* Added test cases for rembert refering to albert and reformer test_tokenization
* removed CURL_CA_BUNDLE='
* Added flag test_sentencepiece_ignore_case and space_between_special_tokens to True
* Overrided test_added_tokens_serialization
* As slow->fast token failed due to the different initialization for [MASK] for slow and fast, Therefore it required to make the initialization for [MASK] token uniform between fast and slow token
* Added few more test cases in test_encode_decode_round_trip and modefied the slow token (mask_token) to have AddedToken instance with lstrip=True
* Added few test cases in test_encoder_decoder round trip and also modified slow tokenizer of rembert to have mask_token as AddedToken with lstrip = True
* Cleaned the code and added fmt: skip to avoid line breaks after make style + added comments to indicate from the copied test cases
* Corrected few comments
* Fixed quality issue
* Ran fix-copies
* Fixed few minor issues as (make fix-copies) broke few test cases while stripping the text
* Reverted the changes made by repo-consistancy
---------
Co-authored-by: Kokane <kokanen@apac.corpdir.net>
* add working convertion script
* first non-working version of modeling code
* update modeling code (working)
* make style
* make fix-copies
* add config docstrings
* add config to ignore docstrings formatage due to unconventional markdown
* fix copies
* fix generation num_return_sequences
* enrich docs
* add and fix tests beside integration tests
* update integration tests
* update repo id
* add tie weights and make style
* correct naming in .md
* fix imports and so on
* correct docstrings
* fix fp16 speech forward
* fix speechencoder attention
* make style
* fix copied from
* rename SeamlessM4Tv2-v2 to SeamlessM4Tv2
* Apply suggestions on configuration
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* remove useless public models
* fix private models + better naming for T2U models
* clean speech encoder relative position embeddings
* refactor chunk attention
* add docstrings to chunk attention method
* improve naming and docstrings
* rename some attention variables + add temperature sampling in T2U model
* rename DOCSTRINGS variable names
* make style + remove 2 useless config parameters
* enrich model card
* remove any attention_head reference + fix temperature in T2U
* new fmt and make style
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* rename spkr_id->speaker_id and change docstrings of get_char_input_ids
* simplify v2attention
* make style
* Update seamless_m4t_v2.md
* update code and tests with last update
* update repo ids
* fill article name, abstract andauthors
* update not_doctested and slow_doc tests
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add distribution head to forecasting
* formatting
* Add generate function for forecasting
* Add generate function to prediction task
* formatting
* use argsort
* add past_observed_mask ordering
* fix arguments
* docs
* add back test_model_outputs_equivalence test
* formatting
* cleanup
* formatting
* use ACT2CLS
* formatting
* fix add_start_docstrings decorator
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* fix typos
* add forecast_masking
* fixed tests
* use set_seed
* fix doc test
* formatting
* Update docs/source/en/model_doc/patchtst.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* better var names
* rename PatchTSTTranspose
* fix argument names and docs string
* remove compute_num_patches and unused class
* remove assert
* renamed to PatchTSTMasking
* use num_labels for classification
* use num_labels
* use default num_labels from super class
* move model_type after docstring
* renamed PatchTSTForMaskPretraining
* bs -> batch_size
* more review fixes
* use hidden_state
* rename encoder layer and block class
* remove commented seed_number
* edit docstring
* Add docstring
* formatting
* use past_observed_mask
* doc suggestion
* make fix-copies
* use Args:
* add docstring
* add docstring
* change some variable names and add PatchTST before some class names
* formatting
* fix argument types
* fix tests
* change x variable to patch_input
* format
* formatting
* fix-copies
* Update tests/models/patchtst/test_modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move loss to forward
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* formatting
* fix a bug when pre_norm is set to True
* output_hidden_states is set to False as default
* set pre_norm=True as default
* format docstring
* format
* output_hidden_states is None by default
* add missing docs
* better var names
* docstring: remove default to False in output_hidden_states
* change labels name to target_values in regression task
* format
* fix tests
* change to forecast_mask_ratios and random_mask_ratio
* change mask names
* change future_values to target_values param in the prediction class
* remove nn.Sequential and make PatchTSTBatchNorm class
* black
* fix argument name for prediction
* add output_attentions option
* add output_attentions to PatchTSTEncoder
* formatting
* Add attention output option to all classes
* Remove PatchTSTEncoderBlock
* create PatchTSTEmbedding class
* use config in PatchTSTPatchify
* Use config in PatchTSTMasking class
* add channel_attn_weights
* Add PatchTSTScaler class
* add output_attentions arg to test function
* format
* Update doc with image patchtst.md
* fix-copies
* rename Forecast <-> Prediction
* change name of a few parameters to match with PatchTSMixer.
