Initial project scaffold

bench/bench_attention.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import argparse
+import statistics
+import sys
+import time
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+import torch
+
+from kernels.triton.flash_attention_fwd import triton_flash_attention_fwd
+from reference.torch_attention import torch_attention
+from tools.lab_extension import build_extension
+
+
+def benchmark(fn, *args, warmup: int = 5, reps: int = 20, **kwargs) -> float:
+    for _ in range(warmup):
+        fn(*args, **kwargs)
+    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, **kwargs)
+        if args[0].is_cuda:
+            torch.cuda.synchronize()
+        times_ms.append((time.perf_counter() - start) * 1e3)
+    return statistics.median(times_ms)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
+    parser.add_argument("--mode", choices=["all", "torch", "triton", "cuda"], default="all")
+    parser.add_argument("--batch", type=int, default=2)
+    parser.add_argument("--heads", type=int, default=8)
+    parser.add_argument("--seq", type=int, default=128)
+    parser.add_argument("--dim", type=int, default=64)
+    parser.add_argument("--causal", action="store_true")
+    args = parser.parse_args()
+
+    q = torch.randn(args.batch, args.heads, args.seq, args.dim, device=args.device)
+    k = torch.randn(args.batch, args.heads, args.seq, args.dim, device=args.device)
+    v = torch.randn(args.batch, args.heads, args.seq, args.dim, device=args.device)
+
+    if args.mode in {"all", "torch"}:
+        elapsed_ms = benchmark(torch_attention, q, k, v, causal=args.causal)
+        print(f"torch: {elapsed_ms:.3f} ms")
+
+    if args.device == "cuda" and args.mode in {"all", "triton"}:
+        try:
+            elapsed_ms = benchmark(triton_flash_attention_fwd, q, k, v, causal=args.causal)
+            print(f"triton: {elapsed_ms:.3f} ms")
+        except (NotImplementedError, RuntimeError) as exc:
+            print(f"triton: skipped ({exc})")
+
+    if args.device == "cuda" and args.mode in {"all", "cuda"}:
+        ext = build_extension(verbose=False)
+        if ext is None or not hasattr(torch.ops, "kernel_lab"):
+            print("cuda: skipped (extension unavailable)")
+        else:
+            try:
+                elapsed_ms = benchmark(
+                    torch.ops.kernel_lab.flash_attention_fwd, q, k, v, args.causal
+                )
+                print(f"cuda: {elapsed_ms:.3f} ms")
+            except Exception as exc:
+                print(f"cuda: skipped ({exc})")
+
+
+if __name__ == "__main__":
+    main()