Add caching mechanism to BERT, RoBERTa (#9183)

* add past_key_values

* add use_cache option

* make mask before cutting ids

* adjust position_ids according to past_key_values

* flatten past_key_values

* fix positional embeds

* fix _reorder_cache

* set use_cache to false when not decoder, fix attention mask init

* add test for caching

* add past_key_values for Roberta

* fix position embeds

* add caching test for roberta

* add doc

* make style

* doc, fix attention mask, test

* small fixes

* adress patrick's comments

* input_ids shouldn't start with pad token

* use_cache only when decoder

* make consistent with bert

* make copies consistent

* add use_cache to encoder

* add past_key_values to tapas attention

* apply suggestions from code review

* make coppies consistent

* add attn mask in tests

* remove copied from longformer

* apply suggestions from code review

* fix bart test

* nit

* simplify model outputs

* fix doc

* fix output ordering

tests/test_modeling_roberta.py

@@ -198,6 +198,74 @@ class RobertaModelTester:
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
+    def create_and_check_decoder_model_past_large_inputs(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.is_decoder = True
+        config.add_cross_attention = True
+        model = RobertaForCausalLM(config=config).to(torch_device).eval()
+
+        # make sure that ids don't start with pad token
+        mask = input_ids.ne(config.pad_token_id).long()
+        input_ids = input_ids * mask
+
+        # first forward pass
+        outputs = model(
+            input_ids,
+            attention_mask=input_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=True,
+        )
+        past_key_values = outputs.past_key_values
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+
+        # make sure that ids don't start with pad token
+        mask = next_tokens.ne(config.pad_token_id).long()
+        next_tokens = next_tokens * mask
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(
+            next_input_ids,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+        output_from_past = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
     def create_and_check_for_masked_lm(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -337,6 +405,10 @@ class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCas
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
 
+    def test_decoder_model_past_with_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
+
     def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)