* Make kwargs uniform for SAM
* Remove unused attribute
* Make point_pad_value part of image_kwargs
* Update annotations
* Code review - use existing methods
* Use ProcessorTesterMixin
* Do not add ProcessorTesterMixin everywhere
* fixup mamba2 - caching and several other small fixes
* fixup cached forward
* correct fix this time
* fixup cache - we do not need to extend the attn mask it's handled by generate (gives total ids + mask at each step)
* remove unnecessary (un)squeeze
* fixup cache position
* simplify a few things
* [run-slow] mamba2
* multi gpu attempt two
* [run-slow] mamba2
* [run-slow] mamba2
* [run-slow] mamba2
* [run-slow] mamba2
* add newer slow path fix
* [run-slow] mamba2
* initial cut of modernbert for transformers
* small bug fixes
* fixes
* Update import
* Use compiled mlp->mlp_norm to match research implementation
* Propagate changes in modular to modeling
* Replace duplicate attn_out_dropout in favor of attention_dropout
cc @warner-benjamin let me know if the two should remain separate!
* Update BOS to CLS and EOS to SEP
Please confirm @warner-benjamin
* Set default classifier bias to False, matching research repo
* Update tie_word_embeddings description
* Fix _init_weights for ForMaskedLM
* Match base_model_prefix
* Add compiled_head to match research repo outputs
* Fix imports for ModernBertForMaskedLM
* Just use "gelu" default outright for classifier
* Fix config name typo: initalizer -> initializer
* Remove some unused parameters in docstring. Still lots to edit there!
* Compile the embeddings forward
Not having this resulted in very slight differences - so small it wasn't even noticed for the base model, only for the large model.
But the tiny difference for large propagated at the embedding layer through the rest of the model, leading to notable differences of ~0.0084 average per value, up to 0.2343 for the worst case.
* Add drafts for ForSequenceClassification/ForTokenClassification
* Add initial SDPA support (not exactly equivalent to FA2 yet!)
During testing, FA2 and SDPA still differ by about 0.0098 per value in the token embeddings. It still predicts the correct mask fills, but I'd like to get it fully 1-1 if possible.
* Only use attention dropout if training
* Add initial eager attention support (also not equivalent to FA2 yet!)
Frustratingly, I also can't get eager to be equivalent to FA2 (or sdpa), but it does get really close, i.e. avg ~0.010 difference per value.
Especially if I use fp32 for both FA2&eager, avg ~0.0029 difference per value
The fill-mask results are good with eager.
* Add initial tests, output_attentions, output_hidden_states, prune_heads
Tests are based on BERT, not all tests pass yet: 23 failed, 79 passed, 100 skipped
* Remove kwargs from ModernBertForMaskedLM
Disable sparse_prediction by default to match the normal HF, can be enabled via config
* Remove/adjust/skip improper tests; warn if padding but no attn mask
* Run formatting etc.
* Run python utils/custom_init_isort.py
* FlexAttention with unpadded sequences(matches FA2 within bf16 numerics)
* Reformat init_weights based on review
* self -> module in attention forwards
* Remove if config.tie_word_embeddings
* Reformat output projection on a different line
* Remove pruning
* Remove assert
* Call contiguous() to simplify paths
* Remove prune_qkv_linear_layer
* Format code
* Keep as kwargs, only use if needed
* Remove unused codepaths & related config options
* Remove 3d attn_mask test; fix token classification tuple output
* Reorder: attention_mask above position_ids, fixes gradient checkpointing
* Fix usage if no FA2 or torch v2.5+
* Make torch.compile/triton optional
Should we rename 'compile'? It's a bit vague
* Separate pooling options into separate functions (cls, mean) - cls as default
* Simplify _pad_modernbert_output, remove unused labels path
* Update tied weights to remove decoder.weight, simplify decoder loading
* Adaptively set config.compile based on hf_device_map/device/resize, etc.
* Update ModernBertConfig docstring
* Satisfy some consistency checks, add unfinished docs
* Only set compile to False if there's more than 1 device
* Add docstrings for public ModernBert classes
* Dont replace docstring returns - ends up being duplicate
* Fix mistake in toctree
* Reformat toctree
* Patched FlexAttention, SDPA, Eager with Local Attention
* Implement FA2 -> SDPA -> Eager attn_impl defaulting, crucial
both to match the original performance, and to get the highest inference speed without requiring users to manually pick FA2
* Patch test edge case with Idefics3 not working with 'attn_implementation="sdpa"'
* Repad all_hidden_states as well
* rename config.compile to reference_compile
* disable flex_attention since it crashes
* Update modernbert.md
* Using dtype min to mask in eager
* Fully remove flex attention for now
It's only compatible with the nightly torch 2.6, so we'll leave it be for now. It's also slower than eager/sdpa.
Also, update compile -> reference_compile in one more case
* Call contiguous to allow for .view()
* Copyright 2020 -> 2024
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update/simplify __init__ structure
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove "... if dropout_prob > 0 else identity"
As dropout with 0.0 should be efficient like identity
* re-use existing pad/unpad functions instead of creating new ones
* remove flexattention method
* Compute attention_mask and local_attention_mask once in modeling
* Simplify sequence classification prediction heads, only CLS now
Users can make custom heads if they feel like it
Also removes the unnecessary pool parameter
* Simplify module.training in eager attn
* Also export ModernBertPreTrainedModel
* Update the documentation with links to finetuning scripts
* Explain local_attention_mask parameter in docstring
* Simplify _autoset_attn_implementation, rely on super()
* Keep "in" to initialize Prediction head
Doublechecked with Benjamin that it's correct/what we used for pretraining
* add back mean pooling
* Use the pooling head in TokenClassification
* update copyright
* Reset config._attn_implementation_internal on failure
* Allow optional attention_mask in ForMaskedLM head
* fix failing run_slow tests
* Add links to the paper
* Remove unpad_no_grad, always pad/unpad without gradients
* local_attention_mask -> sliding_window_mask
* Revert "Use the pooling head in TokenClassification"
This reverts commit 99c38badd1.
There was no real motivation, no info on whether having this bigger head does anything useful.
* Simplify pooling, 2 options via if-else
---------
Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com>
Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>
Co-authored-by: Said Taghadouini <taghadouinisaid@gmail.com>
Co-authored-by: Benjamin Clavié <ben@clavie.eu>
Co-authored-by: Antoine Chaffin <ant54600@hotmail.fr>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial commit for PR
Co-authored-by: Gabe Goodhart <gabe.l.hart@gmail.com>
* rename dynamic cache
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add more unit tests
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add integration test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add integration test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Add modular bamba file
* Remove trainer changes from unrelated PR
* Modify modular and cofig to get model running
* Fix some CI errors and beam search
* Fix a plethora of bugs from CI/docs/etc
* Add bamba to models with special caches
* Updat to newer mamba PR for mamba sublayer
* fix test_left_padding_compatibility
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix remaining tests
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* missed this test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* ran make style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* move slow tag to integration obj
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* make style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* address comments
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix modular
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* left out one part of modular
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* change model
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Make Rotary modular as well
* Update bamba.md
Added overview, update Model inference card and added config
* Update bamba.md
* Update bamba.md
* Update bamba.md
Minor fixes
* Add docs for config and model back
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Add warning when using fast kernels
* replaced generate example
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Address comments from PR
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Propagate attention fixes
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Fix attention interfaces to the new API
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Fix API for decoder layer
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Remove extra weights
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
---------
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
Co-authored-by: Gabe Goodhart <gabe.l.hart@gmail.com>
Co-authored-by: Antoni Viros i Martin <aviros@ibm.com>
Co-authored-by: divya-kumari32 <72085811+divya-kumari32@users.noreply.github.com>
Co-authored-by: Antoni Viros <ani300@gmail.com>
* do not remove decoder_input_ids for the first segment
* do not remove eos token in generate_with_fallback
* when removing padding tokens, do not remove eos token
* remove eos token in generate (and not in generate_with_fallback!)
* reconciliate short-from/ long-form behavior
* correct avg_logprobs calculation
* handle eos token in segments
* handle decoder_input_ids and eos token in _prepare_decoder_input_ids
* fix incorrect time precision
* always remove eos token
* always remove decoder_input_ids
* no need to handle decoder_inputs_ids and eos token
* no need to remove decoder_input_ids
* no need to handle eos token
* fix num_beams in _retrieve_logit_processors
* remove todo unconsistency
* no need to add eos token
* last_timestamp_pos should indeed be timestamp token pos
* patch generate to enable compatibility with GenerationTesterMixin tests
* adapt test_generate_continue_from_past_key_values
* adapt test_prompt_lookup_decoding_matches_greedy_search
* adapt generic GenerationMixin tests to whisper's generate
* fix speculative decoding
* fix
* [run-slow] whisper
* change HF_HUB_TOKEN for require_read_token
* [run-slow] whisper
* prioritize kwargs over generation_config
* remove unnecessary args
* [run-slow] whisper
* update tests
* [run-slow] whisper
* add comment
* update test
* [run-slow] whisper
* update test + revert require_read_token
* docstring updates
* revert tokenizer decode args change
* do not use a patch + docstring updates
* [run-slow] whisper
* make
* [run-slow] whisper
* add a flag to force unique call to generate
* test update
* [run-slow] whisper
* add force_unique_generate_call arg
* do not use a patch
* correct the timestamps for the pad tokens
* docstring update
* docstring update
* docstring update
* upodate TF tests
* add require_read_token
* [run-slow] whisper
* test reset dynamo
* [run-slow] whisper
* fix
* [run-slow] whisper
* avoid iterating twice on current_segments
* [run-slow] whisper
* [run-slow] whisper
---------
Co-authored-by: Eustache Le Bihan <eustlb@users.noreply.huggingface.co>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* feat: add support for sdpa and gradient checkpointing
* fix: ruff format
* fix: config sdpa
* fix: sdpa layer naming convention
* fix: update test_eager_matches_sdpa_inference to handle vision_hidden_states
* test: skip incompatible tests and fix loading issue with sdpa
- Updated tests to skip cases flash and dynamic compile.
- Minor adjustment to ensure correct loading of model with sdpa for dispatch test.
* style: apply Ruff formatting
* ruff fix again after rebase
* [run-slow] sam
* [run-slow] sam
* refactor: Address review comments and improve sub-config handling in SAM model tests
- Added attributes for sub_configs as per PR #34410.
- Enabled tests for configs, ensuring the composite model (SAM) has several sub-configs in the main config.
- Added class attribute _is_composite=True to the tester class
- test_sdpa_can_dispatch_composite_models added
* [run-slow] sam
* style: ruff
* [run-slow] sam
* style: ruff again ...
* [run-slow] sam
* refactor image_processing_auto logic
* fix fast image processor tests
* Fix tests fast vit image processor
* Add safeguard when use_fast True and torchvision not available
* change default use_fast back to None, add warnings
* remove debugging print
* call get_image_processor_class_from_name once
* add more cases
* fix method not found in unittest
Signed-off-by: Lin, Fanli <fanli.lin@intel.com>
* fix more cases
* add more models
* add all
* no unittest.case
* remove for oneformer
* fix style
---------
Signed-off-by: Lin, Fanli <fanli.lin@intel.com>
* draft, run model as compreszed/uncompressed mode
* draft
* run run_compressed=False
* run_compressed as attr
* set run_compressed=False using quantization_config
* remove redundant line
* make is_qat_trainable dependent on run_compressed status
* add tests
* lint
* full in docstring
* add decompress
* comments
* decompress if model is compresssed and not run_compressed
* apply_quant_config logic fix -- populate statedict properly
* comments
* remove non compressed model
* make is_compressed as property
* cosmetic
* run apply_quant_config for non-compressed models -- popualte scales and zeropoints
* add pahtway for decompressing sparse models
* typo on is_quantization_compressed
* lint
* fix typo
* Add files
* Init
* Add TimmWrapperModel
* Fix up
* Some fixes
* Fix up
* Remove old file
* Sort out import orders
* Fix some model loading
* Compatible with pipeline and trainer
* Fix up
* Delete test_timm_model_1/config.json
* Remove accidentally commited files
* Delete src/transformers/models/modeling_timm_wrapper.py
* Remove empty imports; fix transformations applied
* Tidy up
* Add image classifcation model to special cases
* Create pretrained model; enable device_map='auto'
* Enable most tests; fix init order
* Sort imports
* [run-slow] timm_wrapper
* Pass num_classes into timm.create_model
* Remove train transforms from image processor
* Update timm creation with pretrained=False
* Fix gamma/beta issue for timm models
* Fixing gamma and beta renaming for timm models
* Simplify config and model creation
* Remove attn_implementation diff
* Fixup
* Docstrings
* Fix warning msg text according to test case
* Fix device_map auto
* Set dtype and device for pixel_values in forward
* Enable output hidden states
* Enable tests for hidden_states and model parallel
* Remove default scriptable arg
* Refactor inner model
* Update timm version
* Fix _find_mismatched_keys function
* Change inheritance for Classification model (fix weights loading with device_map)
* Minor bugfix
* Disable save pretrained for image processor
* Rename hook method for loaded keys correction
* Rename state dict keys on save, remove `timm_model` prefix, make checkpoint compatible with `timm`
* Managing num_labels <-> num_classes attributes
* Enable loading checkpoints in Trainer to resume training
* Update error message for output_hidden_states
* Add output hidden states test
* Decouple base and classification models
* Add more test cases
* Add save-load-to-timm test
* Fix test name
* Fixup
* Add do_pooling
* Add test for do_pooling
* Fix doc
* Add tests for TimmWrapperModel
* Add validation for `num_classes=0` in timm config + test for DINO checkpoint
* Adjust atol for test
* Fix docs
* dev-ci
* dev-ci
* Add tests for image processor
* Update docs
* Update init to new format
* Update docs in configuration
* Fix some docs in image processor
* Improve docs for modeling
* fix for is_timm_checkpoint
* Update code examples
* Fix header
* Fix typehint
* Increase tolerance a bit
* Fix Path
* Fixing model parallel tests
* Disable "parallel" tests
* Add comment for metadata
* Refactor AutoImageProcessor for timm wrapper loading
* Remove custom test_model_outputs_equivalence
* Add require_timm decorator
* Fix comment
* Make image processor work with older timm versions and tensor input
* Save config instead of whole model in image processor tests
* Add docstring for `image_processor_filename`
* Sanitize kwargs for timm image processor
* Fix doc style
* Update check for tensor input
* Update normalize
* Remove _load_timm_model function
---------
Co-authored-by: Amy Roberts <22614925+amyeroberts@users.noreply.github.com>
Original issue: https://github.com/huggingface/peft/issues/2256
There is a potential error when using load_best_model_at_end=True with a
prompt learning PEFT method. This is because Trainer uses load_adapter
under the hood but with some prompt learning methods, there is an
optimization on the saved model to remove parameters that are not
required for inference, which in turn requires a change to the model
architecture. This is why load_adapter will fail in such cases and users
should instead set load_best_model_at_end=False and use
PeftModel.from_pretrained. As this is not obvious, we now intercept the
error and add a helpful error message.
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
* Correct the new defaults (#34377)
* Correct the new defaults
* CIs
* add check
* Update utils.py
* Update utils.py
* Add the max_length in generate test checking shape without passing length
* style
* CIs
* fix fx CI issue
* [auto. ping] Avoid sending empty info + add more team members (#34383)
* update
* update
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix glm (#34388)
* Fix duplicated
* fix import
* Use non nested images and batched text Idefics2/3 (#34222)
* add support for non nested images and add tests
* add tests error scenario
* fix style
* added single and no image to error tests
* Fix onnx non-expotable inplace aten op (#34376)
* fix onnx non-expotable inplace op
* mistral, qwen2, qwen2_vl, starcoder2
* fixup copies
* Fix right padding in LLaVA models (#34305)
* fix right pad llavas
* device mismatch
* no filter (#34391)
* no filter
* no filter
* no filter
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* SynthID: better example (#34372)
* better example
* Update src/transformers/generation/configuration_utils.py
* Update src/transformers/generation/logits_process.py
* nits
* Tests: upgrade `test_eager_matches_sdpa_generate` (#34386)
* Fix bnb training test failure (#34414)
* Fix bnb training test: compatibility with OPTSdpaAttention
* Avoid check expected exception when it is on CUDA (#34408)
* update
* update
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix typos in agents_advanced.md (#34405)
* [docs] Cache implementations (#34325)
cache
* [run-slow] hubert
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
Add conversion integration test, and make batchnorm explicit variable
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
fix make fixup styling changes
* [run-slow] hubert
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
* [run-slow] hubert
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
Add conversion integration test, and make batchnorm explicit variable
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
fix make fixup styling changes
* [run-slow] hubert
* [run-slow] hubert
---------
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: Ilyas Moutawwakil <57442720+IlyasMoutawwakil@users.noreply.github.com>
Co-authored-by: Raushan Turganbay <raushan@huggingface.co>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
Co-authored-by: Rudy Delouya <rudy.delouya@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* fix GA bugs and add unit test
* narrow down model loss unit test diff gap
* format code to make ruff happy
* send num_items_in_batch argument to decoder
* fix GA loss bug in BertLMHeadModel
* use TinyStories-33M to narrow down diff gap
* fotmat code
* missing .config
* avoid add extra args
---------
Co-authored-by: kangsheng <kangsheng@meituan.com>
* gpt neox flex attention + refactor
* some formatting
* small fix on dropout
* add assertion on flex attn test
* flaky ci :(
* add head mask support
* style
* handle dtype, replace torch where
* fixup flex with output attns
* code review and several other fixes
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* style
* remove unnecessary comment
* remove incorrect comment
* make flex attn check more agnostic tor versions and centralized
* change peft input dtype check to value since q and k could be affected by other stuff like RoPE
* i forgor
* flaky
* code review and small fixes
* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Use torch.nn.attention.sdpa_kernel instead of deprecated torch.backends.cuda.sdp_kernel
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* Fix test_eager_matches_sdpa_inference for XPU backend
As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
which is implemented on PyTorch level using aten operators and is device
agnostic with respect to implementation of each aten operator. Thus, we can
reuse CUDA (or CPU) MATH weights for XPU.
Fixes: #34888
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* Use torch.amp.autocast instead of deprecated torch.cuda.amp.autocast in nemotron
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
---------
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* [PEFT] Set eval mode when loading PEFT adapter
Resolves#34469
When calling model.load_adapter to load a PEFT adapter, by default the
adapter should be set to eval mode. This is now correctly done. Users
can still pass is_trainable=True to load the adapter in training mode.