* Remove *ForForecasting class to match with other time series models.
* make style
* Remove PatchTSTForForecasting in the test
* remove PatchTSTForForecastingOutput class
* change test_forecast_head to test_prediction_head
* style
* fix docs
* fix tests
* change num_labels to num_targets
* Remove PatchTSTTranspose
* remove arguments in PatchTSTMeanScaler
* remove arguments in PatchTSTStdScaler
* add config as an argument to all the scaler classes
* reformat
* Add norm_eps for batchnorm and layernorm
* reformat.
* reformat
* edit docstring
* update docstring
* change variable name pooling to pooling_type
* fix output_hidden_states as tuple
* fix bug when calling PatchTSTBatchNorm
* change stride to patch_stride
* create PatchTSTPositionalEncoding class and restructure the PatchTSTEncoder
* formatting
* initialize scalers with configs
* edit output_hidden_states
* style
* fix forecast_mask_patches doc string
* doc improvements
* move summary to the start
* typo
* fix docstring
* turn off masking when using prediction, regression, classification
* return scaled output
* adjust output when using distribution head
* remove _num_patches function in the config
* get config.num_patches from patchifier init
* add output_attentions docstring, remove tuple in output_hidden_states
* change SamplePatchTSTPredictionOutput and SamplePatchTSTRegressionOutput to SamplePatchTSTOutput
* remove print("model_class: ", model_class)
* change encoder_attention_heads to num_attention_heads
* change norm to norm_layer
* change encoder_layers to num_hidden_layers
* change shared_embedding to share_embedding, shared_projection to share_projection
* add output_attentions
* more robust check of norm_type
* change dropout_path to path_dropout
* edit docstring
* remove positional_encoding function and add _init_pe in PatchTSTPositionalEncoding
* edit shape of cls_token and initialize it
* add a check on the num_input_channels.
* edit head_dim in the Prediction class to allow the use of cls_token
* remove some positional_encoding_type options, remove learn_pe arg, initalize pe
* change Exception to ValueError
* format
* norm_type is "batchnorm"
* make style
* change cls_token shape
* Change forecast_mask_patches to num_mask_patches. Remove forecast_mask_ratios.
* Bring PatchTSTClassificationHead on top of PatchTSTForClassification
* change encoder_ffn_dim to ffn_dim and edit the docstring.
* update variable names to match with the config
* add generation tests
* change num_mask_patches to num_forecast_mask_patches
* Add examples explaining the use of these models
* make style
* Revert "Revert "[time series] Add PatchTST (#25927)" (#27486)"
This reverts commit 78f6ed6c70.
* make style
* fix default std scaler's minimum_scale
* fix docstring
* close code blocks
* Update docs/source/en/model_doc/patchtst.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/patchtst/test_modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/configuration_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix tests
* add add_start_docstrings
* move examples to the forward's docstrings
* update prepare_batch
* update test
* fix test_prediction_head
* fix generation test
* use seed to create generator
* add output_hidden_states and config.num_patches
* add loc and scale args in PatchTSTForPredictionOutput
* edit outputs if if not return_dict
* use self.share_embedding to check instead checking type.
* remove seed
* make style
* seed is an optional int
* fix test
* generator device
* Fix assertTrue test
* swap order of items in outputs when return_dict=False.
