Allow to use a custom class in TasksManager & use canonical tasks names

docs/source/exporters/onnx/usage_guides/contribute.mdx

@@ -157,8 +157,8 @@ For BERT, it looks as follows:
 ```python
  "bert": supported_tasks_mapping(
  "default",
- "masked-lm",
- "causal-lm",
+ "fill-mask",
+ "text-generation",
  "sequence-classification",
  "multiple-choice",
  "token-classification",

docs/source/exporters/onnx/usage_guides/export_a_model.mdx

@@ -87,7 +87,7 @@ Required arguments:
  output Path indicating the directory where to store generated ONNX model.
 
 Optional arguments:
- --task TASK The task to export the model for. If not specified, the task will be auto-inferred based on the model. Available tasks depend on the model, but are among: ['default', 'masked-lm', 'causal-lm', 'seq2seq-lm', 'sequence-classification', 'token-classification', 'multiple-choice', 'object-detection', 'question-answering', 'image-classification', 'image-segmentation', 'masked-im', 'semantic-segmentation', 'speech2seq-lm', 'audio-classification', 'audio-frame-classification', 'audio-ctc', 'audio-xvector', 'vision2seq-lm', 'stable-diffusion', 'zero-shot-object-detection']. For decoder models, use `xxx-with-past` to export the model using past key values in the decoder.
+ --task TASK The task to export the model for. If not specified, the task will be auto-inferred based on the model. Available tasks depend on the model, but are among: ['default', 'fill-mask', 'text-generation', 'text2text-generation', 'sequence-classification', 'token-classification', 'multiple-choice', 'object-detection', 'question-answering', 'image-classification', 'image-segmentation', 'masked-im', 'semantic-segmentation', 'automatic-speech-recognition', 'audio-classification', 'audio-frame-classification', 'audio-ctc', 'audio-xvector', 'image-to-text', 'stable-diffusion', 'zero-shot-object-detection']. For decoder models, use `xxx-with-past` to export the model using past key values in the decoder.
  --monolith Force to export the model as a single ONNX file. By default, the ONNX exporter may break the model in several ONNX files, for example for encoder-decoder models where the encoder should be run only once while the decoder is looped over.
  --device DEVICE The device to use to do the export. Defaults to "cpu".
  --opset OPSET If specified, ONNX opset version to export the model with. Otherwise, the default opset will be used.
@@ -198,7 +198,7 @@ Models exported through `optimum-cli export onnx` can be used directly in [`~onn
 
 When exporting a decoder model used for generation, it can be useful to encapsulate in the exported ONNX the [reuse of past keys and values](https://discuss.huggingface.co/t/what-is-the-purpose-of-use-cache-in-decoder/958/2). This allows to avoid recomputing the same intermediate activations during the generation.
 
-In the ONNX export, the past keys/values are reused by default. This behavior corresponds to `--task seq2seq-lm-with-past`, `--task causal-lm-with-past`, or `--task speech2seq-lm-with-past`. If for any purpose you would like to disable the export with past keys/values reuse, passing explicitly to `optimum-cli export onnx` the task `seq2seq-lm`, `causal-lm` or `speech2seq-lm` is required.
+In the ONNX export, the past keys/values are reused by default. This behavior corresponds to `--task text2text-generation-with-past`, `--task text-generation-with-past`, or `--task automatic-speech-recognition-with-past`. If for any purpose you would like to disable the export with past keys/values reuse, passing explicitly to `optimum-cli export onnx` the task `text2text-generation`, `text-generation` or `automatic-speech-recognition` is required.
 
 A model exported using past key/values can be reused directly into Optimum's [`~onnxruntime.ORTModel`]:
 
@@ -235,7 +235,7 @@ For each model architecture, you can find the list of supported tasks via the [`
 
 >>> distilbert_tasks = list(TasksManager.get_supported_tasks_for_model_type("distilbert", "onnx").keys())
 >>> print(distilbert_tasks)
-['default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering']
+['default', 'fill-mask', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering']
 ```
 
 You can then pass one of these tasks to the `--task` argument in the `optimum-cli export onnx` command, as mentioned above.

docs/source/exporters/task_manager.mdx

@@ -35,7 +35,7 @@ It is possible to know which tasks are supported for a model for a given backend
 >>> distilbert_tasks = list(TasksManager.get_supported_tasks_for_model_type(model_type, backend).keys())
 
