[Bart/Memory] Two separate, smaller decoder attention masks (#3371)

src/transformers/modeling_bart.py

@@ -74,39 +74,37 @@ BART_INPUTS_DOCSTRING = r"""
             ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
         decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
             Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids right, following the paper.
-        decoder_attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, 1, tgt_seq_len, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
-            Default behavior: generate a tensor that ignores pad tokens and future tokens, as in the paper.
+        decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
+            Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
             If you want to change padding behavior, you should read :func:`~transformers.modeling_bart._prepare_decoder_inputs` and modify.
             See diagram 1 in the paper for more info on the default strategy
 """
-LARGE_NEGATIVE = -1e8
+
+
+def invert_mask(attention_mask):
+    assert attention_mask.dim() == 2
+    return attention_mask.eq(0)
 
 
 def _prepare_bart_decoder_inputs(
-    config, input_ids, decoder_input_ids=None, decoder_attn_mask=None, mask_dtype=None,
+    config, input_ids, decoder_input_ids=None, decoder_padding_mask=None, causal_mask_dtype=torch.float32
 ):
-    """Prepare masks that ignore padding tokens in the decoder and a causal lm mask for the decoder if
+    """Prepare masks that ignore padding tokens in the decoder and a causal mask for the decoder if
     none are provided. This mimics the default behavior in fairseq. To override it pass in masks.
     Note: this is not called during generation
     """
     pad_token_id = config.pad_token_id
-    need_causal_mask = not config.output_past
     if decoder_input_ids is None:
         decoder_input_ids = shift_tokens_right(input_ids, pad_token_id)
-    bsz, tgt_len = decoder_input_ids.size()[:2]
-    if decoder_attn_mask is None:
+    bsz, tgt_len = decoder_input_ids.size()
+    if decoder_padding_mask is None:
         decoder_padding_mask = make_padding_mask(decoder_input_ids, pad_token_id)
-        if need_causal_mask:
-            causal_lm_mask = torch.triu(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len)), 1)
-        else:
-            causal_lm_mask = None
-        new_shape = (bsz, tgt_len, tgt_len)
-        # make it broadcastable so can just be added to the attention coefficients
-        decoder_attn_mask = _combine_masks(decoder_padding_mask, causal_lm_mask, new_shape).to(device=input_ids.device)
-        if mask_dtype is not None:
-            decoder_attn_mask = decoder_attn_mask.to(mask_dtype)
-    assert decoder_attn_mask is None or decoder_attn_mask.shape == (bsz, 1, tgt_len, tgt_len)
-    return decoder_input_ids, decoder_attn_mask
+    else:
+        decoder_padding_mask = invert_mask(decoder_padding_mask)
+    causal_mask = torch.triu(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len)), 1).to(
+        dtype=causal_mask_dtype, device=decoder_input_ids.device
+    )
+    return decoder_input_ids, decoder_padding_mask, causal_mask
 
 
 class PretrainedBartModel(PreTrainedModel):
@@ -130,12 +128,9 @@ class PretrainedBartModel(PreTrainedModel):
     def dummy_inputs(self):
         pad_token = self.config.pad_token_id
         input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
-        decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(self.config, input_ids)
         dummy_inputs = {
-            "decoder_input_ids": decoder_input_ids,
             "attention_mask": input_ids.ne(pad_token),
             "input_ids": input_ids,
-            "decoder_attention_mask": decoder_attn_mask,
         }
         return dummy_inputs
 
@@ -153,21 +148,6 @@ def _check_shapes(shape_1, shape2):
         raise AssertionError("shape mismatch: {} != {}".format(shape_1, shape2))
 
 
-def _combine_masks(key_padding_mask, causal_lm_mask, targ_size):
-    """Make one mask of shape (bsz, 1, tgt_len, src_len) """
-    a = torch.zeros(targ_size)  # targ_size is(bsz, tgt_len, src_len)
-    b = torch.zeros(targ_size)
-    if key_padding_mask is not None:  # (bsz, tgt_len) -> targ_size
-        _check_shapes(key_padding_mask.shape, targ_size[:2])
-        reshaped = key_padding_mask.unsqueeze(2).expand(*targ_size)
-        a[reshaped] = LARGE_NEGATIVE
-
-    if causal_lm_mask is not None:  # (tgt_len, src_len) -> targ_size
-        _check_shapes(causal_lm_mask.shape, targ_size[-2:])
-        b = causal_lm_mask.unsqueeze(0).expand(*targ_size)
-    return (a + b).unsqueeze(1).clamp(LARGE_NEGATIVE,)
-
-
 def shift_tokens_right(input_ids, pad_token_id):
     """Shift input ids one token to the right, and wrap the last non pad token (usually <eos>)."""
     prev_output_tokens = input_ids.clone()
@@ -281,8 +261,7 @@ class BartEncoder(nn.Module):
         """
         # check attention mask and invert
         if attention_mask is not None:
-            assert attention_mask.dim() == 2
-            attention_mask = attention_mask.eq(0)
+            attention_mask = invert_mask(attention_mask)
 
