def get_error(model_files, dicts, source_file, target_file, devices):
logging.info("Loading model options from {}".format(model_files[0]))
with open(model_files[0], "r") as f:
model_options = json.load(f)
global dictionaries
logging.info("loading dictionaries from {}, {}".format(*dicts))
with open(dicts[0], "r") as f1, open(dicts[1], "r") as f2:
dictionaries = [json.load(f1), json.load(f2)]
logging.info("loading parameters from {}".format(model_files[1]))
params = load_params(model_files[1])
global in_queue
global out_queue
in_queue = Queue()
out_queue = Queue()
processes = [Process(target=error_process, name="process_{}".format(device),
args=(params, device), kwargs=model_options)
for device in devices.split(",")]
for p in processes:
p.daemon = True
p.start()
ti = TextIterator(source_file=source_file, target_file=target_file,
source_dict=dictionaries[0], target_dict=dictionaries[1],
maxlen=model_options["maxlen"],
n_words_source=model_options["n_words_source"],
n_words_target=model_options["n_words_target"],
raw_characters=model_options["characters"])
num_batches = 0
for batch in ti:
in_queue.put(batch)
num_batches += 1
for _ in processes:
in_queue.put("STOP")
costs = []
for num_processed in range(num_batches):
costs.append(out_queue.get())
percentage_done = (num_processed / num_batches) * 100
print("{}: {:.2f}% of input processed".format(model_files[1], percentage_done),
end="\r", flush=True)
print()
mean_cost = np.mean(costs)
print(model_files[1], mean_cost)
return mean_cost
def test_suite(argv):
"""
the main test suite driver
"""
# parse the commandline arguments
args = test_util.get_args(arg_string=argv)
# read in the test information
suite, test_list = params.load_params(args)
active_test_list = [t.name for t in test_list]
test_list = suite.get_tests_to_run(test_list)
suite.log.skip()
suite.log.bold("running tests: ")
suite.log.indent()
for obj in test_list:
suite.log.log(obj.name)
suite.log.outdent()
if not args.complete_report_from_crash == "":
# make sure the web directory from the crash run exists
suite.full_web_dir = "{}/{}/".format(
suite.webTopDir, args.complete_report_from_crash)
if not os.path.isdir(suite.full_web_dir):
suite.log.fail("Crash directory does not exist")
suite.test_dir = args.complete_report_from_crash
# find all the tests that completed in that web directory
tests = []
test_file = ""
was_benchmark_run = 0
for sfile in os.listdir(suite.full_web_dir):
if os.path.isfile(sfile) and sfile.endswith(".status"):
index = string.rfind(sfile, ".status")
tests.append(sfile[:index])
with open(suite.full_web_dir + sfile, "r") as f:
for line in f:
if line.find("benchmarks updated") > 0:
was_benchmark_run = 1
if os.path.isfile(sfile) and sfile.endswith(".ini"):
test_file = sfile
# create the report for this test run
num_failed = report.report_this_test_run(suite, was_benchmark_run,
"recreated report after crash of suite",
"", tests, test_file)
# create the suite report
suite.log.bold("creating suite report...")
report.report_all_runs(suite, active_test_list)
suite.log.close_log()
sys.exit("done")
#--------------------------------------------------------------------------
# check bench dir and create output directories
#--------------------------------------------------------------------------
all_compile = all([t.compileTest == 1 for t in test_list])
if not all_compile:
bench_dir = suite.get_bench_dir()
if not args.copy_benchmarks is None:
last_run = suite.get_last_run()
suite.make_test_dirs()
if suite.slack_post:
msg = "{} ({}) test suite started, id: {}\n{}".format(
suite.suiteName, suite.sub_title, suite.test_dir, args.note)
suite.slack_post_it(msg)
if not args.copy_benchmarks is None:
old_full_test_dir = suite.testTopDir + suite.suiteName + "-tests/" + last_run
copy_benchmarks(old_full_test_dir, suite.full_web_dir,
test_list, bench_dir, suite.log)
# here, args.copy_benchmarks plays the role of make_benchmarks
num_failed = report.report_this_test_run(suite, args.copy_benchmarks,
"copy_benchmarks used -- no new tests run",
"",
test_list, args.input_file[0])
report.report_all_runs(suite, active_test_list)
if suite.slack_post:
msg = "copied benchmarks\n{}".format(args.copy_benchmarks)
suite.slack_post_it(msg)
sys.exit("done")
#--------------------------------------------------------------------------
