Change BartLearnedPositionalEmbedding's forward method signature to support...

Change BartLearnedPositionalEmbedding's forward method signature to support Opacus training (#18486)

* changing BartLearnedPositionalEmbedding forward signature and references to it

* removing debugging dead code (thanks style checker)

* blackened modeling_bart file

* removing copy inconsistencies via make fix-copies

* changing references to copied signatures in Bart variants

* make fix-copies once more

* using expand over repeat (thanks @michaelbenayoun)

* expand instead of repeat for all model copies
Co-authored-by: Daniel Jones <jonesdaniel@microsoft.com>

src/transformers/models/bart/modeling_bart.py

@@ -128,12 +128,14 @@ class BartLearnedPositionalEmbedding(nn.Embedding):
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
+    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        bsz, seq_len = input_ids.shape[:2]
         positions = torch.arange(
             past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
+        ).expand(bsz, -1)
+
         return super().forward(positions + self.offset)
 
 
@@ -788,17 +790,17 @@ class BartEncoder(BartPretrainedModel):
         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:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
+            input = input_ids
+            input_ids = input_ids.view(-1, input_ids.shape[-1])
         elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
 
-        embed_pos = self.embed_positions(input_shape)
+        embed_pos = self.embed_positions(input)
 
         hidden_states = inputs_embeds + embed_pos
         hidden_states = self.layernorm_embedding(hidden_states)
@@ -1015,10 +1017,12 @@ class BartDecoder(BartPretrainedModel):
         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:
-            input_shape = input_ids.size()
+            input = input_ids
+            input_shape = input.shape
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
@@ -1026,7 +1030,7 @@ class BartDecoder(BartPretrainedModel):
         past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
 
         if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+            inputs_embeds = self.embed_tokens(input) * self.embed_scale
 
         attention_mask = self._prepare_decoder_attention_mask(
             attention_mask, input_shape, inputs_embeds, past_key_values_length
@@ -1038,7 +1042,7 @@ class BartDecoder(BartPretrainedModel):
             encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
 
         # embed positions
-        positions = self.embed_positions(input_shape, past_key_values_length)
+        positions = self.embed_positions(input, past_key_values_length)
 
         hidden_states = inputs_embeds + positions
         hidden_states = self.layernorm_embedding(hidden_states)

src/transformers/models/mbart/modeling_mbart.py

@@ -134,12 +134,14 @@ class MBartLearnedPositionalEmbedding(nn.Embedding):
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
+    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        bsz, seq_len = input_ids.shape[:2]
         positions = torch.arange(
             past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
+        ).expand(bsz, -1)
+
         return super().forward(positions + self.offset)
 
 
@@ -783,17 +785,18 @@ class MBartEncoder(MBartPreTrainedModel):
         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:
-            input_shape = input_ids.size()
+            input = input_ids
+            input_shape = input.shape
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
 
-        embed_pos = self.embed_positions(input_shape)
+        embed_pos = self.embed_positions(input)
 
         hidden_states = inputs_embeds + embed_pos
         hidden_states = self.layernorm_embedding(hidden_states)
@@ -1013,10 +1016,12 @@ class MBartDecoder(MBartPreTrainedModel):
         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:
-            input_shape = input_ids.size()
+            input = input_ids
+            input_shape = input.size()
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
@@ -1036,7 +1041,7 @@ class MBartDecoder(MBartPreTrainedModel):
             encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
 
         # embed positions
-        positions = self.embed_positions(input_shape, past_key_values_length)
+        positions = self.embed_positions(input, past_key_values_length)
 
         hidden_states = inputs_embeds + positions
         hidden_states = self.layernorm_embedding(hidden_states)

src/transformers/models/mvp/modeling_mvp.py

@@ -134,12 +134,14 @@ class MvpLearnedPositionalEmbedding(nn.Embedding):
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
+    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        bsz, seq_len = input_ids.shape[:2]
         positions = torch.arange(
             past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
+        ).expand(bsz, -1)
+
         return super().forward(positions + self.offset)
 
