Removing duplicated code for Translation,Summarization and Text2TextGeneration pipelines (#9433)

* Merging all duplicated codes for Text2TextPipeline while preserving
backward compat.

* Fixing TranslationPipeline Hierarchy + return_name

* torch import guard.

* Update isort version.

* Remove code from other PR disentanglement.

* Removed named example to something more agnostic.

src/transformers/pipelines/text2text_generation.py

@@ -6,7 +6,7 @@ from .base import PIPELINE_INIT_ARGS, Pipeline
 if is_tf_available():
     import tensorflow as tf
 
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 
 if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
@@ -14,242 +14,6 @@ if is_torch_available():
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
-class SummarizationPipeline(Pipeline):
-    """
-    Summarize news articles and other documents.
-
-    This summarizing pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
-    identifier: :obj:`"summarization"`.
-
-    The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
-    currently, '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'. See the up-to-date
-    list of available models on `huggingface.co/models <https://huggingface.co/models?filter=summarization>`__.
-
-    Usage::
-
-        # use bart in pytorch
-        summarizer = pipeline("summarization")
-        summarizer("Sam Shleifer writes the best docstring examples in the whole world.", min_length=5, max_length=20)
-
-        # use t5 in tf
-        summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
-        summarizer("Sam Shleifer writes the best docstring examples in the whole world.", min_length=5, max_length=20)
-    """
-
-    def __init__(self, *args, **kwargs):
-        kwargs.update(task="summarization")
-        super().__init__(*args, **kwargs)
-
-        self.check_model_type(
-            TF_MODEL_WITH_LM_HEAD_MAPPING if self.framework == "tf" else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
-        )
-
-    def __call__(
-        self, *documents, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
-    ):
-        r"""
-        Summarize the text(s) given as inputs.
-
-        Args:
-            documents (`str` or :obj:`List[str]`):
-                One or several articles (or one list of articles) to summarize.
-            return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                Whether or not to include the decoded texts in the outputs
-            return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not to include the tensors of predictions (as token indices) in the outputs.
-            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not to clean up the potential extra spaces in the text output.
-            generate_kwargs:
-                Additional keyword arguments to pass along to the generate method of the model (see the generate method
-                corresponding to your framework `here <./model.html#generative-models>`__).
-
-        Return:
-            A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
-
-            - **summary_text** (:obj:`str`, present when ``return_text=True``) -- The summary of the corresponding
-              input.
-            - **summary_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) --
-              The token ids of the summary.
-        """
-        assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
-        assert len(documents) > 0, "Please provide a document to summarize"
-
-        prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
-
-        if isinstance(documents[0], list):
-            assert (
-                self.tokenizer.pad_token_id is not None
-            ), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
-
-            documents = ([prefix + document for document in documents[0]],)
-            padding = True
-
-        elif isinstance(documents[0], str):
-            documents = (prefix + documents[0],)
-            padding = False
-        else:
-            raise ValueError(
-                " `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
-                    documents[0]
-                )
-            )
-
-        with self.device_placement():
-            inputs = self._parse_and_tokenize(*documents, padding=padding)
-
-            if self.framework == "pt":
-                inputs = self.ensure_tensor_on_device(**inputs)
-                input_length = inputs["input_ids"].shape[-1]
-            elif self.framework == "tf":
-                input_length = tf.shape(inputs["input_ids"])[-1].numpy()
-
-            min_length = generate_kwargs.get("min_length", self.model.config.min_length)
-            if input_length < min_length // 2:
-                logger.warning(
-                    "Your min_length is set to {}, but you input_length is only {}. You might consider decreasing min_length manually, e.g. summarizer('...', min_length=10)".format(
-                        min_length, input_length
-                    )
-                )
-
-            max_length = generate_kwargs.get("max_length", self.model.config.max_length)
-            if input_length < max_length:
-                logger.warning(
-                    "Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format(
-                        max_length, input_length
-                    )
-                )
-
-            summaries = self.model.generate(
-                inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                **generate_kwargs,
-            )
-
-            results = []
-            for summary in summaries:
-                record = {}
-                if return_tensors:
-                    record["summary_token_ids"] = summary
-                if return_text:
-                    record["summary_text"] = self.tokenizer.decode(
-                        summary,
-                        skip_special_tokens=True,
-                        clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-                    )
-                results.append(record)
-            return results
-
-
-@add_end_docstrings(PIPELINE_INIT_ARGS)
-class TranslationPipeline(Pipeline):
-    """
-    Translates from one language to another.
-
-    This translation pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
-    identifier: :obj:`"translation_xx_to_yy"`.
-
-    The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
-    up-to-date list of available models on `huggingface.co/models
-    <https://huggingface.co/models?filter=translation>`__.
-
-    Usage::
-        en_fr_translator = pipeline("translation_en_to_fr")
-        en_fr_translator("How old are you?")
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        self.check_model_type(
-            TF_MODEL_WITH_LM_HEAD_MAPPING if self.framework == "tf" else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
-        )
-
-    def __call__(
-        self, *args, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
-    ):
-        r"""
-        Translate the text(s) given as inputs.
-
-        Args:
-            args (:obj:`str` or :obj:`List[str]`):
-                Texts to be translated.
-            return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not to include the tensors of predictions (as token indices) in the outputs.
-            return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                Whether or not to include the decoded texts in the outputs.
-            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not to clean up the potential extra spaces in the text output.
-            generate_kwargs:
-                Additional keyword arguments to pass along to the generate method of the model (see the generate method
-                corresponding to your framework `here <./model.html#generative-models>`__).
-
-        Return:
-            A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
-
-            - **translation_text** (:obj:`str`, present when ``return_text=True``) -- The translation.
-            - **translation_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``)
-              -- The token ids of the translation.
-        """
-        assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
-
-        prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
-
-        if isinstance(args[0], list):
-            assert (
-                self.tokenizer.pad_token_id is not None
-            ), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
-            args = ([prefix + text for text in args[0]],)
-            padding = True
-
-        elif isinstance(args[0], str):
-            args = (prefix + args[0],)
-            padding = False
-        else:
-            raise ValueError(
-                " `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
-                    args[0]
-                )
-            )
-
-        with self.device_placement():
-            inputs = self._parse_and_tokenize(*args, padding=padding)
-
-            if self.framework == "pt":
-                inputs = self.ensure_tensor_on_device(**inputs)
-                input_length = inputs["input_ids"].shape[-1]
-
-            elif self.framework == "tf":
-                input_length = tf.shape(inputs["input_ids"])[-1].numpy()
-
-            max_length = generate_kwargs.get("max_length", self.model.config.max_length)
-            if input_length > 0.9 * max_length:
-                logger.warning(
-                    "Your input_length: {} is bigger than 0.9 * max_length: {}. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)".format(
-                        input_length, max_length
-                    )
-                )
-
-            translations = self.model.generate(
-                inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                **generate_kwargs,
-            )
-            results = []
-            for translation in translations:
-                record = {}
-                if return_tensors:
-                    record["translation_token_ids"] = translation
-                if return_text:
-                    record["translation_text"] = self.tokenizer.decode(
-                        translation,
-                        skip_special_tokens=True,
-                        clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-                    )
-                results.append(record)
-            return results
-
-
 @add_end_docstrings(PIPELINE_INIT_ARGS)
 class Text2TextGenerationPipeline(Pipeline):
     """
@@ -267,6 +31,9 @@ class Text2TextGenerationPipeline(Pipeline):
         text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything")
     """
 
