[Design proposal] Fix EncoderDecoderModel classes to be more like BART and T5

src/transformers/models/encoder_decoder/modeling_encoder_decoder.py

@@ -14,9 +14,12 @@
 # limitations under the License.
 """ Classes to support Encoder-Decoder architectures """
 
-
+import warnings
 from typing import Optional
 
+import torch
+from torch.nn import CrossEntropyLoss
+
 from ...configuration_utils import PretrainedConfig
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
 from ...modeling_outputs import Seq2SeqLMOutput
@@ -29,6 +32,13 @@
 
 _CONFIG_FOR_DOC = "EncoderDecoderConfig"
 
+DEPRECATION_WARNING = (
+    "Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the "
+    "encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning "
+    "a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no "
+    "need to pass them yourself anymore."
+)
+
 ENCODER_DECODER_START_DOCSTRING = r"""
     This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
     encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
@@ -136,6 +146,24 @@
 """
 
 
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+    """
+    Shift input ids one token to the right.
+    """
+    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+    if decoder_start_token_id is None:
+        raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
+    shifted_input_ids[:, 0] = decoder_start_token_id
+
+    if pad_token_id is None:
+        raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
+    # replace possible -100 values in labels by `pad_token_id`
+    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+    return shifted_input_ids
+
+
 @add_start_docstrings(ENCODER_DECODER_START_DOCSTRING)
 class EncoderDecoderModel(PreTrainedModel):
     r"""
@@ -434,14 +462,18 @@ def forward(
 
         encoder_hidden_states = encoder_outputs[0]
 
+        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+            decoder_input_ids = shift_tokens_right(
+                labels, self.config.pad_token_id, self.config.decoder_start_token_id
+            )
+
         # Decode
         decoder_outputs = self.decoder(
             input_ids=decoder_input_ids,
             attention_mask=decoder_attention_mask,
             encoder_hidden_states=encoder_hidden_states,
             encoder_attention_mask=attention_mask,
             inputs_embeds=decoder_inputs_embeds,
-            labels=labels,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             use_cache=use_cache,
@@ -450,11 +482,22 @@ def forward(
             **kwargs_decoder,
         )
 
+        # Compute loss independent from decoder (as some shift the logits inside them)
+        loss = None
+        if labels is not None:
+            warnings.warn(DEPRECATION_WARNING, FutureWarning)
+            logits = decoder_outputs.logits if return_dict else decoder_outputs[1]
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))
+
         if not return_dict:
-            return decoder_outputs + encoder_outputs
+            if loss is not None:
+                return (loss,) + decoder_outputs + encoder_outputs
+            else:
+                return decoder_outputs + encoder_outputs
 
         return Seq2SeqLMOutput(
-            loss=decoder_outputs.loss,
+            loss=loss,
             logits=decoder_outputs.logits,
             past_key_values=decoder_outputs.past_key_values,
             decoder_hidden_states=decoder_outputs.hidden_states,
@@ -465,6 +508,9 @@ def forward(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
     def prepare_inputs_for_generation(
         self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
     ):

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -17,7 +17,9 @@
 
 from typing import Optional
 
+import torch
 from torch import nn
+from torch.nn import CrossEntropyLoss
 
 from ...configuration_utils import PretrainedConfig
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
@@ -149,6 +151,25 @@
 """
 
 
+# Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+    """
+    Shift input ids one token to the right.
+    """
+    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+    if decoder_start_token_id is None:
+        raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
+    shifted_input_ids[:, 0] = decoder_start_token_id
+
+    if pad_token_id is None:
+        raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
+    # replace possible -100 values in labels by `pad_token_id`
+    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+    return shifted_input_ids
+
+
 @add_start_docstrings(SPEECH_ENCODER_DECODER_START_DOCSTRING)
 class SpeechEncoderDecoderModel(PreTrainedModel):
     r"""
@@ -467,6 +488,11 @@ def forward(
         else:
             encoder_attention_mask = None
 
+        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+            decoder_input_ids = shift_tokens_right(
+                labels, self.config.pad_token_id, self.config.decoder_start_token_id
+            )
+
         # Decode
         decoder_outputs = self.decoder(
             input_ids=decoder_input_ids,
@@ -482,20 +508,34 @@ def forward(
             **kwargs_decoder,
         )
 
