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Enable shape-sensitive analysis in ProfileExplorer for GPU kernels (#13647)

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### Description
Improve the profile explorer by enabling shape sensitivity for GPU
kernels.



### Motivation and Context
Due to problems with the ROCM profiler, it was previously challenging to
retrieve the shapes corresponding to a GPU kernel event. [PR
13546](https://github.com/microsoft/onnxruntime/pull/13549) addresses
these problems, so it's now possible to retrieve shapes from the ORT
ROCM/CUDA profilers. This PR leverages [PR
13546](https://github.com/microsoft/onnxruntime/pull/13549) to enable
shape-sensitive GPU kernel ranking.

Co-authored-by: Abhishek Udupa <abhishek.udupa@microsoft.com>
This commit is contained in:
Abhishek Udupa 2022-11-15 10:05:40 -08:00 committed by GitHub
parent 4cd8b4269a
commit 9c6c219949
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GPG key ID: 4AEE18F83AFDEB23
1 changed files with 31 additions and 4 deletions
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35
onnxruntime/python/tools/profile_explorer/profile_explorer.py
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@ -73,6 +73,10 @@ def _json_to_df(profile_path, filter_matcher):
if isinstance(data, dict):
data = data["traceEvents"]
most_recent_kernel_launch_event = None
num_missing_kernel_launch_events = 0
total_kernel_events = 0
for item in data:
cat = item.get("cat")
if cat is None:
@ -92,6 +96,8 @@ def _json_to_df(profile_path, filter_matcher):
if cat != "Kernel" and not name.endswith("kernel_time"):
continue
elif name.endswith("kernel_time"):
most_recent_kernel_launch_event = item
block_x = arg.get("block_x", -1)
block_y = arg.get("block_y", -1)
@ -107,18 +113,31 @@ def _json_to_df(profile_path, filter_matcher):
"duration": dur,
"dimensions": f"{block_x}_{block_y}_{block_z}_{grid_x}_{grid_y}_{grid_z}",
"op_name": op_name,
"input_type_shape": (
_shape_to_string(most_recent_kernel_launch_event["args"]["input_type_shape"])
if most_recent_kernel_launch_event is not None
else "unknown"
),
}
)
total_kernel_events += 1
if gpu_entries[-1]["input_type_shape"] == "unknown" and "hipMem" not in gpu_entries[-1]["name"]:
num_missing_kernel_launch_events += 1
else:
cpu_entries.append(
{
"name": item["args"]["op_name"],
"duration": dur,
"input_shape": _shape_to_string(item["args"]["input_type_shape"]),
"output_shape": _shape_to_string(item["args"]["output_type_shape"]),
"input_type_shape": _shape_to_string(item["args"]["input_type_shape"]),
"output_type_shape": _shape_to_string(item["args"]["output_type_shape"]),
}
)
if num_missing_kernel_launch_events > 0:
print(
f"WARNNG: Could not resolve shapes for {num_missing_kernel_launch_events} of {total_kernel_events} kernels."
)
cpu_df = pd.DataFrame(cpu_entries)
gpu_df = pd.DataFrame(gpu_entries)
cpu_df["count"] = 1
@ -131,7 +150,10 @@ def _print_cpu_top_hitters(frame, args):
print("No CPU entries found!")
return
top = args.count
group_key = ["name", "input_shape"] if args.shape_sensitive else ["name"]
group_key = ["name"]
if args.shape_sensitive:
group_key.append("input_type_shape")
frame2 = frame[["duration", "count"]].sum()
frame["pct"] = 100 * (frame["duration"] / frame2["duration"])
fields = group_key + ["duration", "pct", "count"]
@ -150,7 +172,12 @@ def _print_gpu_top_hitters(frame, args):
print("No GPU entries found!")
return
top = args.count
group_key = ["name", "dimensions"] if args.dimension_sensitive else ["name"]
group_key = ["name"]
if args.dimension_sensitive:
group_key.append("dimensions")
if args.shape_sensitive:
group_key.append("input_type_shape")
frame2 = frame[["duration", "count"]].sum()
frame["pct"] = 100 * (frame["duration"] / frame2["duration"])
fields = group_key + ["duration", "pct", "count"]