[Perf][NPU] Fuse MiniMax H3 Qwen3-VL SwiGLU (#6167)

Signed-off-by: Wenjia Li <wjialish@gmail.com>

tests/diffusion/models/minimax_h3/test_minimax_h3_qwen3vl_swiglu.py

@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm_omni.platforms import current_omni_platform
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]
+
+
+@pytest.mark.skipif(not current_omni_platform.is_npu(), reason="requires Ascend NPU")
+@pytest.mark.parametrize("shape", [(1, 16, 256), (2, 1, 256)])
+def test_qwen3_vl_npu_swiglu_matches_split_reference(
+    shape: tuple[int, int, int],
+) -> None:
+    from vllm_omni.platforms.npu.models.minimax_h3 import npu_swiglu_from_packed
+
+    packed = torch.randn(*shape, device="npu", dtype=torch.bfloat16)
+    gate, up = packed.chunk(2, dim=-1)
+
+    torch.testing.assert_close(
+        npu_swiglu_from_packed(packed),
+        F.silu(gate) * up,
+        atol=2e-2,
+        rtol=2e-2,
+    )
+
+
+@pytest.mark.skipif(not current_omni_platform.is_npu(), reason="requires Ascend NPU")
+def test_qwen3_vl_npu_swiglu_matches_two_projection_reference() -> None:
+    from vllm_omni.platforms.npu.models.minimax_h3 import npu_swiglu_from_packed
+
+    x = torch.randn(1, 16, 128, device="npu", dtype=torch.bfloat16)
+    weight = torch.randn(256, 128, device="npu", dtype=torch.bfloat16)
+    packed = F.linear(x, weight)
+    gate = F.linear(x, weight[:128])
+    up = F.linear(x, weight[128:])
+
+    torch.testing.assert_close(
+        npu_swiglu_from_packed(packed),
+        F.silu(gate) * up,
+        atol=2e-2,
+        rtol=2e-2,
+    )