* Initial commit
* Just a copy of modeling_idefics.py that will be ported to TF
* - Prepend TF to the name of all classes
- Convert pytorch ops to TF (not all operations are converted yet)
* Add TF imports
* Add autotranslated files
* Add TF classes to model_tf_auto.py
* Add the TF classes in model_doc
* include auto-translated code
* Adopted from auto-translated version
* Add a forgotten super().build
* Add test code for TF version.
* Fix indentation and load pytorch weights for now
* Some fixes. Many tests are still failing but some are passing now.
- I have added TODO's for some of the hacks I made to unblock me
and I will address them soon
- I have the processing_idefics.py hacked in my view to support TF temporarily
* Add ALL_LAYERNORM_LAYERS to match pytorch
* Revert "Add ALL_LAYERNORM_LAYERS to match pytorch"
This reverts commit 7e0a35119b4d7a6284d04d8c543fba1b29e573c9 as it
is not needed in the tf implementation.
* Fix freeze_relevant_params()
* Some more fixes
* Fix test_attention_outputs
* Add tf stuff to processing_idefics.py
processing_idefics.py supports both pytorch and tf now.
test_processor_idefics.py for pytorch is passing, so i didn't break anything
but still some issues with tf. I also need to add tf tests in
test_processor_idefics.py.
* Pass return_tensors to image processing code and fix test
* Pass return_tensors to the image processor __init__
* Fix several test cases
- Make input to some of the forward pass of type `TFModelInputType`
- Decorate main layer forward pass with `@unpack_inputs`
- Decorate main layer with `@keras_serializable`
- Pass `inputs` to TFIdeficsModel
* Some more fixes forgotten in last commit
* Fix processing code and vision_tf.py
* Fix perceiver bug
* Import from
* Auto-add build() methods + style pass
* Fix build() errors due to `None` being passed as shape to some layers
* Change name in TFIdeficsForVisionText2Text to attribute in IdeficsForVisionText2Text
* Fix pytorch weights load for tf2
There were a lot of `name=` missing in weight initialization code.
* Attempt to fix CI
* Add back accidently removed line
* Remove torch-specific stuff from the TF test file
* make fix-copies, make style, remove autotranslated files
* Fixes to imports/docstrings
* Let's try the from future import in desperation
* Fix the core random_attention_mask fn to match the torch/flax behaviour
* Clean random_attention_mask up correctly
* Remove torch-only test
* Fix loss shape, couple of nits
* make style
* Don't test for OOB embeddings because IDEFICS uses those deliberately
* Fix loss computation to handle masking
* Fix test failures when flattening
* Fix some test failures
- Add cross attention gate which was missing and wasn't being passed arround
- Fix overwriting of image_attention_mask due to hack I had for dummy inputs
* Add a proper stateless scaled_dot_product_attention
* make style
* Adding missing attribute from the PyTorch version
* Small cleanups to decoupledlinearlayer in case that helps
* Pass epsilon to LayerNormalization
* Attemp to fix pytorch weight cross-loading for TFIdeficsEmbedding
* Fix a bug in TFIdeficsGatedCrossAttentionLayer
* Patching up build() methods
* Constant self.inv_freq
* Constant self.inv_freq
* First working version
The TF implementation works now, there was a bug in the TFIdeficsDecoupledLinear
where the weights were mis-intialized (in_features,out_features)
when it should be: (out_features, in_features)
I have tested this so far with tiny-random and idefics-9b-instruct
and gives correct output.
I also dumped the final outputs for both pytorch and TF
and they are identical.
* Fix some test failures
* remove print statement
* Fix return_tensors
* Fix CI test failure check_code_quality
* Attempt to fix CI failures by running `make fixup`
The hardcoded IDs in test_modeling_tf_idefics.py are for the integration
test and makes that file unreadable and should probably be moved to a seperate file.
* Attempt to fix tests_pr_documentation_tests
* Fix a test failure in test_image_processing_idefics.py
* Fix test test_pt_tf_model_equivalence
* Fix a few failures
* Tiny fix
* Some minor fixes
* Remove a duplicate test
* Override a few test failures for IDEFICS
- `test_keras_save_load` is passing now
- `test_compile_tf_model` is still failing
* Fix processing_idefics.py after rebase
* Guard import keras with is_tf_available
* fix check code quality
* fix check code quality
* Minor fixes
* Skip test_save_load temporarily
This test passed on my local box but fails on the CI, skipping
for now to see if there are other remaining failures on the CI.
* Run `ruff format tests src utils`
* Fix last failing test, `test_compile_tf_model`
* Add fixes for vision_tf.py
I forgot to add this file in last commit.
* Minor fixes
* Replace "<<<" with "<<" for doc tests
IDEFICS-9B is too big for doctest runner, so don't run it there
* Make code more readable
* Fix bug after code review
I added a layer_norm_eps to IdeficsConfig but I don't even need it
since the vision config has a layer_norm_eps.