* Linter
* Initial draft
* Add .jinja file loading for processors
* Add processor saving of naked chat template files
* make fixup
* Add save-load test for tokenizers
* Add save-load test for tokenizers
* stash commit
* Try popping the file
* make fixup
* Pop the arg correctly
* Pop the arg correctly
* Add processor test
* Fix processor code
* stash commit
* Processor clobbers child tokenizer's chat template
* Processor clobbers child tokenizer's chat template
* make fixup
* Split processor/tokenizer files to avoid interactions
* fix test
* Expand processor tests
* Rename arg to "save_raw_chat_template" across all classes
* Update processor warning
* Move templates to single file
* Move templates to single file
* Improve testing for processor/tokenizer clashes
* Improve testing for processor/tokenizer clashes
* Extend saving test
* Test file priority correctly
* make fixup
* Don't pop the chat template file before the slow tokenizer gets a look
* Remove breakpoint
* make fixup
* Fix error
* fix test_tiny_timestamp_generation
* fix test_large_timestamp_generation
* fix test_whisper_shortform_single_batch_prev_cond
* fix test_whisper_shortform_multi_batch_hard_prev_cond
* return_timestamps necessary with long form
* fix test_default_multilingual_transcription_long_form
* fix test_tiny_token_timestamp_generation_longform
* fix test_whisper_longform_multi_batch_hard
* Update tests/models/whisper/test_modeling_whisper.py
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* fix typo
* do not expect special tokens
* fix test_whisper_longform_single_batch_beam
* fix test_whisper_longform_multi_batch_hard_prev_cond
* update test_whisper_longform_multi_batch_hard_prev_cond
* update test_whisper_longform_multi_batch_hard_prev_cond
* these tests does not make sense anymore
* this test does not make sense anymore
* make fixup
* suggested nits
* add test with forced_decoder_ids
* this test does not make sense anymore
* change assert for unittest test cases
* make fixup
* test with prompt_ids and task and language
* fix unittest test case call
* fix test_tiny_generation
* fix test_tiny_en_generation
* fix test_tiny_en_batched_generation
* fix test_tiny_longform_timestamps_generation
* fix test_tiny_timestamp_generation
* fix test_large_generation
* fix test_large_batched_generation
* fix test_large_generation_multilingual
* fix test_large_timestamp_generation
* fix test_large_timestamp_generation
* fix test_tiny_token_timestamp_generation_longform
* fix test_tiny_en_batched_generation
* make fixup
* [run-slow] whisper
---------
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* allow unused parameter passthrough when chunking in asr pipelines
* format code
* format
* run fixup
* update tests
* update parameters to pipline in test
* updates parametrs in tests
* change spelling in gitignore
* revert .gitignore to main
* add git ignore of devcontainer folder
* assert asr output follows expected inference output type
* run fixup
* Remove .devcontainer from .gitignore
* remove compliance check
* Add Nemotron GGUF Loading Support
* fix the Nemotron architecture assignation
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Do not load for meta device
* Make some minor improvements
* Add test
* Update tests/utils/test_modeling_utils.py
Update test parameters
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Make the test simpler
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add deformable detr image processor fast
* add fast processor to doc
* fix copies
* nit docstring
* Add tests gpu/cpu and fix docstrings
* fix docstring
* import changes from detr
* fix imports
* rebase and fix
* fix input data format change in detr and rtdetr fast
* add support for openai api image_url input
* change continue to elif
* Explicitely add support for OpenAI/TGI chat format
* rewrite content to transformers chat format and add tests
* Add support for typing of image type in chat templates
* add base64 to possible image types
* refactor nesting
* softcapping
* soft cap before the mask
* style
* ...
* super nit
* update
* fixes
* update
* small issue with modular
* fix modular imports
* update
* fixup
* simplify a hell lot
* simplify cleaning imports
* finish fixing
* update our design
* nits
* use a deprecation cycle
* updates
* Fix modular (recursive deps need to always be computed after merges!)
* push
* fix
* update
* fix modular order
* make fix-copies
* updates
* update
* ?
* don't compile for now
* ?
* fix some stuff
* donc!
* fix copies
* update
* fixup
* ?
* fix two tests
* fix?
* for now, don't use head info
* eager when output attentoin and sdpa or flash as it's the simplest behaviour (for our tests as well :))
* fix-copies
* revert sdpa check
* Apply suggestions from code review
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
* rebase, fix-copies and push
* add a slow integration test
* update the test
* fix left padding issue
* fix test
* remove duplicate scaling
* quality
* add a small test and make sure it works
* 2b
---------
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
19d58d31f has introduced a context manager to manage subtests of
test_training_gradient_checkpointing. However, test body was not
moved under "with" statement. Thus, while tests are correctly
marked as skipped, test bodies were still executed. In some cases,
as with llama this caused attribute errors.
Fixes: #34722
Fixes: 19d58d31f ("Add MLLama (#33703)")
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* Add model skeletion with transformers-cli add-new-model-like
* Convert config to modular, add rms_norm_eps, delete clip_qkv
* Convert model to modular, add RMSNorm
* Add flash attention with qk norm and no qkv clipping
* Add decoder layer with RMSNorm after attention/feedforward layers
* Add base and causal model
* Add converter improvements from OLMo repo
* Update weight loading in OLMo to HF converter
* Set correct default for rms_norm_eps
* Set correct pipeline_model_mapping in test
* Run make fixup
* Fix model type
* Re-run modular conversion
* Manually set config docs to fix build errors
* Convert olmo-1124 to olmo_1124 to fix flash attention docs errors
* Start updating tests
* Update tests
* Copy upstream test_eager_matches_sdpa_inference_1_bfloat16 changes to olmo_1124
* Rename input_layernorm and post_attention_layernorm to reflect their ops better
* Use correct tokenizer
* Remove test unsupported by GPT2 tokenizer
* Create GenerationConfig outside of from_pretrained call
* Use simpler init file structure
* Add explicit __all__ to support simplified init
* Make safetensor serialization the default
* Update OLMo November 2024 docs
* remove v4.44 deprecations
* PR comments
* deprecations scheduled for v4.50
* hub version update
* make fiuxp
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove FSDP wrapping from sub-models.
* solve conflict trainer.py
* make fixup
* add unit test for fsdp_auto_wrap_policy when using auto_find_batch_size
* put back extract_model_from_parallel
* use transformers unwrap_model
* Retain newlines in chat template when
* Add try/except
* Add regression test
* Simplify test
* Apply suggestions from code review
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* save/load sub-configs
* nit forgot these
* fix copies
* move test to common
* use dict for sub-configs
* add load-save-laod test
* clean up modeling check
* oops this are correct keys
* fix some tests, missed some composite configs
* this model was missed
FIX Broken repr of TorchAoConfig
The __repr__ method references a non-existent self.kwargs. This is now
fixed.
There does not appear to be a uniform way of defining __repr__ for
quantization configs. I copied the method as implemented for HQQ:
e2ac16b28a/src/transformers/utils/quantization_config.py (L285-L287)
* kinda works
* update
* add tests
* update
* use special tokens in processors
* typo
* fix copies
* fix
* fix moshi after rebase
* update
* fix tests
* update
* Update docs/source/en/main_classes/tokenizer.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update docs
* test for load time adding tokens
* fix some more tests which are now fetched better
* one more fix
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update trainer for easier handling of accumulate + proper reporting
* test
* Fixup tests
* Full fix
* Fix style
* rm comment
* Fix tests
* Minimize test + remove py 311 check
* Unused import
* Forward contrib credits from discussions
* Fix reported metrics
* Refactor, good as it's going to get
* rm pad tok id check
* object detection and audio are being annoying
* Fin
* Fin x2
---------
Co-authored-by: Gyanateet Dutta <Ryukijano@users.noreply.github.com>
* blip2 tests
* instructblips
* copies
* fix slow tests
* fix
* uncomment this
* clean up after rebase
* should be model main input
* fix overwritten tests
* oops len should be multiple of frame number
* style
* fix some tests
* Standardize image-text-to-text-models-output
add post_process_image_text_to_text to chameleon and cleanup
Fix legacy kwarg behavior and deprecation warning
add post_process_image_text_to_text to qwen2_vl and llava_onevision
Add post_process_image_text_to_text to idefics3, mllama, pixtral processor
* nit var name post_process_image_text_to_text udop
* nit fix deprecation warnings
* Add image-text-to-text pipeline
* add support for image url in chat template for pipeline
* Reformat to be fully compatible with chat templates
* Add tests chat template
* Fix imports and tests
* Add pipeline tag
* change logic handling of single prompt ans multiple images
* add pipeline mapping to models
* fix batched inference
* fix tests
* Add manual batching for preprocessing
* Fix outputs with nested images
* Add support for all common processing kwargs
* Add default padding when multiple text inputs (batch size>1)
* nit change version deprecation warning
* Add support for text only inference
* add chat_template warnings
* Add pipeline tests and add copied from post process function
* Fix batched pipeline tests
* nit
* Fix pipeline tests blip2
* remove unnecessary max_new_tokens
* revert processing kosmos2 and remove unnecessary max_new_tokens
* fix pipeline tests idefics
* Force try loading processor if pipeline supports it
* revert load_processor change
* hardcode loading only processor
* remove unnecessary try except
* skip imagetexttotext tests for kosmos2 as tiny model causes problems
* Make code clearer
* Address review comments
* remove preprocessing logic from pipeline
* fix fuyu
* add BC resize fuyu
* Move post_process_image_text_to_text to ProcessorMixin
* add guard in post_process
* fix zero shot object detection pipeline
* add support for generator input in pipeline
* nit
* change default image-text-to-text model to llava onevision
* fix owlv2 size dict
* Change legacy deprecation warning to only show when True
* add fast image processor rtdetr
* add gpu/cpu test and fix docstring
* remove prints
* add to doc
* nit docstring
* avoid iterating over images/annotations several times
* change torch typing
* Add image processor fast documentation
* add mamba architecture for gguf
* add logic for weights conversion, some fixes and refactoring
* add lm_head layers, unit test refactoring
* more fixes for tests
* remove lm_head creation
* remove unused comments
* tmp commit
* tmp commit
* cull overwrites of deleted tests
* typo
* more specific docstring
* make fixup
* parameterize at the top?
* correction
* more deletions :D
* tmp commit
* for VLMs too
* fix _check_outputs
* test nit
* make fixup
* fix another flaky
* test_generate_from_inputs_embeds -- handle missing attention mask
* feat: Added int conversion and unwrapping
* test: added tests for post_process_keypoint_detection of SuperPointImageProcessor
* docs: changed docs to include post_process_keypoint_detection method and switched from opencv to matplotlib
* test: changed test to not depend on SuperPointModel forward
* test: added missing require_torch decorator
* docs: changed pyplot parameters for the keypoints to be more visible in the example
* tests: changed import torch location to make test_flax and test_tf
* Revert "tests: changed import torch location to make test_flax and test_tf"
This reverts commit 39b32a2f69.
* tests: fixed import
* chore: applied suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* tests: fixed import
* tests: fixed import (bis)
* tests: fixed import (ter)
* feat: added choice of type for target_size and changed tests accordingly
* docs: updated code snippet to reflect the addition of target size type choice in post process method
* tests: fixed imports (...)
* tests: fixed imports (...)
* style: formatting file
* docs: fixed typo from image[0] to image.size[0]
* docs: added output image and fixed some tests
* Update docs/source/en/model_doc/superpoint.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fix: included SuperPointKeypointDescriptionOutput in TYPE_CHECKING if statement and changed tests results to reflect changes to SuperPoint from absolute keypoints coordinates to relative
* docs: changed SuperPoint's docs to print output instead of just accessing
* style: applied make style
* docs: added missing output type and precision in docstring of post_process_keypoint_detection
* perf: deleted loop to perform keypoint conversion in one statement
* fix: moved keypoint conversion at the end of model forward
* docs: changed SuperPointInterestPointDecoder to SuperPointKeypointDecoder class name and added relative (x, y) coordinates information to its method
* fix: changed type hint
* refactor: removed unnecessary brackets
* revert: SuperPointKeypointDecoder to SuperPointInterestPointDecoder
* Update docs/source/en/model_doc/superpoint.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
---------
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Add _determine_best_metric and new saving logic.
1. Logic to determine the best logic was separated out from
`_save_checkpoint`.
2. In `_maybe_log_save_evaluate`, whether or not a new best metric was
achieved is determined after each evaluation, and if the save strategy
is "best' then the TrainerControl is updated accordingly.
* Added SaveStrategy.
Same as IntervalStrategy, but with a new attribute called BEST.
* IntervalStrategy -> SaveStrategy
* IntervalStratgy -> SaveStrategy for save_strat.
* Interval -> Save in docstring.
* Updated docstring for save_strategy.
* Added SaveStrategy and made according changes.
`save_strategy` previously followed `IntervalStrategy` but now follows
`SaveStrategy`.
Changes were made accordingly to the code and the docstring.
* Changes from `make fixup`.
* Removed redundant metrics argument.
* Added new test_save_best_checkpoint test.
1. Checks for both cases where `metric_for_best_model` is explicitly
provided and when it's not provided.
2. The first case should have two checkpoints saved, whereas the second
should have three saved.
* Changed should_training_end saving logic.
The Trainer saves a checkpoints at the end of training by default as
long as `save_strategy != SaveStrategy.NO`. This condition was modified
to include `SaveStrategy.BEST` because it would be counterintuitive that
we'd only want the best checkpoint to be saved but the last one is as
well.
* `args.metric_for_best_model` default to loss.
* Undo metric_for_best_model update.
* Remove checking metric_for_best_model.
* Added test cases for loss and no metric.
* Added error for metric and changed default best_metric.
* Removed unused import.
* `new_best_metric` -> `is_new_best_metric`
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Applied `is_new_best_metric` to all.
Changes were made for consistency and also to fix a potential bug.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* exclude fsdp from delay_optimizer_creation
* add test case for trainer: FSDP mode and fp8 as mixed precision
* rearrange imports
* ruff formatted
* adapt _init_fsdp to fp8
* use _init_fsdp only when resume_from_checkpoint
* In case of FDP, self.layer will be CheckpointWrapper which has no len() method
* delete _init_fsdp
* solve conflict
* fix conflict
* make fixup
* Correct the new defaults
* CIs
* add check
* Update utils.py
* Update utils.py
* Add the max_length in generate test checking shape without passing length
* style
* CIs
* fix fx CI issue
When loading a LoRA adapter, so far, there was only a warning when there
were unexpected keys in the checkpoint. Now, there is also a warning
when there are missing keys.
This change is consistent with
https://github.com/huggingface/peft/pull/2118 in PEFT and the planned PR
https://github.com/huggingface/diffusers/pull/9622 in diffusers.
Apart from this change, the error message for unexpected keys was
slightly altered for consistency (it should be more readable now). Also,
besides adding a test for the missing keys warning, a test for
unexpected keys warning was also added, as it was missing so far.
* Add SynthIDTextWatermarkLogitsProcessor
* esolving comments.
* Resolving comments.
* esolving commits,
* Improving SynthIDWatermark tests.
* switch to PT version
* detector as pretrained model + style
* update training + style
* rebase
* Update logits_process.py
* Improving SynthIDWatermark tests.
* Shift detector training to wikitext negatives and stabilize with lower learning rate.
* Clean up.
* in for 7B
* cleanup
* upport python 3.8.
* README and final cleanup.
* HF Hub upload and initiaze.
* Update requirements for synthid_text.
* Adding SynthIDTextWatermarkDetector.
* Detector testing.
* Documentation changes.
* Copyrights fix.
* Fix detector api.
* ironing out errors
* ironing out errors
* training checks
* make fixup and make fix-copies
* docstrings and add to docs
* copyright
* BC
* test docstrings
* move import
* protect type hints
* top level imports
* watermarking example
* direct imports
* tpr fpr meaning
* process_kwargs
* SynthIDTextWatermarkingConfig docstring
* assert -> exception
* example updates
* no immutable dict (cant be serialized)
* pack fn
* einsum equivalent
* import order
* fix test on gpu
* add detector example
---------
Co-authored-by: Sumedh Ghaisas <sumedhg@google.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: sumedhghaisas2 <138781311+sumedhghaisas2@users.noreply.github.com>
Co-authored-by: raushan <raushan@huggingface.co>
* Enable grad accum fix across all models + trainer fully in forward()
* handle peft case
* Account for DDP: need to run scale tests
* Use accelerator state
* Quality
* Guard
* Experiment w/ only fairseq fix
* Fairseq only
* Revert multiply_grads fix
* Mult by grad accum to fully bring back solution
* Style
* Good to go now
* Skip fx tests for now
* Bookmark
* Working now
* Add option for running ffmpeg_microphone_live as a background process
* Code quality checks for audio_utils
* Code clean up for audio_utils
* Fixing logic in ffmpeg_microphone calls in audio_utils
* Allowing any arbitrary arguments to be passed to ffmpeg_microphone_live
* Formatting
* Fixing last problems with adding ffmpeg_additional_args
* Fixing default arguments and formatting issues
* Fixing comments for ffmpeg_additional_args
* Adding two shorts tests for ffmpeg_microphone_live
* Fixing test bug
* add colorize_depth and matplotlib availability check
* add post_process_depth_estimation for zoedepth + tests
* add post_process_depth_estimation for DPT + tests
* add post_process_depth_estimation in DepthEstimationPipeline & special case for zoedepth
* run `make fixup`
* fix import related error on tests
* fix more import related errors on test
* forgot some `torch` calls in declerations
* remove `torch` call in zoedepth tests that caused error
* updated docs for depth estimation
* small fix for `colorize` input/output types
* remove `colorize_depth`, fix various names, remove matplotlib dependency
* fix formatting
* run fixup
* different images for test
* update examples in `forward` functions
* fixed broken links
* fix output types for docs
* possible format fix inside `<Tip>`
* Readability related updates
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Readability related update
* cleanup after merge
* refactor `post_process_depth_estimation` to return dict; simplify ZoeDepth's `post_process_depth_estimation`
* rewrite dict merging to support python 3.8
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* this worked in normal generation, needs more tests
* fix almost all tests in t5
* nit
* longt5, umt5, mt5
* style
* udop, pix2struct
* more models
* fix some tests
* fix onnx tests
* tracing tests fixed
* compile enabled and tested for t5 models
* fix small bug in slow tests
* [run-slow] t5
* uncomment
* style
* update with new generation refactoring
* nit
* fix copies
* this is the fix, had to change t5 to fix copies
* update
* [run-slow] t5
* [run-slow] t5
* update
* add test for encoder only T5
* clean up after rebase
* fix pop2piano
* add comment
* style
* fix copies after rebase
* fix copies missed this one
* first try
* codestyle
* idefics2 is happy
* [run-slow] llava, llava_next, video_llava, vipllava, llava_next_video, idefics, idefics2, kosmos2, fuyu, blip, blip_2, instructblip, instructblipvideo, paligemma
* fix-copies
* [run-slow] llava, llava_next, video_llava, vipllava, llava_next_video, idefics, idefics2, kosmos2, fuyu, blip, blip_2, instructblip, instructblipvideo
* blip-2 needs to init vision from config
* when was this removed O_o
* minor fix
* tests
* this way?