         inputs_embeds = self.embed_tokens(input_ids)
         embed_pos = self.embed_positions(input_ids)
@@ -339,7 +318,13 @@ class DecoderLayer(nn.Module):
         self.final_layer_norm = LayerNorm(self.embed_dim)
 
     def forward(
-        self, x, encoder_hidden_states, encoder_attn_mask=None, layer_state=None, attention_mask=None,
+        self,
+        x,
+        encoder_hidden_states,
+        encoder_attn_mask=None,
+        layer_state=None,
+        causal_mask=None,
+        decoder_padding_mask=None,
     ):
         residual = x
 
@@ -347,7 +332,12 @@ class DecoderLayer(nn.Module):
             layer_state = {}
         # next line mutates layer state
         x, self_attn_weights = self.self_attn(
-            query=x, key=x, layer_state=layer_state, attn_mask=attention_mask, need_weights=self.output_attentions
+            query=x,
+            key=x,
+            layer_state=layer_state,
+            key_padding_mask=decoder_padding_mask,
+            attn_mask=causal_mask,
+            need_weights=self.output_attentions,
         )
         x = F.dropout(x, p=self.dropout, training=self.training)
         x = residual + x
@@ -412,7 +402,8 @@ class BartDecoder(nn.Module):
         input_ids,
         encoder_hidden_states,
         encoder_padding_mask,
-        combined_mask,
+        decoder_padding_mask,
+        decoder_causal_mask,
         decoder_cached_states=None,
         generation_mode=False,
         **unused
@@ -437,8 +428,7 @@ class BartDecoder(nn.Module):
         """
         # check attention mask and invert
         if encoder_padding_mask is not None:
-            assert encoder_padding_mask.dim() == 2
-            encoder_padding_mask = encoder_padding_mask.eq(0)
+            encoder_padding_mask = invert_mask(encoder_padding_mask)
 
         # embed positions
         positions = self.embed_positions(input_ids, generation_mode=generation_mode)
@@ -458,7 +448,6 @@ class BartDecoder(nn.Module):
         all_hidden_states = ()
         all_self_attns = ()
         next_decoder_cache = []
-
         for i, decoder_layer in enumerate(self.layers):
             decoder_layer  # type: DecoderLayer
             # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
@@ -468,7 +457,12 @@ class BartDecoder(nn.Module):
 
             layer_state = decoder_cached_states[i] if decoder_cached_states is not None else None
             x, layer_self_attn, layer_past = decoder_layer(
-                x, encoder_hidden_states, encoder_padding_mask, layer_state=layer_state, attention_mask=combined_mask,
+                x,
+                encoder_hidden_states,
+                encoder_attn_mask=encoder_padding_mask,
+                decoder_padding_mask=decoder_padding_mask,
+                layer_state=layer_state,
+                causal_mask=decoder_causal_mask,
             )
 
             if self.output_past:
@@ -736,6 +730,8 @@ def _filter_out_falsey_values(tup) -> Tuple:
 
 
 # Public API
+def _get_shape(t):
+    return getattr(t, "shape", None)
 
 
 @add_start_docstrings(
@@ -769,13 +765,16 @@ class BartModel(PretrainedBartModel):
 
         # make masks if user doesn't supply
         if not generation_mode:
-            decoder_input_ids, decoder_attention_mask = _prepare_bart_decoder_inputs(
+            decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_bart_decoder_inputs(
                 self.config,
                 input_ids,
                 decoder_input_ids=decoder_input_ids,
-                decoder_attn_mask=decoder_attention_mask,
-                mask_dtype=self.shared.weight.dtype,
+                decoder_padding_mask=decoder_attention_mask,
+                causal_mask_dtype=self.shared.weight.dtype,
             )
+        else:
+            decoder_padding_mask, causal_mask = None, None
+
         assert decoder_input_ids is not None
         if encoder_outputs is None:
             encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
@@ -785,7 +784,8 @@ class BartModel(PretrainedBartModel):
             decoder_input_ids,
             encoder_outputs[0],
             attention_mask,
-            decoder_attention_mask,
+            decoder_padding_mask,
+            decoder_causal_mask=causal_mask,
             decoder_cached_states=decoder_cached_states,
             generation_mode=generation_mode,
         )

tests/test_modeling_bart.py

@@ -36,8 +36,8 @@ if is_torch_available():
     from transformers.modeling_bart import (
         BART_PRETRAINED_MODEL_ARCHIVE_MAP,
         shift_tokens_right,
+        invert_mask,
         _prepare_bart_decoder_inputs,
-        LARGE_NEGATIVE,
     )
     from transformers.tokenization_bart import BartTokenizer
 
