[opt kimi k2 1 / n] Add kimi k2 moe fused gate (#13287)

sgl-kernel/benchmark/bench_kimi_k2_moe_fused_gate.py

@@ -0,0 +1,117 @@
+import itertools
+import math
+import os
+
+import torch
+import triton
+import triton.language as tl
+from sgl_kernel import kimi_k2_moe_fused_gate
+
+from sglang.srt.layers.moe.topk import kimi_k2_biased_topk_impl
+
+# CI environment detection
+IS_CI = (
+    os.getenv("CI", "false").lower() == "true"
+    or os.getenv("GITHUB_ACTIONS", "false").lower() == "true"
+)
+
+
+def kimi_k2_biased_topk_torch_compile(scores, bias, topk, routed_scaling_factor):
+    """Original torch.compile-based implementation"""
+    return kimi_k2_biased_topk_impl(
+        scores,
+        scores,
+        bias,
+        topk=topk,
+        renormalize=True,
+        routed_scaling_factor=routed_scaling_factor,
+    )
+
+
+def kimi_k2_biased_topk_fused_kernel(scores, bias, topk, routed_scaling_factor):
+    """Our fused CUDA kernel implementation"""
+    return kimi_k2_moe_fused_gate(
+        scores,
+        bias,
+        topk=topk,
+        renormalize=True,
+        routed_scaling_factor=routed_scaling_factor,
+    )
+
+
+# CI environment uses simplified parameters
+if IS_CI:
+    seq_length_range = [5000]  # Only test one sequence length in CI
+else:
+    seq_length_range = [
+        1,
+        8,
+        16,
+        32,
+        64,
+        128,
+        256,
+        512,
+        1024,
+        2048,
+        4096,
+        10000,
+        15000,
+        20000,
+        25000,
+        30000,
+        35000,
+        40000,
+    ]
+
+configs = [(sq,) for sq in seq_length_range]
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["seq_length"],
+        x_vals=[list(_) for _ in configs],
+        line_arg="provider",
+        line_vals=["torch_compile", "fused_kernel"],
+        line_names=["Torch Compile", "Fused Kernel"],
+        styles=[("blue", "-"), ("red", "-")],
+        ylabel="us",
+        plot_name="kimi-k2-moe-fused-gate-performance",
+        args={},
+    )
+)
+def benchmark(seq_length, provider):
+    dtype = torch.float32
+    device = torch.device("cuda")
+    num_experts, topk = 384, 6  # Kimi K2 configuration
+    routed_scaling_factor = 2.872  # Kimi K2's routed scaling factor
+
+    scores = torch.randn((seq_length, num_experts), device=device, dtype=dtype)
+    bias = torch.rand(num_experts, device=device, dtype=dtype)
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if provider == "torch_compile":
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: kimi_k2_biased_topk_torch_compile(
+                scores.clone(), bias.clone(), topk, routed_scaling_factor
+            ),
+            quantiles=quantiles,
+        )
+    elif provider == "fused_kernel":
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: kimi_k2_biased_topk_fused_kernel(
+                scores.clone(), bias.clone(), topk, routed_scaling_factor
+            ),
+            quantiles=quantiles,
+        )
+
+    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+
+if __name__ == "__main__":
+    print("=" * 80)
+    print("Benchmarking Kimi K2 MoE Fused Gate Performance")
+    print("=" * 80)
+    print("\nPerformance vs Sequence Length (384 experts, topk=6)")
+    benchmark.run(print_data=True, save_path=".")