* add mask_type and random_mask_ratio to unittest
* Update modeling_patchtst.py
* add add_start_docstrings for regression model
* make style
* update model path
* Edit the ValueError comment in forecast_masking
* update examples
* make style
* fix commented code
* update examples: remove config from from_pretrained call
* Edit example outputs
* Set default target_values to None
* remove config setting in regression example
* Update configuration_patchtst.py
* Update configuration_patchtst.py
* remove config from examples
* change default d_model and ffn_dim
* norm_eps default
* set has_attentions to Trye and define self.seq_length = self.num_patche
* update docstring
* change variable mask_input to do_mask_input
* fix blank space.
* change logger.debug to logger.warning.
* remove unused PATCHTST_INPUTS_DOCSTRING
* remove all_generative_model_classes
* set test_missing_keys=True
* remove undefined params in the docstring.
---------
Co-authored-by: nnguyen <nnguyen@us.ibm.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix mistral generate for long prompt / response
* Add unit test
* fix linter
* fix linter
* fix test
* add assisted generation test for mistral and load the model in 4 bit + fa2
* initial commit
* Add inital testing files and modify __init__ files to add UnivNet imports.
* Fix some bugs
* Add checkpoint conversion script and add references to transformers pre-trained model.
* Add UnivNet entries for auto.
* Add initial docs for UnivNet.
* Handle input and output shapes in UnivNetGan.forward and add initial docstrings.
* Write tests and make them pass.
* Write docs.
* Add UnivNet doc to _toctree.yml and improve docs.
* fix typo
* make fixup
* make fix-copies
* Add upsample_rates parameter to config and improve config documentation.
* make fixup
* make fix-copies
* Remove unused upsample_rates config parameter.
* apply suggestions from review
* make style
* Verify and add reason for skipped tests inherited from ModelTesterMixin.
* Add initial UnivNetGan integration tests
* make style
* Remove noise_length input to UnivNetGan and improve integration tests.
* Fix bug and make style
* Make UnivNet integration tests pass
* Add initial code for UnivNetFeatureExtractor.
* make style
* Add initial tests for UnivNetFeatureExtractor.
* make style
* Properly initialize weights for UnivNetGan
* Get feature extractor fast tests passing
* make style
* Get feature extractor integration tests passing
* Get UnivNet integration tests passing
* make style
* Add UnivNetGan usage example
* make style and use feature extractor from hub in integration tests
* Update tips in docs
* apply suggestions from review
* make style
* Calculate padding directly instead of using get_padding methods.
* Update UnivNetFeatureExtractor.to_dict to be UnivNet-specific.
* Update feature extractor to support using model(**inputs) and add the ability to generate noise and pad the end of the spectrogram in __call__.
* Perform padding before generating noise to ensure the shapes are correct.
* Rename UnivNetGan.forward's noise_waveform argument to noise_sequence.
* make style
* Add tests to test generating noise and padding the end for UnivNetFeatureExtractor.__call__.
* Add tests for checking batched vs unbatched inputs for UnivNet feature extractor and model.
* Add expected mean and stddev checks to the integration tests and make them pass.
* make style
* Make it possible to use model(**inputs), where inputs is the output of the feature extractor.
* fix typo in UnivNetGanConfig example
* Calculate spectrogram_zero from other config values.
* apply suggestions from review
* make style
* Refactor UnivNet conversion script to use load_state_dict (following persimmon).
* Rename UnivNetFeatureExtractor to UnivNetGanFeatureExtractor.
* make style
* Switch to using torch.tensor and torch.testing.assert_close for testing expected values/slices.
* make style
* Use config in UnivNetGan modeling blocks.
* make style
* Rename the spectrogram argument of UnivNetGan.forward to input_features, following Whisper.
* make style
* Improving padding documentation.
* Add UnivNet usage example to the docs.
* apply suggestions from review
* Move dynamic_range_compression computation into the mel_spectrogram method of the feature extractor.
* Improve UnivNetGan.forward return docstring.