* tests
* model-agnostic code
* codestyle
* add tests for idefics
* modify general test for VLMs
* no generation test for vlm yet!
* no generation test here also
* wanr in VIT-SDPA if output attn
* add more tests
* user can pass dict as attn impl
* repo consistency
* update
* muicgen
* no prints
* forgot speech enc-dec and clip
* how many composite models we have?
* musicgen meelody is same as mudicgen
* +siglip
* fix tests + add some more
* remove idefics custom overriden code
* make idefics2 automappable
* nits
* skip tests
* doctests
* Update src/transformers/models/idefics2/configuration_idefics2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/clip/test_modeling_clip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics2/test_modeling_idefics2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics2/test_modeling_idefics2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* major update, no need for automap
* clean up
* add FA2 test
* more tests
* style
* skip tests
* why did these started failing now?
* no attributes for FA2 needed
* one tiny test
* address comment about FA2 false warning
* style
* add new models and resolve conflicts
* fix copies
* let it be this way for now, come back tomorrow to review
* some more fixes
* update
* more updates
* update
* fix copies
* style and tests
* another big update
* fix tests
* fix tests
* update
* another update
* fix tests
* fix copies
* fix tests
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* bookmark
* Bookmark
* Bookmark
* Actually implement
* Pass in kwarg explicitly
* Adjust for if we do or don't have labels
* Bookmark fix for od
* bookmark
* Fin
* closer
* Negate accelerate grad accum div
* Fixup not training long enough
* Add in compute_loss to take full model output
* Document
* compute_loss -> compute_loss_fn
* Add a test
* Refactor
* Refactor
* Uncomment tests
* Update tests/trainer/test_trainer.py
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* auto-gptq requirement is removed & model is changed & tokenizer pad token is assigned
* values func is changed with extensions & sequence key value bug is fixed
* map key value check is added in ExtensionsTree
* empty trimmed_ids bug is fixed
* tail_id IndexError is fixed
* empty trimmed_ids bug fix is updated for failed test
* too much specific case for specific tokenizer is removed
* input_ids check is updated
* require auto-gptq import is removed
* key error check is changed with empty list check
* empty input_ids check is added
* empty trimmed_ids fix is checked with numel function
* usage change comments are added
* test changes are commented
* comment style and quality bugs are fixed
* test comment style and quality bug is fixed
* Fix FSDP Initialization for resume training
* Added init_fsdp function to work with dummy values
* Fix FSDP initialization for resuming training
* Added CUDA decorator for tests
* Added torch_gpu decorator to FSDP tests
* Fixup for failing code quality tests
* add idefics
* conflicts after merging main
* enable tests but need to fix some
* fix tests
* no print
* fix/skip some slow tests
* continue not skip
* rebasing broken smth, this is the fix
* mistral qna start
* mixtral qna
* oops
* qwen2 qna
* qwen2moe qna
* add missing input embed methods
* add copied to all methods, can't directly from llama due to the prefix
* make top level copied from
* Generate using exported model and enable gemma2-2b in ExecuTorch
* [run_slow] gemma, gemma2
* truncate expected output message
* Bump required torch version to support gemma2 export
* [run_slow] gemma, gemma2
---------
Co-authored-by: Guang Yang <guangyang@fb.com>
* Default synced_gpus to True when using FullyShardedDataParallel
Fixes#30228
Related:
* https://github.com/pytorch/pytorch/issues/100069
* https://github.com/pytorch/pytorch/issues/123962
Similar to DeepSpeed ZeRO Stage 3, when using FSDP with multiple GPUs and differently sized data per rank, the ranks reach different synchronization points at the same time, leading to deadlock
To avoid this, we can automatically set synced_gpus to True if we detect that a PreTrainedModel is being managed by FSDP using _is_fsdp_managed_module, which was added in 2.0.0 for torch.compile: https://github.com/pytorch/pytorch/blob/v2.0.0/torch/distributed/fsdp/_dynamo_utils.py
* Remove test file
* ruff formatting
* ruff format
* Update copyright year
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add test for FSDP-wrapped model generation
Before #33483, these tests would have hung for 10 minutes before crashing due to a timeout error
* Ruff format
* Move argparse import
* Remove barrier
I think this might cause more problems if one of the workers was killed
* Move import into function to decrease load time
https://github.com/huggingface/transformers/pull/33483#discussion_r1787972735
* Add test for accelerate and Trainer
https://github.com/huggingface/transformers/pull/33483#discussion_r1790309675
* Refactor imports
* Ruff format
* Use nullcontext
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Allow for hyphenated field names in long-options
argparse converts hyphens into underscores before assignment (e.g., an
option passed as `--long-option` will be stored under `long_option`), So
there is no need to pass options as literal attributes, as in
`--long_option` (with an underscore instead of a hyphen). This commit
ensures that this behavior is respected by `parse_args_into_dataclasses`
as well.
Issue: #33933
Co-authored-by: Daniel Marti <mrtidm@amazon.com>
* add sdpa to OPT
* chore: remove redundant whitespace in OPTDecoder class
* fixup
* bug fix
* add sdpa and attention generate test
* fixup
* Refactor OPTAttention forward method for improved readability and maintainability
* undo refactor for _shape and key,val states
* add OPT to doc, fixup didn't find it for some reason
* change order
* change default attn_implemntation in testing to eager
* [run-slow] opt
* change test_eager_matches_sdpa_generate to the one llama
* Update default attention implementation in testing common
* [run-slow] opt
* remove uneeded print
* [run-slow] opt
* refactor model testers to have attn_implementation="eager"
* [run-slow] opt
* convert test_eager_matches_sdpa_generate to opt-350M
* bug fix when creating mask for opt
* [run-slow] opt
* if layer head mask default to eager
* if head mask is not none fall to eager
* [run-slow] opt
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Clean up Unpack imports (#33631)
clean up Unpack imports
* Fix DPT /Dinov2 sdpa regression on main (#33660)
* fallback to eager if output attentions.
* fix copies
* handle dependency errors in check_imports (#33622)
* handle dependency errors in check_imports
* change log level to warning
* add back self.max_position_embeddings = config.max_position_embeddings (#33550)
* add back self.max_position_embeddings = config.max_position_embeddings
* fix-copies
* Fix Llava conversion for LlavaQwen2ForCausalLM with Clip vision tower (#33613)
fix llavaqwen2 model conversion
* Uniformize kwargs for Udop processor and update docs (#33628)
* Add optional kwargs and uniformize udop
* cleanup Unpack
* nit Udop
* Generation: deprecate `PreTrainedModel` inheriting from `GenerationMixin` (#33203)
* Enable BNB multi-backend support (#31098)
* enable cpu bnb path
* fix style
* fix code style
* fix 4 bit path
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* add multi backend refactor tests
* fix style
* tweak 4bit quantizer + fix corresponding tests
* tweak 8bit quantizer + *try* fixing corresponding tests
* fix dequant bnb 8bit
* account for Intel CPU in variability of expected outputs
* enable cpu and xpu device map
* further tweaks to account for Intel CPU
* fix autocast to work with both cpu + cuda
* fix comments
* fix comments
* switch to testing_utils.torch_device
* allow for xpu in multi-gpu tests
* fix tests 4bit for CPU NF4
* fix bug with is_torch_xpu_available needing to be called as func
* avoid issue where test reports attr err due to other failure
* fix formatting
* fix typo from resolving of merge conflict
* polish based on last PR review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix CI
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix error log
* fix error msg
* add \n in error log
* make quality
* rm bnb cuda restriction in doc
* cpu model don't need dispatch
* fix doc
* fix style
* check cuda avaliable in testing
* fix tests
* Update docs/source/en/model_doc/chameleon.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/model_doc/llava_next.md
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix doc
* fix check multibackends
* fix import sort
* remove check torch in bnb
* docs: update bitsandbytes references with multi-backend info
* docs: fix small mistakes in bnb paragraph
* run formatting
* reveret bnb check
* move bnb multi-backend check to import_utils
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix bnb check
* minor fix for bnb
* check lib first
* fix code style
* Revert "run formatting"
This reverts commit ac108c6d6b.
* fix format
* give warning when bnb version is low and no cuda found]
* fix device assignment check to be multi-device capable
* address akx feedback on get_avlbl_dev fn
* revert partially, as we don't want the function that public, as docs would be too much (enforced)
---------
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix error string after refactoring into get_chat_template (#33652)
* Fix error string after refactoring into get_chat_template
* Take suggestion from CR
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* uniformize git processor (#33668)
* uniformize git processor
* update doctring
* Modular `transformers`: modularity and inheritance for new model additions (#33248)
* update exampel
* update
* push the converted diff files for testing and ci
* correct one example
* fix class attributes and docstring
* nits
* oups
* fixed config!
* update
* nitd
* class attributes are not matched against the other, this is missing
* fixed overwriting self.xxx now onto the attributes I think
* partial fix, now order with docstring
* fix docstring order?
* more fixes
* update
* fix missing docstrings!
* examples don't all work yet
* fixup
* nit
* updated
* hick
* update
* delete
* update
* update
* update
* fix
* all default
* no local import
* fix more diff
* some fix related to "safe imports"
* push fixed
* add helper!
* style
* add a check
* all by default
* add the
* update
* FINALLY!
* nit
* fix config dependencies
* man that is it
* fix fix
* update diffs
* fix the last issue
* re-default to all
* alll the fixes
* nice
* fix properties vs setter
* fixup
* updates
* update dependencies
* make sure to install what needs to be installed
* fixup
* quick fix for now
* fix!
* fixup
* update
* update
* updates
* whitespaces
* nit
* fix
* simplify everything, and make it file agnostic (should work for image processors)
* style
* finish fixing all import issues
* fixup
* empty modeling should not be written!
* Add logic to find who depends on what
* update
* cleanup
* update
* update gemma to support positions
* some small nits
* this is the correct docstring for gemma2
* fix merging of docstrings
* update
* fixup
* update
* take doc into account
* styling
* update
* fix hidden activation
* more fixes
* final fixes!
* fixup
* fixup instruct blip video
* update
* fix bugs
* align gemma2 with the rest as well
* updats
* revert
* update
* more reversiom
* grind
* more
* arf
* update
* order will matter
* finish del stuff
* update
* rename to modular
* fixup
* nits
* update makefile
* fixup
* update order of the checks!
* fix
* fix docstring that has a call inside
* fiix conversion check
* style
* add some initial documentation
* update
* update doc
* some fixup
* updates
* yups
* Mostly todo gimme a minut
* update
* fixup
* revert some stuff
* Review docs for the modular transformers (#33472)
Docs
* good update
* fixup
* mmm current updates lead to this code
* okay, this fixes it
* cool
* fixes
* update
* nit
* updates
* nits
* fix doc
* update
* revert bad changes
* update
* updates
* proper update
* update
* update?
* up
* update
* cool
* nits
* nits
* bon bon
* fix
* ?
* minimise changes
* update
* update
* update
* updates?
* fixed gemma2
* kind of a hack
* nits
* update
* remove `diffs` in favor of `modular`
* fix make fix copies
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Fix CIs post merging modular transformers (#33681)
update
* Fixed docstring for cohere model regarding unavailability of prune_he… (#33253)
* Fixed docstring for cohere model regarding unavailability of prune_head() methods
The docstring mentions that cohere model supports prune_heads() methods. I have fixed the docstring by explicitly mentioning that it doesn't support that functionality.
* Update src/transformers/models/cohere/modeling_cohere.py
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Generation tests: update imagegpt input name, remove unused functions (#33663)
* Improve Error Messaging for Flash Attention 2 on CPU (#33655)
Update flash-attn error message on CPU
Rebased to latest branch
* Gemma2: fix config initialization (`cache_implementation`) (#33684)
* Fix ByteLevel alphabet missing when Sequence pretokenizer is used (#33556)
* Fix ByteLevel alphabet missing when Sequence pretokenizer is used
* Fixed formatting with `ruff`.
* Uniformize kwargs for image-text-to-text processors (#32544)
* uniformize FUYU processor kwargs
* Uniformize instructblip processor kwargs
* Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2
* Uniformize llava_next processor
* Fix save_load test for processor with chat_template only as extra init args
* Fix import Unpack
* Fix Fuyu Processor import
* Fix FuyuProcessor import
* Fix FuyuProcessor
* Add defaults for specific kwargs kosmos2
* Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs
* Add tests processor Udop
* remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature
* Fix overwrite tests kwargs processors
* Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop
* Fix processing test fuyu
* remove unnecessary pad_token check in instructblip ProcessorTest
* Fix BC tests and cleanup
* FIx imports fuyu
* Uniformize Pix2Struct
* Fix wrong name for FuyuProcessorKwargs
* Fix slow tests reversed inputs align fuyu llava-next, change udop warning
* Fix wrong logging import udop
* Add check images text input order
* Fix copies
* change text pair handling when positional arg
* rebase on main, fix imports in test_processing_common
* remove optional args and udop uniformization from this PR
* fix failing tests
* remove unnecessary test, fix processing utils and test processing common
* cleanup Unpack
* cleanup
* fix conflict grounding dino
* 🚨🚨 Setting default behavior of assisted decoding (#33657)
* tests: fix pytorch tensor placement errors (#33485)
This commit fixes the following errors:
* Fix "expected all tensors to be on the same device" error
* Fix "can't convert device type tensor to numpy"
According to pytorch documentation torch.Tensor.numpy(force=False)
performs conversion only if tensor is on CPU (plus few other restrictions)
which is not the case. For our case we need force=True since we just
need a data and don't care about tensors coherency.
Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* bump tokenizers, fix added tokens fast (#32535)
* update based on tokenizers release
* update
* nits
* update
* revert re addition
* don't break that yet
* fmt
* revert unwanted
* update tokenizers version
* update dep table
* update
* update in conversion script as well
* some fix
* revert
* fully revert
* fix training
* remove set trace
* fixup
* update
* update
* [Pixtral] Improve docs, rename model (#33491)
* Improve docs, rename model
* Fix style
* Update repo id
* fix code quality after merge
* HFQuantizer implementation for compressed-tensors library (#31704)
* Add compressed-tensors HFQuantizer implementation
* flag serializable as False
* run
* revive lines deleted by ruff
* fixes to load+save from sparseml, edit config to quantization_config, and load back
* address satrat comment
* compressed_tensors to compressed-tensors and revert back is_serializable
* rename quant_method from sparseml to compressed-tensors
* tests
* edit tests
* clean up tests
* make style
* cleanup
* cleanup
* add test skip for when compressed tensors is not installed
* remove pydantic import + style
* delay torch import in test
* initial docs
* update main init for compressed tensors config
* make fix-copies
* docstring
* remove fill_docstring
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* review comments
* review comments
* comments - suppress warnings on state dict load, tests, fixes
* bug-fix - remove unnecessary call to apply quant lifecycle
* run_compressed compatability
* revert changes not needed for compression
* no longer need unexpected keys fn
* unexpected keys not needed either
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add to_diff_dict
* update docs and expand testing
* Update _toctree.yml with compressed-tensors
* Update src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update doc
* add note about saving a loaded model
---------
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
* update model card for opt
* add batch size to inference table
* [slow-run] opt
* [run-slow] opt
---------
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: chengchengpei <5881383+chengchengpei@users.noreply.github.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Tibor Reiss <75096465+tibor-reiss@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: Muhammad Naufil <m.naufil1@gmail.com>
Co-authored-by: sizhky <yyeshr@gmail.com>
Co-authored-by: Umar Butler <umar@umar.au>
Co-authored-by: Jonathan Mamou <jonathan.mamou@intel.com>
Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Benjamin Fineran <bfineran@users.noreply.github.com>
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
* rebasing changes
* fixing style
* adding some doc to functions
* remove bitblas
* change dtype
* fixing check_code_quality
* fixing import order
* adding doc to tree
* Small update on BitLinear
* adding some tests
* sorting imports
* small update
* reformatting
* reformatting
* reformatting with ruff
* adding assert
* changes after review
* update disk offloading
* adapting after review
* Update after review
* add is_serializable back
* fixing style
* adding serialization test
* make style
* small updates after review
* Fix Failed tests with mobile bert
* Cast to the correct dtype
* Code fixup
* Fix padding_idx larger that embedding_size
* Reduce covariance more. use 1e-7 instead of 1e-5
* Comment fix
* Reduce covariance more. use 1e-9 instead of 1e-7
* Copy new config
* all but MRA fixed
* fix mra
* very flaky
* skip instead
* make fixup
---------
Co-authored-by: Joao Gante <joao@huggingface.co>
* Update many similar visual pipelines
* Add input tests
* Add ImageToText as well
* Add output tests
* Add output tests
* Add output tests
* OutputElement -> Output
* Correctly test elements
* make fixup
* fix typo in the task list
* Fix VQA testing
* Add copyright to image_classification.py
* Revert changes to VQA pipeline because outputs have differences - will move to another PR
* make fixup
* Remove deprecation warnings
* Add Auto model for image-text-to-text
* Remove donut from processing auto, add chameleon ti image text to text models
* add qwen2_vl and llava_onevision
* add pixtral to auto model for image-text-to-text
* add mllama and idefics3
* remove models in IGNORE_NON_AUTO_CONFIGURED
* add AutoModelForImageTextToText to tests and doc
* Initial commit for MyT5 model
* custom implementation of MyT5 tokenizer, unused files deleted
* unittest for myt5 tokenizer
* upadate of import structure and style
* removed remmanents of MyT5Config
* fixed docstrings
* Updates after review: filled documentaion file, new docstrings and tests added
* Fixed code style issues
* fixed copied from to refer to function
* updated loading myt5 tokenizer in tests, added sample byte map file to fixtures
* changes after review
* removed redundant copied from
* removed redundant copied from
* optimalization and loading model from hf
* [run_slow] myt5
* [run-slow] myt5
* Updated en documentation for myt5
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* onboard phimoe model
* removed debug code
* added unit tests
* updated docs
* formatted
* fixed unit tests
* fixed test case
* fixed format
* refactored code
* fixed expected outputs in the integration tests
* Added a warning msg
* Addressed comments
* Addressed comments
* fixed test cases
* added paper link
* Addressed comments
* Refactored PhimoeForCausalLM forward fn
* Refactored PhimoeRotaryEmbedding class
* fixed test cases
* fixed testcase
* fixed test case
* Addressed comments
* fixed test cases
* fixed testcases
* Used cache position instead to get the seq len
* intilize new embeddings from normal distrib
* Fix typo in comments
* Fix typo in comments
* Fix style
* Fix variables naming
* Add tests
* Fix style
* code consistency nit
* Add deepspeed support
* Add deepspeed support
* Conver embeddings weights to float32 before computations
* Add deepspeed tests
* Cover when vocab_size is smaller than embedding_size
* Style fix
* Add tests for vocab_size smaller than hiddin_size
* Style fix
* Nits in tests
* Nits in tests
* Check for deepspeed before importing it
* Increase vocab_size for positive definite covariance matrix test
* Add warning
* Add multivariate_resizing flag and implement resizing for lm_heads
* Fix typo
* Fix wrong bias indexing
* Fix bias is zero check
* remove multivariate_resizing flag from tests
* Intialize bias from old bias normal distribution
* Fixup
* Code usability
* Use mean_resizing instead of multivariate_resizing
* Fix up
* Fix comments and docs
* enable cpu awq ipex linear
* add doc for cpu awq with ipex kernel
* add tests for cpu awq
* fix code style
* fix doc and tests
* Update docs/source/en/quantization/awq.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/autoawq/test_awq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix comments
* fix log
* fix log
* fix style
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* [PEFT] Support low_cpu_mem_usage for PEFT loading
PEFT added support for low_cpu_mem_usage=True when loading adapters in
https://github.com/huggingface/peft/pull/1961. This feature is now
available when installing PEFT v0.13.0. With this PR, this option is
also supported when loading PEFT adapters directly into transformers
models.
