* [FT] First commit for graphormer architecture.
The model has no tokenizer, as it uses a collator and preprocessing function for its input management.
Architecture to be tested against original one.
The arch might need to be changed to fit the checkpoint, but a revert to the original arch will make the code less nice to read.
TODO: doc
* [FIX] removed test model
* [FIX] import error
* [FIX] black and flake
* [DOC] added paper refs
* [FIX] [DOC]
* [FIX] black
* [DOC] Updated READMEs
* [FIX] Order of imports + rm Tokenizer calls
* [FIX] Moved assert in class to prevent doc build failure
* [FIX] make fix-copies
* [Doc] update from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [FIX] Removed Graphormer from Sequence classification model list
* [DOC] Added HF copyright to Cython file
* [DOC] Fixed comments
* [FIX] typos in class doc + removed config classes.
Todo: update doc from paper definitions
* [FIX] Removed dependency to fairseq, and replaced all asserts with Exception management
* [FIX] Homogeneized initialization of weights to pretrained constructor
* [FIX] [CP] Updated multi_hop parameter to get same results as in original implementation
* [DOC] Relevant parameter description in the configuration file
* [DOC] Updated doc and comments in main graphormer file
* [FIX] make style and quality checks
* [DOC] Fix doc format
* [FIX] [WIP] Updated part of the tests, though still a wip
* [FIX] [WIP]
* [FIX] repo consistency
* [FIX] Changed input names for more understandability
* [FIX] [BUG] updated num_classes params for propagation in the model
* simplified collator
* [FIX] Updated tests to follow new naming pattern
* [TESTS] Updated test suite along with model
* |FIX] rm tokenizer import
* [DOC] add link to graphormerdoc
* Changed section in doc from text model to graph model
* Apply suggestions from code review
Spacing, inits
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [DOC] Explain algos_graphormer functions
* Cython soft import protection
* Rm call to Callable in configuration graphormer
* [FIX] replaced asserts with Exceptions
* Add org to graphormer checkpoints
* Prefixed classes with Graphormer
* Management of init functions
* format
* fixes
* fix length file
* update indent
* relaunching ci
* Errors for missing cython imports
* fix style
* fix style doc
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* `blip` support for training
* remove labels creation
* remove unneeded `decoder_input_ids` creation
* final changes
- add colab link to documentation
- reduction = mean for loss
* fix nits
* update link
* clearer error message
* torch.jit._state
* Fix past CI
* Fix for perceiver
* Fix REALM
* Fix for Bloom
* Fix for SwinMode
* Fix for TrajectoryTransformerModel
* Fix for test_wav2vec2_with_lm
* make style
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add templates for gpt-sw3
* Add templates for gpt-sw3
* Added sentencepiece tokenizer
* intermediate commit with many changes
* fixed conflicts
* Init commit for tokenization port
* Tokenization progress
* Remove fast tokenizer
* Clean up and rename spm.model -> spiece.model
* Remove TF -> PT conversion script template, Clean up Megatron -> PT script
* Optimize encode & decode performance
* added new attention
* added new attention
* attention for gpt-sw3 working
* attention good
* Cache is now working
* fixed attention mask so that it works with causal attention
* fixed badbmm bug for cpu and caching
* updated config with correct parameters
* Refactor and leave optimizations as separate functions to avoid breaking expected functionality
* Fix special tokens mapping for both tokenizers
* cleaning up of code and comments
* HF compatible attention outputs
* Tokenizer now passing tests, add documentation
* Update documentation
* reverted back to base implementation after checking that it is identical to pretrained model
* updated gpt-sw3 config
* updated conversion script
* aligned parameters with gpt-sw3 config
* changed default scale_attn_by_inverse_layer_idx to true
* removed flag from conversion script
* added temporary model path
* reverted back to functioning convert script
* small changes to default config
* updated tests for gpt-sw3
* make style, make quality, minor cleanup
* Change local paths to testing online repository
* Change name: GptSw3 -> GPTSw3
* Remove GPTSw3TokenizerFast references
* Use official model repository and add more model sizes
* Added reference to 6.7b model
* Add GPTSw3DoubleHeadsModel to IGNORE_NON_AUTO_CONFIGURED, like GPT2DoubleHeadsModel
* Remove pointers to non-existing TFGPTSw3
* Add GPTSw3 to docs/_toctree.yml
* Remove TF artifacts from GPTSw3 in __init__ files
* Update README:s with 'make fix-copies'
* Add 20b model to archive list
* Add documentation for GPT-Sw3
* Fix typo in documentation for GPT-Sw3
