Enabling multilingual models for translation pipelines. (#10536)

* [WIP] Enabling multilingual models for translation pipelines.

* decoder_input_ids -> forced_bos_token_id

* Improve docstring.

* Rebase

* Fixing 2 bugs

- Type token_ids coming from `_parse_and_tokenize`
- Wrong index from tgt_lang.

* Fixing black version.

* Adding tests for _build_translation_inputs and add them for all
tokenizers.

* Mbart actually puts the lang code at the end.

* Fixing m2m100.

* Adding TF support to `deep_round`.

* Update src/transformers/pipelines/text2text_generation.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Adding one line comment.

* Fixing M2M100 `_build_translation_input_ids`, and fix the call site.

* Fixing tests + deep_round -> nested_simplify
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/models/m2m_100/tokenization_m2m_100.py

@@ -288,6 +288,16 @@ class M2M100Tokenizer(PreTrainedTokenizer):
         self.set_src_lang_special_tokens(self.src_lang)
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
+    def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if src_lang is None or tgt_lang is None:
+            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+        self.src_lang = src_lang
+        inputs = self(raw_inputs, add_special_tokens=True, return_tensors="pt", **extra_kwargs)
+        tgt_lang_id = self.get_lang_id(tgt_lang)
+        inputs["forced_bos_token_id"] = tgt_lang_id
+        return inputs
+
     @contextmanager
     def as_target_tokenizer(self):
         """

src/transformers/models/mbart/tokenization_mbart.py

@@ -186,6 +186,16 @@ class MBartTokenizer(XLMRobertaTokenizer):
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
 
+    def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if src_lang is None or tgt_lang is None:
+            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+        self.src_lang = src_lang
+        inputs = self(raw_inputs, add_special_tokens=True, return_tensors="pt", **extra_kwargs)
+        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+        inputs["forced_bos_token_id"] = tgt_lang_id
+        return inputs
+
     def prepare_seq2seq_batch(
         self,
         src_texts: List[str],

src/transformers/models/mbart/tokenization_mbart50.py

@@ -278,6 +278,16 @@ class MBart50Tokenizer(PreTrainedTokenizer):
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
 
+    def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if src_lang is None or tgt_lang is None:
+            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+        self.src_lang = src_lang
+        inputs = self(raw_inputs, add_special_tokens=True, return_tensors="pt", **extra_kwargs)
+        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+        inputs["forced_bos_token_id"] = tgt_lang_id
+        return inputs
+
     def prepare_seq2seq_batch(
         self,
         src_texts: List[str],

src/transformers/models/mbart/tokenization_mbart50_fast.py

@@ -241,6 +241,16 @@ class MBart50TokenizerFast(PreTrainedTokenizerFast):
             special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
         )
 
+    def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if src_lang is None or tgt_lang is None:
+            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+        self.src_lang = src_lang
+        inputs = self(raw_inputs, add_special_tokens=True, return_tensors="pt", **extra_kwargs)
+        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+        inputs["forced_bos_token_id"] = tgt_lang_id
+        return inputs
+
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory")

src/transformers/models/mbart/tokenization_mbart_fast.py

@@ -160,6 +160,16 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
 
+    def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if src_lang is None or tgt_lang is None:
+            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+        self.src_lang = src_lang
+        inputs = self(raw_inputs, add_special_tokens=True, return_tensors="pt", **extra_kwargs)
+        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+        inputs["forced_bos_token_id"] = tgt_lang_id
+        return inputs
+
     def prepare_seq2seq_batch(
         self,
         src_texts: List[str],

src/transformers/pipelines/base.py

@@ -616,7 +616,10 @@ class Pipeline(_ScikitCompat):
         Return:
             :obj:`Dict[str, torch.Tensor]`: The same as :obj:`inputs` but on the proper device.
         """
-        return {name: tensor.to(self.device) for name, tensor in inputs.items()}
+        return {
+            name: tensor.to(self.device) if isinstance(tensor, torch.Tensor) else tensor
+            for name, tensor in inputs.items()
+        }
 
     def check_model_type(self, supported_models: Union[List[str], dict]):
         """

src/transformers/pipelines/text2text_generation.py

+from typing import Optional
+
 from ..file_utils import add_end_docstrings, is_tf_available, is_torch_available
 from ..tokenization_utils import TruncationStrategy
 from ..utils import logging
@@ -50,6 +52,28 @@ class Text2TextGenerationPipeline(Pipeline):
         """
         return True
 