Additionally, with this PR,
https://github.com/huggingface/diffusers/pull/9510 will be unblocked,
which implements this option in diffusers.
* Fix typo
* fix beam indices in token_timestamps
* fix attention_mask in FA2
* correct translation example with the right example
* correct how somes tests are using outputs + correct num_frames
* fix shortform batch prev cond tests
* make fix-copies
* make fix-copies
* take care of shifting beam indices
* [run-slow] whisper
* [run-slow] whisper
* add unit tests for splinter_tokenizer
* add unit test for splinter tokenizer, pass in the question_token to be saved on save_pretrained called
* remove unused import
* remove vocab_splinter.txt, add Copied from, use fmt:on and fmt:off to prevent autoformatting on long lines
* remove all the spaces
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Trainer - deprecate tokenizer for processing_class
* Extend chage across Seq2Seq trainer and docs
* Add tests
* Update to FutureWarning and add deprecation version
* add support for custom inputs and batched inputs in ProcessorTesterMixin
* Fix batch_size behavior ProcessorTesterMixin
* Change format prepare inputs batched
* Remove override test pixtral processor
* Remove unnecessary tests and cleanup after new prepare_inputs functions
* Fix instructBlipVideo image processor
* Remove max_new_tokens arg
* Add ASR pipeline to testing
* make fixup
* Factor the output test out into a util
* Full error reporting
* Full error reporting
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Small comment
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Fix Mamba slow path bug with dtype mismatch.
* Update test_modeling_mamba.py
* Improve style.
* Fix issue with cache position of dtype mismatch test.
* Change test for slow path.
* Revert changes.
* Switch to buggy code and add test to catch it.
* Fix the dtype mismatch bug and add test code to verify it.
* Fix minor bug with test.
* Fix incorrect dtype of model output.
* Fix incorrect dtype of cache.
* Fix incorrect dtype of ssm cache.
* Fix incorrect dtype of conv state.
* Remove assertion for ssm state.
* Add assertion for conv state dtype.
* Fix all issues with dtype mismatch test.
* HQQ model serialization attempt
* fix hqq dispatch and unexpected keys
* style
* remove check_old_param
* revert to check HQQLinear in quantizer_hqq.py
* revert to check HQQLinear in quantizer_hqq.py
* update HqqConfig default params
* make ci happy
* make ci happy
* revert to HQQLinear check in quantizer_hqq.py
* check hqq_min version 0.2.0
* set axis=1 as default in quantization_config.py
* validate_env with hqq>=0.2.0 version message
* deprecated hqq kwargs message
* make ci happy
* remove run_expected_keys_check hack + bump to 0.2.1 min hqq version
* fix unexpected_keys hqq update
* add pre_quantized check
* add update_expected_keys to base quantizerr
* ci base.py fix?
* ci base.py fix?
* fix "quantization typo" src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix post merge
---------
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Make audio classification pipeline spec-compliant and add test
* Check that test actually running in CI
* Try a different pipeline for the CI
* Move the test so it gets triggered
* Move it again, this time into task_tests!
* make fixup
* indentation fix
* comment
* Move everything from testing_utils to test_pipeline_mixin
* Add output testing too
* revert small diff with main
* make fixup
* Clarify comment
* Update tests/pipelines/test_pipelines_audio_classification.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Update tests/test_pipeline_mixin.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Rename function and js_args -> hub_args
* Cleanup the spec recursion
* Check keys for all outputs
---------
Co-authored-by: Lucain <lucainp@gmail.com>
* add bloom arch support for gguf
* apply format
* small refactoring, bug fix in GGUF_TENSOR_MAPPING naming
* optimize bloom GGUF_TENSOR_MAPPING
* implement reverse reshaping for bloom gguf
* add qkv weights test
* add q_8 test for bloom
* clean_up_tokenization_spaces=False if unset
* deprecate warning
* updating param for old models
* update models
* make fix-copies
* fix-copies and update bert models
* warning msg
* update prophet and clvp
* updating test since space before is arbitrarily removed
* remove warning for 4.45
* Add Idefics 3!
* fixes to make both pipelines identical
* fix for quantized models
* First pass at the review
* remove vocab size from the main config (it's still in the text_config)
* hot fix for merve
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* re-add model_type for text_config
* remove support for old_cache
* remove hidden_size from main config
* rename idefics3 HF repo
* few changes suggested in the PR
* fix to input_data_format computation
* remove overwrite of _autoset_attn_implementation following @zucchini-nlp suggestion
* improve example
* few improvements from amy's review
* big change to enable processing input images as numpy arrays
* Changes to the code to uniformize processor kwargs
* image processing tests
* image processing tests fixes and some bugs they discovered
* addressed review comments from Yoni
* fix modeling tests
* remove special tokens that are not special
* fixes tests
* skip failing tests - they also fail for idefics2
* added paper and readded the tests with multi gpu, who knows
* Update docs/source/en/model_doc/idefics3.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* review amy until image_processing_idefics3
* last comments from Amy
* review amy
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/modeling_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/model_doc/idefics3.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* doc improvement - amy review
* fix runtime error during fine-tuning
* amy's review
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/modeling_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* ruff
* amy's comment on the order
* ruff ruff
* fix copies
* square images when they are not splitted
* ruff :(
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics3/test_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix small bug introduced in refactor
* amy's image processing changes
* fixes peft tests and ruff
* modify to_pil_image from transformers. and review from emanuele.
* add modified to_pil_image
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add AdEMAMix optimizer
* Fix test
* Update tests/trainer/test_trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Add compressed-tensors HFQuantizer implementation
* flag serializable as False
* run
* revive lines deleted by ruff
* fixes to load+save from sparseml, edit config to quantization_config, and load back
* address satrat comment
* compressed_tensors to compressed-tensors and revert back is_serializable
* rename quant_method from sparseml to compressed-tensors
* tests
* edit tests
* clean up tests
* make style
* cleanup
* cleanup
* add test skip for when compressed tensors is not installed
* remove pydantic import + style
* delay torch import in test
* initial docs
* update main init for compressed tensors config
* make fix-copies
* docstring
* remove fill_docstring
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* review comments
* review comments
* comments - suppress warnings on state dict load, tests, fixes
* bug-fix - remove unnecessary call to apply quant lifecycle
* run_compressed compatability
* revert changes not needed for compression
* no longer need unexpected keys fn
* unexpected keys not needed either
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add to_diff_dict
* update docs and expand testing
* Update _toctree.yml with compressed-tensors
* Update src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update doc
* add note about saving a loaded model
---------
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
This commit fixes the following errors:
* Fix "expected all tensors to be on the same device" error
* Fix "can't convert device type tensor to numpy"
According to pytorch documentation torch.Tensor.numpy(force=False)
performs conversion only if tensor is on CPU (plus few other restrictions)
which is not the case. For our case we need force=True since we just
need a data and don't care about tensors coherency.
Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* enable cpu bnb path
* fix style
* fix code style
* fix 4 bit path
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* add multi backend refactor tests
* fix style
* tweak 4bit quantizer + fix corresponding tests
* tweak 8bit quantizer + *try* fixing corresponding tests
* fix dequant bnb 8bit
* account for Intel CPU in variability of expected outputs
* enable cpu and xpu device map
* further tweaks to account for Intel CPU
* fix autocast to work with both cpu + cuda
* fix comments
* fix comments
* switch to testing_utils.torch_device
* allow for xpu in multi-gpu tests
* fix tests 4bit for CPU NF4
* fix bug with is_torch_xpu_available needing to be called as func
* avoid issue where test reports attr err due to other failure
* fix formatting
* fix typo from resolving of merge conflict
* polish based on last PR review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix CI
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix error log
* fix error msg
* add \n in error log
* make quality
* rm bnb cuda restriction in doc
* cpu model don't need dispatch
* fix doc
* fix style
* check cuda avaliable in testing
* fix tests
* Update docs/source/en/model_doc/chameleon.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/model_doc/llava_next.md
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix doc
* fix check multibackends
* fix import sort
* remove check torch in bnb
* docs: update bitsandbytes references with multi-backend info
* docs: fix small mistakes in bnb paragraph
* run formatting
* reveret bnb check
* move bnb multi-backend check to import_utils
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix bnb check
* minor fix for bnb
* check lib first
* fix code style
* Revert "run formatting"
This reverts commit ac108c6d6b.
* fix format
* give warning when bnb version is low and no cuda found]
* fix device assignment check to be multi-device capable
* address akx feedback on get_avlbl_dev fn
* revert partially, as we don't want the function that public, as docs would be too much (enforced)
---------
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add sdpa to dinov2
* fixup
* add dinov2 to sdpa doc
* update doc order
* [run-slow] dinov2
* common to eager
* [run-slow] dinov2
* update attn implementation in common
* update test_modeling_dinov2 to have mask_ration, num_masks and mask_length similar to vit
* [run-slow] dinov2
---------
Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il>
* enable low-precision pipeline
* fix parameter for ASR
* reformat
* fix asr bug
* fix bug for zero-shot
* add dtype check
* rm useless comments
* add np.float16 check
* Update src/transformers/pipelines/image_classification.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix comments
* fix asr check
* make fixup
* No more need for is_torch_available()
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Matt <rocketknight1@gmail.com>
* fix: handle padding in contrastive search for decoder-only models
* fix: handle padding in contrastive search for encoder-decoder models
* tests: move padding contrastive test to test_util, add t5 test
* fix: handle if model_kwargs["decoder_attention_mask"] is None
* refactor: improve padding input contrastive search generation tests
* chore: _ranking_fast to use LongTensor for cosine_matrix_mask
* add check and prepare args for BC to ProcessorMixin, improve ProcessorTesterMixin
* change size and crop_size in processor kwargs tests to do_rescale and rescale_factor
* remove unnecessary llava processor kwargs test overwrite
* nit
* change data_arg_name to input_name
* Remove unnecessary test override
* Remove unnecessary tests Paligemma
* Move test_prepare_and_validate_optional_call_args to TesterMixin, add docstring
* change sequence_bias type of SequenceBiasLogitsProcessor tp list, add config tests for all processors
* fix format
* small fix for all_token_bias_pairs_are_valid internal func
* small typo fix in description
* improve test impl, some SequenceBiasLogitsProcessor refactoring
* add tests
* fix whisper
* update
* nit
* add qwen2-vl
* more updates!
* better this way
* fix this one
* fix more tests
* fix final tests, hope so
* fix led
* Update tests/generation/test_utils.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* pr comments
* not pass pixels and extra for low-mem tests, very flaky because of visio tower
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* clean mimi commit
* some nits suggestions from Arthur
* make fixup
* rename repo id + change readme
* Update docs/source/en/model_doc/mimi.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add flaky flag to batching equivalence due to audio_codes failing sometimes
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* _decode signature change and quick return
* added bunch of decoding tests
* signature match and return
* added tests for decoding
* merged decoding test
* more tests for special tokens
* cosmetics
* fixed param
* ruffed the file
* refinement for single special tokens
* added test for single special tokens
* slight change to test name
Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
* minor change test name for skip tokens
Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
* killed already defined var
Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
* minor update with vars
Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
* killed already defined var once more
Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
---------
Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
* fix patch_attention_mask incorrect setting which leads to the difference in the generated text if batch > 1
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* fix format
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* [run_slow] idefics2
---------
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* added sequences_scores to the output
* added beam_indices to output
* added test to check for beam_indices, sequences_scores and their shape
* removed redundant whitespaces
* make fixup
* idefics2 enable_input_require_grads not aligned with disable_input_require_grads
make peft+idefics2 checkpoints disable fail
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* split test case
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* fix ci failure
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* refine test
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* refactor weight_norm + propose uniformed solution to reconcile meta load_state_dict with classic loading
* make style
* fix sew
* fix sew and sew_d tests
* Fix failing tensor placement in Whisper
* fix long form generation tests
* more return_timestamps=True
* make fixup
* [run_slow] whisper
* [run_slow] whisper
* Uniformize kwargs for LlaVa and update docs
* Change order of processor inputs in docstring
* Improve BC support for reversed images and text inputs
* cleanup llava processor call docstring
* Add encoded inputs as valid text inputs in reverse input check, add deprecation version in warning
* Put function check reversed images text outside base processor class
* Refactor _validate_images_text_input_order
* Add ProcessingUtilTester
* fix processing and test_processing
* initial commit
* gloups
* updates
* work
* weights match
* nits
* nits
* updates to support the tokenizer :)
* updates
* Pixtral processor (#33454)
* rough outline
* Add in image break and end tokens
* Fix
* Udo some formatting changes
* Set patch_size default
* Fix
* Fix token expansion
* nit in conversion script
* Fix image token list creation
* done
* add expected results
* Process list of list of images (#33465)
* updates
* working image and processor
* this is the expected format
* some fixes
* push current updated
* working mult images!
* add a small integration test
* Uodate configuration docstring
* Formatting
* Config docstring fix
* simplify model test
* fixup modeling and etests
* Return BatchMixFeature in image processor
* fix some copies
* update
* nits
* Update model docstring
* Apply suggestions from code review
* Fix up
* updates
* revert modeling changes
* update
* update
* fix load safe
* addd liscence
* update
* use pixel_values as required by the model
* skip some tests and refactor
* Add pixtral image processing tests (#33476)
* Image processing tests
* Add processing tests
* woops
* defaults reflect pixtral image processor
* fixup post merge
* images -> pixel values
* oups sorry Mr docbuilder
* isort
* fix
* fix processor tests
* small fixes
* nit
* update
* last nits
* oups this was really breaking!
* nits
* is composition needs to be true
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix long seq bug
* fixed format
* fixed fn copy inconsistency
* fix long seq bug
* fixed format
* fixed fn copy inconsistency
* Addressed comments
* added a unit test
* fixed cache position
* Added a warning msg to the forward fn
* fixed test case
* test(tokenizers): add a test showing conflict with sentencepiece
This is due to the fact that protobuf C implementation uses a global
pool for all added descriptors, so if two different files add
descriptors, they will end up conflicting.
* fix(tokenizers): mitigate sentencepiece/protobuf conflict
When sentencepiece is available, use that protobuf instead of the
internal one.
* chore(style): fix with ruff
* Update tokenization_whisper.py
Fix issue with flax whisper model
* Update tokenization_whisper_fast.py
Fix issue with flax whisper model
* Update tokenization_whisper.py
just check len of token_ids
* Update tokenization_whisper_fast.py
just use len of token_ids
* Update tokenization_whisper_fast.py and revert changes in _strip_prompt and add support to jax arrays in _convert_to_list
* Update tokenization_whisper.py and revert changes in _strip_prompt and add support to jax arrays in _convert_to_list
* Update test_tokenization_whisper.py to add test for _convert_to_list method
* Update test_tokenization_whisper.py to fix code style issues
* Fix code style
* Fix code check again
* Update test_tokenization)whisper.py to Improve code style
* Update test_tokenization_whisper.py to run each of jax, tf and flax modules if available
* Update tests/models/whisper/test_tokenization_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update test_tokenization_whisper.py and use require_xxx decorators instead of `is_xxx_available()` method
* Revert the changes automatically applied by formatter and was unrelated to PR
* Format for minimal changes
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add tests for linear shape behavior
* fix linear shape behavior
ended up adding the reshape at the end, after f8f8bf16_rowwise, because adding
it directly after quantize_fp8_per_row caused f8f8bf16_rowwise to drop the
seq_len dimension. (i.e., (17, 23, 1014) -> (17, 1024))
* save shape up front + comment
* Make StaticCache configurable at model construct time
* integrations import structure
* add new doc file to toc
---------
Co-authored-by: Guang Yang <guangyang@fb.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
* Update docs for GGUF supported models
* Add tensor mappings and define class GGUFPhi3Converter
* Fix tokenizer
* Working version
* Attempt to fix some CI failures
* Run ruff format
* Add vocab, merges, decoder methods like LlamaConverter
* Resolve conflicts since Qwen2Moe was added to gguf
- I missed one place when resolving conflict
- I also made a mistake with tests_ggml.py and now has been fixed to reflect
its master version.
* Import structure & first three model refactors
* Register -> Export. Export all in __all__. Sensible defaults according to filename.
* Apply most comments from Amy and some comments from Lucain
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Lucain Pouget <lucainp@gmail.com>
* Style
* Add comment
* Clearer .py management
* Raise if not in backend mapping
* More specific type
* More efficient listdir
* Misc fixes
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Lucain Pouget <lucainp@gmail.com>
* add self.head_dim for VisionAttention in Qwen2-VL
* add self.head_dim for VisionAttention in Qwen2-VL
* fix ci
* black the test_modeling_qwen2_vl.py
* use ruff to format test_modeling_qwen2_vl.py
* [run-slow] qwen2_vl
* use tying for python3.8
* fix the import format
* use ruff to fix the ci error I001
* [run-slow] qwen2_vl
* remove unused import
* commit for rebase
* use ruff fix ci
* [run-slow] qwen2_vl
---------
Co-authored-by: root <liji>
* Add validation for maximum sequence length in modeling_whisper.py
Added a validation check to ensure that the sequence length of labels does not exceed the maximum allowed length of 448 tokens. If the sequence length exceeds this limit, a ValueError is raised with a descriptive error message.
This change prevents the model from encountering errors or unexpected behavior due to excessively long sequences during training or fine-tuning, ensuring consistent input dimensions and improving overall robustness.