* Do 'make fix-copies' again after having updated docs
* Fix some typos in docs
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/models/gpt_sw3/test_tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Resolve comments from PR feedback
* Resolve more comments from PR feedback, also set use_cache=True in convert script
* Add '# Copied from' comments for GPTSw3 modeling
* Set 'is_parallelizable = False'
* Remove '# Copied from' where code was modified and add 'with x->y' when appropriate
* Remove parallelize in mdx
* make style, make quality
* Update GPTSw3Config default values and corresponding documentation
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean up and protect GPTSw3Tokenizer imports with is_sentencepiece_available
* Make style, make quality
* Add dummy object for GPTSw3Tokenizer via 'make fix-copies'
* make fix-copies
* Remove GPTSw3 modeling classes
* make style, make quality
* Add GPTSw3 auto-mappings for other GPT2 heads
* Update docs/source/en/model_doc/gpt-sw3.mdx
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove old TODO-comment
* Add example usage to GPTSw3Tokenizer docstring
* make style, make quality
* Add implementation details and example usage to gpt-sw3.mdx
Co-authored-by: JoeyOhman <joeyoh@kth.se>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* read to load
* base functionality
* revert init
* fix dummy data
* moving right along
* moving right along
* finally
* cleanup
* pull out comment
* add test
* update docstring for main class
* flake comments and rewriting copies from make repo-consistency`
* remove irrelevant differences/accidental spaces
* put copies back after space removals
* mid
* final test pass
* stray comment
* update test file
* update test file
* fixup
* black
* missed
* black missed one more
* sytle
* add doc update
* fix order of output class
* comment
* Revert "comment"
This reverts commit 03f86b6948.
* remove redundant function, and redundant reshape
* move change out of common
* style
* put common spaces back
* reorder kwargs in output
* doc style
* biogpt initial commit
* updated init
* fix faster decoding with use_cache
* 1. fix input_ids and input_embeds with correct device
2. added _keys_to_ignore_on_load_missing
3. updated prepare_inputs_for_generation
* add activation_dropout and scale_embedding
* replace fsmt attention with bart attention
* added test
* run make fix-copies
* doc init and fix build
* updated README with proper information
* 1. added tips to docs
2. updated BioGptTokenizer func
* 1. added tokenizer test
2. refactor tokenizer
* make fixup
* add biogpt fairseq to hf converter
* updated layer names more
similar to original checkpoints
* config update doc string and set defaults
* added "#copied" from bart model and
updated doc strings
* enable model_input_names in tokenizer
* 1. positionalembedding depending on attention_mask
2. added attention mask to prepare for generation
* added test to verify past and generation
* BioGptLMHeadModel -> BioGptForCausalLM
* fix typo
* tokenization and test
Copyright and updated assertion
* updated Copyright and
one func at time in line
* Copyright updates and
minor doc fix
* replace assertion with ValueError
* rm extra space
* added code syntax
* revert cmnt position change
* add tokenizer to auto
* updated doc string
* tokenizer doc string update
* biogpt hub model update to microsoft/biogpt
* make fixup
* rm cmnt to fix flake8 5.0.4 vs 6 error
* add minimal working gpt2 tokenizer
* graph mode and output equivalence tests working
* not today tensorflow. serialization test passing!
* fix style, documentation, docstrings and all that jazz
* passing consistency checks
* move keras nlp to tf dependencies
* fix tf modeling utils and gpt2 attention to enable compiling
* fix (I hope) keras nlp dependencies
* rever changes on generation
* remove debug prints
* remove redundant tf dummy objects
* add from config, get config and max length settings to address review
* let flake ignore the error on distillation you are welcome
* test from config
* add padding test
* address sgugger review
* Add Donut image processor
* Update src/transformers/image_transforms.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Fix docstrings
* Full var names in docstring
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* add model files etc for MobileNetV2
rename files for MobileNetV1
initial implementation of MobileNetV1
fix conversion script
cleanup
write docs
tweaks
fix conversion script
extract hidden states
fix test cases
make fixup
fixup it all
remove main from doc link
fixes
fix tests
fix up
use google org
fix weird assert
* fixup
* use google organization for checkpoints
* Add DiNAT
* Adds DiNAT + tests
* Minor fixes
* Added HF model
* Add natten to dependencies.
* Cleanup
* Minor fixup
* Reformat
* Optional NATTEN import.
