[Perf] MiniMax H3 Qwen3-VL support fused RMSNorm on NPU (#5915)

Signed-off-by: Wenjia Li <wjialish@gmail.com>
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

tests/diffusion/models/minimax_h3/test_minimax_h3_rmsnorm.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.models.minimax_h3.encoder import MiniMaxH3Qwen3VLRMSNorm
+
+
+def test_qwen3_vl_rmsnorm_uses_common_fused_rmsnorm_contract() -> None:
+    """Qwen keeps BF16 gamma while native fallback accumulates in FP32."""
+    eps = 1e-6
+    norm = MiniMaxH3Qwen3VLRMSNorm(hidden_size=4, eps=eps, dtype=torch.bfloat16)
+    norm.weight.data.copy_(torch.tensor([1.0, 0.5, 1.5, 2.0], dtype=torch.bfloat16))
+    x = torch.tensor(
+        [[1.0, -2.0, 3.0, -4.0], [-5.0, 6.0, -7.0, 8.0]],
+        dtype=torch.bfloat16,
+    )
+
+    x_fp32 = x.float()
+    expected = (x_fp32 * torch.rsqrt(x_fp32.square().mean(-1, keepdim=True) + eps) * norm.weight.float()).to(x.dtype)
+
+    assert isinstance(norm, RMSNorm)
+    assert norm.weight.dtype == torch.bfloat16
+    assert set(norm.state_dict()) == {"weight"}
+    torch.testing.assert_close(norm.forward_native(x), expected, atol=0, rtol=0)