Fix attn mask gpt2 when using past (#3033)

* fix issue and add some tests

* fix issue and add some tests

* updated doc string gpt2

src/transformers/modeling_gpt2.py

@@ -276,14 +276,17 @@ GPT2_START_DOCSTRING = r"""
 
 GPT2_INPUTS_DOCSTRING = r"""
     Args:
-        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
+            `input_ids_length` = `sequence_length if `past` is None else 1
             Indices of input sequence tokens in the vocabulary.
+            If using `past` as an input make sure that `input_ids` are those of the last position.
 
             Indices can be obtained using :class:`transformers.GPT2Tokenizer`.
             See :func:`transformers.PreTrainedTokenizer.encode` and
             :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
 
             `What are input IDs? <../glossary.html#input-ids>`__
+
         past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
             Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
             (see `past` output below). Can be used to speed up sequential decoding. The token ids which have their past given to this model
@@ -294,10 +297,12 @@ GPT2_INPUTS_DOCSTRING = r"""
             ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
 
             `What are attention masks? <../glossary.html#attention-mask>`__
-        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`, `optional`, defaults to :obj:`None`):
+            `input_ids_length` = `sequence_length if `past` is None else 1
             Segment token indices to indicate first and second portions of the inputs.
             Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
             corresponds to a `sentence B` token
+            If using `past` as an input make sure that `token_type_ids` correspond to the `input_ids` of the last position.
 
             `What are token type IDs? <../glossary.html#token-type-ids>`_
         position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
@@ -419,7 +424,8 @@ class GPT2Model(GPT2PreTrainedModel):
 
         # Attention mask.
         if attention_mask is not None:
-            attention_mask = attention_mask.view(-1, input_shape[-1])
+            batch_size = input_ids.shape[0]
+            attention_mask = attention_mask.view(batch_size, -1)
             # We create a 3D attention mask from a 2D tensor mask.
             # Sizes are [batch_size, 1, 1, to_seq_length]
             # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]

tests/test_modeling_gpt2.py

@@ -170,6 +170,72 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
             )
             self.parent.assertEqual(len(result["presents"]), config.n_layer)
 
+        def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+            model = GPT2Model(config=config)
+            model.to(torch_device)
+            model.eval()
+
+            # first forward pass
+            output, past = model(input_ids, token_type_ids=token_type_ids)
+
+            # create hypothetical next token and extent to next_input_ids
+            next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+            next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
+
+            # append to next input_ids and token_type_ids
+            next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+            next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
+
+            output_from_no_past, _ = model(next_input_ids, token_type_ids=next_token_type_ids)
+            output_from_past, _ = model(next_tokens, token_type_ids=next_token_types, past=past)
+
+            # select random slice
+            random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+            output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
+            output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
+
+            # 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_gpt2_model_attention_mask_past(
+            self, config, input_ids, input_mask, head_mask, token_type_ids, *args
+        ):
+            model = GPT2Model(config=config)
+            model.to(torch_device)
+            model.eval()
+
+            # create attention mask
+            attn_mask = torch.ones(input_ids.shape).long()
+            half_seq_length = self.seq_length // 2
+            attn_mask[:, half_seq_length:] = 0
+
+            # first forward pass
+            output, past = model(input_ids, attention_mask=attn_mask)
+
+            # create hypothetical next token and extent to next_input_ids
+            next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+
+            # change a random masked slice from input_ids
+            random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
+            random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
+            input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
+
+            # append to next input_ids and attn_mask
+            next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+            attn_mask = torch.cat([attn_mask, torch.ones((attn_mask.shape[0], 1)).long()], dim=1)
+
+            # get two different outputs
+            output_from_no_past, _ = model(next_input_ids, attention_mask=attn_mask)
+            output_from_past, _ = model(next_tokens, past=past, attention_mask=attn_mask)
+
+            # select random slice
+            random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+            output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
+            output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
+
+            # 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_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
             model = GPT2LMHeadModel(config)
             model.to(torch_device)
@@ -248,6 +314,14 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
 
+    def test_gpt2_model_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)
+
+    def test_gpt2_model_att_mask_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
+
     def test_gpt2_lm_head_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*config_and_inputs)