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Cleaned up and added check for "Total wall time" in the output

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This commit is contained in:
Trung Nguyen
2024-08-13 11:03:35 -05:00
parent bbd72a8960
commit 700a22b7cd
1 changed files with 52 additions and 33 deletions
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85
tools/regression-tests/run_tests.py
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@ -1,6 +1,6 @@
#!/usr/bin/env python3
'''
UPDATE: July 26, 2024:
UPDATE: August 13, 2024:
Launching the LAMMPS binary under testing using a configuration defined in a yaml file (e.g. config.yaml).
Comparing the output thermo with that in the existing log file (with the same nprocs)
+ data in the log files are extracted and converted into yaml data structure
@ -19,7 +19,7 @@ With the current features, users can:
Limitations:
- input scripts use thermo style multi (e.g., examples/peptide) do not work with the expected thermo output format
- input scripts that require partition runs (e.g. examples/neb) need a separate config file, e.g. "args: --partition 3x1"
- input scripts that require partition runs (e.g. examples/neb) need a separate config file, e.g. args: "--partition 3x1"
- testing accelerator packages (GPU, INTEL, KOKKOS, OPENMP) need separate config files, "args: -sf omp -pk omp 4"
TODO:
@ -60,16 +60,15 @@ Example usage:
of run_tests.py simultaneously.
'''
import os
from argparse import ArgumentParser
import datetime
import fnmatch
import logging
import os
import re
import subprocess
from argparse import ArgumentParser
#from multiprocessing import Pool
from multiprocessing import Pool
import logging
# need "pip install numpy pyyaml"
import numpy as np
import yaml
@ -82,6 +81,9 @@ try:
except ImportError:
from yaml import SafeLoader as Loader
'''
data structure to store the test result
'''
class TestResult:
def __init__(self, name, output=None, time=None, checks=0, status=None):
self.name = name
@ -105,22 +107,6 @@ class TestResult:
stat : a dictionary that lists the number of passed, skipped, failed tests
progress_file: yaml file that stores the tested input script and status
last_progress: the dictionary that shows the status of the last tests
NOTE:
To map a function to individual workers:
def func(input1, input2, output_buf):
# do smth
return result
# args is a list of num_workers tuples, each tuple contains the arguments passed to the function executed by a worker
args = []
for i in range(num_workers):
args.append((input1, input2, output_buf))
with Pool(num_workers) as pool:
results = pool.starmap(func, args)
'''
def iterate(lmp_binary, input_folder, input_list, config, results, progress_file, last_progress=None, output_buf=None):
@ -284,11 +270,13 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
# check if the output contains ERROR
if "ERROR" in output:
cmd_str = "grep ERROR log.lammps"
p = subprocess.run(cmd_str, shell=True, text=True, capture_output=True)
error_line = p.stdout.split('\n')[0]
error_line = ""
for line in output:
if "ERROR" in line:
error_line = line
break
logger.info(f" The run terminated with {input_test} gives the following output:")
logger.info(f" {error_line}")
logger.info(f" {error_line}")
if "Unrecognized" in output:
result.status = "error, unrecognized command, package not installed"
elif "Unknown" in output:
@ -309,6 +297,17 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
continue
# if there is no ERROR in the output, then there is something irregular in the run
if "Total wall time" not in output:
logger.info(f" ERROR: no Total wall time in the output.\n")
logger.info(f"\n{input_test}:")
logger.info(f"\n Output:\n{output}")
logger.info(f"\n Error:\n{error}")
progress.write(f"{input}: {{ folder: {input_folder}, status: \"error, no Total wall time in the output.\" }}\n")
progress.close()
num_error = num_error + 1
test_id = test_id + 1
continue
if "Step" not in output or "Loop" not in output:
logger.info(f" ERROR: no Step nor Loop in the output.\n")
logger.info(f"\n{input_test}:")
@ -332,11 +331,11 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
test_id = test_id + 1
continue
else:
# save a copy of the log file
# save a copy of the log file for further inspection
cmd_str = f"cp log.lammps log.{basename}.{nprocs}"
p = subprocess.run(cmd_str, shell=True, text=True, capture_output=True)
# process thermo output in log.lammps from the run
# parse thermo output in log.lammps from the run
thermo = extract_data_to_yaml("log.lammps")
num_runs = len(thermo)
@ -365,10 +364,12 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
# At this point, the run completed without trivial errors, proceed with numerical checks
# check if there is a reference log file for this input
if logfile_exist:
# parse the thermo output in reference log file
thermo_ref = extract_data_to_yaml(thermo_ref_file)
if thermo_ref:
num_runs_ref = len(thermo_ref)
else:
# dictionary is empty
logger.info(f" ERROR: Error parsing the reference log file {thermo_ref_file}.")