# figure out what needs updating and do the git updates, save the
# current hash / HEAD, and make a ChangeLog
# --------------------------------------------------------------------------
now = time.localtime(time.time())
update_time = time.strftime("%Y-%m-%d %H:%M:%S %Z", now)
no_update = args.no_update.lower()
if not args.copy_benchmarks is None:
no_update = "all"
# the default is to update everything, unless we specified a hash
# when constructing the Repo object
if no_update == "none":
pass
elif no_update == "all":
for k in suite.repos:
suite.repos[k].update = False
else:
nouplist = [k.strip() for k in no_update.split(",")]
for repo in suite.repos.keys():
if repo.lower() in nouplist:
suite.repos[repo].update = False
os.chdir(suite.testTopDir)
for k in suite.repos:
suite.log.skip()
suite.log.bold("repo: {}".format(suite.repos[k].name))
suite.log.indent()
if suite.repos[k].update or suite.repos[k].hash_wanted:
suite.repos[k].git_update()
suite.repos[k].save_head()
if suite.repos[k].update:
suite.repos[k].make_changelog()
suite.log.outdent()
# keep track if we are running on any branch that is not the suite
# default
branches = [suite.repos[r].branch_wanted for r in suite.repos]
if not all(suite.default_branch == b for b in branches):
suite.log.warn("some git repos are not on the default branch")
bf = open("{}/branch.status".format(suite.full_web_dir), "w")
bf.write("branch different than suite default")
bf.close()
#--------------------------------------------------------------------------
# build the tools and do a make clean, only once per build directory
#--------------------------------------------------------------------------
suite.build_tools(test_list)
all_build_dirs = find_build_dirs(test_list)
suite.log.skip()
suite.log.bold("make clean in...")
for d, source_tree in all_build_dirs:
if not source_tree == "":
suite.log.log("{} in {}".format(d, source_tree))
os.chdir(suite.repos[source_tree].dir + d)
suite.make_realclean(repo=source_tree)
else:
suite.log.log("{}".format(d))
os.chdir(suite.source_dir + d)
if suite.sourceTree == "BoxLib":
suite.make_realclean(repo="BoxLib")
else:
suite.make_realclean()
os.chdir(suite.testTopDir)
#--------------------------------------------------------------------------
# main loop over tests
#--------------------------------------------------------------------------
for test in test_list:
suite.log.outdent() # just to make sure we have no indentation
suite.log.skip()
suite.log.bold("working on test: {}".format(test.name))
suite.log.indent()
if not args.make_benchmarks is None and (test.restartTest or test.compileTest or
test.selfTest):
suite.log.warn("benchmarks not needed for test {}".format(test.name))
continue
output_dir = suite.full_test_dir + test.name + '/'
os.mkdir(output_dir)
test.output_dir = output_dir
#----------------------------------------------------------------------
# compile the code
#----------------------------------------------------------------------
if not test.extra_build_dir == "":
bdir = suite.repos[test.extra_build_dir].dir + test.buildDir
else:
bdir = suite.source_dir + test.buildDir
os.chdir(bdir)
if test.reClean == 1:
# for one reason or another, multiple tests use different
# build options, make clean again to be safe
suite.log.log("re-making clean...")
if not test.extra_build_dir == "":
suite.make_realclean(repo=test.extra_build_dir)
else:
suite.make_realclean()
suite.log.log("building...")
coutfile="{}/{}.make.out".format(output_dir, test.name)
if suite.sourceTree == "C_Src" or test.testSrcTree == "C_Src":
comp_string, rc = suite.build_c(test=test, outfile=coutfile)
executable = test_util.get_recent_filename(bdir, "", ".ex")
elif suite.sourceTree == "F_Src" or test.testSrcTree == "F_Src":
comp_string, rc = suite.build_f(test=test, outfile=coutfile)
executable = test_util.get_recent_filename(bdir, "main", ".exe")
test.comp_string = comp_string
# make return code is 0 if build was successful
if rc == 0: test.compile_successful = True
# copy the make.out into the web directory
shutil.copy("{}/{}.make.out".format(output_dir, test.name), suite.full_web_dir)
if not test.compile_successful:
error_msg = "ERROR: compilation failed"
report.report_single_test(suite, test, test_list, failure_msg=error_msg)
continue
if test.compileTest:
suite.log.log("creating problem test report ...")