* Fix after code review
Use original code tokenizer.convert_tokens_to_ids
* Keep PyTorch as the default return_tensors
* Fixes to modeling_tf after code review
* Fixes from code review
- Remove all references of `TF_IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST`
- Pass 1e-5 to LayerNormalization in perceiver
* Run ruff
* Undo a change
* Refactor processing code after Matt's suggestion
* Remove TODO's that aren't needed anymore
* For pytorch, Use original pytorch processing code from main
Since this PR is a TF port it shouldn't make any modifications
to pytorch IDEFICS code. This changes undo's the pytorch processing
modifications I made and uses original code from main.
* Update tests/models/idefics/test_modeling_idefics.py
* Update tests/models/idefics/test_modeling_tf_idefics.py
* Add missing imports for is_pt_tf_cross_test
* [DO NOT MERGE]: This is a commit for debugging and will be reverted
The cross test `test_pt_tf_model_equivalence` passes locally but
fails when running on the CI. This commit is to help debug that
and will be reverted.
* Revert "[DO NOT MERGE]: This is a commit for debugging and will be reverted"
This reverts commit 8f0d709ec5bd46685fb0b4259d914ffee794875b.
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 998cc38b8c3d313bf5e5eb55a7f5b7b881897b89.
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 1c695ac4219c4ae4d39b330b01744dc27deb7dd4.
* Don't skip test_save_load
IIRC test_save_load was also failing on the CI but not on my local
box, it might be easier to debug that on the CI first than the cross tests
* Debugging commit, will be reverted
* Revert "Debugging commit, will be reverted"
This reverts commit 8eafc8e41e20c4e95a3a90834f06a6e9f445e2d5.
* Override `test_save_load` and push model to save
Maybe this will help me repro this weird bug
* pass my repo_id
* add endpoint
* Pass a temp (write) token just for this CI
* Undo last few commits, still pushing to hub for model debugging
The issue seems to be with save_pretrained(), when I looked at the model saved
from the CI test failure it is basically empty and has no weights.
`self.save_weights(..)` seems to be failing in save_pretrained but needs
more debugging
* Add logging to modeling tf utils, will be reverted just for debugging
* Debugging, will revert
* Revert "Debugging, will revert"
This reverts commit 9d0d3075fb7c82d8cde3a5c76bc8f3876c5c55d3.
* Revert "Add logging to modeling tf utils, will be reverted just for debugging"
This reverts commit 774b6b7b1c17b3ce5d7634ade768f2f686cee617.
* Remove `test_save_load`
The CI failures are gone after my latest rebase, no idea why
but I was still saving the model to my hub on HF and the tf_model.h5
file now has everything.
* Run make fix-copies
* Run ruff format tests src utils
* Debugging commit, will be reverted
* Run ruff, also trigger CI run
* Run ruff again
* Undo debugging commit
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Create CodeAgent and ReactAgent
* Fix formatting errors
* Update documentation for agents
* Add custom errors, improve logging
* Support variable usage in ReactAgent
* add messages
* Add message passing format
* Create React Code Agent
* Update
* Refactoring
* Fix errors
* Improve python interpreter
* Only non-tensor inputs should be sent to device
* Calculator tool slight refactor
* Improve docstrings
* Refactor
* Fix tests
* Fix more tests
* Fix even more tests
* Fix tests by replacing output and input types
* Fix operand type issue
* two small fixes
* EM TTS
* Fix agent running type errors
* Change text to speech tests to allow changed outputs
* Update doc with new agent types
* Improve code interpreter
* If max iterations reached, provide a real answer instead of an error
* Add edge case in interpreter
* Add safe imports to the interpreter
* Interpreter tweaks: tuples and listcomp
* Make style
* Make quality
* Add dictcomp to interpreter
* Rename ReactJSONAgent to ReactJsonAgent
* Misc changes
* ToolCollection
* Rename agent's logger to self.logger
* Add while loops to interpreter
* Update doc with new tools. still need to mention collections
* Add collections to the doc
* Small fixes on logs and interpretor
* Fix toolbox return type
* Docs + fixup
* Skip doctests
* Correct prompts with improved examples and formatting
* Update prompt
* Remove outdated docs
* Change agent to accept Toolbox object for tools
* Remove calculator tool
* Propagate removal of calculator in doc
* Fix 2 failing workflows
* Simplify additional argument passing
* AgentType audio
* Minor changes: function name, types
* Remove calculator tests
* Fix test
* Fix torch requirement
* Fix final answer tests
* Style fixes
* Fix tests
* Update docstrings with calculator removal
* Small type hint fixes
* Update tests/agents/test_translation.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_python_interpreter.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/default_tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_agents.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bert/configuration_bert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/tools.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/speech_to_text.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_speech_to_text.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/agents/test_tools_common.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* pygments
* Answer comments
* Cleaning up
* Simplifying init for all agents
* Improving prompts and making code nicer
* Style fixes
* Add multiple comparator test in interpreter
* Style fixes
* Improve BERT example in documentation
* Add examples to doc
* Fix python interpreter quality
* Logging improvements
* Change test flag to agents
* Quality fix
* Add example for HfEngine
* Improve conversation example for HfEngine
* typo fix
* Verify doc
* Update docs/source/en/agents.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/agents.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/prompts.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/agents/python_interpreter.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/agents.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix style issues
* local s2t tool
---------
Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com>
Co-authored-by: Lysandre <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Adding SDPA support for BERT
* Using the proper input name for testing model input in inference()
* Adding documentation for SDPA in BERT model page
* Use the stable link for the documentation
* Adding a gate to only call .contiguous() for torch < 2.2.0
* Additions and fixes to the documentation
* Minor updates to documentation
* Adding extra requirements needed for the contiguous() bug
* Adding "Adapted from" in plcae of the "Copied from"
* Add benchmark speedup tables to the documentation
* Minor fixes to the documentation
* Use ClapText as a replacemenet for Bert in the Copied-From
* Some more fixes for the fix-copies references
* Overriding the test_eager_matches_sdpa_generate in bert tests to not load with low_cpu_mem_usage
[test all]
* Undo changes to separate test
* Refactored SDPA self attention code for KV projections
* Change use_sdpa to attn_implementation
* Fix test_sdpa_can_dispatch_on_flash by preparing input (required for MultipleChoice models)
* Draft tutorial for talking to chat models
* Reformat lists and text snippets
* Cleanups and clarifications
* Finish up remaining TODOs
* Correct section link
* Small fix
* Add proper quantization examples
* Add proper quantization examples
* Add proper quantization examples
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/_toctree.yml
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/conversations.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Fix Text Generation Pipeline link and add a ref to the LLM inference guide
* intelligent -> capable
* Small intro cleanup
* Small text cleanup
* Small text cleanup
* Clarification about system message
* Clarification about system message
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* chore(root): Initial commit of Phi-3 files.
* fix(root): Fixes Phi-3 missing on readme.
* fix(root): Ensures files are consistent.
* fix(phi3): Fixes unit tests.
* fix(tests): Fixes style of phi-3 test file.
* chore(tests): Adds integration tests for Phi-3.
* fix(phi3): Removes additional flash-attention usage, .e.g, swiglu and rmsnorm.
* fix(phi3): Fixes incorrect docstrings.
* fix(phi3): Fixes docstring typos.
* fix(phi3): Adds support for Su and Yarn embeddings.
* fix(phi3): Improves according first batch of reviews.
* fix(phi3): Uses up_states instead of y in Phi3MLP.
* fix(phi3): Uses gemma rotary embedding to support torch.compile.
* fix(phi3): Improves how rotary embedding classes are defined.
* fix(phi3): Fixes inv_freq not being re-computed for extended RoPE.
* fix(phi3): Adds last suggestions to modeling file.
* fix(phi3): Splits inv_freq calculation in two lines.
* [FEAT]: EETQ quantizer support
* Update quantization.md
* Update docs/source/en/main_classes/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/__init__.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/__init__.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/integrations/eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/auto.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update src/transformers/quantizers/quantizer_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/eetq_integration/test_eetq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* [FEAT]: EETQ quantizer support
* [FEAT]: EETQ quantizer support
* remove whitespaces
* update quantization.md
* style
* Update docs/source/en/quantization.md
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* add copyright
* Update quantization.md
* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Address the comments by amyeroberts
* style
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Duplicate swiftformer
* Convert SwiftFormerPatchEmbedding
* Convert SwiftFormerEmbeddings
* Convert TFSwiftFormerMlp
* Convert TFSwiftFormerConvEncoder
* Convert TFSwiftFormerLocalRepresentation
* convert TFSwiftFormerEncoderBlock
* Convert SwiftFormerStage
* Convert SwiftFormerEncoder
* Add TFSWiftFormerPreTrainedModel
* Convert SwiftFormerForImageClassification
* Add kwargs and start drop path
* Fix syntax
* Change Model class name
* Add TFSwiftFormer to __init__
* Duplicate test_modeling_swiftformer
* First test conversions
* Change require_torch to require_tf
* Add exports to swiftformer __init__
* Add TFSwiftFormerModel wrapper
* Fix __init__ and run black
* Remove docstring from MainLayer, fix padding
* Use keras.layers.Activation on keras.Sequential
* Fix swiftformer exports
* Fix activation layer from config
* Remove post_inits
* Use tf.keras.layers.ZeroPadding2D
* Convert torch normalize
* Change tf test input shape
* Fix softmax and reduce_sum
* Convert expand_dims and repeat
* Add missing reshape and tranpose
* Simplify TFSwiftFormerEncoderBlock.call
* Fix mismatch in patch embeddings
* Fix expected output shape to match channels last
* Fix swiftformer typo
* Disable test_onnx
* Fix TFSwiftFormerForImageClassification call
* Add unpack inputs
* Convert flatten(2).mean(-1)
* Change vision dummy inputs (to be reviewed)
* Change test_forward_signature to use .call
* Fix @unpack_inputs
* Set return_tensors="tf" and rename class
* Rename wrongly named patch_embeddings layer
* Add serving_output and change dummy_input shape
* Make dimensions BCHW and transpose inside embedding layer
* Change SwiftFormerEncoderBlock
* Fix ruff problems
* Add image size to swiftformer config
* Change tranpose to MainLayer and use -1 for reshape
* Remove serving_outputs and dummy_inputs
* Remove test_initialization test from tf model
* Make Sequential component a separate layer
* Fix layers' names
* Tranpose encoder outputs
* Fix tests and check if hidden states is not None
* Fix TFSwiftFormerForImageClassification
* Run make fixup
* Run make fix-copies
* Update modeling_tf_auto
* Update docs
* Fix modeling auto mapping
* Update modelint_tf_swiftformer docs
* Fill image_size doc and type
* Add reduction=None to loss computation
* Update docs
* make style
* Debug: Delete the tip to see if that changes anything
* Re-add tip
* Remove add_code_sample_docstrings
* Remove unused import
* Get the debug to actually tell us the problem it has with the docs
* Try a substitution to match the PyTorch file?
