asserted we have text. otherwise we can't specify the audio special token.
* assering the number of audio-symbols/audios match correctly.
running get validated_audios only when audio is present
* indentation bugfix + supporting different feature lengths when expanding audio.
* redundant, done in _get_validated_text
* adapting the tests:
- we must have text (not either audio or text)
- _get_num_audio_features takes a list of raw lengths, provided it insetad.
* Minor cleanup, remove unused import
* Add more tests for batch feature processing
* Allow setting offset in rel position embeddings
* Add config option for warning if peft is not installed w/ lora
* Port blip2 qformer code into granite speech
* Add sad test for numpy arr processing
* Allow numpy arrays / tuples in granite speech processor
* Fix config type for projector
* - pad instead of creating a zeros tensor, to keep the original dtype/device (support bfloat16)
- cast input_features to the model dtype (support bfloat16)
* merge Blip2QFormerConfig to GraniteSpeechProjectorConfig
* prevent a crash when re-saving/loading the model (line 109)
* consider additional edge cases during preprocessing.
* consider additional edge cases during preprocessing.
* add features mask for batched inference (bugfix)
* Minor refactor, remove multiaudio processor tests
* Add set input/output embeddings for granite speech
* Fix feature dim check in processor test
* Pop input features in embed test for granite speech
* Small fixes for test edge cases
Add granite speech to seq2seq causal lm mapping names
* Add small tests for granite speech model
* Fix data parallelism test
* Standardize model class names
* Fix check for copies
* Fix misaligned init check
* Skip granite speech in checkpoint check
* Use default for tie_word_embeddings in granite speech
* Fix non documentation granite speech repo issues
* Fix comments and docstring checks
* Add placeholder docs for granite speech
* Fix test naming collision
* Code formatting
* Rerun torch dummy obj regen
* Fix save pretrained for granite speech
* Import sorting
* Fix tests typo
* Remove offset hack
* Pass args through encoder config
* Remove unused prune heads from blip2
* removing einsum. replaced with explicit multiplication (relative positional encodings) and sdpa attention.
* remove Sequential from ConformerFeedForward and ConformerConvModule. + fix for sdpa attention
* remove GraniteSpeechConformerScale
* rename to hidden_states
* rename conformer layers to self.layers, remove the first linear from the list to keep the list homogenous.
* move pre-norm to the attention/feedforward blocks (avoid complex module wrapping)
* adding pre_norm into forward
* feature extractor refactoring to resemble how it's done in phi4multimodal.
* rename feature_extractor to audio_processor
* bugfix: input_feature_mask fix to get the exact number tokens.
* Fix pytest decorator in processor test
* Add (disabled) integration tests for granite speech
* Fix handling of optional feature masking
* Loosen validation in processing for vLLM compatability
* Formatting fixes
* Update init structure to mirror llama
* Make granite speech projector generic
* Update test config to reflect generic projector
* Formatting fixes
* Fix typos, add license
* Fix undefined var in input processing
* Cleanup and expose ctc encoder
* Add missing config docstrings
* Better var names, type hints, etc
* Set attn context size in init
* Add max pos emb to encoder config
* Cleanup feature extractor
* Add granite speech architecture details
* Remove granite speech qformer ref
* Add paper link, explicit calc for qkv
* Calculate padding directly in depthwise conv1d init
* Raise value error instead of asserting
* Reorder class defs (classes used at top)
* Precompute relpos distances
* Run formatting
* Pass attention distances through forward
* Apply suggestions from code review
Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com>
* Add todo for using common batch feature extraction
* Rename audios/features
* Ensure chat template may be provided to processor
* Move granite speech docs to audio models
* Add todos for input proc refactoring
* Fix import order
* Guard torch import
* Use relative imports
* Require torch backend for processor in granite speech
* Add backend guards in feature extractor
---------
Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com>
Co-authored-by: Avihu Dekel <avihu.dekel@ibm.com>
Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com>
* Add saving in the new format (but no loading yet!)
* Add saving in the new format (but no loading yet!)
* A new approach to template files!
* make fixup
* make fixup, set correct dir
* Some progress but need to rework for cached_file
* Rework loading handling again
* Small fixes
* Looks like it's working now!
* make fixup
* Working!
* make fixup
* make fixup
* Add TODO so I don't miss it
* Cleaner control flow with one less indent
* Copy the new logic to processing_utils as well
* Proper support for dicts of templates
* make fixup
* define the file/dir names in a single place
* Update the processor chat template reload test as well
* Add processor loading of multiple templates
* Flatten correctly to match tokenizers
* Better support when files are empty sometimes
* Stop creating those empty templates
* Revert changes now we don't have empty templates
* Revert changes now we don't have empty templates
* Don't support separate template files on the legacy path
* Rework/simplify loading code
* Make sure it's always a chat_template key in chat_template.json
* Update processor handling of multiple templates
* Add a full save-loading test to the tokenizer tests as well
* Correct un-flattening
* New test was incorrect
* Correct error/offline handling
* Better exception handling
* More error handling cleanup
* Add skips for test failing on main
* Reorder to fix errors
* make fixup
* clarify legacy processor file docs and location
* Update src/transformers/processing_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Update src/transformers/processing_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Update src/transformers/processing_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Update src/transformers/processing_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Rename to _jinja and _legacy
* Stop saving multiple templates in the legacy format
* Cleanup the processing code
* Cleanup the processing code more
* make fixup
* make fixup
* correct reformatting
* Use correct dir name
* Fix import location
* Use save_jinja_files instead of save_raw_chat_template_files
* Correct the test for saving multiple processor templates
* Fix type hint
* Update src/transformers/utils/hub.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Patch llava_onevision test
* Update src/transformers/processing_utils.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Refactor chat template saving out into a separate function
* Update tests for the new default
* Don't do chat template saving logic when chat template isn't there
* Ensure save_jinja_files is propagated to tokenizer correctly
* Trigger tests
* Update more tests to new default
* Trigger tests
---------
Co-authored-by: Lucain <lucainp@gmail.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Previously, the identity function was used for dropped tokens
with a weight from the expert that was not applied to the hidden states.
This was misleading, because dropping means, the expert weight is zero.
Instead of trying to fix the weight, we take an easier approach by initializing with zeros.
Fixes issue https://github.com/huggingface/transformers/issues/37017
* add classifier head to donut
* add to transformers __init__
* add to auto model
* fix typo
* add loss for image classification
* add checkpoint
* remove no needed import
* reoder import
* format
* consistency
* add test of classifier
* add doc
* try ignore
* update loss for all swin models
