Fix diffusion fallback guards and validation

python/sglang/jit_kernel/diffusion/cutedsl/scale_residual_norm_scale_shift.py

@@ -257,11 +257,12 @@ def validate_scale_shift(t: torch.Tensor, B: int, S: int, D: int):
         (t.shape[0] not in (1, B)) or (t.shape[1] not in (1, S) or t.shape[2] != D)
     ):
         failed = True
-    elif t.ndim == 4 and (t.shape[0] != B or t.shape[2] != 1 or t.shape[3] != D):
+    elif t.ndim == 4:
         F = t.shape[1]
-        if S % F != 0:
+        if t.shape[0] != B or t.shape[2] != 1 or t.shape[3] != D:
+            failed = True
+        elif S % F != 0:
             raise ValueError(f"Validate failed: S({S}) must be divisible by F({F}).")
-        failed = True
     if failed:
         raise ValueError(f"Validate failed: unsupported tensor shape: {t.shape}.")
     if t.stride()[-1] != 1:

python/sglang/jit_kernel/diffusion/triton/npu_fallback.py

@@ -20,6 +20,9 @@ def fuse_scale_shift_native(
 def apply_rotary_embedding_native(
     x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
 ) -> torch.Tensor:
+    if interleaved and cos.shape[-1] == x.shape[-1]:
+        cos = cos[..., ::2]
+        sin = sin[..., ::2]
     cos = cos.unsqueeze(-2).to(x.dtype)
     sin = sin.unsqueeze(-2).to(x.dtype)
 

python/sglang/jit_kernel/diffusion/triton/torch_fallback.py

@@ -48,6 +48,9 @@ def apply_rotary_embedding_native(
     x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
 ) -> torch.Tensor:
     """Native fallback for rotary embedding (shared with NPU implementation)."""
+    if interleaved and cos.shape[-1] == x.shape[-1]:
+        cos = cos[..., ::2]
+        sin = sin[..., ::2]
     cos = cos.unsqueeze(-2).to(x.dtype)
     sin = sin.unsqueeze(-2).to(x.dtype)
     x1 = x[..., ::2]

python/sglang/jit_kernel/tests/diffusion/test_fused_norm_scale_shift.py

@@ -9,6 +9,7 @@
 from sglang.jit_kernel.diffusion.cutedsl.scale_residual_norm_scale_shift import (
     fused_norm_scale_shift,
     fused_scale_residual_norm_scale_shift,
+    validate_scale_shift,
 )
 from sglang.test.ci.ci_register import register_cuda_ci
 
@@ -125,6 +126,16 @@ def _make_tensor(index_mode: str, shape: Tuple, dtype: torch.dtype):
     return torch.randn(*SHAPE_MAP[index_mode](*shape), device=DEVICE, dtype=dtype)
 
 
+def test_validate_scale_shift_rejects_non_divisible_frames():
+    with pytest.raises(ValueError, match=r"S\(10\) must be divisible by F\(4\)"):
+        validate_scale_shift(
+            torch.empty((1, 4, 1, 256), device=DEVICE, dtype=torch.float16),
+            1,
+            10,
+            256,
+        )
+
+
 @torch.no_grad()
 def run_norm_scale_shift(
     shape=SHAPES[0],