* Update table in docs/source/en/index.md.
* make fix-copies
* Rename UnivNet components to have pattern UnivNet*.
* make style
* make fix-copies
* Update docs
* make style
* Increase tolerance on flaky unbatched integration test.
* Remove torch.no_grad decorators from UnivNet integration tests to try to avoid flax/Tensorflow test errors.
* Add padding_mask argument to UnivNetModel.forward and add batch_decode feature extractor method to remove padding.
* Update documentation and clean up padding code.
* make style
* make style
* Remove torch dependency from UnivNetFeatureExtractor.
* make style
* Fix UnivNetModel usage example
* Clean up feature extractor code/docstrings.
* apply suggestions from review
* make style
* Add comments for tests skipped via ModelTesterMixin flags.
* Add comment for model parallel tests skipped via the test_model_parallel ModelTesterMixin flag.
* Add # Copied from statements to copied UnivNetFeatureExtractionTest tests.
* Simplify UnivNetFeatureExtractorTest.test_batch_decode.
* Add support for unbatched padding_masks in UnivNetModel.forward.
* Refactor unbatched padding_mask support.
* make style
* [Whisper] Add seq gen
* [Whisper] Add seq gen
* more debug
* Fix whisper logit processor
* Improve whisper code further
* Fix more
* more debug
* more debug
* Improve further
* Add tests
* Prep for batch size > 1
* Get batch_size>1 working
* Correct more
* Add extensive tests
* more debug
* more debug
* more debug
* add more tests
* more debug
* Apply suggestions from code review
* more debug
* add comments to explain the code better
* add comments to explain the code better
* add comments to explain the code better
* Add more examples
* add comments to explain the code better
* fix more
* add comments to explain the code better
* add comments to explain the code better
* correct
* correct
* finalize
* Apply suggestions from code review
* Apply suggestions from code review
* tvp model for video grounding
add tokenizer auto
fix param in TVPProcessor
add docs
clear comments and enable different torch dtype
add image processor test and model test and fix code style
* fix conflict
* fix model doc
* fix image processing tests
* fix tvp tests
* remove torch in processor
* fix grammar error
* add more details on tvp.md
* fix model arch for loss, grammar, and processor
* add docstring and do not regard TvpTransformer, TvpVisionModel as individual model
* use pad_image
* update copyright
* control first downsample stride
* reduce first only works for ResNetBottleNeckLayer
* fix param name
* fix style
* add testing
* fix style
* rm init_weight
* fix style
* add post init
* fix comments
* do not test TvpTransformer
* fix warning
* fix style
* fix example
* fix config map
* add link in config
* fix comments
* fix style
* rm useless param
* change attention
* change test
* add notes
* fix comments
* fix tvp
* import checkpointing
* fix gradient checkpointing
* Use a more accurate example in readme
* update
* fix copy
* fix style
* update readme
* delete print
* remove tvp test_forward_signature
* remove TvpTransformer
* fix test init model
* merge main and make style
* fix tests and others
* fix image processor
* fix style and model_input_names
* fix tests
* fix image_attention gate in idefics modeling
* update comment
* cleaner gating
* fix gate condition
* create attention gate once
* update comment
* update doc of cross-attention forward
* improve comment
* bring back no_images
* pass cross_attention_gate similarly to no_images gate
* add information on gate shape
* fix no_images placement
* make tests for gate
* take off no_images logic
* update test based on comments
* raise value error if cross_attention_gate is None
* send cross_attention_gate to device
* Revert "send cross_attention_gate to device"
This reverts commit 054f842284.
* send cross_attention_gate to device
* fix device in test + nit
* fill hidden_states with zeros instead of multiplying with the gate
* style
* Update src/transformers/models/idefics/modeling_idefics.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/idefics/modeling_idefics.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Load idx2sym from pretrained vocab file in Transformer XL
When loading vocab file from a pretrained tokenizer for Transformer XL,
although the pickled vocabulary file contains a idx2sym key, it isn't
loaded, because it is discarded as the empty list already exists as
an attribute.
