[cutlass backend] add teraflops and increase rep for benchmark script (#154944)

Differential Revision: [D75840023](https://our.internmc.facebook.com/intern/diff/D75840023/)

I think I will continue to use do_bench for now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154944
Approved by: https://github.com/mlazos

benchmarks/inductor_backends/cutlass.py

@@ -1,4 +1,5 @@
 import os
+import sys
 
 
 os.environ["TORCH_LOGS"] = "inductor"
@@ -32,6 +33,7 @@ inductor_config.autotune_local_cache = False
 UNITS = {
     "name": "",
     "forward_time": " (us)",
+    "teraflops": " (TFLOPS)",
     "compilation_time": " (s)",
 }
 PERF_OVER_ATEN_STR: str = "perf_over_aten (%)"
@@ -75,7 +77,7 @@ CUTLASS_INSTANTIATION_LEVELS = [
 
 
 def benchmark_torch_function_in_microseconds(func: Callable, *args, **kwargs) -> float:
-    return do_bench(lambda: func(*args, **kwargs)) * 1e3
+    return do_bench(lambda: func(*args, **kwargs), warmup=100, rep=10000) * 1e3
 
 
 @dataclass(frozen=True, kw_only=True)
@@ -162,6 +164,7 @@ class ExperimentGroupConfig:
 class ExperimentResults:
     name: str
     forward_time: float
+    teraflops: float
     compilation_time: float
 
     def asdict(self):
@@ -211,7 +214,10 @@ def run_single_experiment_group(
     for config in group_config.experiments:
         torch._dynamo.reset()
         torch._inductor.utils.clear_inductor_caches()
-        compiled_op = torch.compile(op, fullgraph=True, options=config.to_options())
+        compiled_op = torch.compile(
+            op,
+            options=config.to_options(),
+        )
 
         start_time = time.perf_counter()
         try:
@@ -227,6 +233,7 @@ def run_single_experiment_group(
                 ExperimentResults(
                     name=config.name(),
                     forward_time=float("inf"),
+                    teraflops=0.0,
                     compilation_time=float("inf"),
                 )
             )
@@ -238,10 +245,18 @@ def run_single_experiment_group(
             *inputs,
         )
 
+        flops = calculate_flops(
+            group_config.op_name,
+            group_config.shape,
+            group_config.batch_size,
+        )
+        teraflops = flops / (forward_time * 1e-6) / 1e12
+
         results.append(
             ExperimentResults(
                 name=config.name(),
                 forward_time=forward_time,
+                teraflops=teraflops,
                 compilation_time=compilation_time,
             )
         )
@@ -336,6 +351,20 @@ def calculate_table_data(results: list[ExperimentResults]) -> dict:
     return table_data
 
 
+def calculate_flops(op_name: str, shape: tuple[int, int, int], batch_size: int) -> int:
+    """
+    Calculate the number of floating point operations based on operation type and shape.
+    """
+    M, N, K = shape
+
+    if op_name == "bmm":
+        return 2 * batch_size * M * N * K
+    elif op_name == "addmm":
+        return 2 * M * N * K + M * N
+    else:
+        return 2 * M * N * K
+
+
 def get_printable_results(experiment_groups: list[ExperimentGroup]) -> list[str]:
     edge_over_aten = defaultdict(list)
     output = []
@@ -390,8 +419,10 @@ def main():
         results.append(
             ExperimentGroup(config=group_config, results=group_results),
         )
-        log.info(f"\nINTERMEDIATE results: {i}/{len(configs)}")  # noqa: G004
-        log.info(get_printable_results(results))
+        sys.stderr.write(
+            f"\nINTERMEDIATE results: {i + 1}/{len(configs)} \n"
+            + get_printable_results(results)
+        )
     print("\nFINAL results...")
     print(get_printable_results(results))