Fixing slow pipeline tests (huggingface#14260)

* Fiixng slow pipeline tests

* Remove the image-segmentaiton override.

* Fixing clamping only in training.

* Wav2vec2.

* Remove last mention of `no_grad`.

* Fixing copies.

* Rename.

src/transformers/models/detr/modeling_detr.py

@@ -648,9 +648,10 @@ def forward(
         hidden_states = residual + hidden_states
         hidden_states = self.final_layer_norm(hidden_states)
 
-        if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
-            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+        if self.training:
+            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
+                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
 
         outputs = (hidden_states,)
 

src/transformers/models/unispeech/modeling_unispeech.py

@@ -947,7 +947,10 @@ def _conv_out_length(input_length, kernel_size, stride):
         return input_lengths
 
     def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
-        output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+        # Effectively attention_mask.sum(-1), but not inplace to be able to run
+        # on inference mode.
+        non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
+        output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long)
         batch_size = attention_mask.shape[0]
 
         attention_mask = torch.zeros(

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -948,7 +948,10 @@ def _conv_out_length(input_length, kernel_size, stride):
         return input_lengths
 
     def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
-        output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+        # Effectively attention_mask.sum(-1), but not inplace to be able to run
+        # on inference mode.
+        non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
+        output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long)
         batch_size = attention_mask.shape[0]
 
         attention_mask = torch.zeros(

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -989,7 +989,10 @@ def _conv_out_length(input_length, kernel_size, stride):
         return input_lengths
 
     def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
-        output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+        # Effectively attention_mask.sum(-1), but not inplace to be able to run
+        # on inference mode.
+        non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
+        output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long)
         batch_size = attention_mask.shape[0]
 
         attention_mask = torch.zeros(

src/transformers/pipelines/image_segmentation.py

@@ -91,9 +91,6 @@ def __call__(self, *args, **kwargs) -> Union[Predictions, List[Prediction]]:
 
         return super().__call__(*args, **kwargs)
 
-    def get_inference_context(self):
-        return torch.no_grad
-
     def preprocess(self, image):
         image = load_image(image)
         target_size = torch.IntTensor([[image.height, image.width]])

src/transformers/pipelines/table_question_answering.py

@@ -93,76 +93,74 @@ def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, *
         )
 
     def batch_inference(self, **inputs):
-        with torch.no_grad():
-            return self.model(**inputs)
+        return self.model(**inputs)
 
     def sequential_inference(self, **inputs):
         """
         Inference used for models that need to process sequences in a sequential fashion, like the SQA models which
         handle conversational query related to a table.
         """
-        with torch.no_grad():
-            all_logits = []
-            all_aggregations = []
-            prev_answers = None
-            batch_size = inputs["input_ids"].shape[0]
-
-            input_ids = inputs["input_ids"].to(self.device)
-            attention_mask = inputs["attention_mask"].to(self.device)
-            token_type_ids = inputs["token_type_ids"].to(self.device)
-            token_type_ids_example = None
-
-            for index in range(batch_size):
-                # If sequences have already been processed, the token type IDs will be created according to the previous
-                # answer.
-                if prev_answers is not None:
-                    prev_labels_example = token_type_ids_example[:, 3]  # shape (seq_len,)
-                    model_labels = np.zeros_like(prev_labels_example.cpu().numpy())  # shape (seq_len,)
-
-                    token_type_ids_example = token_type_ids[index]  # shape (seq_len, 7)
-                    for i in range(model_labels.shape[0]):
-                        segment_id = token_type_ids_example[:, 0].tolist()[i]
-                        col_id = token_type_ids_example[:, 1].tolist()[i] - 1
-                        row_id = token_type_ids_example[:, 2].tolist()[i] - 1
-
-                        if row_id >= 0 and col_id >= 0 and segment_id == 1:
-                            model_labels[i] = int(prev_answers[(col_id, row_id)])
-
-                    token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device)
-
-                input_ids_example = input_ids[index]
-                attention_mask_example = attention_mask[index]  # shape (seq_len,)
+        all_logits = []
+        all_aggregations = []
+        prev_answers = None
+        batch_size = inputs["input_ids"].shape[0]
+
+        input_ids = inputs["input_ids"].to(self.device)
+        attention_mask = inputs["attention_mask"].to(self.device)
+        token_type_ids = inputs["token_type_ids"].to(self.device)
+        token_type_ids_example = None
+
+        for index in range(batch_size):
+            # If sequences have already been processed, the token type IDs will be created according to the previous
+            # answer.
+            if prev_answers is not None:
+                prev_labels_example = token_type_ids_example[:, 3]  # shape (seq_len,)
+                model_labels = np.zeros_like(prev_labels_example.cpu().numpy())  # shape (seq_len,)
+
                 token_type_ids_example = token_type_ids[index]  # shape (seq_len, 7)
-                outputs = self.model(
-                    input_ids=input_ids_example.unsqueeze(0),
-                    attention_mask=attention_mask_example.unsqueeze(0),
-                    token_type_ids=token_type_ids_example.unsqueeze(0),
-                )
-                logits = outputs.logits
+                for i in range(model_labels.shape[0]):
+                    segment_id = token_type_ids_example[:, 0].tolist()[i]
+                    col_id = token_type_ids_example[:, 1].tolist()[i] - 1
+                    row_id = token_type_ids_example[:, 2].tolist()[i] - 1
 
