fix copy

src/transformers/models/jetmoe/modeling_jetmoe.py

@@ -1215,37 +1215,27 @@ def _update_causal_mask(
                 else past_seen_tokens + sequence_length + 1
             )
 
-        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
-        if attention_mask is not None:
-            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-            if attention_mask.dim() == 2:
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                 mask_length = attention_mask.shape[-1]
                 padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
                 padding_mask = padding_mask == 0
                 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                     padding_mask, min_dtype
                 )
-            elif attention_mask.dim() == 4:
-                # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
-                # cache. In that case, the 4D attention mask attends to the newest tokens only.
-                if attention_mask.shape[-2] < cache_position[0] + sequence_length:
-                    logger.warning_once(
-                        "Passing a 4d mask shorter than the input length is deprecated and will be removed in "
-                        "transformers v4.42.0"
-                    )
-                    offset = cache_position[0]
-                else:
-                    offset = 0
-                mask_shape = attention_mask.shape
-                mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
-                causal_mask[
-                    : mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]
-                ] = mask_slice
-
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None