Solution is to explicitly take it into account, just like for sym2idx.
* ran make style
* try to stylify using ruff
* might need to remove these changes?
* use ruf format andruff check
* use isinstance instead of type comparision
* use # fmt: skip
* use # fmt: skip
* nits
* soem styling changes
* update ci job
* nits isinstance
* more files update
* nits
* more nits
* small nits
* check and format
* revert wrong changes
* actually use formatter instead of checker
* nits
* well docbuilder is overwriting this commit
* revert notebook changes
* try to nuke docbuilder
* style
* fix feature exrtaction test
* remve `indent-width = 4`
* fixup
* more nits
* update the ruff version that we use
* style
* nuke docbuilder styling
* leve the print for detected changes
* nits
* Remove file I/O
Co-authored-by: charliermarsh
<charlie.r.marsh@gmail.com>
* style
* nits
* revert notebook changes
* Add # fmt skip when possible
* Add # fmt skip when possible
* Fix
* More ` # fmt: skip` usage
* More ` # fmt: skip` usage
* More ` # fmt: skip` usage
* NIts
* more fixes
* fix tapas
* Another way to skip
* Recommended way
* Fix two more fiels
* Remove asynch
Remove asynch
---------
Co-authored-by: charliermarsh <charlie.r.marsh@gmail.com>
* fix speecht5 wrong attention mask when padding
* enable batch generation and add parameter attention_mask
* fix doc
* fix format
* batch postnet inputs, return batched lengths, and consistent to old api
* fix format
* fix format
* fix the format
* fix doc-builder error
* add test, cross attention and docstring
* optimize code based on reviews
* docbuild
* refine
* not skip slow test
* add consistent dropout for batching
* loose atol
* add another test regarding to the consistency of vocoder
* fix format
* refactor
* add return_concrete_lengths as parameter for consistency w/wo batching
* fix review issues
* fix cross_attention issue
* Initial commit of PatchTST model classes
Co-authored-by: Phanwadee Sinthong <phsinthong@gmail.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com>
Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
* Add PatchTSTForPretraining
* update to include classification
Co-authored-by: Phanwadee Sinthong <phsinthong@gmail.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com>
Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
* clean up auto files
* Add PatchTSTForPrediction
* Fix relative import
* Replace original PatchTSTEncoder with ChannelAttentionPatchTSTEncoder
* temporary adding absolute path + add PatchTSTForForecasting class
* Update base PatchTSTModel + Unittest
* Update ForecastHead to use the config class
* edit cv_random_masking, add mask to model output
* Update configuration_patchtst.py
* add masked_loss to the pretraining
* add PatchEmbeddings
* Update configuration_patchtst.py
* edit loss which considers mask in the pretraining
* remove patch_last option
* Add commits from internal repo
* Update ForecastHead
* Add model weight initilization + unittest
* Update PatchTST unittest to use local import
* PatchTST integration tests for pretraining and prediction
* Added PatchTSTForRegression + update unittest to include label generation
* Revert unrelated model test file
* Combine similar output classes
* update PredictionHead
* Update configuration_patchtst.py
* Add Revin
* small edit to PatchTSTModelOutputWithNoAttention
* Update modeling_patchtst.py
* Updating integration test for forecasting
* Fix unittest after class structure changed
* docstring updates
* change input_size to num_input_channels
* more formatting
* Remove some unused params
* Add a comment for pretrained models
* add channel_attention option
add channel_attention option and remove unused positional encoders.
