Adding support for multiple mask tokens. (#14716)

* Adding support for multiple mask tokens.

- Original implem: #10222

Co-authored-by: njafer <[email protected]>

* In order to accomodate optionally multimodal models like Perceiver

we add information to the tasks to specify tasks where we know for sure
if we need the tokenizer/feature_extractor or not.

* Adding info in the documentation about multi masks.

+ marked as experimental.

* Add a copy() to prevent overriding the same tensor over and over.

* Fixup.

* Adding small test for multi mask with real values..

Co-authored-by: njafer <[email protected]>

src/transformers/pipelines/__init__.py

@@ -125,18 +125,21 @@
         "tf": (),
         "pt": (AutoModelForAudioClassification,) if is_torch_available() else (),
         "default": {"model": {"pt": "superb/wav2vec2-base-superb-ks"}},
+        "type": "audio",
     },
     "automatic-speech-recognition": {
         "impl": AutomaticSpeechRecognitionPipeline,
         "tf": (),
         "pt": (AutoModelForCTC, AutoModelForSpeechSeq2Seq) if is_torch_available() else (),
         "default": {"model": {"pt": "facebook/wav2vec2-base-960h"}},
+        "type": "multimodal",
     },
     "feature-extraction": {
         "impl": FeatureExtractionPipeline,
         "tf": (TFAutoModel,) if is_tf_available() else (),
         "pt": (AutoModel,) if is_torch_available() else (),
         "default": {"model": {"pt": "distilbert-base-cased", "tf": "distilbert-base-cased"}},
+        "type": "multimodal",
     },
     "text-classification": {
         "impl": TextClassificationPipeline,
@@ -148,6 +151,7 @@
                 "tf": "distilbert-base-uncased-finetuned-sst-2-english",
             },
         },
+        "type": "text",
     },
     "token-classification": {
         "impl": TokenClassificationPipeline,
@@ -159,6 +163,7 @@
                 "tf": "dbmdz/bert-large-cased-finetuned-conll03-english",
             },
         },
+        "type": "text",
     },
     "question-answering": {
         "impl": QuestionAnsweringPipeline,
@@ -167,6 +172,7 @@
         "default": {
             "model": {"pt": "distilbert-base-cased-distilled-squad", "tf": "distilbert-base-cased-distilled-squad"},
         },
+        "type": "text",
     },
     "table-question-answering": {
         "impl": TableQuestionAnsweringPipeline,
@@ -179,18 +185,21 @@
                 "tf": "google/tapas-base-finetuned-wtq",
             },
         },
+        "type": "text",
     },
     "fill-mask": {
         "impl": FillMaskPipeline,
         "tf": (TFAutoModelForMaskedLM,) if is_tf_available() else (),
         "pt": (AutoModelForMaskedLM,) if is_torch_available() else (),
         "default": {"model": {"pt": "distilroberta-base", "tf": "distilroberta-base"}},
+        "type": "text",
     },
     "summarization": {
         "impl": SummarizationPipeline,
         "tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
         "pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
         "default": {"model": {"pt": "sshleifer/distilbart-cnn-12-6", "tf": "t5-small"}},
+        "type": "text",
     },
     # This task is a special case as it's parametrized by SRC, TGT languages.
     "translation": {
@@ -202,18 +211,21 @@
             ("en", "de"): {"model": {"pt": "t5-base", "tf": "t5-base"}},
             ("en", "ro"): {"model": {"pt": "t5-base", "tf": "t5-base"}},
         },
+        "type": "text",
     },
     "text2text-generation": {
         "impl": Text2TextGenerationPipeline,
         "tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
         "pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
         "default": {"model": {"pt": "t5-base", "tf": "t5-base"}},
+        "type": "text",
     },
     "text-generation": {
         "impl": TextGenerationPipeline,
         "tf": (TFAutoModelForCausalLM,) if is_tf_available() else (),
         "pt": (AutoModelForCausalLM,) if is_torch_available() else (),
         "default": {"model": {"pt": "gpt2", "tf": "gpt2"}},
+        "type": "text",
     },
     "zero-shot-classification": {
         "impl": ZeroShotClassificationPipeline,
@@ -224,33 +236,48 @@
             "config": {"pt": "facebook/bart-large-mnli", "tf": "roberta-large-mnli"},
             "tokenizer": {"pt": "facebook/bart-large-mnli", "tf": "roberta-large-mnli"},
         },
+        "type": "text",
     },
     "conversational": {
         "impl": ConversationalPipeline,
         "tf": (TFAutoModelForSeq2SeqLM, TFAutoModelForCausalLM) if is_tf_available() else (),
         "pt": (AutoModelForSeq2SeqLM, AutoModelForCausalLM) if is_torch_available() else (),
         "default": {"model": {"pt": "microsoft/DialoGPT-medium", "tf": "microsoft/DialoGPT-medium"}},
+        "type": "text",
     },
     "image-classification": {
         "impl": ImageClassificationPipeline,
         "tf": (),
         "pt": (AutoModelForImageClassification,) if is_torch_available() else (),
         "default": {"model": {"pt": "google/vit-base-patch16-224"}},
+        "type": "image",
     },
     "image-segmentation": {
         "impl": ImageSegmentationPipeline,
         "tf": (),
         "pt": (AutoModelForImageSegmentation,) if is_torch_available() else (),
         "default": {"model": {"pt": "facebook/detr-resnet-50-panoptic"}},
+        "type": "image",
     },
     "object-detection": {
         "impl": ObjectDetectionPipeline,
         "tf": (),
         "pt": (AutoModelForObjectDetection,) if is_torch_available() else (),
         "default": {"model": {"pt": "facebook/detr-resnet-50"}},
+        "type": "image",
     },
 }
 