+    def _parse_and_tokenize(self, *args, truncation):
+        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(
+                f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"
+            )
+        inputs = super()._parse_and_tokenize(*args, padding=padding, truncation=truncation)
+        # This is produced by tokenizers but is an invalid generate kwargs
+        if "token_type_ids" in inputs:
+            del inputs["token_type_ids"]
+        return inputs
+
     def __call__(
         self,
         *args,
@@ -88,25 +112,13 @@ 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(
-                f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"
-            )
-
         with self.device_placement():
-            inputs = self._parse_and_tokenize(*args, padding=padding, truncation=truncation)
+            inputs = self._parse_and_tokenize(*args, truncation=truncation)
+            return self._generate(inputs, return_tensors, return_text, clean_up_tokenization_spaces, generate_kwargs)
 
+    def _generate(
+        self, inputs, return_tensors: bool, return_text: bool, clean_up_tokenization_spaces: bool, generate_kwargs
+    ):
         if self.framework == "pt":
             inputs = self.ensure_tensor_on_device(**inputs)
             input_length = inputs["input_ids"].shape[-1]
@@ -117,9 +129,9 @@ class Text2TextGenerationPipeline(Pipeline):
         max_length = generate_kwargs.get("max_length", self.model.config.max_length)
         self.check_inputs(input_length, min_length, max_length)
 
+        generate_kwargs.update(inputs)
+
         generations = self.model.generate(
-                inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
             **generate_kwargs,
         )
         results = []
@@ -226,6 +238,23 @@ class TranslationPipeline(Text2TextGenerationPipeline):
 
     # Used in the return key of the pipeline.
     return_name = "translation"
+    src_lang: Optional[str] = None
+    tgt_lang: Optional[str] = None
+
+    def __init__(self, *args, src_lang=None, tgt_lang=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        if src_lang is not None:
+            self.src_lang = src_lang
+        if tgt_lang is not None:
+            self.tgt_lang = tgt_lang
+        if src_lang is None and tgt_lang is None:
+            # Backward compatibility, direct arguments use is preferred.
+            task = kwargs.get("task", "")
+            items = task.split("_")
+            if task and len(items) == 4:
+                # translation, XX, to YY
+                self.src_lang = items[1]
+                self.tgt_lang = items[3]
 
     def check_inputs(self, input_length: int, min_length: int, max_length: int):
         if input_length > 0.9 * max_length:
@@ -233,8 +262,27 @@ class TranslationPipeline(Text2TextGenerationPipeline):
                 f"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
                 "increasing your max_length manually, e.g. translator('...', max_length=400)"
             )
+        return True
 
-    def __call__(self, *args, **kwargs):
+    def _parse_and_tokenize(self, *args, src_lang, tgt_lang, truncation):
+        if getattr(self.tokenizer, "_build_translation_inputs", None):
+            return self.tokenizer._build_translation_inputs(
+                *args, src_lang=src_lang, tgt_lang=tgt_lang, truncation=truncation
+            )
+        else:
+            return super()._parse_and_tokenize(*args, truncation=truncation)
+
+    def __call__(
+        self,
+        *args,
+        return_tensors=False,
+        return_text=True,
+        clean_up_tokenization_spaces=False,
+        truncation=TruncationStrategy.DO_NOT_TRUNCATE,
+        src_lang=None,
+        tgt_lang=None,
+        **generate_kwargs
+    ):
         r"""
         Translate the text(s) given as inputs.
 
