* [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
* 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>
* 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
* 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>
* 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: 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
* 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>