+    # Used in the return key of the pipeline.
+    return_name = "generated"
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -276,6 +43,12 @@ class Text2TextGenerationPipeline(Pipeline):
             else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
         )
 
+    def check_inputs(self, input_length: int, min_length: int, max_length: int):
+        """
+        Checks wether there might be something wrong with given input with regard to the model.
+        """
+        return True
+
     def __call__(
         self, *args, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
     ):
@@ -304,26 +77,39 @@ class Text2TextGenerationPipeline(Pipeline):
         """
         assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
 
+        prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
         if isinstance(args[0], list):
             assert (
                 self.tokenizer.pad_token_id is not None
             ), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
+            args = ([prefix + arg for arg in args[0]],)
             padding = True
 
         elif isinstance(args[0], str):
+            args = (prefix + args[0],)
             padding = False
         else:
             raise ValueError(
-                " `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
+                " `args[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
                     args[0]
                 )
             )
 
         with self.device_placement():
-            inputs = self._parse_and_tokenize(*args, padding=padding)
+            inputs = self._parse_and_tokenize(*args, padding=padding, **generate_kwargs)
 
             if self.framework == "pt":
                 inputs = self.ensure_tensor_on_device(**inputs)
+                input_length = inputs["input_ids"].shape[-1]
+            elif self.framework == "tf":
+                input_length = tf.shape(inputs["input_ids"])[-1].numpy()
+
+            min_length = generate_kwargs.get("min_length", self.model.config.min_length)
+            max_length = generate_kwargs.get("max_length", self.model.config.max_length)
+            self.check_inputs(input_length, min_length, max_length)
+
+            # truncation should be used by _parse_and_tokenize
+            generate_kwargs.pop("truncation", None)
 
