fixes to matmul and linear benchmarks (#320)

Summary:
Pull Request resolved: #320

for matmul benchmarks, unbreaks them - we need the scales to be fp32, not integers

for linear benchmarks, aligns default settings to current best supported path (compile on, dynamic scaling)

Reviewed By: awgu

Differential Revision: D59877198

fbshipit-source-id: 092daaffeb0096f9fbd12ca407701bc3aa80c97c

benchmarks/bench_linear_float8.py

@@ -91,13 +91,13 @@ def float8_pct_top_peak(self):
 
 def main(
     sweep_path: Optional[Path] = None,
-    compile: bool = False,
+    compile: bool = True,
     n_limit: Optional[int] = None,
     fast_accum_filter: Optional[bool] = None,
     shape_name_filter: Optional[str] = None,
-    scaling_type_x: str = "delayed",
-    scaling_type_w: str = "delayed",
-    scaling_type_dL_dY: str = "delayed",
+    scaling_type_x: str = "dynamic",
+    scaling_type_w: str = "dynamic",
+    scaling_type_dL_dY: str = "dynamic",
 ):
     device = "cuda"
     print(f"Compile is set to             | {compile}")
@@ -274,7 +274,7 @@ def wrapper(*args, **kwargs):
 def invoke_main() -> None:
     parser = argparse.ArgumentParser()
     parser.add_argument("-o", "--output_path", type=str, required=False)
-    parser.add_argument("--compile", action="store_true")
+    parser.add_argument("--disable_compile", action="store_true")
     parser.add_argument("-n", "--n_limit", type=int, required=False)
     parser.add_argument("--fast_accum_filter", type=bool, required=False)
     parser.add_argument("--shape_name_filter", type=str, required=False)
@@ -292,7 +292,7 @@ def invoke_main() -> None:
         kwargs["scaling_type_dL_dY"] = args.scaling_type_dL_dY
     main(
         output_path,
-        args.compile,
+        not args.disable_compile,
         args.n_limit,
         args.fast_accum_filter,
         args.shape_name_filter,

benchmarks/bench_matmul.py

@@ -101,8 +101,8 @@ def run(n_limit: Optional[int] = None):
         B = torch.zeros(K, N, device=device, dtype=d2).t().contiguous().t()
 
         def do_matmul(A, B):
-            scale_a = torch.tensor([1], device=device)
-            scale_b = torch.tensor([1], device=device)
+            scale_a = torch.tensor([1.0], device=device)
+            scale_b = torch.tensor([1.0], device=device)
             return torch._scaled_mm(
                 A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=False
             )