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#!/usr/bin/env python3
# ########################################################################
# Copyright (C) 2022 Advanced Micro Devices, Inc. All rights Reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ########################################################################
import argparse
import subprocess
import os
import re # regexp package
import sys
import tempfile
import json
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--verbose', required=False, default = False, action = "store_true")
parser.add_argument('-t', '--tol', required=True, default = 2.0,type=float)
user_args, unknown_args = parser.parse_known_args()
verbose=user_args.verbose
percentage_tol = user_args.tol
data = []
num_files = len(unknown_args)
titles = []
for file_index in range(num_files):
titles.append(os.path.basename(os.path.splitext(unknown_args[file_index])[0]))
for file_index in range(num_files):
with open(unknown_args[file_index],"r") as f:
data.append(json.load(f))
cmd = [d['cmdline'] for d in data]
xargs = [d['xargs'] for d in data]
yargs = [d['yargs'] for d in data]
samples = [d['results'] for d in data]
num_samples = len(samples[0])
len_xargs = len(xargs[0])
if verbose:
print('//rocalution-bench-regression CONFIG')
for i in range(num_files):
print('//rocalution-bench-regression file'+str(i) +' : \'' + unknown_args[i] + '\'')
print('//rocalution-bench-regression COMPARISON')
####
for i in range(1,num_files):
if xargs[0] != xargs[i]:
print('xargs\'s must be equal, xargs from file \''+unknown_args[i]+'\' is not equal to xargs from file \''+unknown_args[0]+'\'')
exit(1)
if verbose:
print('//rocalution-bench-regression - xargs checked.')
####
for i in range(1,num_files):
if yargs[0] != yargs[i]:
print('yargs\'s must be equal, yargs from file \''+unknown_args[i]+'\' is not equal to yargs from file \''+unknown_args[0]+'\'')
exit(1)
if verbose:
print('//rocalution-bench-regression - yargs checked.')
####
for i in range(1,num_files):
if num_samples != len(samples[i]):
print('num_samples\'s must be equal, num_samples from file \''+unknown_args[i]+'\' is not equal to num_samples from file \''+unknown_args[0]+'\'')
exit(1)
if verbose:
print('//rocalution-bench-regression - num samples checked.')
####
if verbose:
print('//rocalution-bench-regression percentage_tol: ' + str(percentage_tol) + '%')
global_regression=False
global_improvement=False
for file_index in range(1,num_files):
print("//rocalution-bench-regression - '"+ unknown_args[file_index])
for iplot in range(len(yargs[0])):
print('//rocalution-bench-regression plot index ' + str(iplot) + ': \'' + yargs[0][iplot] + '\'',end='')
mx_rel_time_analyze=0
mx_rel_time_solve=0
mx_rel_iter=0
mx_rel_norm_residual=0
mn_rel_time_analyze=0
mn_rel_time_solve=0
mn_rel_iter=0
mn_rel_norm_residual=0
regression=False
improvement=False
regression_time_solve=0
regression_time_analyze=0
regression_iter=0
regression_norm_residual=0
for ixarg in range(len_xargs):
isample = iplot * len_xargs + ixarg
tg = samples[file_index][isample]["timing"]
tg0=samples[0][isample]["timing"]
rel_time_analyze = 100*(float(tg["median"]["time_analyze"])-float(tg0["median"]["time_analyze"]))/float(tg0["median"]["time_analyze"])
rel_time_solve = 100*(float(tg["median"]["time_solve"])-float(tg0["median"]["time_solve"]))/float(tg0["median"]["time_solve"])
rel_iter = 100*(float(tg["median"]["iter"])-float(tg0["median"]["iter"]))/float(tg0["median"]["iter"])
rel_norm_residual = 100*(float(tg["median"]["norm_residual"])-float(tg0["median"]["norm_residual"]))/float(tg0["median"]["norm_residual"])
if ixarg > 0:
