* fix stablelm dropout argument type error
* fix docs of _flash_attention_forward
* fix all docs of _flash_attention_forward
* fix docs of _flash_attention_forward in starcoder2
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Co-authored-by: oliang <oliang@tencent.com>
* fix image-to-text batch incorrect output issue
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* add ci test
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* update ci test
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* left-padding test revisited
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Set `inputs` as kwarg in `TextClassificationPipeline`
This change has been done to align the `TextClassificationPipeline` with the rest of the pipelines, and to be able to e.g. `pipeline(**{"inputs": "text"})` which wouldn't be possible since the `*args` were being used instead.
* Add `noqa: C409` on `tuple([inputs],)`
Even though is discouraged by the linter, the cast `tuple(list(...),)` is required here, as otherwise the original list in `inputs` will be transformed into a `tuple` and the elements 1...N will be ignored by the `Pipeline`
* Run `ruff format`
* Simplify `tuple` conversion with `(inputs,)`
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
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Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Fix: Disable torch.autocast in RotaryEmbedding of Gemma and LLaMa for MPS devices
* Update src/transformers/models/gemma/modeling_gemma.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update llama ang gemma rope use cpu in mps device
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* try to fix gemma mem use
* fix: handle attention mask dim==2 case
* remove logits=logits.float()
* clean up + add llama
* apply formatting
* readability edit: swap order of items being multiplied
* revert change unrelated to PR
* revert black autoformat
* switch to one .to
* Accept style edits
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* added the max_matching_ngram_size parameter into the GenerationConfig, for the PromptLookupCandidateGenerator
* switched back to keyword arguments
* added PromptLookupCandidateGenerator docstring for its parameters
* ruff reformat
* Update src/transformers/generation/configuration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix TrainingArguments regression with torch <2.0.0 for dataloader_prefetch_factor
dataloader_prefetch_factor was added to TrainingArguments in #28498 with the default value None, but versions of torch<2.0.0 do not accept None and will raise an error if num_workers == 0 and prefetch_factor != 2
* Add is_torch_available() check
* Use is_torch_greater_or_equal_than_2_0
add back check for dataloader_prefetch_factor
* initial-commit
* start cleaning
* small nits
* small nits
* current updates
* add kernels
* small refactoring little step
* add comments
* styling
* nit
* nits
* Style
* Small changes
* Push dummy mambda simple slow
* nit
* Use original names
* Use original names and remove norm
* Updates for inference params
* Style nd updates
* nits
* Match logits
* Add a test
* Add expected generated text
* nits doc, imports and styling
* style
* oups
* dont install kernels, invite users to install the required kernels
* let use use the original packages
* styling
* nits
* fix some copieds
* update doc
* fix-copies
* styling done
* nits
* fix import check
* run but wrong cuda ress
* mamba CUDA works :)
* fix the fast path
* config naming nits
* conversion script is not required at this stage
* finish fixing the fast path: generation make sense now!
* nit
* Let's start working on the CIs
* style
* better style
* more nits
* test nit
* quick fix for now
* nits
* nit
* nit
* nit
* nits
* update test rest
* fixup
* update test
* nit
* some fixes
* nits
* update test values
* fix styling
* nit
* support peft
* integrations tests require torchg
* also add slow markers
* styling
* chose forward wisely
* nits
* update tests
* fix gradient checkpointing
* fixup
* nit
* fix doc
* check copies
* fix the docstring
* fix some more tests
* style
* fix beam search
* add init schene
* update
* nit
* fix
* fixup the doc
* fix the doc
* fixup
* tentative update but slow is no longer good
* nit
* should we always use float32?
* nits
* revert wrong changes
* res in float32
* cleanup
* skip fmt for now
* update generation values
* update test values running original model
* fixup
* update tests + rename inference_params to cache_params + make sure training does not use cache_params
* small nits
* more nits
* fix final CIs
* style
* nit doc
* I hope final doc nits
* nit
* 🫠
* final touch!
* fix torch import
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Apply suggestions from code review
* fix fix and fix
* fix base model prefix!
* nit
* Update src/transformers/models/mamba/__init__.py
* Update docs/source/en/model_doc/mamba.md
Co-authored-by: Lysandre Debut <hi@lysand.re>
* nit
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Co-authored-by: Lysandre Debut <hi@lysand.re>
* added exllama kernels support for awq models
* doc
* style
* Update src/transformers/modeling_utils.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* refactor
* moved exllama post init to after device dispatching
* bump autoawq version
* added exllama test
* style
* configurable exllama kernels
* copy exllama_config from gptq
* moved exllama version check to post init
* moved to quantization dockerfile
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Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