             generations = self.model.generate(
                 inputs["input_ids"],
@@ -334,12 +120,139 @@ class Text2TextGenerationPipeline(Pipeline):
             for generation in generations:
                 record = {}
                 if return_tensors:
-                    record["generated_token_ids"] = generation
+                    record[f"{self.return_name}_token_ids"] = generation
                 if return_text:
-                    record["generated_text"] = self.tokenizer.decode(
+                    record[f"{self.return_name}_text"] = self.tokenizer.decode(
                         generation,
                         skip_special_tokens=True,
                         clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                     )
                 results.append(record)
             return results
+
+
+@add_end_docstrings(PIPELINE_INIT_ARGS)
+class SummarizationPipeline(Text2TextGenerationPipeline):
+    """
+    Summarize news articles and other documents.
+
+    This summarizing pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
+    identifier: :obj:`"summarization"`.
+
+    The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
+    currently, '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'. See the up-to-date
+    list of available models on `huggingface.co/models <https://huggingface.co/models?filter=summarization>`__.
+
+    Usage::
+
+        # use bart in pytorch
+        summarizer = pipeline("summarization")
+        summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
+
+        # use t5 in tf
+        summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
+        summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
+    """
+
+    # Used in the return key of the pipeline.
+    return_name = "summary"
+
+    def __call__(self, *args, **kwargs):
+        r"""
+        Summarize the text(s) given as inputs.
+
+        Args:
+            documents (`str` or :obj:`List[str]`):
+                One or several articles (or one list of articles) to summarize.
+            return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
+                Whether or not to include the decoded texts in the outputs
+            return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to include the tensors of predictions (as token indices) in the outputs.
+            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to clean up the potential extra spaces in the text output.
+            generate_kwargs:
+                Additional keyword arguments to pass along to the generate method of the model (see the generate method
+                corresponding to your framework `here <./model.html#generative-models>`__).
+
+        Return:
+            A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
+
+            - **summary_text** (:obj:`str`, present when ``return_text=True``) -- The summary of the corresponding
+              input.
+            - **summary_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) --
+              The token ids of the summary.
+        """
+        return super().__call__(*args, **kwargs)
+
+    def check_inputs(self, input_length: int, min_length: int, max_length: int) -> bool:
+        """
+        Checks wether there might be something wrong with given input with regard to the model.
+        """
+        if input_length < min_length // 2:
+            logger.warning(
+                "Your min_length is set to {}, but you input_length is only {}. You might consider decreasing min_length manually, e.g. summarizer('...', min_length=10)".format(
+                    min_length, input_length
+                )
+            )
+
+        if input_length < max_length:
+            logger.warning(
+                "Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format(
+                    max_length, input_length
+                )
+            )
+
+
+@add_end_docstrings(PIPELINE_INIT_ARGS)
+class TranslationPipeline(Text2TextGenerationPipeline):
+    """
+    Translates from one language to another.
+
+    This translation pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task
+    identifier: :obj:`"translation_xx_to_yy"`.
+
+    The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
+    up-to-date list of available models on `huggingface.co/models
+    <https://huggingface.co/models?filter=translation>`__.
+
+    Usage::
+        en_fr_translator = pipeline("translation_en_to_fr")
+        en_fr_translator("How old are you?")
+    """
+
+    # Used in the return key of the pipeline.
+    return_name = "translation"
+
+    def check_inputs(self, input_length: int, min_length: int, max_length: int):
+        if input_length > 0.9 * max_length:
+            logger.warning(
+                "Your input_length: {} is bigger than 0.9 * max_length: {}. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)".format(
+                    input_length, max_length
+                )
+            )
+
+    def __call__(self, *args, **kwargs):
+        r"""
+        Translate the text(s) given as inputs.
+
+        Args:
+            args (:obj:`str` or :obj:`List[str]`):
+                Texts to be translated.
+            return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to include the tensors of predictions (as token indices) in the outputs.
+            return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
+                Whether or not to include the decoded texts in the outputs.
+            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to clean up the potential extra spaces in the text output.
+            generate_kwargs:
+                Additional keyword arguments to pass along to the generate method of the model (see the generate method
+                corresponding to your framework `here <./model.html#generative-models>`__).
+
+        Return:
+            A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:
+
+            - **translation_text** (:obj:`str`, present when ``return_text=True``) -- The translation.
+            - **translation_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``)
+              -- The token ids of the translation.
+        """
+        return super().__call__(*args, **kwargs)