* Change exception message in src/transformers/models/whisper/modeling_whisper.py
The exception message is for whisper's label's sequence max length.
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* Change 448 to config.max_target_positions in src/transformers/models/whisper/modeling_whisper.py
It's for whisper's config.max_target_positions.
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* Change method's documentation in src/transformers/models/whisper/modeling_whisper.py
* Add test for maximum label's sequence length in test_modeling_whisper.py
* Add self to modeling_whisper.py
* Update test_modeling_whisper.py with respect to automatic validations
* Update modeling_whisper.py with respect to ci/circleci: check_code_quality
* Update test_modeling_whisper.py with respect to ci/circleci: check_code_quality
* Update test_modeling_whisper.py with respect to ci/circleci: tests_generate
* Update test_modeling_whisper.py with respect to ci/circleci: tests_generate
* Update test_modeling_whisper.py with respect to ci/circleci: check_code_quality
* Separate test_labels_sequence_max_length tests in test_modeling_whisper.py
* Update test_modeling_whisper.py with respect to ci/circleci: check_code_quality
* Remove assert from test_modeling_whisper.py
* Add max_target_positions to WhisperModelTester in test_modeling_whisper.py
* Update test_modeling_whisper.py with respect to ci/circleci: check_code_quality
* Update test_modeling_whisper.py with respect to ci/circleci: tests_generate
* Update test_modeling_whisper.py
* Change test_labels_sequence_max_length_error_after_changing_config in test_modeling_whisper.py
* Change self.config.max_target_positions to self.max_target_positions modeling_whisper.py
* Add new tests in test_modeling_whisper.py
* Update test_modeling_whisper.py
---------
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* Load remote code only once
* Use hash as load indicator
* Add a new option `force_reload` for old behavior (i.e. always reload)
* Add test for dynamic module is cached
* Add more type annotations to improve code readability
* Address comments from code review
* Add validate images and test processing utils
* Remove encoded text from possible inputs in tests
* Removed encoded inputs as valid in processing_utils
* change text input check to be recursive
* change text check to all element of lists and not just the first one in recursive checks
* [InstructBLIP] qformer_tokenizer is required input
* Bit safer
* Add to instructblipvideo processor
* Fix up
* Use video inputs
* Update tests/models/instructblipvideo/test_processor_instructblipvideo.py
* Fixing a bug in the way "attention_factor" is validated in ROPE utilities.
* Fixing a bug in the way "attention_factor" is validated in ROPE utilities.
* Fixing a bug in the way "attention_factor" is validated in ROPE utilities.
* use gguf internal dequantize
* add Q5_0 test
* add iq1 test
* add remained test
* remove duplicated test
* update docs
* add gguf version limit
* make style
* update gguf import catch
* revert vocab_size patch
* make style
* use GGUF_MIN_VERSION everywhere
* remove to restiction for 4-bit model
* Update src/transformers/modeling_utils.py
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* bitsandbytes: prevent dtype casting while allowing device movement with .to or .cuda
* quality fix
* Improve warning message for .to() and .cuda() on bnb quantized models
---------
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* don't run custom when not needed?
* update test fetcher filtering
* fixup and updates
* update
* update
* reduce burden
* nit
* nit
* mising comma
* this?
* this?
* more parallelism
* more
* nit for real parallelism on tf and torch examples
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update to make it more custom
* update to make it more custom
* update to make it more custom
* update to make it more custom
* update
* update
* update
* update
* update
* update
* use correct path
* fix path to test files and examples
* filter-tests
* filter?
* filter?
* filter?
* nits
* fix naming of the artifacts to be pushed
* list vs files
* list vs files
* fixup
* fix list of all tests
* fix the install steps
* fix the install steps
* fix the config
* fix the config
* only split if needed
* only split if needed
* extend should fix it
* extend should fix it
* arg
* arg
* update
* update
* run tests
* run tests
* run tests
* more nits
* update
* update
* update
* update
* update
* update
* update
* simpler way to show the test, reduces the complexity of the generated config
* simpler way to show the test, reduces the complexity of the generated config
* style
* oups
* oups
* fix import errors
* skip some tests for now
* update doctestjob
* more parallelism
* fixup
* test only the test in examples
* test only the test in examples
* nits
* from Arthur
* fix generated congi
* update
* update
* show tests
* oups
* oups
* fix torch job for now
* use single upload setp
* oups
* fu**k
* fix
* nit
* update
* nit
* fix
* fixes
* [test-all]
* add generate marker and generate job
* oups
* torch job runs not generate tests
* let repo utils test all utils
* UPdate
* styling
* fix repo utils test
* more parallel please
* don't test
* update
* bit more verbose sir
* more
* hub were skipped
* split by classname
* revert
* maybe?
* Amazing catch
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* fix
* update
* update
* maybe non capturing
* manual convert?
* pass artifacts as parameters as otherwise the config is too long
* artifact.json
* store output
* might not be safe?
* my token
* mmm?
* use CI job IS
* can't get a proper id?
* ups
* build num
* update
* echo url
* this?
* this!
* fix
* wget
* ish
* dang
* udpdate
* there we go
* update
* update
* pass all
* not .txt
* update
* fetcg
* fix naming
* fix
* up
* update
* update
* ??
* update
* more updates
* update
* more
* skip
* oups
* pr documentation tests are currently created differently
* update
* hmmmm
* oups
* curl -L
* update
* ????
* nit
* mmmm
* ish
* ouf
* update
* ish
* update
* update
* updatea
* nit
* nit
* up
* oups
* documentation_test fix
* test hub tests everything, just marker
* update
* fix
* test_hub is the only annoying one now
* tf threads?
* oups
* not sure what is happening?
* fix?
* just use folder for stating hub
* I am getting fucking annoyed
* fix the test?
* update
* uupdate
* ?
* fixes
* add comment!
* nit
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* Adding SDPA support for RoBERTa-based models
* add not is_cross_attention
* fix copies
* fix test
* add minimal test for camembert and xlm_roberta as their test class does not inherit from ModelTesterMixin
* address some review comments
* use copied from
* style
* consistency
* fix lists
---------
Co-authored-by: fxmarty <9808326+fxmarty@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* init fix
* fix mask during cached forward, move mask related stuff to own function
* adjust tests as left padding does not change logits as much anymore + batch gen (with todo on logits comp)
* revert overwriting new integration tests
* move some comments to docstring
* add Blip2ForImageTextRetrieval
* use one line and remove unnecessary space in tests
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* use value from the config, rather than hardcoded
* change order of params in Blip2QFormerModel.forward
* update docstring
* fix style
* update test_inference_opt
* move embeddings out of Blip2QFormerModel
* remove from_vision_qformer_configs
* remove autocast float16 in Blip2QFormerModel
* rename fiels into vision_projection,text_projection,use_image_text_matching_head
* use CLIPOutput for Blip2ImageTextMatchingModelOutput
* remove past_key_values_length from Blip2TextEmbeddings
* fix small typo in the CLIPOutput docstring
* add Blip2ForImageTextRetrieval to Zero Shot Image Classification mapping
* update docstring and add require_torch_fp16
* rollback test_inference_opt
* use use_image_text_matching_head=True in convert
* skip test_model_get_set_embeddings
* fix create_rename_keys error on new itm fields
* revert to do scale after dot product between "query" and "key"
* fix ValueError on convert script for blip2-opt-2.7b
* update org of paths to Salesforce
* add is_pipeline_test_to_skip for VisualQuestionAnsweringPipelineTests
* [run_slow] blip_2
* removed Blip2ForImageTextRetrieval from IGNORE_NON_AUTO_CONFIGURED
* fix docstring of Blip2ImageTextMatchingModelOutput
* [run_slow] blip_2
* fix multi-gpu tests
* [run_slow] blip_2
* [run_slow] blip_2
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add a fix for the case when tokenizers are passed as a string
* Support image processors and feature extractors as well
* Reverting load_feature_extractor and load_image_processor
* Add test
* Test is torch-only
* Add tests for preprocessors and feature extractors and move test
* Extremely experimental fix
* Revert that change, wrong branch!
* Typo!
* Split tests
* fix param not being passed in tested; add exceptions
* better source of model name
* Update utils/create_dummy_models.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix: multilingual midel convert to tflite get wrong token
* fix: modify test_force_tokens_logits_processor the checking value as scores.dtype.min
---------
Co-authored-by: kent.sc.hung <kent.sc.hung@benq.com>
Co-authored-by: Aya <[kent831217@gmail.com]>
* Add new Jinja features:
- Do extension
- Break/continue in loops
- Call strftime to get current datetime in any format
* Add new Jinja features:
- Do extension
- Break/continue in loops
- Call strftime to get current datetime in any format
* Fix strftime template
* Add template strip() just to be safe
* Remove the do extension to make porting easier, and also because it's the least useful
* Rename test
* strftime -> strftime_now
* Split test
* Update test to use strftime_now
* Refactor everything out into chat_template_utils
* Refactor everything out into chat_template_utils
* Refactor everything out into chat_template_utils
* Refactor everything out into chat_template_utils
* Refactor everything out into chat_template_utils
* Add .float() in all generation methods logit outputs
* Switch float-casting of logits to training only for main models
* Add `num_logits_to_keep` in Llama and add it by default in generate
* Apply style
* Add num_logits_to_keep as arg in prepare_input_for_generation
* Add support for Mistral
* Revert models except llama and mistral
* Fix default None value in _supports_num_logits_to_keep()
* Fix dimension of dummy input
* Add exception for prophetnet in _supports_num_logits_to_keep()
* Update _supports_num_logits_to_keep() to use inspect.signature()
* Add deprecation cycle + remove modification with pretraining_tp
* Apply style
* Add most used models
* Apply style
* Make `num_logits_to_keep` an int in all cases to remove if-else clause
* Add compile check for the warning
* Fix torch versions
* style
* Add gemma2
* Update warning version
* Add comment about .float operations in generation utils
* Add tests in GenerationTesterMixin and ModelTesterMixin
* Fix batch size for assisted decoding in tests
* fix small issues in test
* refacor test
* fix slicing removing dim issue
* Add nemotron support (should fix check-copy issue in CIs)
* Trigger new CIs
* Trigger new CIs
* Bump version
* Bump version in TODO
* Trigger CIs
* remove blank space
* Trigger CIs
* more precise name
* better docstrings
* Update src/transformers/cache_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add TorchAOHfQuantizer
Summary:
Enable loading torchao quantized model in huggingface.
Test Plan:
local test
Reviewers:
Subscribers:
Tasks:
Tags:
* Fix a few issues
* style
* Added tests and addressed some comments about dtype conversion
* fix torch_dtype warning message
* fix tests
* style
* TorchAOConfig -> TorchAoConfig
* enable offload + fix memory with multi-gpu
* update torchao version requirement to 0.4.0
* better comments
* add torch.compile to torchao README, add perf number link
---------
Co-authored-by: Marc Sun <marc@huggingface.co>
* Add padding="max_length" to tokenizer kwargs and change crop_size to size for image_processor kwargs
* remove crop_size argument in align processor tests to be coherent with base tests
* Add pad_token when loading tokenizer if needed, change test override tokenizer kwargs, remove unnecessary test overwrites in grounding dino
* fix typo
* uniform kwargs
* make style
* add comments
* remove return_tensors
* remove common_kwargs from processor since it propagates
* make style
* return_token_type_ids to True
* revert the default imagekwargs since does not accept any value in the image processro
* revert processing_utils.py
* make style
* add molbap's commit
* fix typo
* fix common processor
* remain
* Revert "add molbap's commit"
This reverts commit a476c6ee88.
* add unsync PR
* revert
* make CI happy
* nit
* import annotationformat
* add new model like
* draft cuda forward - mismatched keys (sharding on conv1)
* match keys successfully
* fix split
* get generation/forward running (wrong gens, norm?)
* :update
* some refactoring
* fixes
* works up until copy to cache
* fix
* update
* NON WORKING VERSION
* version that work?
* nit
* fix config
* fix conversion script
* working cuda forward
* nit
* update
* simplifcation
* make mamba slow simple work
* no einops
* todo
* fix style
* no einops
* update fix no einsum
* nit
* remove einops
* bug: scan_output differs strongly
* add rms norm option
* fix fast + slow generation with and w/o cache ✔️
* draft integration tests
* remove a big chunk of the einsum
* fix slow, fast generations, without any einsum
* fix copies
* fix structure
* fix up modeling and tests
* fix tests
* clamping is indeed worse
* recover mamba2 cache test
* fix copies
* no cache position (yet)
* fix tf tests
* fix matmul for generate
* fixup
* skip cache tests for now
* [run-slow]mamba2
* tune out hidden states for padding
* test batched generation
* propagate attention mask changes
* fix past length
* fix integration test
* style
* address comments
* update readme
* add mamba2 version check
* fix tests
* [run-slow]mamba2
* skip edge tests
* [run-slow]mamba2
* last fixup
* [run-slow]mamba2
* update README
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
* save total_vocab_size = vocab_size + user added tokens to speed up operation
* updating length when added_tokens_decoder is set
* add test len(tokenizer)
* Test this zach
* Test for improper init w/o zero3
* Move back
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Get rid of stars in warning
* Make private
* Make clear
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Initial implementation of OffloadedCache
* enable usage via cache_implementation
* Address feedback, add tests, remove legacy methods.
* Remove flash-attn, discover synchronization bugs, fix bugs
* Prevent usage in CPU only mode
* Add a section about offloaded KV cache to the docs
* Fix typos in docs
* Clarifications and better explanation of streams
* Fix conflicting key in init kwargs in PreTrainedTokenizerBase
* Update code to check for callable key in save_pretrained
* Apply PR suggestions
* Invoke CI
* Updates based on PR suggestion
* Remove user-defined tokens which can be obtained through merges
* Remove debug line
* formatting
* Refactor spm slow -> fast converter
* revert unnecessary refactor
* set comprehension
* remove test files
* Use `vocab_scores`
* Always replace spiece underline with space in decode
* we no longer need token filtering
* Add save fast load slow unit test
* Remove tokenizers version check
* Remove duplicate code
* Make `<start_of_turn>` and `<end_of_turn>` special tokens
* Bias merge priority with length if score is the same
* Add unit test for merge priority
* CI
* mvp
* added test (a few models need fixes)
* fix a few test cases
* test nits
* harder test 😈
* revert changes in stablelm
* test with improved condition
* add todo
* tmp commit
* merged with main
* nits
* add todo
* final corrections
* add docs for generation compilation
* docs nits
* add tip
* PR suggestions
* add more details to the compilation docs
* fix cache positions
* cache is now init in generate; update docs
* tag test as flaky
* docs
* post rebase make fixup and other nits
* remove unintended changes
* whisper (encoder-decoder) not supported
* move token default updates to ; add tests for token defaults
* push changes
* manual rebase
* chameleon doesn't support this
* fix test_static_cache_mha_mqa_gqa (broken in another PR)
* docs: dynamic is better with end-to-end compilation
* No more default chat templates
* Add the template to the GPT-SW3 tests since it's not available by default now
* Fix GPT2 test
* Fix Bloom test
* Fix Bloom test
* Remove default templates again
* Updated ruff version and fixed the required code accorindg to the latest version.
* Updated ruff version and fixed the required code accorindg to the latest version.
* Added noqa directive to ignore 1 error shown by ruff
* add DataCollatorBatchFlattening
* Update data_collator.py
* change name
* new FA2 flow if position_ids is provided
* add comments
* minor fix
* minor fix data collator
* add test cases for models
* add test case for data collator
* remove extra code
* formating for ruff check and check_repo.py
* ruff format
ruff format tests src utils
* custom_init_isort.py
* gguf conversion forces add_prefix_space=False for llama3, this is not required and forces from_slow, which fails. changing to None + test
* typo
* clean test
* Change resize_token_embeddings to make it return same Class that is passed to it
* Add explanatory comment as requested in review
* Add explanatory comments for add resizing function in lxmert
* Add comment for padding_idx and moving _resize_bias in lxmert to LxmertForPreTraining
---------
Co-authored-by: Prashanth Sateesh <prasatee@Prashanths-MBP.attlocal.net>
Co-authored-by: Prashanth Sateesh <prasatee@Prashanths-MacBook-Pro.local>
* Add YaRN and Dynamic-YaRN RoPE Scaling Methods
YaRN (Yet another RoPE extension method) combines the NTK-By-Parts
Interpolation and Attention Scaling methods, improving upon existing
RoPE interpolation methods for longer context window sizes.
Fine-tuned models maintain their original performance across benchmarks
while enabling efficient extrapolation and transfer learning for
quicker convergence, especially in compute-limited environments.
We implement YaRN and Dynamic-YaRN for the following list of models:
- LLaMA
- Falcon
- GPT-NeoX
- Olmo
- Persimmon
- Phi
- StableLM
- OpenLLaMA
New unit tests are added to assert YaRN's correct behavior on both
short and long sequence inputs.
For more details, please refer to https://arxiv.org/abs/2309.00071.
Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>
* Refactor YaRN implementation for LLaMA
Iterate on YaRN implementation for LLaMA and remove diff from remaining
models for increased PR modularity.
This commit includes the following changes:
- Merge 'yarn_rope_scaling' and 'rope_scaling' dictionaries
- Remove unnecessary attributes ('extrapolation_factor' and 'finetuned')
from YaRN classes
- Inherit 'forward' method in YaRN classes from superclass
- Rename 'yarn' method to 'compute_yarn_scaling'
- Extend YaRN tests with further assertions
- Fix style inconsistencies
Co-authored-by: Miguel Monte e Freitas <miguelmontefreitas@tecnico.ulisboa.pt>
* Refactor Tensor Building Logic for YaRN
- Comply with the the tensor building logic introduced in #30743
- Add referencing to the optimized Attention Factor equation
- Remove Dynamic YaRN for a more agile deployment
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
* remove unwanted file
---------
Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
* fix mask creation of gpt2 and gpt_neox caused by me
* forgot the reshape of masks when shape > 2
* add tests for gpt neox and gpt2
* nit on a comment
* add language to words
_collate_word_timestamps uses the return_language flag to determine whether the language of the chunk should be added to the word's information
* ran style checks
added missing comma
* add new language test
test that the pipeline can return both the language and timestamp
* remove model configuration in test
Removed model configurations that do not influence test results
* remove model configuration in test
Removed model configurations that do not influence test results
* 1,100%!
* Clean
* Don't touch DS
* Experiment with dtype allocation
* skip test_load_save_without_tied_weights test
* A little faster
* Include proper upscaling?
* Fixup tests
* Potentially skip?
