[diffusion] fix: skip USP for cross-attention with replicated KV for wan (#19419)

Co-authored-by: Mick <mickjagger19@icloud.com>

python/sglang/multimodal_gen/runtime/layers/attention/layer.py

@@ -22,7 +22,6 @@ from sglang.multimodal_gen.runtime.layers.attention.backends.attention_backend i
 )
 from sglang.multimodal_gen.runtime.layers.attention.selector import get_attn_backend
 from sglang.multimodal_gen.runtime.layers.usp import (
-    _ulysses_input_split,
     _usp_input_all_to_all,
     _usp_output_all_to_all,
     ring_attn,
@@ -304,9 +303,17 @@ class USPAttention(nn.Module):
         supported_attention_backends: set[AttentionBackendEnum] | None = None,
         prefix: str = "",
         dropout_rate: float = 0.0,
-        is_cross_attention: bool = False,
+        skip_sequence_parallel: bool = False,
         **extra_impl_args,
     ) -> None:
+        """
+        Args:
+            skip_sequence_parallel:
+              when KV is replicated across all SP ranks (e.g. cross-attention to
+              text/image encoder outputs), the full USP pipeline is redundant:
+              each rank's local Q shard can attend directly to the locally-held
+              full KV without any collective communication.
+        """
         super().__init__()
         if softmax_scale is None:
             self.softmax_scale = head_size**-0.5
@@ -337,7 +344,8 @@ class USPAttention(nn.Module):
         self.dtype = dtype
         self.causal = causal
         self.dropout_p = dropout_rate
-        self.is_cross_attention = is_cross_attention
+
+        self.skip_sequence_parallel = skip_sequence_parallel
 
     def forward(
         self,
@@ -354,13 +362,16 @@ class USPAttention(nn.Module):
             q, k, v: [B, S_local, H, D]
 
         Note: Replicated tensors are not supported in this implementation.
+        When skip_sequence_parallel=True (set at construction time), all SP
+        communication is bypassed — use this for cross-attention where KV
+        content is replicated across ranks (distinct from replicated_k/v args).
         """
         assert (
             replicated_q is None and replicated_k is None and replicated_v is None
         ), "USPAttention does not support replicated_qkv."
         forward_context: ForwardContext = get_forward_context()
         ctx_attn_metadata = forward_context.attn_metadata
-        if get_sequence_parallel_world_size() == 1:
+        if self.skip_sequence_parallel or get_sequence_parallel_world_size() == 1:
             # No sequence parallelism, just run local attention.
             out = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
             return out
@@ -369,15 +380,11 @@ class USPAttention(nn.Module):
         if get_ulysses_parallel_world_size() > 1:
             # -> [B, S, H_local, D]
             q = _usp_input_all_to_all(q, head_dim=2)
-            if self.is_cross_attention:
-                k = _ulysses_input_split(k, dim=2)
-                v = _ulysses_input_split(v, dim=2)
-            else:
-                k = _usp_input_all_to_all(k, head_dim=2)
-                v = _usp_input_all_to_all(v, head_dim=2)
+            k = _usp_input_all_to_all(k, head_dim=2)
+            v = _usp_input_all_to_all(v, head_dim=2)
 
         # Ring Attention within subgroups or local attention
-        if get_ring_parallel_world_size() > 1 and not self.is_cross_attention:
+        if get_ring_parallel_world_size() > 1:
             out = ring_attn(
                 q,
                 k,

python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py

@@ -174,27 +174,20 @@ class WanSelfAttention(nn.Module):
             softmax_scale=None,
             causal=False,
             supported_attention_backends=supported_attention_backends,
-            is_cross_attention=is_cross_attention,
+            skip_sequence_parallel=is_cross_attention,
         )
 
     def forward(self, x: torch.Tensor, context: torch.Tensor, context_lens: int):
         r"""
         Args:
             x(Tensor): Shape [B, L, num_heads, C / num_heads]
-            seq_lens(Tensor): Shape [B]
-            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
-            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
         pass
 
 
 class WanT2VCrossAttention(WanSelfAttention):
     def __init__(self, *args, **kwargs):
-        super().__init__(
-            *args,
-            **kwargs,
-            is_cross_attention=True,
-        )
+        super().__init__(*args, **kwargs, is_cross_attention=True)
 
     def forward(self, x, context, context_lens):
         r"""