Multi-dim prompt tuning mask padding

src/adapters/models/bert/modeling_bert.py

@@ -157,6 +157,8 @@ def forward(
         past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
     ) -> Tuple[torch.Tensor]:
+        attention_mask = prefix_attention_mask(attention_mask, [2, 3])  # type: ignore
+
         if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
             # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once implemented.
             logger.warning_once(

src/adapters/utils.py

@@ -865,7 +865,7 @@ def get_adapter_info(adapter_id: str, source: str = "ah") -> Optional[AdapterInf
         raise ValueError("Please specify either 'ah' or 'hf' as source.")
 
 
-def prefix_attention_mask(attention_mask, dim: int = 3, prefix_value: int = 0):
+def prefix_attention_mask(attention_mask, dim: Union[int, List[int]] = 3, prefix_value: int = 0):
     """
     Adds a prefix to an attention mask. The length of the prefix is determined by the `prefix_attention_mask_length`
     attribute in the ForwardContext.
@@ -890,18 +890,21 @@ def prefix_attention_mask(attention_mask, dim: int = 3, prefix_value: int = 0):
         and forward_context is not None
         and getattr(forward_context, "prompt_tokens_length", None) is not None
     ):
-        # Create a tensor of ones with the desired shape
-        ones_shape = list(attention_mask.shape)
-        ones_shape[dim] = forward_context.prompt_tokens_length
-
-        prefix_attention_mask = torch.full(
-            ones_shape,
-            prefix_value,
-            dtype=attention_mask.dtype,
-        ).to(attention_mask.device)
-
-        # Concatenate the prefix_attention_mask along the specified dimension
-        attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=dim)
+        if isinstance(dim, int):
+            dim = [dim]
+        for d in dim:
+            # Create a tensor of ones with the desired shape
+            ones_shape = list(attention_mask.shape)
+            ones_shape[d] = forward_context.prompt_tokens_length
+
+            prefix_attention_mask = torch.full(
+                ones_shape,
+                prefix_value,
+                dtype=attention_mask.dtype,
+            ).to(attention_mask.device)
+
+            # Concatenate the prefix_attention_mask along the specified dimension
+            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=d)
 
     return attention_mask