* Reformat & add doc to _toctree
* Reformat (finally)
* Dummy objects for DiNAT
* Add NAT + minor changes
Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.
* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests
* Minor fixes.
* Fix READMEs.
* Requested changes to docs + minor fixes.
* Requested changes.
* Add NAT/DiNAT tests to layoutlm_job
* Correction to Dinat doc.
* Requested changes.
* Add resources of OpenAI GPT
* Delete Deploy section and add .
* Add scripts
* Update docs/source/en/model_doc/openai-gpt.mdx
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Delete causal-language-modeling section
* Add TFOpenAIGPTLMHeadModel
* Add resources from community
* Delete a link
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Adds image-guided object detection method to OwlViTForObjectDetection class as described in the original paper. One-shot/ image-guided object detection enables users to use a query image to search for similar objects in the input image.
Co-Authored-By: Dhruv Karan k4r4n.dhruv@gmail.com
* WIP: Added CLIP resources from HuggingFace blog
* ADD: Notebooks documentation to clip
* Add link straight to notebook
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Change notebook links to colab
Co-authored-by: Ambuj Pawar <your_email@abc.example>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* allow loading projection in text and vision model
* begin tests
* finish test for CLIPTextModelTest
* style
* add slow tests
* add new classes for projection heads
* remove with_projection
* add in init
* add in doc
* fix tests
* fix some more tests
* fix copies
* fix docs
* remove leftover from fix-copies
* add the head models in IGNORE_NON_AUTO_CONFIGURED
* fix docstr
* fix tests
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add docstr for models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add model files etc for MobileNetV2
* rename files for MobileNetV1
* initial implementation of MobileNetV1
* fix conversion script
* cleanup
* write docs
* tweaks
* fix conversion script
* extract hidden states
* fix test cases
* make fixup
* fixup it all
* rename V1 to V2
* fix checkpoints
* fixup
* implement first block + weight conversion
* add remaining layers
* add output stride and dilation
* fixup
* add tests
* add deeplabv3+ head
* a bit of fixup
* finish deeplab conversion
* add link to doc
* fix issue with JIT trace
in_height and in_width would be Tensor objects during JIT trace, which caused Core ML conversion to fail on the remainder op. By making them ints, the result of the padding calculation becomes a constant value.
* cleanup
* fix order of models
* fix rebase error
* remove main from doc link
* add image processor
* remove old feature extractor
* fix converter + other issues
* fixup
* fix unit test
* add to onnx tests (but these appear broken now)
* add post_process_semantic_segmentation
* use google org
* remove unused imports
* move args
* replace weird assert
* move generation_*.py src files into generation/*.py
* populate generation.__init__ with lazy loading
* move imports and references from generation.xxx.object to generation.object
* Add first draft
* Update conversion script
* Improve conversion script
* Improve conversion script some more
* Add conditional embeddings
* Add initial decoder
* Fix activation function of decoder
* Make decoder outputs match original implementation
* Make decoder outputs match original implementation
* Add more copied from statements
* Improve model outputs
* Fix auto tokenizer file
* Fix more tests
* Add test
* Improve README and docs, improve conditional embeddings
* Fix more tests
* Remove print statements
* Remove initial embeddings
* Improve conversion script
* Add interpolation of position embeddings
* Finish addition of interpolation of position embeddings
* Add support for refined checkpoint
* Fix refined checkpoint
* Remove unused parameter
* Improve conversion script
* Add support for training
* Fix conversion script
* Add CLIPSegFeatureExtractor
* Fix processor
* Fix CLIPSegProcessor
* Fix conversion script
* Fix most tests
* Fix equivalence test
* Fix README
* Add model to doc tests
* Use better variable name
* Convert other checkpoint as well
* Update config, add link to paper
* Add docs
* Update organization
* Replace base_model_prefix with clip
* Fix base_model_prefix
* Fix checkpoint of config
* Fix config checkpoint
* Remove file
* Use logits for output
* Fix tests
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* docs: Fix typo in ONNX parser help: 'tolerence' => 'tolerance'
* docs: Resolve many typos in the English docs
Typos found via 'codespell ./docs/source/en'
* initial commit
* First draft that gets outputs without crashing!