* Add swiftformer to ignore list
* Add build() methods
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove FIXME comment
* Remove from_pt
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Rename one-letter variables
* Remove FIXMEs related to momentum
* Remove old TODO comment
* Remove outstanding FIXME comments
* Get dropout rate from config
* Add specific dropout config for MLP
* Add convencoder dropout to config
* Pass config to SwiftFormerDropPath layer
* Fix drop_path variable name and add Adapted from comment
* Run ruff
* Removed copied from comment
* Run fix copies
* Change drop_path to identity to match pt
* Cleanup build() methods and move to new keras imports
* Update docs/source/en/model_doc/swiftformer.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Raise error if drop_path_rate > 0.0
* Apply suggestions from code review
Replace (self.dim), with self.dim,
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Remove drop_path function
* Add training to TFSwiftFormerEncoder
* Set self.built = True last
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Should have been added to previous commit
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Change default_feature_extractor to default_image_processor
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Import Keras from modeling_tf_utils
* Remove relative import
* Run ruff --fix
* Move import keras to tf_available
* Add copied from comment to test_forward_signature
* Reduce batch size and num_labels
* Extract loss logic to hf_compute_loss
* Run ruff format
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* initial commit, remove warnings on default chat templates
* stash commit
* Raise a much sterner warning for default chat templates, and prepare for depreciation
* Update the docs
* wip
* fix __init__.py
* add docs
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* address comments 1
* work on make fixup
* pass configs down
* add sdpa attention
* remove DbrxBlock
* add to configuration_auto
* docstring now passes formatting test
* fix style
* update READMEs
* add dbrx to modeling_auto
* make fix-copies generated this
* add DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* config docstring passes formatting test
* rename moe_loss_weight to router_aux_loss_coef
* add to flash-attn documentation
* fix model-path in tests
* Explicitly make `"suli"` the default `ffn_act_fn`
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* default to using router_aux_loss_coef over ffn_config[moe_loss_weight]
* fix _flash_attn_uses_top_left_mask and is_causal
* fix tests path
* don't use token type IDs
* follow Llama and remove token_type_ids from test
* init ConfigTester differently so tests pass
* remove multiple choice test
* remove question + answer test
* remove sequence classification test
* remove token classification test
* copy Llama tests and remove token_type_ids from test inputs
* do not test pruning or headmasking; style code
* add _tied_weights_keys parameter to pass test
* add type hints
* fix type check
* update config tester
* remove masked_lm test
* remove encoder tests
* initialize DbrxModelTester with correct params
* style
* torch_dtype does not rely on torch
* run make fixup, fix-copies
* use https://huggingface.co/v2ray/dbrx-base-fixed/blob/main/modeling_dbrx.py
* add copyright info
* fix imports and DbrxRotaryEmbedding
* update DbrxModel docstring
* use copies
* change model path in docstring
* use config in DbrxFFN
* fix flashattention2, sdpaattention
* input config to DbrXAttention, DbrxNormAttentionNorm
* more fixes
* fix
* fix again!
* add informative comment
* fix ruff?
* remove print statement + style
* change doc-test
* fix doc-test
* fix docstring
* delete commented out text
* make defaults match dbrx-instruct
* replace `router_aux_loss_coef` with `moe_loss_weight`
* is_decoder=True
* remove is_decoder from configtester
* implement sdpa properly
* make is_decoder pass tests
* start on the GenerationTesterMixin tests
* add dbrx to sdpa documentation
* skip weight typing test
* style
* initialize smaller model
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Add DBRX to toctree
* skip test_new_cache_format
* make config defaults smaller again
* add pad_token_id
* remove pad_token_id from config
* Remove all references to DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* Update src/transformers/models/dbrx/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix typo
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update docs, fix configuration_auto.py
* address pr comments
* remove is_decoder flag
* slice
* fix requires grad
* remove grad
* disconnect differently
* remove grad
* enable grads
* patch
* detach expert
* nissan al ghaib
* Update modeling_dbrx.py
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* replace "Gemma" with "Dbrx"
* remove # type: ignore
* don't hardcode vocab_size
* remove ToDo
* Re-add removed idefics2 line
* Update test to use tiny-random!
