Add a benchmark script for the HPC-Ops bf16xfp32 router GEMM (#32642)

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

test/registered/kernels/benchmark/gemm/bench_bf16xfp32_router_gemm.py

@@ -0,0 +1,68 @@
+"""Benchmark for the HPC-Ops bf16xfp32 router GEMM (HPC-Ops vs cublas fp32).
+
+`linear_bf16_fp32` computes `x[m, k](bf16) @ w[n, k](fp32)^T`. The `hpc`
+provider requires HPC-Ops (https://github.com/Tencent/hpc-ops) installed and
+a Hopper GPU (sm90a); it decomposes the fp32 weight into two cached bf16
+halves and runs both bf16 GEMMs fused on tensor cores. The `cublas` provider
+upcasts the activation to fp32 and is what every model uses without HPC-Ops.
+
+Shapes are the LongCat-Flash router shapes: (hidden_size, n_routed_experts +
+zero_experts) = (6144, 768) for Chat and (3072, 384) for Lite.
+
+Run on a Hopper (SM90) GPU with HPC-Ops installed:
+    python -m sglang.kernels.jit.benchmark.bench_bf16xfp32_router_gemm
+"""
+
+import torch
+
+from sglang.kernels.jit.benchmark import marker
+from sglang.kernels.jit.benchmark.utils import create_random
+from sglang.kernels.jit.utils import get_jit_cuda_arch, is_hip_runtime
+from sglang.kernels.ops.attention.dsv4.gemm import (
+    _linear_bf16_fp32_hpc,
+    mark_hpc_bf16xfp32_gemm_enabled,
+)
+from sglang.test.ci.ci_register import register_cuda_ci
+
+register_cuda_ci(
+    est_time=5, stage="base-b-kernel-benchmark", runner_config="1-gpu-large"
+)
+
+mark_hpc_bf16xfp32_gemm_enabled()
+
+
+def _cublas_fp32(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+    return torch.mm(x.float(), w.t())
+
+
+def _hpc(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+    out = _linear_bf16_fp32_hpc(x, w, min_m=1)
+    if out is None:
+        marker.skip("HPC-Ops is not installed, or this GPU is not Hopper (sm90a)")
+    return out
+
+
+FN_MAP = {
+    "cublas": _cublas_fp32,
+    "hpc": _hpc,
+}
+
+
+@marker.parametrize(
+    "m", [1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192], [1, 64, 8192]
+)
+@marker.parametrize("n,k", [(768, 6144), (384, 3072)], [(768, 6144)])
+@marker.benchmark("provider", ["cublas", "hpc"])
+def benchmark(m, n, k, provider):
+    x = create_random(m, k, dtype=torch.bfloat16)
+    w = create_random(n, k, dtype=torch.float32)
+    return marker.do_bench(FN_MAP[provider], input_args=(x, w))
+
+
+if __name__ == "__main__":
+    if is_hip_runtime() or get_jit_cuda_arch().major != 9:
+        print(
+            "The HPC-Ops bf16xfp32 GEMM requires a Hopper (sm90a) CUDA GPU. Skipping."
+        )
+    else:
+        benchmark.run()