Create 01_matmul_add.py

01_matmul_add.py

@@ -0,0 +1,77 @@
+import argparse
+import os
+import torch
+
+
+def parse_arguments():
+    p = argparse.ArgumentParser()
+    p.add_argument("--size", type=int, default=64)
+    p.add_argument("--dtype", choices=["bf16", "fp32"], default="bf16")
+    p.add_argument("--compile", action="store_true")
+    p.add_argument("--warmup", action="store_true")
+    p.add_argument("--trace_dir", default="./traces/01_matmul_add")
+    return p.parse_args()
+
+
+def main():
+    args = parse_arguments()
+
+    device = "cuda"
+    dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32
+
+    x = torch.randn(args.size, args.size, device=device, dtype=dtype)
+    w = torch.randn(args.size, args.size, device=device, dtype=dtype)
+    b = torch.randn(args.size, args.size, device=device, dtype=dtype)
+
+    def fn(x, w, b):
+        return torch.add(torch.matmul(x, w), b)
+
+    fn = torch.compile(fn) if args.compile else fn
+
+    def step():
+        with torch.profiler.record_function("matmul_add"):
+            return fn(x, w, b)
+
+    if args.warmup:
+        for _ in range(3):
+            step()
+        # you're flushing the queue so the upcoming profiled steps aren't credited for prior work
+        torch.cuda.synchronize()
+
+    os.makedirs(args.trace_dir, exist_ok=True)
+    compile_tag = "compile" if args.compile else "eager"
+    warmup_tag = "warm" if args.warmup else "cold"
+    tag = f"{args.size}_{args.dtype}_{warmup_tag}_{compile_tag}"
+
+    table_path = os.path.join(args.trace_dir, f"{tag}.txt")
+    trace_path = os.path.join(args.trace_dir, f"{tag}.json")
+
+    # wait skips noisy init, warmup runs through the profiler without
+    # recording (so caches/autotune settle), active is what shows up in
+    # the table/trace.
+    schedule = torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=1)
+    with torch.profiler.profile(
+        activities=[
+            torch.profiler.ProfilerActivity.CPU,
+            torch.profiler.ProfilerActivity.CUDA,
+        ],
+        schedule=schedule,
+        record_shapes=False,  # adds CPU overhead
+        profile_memory=False,  # adds CPU overhead
+        with_stack=False,
+    ) as prof:
+        for _ in range(5):
+            step()
+            prof.step()
+
+    torch.cuda.synchronize()
+
+    print(f"saving traces ... {trace_path}")
+    prof.export_chrome_trace(trace_path)
+
+    with open(table_path, "w") as f:
+        f.write(prof.key_averages().table(sort_by="cuda_time_total", row_limit=15))
+
+
+if __name__ == "__main__":
+    main()