 >>> print(distilbert_tasks)
-['default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering']
+['default', 'fill-mask', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering']
 ```
 
 </Tip>
@@ -44,11 +44,11 @@ It is possible to know which tasks are supported for a model for a given backend
 
 | Task | Auto Class |
 |--------------------------------------|--------------------------------------|
-| `causal-lm`, `causal-lm-with-past` | `AutoModelForCausalLM` |
-| `default`, `default-with-past` | `AutoModel` |
-| `masked-lm` | `AutoModelForMaskedLM` |
+| `text-generation`, `text-generation-with-past` | `AutoModelForCausalLM` |
+| `feature-extraction`, `feature-extraction-with-past` | `AutoModel` |
+| `fill-mask` | `AutoModelForMaskedLM` |
 | `question-answering` | `AutoModelForQuestionAnswering` |
-| `seq2seq-lm`, `seq2seq-lm-with-past` | `AutoModelForSeq2SeqLM` |
+| `text2text-generation`, `text2text-generation-with-past` | `AutoModelForSeq2SeqLM` |
 | `sequence-classification` | `AutoModelForSequenceClassification` |
 | `token-classification` | `AutoModelForTokenClassification` |
 | `multiple-choice` | `AutoModelForMultipleChoice` |
@@ -57,17 +57,17 @@ It is possible to know which tasks are supported for a model for a given backend
 | `image-segmentation` | `AutoModelForImageSegmentation` |
 | `masked-im` | `AutoModelForMaskedImageModeling` |
 | `semantic-segmentation` | `AutoModelForSemanticSegmentation` |
-| `speech2seq-lm` | `AutoModelForSpeechSeq2Seq` |
+| `automatic-speech-recognition` | `AutoModelForSpeechSeq2Seq` |
 
 ### TensorFlow
 
 | Task | Auto Class |
 |--------------------------------------|----------------------------------------|
-| `causal-lm`, `causal-lm-with-past` | `TFAutoModelForCausalLM` |
+| `text-generation`, `text-generation-with-past` | `TFAutoModelForCausalLM` |
 | `default`, `default-with-past` | `TFAutoModel` |
-| `masked-lm` | `TFAutoModelForMaskedLM` |
+| `fill-mask` | `TFAutoModelForMaskedLM` |
 | `question-answering` | `TFAutoModelForQuestionAnswering` |
-| `seq2seq-lm`, `seq2seq-lm-with-past` | `TFAutoModelForSeq2SeqLM` |
+| `text2text-generation`, `text2text-generation-with-past` | `TFAutoModelForSeq2SeqLM` |
 | `sequence-classification` | `TFAutoModelForSequenceClassification` |
 | `token-classification` | `TFAutoModelForTokenClassification` |
 | `multiple-choice` | `TFAutoModelForMultipleChoice` |

docs/source/exporters/tflite/usage_guides/export_a_model.mdx

@@ -56,9 +56,9 @@ Required arguments:
 
 Optional arguments:
  --task TASK The task to export the model for. If not specified, the task will be auto-inferred based on the model. Available tasks depend on
- the model, but are among: ['default', 'masked-lm', 'causal-lm', 'seq2seq-lm', 'sequence-classification', 'token-classification',
+ the model, but are among: ['default', 'fill-mask', 'text-generation', 'text2text-generation', 'sequence-classification', 'token-classification',
  'multiple-choice', 'object-detection', 'question-answering', 'image-classification', 'image-segmentation', 'masked-im', 'semantic-
- segmentation', 'speech2seq-lm', 'audio-classification', 'audio-frame-classification', 'audio-ctc', 'audio-xvector', 'vision2seq-
+ segmentation', 'automatic-speech-recognition', 'audio-classification', 'audio-frame-classification', 'audio-ctc', 'audio-xvector', 'vision2seq-
  lm', 'stable-diffusion', 'zero-shot-object-detection']. For decoder models, use `xxx-with-past` to export the model using past key
  values in the decoder.
  --atol ATOL If specified, the absolute difference tolerance when validating the model. Otherwise, the default atol for the model will be used.

docs/source/onnxruntime/usage_guides/trainer.mdx

@@ -159,7 +159,7 @@ empowered by ONNX Runtime. Here is an example of how to use `ORTSeq2SeqTrainer`
  model=model,
  args=training_args,
  train_dataset=train_dataset,
-+ feature="seq2seq-lm",
++ feature="text2text-generation",
  ...
 )
 

examples/onnxruntime/training/language-modeling/run_clm.py

@@ -528,7 +528,7 @@ def compute_metrics(eval_preds):
  preprocess_logits_for_metrics=preprocess_logits_for_metrics
  if training_args.do_eval and not is_torch_tpu_available()
  else None,
