Add deepseek style fused moe group gate selection kernel (#4530)

sgl-kernel/benchmark/bench_moe_fused_gate.py

@@ -0,0 +1,74 @@
+import itertools
+import math
+
+import torch
+import triton
+import triton.language as tl
+from sgl_kernel import moe_fused_gate
+
+from sglang.srt.layers.moe.topk import biased_grouped_topk
+
+
+def biased_grouped_topk_org(scores, bias, num_expert_group, topk_group, topk):
+    return biased_grouped_topk(
+        scores,
+        scores,
+        bias,
+        topk=topk,
+        renormalize=True,
+        num_expert_group=num_expert_group,
+        topk_group=topk_group,
+    )
+
+
+def biased_grouped_topk_org_kernel(scores, bias, num_expert_group, topk_group, topk):
+    return moe_fused_gate(scores, bias, num_expert_group, topk_group, topk)
+
+
+seq_length_range = [5000, 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=["original", "kernel"],
+        line_names=["Original", "SGL Kernel"],
+        styles=[("blue", "-"), ("red", "-")],
+        ylabel="us",
+        plot_name="moe-fused-gate-performance",
+        args={},
+    )
+)
+def benchmark(seq_length, provider):
+    dtype = torch.bfloat16
+    device = torch.device("cuda")
+    num_experts, num_expert_group, topk_group, topk = 256, 8, 4, 8
+
+    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 == "original":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: biased_grouped_topk_org(
+                scores.clone(), bias.clone(), num_expert_group, topk_group, topk
+            ),
+            quantiles=quantiles,
+        )
+    elif provider == "kernel":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: biased_grouped_topk_org_kernel(
+                scores.clone(), bias.clone(), num_expert_group, topk_group, topk
+            ),
+            quantiles=quantiles,
+        )
+
+    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+
+if __name__ == "__main__":
+    benchmark.run(print_data=True)