report.report_single_test(suite, test, test_list)
continue
#----------------------------------------------------------------------
# copy the necessary files over to the run directory
#----------------------------------------------------------------------
suite.log.log("copying files to run directory...")
needed_files = []
needed_files.append((executable, "move"))
needed_files.append((test.inputFile, "copy"))
# strip out any sub-directory from the build dir
test.inputFile = os.path.basename(test.inputFile)
if test.probinFile != "":
needed_files.append((test.probinFile, "copy"))
# strip out any sub-directory from the build dir
test.probinFile = os.path.basename(test.probinFile)
for auxf in test.auxFiles:
needed_files.append((auxf, "copy"))
# if any copy/move fail, we move onto the next test
skip_to_next_test = 0
for nfile, action in needed_files:
if action == "copy":
act = shutil.copy
elif action == "move":
act = shutil.move
else:
suite.log.fail("invalid action")
try: act(nfile, output_dir)
except IOError:
error_msg = "ERROR: unable to {} file {}".format(action, nfile)
report.report_single_test(suite, test, test_list, failure_msg=error_msg)
skip_to_next_test = 1
break
if skip_to_next_test: continue
skip_to_next_test = 0
for lfile in test.linkFiles:
if not os.path.exists(lfile):
error_msg = "ERROR: link file {} does not exist".format(lfile)
report.report_single_test(suite, test, test_list, failure_msg=error_msg)
skip_to_next_test = 1
break
else:
link_source = os.path.abspath(lfile)
link_name = os.path.join(output_dir, os.path.basename(lfile))
try: os.symlink(link_source, link_name)
except IOError:
error_msg = "ERROR: unable to symlink link file: {}".format(lfile)
report.report_single_test(suite, test, test_list, failure_msg=error_msg)
skip_to_next_test = 1
break
if skip_to_next_test: continue
#----------------------------------------------------------------------
# run the test
#----------------------------------------------------------------------
suite.log.log("running the test...")
os.chdir(output_dir)
test.wall_time = time.time()
if suite.sourceTree == "C_Src" or test.testSrcTree == "C_Src":
base_cmd = "./{} {} amr.plot_file={}_plt amr.check_file={}_chk".format(
executable, test.inputFile, test.name, test.name)
# keep around the checkpoint files only for the restart runs
if test.restartTest:
base_cmd += " amr.checkpoint_files_output=1 amr.check_int=%d" % \
(test.restartFileNum)
else:
base_cmd += " amr.checkpoint_files_output=0"
base_cmd += "{} {}".format(suite.globalAddToExecString, test.runtime_params)
elif suite.sourceTree == "F_Src" or test.testSrcTree == "F_Src":
base_cmd = "./{} {} --plot_base_name {}_plt --check_base_name {}_chk ".format(
executable, test.inputFile, test.name, test.name)
# keep around the checkpoint files only for the restart runs
if not test.restartTest: base_cmd += " --chk_int 0 "
base_cmd += "{} {}".format(suite.globalAddToExecString, test.runtime_params)
if args.with_valgrind:
base_cmd = "valgrind " + args.valgrind_options + " " + base_cmd
suite.run_test(test, base_cmd)
# if it is a restart test, then rename the final output file and
# restart the test
if test.restartTest:
skip_restart = False
last_file = test.get_last_plotfile(output_dir=output_dir)
if last_file == "":
error_msg = "ERROR: test did not produce output. Restart test not possible"
skip_restart = True
if len(test.find_backtrace()) > 0:
error_msg = "ERROR: test produced backtraces. Restart test not possible"
skip_restart = True
if skip_restart:
# copy what we can
test.wall_time = time.time() - test.wall_time
shutil.copy("{}.run.out".format(test.name), suite.full_web_dir)
if os.path.isfile("{}.err.out".format(test.name)):
shutil.copy("{}.err.out".format(test.name), suite.full_web_dir)
test.has_stderr = True
suite.copy_backtrace(test)
report.report_single_test(suite, test, test_list, failure_msg=error_msg)
continue
orig_last_file = "orig_{}".format(last_file)
shutil.move(last_file, orig_last_file)
if test.diffDir:
orig_diff_dir = "orig_{}".format(test.diffDir)
shutil.move(test.diffDir, orig_diff_dir)
# get the file number to restart from
restart_file = "%s_chk%5.5d" % (test.name, test.restartFileNum)
suite.log.log("restarting from {} ... ".format(restart_file))
