[NPU]:Support USP feature in NPU

diffsynth/utils/xfuser/xdit_context_parallel.py

@@ -1,10 +1,13 @@
 import torch
 from typing import Optional
 from einops import rearrange
+from yunchang.kernels import AttnType
 from xfuser.core.distributed import (get_sequence_parallel_rank,
                                      get_sequence_parallel_world_size,
                                      get_sp_group)
 from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+
+from ... import IS_NPU_AVAILABLE
 from ...core.device import parse_nccl_backend, parse_device_type
 
 
@@ -30,13 +33,16 @@ def sinusoidal_embedding_1d(dim, position):
 def pad_freqs(original_tensor, target_len):
     seq_len, s1, s2 = original_tensor.shape
     pad_size = target_len - seq_len
+    original_tensor_device = original_tensor.device
+    if original_tensor.device == "npu":
+        original_tensor = original_tensor.cpu()
     padding_tensor = torch.ones(
         pad_size,
         s1,
         s2,
         dtype=original_tensor.dtype,
         device=original_tensor.device)
-    padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
+    padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0).to(device=original_tensor_device)
     return padded_tensor
 
 def rope_apply(x, freqs, num_heads):
@@ -133,7 +139,12 @@ def usp_attn_forward(self, x, freqs):
     k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads)
     v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads)
 
-    x = xFuserLongContextAttention()(
+    attn_type = AttnType.FA
+    ring_impl_type = "basic"
+    if IS_NPU_AVAILABLE:
+        attn_type = AttnType.NPU
+        ring_impl_type = "basic_npu"
+    x = xFuserLongContextAttention(attn_type=attn_type, ring_impl_type=ring_impl_type)(
         None,
         query=q,
         key=k,

docs/en/Pipeline_Usage/GPU_support.md

@@ -58,6 +58,14 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=5)
 ```
 
+#### USP(Unified Sequence Parallel)
+If you want to use this feature on NPU, please install additional third-party libraries as follows:
+```shell
+pip install git+https://github.com/feifeibear/long-context-attention.git
+pip install git+https://github.com/xdit-project/xDiT.git
+```
+
+
 ### Training
 NPU startup script samples have been added for each type of model,the scripts are stored in the `examples/xxx/special/npu_training`, for example `examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh`.
 

docs/zh/Pipeline_Usage/GPU_support.md

@@ -58,6 +58,13 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=5)
 ```
 
+#### USP(Unified Sequence Parallel)
+如果想要在NPU上使用该特性，请通过如下方式安装额外的第三方库：
+```shell
+pip install git+https://github.com/feifeibear/long-context-attention.git
+pip install git+https://github.com/xdit-project/xDiT.git
+```
+
 ### 训练
 当前已为每类模型添加NPU的启动脚本样例，脚本存放在`examples/xxx/special/npu_training`目录下，例如 `examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh`。