* Remove TODO
* Remove one more case of loading the entire dbrx-instruct in the tests
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* address some comments
* small model
* add dbrx to tokenization_auto
* More docstrings with add_start_docstrings
* Dbrx for now
* add PipelineTesterMixin
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove flash-attn2 import error
* fix docstring
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add useage example
* put on one line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix ffn_act_fn
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* change "dbrx" to "DBRX" for display purposes.
* fix __init__.py?
* fix __init__.py
* fix README
* return the aux_loss
* remove extra spaces
* fix configuration_auto.py
* fix format in tokenization_auto
* remove new line
* add more useage examples
---------
Co-authored-by: Abhi Venigalla <abhi.venigalla@databricks.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Eitan Turok <eitan.turok@databricks.com>
Co-authored-by: Eitan Turok <150733043+eitanturok@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Eitan Turok <eitanturok@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add jamba arch
* apply "make fix-copies" changes
* fix link to model in JambaConfig docstring
* Add n_ctx in modeling file because repo-consistency wants that
* Add jamba to flash attention and sdpa documentation
* mamba dt_proj quant fix now works for LoRA as well
* override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers
* add jamba to tokenization auto
* fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24)
* simple PR fixes
* remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer
* remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530)
* Add copied comment on JambaMLP (it's the same as MixtralMLP)
* remove padding_mask warnings. It's not supported anymore
* fix docstring. Float instead of int
* A few more minor PR fixes
* (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass
* Return None attention weights from mamba layers. Append to all attentions only if not None.
* remove some leftover jamba archive lists
* Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel
* no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers
* Add Jamba paper on READMEs
* (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes)
* Add copied from comment
* remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms
* clearer docstring for _convert_to_standard_cache
* style fixes
* Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs
* rename test so it still overrides what its meant to override
* draft
* oups
* nit
* remove more complexe logic
* fix names used in config
* fix fix fix
* style
* fix some more failing tests
* generate did not init the cache 🙃
* more small nits
* typo
* config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes
* fix init of pkv with torch.tensor()
* empty tensor
* fix some init issues
* stupid changes required by generate because it does not even support it's own DynamicCache class
* more fixes
* fix general assisted gen cache_position bug
* tests passing
* Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py
* fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache
* no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore
* fix docstrings and typehints for past_key_values
* style fixes
* fix docs
* change typehint due to copy from Mixtral
* forgot import
* import order
* Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward)
* Add integration test with tiny tandom Jamba model on hub
* fix flash attention cache shapes
* bring back forgotten hidden states
* rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model
* align integration test after modeling fixes
* bugfix - mamba can use precomputed states only of forward pass is on a single token
* bugfix - mamba can use precomputed states only if they match the batch size
* typo
* remove making _prepare_4d_causal_attention_mask a leaf function
* stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
* Add OLMo using add-new-model-like with Llama
* Fix incorrect tokenizer for OLMo
* Copy-paste relevant OLMo methods and their imports
* Add OLMo config
* Modify OLMo config to follow HF conventions
* Remove unneeded Llama code from OLMo model
* Add ability for OLMo model to output attentions
* Add OLMoPreTrainedModel and OLMoModel
* Add OLMoForCausalLM
* Minor fixes to OLMo model for style and missing functions
* Implement OLMo tokenizer
* Implement OLMo to HF conversion script
* Add tests for OLMo model
* Add tests for OLMo fast tokenizer
* Add auto-generated dummy objects
* Remove unimplemented OLMo classes from auto and init classes and re-format
* Add README and associated auto-generated files
* Use OLMo names for common properties
* Run make fixup
* Remove `|` from OLMo typing
* Remove unneeded tokenization_olmo.py
* Revert model, config and converter to add-new-model-like Llama
* Move logic for adding bos/eos token into GPTNeoxTokenizerFast
* Change OLMoConfig defaults to match OLMo-7B
* Use GPTNeoXToknizerFast in OLMo tokenizer tests
* Modify auto-generated OLMoModelTests to work for OLMo
* Add non-parametric layer norm OLMoLayerNorm
* Update weight conversion script for OLMo
* Fix __init__ and auto structure for OLMo
* Fix errors from make fixup
* Remove OLMoTokenizerFast from documentation
* Add missing 'Copied from' for OLMoModel._update_causal_mask
* Run make fix-copies
* Rearrange string replacements in OLMoForCausalLM Copied from
* Move OLMo and Llama CausalLM.forward example into global constants
* Fix OLMO_GENERATION_EXAMPLE doc string typo
* Add option for qkv clipping to OLMo
* Rearrange OLMoConfig kwargs in convert_olmo_weights_to_hf
* Add clip_qkv to OLMoConfig in convert_olmo_weights_to_hf
* Fix OLMo tokenization bug using conversion script
* Keep model in full precision after conversion
* Do not add eos token automatically
* Update references to OLMo model in HF Hub
* Do not add eos token during encoding by default
* Fix Llama generation example
* Run make fixup
* OLMo 7B integration test fix
* Remove unneeded special case for OLMoConfig
* OLMo 7B Twin 2T integration test fix
* Fix test_model_7b_greedy_generation
* Remove test_compile_static_cache
* Fix OLMo and Llama generation example
* Run make fixup
* Revert "OLMo 7B integration test fix"
This reverts commit 4df56a4b15.