mx_rel_time_solve=max(mx_rel_time_solve,rel_time_solve)
mn_rel_time_solve=min(mn_rel_time_solve,rel_time_solve)
mx_rel_time_analyze=max(mx_rel_time_analyze,rel_time_analyze)
mn_rel_time_analyze=min(mn_rel_time_analyze,rel_time_analyze)
mx_rel_iter=max(mx_rel_iter,rel_iter)
mn_rel_iter=min(mn_rel_iter,rel_iter)
mx_rel_norm_residual=max(mx_rel_norm_residual,rel_norm_residual)
mn_rel_norm_residual=min(mn_rel_norm_residual,rel_norm_residual)
else:
mx_rel_norm_residual=rel_norm_residual
mn_rel_norm_residual=rel_norm_residual
mx_rel_iter=rel_iter
mn_rel_iter=rel_iter
mx_rel_time_analyze=rel_time_analyze
mn_rel_time_analyze=rel_time_analyze
mx_rel_time_solve=rel_time_solve
mn_rel_time_solve=rel_time_solve
if (rel_time_solve > percentage_tol):
regression_time_solve = -1
elif (rel_time_solve < -percentage_tol):
regression_time_solve = 1
if (rel_time_analyze > percentage_tol):
regression_time_analyze = -1
elif (rel_time_analyze < -percentage_tol):
regression_time_analyze = 1
if (rel_iter > percentage_tol):
regression_iter = -1
elif (rel_iter < -percentage_tol):
regression_iter = 1
if (rel_norm_residual > percentage_tol):
regression_norm_residual = -1
elif (rel_norm_residual < -percentage_tol):
regression_norm_residual = 1
if (regression_time_solve == -1) or (regression_time_analyze == -1) or (regression_iter == -1) or (regression_norm_residual == -1):
print("")
if (regression_time_solve == 1):
improvement=True
if (regression_time_analyze == 1):
improvement=True
if (regression_iter == 1):
improvement=True
if (regression_norm_residual == 1):
improvement=True
if (regression_time_solve == -1):
improvement=False
regression=True
print("//rocalution-bench-regression FAIL time_solve exceeds tolerance of " + str(percentage_tol) + "%, [" +"{:.2f}".format(mn_rel_time_solve) + "," + "{:.2f}".format(mx_rel_time_solve) + "] from '" + xargs[file_index][ixarg] + "'")
if (regression_time_analyze == -1):
improvement=False
regression=True
print("//rocalution-bench-regression FAIL time_analyze exceeds tolerance of " + str(percentage_tol) + "%, [" +"{:.2f}".format(mn_rel_time_analyze) + "," + "{:.2f}".format(mx_rel_time_analyze) + "] from '" + xargs[file_index][ixarg] + "'")
if (regression_iter == -1):
improvement=False
regression=True
print("//rocalution-bench-regression FAIL iter exceeds tolerance of " + str(percentage_tol) + "%, [" +"{:.2f}".format(mn_rel_iter) + "," + "{:.2f}".format(mx_rel_iter) + "] from '" + xargs[file_index][ixarg] + "'")
if (regression_norm_residual == -1):
improvement=False
regression=True
print("//rocalution-bench-regression FAIL norm_residual exceeds tolerance of " + str(percentage_tol) + "%, [" +"{:.2f}".format(mn_rel_norm_residual) + "," + "{:.2f}".format(mx_rel_norm_residual) + "] from '" + xargs[file_index][ixarg] + "'")
if not improvement:
global_improvement=False
if regression:
global_regression=True
if regression:
print(" FAILED")
print('//rocalution-bench-regression plot index ' + str(iplot) + ': \'' + yargs[0][iplot] + '\' FAILED')
else:
if improvement:
print(" IMPROVED")
else:
print(" PASSED")
if verbose:
print("//rocalution-bench-regression time_solve [" +"{:.2f}".format(mn_rel_time_solve) + "," + "{:.2f}".format(mx_rel_time_solve) + "], " + "time_analyze [" +"{:.2f}".format(mn_rel_time_analyze) + "," + "{:.2f}".format(mx_rel_time_analyze) + "], " + "iter [" +"{:.2f}".format(mn_rel_iter) + "," + "{:.2f}".format(mx_rel_iter) + "], " + "norm_residual [" +"{:.2f}".format(mn_rel_norm_residual) + "," + "{:.2f}".format(mx_rel_norm_residual) )
if global_regression:
exit(1)
else:
exit(0)
if __name__ == "__main__":
main()
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