* Add all the ported openfold dependencies
* testing
* Restructure config files for ESMFold
* Debugging to find output discrepancies
* Mainly style
* Make model runnable without extra deps
* Remove utils and merge them to the modeling file
* Use correct gelu and remove some debug prints
* More cleanup
* Update esm docs
* Update conversion script to support ESMFold properly
* Port some top-level changes from ESMFold repo
* Expand EsmFold docstrings
* Make attention_mask optional (default to all 1s)
* Add inference test for ESMFold
* Use config and not n kwargs
* Add modeling output class
* Remove einops
* Remove chunking in ESM FFN
* Update tests for ESMFold
* Quality
* REpo consistency
* Remove tree dependency from ESMFold
* make fixup
* Add an error in case my structure map function breaks later
* Remove needless code
* Stop auto-casting the LM to float16 so CPU tests pass
* Stop auto-casting the LM to float16 so CPU tests pass
* Final test updates
* Split test file
* Copyright and quality
* Unpin PyTorch to see built doc
* Fix config file to_dict() method
* Add some docstrings to the output
* Skip TF checkpoint tests for ESM until we reupload those
* make fixup
* More docstrings
* Unpin to get even with main
* Flag example to write
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Partial TF port for ESM model
* Add ESM-TF tests
* Add the various imports for TF-ESM
* TF weight conversion almost ready
* Stop ignoring the decoder weights in PT
* Add tests and lots of fixes
* fix-copies
* Fix imports, add model docs
* Add get_vocab() to tokenizer
* Fix vocab links for pretrained files
* Allow multiple inputs with a sep
* Use EOS as SEP token because ESM vocab lacks SEP
* Correctly return special tokens mask from ESM tokenizer
* make fixup
* Stop testing unsupported embedding resizing
* Handle TF bias correctly
* Skip all models with slow tokenizers in the token classification test
* Fixing the batch/unbatcher of pipelines to accomodate the `None` being
passed around.
* Fixing pipeline bug caused by slow tokenizer being different.
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update set_input_embeddings and the copyright notices
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Add initial files for depth estimation pipelines
* Add test file for depth estimation pipeline
* Update model mapping names
* Add updates for depth estimation output
* Add generic test
* Hopefully fixing the tests.
* Check if test passes
* Add make fixup and make fix-copies changes after rebase with main
* Rebase with main
* Fixing up depth pipeline.
* This is not used anymore.
* Fixing the test. `Image` is a module `Image.Image` is the type.
* Update docs/source/en/main_classes/pipelines.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Fix more things
* Improve more things
* Remove some head models
* Fix more things
* Add missing layers
* Remove tokenizer
* Fix more things
* Fix copied from statements
* Make all tests pass
* Remove print statements
* Remove files
* Fix README and docs
* Add integration test and fix organization
* Add tips
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Make tests faster, improve docs
* Fix doc tests
* Add model to toctree
* Add docs
* Add note about creating new checkpoint
* Remove is_decoder
* Make tests smaller, add docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* implemented TFCvtModel and TFCvtForImageClassification and modified relevant files, added an exception in convert_tf_weight_name_to_pt_weight_name, added quick testing file to compare with pytorch model
* added docstring + testing file in transformers testing suite
* added test in testing file, modified docs to pass repo-consistency, passed formatting test
* refactoring + passing all test
* small refacto, removing unwanted comments
* improved testing config
* corrected import error
* modified acces to pretrained model archive list, to pass tf_test
* corrected import structure in init files
* modified testing for keras_fit with cpu
* correcting PR issues + Refactoring
* Refactoring : improving readability and reducing the number of permutations
* corrected momentum value + cls_token initialization
* removed from_pt as weights were added to the hub
* Update tests/models/cvt/test_modeling_tf_cvt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Add `OPTForQuestionAnswering`
- added `OPTForQuestionAnswering` class based on `BloomForQuestionAnswering`
- added `OPTForQuestionAnswering` in common tests
- all common tests pass
- make fixup done
* added docstrings for OPTForQuestionAnswering
* Fix docstrings for OPTForQuestionAnswering
* Add ZeroShotObjectDetectionPipeline (#18445)
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add batching for ZeroShotObjectDetectionPipeline
* Fix doc-string ZeroShotObjectDetectionPipeline
* Fix output format: ZeroShotObjectDetectionPipeline
- Improves MaskFormer docs, corrects minor typos
- Restructures MaskFormerFeatureExtractor.post_process_panoptic_segmentation for better readability, adds target_sizes argument for optional resizing
- Adds post_process_semantic_segmentation and post_process_instance_segmentation methods.
