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)]
@@ -142,6 +144,7 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 attentions = outputs[-1]
                 self.assertEqual(model.config.output_attentions, True)
@@ -173,6 +176,7 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
+                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)
@@ -273,6 +277,7 @@ class CommonTestCases:
                 inputs = inputs_dict.copy()
                 inputs['head_mask'] = head_mask
 
+                with torch.no_grad():
                     outputs = model(**inputs)
 
                 # Test that we can get a gradient back for importance score computation
@@ -320,6 +325,7 @@ class CommonTestCases:
                 heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
                                 -1: [0]}
                 model.prune_heads(heads_to_prune)
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
 
                 attentions = outputs[-1]
@@ -356,6 +362,7 @@ class CommonTestCases:
                 model = model_class.from_pretrained(directory)
                 model.to(torch_device)
 
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 attentions = outputs[-1]
                 self.assertEqual(attentions[0].shape[-3], 1)
@@ -385,6 +392,7 @@ class CommonTestCases:
                 model.to(torch_device)
                 model.eval()
 
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
@@ -412,6 +420,7 @@ class CommonTestCases:
                 model.to(torch_device)
                 model.eval()
 
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
@@ -429,6 +438,7 @@ class CommonTestCases:
                 model.to(torch_device)
                 shutil.rmtree(directory)
 
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
@@ -440,6 +450,7 @@ class CommonTestCases:
                 heads_to_prune = {0: [0], 2: [1, 2]}
                 model.prune_heads(heads_to_prune)
 
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 attentions = outputs[-1]
 
@@ -459,6 +470,7 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
                 hidden_states = outputs[-1]
                 self.assertEqual(model.config.output_attentions, False)
@@ -594,6 +606,7 @@ class CommonTestCases:
                     inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids)
                     inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids)
 
+                with torch.no_grad():
                     outputs = model(**inputs_dict)
 
     class GPTModelTester(CommonModelTester):
@@ -682,6 +695,7 @@ class CommonTestCases:
             model.to(torch_device)
             model.eval()
 
+            with torch.no_grad():
                 outputs = model(input_ids, position_ids, token_type_ids)
                 outputs = model(input_ids, position_ids)
                 outputs = model(input_ids)
@@ -697,6 +711,7 @@ class CommonTestCases:
             model = self.lm_head_model_class(config)
             model.to(torch_device)
             model.eval()
+            with torch.no_grad():
                 outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
             loss, lm_logits = outputs[:2]
 
@@ -714,6 +729,7 @@ class CommonTestCases:
                 model = model_class(config)
                 model.to(torch_device)
                 model.eval()
+                with torch.no_grad():
                     outputs = model(input_ids)
                 presents = outputs[-1]
                 self.parent.assertEqual(self.num_hidden_layers, len(presents))
@@ -727,6 +743,7 @@ class CommonTestCases:
             model = self.double_head_model_class(config)
             model.to(torch_device)
             model.eval()
+            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]