Use NVTX filtering to limit NCU profile collection

Summary:
Previously, we used `--replay-mode range`, but that did not give us per-kernel
metrics, so it was changed to `---replay-mode kernel` (the default). However,
that can causes us to profile a lot more kernels outside the ones in the
desired benchmark.

It appears we can instead use NVTX filtering to solve this problem. Relevant docs:
https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html#nvtx-filtering

I also tacked on a minor change to the ncu invocation, adding `--import-source yes`.
This makes it easier to analyze the traces on a different machine from the one doing
the profiling.

Reviewed By: chenyang78

Differential Revision: D58711358

fbshipit-source-id: 28aec4f71a736c7427b1886335297ece4a2a54a8

torchbenchmark/_components/ncu/__init__.py

@@ -1,7 +1,14 @@
-
 from typing import Callable
 
-def do_bench_ncu_in_task(fn: Callable, warmup=25, grad_to_none=None, fast_flush=True, output_dir=None) -> None:
+
+def do_bench_ncu_in_task(
+    fn: Callable,
+    warmup=25,
+    grad_to_none=None,
+    fast_flush=True,
+    output_dir=None,
+    range_name: str = "",
+) -> None:
     """
     Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with
     the 20-th and 80-th performance percentile.
@@ -46,8 +53,6 @@ def do_bench_ncu_in_task(fn: Callable, warmup=25, grad_to_none=None, fast_flush=
     # Warm-up
     for _ in range(n_warmup):
         fn()
-    # Start ncu profiling
-    torch.cuda.cudart().cudaProfilerStart()
     # we don't want `fn` to accumulate gradient values
     # if it contains a backward pass. So we clear the
     # provided gradients
@@ -56,5 +61,5 @@ def do_bench_ncu_in_task(fn: Callable, warmup=25, grad_to_none=None, fast_flush=
             x.grad = None
     # we clear the L2 cache before run
     cache.zero_()
-    fn()
-    torch.cuda.cudart().cudaProfilerStop()
+    with torch.cuda.nvtx.range(range_name):
+        fn()

torchbenchmark/util/triton_op.py

@@ -414,6 +414,10 @@ def __call__(cls, *args, **kwargs):
         obj.__post__init__()
         return obj
 
+
+_RANGE_NAME = "tritonbench_range"
+
+
 class BenchmarkOperator(metaclass=PostInitProcessor):
     mode: Mode = Mode.FWD
     test: str = "eval"
@@ -827,6 +831,7 @@ def _init_extra_metrics() -> Dict[str, Any]:
                     fn=fn,
                     warmup=warmup,
                     grad_to_none=self.get_grad_to_none(self.example_inputs),
+                    range_name=_RANGE_NAME,
                 )
                 metrics.extra_metrics["_ncu_trace_in_task"] = "success"
             # generate customized metrics
@@ -901,26 +906,27 @@ def ncu_trace(self, input_id: int, fn_name: str, replay: bool=False) -> str:
             "ncu",
             "--set",
             "full",
-            "--replay-mode",
-            "kernel",
+            "--nvtx",
+            "--nvtx-include",
+            f"{_RANGE_NAME}/",
             "--target-processes",
             "all",
-            "--csv",
-            "-f",
-            "--log-file",
-            str(ncu_output_file.resolve()),
-        ] if not replay else [
-            "ncu",
-            "--set",
-            "full",
-            "--replay-mode",
-            "kernel",
-            "--target-processes",
-            "all",
-            "-f",
-            "-o",
-            str(ncu_output_file.resolve()),
+            "--import-source",
+            "yes",
         ]
+        if replay:
+            ncu_args.extend([
+                "-f",
+                "-o",
+                str(ncu_output_file.resolve()),
+            ])
+        else:
+            ncu_args.extend([
+                "--csv",
+                "-f",
+                "--log-file",
+                str(ncu_output_file.resolve()),
+            ])
         ncu_args.extend(op_task_args)
         subprocess.check_call(ncu_args)
         return str(ncu_output_file.resolve())