+NO_FEATURE_EXTRACTOR_TASKS = set()
+NO_TOKENIZER_TASKS = set()
+for task, values in SUPPORTED_TASKS.items():
+    if values["type"] == "text":
+        NO_FEATURE_EXTRACTOR_TASKS.add(task)
+    elif values["type"] in {"audio", "image"}:
+        NO_TOKENIZER_TASKS.add(task)
+    elif values["type"] != "multimodal":
+        raise ValueError(f"SUPPORTED_TASK {task} contains invalid type {values['type']}")
+
 
 def get_supported_tasks() -> List[str]:
     """
@@ -528,12 +555,14 @@ def pipeline(
     load_tokenizer = type(model_config) in TOKENIZER_MAPPING or model_config.tokenizer_class is not None
     load_feature_extractor = type(model_config) in FEATURE_EXTRACTOR_MAPPING or feature_extractor is not None
 
-    if task in {"audio-classification", "image-classification"}:
+    if task in NO_TOKENIZER_TASKS:
         # These will never require a tokenizer.
         # the model on the other hand might have a tokenizer, but
         # the files could be missing from the hub, instead of failing
         # on such repos, we just force to not load it.
         load_tokenizer = False
+    if task in NO_FEATURE_EXTRACTOR_TASKS:
+        load_feature_extractor = False
 
     if load_tokenizer:
         # Try to infer tokenizer from model or config name (if provided as str)

src/transformers/pipelines/fill_mask.py

@@ -44,7 +44,9 @@ class FillMaskPipeline(Pipeline):
 
     .. note::
 
-        This pipeline only works for inputs with exactly one token masked.
+        This pipeline only works for inputs with exactly one token masked. Experimental: We added support for multiple
+        masks. The returned values are raw model output, and correspond to disjoint probabilities where one might
+        expect joint probabilities (See `discussion <https://github.com/huggingface/transformers/pull/10222>`__).
     """
 
     def get_masked_index(self, input_ids: GenericTensor) -> np.ndarray:
@@ -59,13 +61,7 @@ def get_masked_index(self, input_ids: GenericTensor) -> np.ndarray:
     def _ensure_exactly_one_mask_token(self, input_ids: GenericTensor) -> np.ndarray:
         masked_index = self.get_masked_index(input_ids)
         numel = np.prod(masked_index.shape)
-        if numel > 1:
-            raise PipelineException(
-                "fill-mask",
-                self.model.base_model_prefix,
-                f"More than one mask_token ({self.tokenizer.mask_token}) is not supported",
-            )
-        elif numel < 1:
+        if numel < 1:
             raise PipelineException(
                 "fill-mask",
                 self.model.base_model_prefix,
@@ -98,46 +94,53 @@ def postprocess(self, model_outputs, top_k=5, target_ids=None):
             top_k = target_ids.shape[0]
         input_ids = model_outputs["input_ids"][0]
         outputs = model_outputs["logits"]
-        result = []
 
         if self.framework == "tf":
-            masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
+            masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()[:, 0]
 
-            # Fill mask pipeline supports only one ${mask_token} per sample
+            outputs = outputs.numpy()
 
