syn with main

fastvideo/models/hunyuan/modules/attenion.py

@@ -83,16 +83,16 @@ def parallel_attention(q, k, v, img_q_len, img_kv_len, text_mask, mask_strategy=
     encoder_sequence_length = encoder_query.size(1)
 
     if mask_strategy[0] is not None:
-        query = torch.cat([tile(query, nccl_info.sp_size), encoder_query], dim=1).transpose(1, 2)
-        key = torch.cat([tile(key, nccl_info.sp_size), encoder_key], dim=1).transpose(1, 2)
-        value = torch.cat([tile(value, nccl_info.sp_size), encoder_value], dim=1).transpose(1, 2)
+        query = torch.cat([tile(query, nccl_info.sp_size), encoder_query], dim=1).transpose(1, 2).contiguous()
+        key = torch.cat([tile(key, nccl_info.sp_size), encoder_key], dim=1).transpose(1, 2).contiguous()
+        value = torch.cat([tile(value, nccl_info.sp_size), encoder_value], dim=1).transpose(1, 2).contiguous()
 
         head_num = query.size(1)
         current_rank = nccl_info.rank_within_group
         start_head = current_rank * head_num
         windows = [mask_strategy[head_idx + start_head] for head_idx in range(head_num)]
 
-        hidden_states = sliding_tile_attention(query, key, value, windows, text_length).transpose(1, 2)
+        hidden_states = sliding_tile_attention(query, key, value, windows, text_length).transpose(1, 2).contiguous()
     else:
         query = torch.cat([query, encoder_query], dim=1)
         key = torch.cat([key, encoder_key], dim=1)