* No more Tuple, List, Dict
* make fixup
* More style fixes
* Docstring fixes with regex replacement
* Trigger tests
* Redo fixes after rebase
* Fix copies
* [test all]
* update
* [test all]
* update
* [test all]
* make style after rebase
* Patch the hf_argparser test
* Patch the hf_argparser test
* style fixes
* style fixes
* style fixes
* Fix docstrings in Cohere test
* [test all]
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* make it go brrrr
* date time
* update
* fix
* up
* uppp
* up
* no number i
* udpate
* fix
* [paligemma] fix processor with suffix (#38365)
fix pg processor
* [video utils] group and reorder by number of frames (#38374)
fix
* Fix convert to original state dict for VLMs (#38385)
* fix convert to original state dict
* fix
* lint
* Update modeling_utils.py
* update
* warn
* no verbose
* fginal
* ouft
* style
---------
Co-authored-by: Raushan Turganbay <raushan@huggingface.co>
Co-authored-by: hoshi-hiyouga <hiyouga@buaa.edu.cn>
* updates
* fixup
* fix tests
* fix test
* fix
* let it be here for now, till monday
* two more fixes
* persimmon
* fixup
* fix
* fixup
* make sure fuyu runs now that LM has new attn API
* fixup + tests
* qwen vl uses new mask interface as well
* qwen image features format
* update
* remove image_sizes
* address comments
* i am dumb...
* 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>
* 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
* Let notification service succeed even when artifacts and reported jobs on github have mismatch
* Use default trace msg if no trace msg available
* Add pop_default helper fn
* style
* 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 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>
* 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>
* 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
* 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>
