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tasks/03_tiled_matmul/bench.py

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+from __future__ import annotations
+
+import statistics
+import sys
+import time
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+import torch
+
+from kernels.triton.tiled_matmul import triton_tiled_matmul
+from reference.torch_matmul import torch_matmul
+
+
+def benchmark(fn, *args, warmup: int = 5, reps: int = 20) -> float:
+    for _ in range(warmup):
+        fn(*args)
+    if args[0].is_cuda:
+        torch.cuda.synchronize()
+    times_ms = []
+    for _ in range(reps):
+        if args[0].is_cuda:
+            torch.cuda.synchronize()
+        start = time.perf_counter()
+        fn(*args)
+        if args[0].is_cuda:
+            torch.cuda.synchronize()
+        times_ms.append((time.perf_counter() - start) * 1e3)
+    return statistics.median(times_ms)
+
+
+def main() -> None:
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    for m, k, n in [(128, 128, 128), (512, 512, 512)]:
+        a = torch.randn(m, k, device=device)
+        b = torch.randn(k, n, device=device)
+        ref_ms = benchmark(torch_matmul, a, b)
+        print(f"torch_matmul {m}x{k}x{n}: {ref_ms:.3f} ms")
+        if device == "cuda":
+            try:
+                triton_ms = benchmark(triton_tiled_matmul, a, b)
+                print(f"triton_tiled_matmul {m}x{k}x{n}: {triton_ms:.3f} ms")
+            except (NotImplementedError, RuntimeError) as exc:
+                print(f"triton_tiled_matmul {m}x{k}x{n}: skipped ({exc})")
+
+
+if __name__ == "__main__":
+    main()