all tests in torch no grad

transformers/tests/modeling_common_test.py

@@ -120,7 +120,9 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
-                first, second = model(**inputs_dict)[0], model(**inputs_dict)[0]
+                with torch.no_grad():
+                    first = model(**inputs_dict)[0]
+                    second = model(**inputs_dict)[0]
                 out_1 = first.cpu().numpy()
                 out_2 = second.cpu().numpy()
                 out_1 = out_1[~np.isnan(out_1)]
@@ -142,7 +144,8 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 attentions = outputs[-1]
                 self.assertEqual(model.config.output_attentions, True)
                 self.assertEqual(model.config.output_hidden_states, False)
@@ -173,7 +176,8 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
                 self.assertEqual(model.config.output_attentions, True)
                 self.assertEqual(model.config.output_hidden_states, True)
@@ -273,7 +277,8 @@ class CommonTestCases:
                 inputs = inputs_dict.copy()
                 inputs['head_mask'] = head_mask
 
-                outputs = model(**inputs)
+                with torch.no_grad():
+                    outputs = model(**inputs)
 
                 # Test that we can get a gradient back for importance score computation
                 output = sum(t.sum() for t in outputs[0])
@@ -320,7 +325,8 @@ class CommonTestCases:
                 heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
                                 -1: [0]}
                 model.prune_heads(heads_to_prune)
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
 
                 attentions = outputs[-1]
 
@@ -356,7 +362,8 @@ class CommonTestCases:
                 model = model_class.from_pretrained(directory)
                 model.to(torch_device)
 
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 attentions = outputs[-1]
                 self.assertEqual(attentions[0].shape[-3], 1)
                 self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
@@ -385,7 +392,8 @@ class CommonTestCases:
                 model.to(torch_device)
                 model.eval()
 
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
                 self.assertEqual(attentions[0].shape[-3], 1)
@@ -412,7 +420,8 @@ class CommonTestCases:
                 model.to(torch_device)
                 model.eval()
 
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
                 self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
@@ -429,7 +438,8 @@ class CommonTestCases:
                 model.to(torch_device)
                 shutil.rmtree(directory)
 
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
                 self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
@@ -440,7 +450,8 @@ class CommonTestCases:
                 heads_to_prune = {0: [0], 2: [1, 2]}
                 model.prune_heads(heads_to_prune)
 
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
                 self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads -1)
@@ -459,7 +470,8 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
                 hidden_states = outputs[-1]
                 self.assertEqual(model.config.output_attentions, False)
                 self.assertEqual(model.config.output_hidden_states, True)
@@ -594,7 +606,8 @@ class CommonTestCases:
                     inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids)
                     inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids)
 
-                outputs = model(**inputs_dict)
+                with torch.no_grad():
+                    outputs = model(**inputs_dict)
 
     class GPTModelTester(CommonModelTester):
 
@@ -682,9 +695,10 @@ class CommonTestCases:
             model.to(torch_device)
             model.eval()
 
-            outputs = model(input_ids, position_ids, token_type_ids)
-            outputs = model(input_ids, position_ids)
-            outputs = model(input_ids)
+            with torch.no_grad():
+                outputs = model(input_ids, position_ids, token_type_ids)
+                outputs = model(input_ids, position_ids)
+                outputs = model(input_ids)
 
             hidden_state = outputs[0]
             self.parent.assertListEqual(
@@ -697,7 +711,8 @@ class CommonTestCases:
             model = self.lm_head_model_class(config)
             model.to(torch_device)
             model.eval()
-            outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
+            with torch.no_grad():
+                outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
             loss, lm_logits = outputs[:2]
 
             total_voc = self.vocab_size
@@ -714,7 +729,8 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
-                outputs = model(input_ids)
+                with torch.no_grad():
+                    outputs = model(input_ids)
                 presents = outputs[-1]
                 self.parent.assertEqual(self.num_hidden_layers, len(presents))
                 self.parent.assertListEqual(
@@ -727,7 +743,8 @@ class CommonTestCases:
             model = self.double_head_model_class(config)
             model.to(torch_device)
             model.eval()
-            outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
+            with torch.no_grad():
+                outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
                             token_type_ids=token_type_ids, position_ids=position_ids)
             lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
             loss = [lm_loss, mc_loss]