* Changed logic for renaming staging directory when saving checkpoint to only operate with the main process.
Added fsync functionality to attempt to flush the write changes in case os.rename is not atomic.
* Updated styling using make fixup
* Updated check for main process to use built-in versions from trainer
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* Fixed incorrect usage of trainer main process checks
Added with open usage to ensure better file closing as suggested from PR
Added rotate_checkpoints into main process logic
* Removed "with open" due to not working with directory. os.open seems to work for directories.
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Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* Fix initialization for missing parameters in `from_pretrained` under ZeRO-3
* Test initialization for missing parameters under ZeRO-3
* Add more tests
* Only enable deepspeed context for per-module level parameters
* Enable deepspeed context only once
* Move class definition inside test case body
* Add first draft
* Use appropriate gelu function
* More improvements
* More improvements
* More improvements
* Convert checkpoint
* More improvements
* Improve docs, remove print statements
* More improvements
* Add link
* remove unused masking function
* begin tokenizer
* do_lower_case
* debug
* set split_special_tokens=True
* Remove script
* Fix style
* Fix rebase
* Use same design as CLIP
* Add fast tokenizer
* Add SiglipTokenizer to init, remove extra_ids
* Improve conversion script
* Use smaller inputs in conversion script
* Update conversion script
* More improvements
* Add processor to conversion script
* Add tests
* Remove print statements
* Add tokenizer tests
* Fix more tests
* More improvements related to weight initialization
* More improvements
* Make more tests pass
* More improvements
* More improvements
* Add copied from
* Add canonicalize_text
* Enable fast tokenizer tests
* More improvements
* Fix most slow tokenizer tests
* Address comments
* Fix style
* Remove script
* Address some comments
* Add copied from to tests
* Add more copied from
* Add more copied from
* Add more copied from
* Remove is_flax_available
* More updates
* Address comment
* Remove SiglipTokenizerFast for now
* Add caching
* Remove umt5 test
* Add canonicalize_text inside _tokenize, thanks Arthur
* Fix image processor tests
* Skip tests which are not applicable
* Skip test_initialization
* More improvements
* Compare pixel values
* Fix doc tests, add integration test
* Add do_normalize
* Remove causal mask and leverage ignore copy
* Fix attention_mask
* Fix remaining tests
* Fix dummies
* Rename temperature and bias
* Address comments
* Add copied from to tokenizer tests
* Add SiglipVisionModel to auto mapping
* Add copied from to image processor tests
* Improve doc
* Remove SiglipVisionModel from index
* Address comments
* Improve docs
* Simplify config
* Add first draft
* Make it like mistral
* More improvements
* Fix attention_mask
* Fix output_attentions
* Add note in docs
* Convert multilingual model
* Convert large checkpoint
* Convert more checkpoints
* Add pipeline support, correct image_mean and image_std
* Use padding=max_length by default
* Make processor like llava
* Add code snippet
* Convert more checkpoints
* Set keep_punctuation_string=None as in OpenCLIP
* Set normalized=False for special tokens
* Fix doc test
* Update integration test
* Add figure
* Update organization
* Happy new year
* Use AutoModel everywhere
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Co-authored-by: patil-suraj <surajp815@gmail.com>
* fix input audio device for windows.
* ffmpeg audio device Windows
* Fixes wrong input device assignment in Windows
* Fixed getting mic on Windows systems by adding _get_microphone_name() function.