* Revert "OLMo 7B Twin 2T integration test fix"
This reverts commit 9ff65a4a29.
* Ungate 7B integration tests and fix greedy generation test
* Add retries for flaky test_eager_matches_sdpa_generate
* Fix output of doc example for OLMoForCausalLM.forward
* Downsize OLMo doc test for OLMoForCausalLM.forward to 1B model
* Try fix incorrect characters in OLMoForCausalLM.forward doct test
* Try fix incorrect characters in OLMoForCausalLM.forward doc test using end quotes
* Remove pretraining_tp from OLMo config and model
* Add missing 'Copied from' instances
* Remove unneeded causal_mask from OLMoModel
* Revert Llama changes
* Ignore copy for OLMoForCausalLM.forward
* Change 'OLMo' to 'Olmo' in classes
* Move minimal OLMo tokenization tests to model tests
* Add missed 'Copied from' for repeat_kv
* Add create token type ids to CodeGenTokenizer
* Fix inconsistent length of token type ids
* Format source codes
* Fix inconsistent order of methods
* Update docstring
* add test_tokenizer_integration test
* Format source codes
* Add `copied from` comment to CodeGenTokenizerFast
* Add doc of create_token_type_ids_from_sequences
* Make return_token_type_ids False by default
* Make test_tokenizer_integration as slow test
* Add return_token_type_ids to tokenizer init arg
* Add test for tokenizer's init return_token_type_ids
* Format source codes
* Configuring Translation Pipelines documents update #27753
Configuring Translation Pipelines documents update
* Language Format Addition
* adding supported list of languages list
* Fork.
* RecurrentGemma initial commit.
* Updating __init__.py.
* Minor modification to how we initialize the cache.
Changing how the config specifies the architecture.
* Reformat code to 4 spaces.
Fixed a few typos.
* Fixed the forward pass.
Still unclear on the cache?
* Fixed the RecurrentGemmaForCausalLM
* Minor comment that we might not need attention_mask and output_attention arguments.
* Now cache should work as well.
* Adding a temporary example to check whether the model generation works.
* Adding the tests and updating imports.
* Adding the example file missing in the previous commit.
* First working example.
* Removing .gitignore and reverting parts of __init__.
* Re-add .gitignore.
* Addressing comments for configuration.
* Move mask creation to `_prepare_inputs_for_generation`.
* First try at integration tests:
1. AttributeError: 'GriffinCausalLMOutput' object has no attribute 'attentions'.
2. `cache_position` not passed
* Transfoering between machines.
* Running normal tests.
* Minor fix.
* More fixes.
* Addressing more comments.
* Minor fixes.
* first stab at cleanup
* more refactoring
* fix copies and else
* renaming and get init to work
* fix causal mask creation
* update
* nit
* fix a hell lot of things
* updates
* update conversion script
* make all keys importable
* nits
* add auto mappings
* properly convert ffw_up and down
* add scaling
* fix generations
* for recurrent dtype
* update
* fix going beyong window
* fixup
* add missing files
* current updates to remove last einops
* finish modeling refactor
* TADA
* fix compile
* fix most failing testt ? ?
* update tests
* refactor and update
* update
* nits, fixup and update tests
* more fixup
* nits
* fix imports
* test format
* fixups
* nits
* tuple typing
* fix code quality
* add model card
* fix doc
* skip most generation tests
* nits
* style
* doc fixes
* fix pr and check_copies?
* last nit
* oupsy
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* update
* Update src/transformers/models/recurrent_gemma/convert_recurrent_gemma_to_hf.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* update based on review
* doc nit
* fix quality
* quality
* fix slow test model path
* update default dype
* ignore attributes that can be safely ignored in check config attributes
* 0lallalala come on
* save nit
* style
* remove to dict update
* make sure we can also run in float16
* style
---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: Aleksandar Botev <botev@google.com>
Co-authored-by: Leonard Berrada <lberrada@users.noreply.github.com>
Co-authored-by: anushanf <anushanf@google.com>
Co-authored-by: botev <botevmg@gmail.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* 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?
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
* add FA2 to o.g Musicgen
* make style
* add FA2 support to Musicgen Melody
* add generation FA2 tests to o.g Musicgen
* make style and fix copies
* add Musicgen to FA2 docs + deprecate list
* add sdpa supports to Musicgen's
* make style and fix copies
* refactor attention implementation arguments
* add Copied from to sdpa tests
* add copied form in sdpa tests melody
* add copied for FA2 generation tests
* add FA2 inference copied from
* make style
* add support for qwen2 MoE models
* update docs
* add support for qwen2 MoE models
* update docs
* update model name & test
* update readme
* update class names & readme & model_doc of Qwen2MoE.