* Let's see if this fixes git history
* Maintain new dtype
* Fin
* Rm hook idea for now
* New approach, see what breaks
* stage
* Clean
* Stash
* Should be fin now, just need to mark failing models
* Clean up
* Simplify
* Deal with weird models
* Enc/Dec
* Skip w/ reason
* Adjust test
* Fix test
* one more test
* Keep experimenting
* Fix ref
* TO REMOVE: testing feedback CI
* Right push
* Update tests/utils/test_modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* disable
* Add new func
* Test nits from Amy
* Update src/transformers/modeling_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Adjust comment
* Adjust comment on skip
* make private
* Fin
* Should be a not flag
* Clarify and rename test
---------
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix the incorrect permutation of gguf
* rename num_kv_heads
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add typing to num_kv_heads
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* rename variables
* refactor permute function name
* update the expected text of the llama3 q4 test
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* tmp commit
* shorter
* nit
* explicit kwargs
* propagate changes
* mass propagation with a few manual touches (let's see how CI behaves)
* fix cacheless case
* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* make fixup
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add warning message for and parameters
* Fix when the warning is raised
* Formatting changes
* Improve testing and remove duplicated warning from _fix_key
* fix galore lr display with lr schedulers
* style
* add some tests to check for displayed lrs
* copy-paste err for warmup steps
* standardize the default lr to be only in the optimizer
* trying out my luck with the reads
* cast image features to model.dtype where needed to support FP16 or other precision in pipelines
* Update src/transformers/pipelines/image_feature_extraction.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use .to instead
* Add FP16 pipeline support for zeroshot audio classification
* Remove unused torch imports
* Add docs on FP16 pipeline
* Remove unused import
* Add FP16 tests to pipeline mixin
* Add fp16 placeholder for mask_generation pipeline test
* Add FP16 tests for all pipelines
* Fix formatting
* Remove torch_dtype arg from is_pipeline_test_to_skip*
* Fix format
* trigger ci
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add siglip loss function
* Update docs
* Enable training tests
[experimental] enable GC training tests as it has worked for my own data
* Remove test_training* overrides to enable training tests
[run_slow] siglip
* Skip training tests for Siglip text model and ImageClassificationModel
[run_slow] siglip
* Skip GC training tests for SiglipForImageClassification
* Explicitly skip training tests for SiglipVisionModel
Add skip reason for training tests for SiglipTextModel
* Remove copied from to fix CI
* Fix init for rt-detr heads
* Fixup
* Add separate prior_prob value to config for initialization
* Add bbox init
* Change to 1 / num_labels init
* Adjust weights init test
* Fix style for test
* add gather_use_object arguments
* fix name and pass the CI test for Seq2SeqTrainer
* make style
* make it to functools
* fix typo
* add accelerate version:
* adding warning
* Update src/transformers/trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* make style
* Update src/transformers/training_args.py
* check function move to initial part
* add test for eval_use_gather_object
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* squash into single commit
* run diff once more
* docstring
* tests
* minor chnages and ready to go
* Update src/transformers/models/llava_next_video/processing_llava_next_video.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/vipllava/test_modeling_vipllava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* [run-slow] llava-next-video
* [run-slow] llava-next-video
* [run-slow] llava_next_video
* fix two tests
* fix slow tests
* remove logit checks due to numeric errors
* run test once more
* [run-slow] llava_next_video
* final try to pass the test
* [run-slow] llava_next_video
* [run-slow] llava_next_video
* [run-slow] llava_next_video
* style
* fix
* style
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* fix llama fsdp
* fixup
* adding FSDP tests for CPU offloading
* fixes
* fix tests
* fix tests
* add it for mixtral
* propagate the changes on other models
* Update src/transformers/models/phi/modeling_phi.py
* Delete utils/testing_scripts/fsdp_cpu_offloading.py
Remove script - FSDP + CPU offloading it tested in the test suite
* Delete utils/testing_scripts/dummy_fsdp_config.yml
* Update + add cache_positions docstring
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* starting support for sdpa in `gptneox` models
* small comment on tests
* fix dropout
* documentation and style
* clarify concrete paths for reference
* generalise attn projections and rope application
added head mask check to sdpa mask creation
handle sdpa memory backend bug via own version flag
* update docs and style
* move dtype casting outside of general attn_projection_and_rope function
fix flash_attn_2 stuff
* more generic attn warning if output_attns or head_mask
* simplify head mask check by moving head mask creation to a later point
* remove copied llama artifact
* remove padding_mask from attention function signature
* removing unnecessary comments, only "save" attn implementation once
* [run_slow] gpt_neox
* Add initial implementation of `spectrogram_batch`
* Format the initial implementation
* Add test suite for the `spectrogram_batch`
* Update `spectrogram_batch` to ensure compatibility with test suite
* Update `spectrogram_batch` to include pre and post-processing
* Add `amplitude_to_db_batch` function and associated tests
* Add `power_to_db_batch` function and associated tests
* Reimplement the test suite for `spectrogram_batch`
* Fix errors in `spectrogram_batch`
* Add the function annotation for `spectrogram_batch`
* Address code quality
* Re-add `test_chroma_equivalence` function
* Update src/transformers/audio_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/audio_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* PR SPLIT: moving origina changes for adding user defined symbols
* adding gemma test and generalizing gemma converter
* ruff
* update common test
* update serialization test
* deberta v2 tests updates as rust version adds '.' as a user added token, so a space is not added
* removing commented lines
* applying feedback - user only added_tokens to add and check piece.type instead of trainer_spec for user_defined_symbols
* add comment referencing sentencepiece
* Fix single letter stop strings
* Change the 0 to a 1 to avoid potential empty vector headaches later
* Restructure for clarity
* Update tests/generation/test_stopping_criteria.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add the unsqueeze
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Improve Python interpreter
* Add with and assert statements
* Prevent overwriting existing tools
* Check interpreter errors are well logged in code agent
* Add lazy evaluation for and and or
* Improve variable assignment
* Fix early return statements in functions
* Add small import fix on interpreter tool
* Pass datasets trust_remote_code
* Pass trust_remote_code in more tests
* Add trust_remote_dataset_code arg to some tests
* Revert "Temporarily pin datasets upper version to fix CI"
This reverts commit b7672826ca.
* Pass trust_remote_code in librispeech_asr_dummy docstrings
* Revert "Pin datasets<2.20.0 for examples"
This reverts commit 833fc17a3e.
* Pass trust_remote_code to all examples
* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects
* Pass trust_remote_code to tests
* Pass trust_remote_code to docstrings
* Fix flax examples tests requirements
* Pass trust_remote_dataset_code arg to tests
* Replace trust_remote_dataset_code with trust_remote_code in one example
* Fix duplicate trust_remote_code
* Replace args.trust_remote_dataset_code with args.trust_remote_code
* Replace trust_remote_dataset_code with trust_remote_code in parser
* Replace trust_remote_dataset_code with trust_remote_code in dataclasses
* Replace trust_remote_dataset_code with trust_remote_code arg
* Draft fast image processors
* Draft working fast version
* py3.8 compatible cache
* Enable loading fast image processors through auto
* Tidy up; rescale behaviour based on input type
* Enable tests for fast image processors
* Smarter rescaling
* Don't default to Fast
* Safer imports
* Add necessary Pillow requirement
* Woops
* Add AutoImageProcessor test
* Fix up
* Fix test for imagegpt
* Fix test
* Review comments
* Add warning for TF and JAX input types
* Rearrange
* Return transforms
* NumpyToTensor transformation
* Rebase - include changes from upstream in ImageProcessingMixin
* Safe typing
* Fix up
* convert mean/std to tesnor to rescale
* Don't store transforms in state
* Fix up
* Update src/transformers/image_processing_utils_fast.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Warn if fast image processor available
* Update src/transformers/models/vit/image_processing_vit_fast.py
* Transpose incoming numpy images to be in CHW format
* Update mapping names based on packages, auto set fast to None
* Fix up
* Fix
* Add AutoImageProcessor.from_pretrained(checkpoint, use_fast=True) test
* Update src/transformers/models/vit/image_processing_vit_fast.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Add equivalence and speed tests
* Fix up
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* First draft, still missing automatic function conversion
* First draft of the automatic schema generator
* Lots of small fixes
* the walrus has betrayed me
* please stop committing your debug breakpoints
* Lots of cleanup and edge cases, looking better now
* Comments and bugfixes for the type hint parser
* More cleanup
* Add tests, update schema generator
* Update tests, proper handling of return values
* Small docstring change
* More doc updates
* More doc updates
* Add json_schema decorator
* Clean up the TODOs and finish the docs
* self.maxDiff = None to see the whole diff for the nested list test
* add import for add_json_schema
* Quick test fix
* Fix something that was bugging me in the chat template docstring
* Less "anyOf" when unnecessary
* Support return types for the templates that need them
* Proper return type tests
* Switch to Google format docstrings
* Update chat templating docs to match new format
* Stop putting the return type in with the other parameters
* Add Tuple support
* No more decorator - we just do it implicitly!
* Add enum support to get_json_schema
* Update docstring
* Add copyright header
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/chat_templating.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/utils/chat_template_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/utils/chat_template_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add copyright header
* make fixup
* Fix indentation
* Reformat chat_template_utils
* Correct return value
* Make regexes module-level
* Support more complex, multi-line arg docstrings
* Update error message for ...
* Update ruff
* Add document type validation
* Refactor docs
* Refactor docs
* Refactor docs
* Clean up Tuple error
* Add an extra test for very complex defs and docstrings and clean everything up for it
* Document enum block
* Quick test fixes
* Stop supporting type hints in docstring to fix bugs and simplify the regex
* Update docs for the regex change
* Clean up enum regex
* Wrap functions in {"type": "function", "function": ...}
* Update src/transformers/utils/chat_template_utils.py
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* Temporary tool calling commit
* Add type hints to chat template utils, partially update docs (incomplete!)
* Code cleanup based on @molbap's suggestion
* Add comments to explain regexes
* Fix up type parsing for unions and lists
* Add custom exception types and adjust tests to look for them
* Update docs with a demo!
* Docs cleanup
* Pass content as string
* Update tool call formatting
* Update docs with new function format
* Update docs
* Update docs with a second tool to show the model choosing correctly
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* Rename to test_model_common_attributes
The method name is misleading - it is testing being able to get and set embeddings, not common attributes to all models
* Explicitly skip
* Update TVP model to interpolate pre-trained image pad prompter encodings
* feat: Add 2D positional embeddings interpolation in TvpVisualInputEmbedding
* added required comments
* Update TVP model to interpolate pre-trained image pad prompter encodings
* feat: Add 2D positional embeddings interpolation in TvpVisualInputEmbedding
* added required comments
* docstring and argument fix
* doc fixes and test case fix suggested in review.
* varibale typo fix
* styling and name fixes for padding interpolation flag.
* Remove ConversationalPipeline and Conversation object, as they have been deprecated for some time and are due for removal
* Update not-doctested.txt
* Fix JA and ZH docs
* Fix JA and ZH docs some more
* Fix JA and ZH docs some more
* Implement JSON dump conversion for torch_dtype in TrainingArguments
* Add unit test for converting torch_dtype in TrainingArguments to JSON
* move unit test for converting torch_dtype into TrainerIntegrationTest class
* reformating using ruff
* convert dict_torch_dtype_to_str to private method _dict_torch_dtype_to_str
---------
Co-authored-by: jun.4 <jun.4@kakaobrain.com>
* Add list check for image and question
* Handle passing two lists and update docstring
* Add tests
* Add support for dataset
* Add test for dataset as input
* fixup
* fix unprotected import
* fix unprotected import
* fix import again
* fix param type
* Initial attempt
* Updates: PR suggestions
* Interpolate the relative position bias when interpolate_pos_encoding is True
* Add slow tag for the added tests
* Add in DATA2VEC_VISION_INPUTS_DOCSTRING
* Added interpolate pos encoding feature and test to deit
* Added interpolate pos encoding feature and test for deit TF model
* readded accidentally delted test for multi_gpu
* storing only patch_size instead of entire config and removed commented code
* Update modeling_tf_deit.py to remove extra line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix the get_size_with_aspect_ratio in max_size situation
* make fix-up
* add more general solution
* consider when max_size is not defined
* fix typo
* fix typo
* simple fix
* fix error
* fix if else error
* fix error of size overwrite
* fix yolos image processing
* fix detr image processing
* make
* add longest related test script
* Update src/transformers/models/yolos/image_processing_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add more test
* add test script about longest size
* remove deprecated
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* token healing impl + trie with extensions
* make fixup
* prefix-robust space tokenization
* examples readme and requirements
* make fixup
* allow input prompt and model
* redundant defaults
* Specialized Trie
* make fixup
* updated tests with new inherited Tree
* input ids to auto device_map
* rm unused import
* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* naming convention
* Revert "naming convention"
This reverts commit dd39d9c5b7a969e2d8a8d2a8e54f121b82dc44f0.
* naming convention
* last -hopefully- changes
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix has_file in offline mode
* harmonize env variable for offline mode
* Switch to HF_HUB_OFFLINE
* fix test
* revert test_offline to test TRANSFORMERS_OFFLINE
* Add new offline test
* merge conflicts
* docs
* seems like `split_special_tokens` is used here
* split special token
* add new line at end of file
* moving split special token test to common tests
* added assertions
* test
* fixup
* add co-author
* passing rest of args to gptsan_japanese, fixing tests
* removing direct comparison of fast and slow models
* adding test support for UDOP and LayoutXLM
* ruff fix
* readd check if slow tokenizer
* modify test to handle bos tokens
* removing commented function
* trigger build
* applying review feedback - updated docstrings, var names, and simplified tests
* ruff fixes
* Update tests/test_tokenization_common.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* applying feedback, comments
* shutil temp directory fix
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Ita Zaporozhets <itazaporozhets@Itas-MBP.localdomain>
Co-authored-by: itazap <itazap@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Ita Zaporozhets <itazaporozhets@Itas-MacBook-Pro.local>
* added interpolation for vitmae model in pytorch as well as tf.
* Update modeling_vit_mae.py
irreugalr import fixed
* small changes and proper formatting
* changes suggested in review.
* modified decoder interpolate_func
* arguments and docstring fix
* Apply suggestions from code review
doc fixes
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add test that currently fails
* test passed
* all perceiver passed
* fixup, style, quality, repo-consistency, all passed
* Apply suggestions from code review: default to False + compute sqrt once only
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix a minor bracket
* replace dim with self._num_channels
* add arguments to the rest preprocessors
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add prefix space ignored in llama #29625
* adding test with add_prefix_space=False
* ruff
---------
Co-authored-by: Ita Zaporozhets <itazaporozhets@Itas-MBP.localdomain>
* clean-up
* Update src/transformers/cache_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/cache_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/cache_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup
* Update tests/quantization/quanto_integration/test_quanto.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/generation/configuration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* more suggestions
* mapping if torch available
* run tests & add 'support_quantized' flag
* fix jamba test
* revert, will be fixed by another PR
* codestyle
* HQQ and versatile cache classes
* final update
* typo
* make tests happy
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
If required padding for a crop larger than input image is odd-numbered,
the padding would be rounded down instead of rounded up, causing the
output dimension to be one smaller than it should be.
* Introduce configured_state
* Include note on tuning
* Allow for users to have defined a state already
* Include tests
* Add note on hpam tune
* Guard a bit better
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Finish rebase
* Finish rebase
* Guard carefully
* Fixup test
* Refactor
* Fin refactor
* Comment
* Update wrt feedback
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix for custom pipeline configuration
* fix for custom pipelines
* remove extra exception
* added test for custom pipelines extra tag
* format with ruff
* limit extra tag for first time only
* format with ruff
* improve tests for custom pipelines
* Add MistralForTokenClassification
* Add tests and docs
* Add token classification for Mixtral and Qwen2
* Save llma for token classification draft
* Add token classification support for Llama, Gemma, Persimmon, StableLm and StarCoder2
* Formatting
* Add token classification support for Qwen2Moe model
* Add dropout layer to each ForTokenClassification model
* Add copied from in tests
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Propagate suggested changes
* Style
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* add interpolation of positional encoding support to swin
* add style changes
* use default image processor and make size a dictionary
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove logits testing
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Refactor image size validation logic when interpolation is disabled
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove asserts in modeling
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add dynamic resolution input support to swinv2
* change size to ensure interpolation encoding path is triggered
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* set interpolate_pos_encoding default value to False
* add dynamic resolution input to donut swin
* add dynamic resolution input to maskformer swin
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Support arbitrary processor
* fix
* nit
* update
* nit
* nit
* fix and revert
* add a small test
* better check
* fixup
* bug so let's just use class for now
* oups
* .
* Fix llama model forward function with attention=True, same-length encoded sequence.
* Fix style
* propagate fix to modeling_cohere, gemma, dbrx, and olmo (which copy the same sdpa masking logic from llama)
* Fix style
* ignore unnecessary sdpa mask converter when output_attentions=True
* add tests checking sdpa and eager outputs match when output_attentions=True
* Split if statements in two lines
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix formatting
* Add fix to new jetmoe model
* Add missing output_attentions argument to jetmoe mask creation
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add method
* change method name
* more comments
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fixup
* add docstrings and fix comment
* warn users on the de-quantized dtype
* Update src/transformers/quantizers/base.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/bitsandbytes.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* final suggestion - use private method
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add support for mixing languages in a single batch
* Update docstring
* Enable different detected languages in batch
* Do not require input_features
* Test list of languages
* Fix comment
* Make init_tokens length-1 if possible, broadcast at the end
* Test for ValueError with language list of incorrect length
* Slow test for batched multilingual transcription
* fixup
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Address review, refactor
* Second attempt to move this line where it was originally
* Split test, fix a bug
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Initial commit
* Just a copy of modeling_idefics.py that will be ported to TF
* - Prepend TF to the name of all classes
- Convert pytorch ops to TF (not all operations are converted yet)
* Add TF imports
* Add autotranslated files
* Add TF classes to model_tf_auto.py
* Add the TF classes in model_doc
* include auto-translated code
* Adopted from auto-translated version
* Add a forgotten super().build
* Add test code for TF version.
* Fix indentation and load pytorch weights for now
* Some fixes. Many tests are still failing but some are passing now.
- I have added TODO's for some of the hacks I made to unblock me
and I will address them soon
- I have the processing_idefics.py hacked in my view to support TF temporarily
* Add ALL_LAYERNORM_LAYERS to match pytorch
* Revert "Add ALL_LAYERNORM_LAYERS to match pytorch"
This reverts commit 7e0a35119b4d7a6284d04d8c543fba1b29e573c9 as it
is not needed in the tf implementation.
* Fix freeze_relevant_params()
* Some more fixes
* Fix test_attention_outputs
* Add tf stuff to processing_idefics.py
processing_idefics.py supports both pytorch and tf now.
test_processor_idefics.py for pytorch is passing, so i didn't break anything
but still some issues with tf. I also need to add tf tests in
test_processor_idefics.py.