@@ -247,6 +295,12 @@ class TranslationPipeline(Text2TextGenerationPipeline):
                 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.
+            src_lang (:obj:`str`, `optional`, defaults to :obj:`None`):
+                The language of the input. Might be required for multilingual models. Will not have any effect for
+                single pair translation models
+            tgt_lang (:obj:`str`, `optional`, defaults to :obj:`None`):
+                The language of the desired output. Might be required for multilingual models. Will not have any effect
+                for single pair translation models
             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>`__).
@@ -258,4 +312,10 @@ class TranslationPipeline(Text2TextGenerationPipeline):
             - **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)
+        assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
+        src_lang = src_lang if src_lang is not None else self.src_lang
+        tgt_lang = tgt_lang if tgt_lang is not None else self.tgt_lang
+
+        with self.device_placement():
+            inputs = self._parse_and_tokenize(*args, truncation=truncation, src_lang=src_lang, tgt_lang=tgt_lang)
+            return self._generate(inputs, return_tensors, return_text, clean_up_tokenization_spaces, generate_kwargs)

src/transformers/testing_utils.py

@@ -361,6 +361,9 @@ if is_torch_available():
 else:
     torch_device = None
 
+if is_tf_available():
+    import tensorflow as tf
+
 
 def require_torch_gpu(test_case):
     """Decorator marking a test that requires CUDA and PyTorch. """
@@ -1174,3 +1177,26 @@ def execute_subprocess_async(cmd, env=None, stdin=None, timeout=180, quiet=False
         raise RuntimeError(f"'{cmd_str}' produced no output.")
 
     return result
+
+
+def nested_simplify(obj, decimals=3):
+    """
+    Simplifies an object by rounding float numbers, and downcasting tensors/numpy arrays to get simple equality test
+    within tests.
+    """
+    from transformers.tokenization_utils import BatchEncoding
+
+    if isinstance(obj, list):
+        return [nested_simplify(item, decimals) for item in obj]
+    elif isinstance(obj, (dict, BatchEncoding)):
+        return {nested_simplify(k, decimals): nested_simplify(v, decimals) for k, v in obj.items()}
+    elif isinstance(obj, (str, int)):
+        return obj
+    elif is_torch_available() and isinstance(obj, torch.Tensor):
+        return nested_simplify(obj.tolist())
+    elif is_tf_available() and tf.is_tensor(obj):
+        return nested_simplify(obj.numpy().tolist())
+    elif isinstance(obj, float):
+        return round(obj, decimals)
+    else:
+        raise Exception(f"Not supported: {type(obj)}")

tests/test_pipelines_translation.py

@@ -17,11 +17,15 @@ import unittest
 import pytest
 
 from transformers import pipeline
-from transformers.testing_utils import is_pipeline_test, require_torch, slow
+from transformers.testing_utils import is_pipeline_test, is_torch_available, require_torch, slow
 
 from .test_pipelines_common import MonoInputPipelineCommonMixin
 
 
+if is_torch_available():
+    from transformers.models.mbart import MBart50TokenizerFast, MBartForConditionalGeneration
+
+
 class TranslationEnToDePipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
     pipeline_task = "translation_en_to_de"
     small_models = ["patrickvonplaten/t5-tiny-random"]  # Default model - Models tested without the @slow decorator
@@ -48,12 +52,38 @@ class TranslationNewFormatPipelineTests(unittest.TestCase):
             pipeline(task="translation_cn_to_ar")
 
         # but we do for this one
-        pipeline(task="translation_en_to_de")
+        translator = pipeline(task="translation_en_to_de")
+        self.assertEquals(translator.src_lang, "en")
+        self.assertEquals(translator.tgt_lang, "de")
+
+    @require_torch
+    @slow
+    def test_multilingual_translation(self):
+        model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+        tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+
+        translator = pipeline(task="translation", model=model, tokenizer=tokenizer)
+        # Missing src_lang, tgt_lang
+        with self.assertRaises(ValueError):
+            translator("This is a test")
+
+        outputs = translator("This is a test", src_lang="en_XX", tgt_lang="ar_AR")
+        self.assertEqual(outputs, [{"translation_text": "هذا إختبار"}])
+
+        outputs = translator("This is a test", src_lang="en_XX", tgt_lang="hi_IN")
+        self.assertEqual(outputs, [{"translation_text": "यह एक परीक्षण है"}])
+
+        # src_lang, tgt_lang can be defined at pipeline call time
+        translator = pipeline(task="translation", model=model, tokenizer=tokenizer, src_lang="en_XX", tgt_lang="ar_AR")
+        outputs = translator("This is a test")
+        self.assertEqual(outputs, [{"translation_text": "هذا إختبار"}])
 