* update architecture name
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fix style
* fix test when there are sparse and non sparse layers
* fixup
* Update README.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup
* fixup
* add archive back
* add support for qwen2 MoE models
* update docs
* update model name & test
* update readme
* update class names & readme & model_doc of Qwen2MoE.
* update architecture name
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fixup
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* fix style
* fix test when there are sparse and non sparse layers
* fixup
* add archive back
* fix integration test
* fixup
---------
Co-authored-by: bozheng-hit <dsoul0621@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* model_summary.md - Add link to Harvard's Annotated Transformer.
* model_summary.md - slight wording change + capitalize name of the paper
* model_summary.md - moves the Annotated Transformer link in a praenthesis next to the link to the original paper (great idea, stevhliu!)
* model_summary.md - moves the Annotated Transformer link in a praenthesis next to the link to the original paper (commit pt. 2, accidentally removed "has" in pt. 1)
* Added SuperPoint docs
* Added tests
* Removed commented part
* Commit to create and fix add_superpoint branch with a new branch
* Fixed dummy_pt_objects
* Committed missing files
* Fixed README.md
* Apply suggestions from code review
Fixed small changes
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Moved ImagePointDescriptionOutput from modeling_outputs.py to modeling_superpoint.py
* Removed AutoModelForKeypointDetection and related stuff
* Fixed inconsistencies in image_processing_superpoint.py
* Moved infer_on_model logic simply in test_inference
* Fixed bugs, added labels to forward method with checks whether it is properly a None value, also added tests about this logic in test_modeling_superpoint.py
* Added tests to SuperPointImageProcessor to ensure that images are properly converted to grayscale
* Removed remaining mentions of MODEL_FOR_KEYPOINT_DETECTION_MAPPING
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fixed from (w, h) to (h, w) as input for tests
* Removed unnecessary condition
* Moved last_hidden_state to be the first returned
* Moved last_hidden_state to be the first returned (bis)
* Moved last_hidden_state to be the first returned (ter)
* Switched image_width and image_height in tests to match recent changes
* Added config as first SuperPointConvBlock init argument
* Reordered README's after merge
* Added missing first config argument to SuperPointConvBlock instantiations
* Removed formatting error
* Added SuperPoint to README's de, pt-br, ru, te and vi
* Checked out README_fr.md
* Fixed README_fr.md
* Test fix README_fr.md
* Test fix README_fr.md
* Last make fix-copies !
* Updated checkpoint path
* Removed unused SuperPoint doc
* Added missing image
* Update src/transformers/models/superpoint/modeling_superpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Removed unnecessary import
* Update src/transformers/models/superpoint/modeling_superpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Added SuperPoint to _toctree.yml
---------
Co-authored-by: steven <steven.bucaillle@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
* add galore v1
* add import
* add tests and doc
* fix doctest
* forward contrib credits from discussions
* forward contrib credits from discussions
* Apply suggestions from code review
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* fix failing tests'
* switch to `optim_target_modules` and clarify docs
* more clarification
* enhance lookup logic
* update a test to add peak memory
* add regex, all-linear and single string support
* add layer-wise optimization through DummyOptimizers and LRSchedulers
* forward contrib credits from discussions and original idea
* add a section about DDP not supported in layerwise
* Update src/transformers/trainer.py
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* fix self
* check only if layer_wise
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* oops
* make use of intervals
* clarify comment
* add matching tests
* GaLoRe -> GaLore
* move to `get_scheduler`
* add note on docs
* add a warning
* adapt a bit the docs
* update docstring
* support original API
* Update docs/source/en/trainer.md
* slightly refactor
* Update docs/source/en/trainer.md
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix args parsing and add tests
* remove warning for regex
* fix type hint
* add note about extra args
* make `is_regex` return optional
---------
Co-authored-by: Maxime <maximegmd @users.noreply.github.com>
Co-authored-by: Wing Lian <winglian @users.noreply.github.com>
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
Co-authored-by: hiyouga <hiyouga@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* Update pipeline_tutorial.md to include gradio
* Update pipeline_tutorial.md
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update pipeline_tutorial.md
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Cohere Model Release (#1)
Cohere Model Release
* Remove unnecessary files and code (#2)
Some cleanup
* Delete cohere-model directory (#3)
* Make Fix (#5)
* Pr fixes (#6)
* fixes for pr
* pr fixes for the format
* pr fixes for the format
* src/transformers/models/auto/tokenization_auto.py
* Tokenizer test (#8)
* tokenizer test
* format fix
* Adding Docs and other minor changes (#7)
* Add modeling tests (#9)
* Smol Fix (#11)
* tokenization tests are fixed
* format fixes
* fix pr doc tests
* fix pr doc tests
* fix pr doc tests
* fix pr style check
* small changes in cohere.md
* FIX: Address final comments for transformers integration (#13)
* fix modeling final nits and add proper test file
* for now leave empty tests
* add integration test
* push new test
* fix modeling cohere (#14)
* Update chat templates to use the new API (#15)
---------
Co-authored-by: ahmetustun <ahmetustun89@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Added pytests for pvt-v2, all passed
* Added pvt_v2 to docs/source/end/model_doc
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat. Added additional type support for image size in config
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Reverted batch eval changes for PR
* Updated index.md
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat
* Ran fix-copies
* Fixed PvtV2Backbone tests
* Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py
* Fixed backbone stuff and fixed tests: all passing
* Ran make fixup
* Made modifications for code checks
