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* Add ALL_LAYERNORM_LAYERS for LayerNorm

* fix bug of appending layer norm

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* get the right slicing index for position_bias

Co-authored-by: Sreyan-G@NVIDIA <sreyang@nvidia.com>

* Return scalar losses instead of per-sample means

* Make loss shape (1,) instead of scalar

* Allow scalar losses in test_loss_computation

* Allow scalar losses in test_loss_computation

* Allow scalar losses in test_loss_computation

* Remove XLA loss function for RAG

* Refactor to inherit from nn.Module instead of nn.ModuleList

* Fix typo

* Empty to trigger CI re-run

Blender Bot tests failing (should be unrelated to this PR) and pass locally). I don't have sufficient permisisons to re-run the CI workflow (totally or from failed)

* Rought TF conversion outline

* Tidy up

* Fix padding differences between layers

* Add back embedder - whoops

* Match test file to main

* Match upstream test file

* Correctly pass and assign image_size parameter
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Add in MainLayer

* Correctly name layer

* Tidy up AdaptivePooler

* Small tidy-up

More accurate type hints and remove whitespaces

* Change AdaptiveAvgPool

Use the AdaptiveAvgPool implementation by @Rocketknight1, which correctly pools if the output shape does not evenly divide by input shape c.f. https://github.com/huggingface/transformers/pull/17554/files/9e26607e22aa8d069c86b50196656012ff0ce62a#r900109509

Co-authored-by: From: matt <rocketknight1@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Use updated AdaptiveAvgPool
Co-authored-by: matt <rocketknight1@gmail.com>

* Make AdaptiveAvgPool compatible with CPU

* Remove image_size from configuration

* Fixup

* Tensorflow -> TensorFlow

* Fix pt references in tests

* Apply suggestions from code review - grammar and wording
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Add TFResNet to doc tests

* PR comments - GlobalAveragePooling and clearer comments

* Remove unused import

* Add in keepdims argument

* Add num_channels check

* grammar fix: by -> of
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Remove transposes - keep NHWC throughout forward pass

* Fixup look sharp

* Add missing layer names

* Final tidy up - remove from_pt now weights on hub
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Exclude Databricks from notebook env only if the runtime is below 11.0

* Dummy commit to trigger CI

* Empty commit to trigger CI

* Empty commit to trigger CI

* Empty commit to trigger CI

* Empty commit to trigger CI

* Empty commit to trigger CI

* Empty commit to trigger CI

* Empty commit to trigger CI

* Shifting labels for causal LM when using label smoother

When training CausalLM, loss is computed within model's foward() function and
labels are shifted internally. However, if label smoothing is applied, loss is
computed in trainer's compute_loss function and labels are not shifted.
This causes unintended confusion during the alignment of labels and corresponding
inputs. This commit is for resolving this confusion.

Resolves #17960

On branch shift_labels_for_causalLM
Changes to be committed:
	modified:   src/transformers/trainer.py
	modified:   src/transformers/trainer_pt_utils.py

* Update trainer.py

* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Copy inputs to train and test step before modifying them, as this breaks things

* Add XLA tests, fix our loss functions to be XLA-compatible

* make fixup

* Update loss computation test to expect vector of per-sample losses

* Patch loss for TFLED

* Patch loss for TFAlbert

* Add a tf_legacy_loss config flag that enables old loss functions

* Stop using config.get() because it's not a dict

* Skip loss computation test for RAG because its loss is very strange and I'm afraid to rewrite it

* make fixup

* Add XLA-compatible RAG loss

* Fix dtype of loss mask for TFAlbert

* Fix test for XLNet too because it overrides the default one

* make fixup

* Fix config test

* No more depending on GPU NaN behaviour

* Add test, avoid potential zero division

* Fix test item assignment

* Fix loss computation masking test

* make fixup

* Fix dtype bugs

* [Flax] Add remat (gradient checkpointing)

* fix variable naming in test

* flip: checkpoint using a method

* fix naming

* fix class naming

* apply PVP's suggestions from code review

* make fix-copies

* fix big-bird, electra, roberta

* cookie-cutter

* fix flax big-bird

* move test to common

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* add onnx support for BLOOM

* use TYPE_CHECKING for type annotations

* fix past_shape for bloom (different from gpt2)

* use logical_or instead of `+` for onnx support

* bigger `atol_for_validation` for larger bloom models

* copied -> taken because it's no longer an exact copy

* remove "copied from" comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fixing fsdp autowrap functionality

* update version and quality

* update torch version to latest stable version

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* skip some gpt_neox tests that require 80G RAM

* remove tests

* fix quality
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* feat: add pipeline registry abstraction

- added `PipelineRegistry` abstraction
- updates `add_new_pipeline.mdx` (english docs) to reflect the api addition
- migrate `check_task` and `get_supported_tasks` from
  transformers/pipelines/__init__.py to
  transformers/pipelines/base.py#PipelineRegistry.{check_task,get_supported_tasks}
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

* fix: update with upstream/main

chore: Apply suggestions from sgugger's code review
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* chore: PR updates

- revert src/transformers/dependency_versions_table.py from upstream/main
- updates pipeline registry to use global variables
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

* tests: add tests for pipeline registry
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

* tests: add test for output warning.
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

* chore: fmt and cleanup unused imports
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

* fix: change imports to top of the file and address comments
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add ONNX support for LayoutLMv3

* Update docstrings

* Update empty description in docstring

* Fix imports and type hints

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* sharded conversion; add flag to control max hidden error

* better hidden name matching

* Add test: load TF from PT shards

* fix test (PT data must be local)

* trigger test failure

* upload revision poc

* Update src/transformers/pipelines/base.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>

* up

* add test

* correct some stuff

* Update src/transformers/pipelines/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* correct require flag
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* doc: Unify training arg type annotations

* wip: extracting enum type from Union

* blackening

* Fix GPT-NeoX-20B past handling, swap attention computation to hopefully avoid NaN, update docs

* 20B tests

* first draft adding Flax-t5-encoder and Flax-mt5-encoder

* imports

* after make fixup

* flax t5 encoder test

* black on test

* make fix-copies

* clean

* all_model_classes -> tuple

* clean test

* is_encoder_decoder=False in t5-enc tester

* remove file docstring before FlaxT5Encoder

* black

* isort

* commit suggestions on src/transformers/models/t5/modeling_flax_t5.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* commit suggestions on src/transformers/models/t5/modeling_flax_t5.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* remove _get_encoder_module

* self.decoder_seq_length -> self.encoder_seq_length as t5-enc does not have decoder

* bugfix - self.module_class is class itself, not instance;

* docs for mt5 and t5

* call -> __call__ in t5 doc

* FlaxMT5EncoderModel to TYPE_HINT

* run doc-builder to allow change the files
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Removed dead position_id code, fix #17893

* Removed unused var

* Now ignores removed (dead) dict key for backward comp