[Longformer] Major Refactor (#5219)

* refactor naming

* add small slow test

* refactor

* refactor naming

* rename selected to extra

* big global attention refactor

* make style

* refactor naming

* save intermed

* refactor functions

* finish function refactor

* fix tests

* fix longformer

* fix longformer

* fix longformer

* fix all tests but one

* finish longformer

* address sams and izs comments

* fix transpose

tests/test_modeling_common.py

@@ -811,7 +811,7 @@ class ModelTesterMixin:
             # Wrap model in nn.DataParallel
             model = torch.nn.DataParallel(model)
             with torch.no_grad():
-                _ = model(**inputs_dict)
+                _ = model(**self._prepare_for_class(inputs_dict, model_class))
 
 
 global_rng = random.Random()

tests/test_modeling_longformer.py

@@ -115,6 +115,18 @@ class LongformerModelTester:
     def check_loss_output(self, result):
         self.parent.assertListEqual(list(result["loss"].size()), [])
 
+    def create_and_check_attention_mask_determinism(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = LongformerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+        output_with_mask = model(input_ids, attention_mask=attention_mask)[0]
+        output_without_mask = model(input_ids)[0]
+        self.parent.assertTrue(torch.allclose(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], atol=1e-4))
+
     def create_and_check_longformer_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -134,6 +146,36 @@ class LongformerModelTester:
         )
         self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
 
+    def create_and_check_longformer_model_with_global_attention_mask(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = LongformerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        global_attention_mask = input_mask.clone()
+        global_attention_mask[:, input_mask.shape[-1] // 2] = 0
+        global_attention_mask = global_attention_mask.to(torch_device)
+
+        sequence_output, pooled_output = model(
+            input_ids,
+            attention_mask=input_mask,
+            global_attention_mask=global_attention_mask,
+            token_type_ids=token_type_ids,
+        )
+        sequence_output, pooled_output = model(
+            input_ids, token_type_ids=token_type_ids, global_attention_mask=global_attention_mask
+        )
+        sequence_output, pooled_output = model(input_ids, global_attention_mask=global_attention_mask)
+
+        result = {
+            "sequence_output": sequence_output,
+            "pooled_output": pooled_output,
+        }
+        self.parent.assertListEqual(
+            list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
+        )
+        self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
+
     def create_and_check_longformer_for_masked_lm(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -243,7 +285,13 @@ class LongformerModelTester:
             token_labels,
             choice_labels,
         ) = config_and_inputs
-        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        global_attention_mask = torch.zeros_like(input_ids)
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask,
+            "global_attention_mask": global_attention_mask,
+        }
         return config, inputs_dict
 
     def prepare_config_and_inputs_for_question_answering(self):
@@ -277,11 +325,10 @@ class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
         (
             LongformerModel,
             LongformerForMaskedLM,
-            # TODO: make tests pass for those models
-            # LongformerForSequenceClassification,
-            # LongformerForQuestionAnswering,
-            # LongformerForTokenClassification,
-            # LongformerForMultipleChoice,
+            LongformerForSequenceClassification,
+            LongformerForQuestionAnswering,
+            LongformerForTokenClassification,
+            LongformerForMultipleChoice,
         )
         if is_torch_available()
         else ()
@@ -298,6 +345,14 @@ class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_longformer_model(*config_and_inputs)
 
+    def test_longformer_model_attention_mask_determinism(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_attention_mask_determinism(*config_and_inputs)
+
+    def test_longformer_model_global_attention_mask(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_longformer_model_with_global_attention_mask(*config_and_inputs)
+
     def test_longformer_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_longformer_for_masked_lm(*config_and_inputs)
@@ -325,15 +380,31 @@ class LongformerModelIntegrationTest(unittest.TestCase):
         model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
         model.to(torch_device)
 
+        # 'Hello world!'
+        input_ids = torch.tensor([[0, 20920, 232, 328, 1437, 2]], dtype=torch.long, device=torch_device)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+        output = model(input_ids, attention_mask=attention_mask)[0]
+        output_without_mask = model(input_ids)[0]
+
+        expected_output_slice = torch.tensor([0.0549, 0.1087, -0.1119, -0.0368, 0.0250], device=torch_device)
+        self.assertTrue(torch.allclose(output[0, 0, -5:], expected_output_slice, atol=1e-4))
+        self.assertTrue(torch.allclose(output_without_mask[0, 0, -5:], expected_output_slice, atol=1e-4))
+
+    @slow
+    def test_inference_no_head_long(self):
+        model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
+        model.to(torch_device)
+
         # 'Hello world! ' repeated 1000 times
         input_ids = torch.tensor(
             [[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device
         )  # long input
 
         attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
-        attention_mask[:, [1, 4, 21]] = 2  # Set global attention on a few random positions
+        global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device)
+        global_attention_mask[:, [1, 4, 21]] = 1  # Set global attention on a few random positions
 
-        output = model(input_ids, attention_mask=attention_mask)[0]
+        output = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)[0]
 
         expected_output_sum = torch.tensor(74585.8594, device=torch_device)
         expected_output_mean = torch.tensor(0.0243, device=torch_device)
@@ -341,7 +412,7 @@ class LongformerModelIntegrationTest(unittest.TestCase):
         self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
 
     @slow
-    def test_inference_masked_lm(self):
+    def test_inference_masked_lm_long(self):
         model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
         model.to(torch_device)
 
@@ -352,9 +423,9 @@ class LongformerModelIntegrationTest(unittest.TestCase):
 
         loss, prediction_scores = model(input_ids, labels=input_ids)
 
-        expected_loss = torch.tensor(0.0620, device=torch_device)
-        expected_prediction_scores_sum = torch.tensor(-6.1599e08, device=torch_device)
-        expected_prediction_scores_mean = torch.tensor(-3.0622, device=torch_device)
+        expected_loss = torch.tensor(0.0074, device=torch_device)
+        expected_prediction_scores_sum = torch.tensor(-6.1048e08, device=torch_device)
+        expected_prediction_scores_mean = torch.tensor(-3.0348, device=torch_device)
         input_ids = input_ids.to(torch_device)
 
         self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-4))