complete t5 more output

paddlenlp/transformers/model_outputs.py

@@ -724,3 +724,123 @@ class CausalLMOutputWithCrossAttentions(ModelOutput):
     hidden_states: Optional[Tuple[paddle.Tensor]] = None
     attentions: Optional[Tuple[paddle.Tensor]] = None
     cross_attentions: Optional[Tuple[paddle.Tensor]] = None
+
+
+@dataclass
+class Seq2SeqModelOutput(ModelOutput):
+    """
+    Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
+    decoding.
+
+    Args:
+        last_hidden_state (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the decoder of the model.
+
+            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+            hidden_size)` is output.
+        past_key_values (`tuple(tuple(paddle.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(paddle.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+        decoder_hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `paddle.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
+        decoder_attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        cross_attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+        encoder_last_hidden_state (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `paddle.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
+        encoder_attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    last_hidden_state: paddle.Tensor = None
+    past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None
+    decoder_hidden_states: Optional[Tuple[paddle.Tensor]] = None
+    decoder_attentions: Optional[Tuple[paddle.Tensor]] = None
+    cross_attentions: Optional[Tuple[paddle.Tensor]] = None
+    encoder_last_hidden_state: Optional[paddle.Tensor] = None
+    encoder_hidden_states: Optional[Tuple[paddle.Tensor]] = None
+    encoder_attentions: Optional[Tuple[paddle.Tensor]] = None   
+
+@dataclass
+class Seq2SeqLMOutput(ModelOutput):
+    """
+    Base class for sequence-to-sequence language models outputs.
+
+    Args:
+        loss (`paddle.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss.
+        logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(paddle.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(paddle.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+        decoder_hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `paddle.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+        decoder_attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        cross_attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+        encoder_last_hidden_state (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `paddle.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+        encoder_attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    loss: Optional[paddle.Tensor] = None
+    logits: paddle.Tensor = None
+    past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None
+    decoder_hidden_states: Optional[Tuple[paddle.Tensor]] = None
+    decoder_attentions: Optional[Tuple[paddle.Tensor]] = None
+    cross_attentions: Optional[Tuple[paddle.Tensor]] = None
+    encoder_last_hidden_state: Optional[paddle.Tensor] = None
+    encoder_hidden_states: Optional[Tuple[paddle.Tensor]] = None
+    encoder_attentions: Optional[Tuple[paddle.Tensor]] = None

paddlenlp/transformers/t5/modeling.py

@@ -26,6 +26,13 @@
 
 from ..model_utils import PretrainedModel, register_base_model
 from ..nezha.modeling import ACT2FN
+from ..model_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    Seq2SeqModelOutput,
+    Seq2SeqLMOutput,
+    BaseModelOutput,
+    ModelOutput,
+)
 
 __all__ = [
     'T5Model', "T5PretrainedModel", 'T5ForConditionalGeneration',
@@ -944,7 +951,8 @@ def forward(self,
                 cache=None,
                 use_cache=False,
                 output_attentions=False,
-                output_hidden_states=False):
+                output_hidden_states=False,
+                return_dict=False):
         assert input_ids is not None, "input_ids can not be None"
         input_shape = input_ids.shape
         input_ids = input_ids.reshape(shape=[-1, input_shape[-1]])
@@ -1051,13 +1059,22 @@ def forward(self,
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states, )
 
-        return tuple(v for v in [
-            hidden_states,
-            present_key_value_states,
-            all_hidden_states,
-            all_attentions,
-            all_cross_attentions,
-        ] if v is not None)
+        if not return_dict:
+            return tuple(v for v in [
+                hidden_states,
+                present_key_value_states,
+                all_hidden_states,
+                all_attentions,
+                all_cross_attentions,
+            ] if v is not None)
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=present_key_value_states,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+            cross_attentions=all_cross_attentions,
+        )
 
     def get_extended_attention_mask(self, attention_mask, input_shape):
         if attention_mask.ndim == 3:
@@ -1293,7 +1310,8 @@ def forward(self,
                 cache=None,
                 use_cache=True,
                 output_attentions=False,
-                output_hidden_states=False):
+                output_hidden_states=False,
+                return_dict=False):
         r"""
         The T5Model forward method, overrides the `__call__()` special method.
 
