[Diffusion][NPU][GPU] Fix SANA model execution error (#24798)

python/sglang/multimodal_gen/runtime/layers/activation.py

@@ -82,6 +82,15 @@ class GeluAndMul(CustomOp):
     def forward_cuda(self, *args, **kwargs) -> Any:
         return self.forward_native(*args, **kwargs)
 
+    def forward_npu(self, x: torch.Tensor) -> torch.Tensor:
+        y_npu, _ = torch_npu.npu_geglu(
+            x,
+            dim=-1,
+            approximate=1 if self.approximate == "tanh" else 0,
+            activate_left=True,
+        )
+        return y_npu
+
     def forward_native(self, x: torch.Tensor) -> torch.Tensor:
         """PyTorch-native implementation equivalent to forward()."""
         d = x.shape[-1] // 2

python/sglang/multimodal_gen/runtime/models/encoders/gemma2.py

@@ -178,31 +178,22 @@ class Gemma2Attention(nn.Module):
         key = k.transpose(1, 2)
         value = v.transpose(1, 2)
 
-        attn_mask = torch.zeros(
-            (seq_len, seq_len), device=hidden_states.device, dtype=torch.float32
-        )
-        causal = torch.triu(
+        attn_mask = torch.tril(
             torch.ones(
                 (seq_len, seq_len), device=hidden_states.device, dtype=torch.bool
-            ),
-            diagonal=1,
+            )
         )
-        attn_mask = attn_mask.masked_fill(causal, float("-inf"))
         if self.is_sliding and self.sliding_window is not None:
             idx = torch.arange(seq_len, device=hidden_states.device)
             dist = idx[None, :] - idx[:, None]
             too_far = dist > self.sliding_window
-            attn_mask = attn_mask.masked_fill(too_far, float("-inf"))
+            attn_mask = attn_mask.masked_fill(too_far, False)
 
         if attention_mask is not None:
-            key_pad = ~attention_mask.to(torch.bool)
             attn_mask = attn_mask[None, None, :, :].expand(
                 batch_size, 1, seq_len, seq_len
             )
-            attn_mask = attn_mask.masked_fill(
-                key_pad[:, None, None, :].expand(batch_size, 1, seq_len, seq_len),
-                float("-inf"),
-            )
+            attn_mask = attn_mask & attention_mask.to(torch.bool)[:, None, None, :]
 
         attn_kwargs = {
             "attn_mask": attn_mask,

python/sglang/multimodal_gen/runtime/models/schedulers/scheduling_dpm_solver_multistep.py

@@ -115,9 +115,18 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
         model_output: torch.Tensor,
         timestep: int,
         sample: torch.Tensor,
-        **kwargs,
+        generator: torch.Generator | None = None,
+        variance_noise: torch.Tensor | None = None,
+        return_dict: bool = True,
     ):
-        return self._inner.step(model_output, timestep, sample, **kwargs)
+        return self._inner.step(
+            model_output,
+            timestep,
+            sample,
+            generator=generator,
+            variance_noise=variance_noise,
+            return_dict=return_dict,
+        )
 
     @property
     def sigmas(self):