+        # Compute loss independent from decoder (as some shift the logits inside them)
+        loss = None
+        if labels is not None:
+            logits = decoder_outputs.logits if return_dict else decoder_outputs[1]
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))
+
         if not return_dict:
-            return decoder_outputs + encoder_outputs
+            if loss is not None:
+                return (loss,) + decoder_outputs + encoder_outputs
+            else:
+                return decoder_outputs + encoder_outputs
 
         return Seq2SeqLMOutput(
+            loss=loss,
             logits=decoder_outputs.logits,
             past_key_values=decoder_outputs.past_key_values,
             decoder_hidden_states=decoder_outputs.hidden_states,
             decoder_attentions=decoder_outputs.attentions,
             cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_hidden_states,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
             encoder_hidden_states=encoder_outputs.hidden_states,
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
     def prepare_inputs_for_generation(
         self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
     ):

src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py

@@ -17,7 +17,9 @@
 
 from typing import Optional
 
+import torch
 from torch import nn
+from torch.nn import CrossEntropyLoss
 
 from ...configuration_utils import PretrainedConfig
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
@@ -29,6 +31,25 @@
 from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
 
 
+# Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+    """
+    Shift input ids one token to the right.
+    """
+    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+    if decoder_start_token_id is None:
+        raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
+    shifted_input_ids[:, 0] = decoder_start_token_id
+
+    if pad_token_id is None:
+        raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
+    # replace possible -100 values in labels by `pad_token_id`
+    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+    return shifted_input_ids
+
+
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "VisionEncoderDecoderConfig"
@@ -448,14 +469,18 @@ def forward(
         # else:
         encoder_attention_mask = None
 
+        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+            decoder_input_ids = shift_tokens_right(
+                labels, self.config.pad_token_id, self.config.decoder_start_token_id
+            )
+
         # Decode
         decoder_outputs = self.decoder(
             input_ids=decoder_input_ids,
             attention_mask=decoder_attention_mask,
             encoder_hidden_states=encoder_hidden_states,
             encoder_attention_mask=encoder_attention_mask,
             inputs_embeds=decoder_inputs_embeds,
-            labels=labels,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             use_cache=use_cache,
@@ -464,21 +489,34 @@ def forward(
             **kwargs_decoder,
         )
 
+        # Compute loss independent from decoder (as some shift the logits inside them)
+        loss = None
+        if labels is not None:
+            logits = decoder_outputs.logits if return_dict else decoder_outputs[1]
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))
+
         if not return_dict:
-            return decoder_outputs + encoder_outputs
+            if loss is not None:
+                return (loss,) + decoder_outputs + encoder_outputs
+            else:
+                return decoder_outputs + encoder_outputs
 
         return Seq2SeqLMOutput(
-            loss=decoder_outputs.loss,
+            loss=loss,
             logits=decoder_outputs.logits,
             past_key_values=decoder_outputs.past_key_values,
             decoder_hidden_states=decoder_outputs.hidden_states,
             decoder_attentions=decoder_outputs.attentions,
             cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_hidden_states,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
             encoder_hidden_states=encoder_outputs.hidden_states,
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
     def prepare_inputs_for_generation(
         self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
     ):

src/transformers/trainer_seq2seq.py

@@ -164,9 +164,15 @@ def prediction_step(
             "synced_gpus": True if is_deepspeed_zero3_enabled() else False,
         }
 
+        if self.tokenizer is not None:
+            generation_inputs = {k: v for k, v in inputs.items() if k in self.tokenizer.model_input_names}
+            # very ugly hack to make it work
+            generation_inputs["input_ids"] = generation_inputs.pop(self.tokenizer.model_input_names[0])
+        else:
+            generation_inputs = inputs["input_ids"]
+
         generated_tokens = self.model.generate(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
+            **generation_inputs,
             **gen_kwargs,
         )
         # in case the batch is shorter than max length, the output should be padded
@@ -197,15 +203,16 @@ def prediction_step(
         return (loss, generated_tokens, labels)
 
     def _pad_tensors_to_max_len(self, tensor, max_length):
-        if self.tokenizer is None:
-            raise ValueError(
-                f"Tensor need to be padded to `max_length={max_length}` but no tokenizer was passed when creating "
-                "this `Trainer`. Make sure to create your `Trainer` with the appropriate tokenizer."
+        if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
+            # If PAD token is not defined at least EOS token has to be defined
+            pad_token_id = (
+                self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
             )
-        # If PAD token is not defined at least EOS token has to be defined
-        pad_token_id = (
-            self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
-        )
+        else:
+            if self.model.config.pad_token_id is not None:
+                pad_token_id = self.model.config.pad_token_id
+            else:
+                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
 
         padded_tensor = pad_token_id * torch.ones(
             (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device