Dcu: format code (#588)

lightx2v_platform/base/dcu.py

@@ -12,6 +12,7 @@ class DcuDevice:
     DCU uses ROCm which provides CUDA-compatible APIs through HIP.
     Most PyTorch operations work transparently through the ROCm backend.
     """
+
     name = "dcu"
 
     @staticmethod
@@ -52,4 +53,3 @@ class DcuDevice:
         # RCCL is compatible with NCCL backend
         dist.init_process_group(backend="nccl")
         torch.cuda.set_device(dist.get_rank())
-

lightx2v_platform/ops/attn/dcu/__init__.py

 from .flash_attn import *
-

lightx2v_platform/ops/attn/dcu/flash_attn.py

@@ -28,7 +28,6 @@ class FlashAttnDcu(AttnWeightTemplate):
     - PyTorch: 2.7.1
     - Python: 3.10
     - Flash Attention: 2.6.1 (ROCm)
-    
     Reference: https://developer.sourcefind.cn/codes/modelzoo/wan2.1_pytorch/-/blob/master/wan/modules/attention.py
     """
 
@@ -56,7 +55,6 @@ class FlashAttnDcu(AttnWeightTemplate):
     ):
         """
         Execute Flash Attention computation.
-        
         Args:
             q: [B, Lq, Nq, C1] Query tensor
             k: [B, Lk, Nk, C1] Key tensor
@@ -68,7 +66,6 @@ class FlashAttnDcu(AttnWeightTemplate):
             causal: Whether to apply causal mask
             window_size: Sliding window size tuple (left, right)
             deterministic: Whether to use deterministic algorithm
-            
         Returns:
             Output tensor: [B, Lq, Nq, C2]
         """
@@ -129,14 +126,12 @@ class FlashAttnDcu(AttnWeightTemplate):
     def _sdpa_fallback(self, q, k, v, causal=False, dropout_p=0.0):
         """
         Fallback to PyTorch Scaled Dot Product Attention.
-        
         Args:
             q: [B, Lq, Nq, C] Query tensor
             k: [B, Lk, Nk, C] Key tensor
             v: [B, Lk, Nk, C] Value tensor
             causal: Whether to apply causal mask
             dropout_p: Dropout probability
-            
         Returns:
             Output tensor: [B, Lq, Nq, C]
         """
@@ -145,10 +140,7 @@ class FlashAttnDcu(AttnWeightTemplate):
         k = k.transpose(1, 2)
         v = v.transpose(1, 2)
 
-        out = torch.nn.functional.scaled_dot_product_attention(
-            q, k, v, attn_mask=None, is_causal=causal, dropout_p=dropout_p
-        )
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=causal, dropout_p=dropout_p)
 
         # Transpose back to [B, Lq, Nq, C]
         return out.transpose(1, 2).contiguous()
-