-                if self.aggregate:
-                    all_aggregations.append(outputs.logits_aggregation)
+                    if row_id >= 0 and col_id >= 0 and segment_id == 1:
+                        model_labels[i] = int(prev_answers[(col_id, row_id)])
 
-                all_logits.append(logits)
+                token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device)
 
-                dist_per_token = torch.distributions.Bernoulli(logits=logits)
-                probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to(
-                    dist_per_token.probs.device
-                )
+            input_ids_example = input_ids[index]
+            attention_mask_example = attention_mask[index]  # shape (seq_len,)
+            token_type_ids_example = token_type_ids[index]  # shape (seq_len, 7)
+            outputs = self.model(
+                input_ids=input_ids_example.unsqueeze(0),
+                attention_mask=attention_mask_example.unsqueeze(0),
+                token_type_ids=token_type_ids_example.unsqueeze(0),
+            )
+            logits = outputs.logits
 
-                coords_to_probs = collections.defaultdict(list)
-                for i, p in enumerate(probabilities.squeeze().tolist()):
-                    segment_id = token_type_ids_example[:, 0].tolist()[i]
-                    col = token_type_ids_example[:, 1].tolist()[i] - 1
-                    row = token_type_ids_example[:, 2].tolist()[i] - 1
-                    if col >= 0 and row >= 0 and segment_id == 1:
-                        coords_to_probs[(col, row)].append(p)
+            if self.aggregate:
+                all_aggregations.append(outputs.logits_aggregation)
+
+            all_logits.append(logits)
+
+            dist_per_token = torch.distributions.Bernoulli(logits=logits)
+            probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to(
+                dist_per_token.probs.device
+            )
+
+            coords_to_probs = collections.defaultdict(list)
+            for i, p in enumerate(probabilities.squeeze().tolist()):
+                segment_id = token_type_ids_example[:, 0].tolist()[i]
+                col = token_type_ids_example[:, 1].tolist()[i] - 1
+                row = token_type_ids_example[:, 2].tolist()[i] - 1
+                if col >= 0 and row >= 0 and segment_id == 1:
+                    coords_to_probs[(col, row)].append(p)
 
-                prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
+            prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
 
-            logits_batch = torch.cat(tuple(all_logits), 0)
+        logits_batch = torch.cat(tuple(all_logits), 0)
 
-            return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0))
+        return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0))
 
     def __call__(self, *args, **kwargs):
         r"""

tests/test_pipelines_audio_classification.py

@@ -117,7 +117,7 @@ def test_large_model_pt(self):
         self.assertEqual(
             nested_simplify(output, decimals=4),
             [
-                {"score": 0.9809, "label": "go"},
+                {"score": 0.981, "label": "go"},
                 {"score": 0.0073, "label": "up"},
                 {"score": 0.0064, "label": "_unknown_"},
                 {"score": 0.0015, "label": "down"},