result.status = "skipped numerical checks due to parsing the reference log file"
results.append(result)
@ -381,13 +382,14 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
msg = f" Cannot find the reference log file for {input_test} with the expected format log.[date].{basename}.*.[nprocs]"
logger.info(msg)
print(msg)
# try to read in the thermo yaml output from the working directory
# attempt to read in the thermo yaml output from the working directory (the following section will be deprecated)
thermo_ref_file = 'thermo.' + input + '.yaml'
file_exist = os.path.isfile(thermo_ref_file)
if file_exist == True:
thermo_ref = extract_thermo(thermo_ref_file)
num_runs_ref = len(thermo_ref)
else:
# mostly will come to here if the reference log file does not exist
logger.info(f" {thermo_ref_file} also does not exist in the working directory.")
result.status = "skipped due to missing the reference log file"
results.append(result)
@ -400,6 +402,7 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
logger.info(f" Comparing thermo output from log.lammps against the reference log file {thermo_ref_file}")
# check if the number of runs matches with that in the reference log file
# maybe due to some changes to the input where the ref log file is not updated yet
if num_runs != num_runs_ref:
logger.info(f" ERROR: Number of runs in log.lammps ({num_runs}) is different from that in the reference log ({num_runs_ref})."
" Check README in the folder, possibly due to using mpirun with partitions or parsing the wrong reference log file.")
@ -411,7 +414,9 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
test_id = test_id + 1
continue
# check if the number of fields match with that in the reference log file in the first run for early exit
# check if the number of fields match with that in the reference log file in the first run
# due to some changes to the input where the ref log file is not updated yet
# for early exit
num_fields = len(thermo[0]['keywords'])
num_fields_ref = len(thermo_ref[0]['keywords'])
if num_fields != num_fields_ref:
@ -543,7 +548,7 @@ def iterate(lmp_binary, input_folder, input_list, config, results, progress_file
# check if memleak detects from valgrind run (need to replace "mpirun" -> valgrind --leak-check=yes mpirun")
msg = "completed"
if 'valgrind' in config['mpiexec']:
if "All heap blocks were free" in error:
if "All heap blocks were freed" in error:
msg += ", no memory leak"
else:
msg += ", memory leaks detected"
@ -705,6 +710,21 @@ def execute(lmp_binary, config, input_file_name, generate_ref_yaml=False):
'''
split a list into a list of N sublists
NOTE:
To map a function to individual workers with multiprocessing.Pool:
def func(input1, input2, output_buf):
# do smth
return result
# args is a list of num_workers tuples, each tuple contains the arguments passed to the function executed by a worker
args = []
for i in range(num_workers):
args.append((input1, input2, output_buf))
with Pool(num_workers) as pool:
results = pool.starmap(func, args)
'''
def divide_into_N(original_list, N):
size = np.ceil(len(original_list) / N)
@ -1002,7 +1022,6 @@ if __name__ == "__main__":
with Pool(num_workers) as pool:
results = pool.starmap(func, args)
'''
for directory in example_subfolders: