* revert back to torch 2.1.1
* run test
* switch to torch 2.2.1
* udapte dockerfile
* fix awq tests
* fix test
* run quanto tests
* update tests
* split quantization tests
* fix
* fix again
* final fix
* fix report artifact
* build docker again
* Revert "build docker again"
This reverts commit 399a5f9d93.
* debug
* revert
* style
* new notification system
* testing notfication
* rebuild docker
* fix_prev_ci_results
* typo
* remove warning
* fix typo
* fix artifact name
* debug
* issue fixed
* debug again
* fix
* fix time
* test notif with faling test
* typo
* issues again
* final fix ?
* run all quantization tests again
* remove name to clear space
* revert modfiication done on workflow
* fix
* build docker
* build only quant docker
* fix quantization ci
* fix
* fix report
* better quantization_matrix
* add print
* revert to the basic one
* See if we can get tests to pass with the fixed weights
* See if we can get tests to pass with the fixed weights
* Replace the revisions now that we don't need them anymore
* init: add StableLm 2 support
* add integration test for parallel residual and qk layernorm
* update(modeling): match qk norm naming for consistency with phi/persimmon
* fix(tests): run fwd/bwd on random init test model to jitter norm weights off identity
* `use_parallel_residual`: add copy pointer to `GPTNeoXLayer.forward`
* refactor: rename head states var in `StableLmLayerNormPerHead`
* tests: update test model and add generate check
* ImportError: Trainer with PyTorch requires accelerate>=0.20.1 Fix
Adding the evaluate and accelerate installs at the beginning of the cell to fix the issue
* ImportError Fix: Trainer with PyTorch requires accelerate>=0.20.1
* Import Error Fix
* Update installation.md
* Update quicktour.md
* rollback other lang changes
* Update _config.py
* updates for other languages
* fixing error
* Tutorial Update
* Update tokenization_utils_base.py
* Just use an optimizer string to pass the doctest?
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Co-authored-by: Matt <rocketknight1@gmail.com>
* add _torch_extract_fbank_features_batch function in feature_extractor_whisper
* reformat feature_extraction_whisper.py file
* handle batching in single function
* add gpu test & doc
* add batch test & device in each __call__
* add device arg in doc string
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Co-authored-by: vaibhav.aggarwal <vaibhav.aggarwal@sprinklr.com>
* separate jobs
* separate jobs
* use channel name directly instead of ID
* use channel name directly instead of ID
* use channel name directly instead of ID
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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Update quantizer_bnb_4bit.py
There is an mistake in ValueError on line 86 of quantizer_bnb_4bit.py. In the error string there should be "....you need to set `llm_int8_enable_fp32_cpu_offload=True`...." instead of "load_in_8bit_fp32_cpu_offload=True". I think you updated the BitsAndBytesConfig() arguments, but forgot to change the ValueError in quantizer_bnb_4bit.py.
* Update quantizer_bnb_4bit.py
Changed ValueError string "...you need to set load_in_8bit_fp32_cpu_offload=True..." to "....you need to set llm_int8_enable_fp32_cpu_offload=True...."
* if output is tuple like facebook/hf-seamless-m4t-medium, waveform is the first element
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* add test and fix batch issue
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* add dict output support for seamless_m4t
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
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Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* Defaulted IdeficsProcessor padding to 'longest', removed manual padding
* make fixup
* Defaulted processor call to padding=False
* Add padding to processor call in IdeficsModelIntegrationTest as well
* Defaulted IdeficsProcessor padding to 'longest', removed manual padding
* make fixup
* Defaulted processor call to padding=False
* Add padding to processor call in IdeficsModelIntegrationTest as well
* redefaulted padding=longest again
* fixup/doc
* implement convert_mamba_ssm_checkpoint_to_pytorch
* Add test test_model_from_mamba_ssm_conversion
* moved convert_ssm_config_to_hf_config to inside mamba_ssm_available check
* fix skipif clause
* moved skips to inside test since skipif decorator isn't working for some reason
* Added validation
* removed test
* fixup
* only compare logits
* remove weight rename
* Update src/transformers/models/mamba/convert_mamba_ssm_checkpoint_to_pytorch.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* nits
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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