     @require_torch
     def test_translation_on_odd_language(self):
         model = "patrickvonplaten/t5-tiny-random"
-        pipeline(task="translation_cn_to_ar", model=model)
+        translator = pipeline(task="translation_cn_to_ar", model=model)
+        self.assertEquals(translator.src_lang, "cn")
+        self.assertEquals(translator.tgt_lang, "ar")
 
     @require_torch
     def test_translation_default_language_selection(self):
@@ -61,6 +91,8 @@ class TranslationNewFormatPipelineTests(unittest.TestCase):
         with pytest.warns(UserWarning, match=r".*translation_en_to_de.*"):
             nlp = pipeline(task="translation", model=model)
         self.assertEqual(nlp.task, "translation_en_to_de")
+        self.assertEquals(nlp.src_lang, "en")
+        self.assertEquals(nlp.tgt_lang, "de")
 
     @require_torch
     def test_translation_with_no_language_no_model_fails(self):

tests/test_tokenization_m2m_100.py

@@ -20,7 +20,7 @@ from shutil import copyfile
 
 from transformers import M2M100Tokenizer, is_torch_available
 from transformers.file_utils import is_sentencepiece_available
-from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch
+from transformers.testing_utils import nested_simplify, require_sentencepiece, require_tokenizers, require_torch
 
 
 if is_sentencepiece_available():
@@ -191,3 +191,18 @@ class M2M100TokenizerIntegrationTest(unittest.TestCase):
             self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")])
             self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
         self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])
+
+    @require_torch
+    def test_tokenizer_translation(self):
+        inputs = self.tokenizer._build_translation_inputs("A test", src_lang="en", tgt_lang="ar")
+
+        self.assertEqual(
+            nested_simplify(inputs),
+            {
+                # en_XX, A, test, EOS
+                "input_ids": [[128022, 58, 4183, 2]],
+                "attention_mask": [[1, 1, 1, 1]],
+                # ar_AR
+                "forced_bos_token_id": 128006,
+            },
+        )

tests/test_tokenization_mbart.py

@@ -17,7 +17,7 @@ import tempfile
 import unittest
 
 from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
-from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch
+from transformers.testing_utils import nested_simplify, require_sentencepiece, require_tokenizers, require_torch
 
 from .test_tokenization_common import TokenizerTesterMixin
 
@@ -232,3 +232,18 @@ class MBartEnroIntegrationTest(unittest.TestCase):
 
         self.assertEqual(batch.input_ids.shape[1], 3)
         self.assertEqual(batch.decoder_input_ids.shape[1], 10)
+
+    @require_torch
+    def test_tokenizer_translation(self):
+        inputs = self.tokenizer._build_translation_inputs("A test", src_lang="en_XX", tgt_lang="ar_AR")
+
+        self.assertEqual(
+            nested_simplify(inputs),
+            {
+                # A, test, EOS, en_XX
+                "input_ids": [[62, 3034, 2, 250004]],
+                "attention_mask": [[1, 1, 1, 1]],
+                # ar_AR
+                "forced_bos_token_id": 250001,
+            },
+        )

tests/test_tokenization_mbart50.py

@@ -17,7 +17,7 @@ import tempfile
 import unittest
 
 from transformers import SPIECE_UNDERLINE, BatchEncoding, MBart50Tokenizer, MBart50TokenizerFast, is_torch_available
-from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch
+from transformers.testing_utils import nested_simplify, require_sentencepiece, require_tokenizers, require_torch
 
 from .test_tokenization_common import TokenizerTesterMixin
 
@@ -194,3 +194,18 @@ class MBartOneToManyIntegrationTest(unittest.TestCase):
 
         self.assertEqual(batch.input_ids.shape[1], 3)
         self.assertEqual(batch.decoder_input_ids.shape[1], 10)
+
+    @require_torch
+    def test_tokenizer_translation(self):
+        inputs = self.tokenizer._build_translation_inputs("A test", src_lang="en_XX", tgt_lang="ar_AR")
+
+        self.assertEqual(
+            nested_simplify(inputs),
+            {
+                # en_XX, A, test, EOS
+                "input_ids": [[250004, 62, 3034, 2]],
+                "attention_mask": [[1, 1, 1, 1]],
+                # ar_AR
+                "forced_bos_token_id": 250001,
+            },
+        )