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#!/usr/bin/env python3
import argparse
import logging
import os
import pandas as pd
from torch._functorch.benchmark_utils import compute_utilization
# process the chrome traces output by the pytorch profiler
# require the json input file's name to be in format {model_name}_chrome_trace_*.json
# the runtimes file should have format (model_name, runtime)
def get_model_name(filename):
"""
Get model name from a file in format {model_name}_chrome_trace_*.json
"""
_, tail = os.path.split(filename)
modelname = tail[: tail.find("_chrome_trace")]
return modelname
def get_total_length(run_times_df, modelname):
return float(run_times_df[run_times_df["name"] == modelname]["runtime"])
def main():
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group(required=True)
parser.add_argument(
"--runtime", "-runf", help="file name of the runtime file", required=True
)
group.add_argument(
"--filename",
"-f",
action="append",
help="a filename of the json file to process",
)
group.add_argument("--folder", "-fd", help="a folder of the json files to process")
args = parser.parse_args()
if args.filename:
filenames = args.filename
elif args.folder:
filenames = []
directory = args.folder
for filename in os.listdir(directory):
f = os.path.join(directory, filename)
if os.path.isfile(f) and f.endswith(".json"):
filenames.append(f)
else:
print("Please provide a filename or a folder name")
print("modelname, GPU Utilization, MM and Conv time")
run_times_df = pd.read_csv(args.runtime)
for filename in filenames:
try:
modelname = get_model_name(filename)
total_length = get_total_length(run_times_df, modelname) * 1e6
utilization, mm_conv_utilization = compute_utilization(
filenames, total_length
)
print(f"{modelname}, {utilization}, {mm_conv_utilization}")
except BaseException:
logging.exception("%s, ERROR", filename)
print(f"{filename}, ERROR")
if __name__ == "__main__":
main()
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