 
@@ -895,17 +897,19 @@ class MvpEncoder(MvpPreTrainedModel):
         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:
-            input_shape = input_ids.size()
+            input = input_ids
+            input_shape = input.shape
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
 
-        embed_pos = self.embed_positions(input_shape)
+        embed_pos = self.embed_positions(input)
 
         hidden_states = inputs_embeds + embed_pos
         hidden_states = self.layernorm_embedding(hidden_states)
@@ -1144,10 +1148,12 @@ class MvpDecoder(MvpPreTrainedModel):
         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:
-            input_shape = input_ids.size()
+            input = input_ids
+            input_shape = input_ids.shape
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
@@ -1167,7 +1173,7 @@ class MvpDecoder(MvpPreTrainedModel):
             encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
 
         # embed positions
-        positions = self.embed_positions(input_shape, past_key_values_length)
+        positions = self.embed_positions(input, past_key_values_length)
 
         hidden_states = inputs_embeds + positions
         hidden_states = self.layernorm_embedding(hidden_states)

src/transformers/models/plbart/modeling_plbart.py

@@ -131,12 +131,14 @@ class PLBartLearnedPositionalEmbedding(nn.Embedding):
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
+    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        bsz, seq_len = input_ids.shape[:2]
         positions = torch.arange(
             past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
+        ).expand(bsz, -1)
+
         return super().forward(positions + self.offset)
 
 
@@ -759,17 +761,17 @@ class PLBartEncoder(PLBartPreTrainedModel):
         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:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
+            input = input_ids
+            input_ids = input_ids.view(-1, input_ids.shape[-1])
         elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
 
-        embed_pos = self.embed_positions(input_shape)
+        embed_pos = self.embed_positions(input)
 
         hidden_states = inputs_embeds + embed_pos
         hidden_states = self.layernorm_embedding(hidden_states)
@@ -987,10 +989,12 @@ class PLBartDecoder(PLBartPreTrainedModel):
         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:
-            input_shape = input_ids.size()
+            input = input_ids
+            input_shape = input.shape
             input_ids = input_ids.view(-1, input_shape[-1])
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
@@ -998,7 +1002,7 @@ class PLBartDecoder(PLBartPreTrainedModel):
         past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
 
         if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+            inputs_embeds = self.embed_tokens(input) * self.embed_scale
 
         attention_mask = self._prepare_decoder_attention_mask(
             attention_mask, input_shape, inputs_embeds, past_key_values_length
@@ -1010,7 +1014,7 @@ class PLBartDecoder(PLBartPreTrainedModel):
             encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
 
         # embed positions
-        positions = self.embed_positions(input_shape, past_key_values_length)
+        positions = self.embed_positions(input, past_key_values_length)
 
         hidden_states = inputs_embeds + positions
         hidden_states = self.layernorm_embedding(hidden_states)

src/transformers/models/trocr/modeling_trocr.py

@@ -87,12 +87,14 @@ class TrOCRLearnedPositionalEmbedding(nn.Embedding):
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
+    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        bsz, seq_len = input_ids.shape[:2]
         positions = torch.arange(
             past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
+        ).expand(bsz, -1)
+
         return super().forward(positions + self.offset)
 
 
@@ -626,10 +628,11 @@ class TrOCRDecoder(TrOCRPreTrainedModel):
         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:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
+            input = input_ids
+            input_ids = input_ids.view(-1, input.shape[-1])
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
         else:
             raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
@@ -640,7 +643,7 @@ class TrOCRDecoder(TrOCRPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
 
         if self.config.use_learned_position_embeddings:
-            embed_pos = self.embed_positions(input_shape, past_key_values_length=past_key_values_length)
+            embed_pos = self.embed_positions(input, past_key_values_length=past_key_values_length)
         else:
             embed_pos = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
 
@@ -651,6 +654,8 @@ class TrOCRDecoder(TrOCRPreTrainedModel):
 
         hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
 
+        input_shape = input.shape
+
         attention_mask = self._prepare_decoder_attention_mask(
             attention_mask, input_shape, inputs_embeds, past_key_values_length
         )