* Update PatchTST models to use HF's MultiHeadAttention module
* Update paper + github urls
* Fix hidden_state return value
* Update integration test to use PatchTSTForForecasting
* Adding dataclass decorator for model output classes
* Run fixup script
* Rename model repos for integration test
* edit argument explanation
* change individual option to shared_projection
* style
* Rename integration test + import cleanup
* Fix outpu_hidden_states return value
* removed unused mode
* added std, mean and nops scaler
* add initial distributional loss for predition
* fix typo in docs
* add generate function
* formatting
* add num_parallel_samples
* Fix a typo
* copy weighted_average function, edit PredictionHead
* edit PredictionHead
* add distribution head to forecasting
* formatting
* Add generate function for forecasting
* Add generate function to prediction task
* formatting
* use argsort
* add past_observed_mask ordering
* fix arguments
* docs
* add back test_model_outputs_equivalence test
* formatting
* cleanup
* formatting
* use ACT2CLS
* formatting
* fix add_start_docstrings decorator
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* fix typos
* add forecast_masking
* fixed tests
* use set_seed
* fix doc test
* formatting
* Update docs/source/en/model_doc/patchtst.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* better var names
* rename PatchTSTTranspose
* fix argument names and docs string
* remove compute_num_patches and unused class
* remove assert
* renamed to PatchTSTMasking
* use num_labels for classification
* use num_labels
* use default num_labels from super class
* move model_type after docstring
* renamed PatchTSTForMaskPretraining
* bs -> batch_size
* more review fixes
* use hidden_state
* rename encoder layer and block class
* remove commented seed_number
* edit docstring
* Add docstring
* formatting
* use past_observed_mask
* doc suggestion
* make fix-copies
* use Args:
* add docstring
* add docstring
* change some variable names and add PatchTST before some class names
* formatting
* fix argument types
* fix tests
* change x variable to patch_input
* format
* formatting
* fix-copies
* Update tests/models/patchtst/test_modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move loss to forward
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* formatting
* fix a bug when pre_norm is set to True
* output_hidden_states is set to False as default
* set pre_norm=True as default
* format docstring
* format
* output_hidden_states is None by default
* add missing docs
* better var names
* docstring: remove default to False in output_hidden_states
* change labels name to target_values in regression task
* format
* fix tests
* change to forecast_mask_ratios and random_mask_ratio
* change mask names
* change future_values to target_values param in the prediction class
* remove nn.Sequential and make PatchTSTBatchNorm class
* black
* fix argument name for prediction
* add output_attentions option
* add output_attentions to PatchTSTEncoder
* formatting
* Add attention output option to all classes
* Remove PatchTSTEncoderBlock
* create PatchTSTEmbedding class
* use config in PatchTSTPatchify
* Use config in PatchTSTMasking class
* add channel_attn_weights
* Add PatchTSTScaler class
* add output_attentions arg to test function
* format
* Update doc with image patchtst.md
* fix-copies
* rename Forecast <-> Prediction
* change name of a few parameters to match with PatchTSMixer.
* Remove *ForForecasting class to match with other time series models.
* make style
* Remove PatchTSTForForecasting in the test
* remove PatchTSTForForecastingOutput class
* change test_forecast_head to test_prediction_head
* style
* fix docs
* fix tests
* change num_labels to num_targets
* Remove PatchTSTTranspose
* remove arguments in PatchTSTMeanScaler
* remove arguments in PatchTSTStdScaler
* add config as an argument to all the scaler classes
* reformat
* Add norm_eps for batchnorm and layernorm
* reformat.
* reformat
* edit docstring
* update docstring
* change variable name pooling to pooling_type
* fix output_hidden_states as tuple
* fix bug when calling PatchTSTBatchNorm
* change stride to patch_stride
* create PatchTSTPositionalEncoding class and restructure the PatchTSTEncoder
* formatting
* initialize scalers with configs
* edit output_hidden_states
* style
* fix forecast_mask_patches doc string
---------
Co-authored-by: Gift Sinthong <gift.sinthong@ibm.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com>
Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
Co-authored-by: Wesley M. Gifford <wmgifford@us.ibm.com>
Co-authored-by: nnguyen <nnguyen@us.ibm.com>
Co-authored-by: Ngoc Diep Do <diiepy@gmail.com>
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* only dir not even init
* init
* tokenizer removed and reference of codegen added
* modeling file updated a lot remaining app_rotary_emb
* conversion script done
* conversion script fixed, a lot of factoring done and most tests pass
* added token_clf and extractive_QA_head
* integration tests pass
* flash attn tests pass!
