Merge branch 'huggingface:main' into t5x_to_pytorch

src/transformers/models/fsmt/modeling_fsmt.py

@@ -272,6 +272,18 @@
             If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
             don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
             `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
+            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
+            input (see `past_key_values`). This is useful if you want more control over how to convert
+            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
+
+            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
+            of `inputs_embeds`.
         use_cache (`bool`, *optional*, defaults to `True`):
             If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
             `past_key_values`).
@@ -470,6 +482,7 @@ def forward(
         self,
         input_ids: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: torch.Tensor = None,
         head_mask: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
         output_hidden_states: bool = False,
@@ -480,6 +493,8 @@ def forward(
             input_ids (`torch.LongTensor`): tokens in the source language of shape
                 *(batch, src_len)*
             attention_mask (`torch.LongTensor`): indicating which indices are padding tokens
+            inputs_embeds (`torch.FloatTensor`):
+                embedding vectors of shape *(batch, src_len, embed_dim)*
             head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
                 Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
 
@@ -499,8 +514,24 @@ def forward(
         if attention_mask is not None:
             attention_mask = invert_mask(attention_mask)
 
-        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-        embed_pos = self.embed_positions(input_ids)
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+            embed_pos = self.embed_positions(input_ids)
+        elif inputs_embeds is not None:
+            inputs_embeds = inputs_embeds * self.embed_scale
+
+            # We assume zeros hidden states correspond to padding tokens
+            # and create `position_ids` where inputs_embeds[:, :, 0] == 0
+            position_ids = inputs_embeds[:, :, 0].masked_fill(
+                inputs_embeds[:, :, 0].eq(0), self.embed_positions.padding_idx
+            )
+
+            embed_pos = self.embed_positions(position_ids)
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
         x = inputs_embeds + embed_pos
         x = nn.functional.dropout(x, p=self.dropout, training=self.training)
 
@@ -675,6 +706,7 @@ def forward(
         decoder_padding_mask: torch.Tensor,
         decoder_causal_mask: torch.Tensor,
         head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
         cross_attn_head_mask: Optional[torch.Tensor] = None,
         past_key_values: Optional[List[torch.FloatTensor]] = None,
         use_cache: bool = False,
@@ -717,15 +749,26 @@ def forward(
         if encoder_padding_mask is not None:
             encoder_padding_mask = invert_mask(encoder_padding_mask)
 
-        # embed positions
-        positions = self.embed_positions(input_ids)  # , use_cache=use_cache)
-
-        if use_cache:
-            input_ids = input_ids[:, -1:]
-            positions = positions[:, -1:]  # happens after we embed them
-            # assert input_ids.ne(self.padding_idx).any()
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            # embed positions
+            positions = self.embed_positions(input_ids)
+            if use_cache:
+                input_ids = input_ids[:, -1:]
+                positions = positions[:, -1:]  # happens after we embed them
+            x = self.embed_tokens(input_ids) * self.embed_scale
+        elif inputs_embeds is not None:
+            # We assume zeros hidden states correspond to padding tokens
+            # and create `position_ids` where inputs_embeds[:, :, 0] == 0
+            position_ids = inputs_embeds[:, :, 0].masked_fill(
+                inputs_embeds[:, :, 0].eq(0), self.embed_positions.padding_idx
+            )
+            positions = self.embed_positions(position_ids)
+            x = inputs_embeds * self.embed_scale
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
-        x = self.embed_tokens(input_ids) * self.embed_scale
         x += positions
         x = nn.functional.dropout(x, p=self.dropout, training=self.training)
 
@@ -1007,6 +1050,12 @@ def __init__(self, config: FSMTConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def get_encoder(self):
+        return self.encoder
+
+    def get_decoder(self):
+        return self.decoder
+
     @add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
@@ -1028,6 +1077,8 @@ def forward(
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
         if decoder_input_ids is None:
@@ -1041,7 +1092,7 @@ def forward(
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         # make masks if user doesn't supply
-        if not use_cache:
+        if not use_cache and input_ids is not None:
             decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_fsmt_decoder_inputs(
                 self.config,
                 input_ids,
@@ -1052,12 +1103,14 @@ def forward(
         else:
             decoder_padding_mask, causal_mask = None, None
 
-        assert decoder_input_ids is not None
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            raise ValueError("Make sure that `decoder_input_ids` or `decoder_inputs_embeds` are passed.")
 
         if encoder_outputs is None:
             encoder_outputs = self.encoder(
                 input_ids=input_ids,
                 attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
                 head_mask=head_mask,
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
@@ -1078,6 +1131,7 @@ def forward(
             attention_mask,
             decoder_padding_mask,
             decoder_causal_mask=causal_mask,
+            inputs_embeds=decoder_inputs_embeds,
             head_mask=decoder_head_mask,
             cross_attn_head_mask=cross_attn_head_mask,
             past_key_values=past_key_values,
@@ -1148,6 +1202,8 @@ def forward(
         cross_attn_head_mask: Optional[torch.Tensor] = None,
         encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
         past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        decoder_inputs_embeds: Optional[torch.Tensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
@@ -1170,8 +1226,10 @@ def forward(
 
         outputs = self.model(
             input_ids,
+            inputs_embeds=inputs_embeds,
             attention_mask=attention_mask,
             decoder_input_ids=decoder_input_ids,
+            decoder_inputs_embeds=decoder_inputs_embeds,
             encoder_outputs=encoder_outputs,
             decoder_attention_mask=decoder_attention_mask,
             head_mask=head_mask,
@@ -1248,6 +1306,9 @@ def _reorder_cache(past, beam_idx):
     def get_encoder(self):
         return self.model.encoder
 
+    def get_decoder(self):
+        return self.model.decoder
+
     def get_output_embeddings(self):
         return self.model.decoder.embed_tokens