* Pass return_tensors to image processing code and fix test
* Pass return_tensors to the image processor __init__
* Fix several test cases
- Make input to some of the forward pass of type `TFModelInputType`
- Decorate main layer forward pass with `@unpack_inputs`
- Decorate main layer with `@keras_serializable`
- Pass `inputs` to TFIdeficsModel
* Some more fixes forgotten in last commit
* Fix processing code and vision_tf.py
* Fix perceiver bug
* Import from
* Auto-add build() methods + style pass
* Fix build() errors due to `None` being passed as shape to some layers
* Change name in TFIdeficsForVisionText2Text to attribute in IdeficsForVisionText2Text
* Fix pytorch weights load for tf2
There were a lot of `name=` missing in weight initialization code.
* Attempt to fix CI
* Add back accidently removed line
* Remove torch-specific stuff from the TF test file
* make fix-copies, make style, remove autotranslated files
* Fixes to imports/docstrings
* Let's try the from future import in desperation
* Fix the core random_attention_mask fn to match the torch/flax behaviour
* Clean random_attention_mask up correctly
* Remove torch-only test
* Fix loss shape, couple of nits
* make style
* Don't test for OOB embeddings because IDEFICS uses those deliberately
* Fix loss computation to handle masking
* Fix test failures when flattening
* Fix some test failures
- Add cross attention gate which was missing and wasn't being passed arround
- Fix overwriting of image_attention_mask due to hack I had for dummy inputs
* Add a proper stateless scaled_dot_product_attention
* make style
* Adding missing attribute from the PyTorch version
* Small cleanups to decoupledlinearlayer in case that helps
* Pass epsilon to LayerNormalization
* Attemp to fix pytorch weight cross-loading for TFIdeficsEmbedding
* Fix a bug in TFIdeficsGatedCrossAttentionLayer
* Patching up build() methods
* Constant self.inv_freq
* Constant self.inv_freq
* First working version
The TF implementation works now, there was a bug in the TFIdeficsDecoupledLinear
where the weights were mis-intialized (in_features,out_features)
when it should be: (out_features, in_features)
I have tested this so far with tiny-random and idefics-9b-instruct
and gives correct output.
I also dumped the final outputs for both pytorch and TF
and they are identical.
* Fix some test failures
* remove print statement
* Fix return_tensors
* Fix CI test failure check_code_quality
* Attempt to fix CI failures by running `make fixup`
The hardcoded IDs in test_modeling_tf_idefics.py are for the integration
test and makes that file unreadable and should probably be moved to a seperate file.
* Attempt to fix tests_pr_documentation_tests
* Fix a test failure in test_image_processing_idefics.py
* Fix test test_pt_tf_model_equivalence
* Fix a few failures
* Tiny fix
* Some minor fixes
* Remove a duplicate test
* Override a few test failures for IDEFICS
- `test_keras_save_load` is passing now
- `test_compile_tf_model` is still failing
* Fix processing_idefics.py after rebase
* Guard import keras with is_tf_available
* fix check code quality
* fix check code quality
* Minor fixes
* Skip test_save_load temporarily
This test passed on my local box but fails on the CI, skipping
for now to see if there are other remaining failures on the CI.
* Run `ruff format tests src utils`
* Fix last failing test, `test_compile_tf_model`
* Add fixes for vision_tf.py
I forgot to add this file in last commit.
* Minor fixes
* Replace "<<<" with "<<" for doc tests
IDEFICS-9B is too big for doctest runner, so don't run it there
* Make code more readable
* Fix bug after code review
I added a layer_norm_eps to IdeficsConfig but I don't even need it
since the vision config has a layer_norm_eps.
* Fix after code review
Use original code tokenizer.convert_tokens_to_ids
* Keep PyTorch as the default return_tensors
* Fixes to modeling_tf after code review
* Fixes from code review
- Remove all references of `TF_IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST`
- Pass 1e-5 to LayerNormalization in perceiver
* Run ruff
* Undo a change
* Refactor processing code after Matt's suggestion
* Remove TODO's that aren't needed anymore
* For pytorch, Use original pytorch processing code from main
Since this PR is a TF port it shouldn't make any modifications
to pytorch IDEFICS code. This changes undo's the pytorch processing
modifications I made and uses original code from main.
* Update tests/models/idefics/test_modeling_idefics.py
* Update tests/models/idefics/test_modeling_tf_idefics.py
* Add missing imports for is_pt_tf_cross_test
* [DO NOT MERGE]: This is a commit for debugging and will be reverted
The cross test `test_pt_tf_model_equivalence` passes locally but
fails when running on the CI. This commit is to help debug that
and will be reverted.
* Revert "[DO NOT MERGE]: This is a commit for debugging and will be reverted"
This reverts commit 8f0d709ec5bd46685fb0b4259d914ffee794875b.
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 998cc38b8c3d313bf5e5eb55a7f5b7b881897b89.
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 1c695ac4219c4ae4d39b330b01744dc27deb7dd4.
* Don't skip test_save_load
IIRC test_save_load was also failing on the CI but not on my local
box, it might be easier to debug that on the CI first than the cross tests
* Debugging commit, will be reverted
* Revert "Debugging commit, will be reverted"
This reverts commit 8eafc8e41e20c4e95a3a90834f06a6e9f445e2d5.
* Override `test_save_load` and push model to save
Maybe this will help me repro this weird bug
* pass my repo_id
* add endpoint
* Pass a temp (write) token just for this CI
* Undo last few commits, still pushing to hub for model debugging
The issue seems to be with save_pretrained(), when I looked at the model saved
from the CI test failure it is basically empty and has no weights.
`self.save_weights(..)` seems to be failing in save_pretrained but needs
more debugging
* Add logging to modeling tf utils, will be reverted just for debugging
* Debugging, will revert
* Revert "Debugging, will revert"
This reverts commit 9d0d3075fb7c82d8cde3a5c76bc8f3876c5c55d3.
* Revert "Add logging to modeling tf utils, will be reverted just for debugging"
This reverts commit 774b6b7b1c17b3ce5d7634ade768f2f686cee617.
* Remove `test_save_load`
The CI failures are gone after my latest rebase, no idea why
but I was still saving the model to my hub on HF and the tf_model.h5
file now has everything.
* Run make fix-copies
* Run ruff format tests src utils
* Debugging commit, will be reverted
* Run ruff, also trigger CI run
* Run ruff again
* Undo debugging commit
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* blip with interpolated pos encoding
* feat: Add interpolate_pos_encoding option to other models from `BLIP` family.
* include check for textual generated content in tests
* immutability fix for seq2seq as well as immutability tests for the collators
* ensure we don't act on none labels and formatting
* remove tf/pt in respective tests as they are not required
* more type error fixes tf/np
* remove todo
* apply suggestions from code review
* formatting / style
* Create CodeAgent and ReactAgent
* Fix formatting errors
* Update documentation for agents
* Add custom errors, improve logging
* Support variable usage in ReactAgent
* add messages
* Add message passing format
* Create React Code Agent
* Update
* Refactoring
* Fix errors
* Improve python interpreter
* Only non-tensor inputs should be sent to device
* Calculator tool slight refactor
* Improve docstrings
* Refactor
* Fix tests
* Fix more tests
* Fix even more tests
* Fix tests by replacing output and input types
* Fix operand type issue
* two small fixes
* EM TTS
* Fix agent running type errors
* Change text to speech tests to allow changed outputs
* Update doc with new agent types
* Improve code interpreter
* If max iterations reached, provide a real answer instead of an error
* Add edge case in interpreter
* Add safe imports to the interpreter
* Interpreter tweaks: tuples and listcomp
* Make style
* Make quality
* Add dictcomp to interpreter
* Rename ReactJSONAgent to ReactJsonAgent
* Misc changes
* ToolCollection
* Rename agent's logger to self.logger
* Add while loops to interpreter
* Update doc with new tools. still need to mention collections
* Add collections to the doc
* Small fixes on logs and interpretor
* Fix toolbox return type
* Docs + fixup
* Skip doctests
* Correct prompts with improved examples and formatting
* Update prompt
* Remove outdated docs
* Change agent to accept Toolbox object for tools
* Remove calculator tool
* Propagate removal of calculator in doc
* Fix 2 failing workflows
* Simplify additional argument passing
* AgentType audio
* Minor changes: function name, types
* Remove calculator tests
* Fix test
* Fix torch requirement
* Fix final answer tests
* Style fixes
* Fix tests
* Update docstrings with calculator removal
* Small type hint fixes
* Update tests/agents/test_translation.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_python_interpreter.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/default_tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_agents.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bert/configuration_bert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/speech_to_text.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_speech_to_text.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_tools_common.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* pygments
* Answer comments
* Cleaning up
* Simplifying init for all agents
* Improving prompts and making code nicer
* Style fixes
* Add multiple comparator test in interpreter
* Style fixes
* Improve BERT example in documentation
* Add examples to doc
* Fix python interpreter quality
* Logging improvements
* Change test flag to agents
* Quality fix
* Add example for HfEngine
* Improve conversation example for HfEngine
* typo fix
* Verify doc
* Update docs/source/en/agents.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/agents.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/prompts.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/python_interpreter.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/agents.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix style issues
* local s2t tool
---------
Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com>
Co-authored-by: Lysandre <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Adding _tie_weights() to prediction heads to support low_cpu_mem_usage=True
* Testing for the non-safe-tensors case, since the default is safe-tensors already
* Running fixup/fix-copies
* Adding accelerate annotations to tests
* Added cache clearing for GPU efficiency.
* Added cache clearing for GPU efficiency.
* Added batch_eval_metrics capability
* Ran make fixup
* Fixed bug
* Fixed whitespace issue
* Fixed outdated condition
* Updated docstrings with instructions for batch_eval_metrics. Updated end of dataloader logic
* Added first version of batch_eval_metrics Trainer test
* Fixed batch_eval_metrics Trainer tests for both eval and predict
* Fixed batch_eval_metrics behavior for new Trainer variables
* Fixed batch_eval_metrics Trainer tests
* Ran fixup
* Trainer: load checkpoint model with multiple adapters
* Trainer._load_from_checkpoint support multiple active adapters
* PeftModel.set_adapter does not support multiple adapters yet
* Trainer._load_from_checkpoint test multiple adapters
---------
Co-authored-by: Clara Luise Pohland <clara-luise.pohland@telekom.de>
* change cis
* nits
* update
* minor updates
* [push-ci-image]
* nit [push-ci-image]
* nitsssss
* [build-ci-image]
* [push-ci-image]
* [push-ci-image]
* both
* [push-ci-image]
* this?
* [push-ci-image]
* pypi-kenlm needs g++
* [push-ci-image]
* nit
* more nits [push-ci-image]
* nits [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* add vision
* [push-ci-image]
* [push-ci-image]
* add new dummy file but will need to update them [push-ci-image]
* [push-ci-image]
* show package size as well
* [push-ci-image]
* potentially ignore failures
* workflow updates
* nits [push-ci-image]
* [push-ci-image]
* fix consistency
* clean nciida triton
* also show big packages [push-ci-image]
* nit
* update
* another one
* line escape?
* add accelerate [push-ci-image]
* updates [push-ci-image]
* nits to run tests, no push-ci
* try to parse skip reason to make sure nothing is skipped that should no be skippped
* nit?
* always show skipped reasons
* nits
* better parsing of the test outputs
* action="store_true",
* failure on failed
* show matched
* debug
* update short summary with skipped, failed and errors
* nits
* nits
* coolu pdates
* remove docbuilder
* fix
* always run checks
* oups
* nits
* don't error out on library printing
* non zero exi codes
* no warning
* nit
* WAT?
* format nit
* [push-ci-image]
* fail if fail is needed
* [push-ci-image]
* sound file for torch light?
* [push-ci-image]
* order is important [push-ci-image]
* [push-ci-image] reduce even further
* [push-ci-image]
* use pytest rich !
* yes [push-ci-image]
* oupsy
* bring back the full traceback, but pytest rich should help
* nit
* [push-ci-image]
* re run
* nit
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* empty push to trigger
* [push-ci-image]
* nit? [push-ci-image]
* empty
* try to install timm with no deps
* [push-ci-image]
* oups [push-ci-image]
* [push-ci-image]
* [push-ci-image] ?
* [push-ci-image] open ssh client for git checkout fast
* empty for torch light
* updates [push-ci-image]
* nit
* @v4 for checkout
* [push-ci-image]
* [push-ci-image]
* fix fetch tests with parallelism
* [push-ci-image]
* more parallelism
* nit
* more nits
* empty to re-trigger
* empty to re-trigger
* split by timing
* did not work with previous commit
* junit.xml
* no path?
* mmm this?
* junitxml format
* split by timing
* nit
* fix junit family
* now we can test if the xunit1 is compatible!
* this?
* fully list tests
* update
* update
* oups
* finally
* use classname
* remove working directory to make sure the path does not interfere
* okay no juni should have the correct path
* name split?
* sort by classname is what make most sense
* some testing
* naem
* oups
* test something fun
* autodetect
* 18?
* nit
* file size?
* uip
* 4 is best
* update to see versions
* better print
* [push-ci-image]
* [push-ci-image]
* please install the correct keras version
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* uv is fucking me up
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* nits
* [push-ci-image]
* [push-ci-image]
* install issues an pins
* tapas as well
* nits
* more paralellism
* short tb
* soundfile
* soundfile
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* oups
* [push-ci-image]
* fix some things
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* use torch-light for hub
* small git lfs for hub job
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* fix tf tapas
* [push-ci-image]
* nits
* [push-ci-image]
* don't update the test
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* no use them
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* update tf proba
* [push-ci-image]
* [push-ci-image]
* woops
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* test with built dockers
* [push-ci-image]
* skip annoying tests
* revert fix copy
* update test values
* update
* last skip and fixup
* nit
* ALL GOOOD
* quality
* Update tests/models/layoutlmv2/test_image_processing_layoutlmv2.py
* Update docker/quality.dockerfile
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Update src/transformers/models/tapas/modeling_tf_tapas.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* use torch-speed
* updates
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* [push-ci-image]
* fuck ken-lm [push-ci-image]
* [push-ci-image]
* [push-ci-image]
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* move scaling to nn.Module
* let the test be here for now (need to fix)
* failing tests
* last failing models
* Revert commit 4c14817f38
* clean-up
* oops forgot
* codestyle
* raise NotImplemented when possible
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* skip tests in respective modeling files
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix seq2seq data collator to respect the given padding strategy
further added tests for the seq2seq data collator in the style of the `data_collator_for_token_classification` (pt, tf, np)
* formatting and change bool equals "==" to "is"
* add missed return types in tests
* update numpy test as it can handle unequal shapes, not like pt or tf
* Enable instantiating model with pretrained backbone weights
* Clarify pretrained import
* Use load_backbone instead
* Add backbone_kwargs to config
* Fix up
* Add tests
* Tidy up
* Enable instantiating model with pretrained backbone weights
* Update tests so backbone checkpoint isn't passed in
* Clarify pretrained import
* Update configs - docs and validation check
* Update src/transformers/utils/backbone_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Clarify exception message
* Update config init in tests
* Add test for when use_timm_backbone=True
* Use load_backbone instead
* Add use_timm_backbone to the model configs
* Add backbone_kwargs to config
* Pass kwargs to constructors
* Draft
* Fix tests
* Add back timm - weight naming
* More tidying up
* Whoops
* Tidy up
* Handle when kwargs are none
* Update tests
* Revert test changes
* Deformable detr test - don't use default
* Don't mutate; correct model attributes
* Add some clarifying comments
* nit - grammar is hard
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Adding SDPA support for BERT
* Using the proper input name for testing model input in inference()
* Adding documentation for SDPA in BERT model page
* Use the stable link for the documentation
* Adding a gate to only call .contiguous() for torch < 2.2.0
* Additions and fixes to the documentation
* Minor updates to documentation
* Adding extra requirements needed for the contiguous() bug
* Adding "Adapted from" in plcae of the "Copied from"
* Add benchmark speedup tables to the documentation
* Minor fixes to the documentation
* Use ClapText as a replacemenet for Bert in the Copied-From
* Some more fixes for the fix-copies references
* Overriding the test_eager_matches_sdpa_generate in bert tests to not load with low_cpu_mem_usage
[test all]
* Undo changes to separate test
* Refactored SDPA self attention code for KV projections
* Change use_sdpa to attn_implementation
* Fix test_sdpa_can_dispatch_on_flash by preparing input (required for MultipleChoice models)
* Introduce saveable callbacks
* Add note
* Test for non-present and flag
* Support early stopping and refusing to train further
* Update docstring
* More saving
* Import oopsie
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make it go through TrainerArguments
* Document
* Fix test
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Rework to allow for duplicates
* CLean
* Fix failing tests
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* first modeling code
* make repository
* still WIP
* update model
* add tests
* add latest change
* clean docstrings and copied from
* update docstrings md and readme
* correct chroma function
* correct copied from and remove unreleated test
* add doc to toctree
* correct imports
* add convert script to notdoctested
* Add suggestion from Sanchit
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* correct get_uncoditional_inputs docstrings
* modify README according to SANCHIT feedback
* add chroma to audio utils
* clean librosa and torchaudio hard dependencies
* fix FE
* refactor audio decoder -> audio encoder for consistency with previous musicgen
* refactor conditional -> encoder
* modify sampling rate logics
* modify license at the beginning
* refactor all_self_attns->all_attentions
* remove ignore copy from causallm generate
* add copied from for from_sub_models
* fix make copies
* add warning if audio is truncated
* add copied from where relevant
* remove artefact
* fix convert script
* fix torchaudio and FE
* modify chroma method according to feedback-> better naming
* refactor input_values->input_features
* refactor input_values->input_features and fix import fe
* add input_features to docstrigs
* correct inputs_embeds logics
* remove dtype conversion
* refactor _prepare_conditional_hidden_states_kwargs_for_generation ->_prepare_encoder_hidden_states_kwargs_for_generation
* change warning for chroma length
* Update src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* change way to save wav, using soundfile
* correct docs and change to soundfile
* fix import
* fix init proj layers
* add draft training
* fix cross entropy
* clean loss computation
* fix labels
* remove line breaks from md
* fix issue with docstrings
* add FE suggestions
* improve is in logics and remove useless imports
* remove custom from_pretrained
* simplify docstring code
* add suggestions for modeling tests
* make style
* update converting script with sanity check
* remove encoder attention mask from conditional generation
* replace musicgen melody checkpoints with official orga
* rename ylacombe->facebook in checkpoints
* fix copies
* remove unecessary warning
* add shape in code docstrings
* add files to slow doc tests
* fix md bug and add md to not_tested
* make fix-copies
* fix hidden states test and batching
* update training code
* add training tests for melody
* add training for o.g musicgen
* fix copied from
* remove final todos
* make style
* fix style
* add suggestions from review
* add ref to the original loss computation code
* rename method + fix labels in tests
* make style
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* chore(root): Initial commit of Phi-3 files.