* config done
* more docs in modeling file
* some style fix
* style and others
* doc test error fix
* more doc fix
* some attention fixes
* most fixes
* style and other fixes
* docs fix and config
* doc fix
* some comments
* conversion script updated
* conversion script updated
* Revert "conversion script updated"
This reverts commit e92378c54084ec0747041b113083d1746ecb6c7f.
* final comments
* add Phi to language_modeling.md
* edit phi.md file
* rebase and fix
* removed phi-1.5 example
* changed model_type from 'phi'->'mixformer-sequential'
* small change
* small change
* revert \small change
* changed mixformer-sequential->phi
* small change
* added phi-1.5 example instead of phi-1
* doc test might pass now
* rebase and small change
* added the dropout layer
* more fixes
* modified .md file
* very very small doc change
* init commit
* attention arch done except rotary emb
* rotary emb done
* text encoder working
* outputs matching
* arch first pass done
* make commands done, tests and docs remaining
* all tests passed, only docs remaining
* docs done
* doc-builder fix
* convert script removed(not relevant)
* minor comments done
* added ckpt conversion script
* tokenizer done
* very minor fix of index.md 2
* mostly make fixup related
* all done except fe and rotary emb
* very small change
* removed unidecode dependency
* style changes
* tokenizer removed require_backends
* added require_inflect to tokenizer tests
* removed VOCAB_FILES in tokenizer test
* inflect dependency removed
* added rotary pos emb cache and simplified the apply method
* style
* little doc change
* more comments
* feature extractor added
* added processor
* auto-regressive config added
* added CLVPConditioningEncoder
* comments done except the test one
* weights added successfull(NOT tested)
* tokenizer fix with numbers
* generate outputs matching
* almost tests passing Integ tests not written
* Integ tests added
* major CUDA error fixed
* docs done
* rebase and multiple fixes
* fixed rebase overwrites
* generate code simplified and tests for AutoRegressive model added
* minor changes
* refectored gpt2 code in clvp file
* weights done and all code refactored
* mostly done except the fast_tokenizer
* doc test fix
* config file's doc fixes
* more config fix
* more comments
* tokenizer comments mostly done
* modeling file mostly refactored and can load modules
* ClvpEncoder tested
* ClvpDecoder, ClvpModel and ClvpForCausalLM tested
* integration and all tests passed
* more fixes
* docs almost done
* ckpt conversion refectored
* style and some failing tests fix
* comments
* temporary output fix but test_assisted_decoding_matches_greedy_search test fails
* majority changes done
* use_cache outputs same now! Along with the asisted_greedy_decoding test fix
* more comments
* more comments
* prepare_inputs_for_generation fixed and _prepare_model_inputs added
* style fix
* clvp.md change
* moved clvpconditionalencoder norms
* add model to new index
* added tokenizer input_ids_with_special_tokens
* small fix
* config mostly done
* added config-tester and changed conversion script
* more comments
* comments
* style fix
* some comments
* tokenizer changed back to prev state
* small commnets
* added output hidden states for the main model
* style fix
* comments
* small change
* revert small change
* .
* Update clvp.md
* Update test_modeling_clvp.py
* :)
* some minor change
* new fixes
* remove to_dict from FE
* add audio_utils usage in the FE of SpeechToText
* clean unecessary parameters of AudioSpectrogramTransformer FE
* add audio_utils usage in AST
* add serialization tests and function to FEs
* make style
* remove use_torchaudio and move to_dict to FE
* test audio_utils usage
* make style and fix import (remove torchaudio dependency import)
* fix torch dependency for jax and tensor tests
* fix typo
* clean tests with suggestions
* add lines to test if is_speech_availble is False
* Use Llama RoPE implementation for Falcon
+ Add copy functionalities
* Use standard cache format for Falcon
* Simplify apply_rotary_pos_emb, copy from Llama
* Remove unnecessary cache conversion test
We don't need to convert any caches anymore!
* Resolve copy complaint
