* Change the .view call to .reshape
* Change the .view call to .reshape to all the copies from bart attention
* Fix copies and style
* Fix copies and style
* Fix copies and style
* Fix copies and style
* Fix copies and style
* Revert unneccessary changes
* Revert unneccessary changes
* Revert unneccessary changes
* Revert unneccessary changes
* trying to figure out whether model is NLP
* drop my changes and apply easier fix
* trying to handle all int input types
* fix logic
---------
Co-authored-by: Stas Bekman <stas@stason.org>
* rounding_mode = "floor" instead of // to prevent behavioral change
* add other TODO
* use `torch_int_div` from pytrch_utils
* same for tests
* fix copies
* style
* use relative imports when needed
* Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* First commit for the improved PT-TF weight loading
* Remove workarounds from TFEncoderDecoder tests
* Allow a custom weight renaming function in from_pretrained and use that to clean up EncoderDecoder
* make fixup
* First attempt at visionencoderdecoder
* Disable tensorfloat32 in tests to get consistent outputs
* Quick fix to tf_vision_encoder_decoder tests
* make fixup
* Update Blenderbot tests
* Remove unused arg in modeling_tf_opt
* load_tf_sharded_weights had strict=True! This meant transfer learning was impossible, so I'm setting it to False.
* Support prefixes when loading sharded TF checkpoints
* make fixup
* Add test to load sharded models with a weight prefix
* Fix sharded weight loading test
* Add a test for transfer from a sharded checkpoint
* make fixup
* Add test to check that crossloading from PT with a prefix works
* Refactor from_pretrained in the encoderdecoder classes
* Refactor from_pretrained in the encoderdecoder classes
* missmatched -> mismatched
* Explicitly check for None
* No comments showing my very impressive and attractive knowledge of Py3.9+
* Disable TF32 across all TF tests
* Add loss for BridgeTowerForMaskedLM and BridgeTowerForImageAndTextRetrieval
* minor fix return_dict
* implement test for loss computation
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Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>
* zero shot object detection part 1
* added batch prediction section
* added image guided object detection section
* make style
* added the task guide to the TOC
* minor polishing
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* added embedded owlvit demo
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* minor fix
* make style
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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* If applied, this commit fixes generate bug in gptj
* Remove extra same code block
* formatting and test fix
* Conflict fix and declaration error fix
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Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix the issue of blip model returning loss even when the label is not provoided
* Fix ruff failure
* Incorporate PR feedbacks
* Incorporate PR feedbacks
* Incorporate PR feedbacks
* Incorporate PR feedbacks
* add pipeline
* update init
* add zero shot to init
* update inits and correct checkpoints
* update base to support input features
* add tests
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* update pieline code
* use tiny checkpoint
* nits and expected value with tiny model
* style
* last nit on tests values
* fix styling
* fix collate fn that was casting t float
* update
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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Override the decoding parameters of Seq2SeqTrainer
* Fix quality
* Fix max_length parameter
* Fix quality
* Remove redundant parameter max_length
* Separate the preprocess of train and validation to use different max_target_length
* Fix resume_from_checkpoint for deepspeed
Fix resume_from_checkpoint for deepspeed, by ensuring that the deepspeed engine is the one to load the checkpoint.
* Empty commit to trigger CI
* Removed deepspeed skipping
Removed deepspeed skipping inside the _load_from_checkpoint function, as it is obsolete
* another adjustment
* Trigger CI
* trigger circleci
* style
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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>
* [flax] adding support for batch norm layers
* fixing bugs related to pt+flax integration
* cleanup, batchnorm support in sharded pt to flax
* support for batchnorm tests in pt+flax integration
* simplifying checking batch norm layer
