* Enabling dataset iteration on pipelines.
Enabling dataset iteration on pipelines.
Unifying parameters under `set_parameters` function.
Small fix.
Last fixes after rebase
Remove print.
Fixing text2text `generate_kwargs`
No more `self.max_length`.
Fixing tf only conversational.
Consistency in start/stop index over TF/PT.
Speeding up drastically on TF (nasty bug where max_length would increase
a ton.)
Adding test for support for non fast tokenizers.
Fixign GPU usage on zero-shot.
Fix working on Tf.
Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Small cleanup.
Remove all asserts + simple format.
* Fixing audio-classification for large PR.
* Overly explicity null checking.
* Encapsulating GPU/CPU pytorch manipulation directly within `base.py`.
* Removed internal state for parameters of the pipeline.
Instead of overriding implicitly internal state, we moved
to real named arguments on every `preprocess`, `_forward`,
`postprocess` function.
Instead `_sanitize_parameters` will be used to split all kwargs
of both __init__ and __call__ into the 3 kinds of named parameters.
* Move import warnings.
* Small fixes.
* Quality.
* Another small fix, using the CI to debug faster.
* Last fixes.
* Last fix.
* Small cleanup of tensor moving.
* is not None.
* Adding a bunch of docs + a iteration test.
* Fixing doc style.
* KeyDataset = None guard.
* RRemoving the Cuda test for pipelines (was testing).
* Even more simple iteration test.
* Correct import .
* Long day.
* Fixes in docs.
* [WIP] migrating object detection.
* Fixed the target_size bug.
* Fixup.
* Bad variable name.
* Fixing `ensure_on_device` respects original ModelOutput.
* Add the audio classification pipeline
* Remove autoconfig exception
* Mark ffmpeg test as slow
* Rearrange pipeline tests
* Add small test
* Replace asserts with ValueError
* Add the ImageClassificationPipeline
* Code review
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Have `load_image` at the module level
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Adding `AutomaticSpeechRecognitionPipeline`.
- Because we added everything to enable this pipeline, we probably
should add it to `transformers`.
- This PR tries to limit the scope and focuses only on the pipeline part
(what should go in, and out).
- The tests are very specific for S2T and Wav2vec2 to make sure both
architectures are supported by the pipeline. We don't use the mixin for
tests right now, because that requires more work in the `pipeline`
function (will be done in a follow up PR).
- Unsure about the "helper" function `ffmpeg_read`. It makes a lot of
sense from a user perspective, it does not add any additional
dependencies (as in hard dependency, because users can always use their
own load mechanism). Meanwhile, it feels slightly clunky to have so much
optional preprocessing.
- The pipeline is not done to support streaming audio right now.
Future work:
- Add `automatic-speech-recognition` as a `task`. And add the
FeatureExtractor.from_pretrained within `pipeline` function.
- Add small models within tests
- Add the Mixin to tests.
- Make the logic between ForCTC vs ForConditionalGeneration better.
* Update tests/test_pipelines_automatic_speech_recognition.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Adding docs + main import + type checking + LICENSE.
* Doc style !.
* Fixing TYPE_HINT.
* Specifying waveform shape in the docs.
* Adding asserts + specify in the documentation the shape of the input
np.ndarray.
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Adding require to tests + move the `feature_extractor` doc.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Important files
* Styling them all
* Revert "Styling them all"
This reverts commit 7d029395fd.