@@ -123,10 +123,9 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_advanced_inputs(self):
+    def test_initialization_more(self):
         # (config, input_ids, token_type_ids, input_mask, *unused) = \
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(config, inputs_dict["input_ids"])
         model = BartModel(config)
         model.to(torch_device)
         model.eval()
@@ -142,9 +141,17 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
         _check_var(model.encoder.layers[0].fc1)
         _check_var(model.encoder.embed_positions)
 
+    def test_advanced_inputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        inputs_dict["input_ids"][:, -2:] = config.pad_token_id
+        decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_bart_decoder_inputs(
+            config, inputs_dict["input_ids"]
+        )
+        model = BartModel(config).to(torch_device).eval()
+
         decoder_features_with_created_mask = model(**inputs_dict)[0]
         decoder_features_with_passed_mask = model(
-            decoder_attention_mask=decoder_attn_mask, decoder_input_ids=decoder_input_ids, **inputs_dict
+            decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict
         )[0]
         _assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask)
         useless_mask = torch.zeros_like(decoder_attn_mask)
@@ -238,7 +245,7 @@ class BartHeadTests(unittest.TestCase):
         lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size).to(torch_device)
         lm_model = BartForConditionalGeneration(config)
         lm_model.to(torch_device)
-        loss, logits, enc_features = lm_model(input_ids=input_ids, lm_labels=lm_labels, decoder_input_ids=input_ids)
+        loss, logits, enc_features = lm_model(input_ids=input_ids, lm_labels=lm_labels)
         expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(logits.shape, expected_shape)
         self.assertIsInstance(loss.item(), float)
@@ -336,41 +343,23 @@ class BartHeadTests(unittest.TestCase):
         model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
 
     def test_dummy_inputs(self):
-        config, *_ = self._get_config_and_data(output_past=True)
+        config, *_ = self._get_config_and_data()
         model = BartForConditionalGeneration(config).eval().to(torch_device)
         model(**model.dummy_inputs)
 
     def test_prepare_bart_decoder_inputs(self):
         config, *_ = self._get_config_and_data(output_past=False)
-        input_ids = _long_tensor(([4, 4, 2]))  # only used for .device if decoder_input_ids is passed
+        input_ids = _long_tensor(([4, 4, 2]))
         decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]])
-        ignore = LARGE_NEGATIVE
-        decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(config, input_ids, decoder_input_ids)
-        expected_mask = torch.tensor(
-            [
-                [0, ignore, ignore],
-                [0, 0, ignore],
-                [ignore, ignore, ignore],  # never attend to the final token, because its pad
-            ]
-        ).to(input_ids.device)
-        self.assertEqual(decoder_attn_mask.size(), (1, 1, 3, 3))
-        self.assertTrue(torch.eq(expected_mask, decoder_attn_mask).all())
-
-        # Test no causal mask
-        config, *_ = self._get_config_and_data(output_past=True)
-        expected_just_padding_mask = torch.tensor(
-            [[0, 0, 0], [0, 0, 0], [ignore, ignore, ignore]]  # never attend to the final token, because its pad
-        ).to(input_ids.device)
-        _, decoder_attn_mask_no_causal_mask = _prepare_bart_decoder_inputs(config, input_ids, decoder_input_ids)
-        self.assertEqual(decoder_attn_mask_no_causal_mask.size(), (1, 1, 3, 3))
-        self.assertTrue(torch.eq(expected_just_padding_mask, decoder_attn_mask_no_causal_mask).all())
-
-        decoder_input_ids = _long_tensor([[0, 26388, 4133, 2]])
-        # Attend to everything if no pad tokens and no causal mask
-        _, decoder_attn_mask_no_padding_no_causal_mask = _prepare_bart_decoder_inputs(
+        ignore = float("-inf")
+        decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_bart_decoder_inputs(
             config, input_ids, decoder_input_ids
         )
-        self.assertTrue(torch.eq(decoder_attn_mask_no_padding_no_causal_mask, 0).all())
+        expected_causal_mask = torch.tensor(
+            [[0, ignore, ignore], [0, 0, ignore], [0, 0, 0]]  # never attend to the final token, because its pad
+        ).to(input_ids.device)
+        self.assertEqual(decoder_attn_mask.size(), decoder_input_ids.size())
+        self.assertTrue(torch.eq(expected_causal_mask, causal_mask).all())
 
     def test_resize_tokens_embeddings_more(self):
         config, input_ids, _ = self._get_config_and_data()