* fix(root): Fixes Phi-3 missing on readme.
* fix(root): Ensures files are consistent.
* fix(phi3): Fixes unit tests.
* fix(tests): Fixes style of phi-3 test file.
* chore(tests): Adds integration tests for Phi-3.
* fix(phi3): Removes additional flash-attention usage, .e.g, swiglu and rmsnorm.
* fix(phi3): Fixes incorrect docstrings.
* fix(phi3): Fixes docstring typos.
* fix(phi3): Adds support for Su and Yarn embeddings.
* fix(phi3): Improves according first batch of reviews.
* fix(phi3): Uses up_states instead of y in Phi3MLP.
* fix(phi3): Uses gemma rotary embedding to support torch.compile.
* fix(phi3): Improves how rotary embedding classes are defined.
* fix(phi3): Fixes inv_freq not being re-computed for extended RoPE.
* fix(phi3): Adds last suggestions to modeling file.
* fix(phi3): Splits inv_freq calculation in two lines.
* Fixed main train issues
* Added loss test
* Update src/transformers/models/seggpt/modeling_seggpt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Added missing labels arg in SegGptModel forward
* Fixed typo
* Added slow test to test loss calculation
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* push legacy to fast as well
* super strange
* Update src/transformers/convert_slow_tokenizer.py
* make sure we are BC
* fix Llama test
* nit
* revert
* more test
* style
* update
* small update w.r.t tokenizers
* nit
* don't split
* lol
* add a test for `add_prefix_space=False`
* fix gemma tokenizer as well
* update
* fix gemma
* nicer failures
* fixup
* update
* fix the example for legacy = False
* use `huggyllama/llama-7b` for the PR doctest
* nit
* use from_slow
* fix llama
* [FEAT]: EETQ quantizer support
* Update quantization.md
* Update docs/source/en/main_classes/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/__init__.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/__init__.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* [FEAT]: EETQ quantizer support
* [FEAT]: EETQ quantizer support
* remove whitespaces
* update quantization.md
* style
* Update docs/source/en/quantization.md
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* add copyright
* Update quantization.md
* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Address the comments by amyeroberts
* style
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* stash commit (will discard all of this)
* stash commit
* First commit - needs a lot of testing!
* Add a test
* Fix imports and make the tests actually test something
* Tests pass!
* Rearrange test
* Add comments (but it's still a bit confusing)
* Stop storing the tokenizer
* Comment fixup
* Fix for input_ids with a single sequence
* Update tests to test single sequences
* make fixup
* Fix incorrect use of isin()
* Expand tests to catch more cases
* Expand tests to catch more cases
* make fixup
* Fix length calculation and update tests
* Handle Ġ as a space replacement too
* Update src/transformers/generation/stopping_criteria.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Add optimizations from Joao's suggestion
* Remove TODO
* Update src/transformers/generation/stopping_criteria.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/generation/test_stopping_criteria.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* make fixup
* Rename some variables and remove some debugging clauses for clarity
* Add tests for the sub-methods
* Clarify one test slightly
* Add stop_strings to GenerationConfig
* generate() supports stop_string arg, asks for tokenizer if not provided
* make fixup
* Cleanup code and rename variables for clarity
* Update tokenizer error
* Update tokenizer passing, handle generation on GPU
* Slightly more explanation cleanup
* More comment cleanup
* Factor out the token cleanup so it's more obvious what we're doing, and we can change it later
* Careful with that cleanup!
* Cleanup + optimizations to _get_matching_positions
* More minor performance tweaks
* Implement caching and eliminate some expensive ops (startup time: 200ms -> 9ms)
* Remove the pin_memory call
* Parallelize across all stop strings!
* Quick fix for tensor devices
* Update embeddings test for the new format
* Fix test imports
* Manual patching for BERT-like tokenizers
* Return a bool vector instead of a single True/False
* Better comment
* Better comment
* Add tests from @zucchini-nlp
* Amy's list creation nit
* tok_list -> token_list
* Push a big expanded docstring (should we put it somewhere else?)
* Expand docstrings
* Docstring fixups
* Rebase
* make fixup
* Make a properly general method for figuring out token strings
* Fix naming throughout the functions
* Move cache, refactor, fix tests
* Add comment
* Remove finished TODO
* Remove finished TODO
* make fixup
* Update src/transformers/generation/stopping_criteria.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update and shorten docstring
* Update tests to be shorter/clearer and test specific cases
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Duplicate swiftformer
* Convert SwiftFormerPatchEmbedding
* Convert SwiftFormerEmbeddings
* Convert TFSwiftFormerMlp
* Convert TFSwiftFormerConvEncoder
* Convert TFSwiftFormerLocalRepresentation
* convert TFSwiftFormerEncoderBlock
* Convert SwiftFormerStage
* Convert SwiftFormerEncoder
* Add TFSWiftFormerPreTrainedModel
* Convert SwiftFormerForImageClassification
* Add kwargs and start drop path
* Fix syntax
* Change Model class name
* Add TFSwiftFormer to __init__
* Duplicate test_modeling_swiftformer
* First test conversions
* Change require_torch to require_tf
* Add exports to swiftformer __init__
* Add TFSwiftFormerModel wrapper
* Fix __init__ and run black
* Remove docstring from MainLayer, fix padding
* Use keras.layers.Activation on keras.Sequential
* Fix swiftformer exports
* Fix activation layer from config
* Remove post_inits
* Use tf.keras.layers.ZeroPadding2D
* Convert torch normalize
* Change tf test input shape
* Fix softmax and reduce_sum
* Convert expand_dims and repeat
* Add missing reshape and tranpose
* Simplify TFSwiftFormerEncoderBlock.call
* Fix mismatch in patch embeddings
* Fix expected output shape to match channels last
* Fix swiftformer typo
* Disable test_onnx
* Fix TFSwiftFormerForImageClassification call
* Add unpack inputs
* Convert flatten(2).mean(-1)
* Change vision dummy inputs (to be reviewed)
* Change test_forward_signature to use .call
* Fix @unpack_inputs
* Set return_tensors="tf" and rename class
* Rename wrongly named patch_embeddings layer
* Add serving_output and change dummy_input shape
* Make dimensions BCHW and transpose inside embedding layer
* Change SwiftFormerEncoderBlock
* Fix ruff problems
* Add image size to swiftformer config
* Change tranpose to MainLayer and use -1 for reshape
* Remove serving_outputs and dummy_inputs
* Remove test_initialization test from tf model
* Make Sequential component a separate layer
* Fix layers' names
* Tranpose encoder outputs
* Fix tests and check if hidden states is not None
* Fix TFSwiftFormerForImageClassification
* Run make fixup
* Run make fix-copies
* Update modeling_tf_auto
* Update docs
* Fix modeling auto mapping
* Update modelint_tf_swiftformer docs
* Fill image_size doc and type
* Add reduction=None to loss computation
* Update docs
* make style
* Debug: Delete the tip to see if that changes anything
* Re-add tip
* Remove add_code_sample_docstrings
* Remove unused import
* Get the debug to actually tell us the problem it has with the docs
* Try a substitution to match the PyTorch file?
* Add swiftformer to ignore list
* Add build() methods
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove FIXME comment
* Remove from_pt
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Rename one-letter variables
* Remove FIXMEs related to momentum
* Remove old TODO comment
* Remove outstanding FIXME comments
* Get dropout rate from config
* Add specific dropout config for MLP
* Add convencoder dropout to config
* Pass config to SwiftFormerDropPath layer
* Fix drop_path variable name and add Adapted from comment
* Run ruff
* Removed copied from comment
* Run fix copies
* Change drop_path to identity to match pt
* Cleanup build() methods and move to new keras imports
* Update docs/source/en/model_doc/swiftformer.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Raise error if drop_path_rate > 0.0
* Apply suggestions from code review
Replace (self.dim), with self.dim,
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Remove drop_path function
* Add training to TFSwiftFormerEncoder
* Set self.built = True last
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Should have been added to previous commit
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Change default_feature_extractor to default_image_processor
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Import Keras from modeling_tf_utils
* Remove relative import
* Run ruff --fix
* Move import keras to tf_available
* Add copied from comment to test_forward_signature
* Reduce batch size and num_labels
* Extract loss logic to hf_compute_loss
* Run ruff format
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* feat: multidevice for resnet
* feat: yes! resnet
* fix: compare all elements in tuple
* feat: support for regnet
* feat: support for convnextv2
* feat: support for bit
* feat: support for cvt
* feat: add support for focalnet
* feat: support for yolos
* feat: support for glpn
* feat: support for imagegpt
* feat: support for levit
* feat: support for mgp_str
* feat: support for mobilnet_v1
* feat: support for mobilnet_v2
* feat: support for mobilevit
* feat: support for mobilevitv2
* feat: support for poolformer
* fix: copies
* fix: code quality check
* update: upstream changes from main
* fix: consistency check
* feat: support for sam
* feat: support for switchformer
* feat: support for swin
* feat: support for swinv2
* feat: support for timesformer
* feat: suport for trocr
* feat: support for upernet
* fix: check copies
* update: rerun CI
* update: rerun again, maybe
* update: one more rerun
---------
Co-authored-by: Jacky Lee <jackylee328@gmail.com>
* wip
* fix __init__.py
* add docs
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* address comments 1
* work on make fixup
* pass configs down
* add sdpa attention
* remove DbrxBlock
* add to configuration_auto
* docstring now passes formatting test
* fix style
* update READMEs
* add dbrx to modeling_auto
* make fix-copies generated this
* add DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* config docstring passes formatting test
* rename moe_loss_weight to router_aux_loss_coef
* add to flash-attn documentation
* fix model-path in tests
* Explicitly make `"suli"` the default `ffn_act_fn`
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* default to using router_aux_loss_coef over ffn_config[moe_loss_weight]
* fix _flash_attn_uses_top_left_mask and is_causal
* fix tests path
* don't use token type IDs
* follow Llama and remove token_type_ids from test
* init ConfigTester differently so tests pass
* remove multiple choice test
* remove question + answer test
* remove sequence classification test
* remove token classification test
* copy Llama tests and remove token_type_ids from test inputs
* do not test pruning or headmasking; style code
* add _tied_weights_keys parameter to pass test
* add type hints
* fix type check
* update config tester
* remove masked_lm test
* remove encoder tests
* initialize DbrxModelTester with correct params
* style
* torch_dtype does not rely on torch
* run make fixup, fix-copies
* use https://huggingface.co/v2ray/dbrx-base-fixed/blob/main/modeling_dbrx.py
* add copyright info
* fix imports and DbrxRotaryEmbedding
* update DbrxModel docstring
* use copies
* change model path in docstring
* use config in DbrxFFN
* fix flashattention2, sdpaattention
* input config to DbrXAttention, DbrxNormAttentionNorm
* more fixes
* fix
* fix again!
* add informative comment
* fix ruff?
* remove print statement + style
* change doc-test
* fix doc-test
* fix docstring
* delete commented out text
* make defaults match dbrx-instruct
* replace `router_aux_loss_coef` with `moe_loss_weight`
* is_decoder=True
* remove is_decoder from configtester
* implement sdpa properly
* make is_decoder pass tests
* start on the GenerationTesterMixin tests
* add dbrx to sdpa documentation
* skip weight typing test
* style
* initialize smaller model
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Add DBRX to toctree
* skip test_new_cache_format
* make config defaults smaller again
* add pad_token_id
* remove pad_token_id from config
* Remove all references to DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* Update src/transformers/models/dbrx/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix typo
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update docs, fix configuration_auto.py
* address pr comments
* remove is_decoder flag
* slice
* fix requires grad
* remove grad
* disconnect differently
* remove grad
* enable grads
* patch
* detach expert
* nissan al ghaib
* Update modeling_dbrx.py
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* replace "Gemma" with "Dbrx"
* remove # type: ignore
* don't hardcode vocab_size
* remove ToDo
* Re-add removed idefics2 line
* Update test to use tiny-random!
* Remove TODO
* Remove one more case of loading the entire dbrx-instruct in the tests
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* address some comments
* small model
* add dbrx to tokenization_auto
* More docstrings with add_start_docstrings
* Dbrx for now
* add PipelineTesterMixin
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove flash-attn2 import error
* fix docstring
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add useage example
* put on one line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix ffn_act_fn
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* change "dbrx" to "DBRX" for display purposes.
* fix __init__.py?
* fix __init__.py
* fix README
* return the aux_loss
* remove extra spaces
* fix configuration_auto.py
* fix format in tokenization_auto
* remove new line
* add more useage examples
---------
Co-authored-by: Abhi Venigalla <abhi.venigalla@databricks.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Eitan Turok <eitan.turok@databricks.com>
Co-authored-by: Eitan Turok <150733043+eitanturok@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Eitan Turok <eitanturok@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add jamba arch
* apply "make fix-copies" changes
* fix link to model in JambaConfig docstring
* Add n_ctx in modeling file because repo-consistency wants that
* Add jamba to flash attention and sdpa documentation
* mamba dt_proj quant fix now works for LoRA as well
* override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers
* add jamba to tokenization auto
* fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24)
* simple PR fixes
* remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer
* remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530)
* Add copied comment on JambaMLP (it's the same as MixtralMLP)
* remove padding_mask warnings. It's not supported anymore
* fix docstring. Float instead of int
* A few more minor PR fixes
* (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass
* Return None attention weights from mamba layers. Append to all attentions only if not None.
* remove some leftover jamba archive lists
* Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel
* no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers
* Add Jamba paper on READMEs
* (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes)
* Add copied from comment
* remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms
* clearer docstring for _convert_to_standard_cache
* style fixes
* Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs
* rename test so it still overrides what its meant to override
* draft
* oups
* nit
* remove more complexe logic
* fix names used in config
* fix fix fix
* style
* fix some more failing tests
* generate did not init the cache 🙃
* more small nits
* typo
* config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes
* fix init of pkv with torch.tensor()
* empty tensor
* fix some init issues
* stupid changes required by generate because it does not even support it's own DynamicCache class
* more fixes
* fix general assisted gen cache_position bug
* tests passing
* Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py
* fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache
* no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore
* fix docstrings and typehints for past_key_values
* style fixes
* fix docs
* change typehint due to copy from Mixtral
* forgot import
* import order
* Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward)
* Add integration test with tiny tandom Jamba model on hub
* fix flash attention cache shapes
* bring back forgotten hidden states
* rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model
* align integration test after modeling fixes
* bugfix - mamba can use precomputed states only of forward pass is on a single token
* bugfix - mamba can use precomputed states only if they match the batch size
* typo
* remove making _prepare_4d_causal_attention_mask a leaf function
* stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
* Add OLMo using add-new-model-like with Llama
* Fix incorrect tokenizer for OLMo
* Copy-paste relevant OLMo methods and their imports
* Add OLMo config
* Modify OLMo config to follow HF conventions
* Remove unneeded Llama code from OLMo model
* Add ability for OLMo model to output attentions
* Add OLMoPreTrainedModel and OLMoModel
* Add OLMoForCausalLM
* Minor fixes to OLMo model for style and missing functions
* Implement OLMo tokenizer
* Implement OLMo to HF conversion script
* Add tests for OLMo model
* Add tests for OLMo fast tokenizer
* Add auto-generated dummy objects
* Remove unimplemented OLMo classes from auto and init classes and re-format
* Add README and associated auto-generated files
* Use OLMo names for common properties
* Run make fixup
* Remove `|` from OLMo typing
* Remove unneeded tokenization_olmo.py
* Revert model, config and converter to add-new-model-like Llama
* Move logic for adding bos/eos token into GPTNeoxTokenizerFast
* Change OLMoConfig defaults to match OLMo-7B
* Use GPTNeoXToknizerFast in OLMo tokenizer tests
* Modify auto-generated OLMoModelTests to work for OLMo
* Add non-parametric layer norm OLMoLayerNorm
* Update weight conversion script for OLMo
* Fix __init__ and auto structure for OLMo
* Fix errors from make fixup
* Remove OLMoTokenizerFast from documentation
* Add missing 'Copied from' for OLMoModel._update_causal_mask
* Run make fix-copies
* Rearrange string replacements in OLMoForCausalLM Copied from
* Move OLMo and Llama CausalLM.forward example into global constants
* Fix OLMO_GENERATION_EXAMPLE doc string typo
* Add option for qkv clipping to OLMo
* Rearrange OLMoConfig kwargs in convert_olmo_weights_to_hf
* Add clip_qkv to OLMoConfig in convert_olmo_weights_to_hf
* Fix OLMo tokenization bug using conversion script
* Keep model in full precision after conversion
* Do not add eos token automatically
* Update references to OLMo model in HF Hub
* Do not add eos token during encoding by default
* Fix Llama generation example
* Run make fixup
* OLMo 7B integration test fix
* Remove unneeded special case for OLMoConfig
* OLMo 7B Twin 2T integration test fix
* Fix test_model_7b_greedy_generation
* Remove test_compile_static_cache
* Fix OLMo and Llama generation example
* Run make fixup
* Revert "OLMo 7B integration test fix"
This reverts commit 4df56a4b15.
* Revert "OLMo 7B Twin 2T integration test fix"
This reverts commit 9ff65a4a29.
* Ungate 7B integration tests and fix greedy generation test
* Add retries for flaky test_eager_matches_sdpa_generate
* Fix output of doc example for OLMoForCausalLM.forward
* Downsize OLMo doc test for OLMoForCausalLM.forward to 1B model
* Try fix incorrect characters in OLMoForCausalLM.forward doct test
* Try fix incorrect characters in OLMoForCausalLM.forward doc test using end quotes
* Remove pretraining_tp from OLMo config and model
* Add missing 'Copied from' instances
* Remove unneeded causal_mask from OLMoModel
* Revert Llama changes
* Ignore copy for OLMoForCausalLM.forward
* Change 'OLMo' to 'Olmo' in classes
* Move minimal OLMo tokenization tests to model tests
* Add missed 'Copied from' for repeat_kv
* Add create token type ids to CodeGenTokenizer
* Fix inconsistent length of token type ids
* Format source codes
* Fix inconsistent order of methods
* Update docstring
* add test_tokenizer_integration test
* Format source codes
* Add `copied from` comment to CodeGenTokenizerFast
* Add doc of create_token_type_ids_from_sequences
* Make return_token_type_ids False by default
* Make test_tokenizer_integration as slow test
* Add return_token_type_ids to tokenizer init arg
* Add test for tokenizer's init return_token_type_ids
* Format source codes
