[T5] make decoder input ids optional for t5 training

src/transformers/modeling_t5.py

@@ -502,6 +502,27 @@ def _init_weights(self, module):
             if module.has_relative_attention_bias:
                 module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
 
+    def _shift_right(self, input_ids):
+        decoder_start_token_id = self.config.decoder_start_token_id
+        pad_token_id = self.config.pad_token_id
+
+        assert (
+            decoder_start_token_id is not None
+        ), "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. See T5 docs for more information"
+
+        # shift inputs to the right
+        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+        shifted_input_ids[..., 0] = decoder_start_token_id
+
+        assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
+        # replace possible -100 values in lm_labels by `pad_token_id`
+        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+        assert torch.all(shifted_input_ids >= 0).item(), "Verify that `lm_labels` has only positive values and -100"
+
+        return shifted_input_ids
+
 
 class T5Stack(T5PreTrainedModel):
     def __init__(self, config, embed_tokens=None):
@@ -931,6 +952,10 @@ def forward(
 
         hidden_states = encoder_outputs[0]
 
+        if lm_labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+            # get decoder inputs from shifting lm labels to the right
+            decoder_input_ids = self._shift_right(lm_labels)
+
         # Decode
         decoder_outputs = self.decoder(
             input_ids=decoder_input_ids,
@@ -949,10 +974,8 @@ def forward(
 
         decoder_outputs = (lm_logits,) + decoder_outputs[1:]  # Add hidden states and attention if they are here
         if lm_labels is not None:
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = lm_labels[..., 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-100)
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1))
             decoder_outputs = (
                 loss,
             ) + decoder_outputs  # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666

src/transformers/modeling_tf_utils.py

@@ -523,8 +523,8 @@ def generate(
             pad_token_id: (`optional`) int
                 Pad token. Defaults to pad_token_id as defined in the models config.
 
-            eos_token_ids: (`optional`) int or list of int
-                End of sequence token or list of tokens to stop the generation. Default to 0.
+            eos_token_id: (`optional`) int
+                EOS token. Defaults to eos_token_id as defined in the models config.
 
             length_penalty: (`optional`) float
                 Exponential penalty to the length. Default to 1.

src/transformers/modeling_utils.py

@@ -721,13 +721,10 @@ def generate(
                 Padding token. Default to specicic model pad_token_id or None if it does not exist.
 
             bos_token_id: (`optional`) int
-                BOS token. Defaults to bos_token_id as defined in the models config.
+                BOS token. Defaults to `bos_token_id` as defined in the models config.
 
-            pad_token_id: (`optional`) int
-                Pad token. Defaults to pad_token_id as defined in the models config.
-
-            eos_token_ids: (`optional`) int or list of int
-                End of sequence token or list of tokens to stop the generation. Default to eos_token_ids as defined in the models config.
+            eos_token_id: (`optional`) int
+                EOS token. Defaults to `eos_token_id` as defined in the models config.
 
             length_penalty: (`optional`) float
                 Exponential penalty to the length. Default to 1.

tests/test_modeling_bart.py

@@ -468,7 +468,7 @@ def test_xsum_summarization_same_as_fairseq(self):
             length_penalty=1.0,
             no_repeat_ngram_size=3,
             early_stopping=True,
-            decoder_start_token_id=model.config.eos_token_ids[0],
+            decoder_start_token_id=model.config.eos_token_id,
         )
 
         decoded = [

tests/test_modeling_t5.py

@@ -24,6 +24,7 @@
 
 
 if is_torch_available():
+    import torch
     from transformers import T5Config, T5Model, T5ForConditionalGeneration
     from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP
 
@@ -57,8 +58,9 @@ def __init__(
             relative_attention_num_buckets=8,
             dropout_rate=0.1,
             initializer_factor=0.002,
-            eos_token_ids=[1],
+            eos_token_id=1,
             pad_token_id=0,
+            decoder_start_token_id=0,
             scope=None,
         ):
             self.parent = parent
@@ -78,8 +80,9 @@ def __init__(
             self.dropout_rate = dropout_rate
             self.initializer_factor = initializer_factor
             self.scope = scope
-            self.eos_token_ids = eos_token_ids
+            self.eos_token_id = eos_token_id
             self.pad_token_id = pad_token_id
+            self.decoder_start_token_id = decoder_start_token_id
 
         def prepare_config_and_inputs(self):
             input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
@@ -106,9 +109,10 @@ def prepare_config_and_inputs(self):
                 relative_attention_num_buckets=self.relative_attention_num_buckets,
                 dropout_rate=self.dropout_rate,
                 initializer_factor=self.initializer_factor,
-                eos_token_ids=self.eos_token_ids,
+                eos_token_id=self.eos_token_id,
                 bos_token_id=self.pad_token_id,
                 pad_token_id=self.pad_token_id,
+                decoder_start_token_id=self.decoder_start_token_id,
             )
 
             return (
@@ -123,6 +127,39 @@ def prepare_config_and_inputs(self):
         def check_loss_output(self, result):
             self.parent.assertListEqual(list(result["loss"].size()), [])
 
+        def check_prepare_lm_labels_via_shift_left(
+            self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
+        ):
+            model = T5Model(config=config)
+            model.to(torch_device)
+            model.eval()
+
+            # make sure that lm_labels are correctly padded from the right
+            lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
+
+            # add casaul pad token mask
+            triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
+            lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
+            decoder_input_ids = model._shift_right(lm_labels)
+
+            for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
+                # first item
+                self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
+                if i < decoder_input_ids_slice.shape[-1]:
+                    if i < decoder_input_ids.shape[-1] - 1:
+                        # items before diagonal
+                        self.parent.assertListEqual(
+                            decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
+                        )
+                    # pad items after diagonal
+                    if i < decoder_input_ids.shape[-1] - 2:
+                        self.parent.assertListEqual(
+                            decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
+                        )
+                else:
+                    # all items after square
+                    self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
+
         def create_and_check_t5_model(
             self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
         ):
@@ -197,6 +234,10 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
+    def test_shift_right(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
+
     def test_t5_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_t5_model(*config_and_inputs)

tests/test_modeling_tf_t5.py

@@ -52,7 +52,7 @@ def __init__(
             relative_attention_num_buckets=8,
             dropout_rate=0.1,
             initializer_factor=0.002,
-            eos_token_ids=[1],
+            eos_token_id=1,
             pad_token_id=0,
             scope=None,
         ):
@@ -71,7 +71,7 @@ def __init__(
             self.relative_attention_num_buckets = relative_attention_num_buckets
             self.dropout_rate = dropout_rate
             self.initializer_factor = initializer_factor
-            self.eos_token_ids = eos_token_ids
+            self.eos_token_id = eos_token_id
             self.pad_token_id = pad_token_id
             self.scope = scope
 
@@ -97,7 +97,7 @@ def prepare_config_and_inputs(self):
                 relative_attention_num_buckets=self.relative_attention_num_buckets,
                 dropout_rate=self.dropout_rate,
                 initializer_factor=self.initializer_factor,
-                eos_token_ids=self.eos_token_ids,
+                eos_token_id=self.eos_token_id,
                 bos_token_id=self.pad_token_id,
                 pad_token_id=self.pad_token_id,
             )