* Little bit change code in get_activation()
* proper area to deffine gelu_activation() in this two file
* Fix github issue
* Mistake some typo
* My mistake to self using to call config
* Reformat my two file
* Update src/transformers/activations.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/electra/modeling_electra.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/convbert/modeling_convbert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Rename gelu_act to activatioin
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix fa2 autocasting when using quantization
* Update src/transformers/models/distilbert/modeling_distilbert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/distilbert/modeling_distilbert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* [DETA] fix freeze/unfreeze function
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add freeze/unfreeze test case in DETA
* fix type
* fix typo 2
* fix : enable aux and enc loss in training pipeline
* Add unsynced variables from original DETA for training
* modification for passing CI test
* make style
* make fix
* manual make fix
* change deta_modeling_test of configuration 'two_stage' default to TRUE and minor change of dist checking
* remove print
* divide configuration in DetaModel and DetaForObjectDetection
* image smaller size than 224 will give topk error
* pred_boxes and logits should be equivalent to two_stage_num_proposals
* add missing part in DetaConfig
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add docstring in configure and prettify TO DO part
* change distribute related code to accelerate
* Update src/transformers/models/deta/configuration_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/deta/test_modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* protect importing accelerate
* change variable name to specific value
* wrong import
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
When running the case on multi-cards server with devcie_map-auto, It will not always be allocated to device 0,
Because other processes may be using these cards. It will select the devices that can accommodate this model.
Signed-off-by: yuanwu <yuan.wu@intel.com>
* Sort es/_toctree.yml like en/_toctree.yml
* Run make style
* Add -Rendimiento y escalabilidad- section to es/_toctree.yml
* Run make style
* Add s to section
* Add translate of performance.md
* Add performance.md to es/_toctree.yml
* Run make styele
* Fix docs links
* Run make style
* remove token_type_ids from model_input_names (like #24788)
* removed test that assumed token_type_ids should be present and updated a model reference so that it points to an available model)
* start - docs, SpeechT5 copy and rename
* add relevant code from FastSpeech2 draft, have tests pass
* make it an actual conformer, demo ex.
* matching inference with original repo, includes debug code
* refactor nn.Sequentials, start more desc. var names
* more renaming
* more renaming
* vocoder scratchwork
* matching vocoder outputs
* hifigan vocoder conversion script
* convert model script, rename some config vars
* replace postnet with speecht5's implementation
* passing common tests, file cleanup
* expand testing, add output hidden states and attention
* tokenizer + passing tokenizer tests
* variety of updates and tests
* g2p_en pckg setup
* import structure edits
* docstrings and cleanup
* repo consistency
* deps
* small cleanup
* forward signature param order
* address comments except for masks and labels
* address comments on attention_mask and labels
* address second round of comments
* remove old unneeded line
* address comments part 1
* address comments pt 2
* rename auto mapping
* fixes for failing tests
* address comments part 3 (bart-like, train loss)
* make style
* pass config where possible
* add forward method + tests to WithHifiGan model
* make style
* address arg passing and generate_speech comments
* address Arthur comments
* address Arthur comments pt2
* lint changes
* Sanchit comment
* add g2p-en to doctest deps
* move up self.encoder
* onnx compatible tensor method
* fix is symbolic
* fix paper url
* move models to espnet org
* make style
* make fix-copies
* update docstring
* Arthur comments
* update docstring w/ new updates
* add model architecture images
* header size
* md wording update
* make style
* Update modeling_whisper.py to support MPS backend
Fixed some issue with MPS backend.
First, the torch.std_mean is not implemented and is not scheduled for implementation, while the single torch.std and torch.mean are.
Second, MPS backend does not support float64, so it can not cast from float32 to float64. Inverting the double() when the matrix is in the cpu fixes the issue while should not change the logic.
* Found another instruction in modeling_whisper.py not implemented byor MPS
After a load test, where I transcribed a 2 hours audio file, I got into a branch that did not fix in the previous commit.
Similar fix, where the torch.std_mean is changed into torch.std and torch.mean
* Update modeling_whisper.py removed trailing white spaces
Removed trailing white spaces
* Update modeling_whisper.py to use is_torch_mps_available()
Using is_torch_mps_available() instead of capturing the NotImplemented exception
* Update modeling_whisper.py sorting the import block
Sorting the utils import block
* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix: minor enhancement and fix in bounding box visualization example
The example that was trying to visualize the bounding box was not considering an edge case,
where the bounding box can be un-normalized. So using the same set of code, we can not get
results with a different dataset with un-normalized bounding box. This commit fixes that.
* run make clean
* add an additional note on the scenarios where the box viz code works
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Co-authored-by: Anindyadeep <anindya@pop-os.localdomain>
Francisco Kurucz