-            logits = outputs[0, masked_index.item(), :]
-            probs = tf.nn.softmax(logits)
+            logits = outputs[0, masked_index, :]
+            probs = tf.nn.softmax(logits, axis=-1)
             if target_ids is not None:
-                probs = tf.gather_nd(probs, tf.reshape(target_ids, (-1, 1)))
+                probs = tf.gather_nd(tf.squeeze(probs, 0), target_ids.reshape(-1, 1))
+                probs = tf.expand_dims(probs, 0)
 
             topk = tf.math.top_k(probs, k=top_k)
             values, predictions = topk.values.numpy(), topk.indices.numpy()
         else:
-            masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False)
+            masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False).squeeze(-1)
             # Fill mask pipeline supports only one ${mask_token} per sample
 
-            logits = outputs[0, masked_index.item(), :]
-            probs = logits.softmax(dim=0)
+            logits = outputs[0, masked_index, :]
+            probs = logits.softmax(dim=-1)
             if target_ids is not None:
                 probs = probs[..., target_ids]
 
             values, predictions = probs.topk(top_k)
 
-        for v, p in zip(values.tolist(), predictions.tolist()):
-            tokens = input_ids.numpy()
-            if target_ids is not None:
-                p = target_ids[p].tolist()
-            tokens[masked_index] = p
-            # Filter padding out:
-            tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
-            result.append(
-                {
-                    "sequence": self.tokenizer.decode(tokens, skip_special_tokens=True),
-                    "score": v,
-                    "token": p,
-                    "token_str": self.tokenizer.decode(p),
-                }
-            )
+        result = []
+        single_mask = values.shape[0] == 1
+        for i, (_values, _predictions) in enumerate(zip(values.tolist(), predictions.tolist())):
+            row = []
+            for v, p in zip(_values, _predictions):
+                # Copy is important since we're going to modify this array in place
+                tokens = input_ids.numpy().copy()
+                if target_ids is not None:
+                    p = target_ids[p].tolist()
+
+                tokens[masked_index[i]] = p
+                # Filter padding out:
+                tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
+                # Originally we skip special tokens to give readable output.
+                # For multi masks though, the other [MASK] would be removed otherwise
+                # making the output look odd, so we add them back
+                sequence = self.tokenizer.decode(tokens, skip_special_tokens=single_mask)
+                proposition = {"score": v, "token": p, "token_str": self.tokenizer.decode(p), "sequence": sequence}
+                row.append(proposition)
+            result.append(row)
+        if single_mask:
+            return result[0]
         return result
 
     def get_target_ids(self, targets, top_k=None):

tests/test_pipelines_fill_mask.py

@@ -104,6 +104,32 @@ def test_small_model_pt(self):
             ],
         )
 
+        outputs = unmasker("My name is <mask> <mask>", top_k=2)
+
+        self.assertEqual(
+            nested_simplify(outputs, decimals=6),
+            [
+                [
+                    {
+                        "score": 2.2e-05,
+                        "token": 35676,
+                        "token_str": " Maul",
+                        "sequence": "<s>My name is Maul<mask></s>",
+                    },
+                    {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
+                ],
+                [
+                    {
+                        "score": 2.2e-05,
+                        "token": 35676,
+                        "token_str": " Maul",
+                        "sequence": "<s>My name is<mask> Maul</s>",
+                    },
+                    {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
+                ],
+            ],
+        )
+
     @slow
     @require_torch
     def test_large_model_pt(self):
@@ -231,9 +257,6 @@ def run_pipeline_test(self, fill_masker, examples):
 
         with self.assertRaises(ValueError):
             fill_masker([None])
-        # Multiple masks
-        with self.assertRaises(PipelineException):
-            fill_masker(f"This is {tokenizer.mask_token} {tokenizer.mask_token}")
         # No mask_token is not supported
         with self.assertRaises(PipelineException):
             fill_masker("This is")
@@ -242,6 +265,7 @@ def run_pipeline_test(self, fill_masker, examples):
         self.run_test_targets(model, tokenizer)
         self.run_test_top_k_targets(model, tokenizer)
         self.fill_mask_with_duplicate_targets_and_top_k(model, tokenizer)
+        self.fill_mask_with_multiple_masks(model, tokenizer)
 
     def run_test_targets(self, model, tokenizer):
         vocab = tokenizer.get_vocab()
@@ -340,3 +364,27 @@ def fill_mask_with_duplicate_targets_and_top_k(self, model, tokenizer):
         # The target list contains duplicates, so we can't output more
         # than them
         self.assertEqual(len(outputs), 3)
+
+    def fill_mask_with_multiple_masks(self, model, tokenizer):
+        fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
+
+        outputs = fill_masker(
+            f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}", top_k=2
+        )
+        self.assertEqual(
+            outputs,
+            [
+                [
+                    {"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
+                    {"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
+                ],
+                [
+                    {"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
+                    {"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
+                ],
+                [
+                    {"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
+                    {"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
+                ],
+            ],
+        )