- Adds a deprecation warning to post_process_segmentation method in favour of post_process_instance_segmentation
* add bloom for question answering
- attempt to add Bloom for question answering
- adapted from `GPTJForQuestionAnswering`
- Fixed `num_labels` to `2` for common tests
- Added a bit of docstring
- All common tests pass
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert changes related to `num_labels`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Rebase ESM PR and update all file formats
* Fix test relative imports
* Add __init__.py to the test dir
* Disable gradient checkpointing
* Remove references to TFESM... FOR NOW >:|
* Remove completed TODOs from tests
* Convert docstrings to mdx, fix-copies from BERT
* fix-copies for the README and index
* Update ESM's __init__.py to the modern format
* Add to _toctree.yml
* Ensure we correctly copy the pad_token_id from the original ESM model
* Ensure we correctly copy the pad_token_id from the original ESM model
* Tiny grammar nitpicks
* Make the layer norm after embeddings an optional flag
* Make the layer norm after embeddings an optional flag
* Update the conversion script to handle other model classes
* Remove token_type_ids entirely, fix attention_masking and add checks to convert_esm.py
* Break the copied from link from BertModel.forward to remove token_type_ids
* Remove debug array saves
* Begin ESM-2 porting
* Add a hacky workaround for the precision issue in original repo
* Code cleanup
* Remove unused checkpoint conversion code
* Remove unused checkpoint conversion code
* Fix copyright notices
* Get rid of all references to the TF weights conversion
* Remove token_type_ids from the tests
* Fix test code
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add credit
* Remove _ args and __ kwargs in rotary embedding
* Assertively remove asserts
* Replace einsum with torch.outer()
* Fix docstring formatting
* Remove assertions in tokenization
* Add paper citation to ESMModel docstring
* Move vocab list to single line
* Remove ESMLayer from init
* Add Facebook copyrights
* Clean up RotaryEmbedding docstring
* Fix docstring formatting
* Fix docstring for config object
* Add explanation for new config methods
* make fix-copies
* Rename all the ESM- classes to Esm-
* Update conversion script to allow pushing to hub
* Update tests to point at my repo for now
* Set config properly for tests
* Remove the gross hack that forced loss of precision in inv_freq and instead copy the data from the model being converted
* make fixup
* Update expected values for slow tests
* make fixup
* Remove EsmForCausalLM for now
* Remove EsmForCausalLM for now
* Fix padding idx test
* Updated README and docs with ESM-1b and ESM-2 separately (#19221)
* Updated README and docs with ESM-1b and ESM-2 separately
* Update READMEs, longer entry with 3 citations
* make fix-copies
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Tom Sercu <tsercu@fb.com>
Co-authored-by: Your Name <you@example.com>
* chore: initial commit
* chore: adding util methods
yet to work on the nn.functional.interpolate port with align_corener=True
* chore: refactor the utils
* used tf.compat.v1.image.resize to align the F.interpolate function
* added type hints to the method signatures
* added references to the gists where one 2 one alignment of torch and tf has been shown
* chore: adding the layers
* chore: porting all the layers from torch to tf
This is the initial draft, nothing is tested yet.
* chore: aligning the layers with reference to tf clip
* chore: aligning the modules
* added demaraction comments
* added copied and adapted from comments
* chore: aligning with CLIP
* chore: wrangling the layers to keep it tf compatible
* chore: aligning the names of the layers for porting
* chore: style changes
* chore: adding docs and inits
* chore: adding tfp dependencis
the code is taken from TAPAS
* chore: initial commit for testing
* chore: aligning the vision embeddings with the vit implementatino
* chore: changing model prefix
* chore: fixing the name of the model and the layer normalization test case
* chore: every test passes but the slow ones
* chore: fix style and integration test
* chore: moving comments below decorators
* chore: make fixup and fix-copies changes
* chore: adding the Vision and Text Model to check_repo
* chore: modifying the prefix name to align it with the torch implementation
* chore: fix typo in configuration
* choer: changing the name of the model variable
* chore: adding segmentation flag
* chore: gante's review
* chore: style refactor
* chore: amy review
* chore: adding shape_list to parts that have been copied from other snippets
* chore: init batchnorm with torch defaults
* chore: adding shape_list to pass the tests
* test fix: adding seed as 0
* set seed
* chore: changing the straight through trick to fix -ve dimensinos
* chore: adding a dimension to the loss
* chore: adding reviewers and contributors names to the docs
* chore: added changes after review
* chore: code quality fixup
* chore: fixing the segmentation snippet
* chore: adding to the layer calls
* chore: changing int32 to int64 for inputs of serving
* chore: review changes
* chore: style changes
* chore: remove from_pt=True
* fix: repo consistency
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
