[Bugfix][NPU] Fix MiniMax-H3 INT8 quantization dispatch (#6876)

Signed-off-by: KrystalRay <keeleiray@gmail.com>
Co-authored-by: KrystalRay <keeleiray@gmail.com>

.buildkite/npu/test-npu-ready.yml

@@ -19,6 +19,7 @@ steps:
         pytest -s -v
         tests/diffusion/layers/test_activation.py
         tests/diffusion/layers/test_fused_qk_norm_rope_npu.py
+        tests/diffusion/models/minimax_h3/test_minimax_h3_qwen3vl_swiglu.py
         -m 'core_model and npu and A3'
         --run-level 'core_model'
 

tests/diffusion/models/minimax_h3/test_minimax_h3_qwen3vl_swiglu.py

@@ -49,3 +49,29 @@ def test_qwen3_vl_npu_swiglu_matches_two_projection_reference() -> None:
         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_forward_uses_projection_module(mocker) -> None:
+    from vllm_omni.platforms.npu.models import minimax_h3
+    from vllm_omni.platforms.npu.models.minimax_h3 import (
+        _forward_minimax_h3_qwen3vl_text_mlp_npu,
+    )
+
+    x = torch.randn(1, 16, 128, device="npu", dtype=torch.bfloat16)
+    packed = torch.randn(1, 16, 256, device="npu", dtype=torch.bfloat16)
+    activated = torch.randn(1, 16, 128, device="npu", dtype=torch.bfloat16)
+    gate_up_proj = mocker.Mock(return_value=packed)
+    down_proj = mocker.Mock(side_effect=lambda value: value)
+    mlp = mocker.Mock(gate_up_proj=gate_up_proj, down_proj=down_proj)
+    swiglu = mocker.patch.object(minimax_h3, "npu_swiglu_from_packed", return_value=activated)
+
+    output = _forward_minimax_h3_qwen3vl_text_mlp_npu(mlp, x)
+
+    gate_up_proj.assert_called_once()
+    assert gate_up_proj.call_args.args[0] is x
+    swiglu.assert_called_once()
+    assert swiglu.call_args.args[0] is packed
+    down_proj.assert_called_once()
+    assert down_proj.call_args.args[0] is activated
+    assert output is activated

vllm_omni/platforms/npu/models/minimax_h3.py

@@ -100,7 +100,7 @@ def npu_swiglu_from_packed(gate_up: torch.Tensor) -> torch.Tensor:
 
 def _forward_minimax_h3_qwen3vl_text_mlp_npu(self: Any, x: torch.Tensor) -> torch.Tensor:
     """Run the Qwen3-VL MLP with one packed GEMM and fused SwiGLU."""
-    gate_up = F.linear(x, self.gate_up_proj.weight)
+    gate_up = self.gate_up_proj(x)
     return self.down_proj(npu_swiglu_from_packed(gate_up))