@@ -1343,8 +1361,16 @@ def forward(self,
             output_hidden_states (bool, optional):
                 Whether or not to return the output of all hidden layers.
                 Defaults to `False`.
+            return_dict (bool, optional):
+                Whether or not to return a class:`~paddlenlp.transformers.model_outputs.Seq2SeqModelOutput`. If `False`, the output
+                will be a tuple of tensors. Defaults to `False`.
+
 
         Returns:
+            An instance of :class:`~paddlenlp.transformers.model_outputs.Seq2SeqModelOutput` if `return_dict=True`. 
+            Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of
+            :class:`~paddlenlp.transformers.model_outputs.Seq2SeqModelOutput`.
+
             tuple: Returns tuple (`last_hidden_state`, `cache`, `decoder_hidden_states`, `decoder_attentions`,
             `cross_attentions`, `encoder_last_hidden_state`, `encoder_hidden_states`, `encoder_attentions`)
 
@@ -1419,8 +1445,10 @@ def forward(self,
                 input_ids=input_ids,
                 attention_mask=attention_mask,
                 output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states)
-
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict)
+        elif return_dict and not isinstance(encoder_output, ModelOutput):
+            encoder_output = convert_encoder_output(encoder_output)
         hidden_states = encoder_output[0]
 
         # Decode
@@ -1432,9 +1460,22 @@ def forward(self,
             encoder_attention_mask=attention_mask,
             use_cache=use_cache,
             output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states)
-
-        return decoder_outputs + encoder_output
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict)
+
+        if not return_dict:
+            return decoder_outputs + encoder_output
+
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            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_output.last_hidden_state,
+            encoder_hidden_states=encoder_output.hidden_states,
+            encoder_attentions=encoder_output.attentions,
+        )
 
 
 class T5ForConditionalGeneration(T5PretrainedModel):
@@ -1490,7 +1531,8 @@ def forward(self,
                 labels=None,
                 use_cache=True,
                 output_attentions=False,
-                output_hidden_states=False):
+                output_hidden_states=False,
+                return_dict=False):
         r"""
 
         Args:
@@ -1518,8 +1560,14 @@ def forward(self,
                 See :class:`T5Model`.
             output_hidden_states (bool, optional):
                 See :class:`T5Model`.
+            return_dict (bool, optional):
+                See :class:`T5Model`.
 
         Returns:
+            An instance of :class:`~paddlenlp.transformers.model_outputs.Seq2SeqLMOutput` if `return_dict=True`. 
+            Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of
+            :class:`~paddlenlp.transformers.model_outputs.Seq2SeqLMOutput`. 
+
             tuple: Returns tuple (`loss`, `logits`, `cache`, `decoder_hidden_states`, `decoder_attentions`,
             `cross_attentions`, `encoder_last_hidden_state`, `encoder_hidden_states`, `encoder_attentions`)
 
@@ -1581,12 +1629,13 @@ def forward(self,
                 input_ids=input_ids,
                 attention_mask=attention_mask,
                 output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states)
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict)
 
-        if isinstance(encoder_output, (tuple, list)):
-            hidden_states = encoder_output[0]
-        else:
-            hidden_states = encoder_output
+        # encoder_output could be a Tensor, tuple or ModelOutput
+        if isinstance(encoder_output, paddle.Tensor):
+            encoder_output = (encoder_output, )
+        hidden_states = encoder_output[0]
 
         if labels is not None and decoder_input_ids is None:
             # get decoder inputs from shifting lm labels to the right
@@ -1610,7 +1659,8 @@ def forward(self,
             encoder_attention_mask=attention_mask,
             use_cache=use_cache,
             output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states)
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict)
 
         sequence_output = decoder_outputs[0]
 
@@ -1631,11 +1681,26 @@ def forward(self,
             loss = loss_fct(lm_logits.reshape(shape=[-1, lm_logits.shape[-1]]),
                             labels.flatten())
 
-        if not isinstance(encoder_output, (list, tuple)):
-            encoder_output = (encoder_output, )
-
-        output = (lm_logits, ) + decoder_outputs[1:] + encoder_output
-        return ((loss, ) + output) if loss is not None else output
+        if not return_dict:
+            # 元组相加
+            output = (lm_logits, ) + decoder_outputs[1:] + encoder_output[0:]
+
+            return ((loss, ) + output) if loss is not None else output
+
+        if not isinstance(encoder_output, ModelOutput):
+            encoder_output = convert_encoder_output(encoder_output)
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_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_output.last_hidden_state,
+            encoder_hidden_states=encoder_output.hidden_states,
+            encoder_attentions=encoder_output.attentions,
+        )
 
     @staticmethod
     def prepare_input_ids_for_generation(bos_token_id, encoder_output=None):
@@ -1817,6 +1882,7 @@ def forward(
         use_cache: Optional[bool] = False,
         output_attentions: Optional[bool] = False,
         output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = False,
     ):
         encoder_outputs = self.encoder(
             input_ids=input_ids,
@@ -1827,9 +1893,21 @@ def forward(
             use_cache=use_cache,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
-        )
+            return_dict=return_dict)
 
         return encoder_outputs
 
 
 T5EncoderModel.base_model_class = T5EncoderModel
+
+
+def convert_encoder_output(encoder_output):
+    """
+    Convert encoder_output which type is tuple to an instance of BaseModelOutput.
+    args: encoder_output = (last_hidden_state, hidden_states, attentions)
+    """
+    return BaseModelOutput(
+        last_hidden_state=encoder_output[0],
+        hidden_states=encoder_output[1] if len(encoder_output) > 1 else None,
+        attentions=encoder_output[2] if len(encoder_output) > 2 else None,
+    )