[Diffusion] Fuse FLUX.2 token concatenation and NVFP4 quantization (#37141)

test/manual/kernels/bench_flux2_token_cat_nvfp4.py

@@ -0,0 +1,90 @@
+import time
+
+import flashinfer
+import torch
+
+from sglang.kernels.ops.diffusion import try_flux2_token_cat_nvfp4
+
+
+def _benchmark(fn, iterations: int = 100) -> float:
+    for _ in range(10):
+        fn()
+    torch.cuda.synchronize()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    start.record()
+    for _ in range(iterations):
+        fn()
+    end.record()
+    torch.cuda.synchronize()
+    return start.elapsed_time(end) * 1000 / iterations
+
+
+def _benchmark_wall(fn, iterations: int = 100) -> float:
+    for _ in range(10):
+        fn()
+    torch.cuda.synchronize()
+    start = time.perf_counter_ns()
+    for _ in range(iterations):
+        fn()
+    torch.cuda.synchronize()
+    return (time.perf_counter_ns() - start) / iterations / 1000
+
+
+def _run_case(token_count: int) -> None:
+    generator = torch.Generator(device="cuda")
+    generator.manual_seed(20260830 + token_count)
+    attention = torch.randn(
+        1,
+        token_count,
+        6144,
+        device="cuda",
+        dtype=torch.bfloat16,
+        generator=generator,
+    )
+    mlp = torch.randn(
+        1,
+        token_count,
+        18432,
+        device="cuda",
+        dtype=torch.bfloat16,
+        generator=generator,
+    )
+    global_scale = torch.tensor(0.625, device="cuda", dtype=torch.float32)
+
+    def baseline():
+        return flashinfer.fp4_quantize(
+            torch.cat([attention, mlp], dim=-1).view(-1, 24576), global_scale
+        )
+
+    def fused():
+        result = try_flux2_token_cat_nvfp4(attention, mlp, global_scale)
+        assert result is not None
+        return result
+
+    expected = baseline()
+    actual = fused()
+    exact = [torch.equal(lhs, rhs) for lhs, rhs in zip(actual, expected)]
+    baseline_us = _benchmark(baseline)
+    fused_us = _benchmark(fused)
+    baseline_wall_us = _benchmark_wall(baseline)
+    fused_wall_us = _benchmark_wall(fused)
+    print(
+        {
+            "tokens": token_count,
+            "baseline_us": baseline_us,
+            "fused_us": fused_us,
+            "speedup": baseline_us / fused_us,
+            "baseline_wall_us": baseline_wall_us,
+            "fused_wall_us": fused_wall_us,
+            "wall_speedup": baseline_wall_us / fused_wall_us,
+            "exact": exact,
+        }
+    )
+
+
+if __name__ == "__main__":
+    if not torch.cuda.is_available() or torch.cuda.get_device_capability() != (10, 3):
+        raise RuntimeError("This benchmark requires an NVIDIA Blackwell SM103 GPU")
+    for tokens in (17, 512, 4096, 4608):
+        _run_case(tokens)