Create 03_simple_mlp.py

03_simple_mlp.py

@@ -0,0 +1,81 @@
+import argparse
+import os
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class SimpleGeGLUMLP(nn.Module):
+    def __init__(self, dim, hidden):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden, bias=False)
+        self.up_proj = nn.Linear(dim, hidden, bias=False)
+        self.down_proj = nn.Linear(hidden, dim, bias=False)
+
+    def forward(self, x):
+        g = self.gate_proj(x)
+        u = self.up_proj(x)
+        h = F.gelu(g, approximate="tanh")
+        m = h * u
+        y = self.down_proj(m)
+        return y
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--batch", type=int, default=64)
+    p.add_argument("--seq", type=int, default=128)
+    p.add_argument("--dim", type=int, default=768)
+    p.add_argument("--hidden", type=int, default=3072)
+    p.add_argument("--compile", action="store_true")
+    p.add_argument("--trace_dir", default="./traces/03_simple_mlp")
+    args = p.parse_args()
+
+    device = "cuda"
+    x = torch.randn(args.batch, args.seq, args.dim, device=device, dtype=torch.bfloat16)
+
+    mlp = SimpleGeGLUMLP(args.dim, args.hidden).to(device, dtype=torch.bfloat16)
+    mlp.eval()
+
+    fwd = torch.compile(mlp) if args.compile else mlp
+
+    def step():
+        with torch.profiler.record_function("mlp_fwd"), torch.no_grad():
+            return fwd(x)
+
+    for _ in range(3):
+        step()
+    torch.cuda.synchronize()
+
+    os.makedirs(args.trace_dir, exist_ok=True)
+    compile_tag = "compile" if args.compile else "eager"
+    tag = f"{args.batch}_{args.seq}_{args.dim}_{args.hidden}_{compile_tag}"
+
+    table_path = os.path.join(args.trace_dir, f"{tag}.txt")
+    trace_path = os.path.join(args.trace_dir, f"{tag}.json")
+
+    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()