- feature="causal-lm",
+ feature="text-generation",
  )
 
  # Training

examples/onnxruntime/training/language-modeling/run_mlm.py

@@ -563,7 +563,7 @@ def compute_metrics(eval_preds):
  preprocess_logits_for_metrics=preprocess_logits_for_metrics
  if training_args.do_eval and not is_torch_tpu_available()
  else None,
- feature="masked-lm",
+ feature="fill-mask",
  )
 
  # Training

examples/onnxruntime/training/summarization/run_summarization.py

@@ -652,7 +652,7 @@ def compute_metrics(eval_preds):
  tokenizer=tokenizer,
  data_collator=data_collator,
  compute_metrics=compute_metrics if training_args.predict_with_generate else None,
- feature="seq2seq-lm",
+ feature="text2text-generation",
  )
 
  # Training

examples/onnxruntime/training/text-classification/run_glue.py

@@ -532,7 +532,7 @@ def compute_metrics(p: EvalPrediction):
  compute_metrics=compute_metrics,
  tokenizer=tokenizer,
  data_collator=data_collator,
- feature="sequence-classification",
+ feature="text-classification",
  )
 
  # Training

examples/onnxruntime/training/translation/run_translation.py

@@ -575,7 +575,7 @@ def compute_metrics(eval_preds):
  tokenizer=tokenizer,
  data_collator=data_collator,
  compute_metrics=compute_metrics if training_args.predict_with_generate else None,
- feature="seq2seq-lm",
+ feature="text2text-generation",
  )
 
  # Training

optimum/commands/export/tflite.py

@@ -189,7 +189,7 @@ def parse_args_tflite(parser: "ArgumentParser"):
  default=None,
  help=(
  "The name of the column in the dataset containing the main data to preprocess. "
- "Only for sequence-classification and token-classification. "
+ "Only for text-classification and token-classification. "
  ),
  )
  calibration_dataset_group.add_argument(
@@ -198,7 +198,7 @@ def parse_args_tflite(parser: "ArgumentParser"):
  default=None,
  help=(
  "The name of the second column in the dataset containing the main data to preprocess, not always needed. "
- "Only for sequence-classification and token-classification. "
+ "Only for text-classification and token-classification. "
  ),
  )
  calibration_dataset_group.add_argument(

optimum/exporters/onnx/__main__.py

@@ -154,6 +154,8 @@ def main_export(
  raise KeyError(
  f"The task could not be automatically inferred. Please provide the argument --task with the task from {', '.join(TasksManager.get_all_tasks())}. Detailed error: {e}"
  )
+ else:
+ task = TasksManager.map_from_legacy(task)
 
  framework = TasksManager.determine_framework(model_name_or_path, subfolder=subfolder, framework=framework)
 
@@ -215,7 +217,7 @@ def main_export(
  needs_pad_token_id = (
  isinstance(onnx_config, OnnxConfigWithPast)
  and getattr(model.config, "pad_token_id", None) is None
- and task in ["sequence_classification"]
+ and task in ["text-classification"]
  )
  if needs_pad_token_id:
  if pad_token_id is not None:
@@ -265,7 +267,7 @@ def main_export(
  model.feature_extractor.save_pretrained(output.joinpath("feature_extractor"))
  model.save_config(output)
  else:
- if model.config.is_encoder_decoder and task.startswith("causal-lm"):
+ if model.config.is_encoder_decoder and task.startswith("text-generation"):
  raise ValueError(
  f"model.config.is_encoder_decoder is True and task is `{task}`, which are incompatible. If the task was auto-inferred, please fill a bug report"
  f"at https://github.com/huggingface/optimum, if --task was explicitely passed, make sure you selected the right task for the model,"
@@ -275,11 +277,18 @@ def main_export(
  onnx_files_subpaths = None
  if (
  model.config.is_encoder_decoder
- and task.startswith(("seq2seq-lm", "speech2seq-lm", "vision2seq-lm", "default-with-past"))
+ and task.startswith(
+ (
+ "text2text-generation",
+ "automatic-speech-recognition",
+ "image-to-text",
+ "feature-extraction-with-past",
+ )
+ )
  and not monolith
  ):
  models_and_onnx_configs = get_encoder_decoder_models_for_export(model, onnx_config)
- elif task.startswith("causal-lm") and not monolith:
+ elif task.startswith("text-generation") and not monolith:
  models_and_onnx_configs = get_decoder_models_for_export(model, onnx_config)
  else:
  models_and_onnx_configs = {"model": (model, onnx_config)}