* Remove ONNX config from configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use explicit image size dict in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make image_size optional in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove _ntuple use in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove reference to fp16_enabled
* Model modules now take config as first argument even when not used
* Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling"
* All LayerNorm now instantiates with config.layer_norm_eps
* Added docstring for depth-wise conv layer
* PvtV2Config now only takes Union[int, Tuple[int, int]] for image size
* Refactored PVTv2 in prep for gradient checkpointing
* Gradient checkpointing ready to test
* Removed override of _set_gradient_checkpointing
* Cleaned out old code
* Applied code fixup
* Applied code fixup
* Began debug of pvt_v2 tests
* Leave handling of num_labels to base pretrained config class
* Deactivated gradient checkpointing tests until it is fixed
* Removed PvtV2ImageProcessor which duped PvtImageProcessor
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Added pvt_v2 to docs/source/end/model_doc
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat. Added additional type support for image size in config
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat
* Ran fix-copies
* Fixed PvtV2Backbone tests
* Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py
* Fixed backbone stuff and fixed tests: all passing
* Ran make fixup
* Made modifications for code checks
* Remove ONNX config from configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use explicit image size dict in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make image_size optional in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove _ntuple use in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove reference to fp16_enabled
* Model modules now take config as first argument even when not used
* Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling"
* All LayerNorm now instantiates with config.layer_norm_eps
* Added docstring for depth-wise conv layer
* PvtV2Config now only takes Union[int, Tuple[int, int]] for image size
* Refactored PVTv2 in prep for gradient checkpointing
* Gradient checkpointing ready to test
* Removed override of _set_gradient_checkpointing
* Cleaned out old code
* Applied code fixup
* Applied code fixup
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Ran fix-copies and fixup. All checks passed
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Fixed config docstring. Added channels property
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Ran fix-copies and fixup. All checks passed
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Fixed config backbone compat
* Ran fix-copies
* Began debug of pvt_v2 tests
* Leave handling of num_labels to base pretrained config class
* Deactivated gradient checkpointing tests until it is fixed
* Removed PvtV2ImageProcessor which duped PvtImageProcessor
* Fixed issue from rebase
* Fixed issue from rebase
* Set tests for gradient checkpointing to skip those using reentrant since it isn't supported
* Fixed issue from rebase
* Fixed issue from rebase
* Changed model name in docs
* Removed duplicate PvtV2Backbone
* Work around type switching issue in tests
* Fix model name in config comments
* Update docs/source/en/model_doc/pvt_v2.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Changed name of variable from 'attn_reduce' to 'sr_type'
* Changed name of variable from 'attn_reduce' to 'sr_type'
* Changed from using 'sr_type' to 'linear_attention' for clarity
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Removed old code
* Changed from using 'sr_type' to 'linear_attention' for clarity
* Fixed Class names to be more descriptive
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Removed outdated code
* Moved paper abstract to single line in pvt_v2.md
* Added usage tips to pvt_v2.md
* Simplified module inits by passing layer_idx
* Fixed typing for hidden_act in PvtV2Config
* Removed unusued import
* Add pvt_v2 to docs/source/en/_toctree.yml
* Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive.
* Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive.
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Move function parameters to single line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Update year of copyright to 2024
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Make code more explicit
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Updated sr_ratio to be more explicit spatial_reduction_ratio
* Removed excess type hints in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Move params to single line in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Removed needless comment in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update copyright date in pvt_v2.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Moved params to single line in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Updated copyright date in configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Cleaned comments in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Renamed spatial_reduction Conv2D operation
* Revert "Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
"
This reverts commit c4a04416dd.
* Updated conversion script to reflect module name change
* Deprecated reshape_last_stage option in config
* Removed unused imports
* Code formatting
* Fixed outdated decorators on test_inference_fp16
* Added "Copied from" comments in test_modeling_pvt_v2.py
* Fixed import listing
* Updated model name
* Force empty commit for PR refresh
* Fixed linting issue
* Removed # Copied from comments
* Added PVTv2 to README_fr.md
* Ran make fix-copies
* Replace all FoamoftheSea hub references with OpenGVLab
* Fixed out_indices and out_features logic in configuration_pvt_v2.py
* Made ImageNet weight conversion verification optional in convert_pvt_v2_to_pytorch.py
* Ran code fixup
* Fixed order of parent classes in PvtV2Config to fix the to_dict method override
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial implementation of flash attention for gptj
* modify flash attention and overwrite test_flash_attn_2_generate_padding_right
* update flash attention support list
* remove the copy line in the `CodeGenBlock`
* address copy mechanism
* Update src/transformers/models/gptj/modeling_gptj.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add GPTJ attention classes
* add expected outputs in the gptj test
* Ensure repo consistency with 'make fix-copies'
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