* Syling them for realsies
* Fix syntax error
* Fix benchmark_utils
* More fixes
* Fix modeling auto and script
* Remove new line
* Fixes
* More fixes
* Fix more files
* Style
* Add FSMT
* More fixes
* More fixes
* More fixes
* More fixes
* Fixes
* More fixes
* More fixes
* Last fixes
* Make sphinx happy
* Clean up model documentation
* Formatting
* Preparation work
* Long lines
* Main work on rst files
* Cleanup all config files
* Syntax fix
* Clean all tokenizers
* Work on first models
* Models beginning
* FaluBERT
* All PyTorch models
* All models
* Long lines again
* Fixes
* More fixes
* Update docs/source/model_doc/bert.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update docs/source/model_doc/electra.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Last fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* initial commit for pipeline implementation
Addition of input processing and history concatenation
* Conversation pipeline tested and working for single & multiple conversation inputs
* Added docstrings for dialogue pipeline
* Addition of dialogue pipeline integration tests
* Delete test_t5.py
* Fixed max code length
* Updated styling
* Fixed test broken by formatting tools
* Removed unused import
* Added unit test for DialoguePipeline
* Fixed Tensorflow compatibility
* Fixed multi-framework support using framework flag
* - Fixed docstring
- Added `min_length_for_response` as an initialization parameter
- Renamed `*args` to `conversations`, `conversations` being a `Conversation` or a `List[Conversation]`
- Updated truncation to truncate entire segments of conversations, instead of cutting in the middle of a user/bot input
* - renamed pipeline name from dialogue to conversational
- removed hardcoded default value of 1000 and use config.max_length instead
- added `append_response` and `set_history` method to the Conversation class to avoid direct fields mutation
- fixed bug in history truncation method
* - Updated ConversationalPipeline to accept only active conversations (otherwise a ValueError is raised)
* - Simplified input tensor conversion
* - Updated attention_mask value for Tensorflow compatibility
* - Updated last dialogue reference to conversational & fixed integration tests
* Fixed conflict with master
* Updates following review comments
* Updated formatting
* Added Conversation and ConversationalPipeline to the library __init__, addition of docstrings for Conversation, added both to the docs
* Update src/transformers/pipelines.py
Updated docsting following review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Quicktour part 1
* Update
* All done
* Typos
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Address comments in quick tour
* Update docs/source/quicktour.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update from feedback
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* [hf_api] Attach all unknown attributes for future-proof compatibility
* [Pipeline] NerPipeline is really a TokenClassificationPipeline
* modelcard.py: I don't think we need to force the download
* Remove config, tokenizer from SUPPORTED_TASKS as we're moving to one model = one weight + one tokenizer
* FillMaskPipeline: also output token in string form
* TextClassificationPipeline: option to return all scores, not just the argmax
* Update docs/source/main_classes/pipelines.rst
* Add GenerationPipeline
* Fix parameter names
* Correct parameter __call__ parameters
* Add model type attribute and correct function calls for prepare_input
* Take out trailing commas from init attributes
* Remove unnecessary tokenization line
* Implement support for multiple text inputs
* Apply generation support for multiple input text prompts
* Take out tensor coersion
* Take out batch index
* Add text prompt to return sequence
* Squeeze token tensore before decoding
* Return only a single list of sequences if only one prompt was used
* Correct results variable name
* Add GenerationPipeline to SUPPORTED_TASKS with the alias , initalized w GPT2
* Registedred AutoModelWithLMHead for both pt and t
* Update docstring for GenerationPipeline
* Add kwargs parameter to mode.generate
* Take out kwargs parameter after all
* Add generation pipeline example in pipeline docstring
* Fix max length by squeezing tokens tensor
* Apply ensure_tensor_on_device to pytorch tensor
* Include generation step in torch.no_grad
* Take out input from prepare_xlm_input and set 'en' as default xlm_language
* Apply framework specific encoding during prepare_input
* Format w make style
* Move GenerationPipeline import to follow proper import sorting
* Take out training comma from generation dict
* Apply requested changes
* Change name to TextGenerationPipeline
* Apply TextGenerationPipeline rename to __init___
* Changing alias to
* Set input mapping as input to ensure_tensor_on_device
* Fix assertion placement
* Add test_text_generation
* Add TextGenerationPipeline to PipelineCommonTests
* Take out whitespace
* Format __init__ w black
* Fix __init__ style
* Forman __init___
* Add line to end of __init__
* Correct model tokenizer set for test_text_generation
* Ensure to return list of list, not list of string (to pass test)
* Limit test models to only 3 to limit runtime to address circleCI timeout error
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Remove argument docstring, __init__, add additional __call__ arguments, and reformat results to list of dict
* Fix blank result list
* Add TextGenerationPipeline to pipelines.rst
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix typos from adding PADDING_TEXT_TOKEN_LENGTH
* Fix incorrectly moved result list
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
* Update src/transformers/pipelines.py
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>
* Add back generation line and make style
* Take out blank whitespace
* Apply new alis, text-generation, to test_pipelines
* Fix text generation alias in test
* Update src/transformers/pipelines.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* passing
* Undo stupid chg
* docs
* undo rename
* delete-cruft
* only import if you have torch
* Dont rely on dict ordering
* Fix dict ordering upstream
* docstring link
* docstring link
* remove trailing comma for 3.5 compat
* new name
* delegate kwarging
* Update kwargs
* Pipeline doc initial commit
* pipeline abstraction
* Remove modelcard argument from pipeline
* Task-specific pipelines can be instantiated with no model or tokenizer
* All pipelines doc