if suite.sourceTree == "C_Src" or test.testSrcTree == "C_Src":
base_cmd = "./{} {} amr.plot_file={}_plt amr.check_file={}_chk amr.checkpoint_files_output=0 amr.restart={}".format(
executable, test.inputFile, test.name, test.name, restart_file)
elif suite.sourceTree == "F_Src" or test.testSrcTree == "F_Src":
base_cmd = "./{} {} --plot_base_name {}_plt --check_base_name {}_chk --chk_int 0 --restart {} {}".format(
executable, test.inputFile, test.name, test.name, test.restartFileNum, suite.globalAddToExecString)
suite.run_test(test, base_cmd)
test.wall_time = time.time() - test.wall_time
#----------------------------------------------------------------------
# do the comparison
#----------------------------------------------------------------------
if not test.selfTest:
if test.outputFile == "":
if test.compareFile == "":
compare_file = test.get_last_plotfile(output_dir=output_dir)
else:
# we specified the name of the file we want to
# compare to -- make sure it exists
compare_file = test.compareFile
if not os.path.isdir(compare_file):
compare_file = ""
output_file = compare_file
else:
output_file = test.outputFile
compare_file = test.name+'_'+output_file
# get the number of levels for reporting
prog = "{} -l {}".format(suite.tools["fboxinfo"], output_file)
stdout0, stderr0, rc = test_util.run(prog)
test.nlevels = stdout0.rstrip('\n')
if not type(params.convert_type(test.nlevels)) is int:
test.nlevels = ""
if args.make_benchmarks is None:
suite.log.log("doing the comparison...")
suite.log.indent()
suite.log.log("comparison file: {}".format(output_file))
test.compare_file_used = output_file
if not test.restartTest:
bench_file = bench_dir + compare_file
else:
bench_file = orig_last_file
# see if it exists
# note, with BoxLib, the plotfiles are actually directories
if not os.path.isdir(bench_file):
suite.log.warn("no corresponding benchmark found")
bench_file = ""
with open("{}.compare.out".format(test.name), 'w') as cf:
cf.write("WARNING: no corresponding benchmark found\n")
cf.write(" unable to do a comparison\n")
else:
if not compare_file == "":
suite.log.log("benchmark file: {}".format(bench_file))
command = "{} -n 0 {} {}".format(
suite.tools["fcompare"], bench_file, output_file)
sout, serr, ierr = test_util.run(command,
outfile="{}.compare.out".format(test.name), store_command=True)
if ierr == 0:
test.compare_successful = True
else:
suite.log.warn("unable to do a comparison")
with open("{}.compare.out".format(test.name), 'w') as cf:
cf.write("WARNING: run did not produce any output\n")
cf.write(" unable to do a comparison\n")
suite.log.outdent()
if not test.diffDir == "":
if not test.restartTest:
diff_dir_bench = bench_dir + '/' + test.name + '_' + test.diffDir
else:
diff_dir_bench = orig_diff_dir
suite.log.log("doing the diff...")
suite.log.log("diff dir: {}".format(test.diffDir))
command = "diff {} -r {} {}".format(
test.diffOpts, diff_dir_bench, test.diffDir)
outfile = "{}.compare.out".format(test.name)
sout, serr, diff_status = test_util.run(command, outfile=outfile, store_command=True)
if diff_status == 0:
diff_successful = True
with open("{}.compare.out".format(test.name), 'a') as cf:
cf.write("\ndiff was SUCCESSFUL\n")
else:
diff_successful = False
test.compare_successful = test.compare_successful and diff_successful
else: # make_benchmarks
suite.log.log("storing output of {} as the new benchmark...".format(test.name))
suite.log.indent()
suite.log.warn("new benchmark file: {}".format(compare_file))
suite.log.outdent()
if not compare_file == "":
if not output_file == compare_file:
source_file = output_file
else:
source_file = compare_file
try: shutil.rmtree("{}/{}".format(bench_dir, compare_file))
except: pass
shutil.copytree(source_file, "{}/{}".format(bench_dir, compare_file))
with open("{}.status".format(test.name), 'w') as cf:
cf.write("benchmarks updated. New file: {}\n".format(compare_file) )
else:
with open("{}.status".format(test.name), 'w') as cf:
cf.write("benchmarks failed")
# copy what we can
shutil.copy("{}.run.out".format(test.name), suite.full_web_dir)
if os.path.isfile("{}.err.out".format(test.name)):
shutil.copy("{}.err.out".format(test.name), suite.full_web_dir)
test.has_stderr = True
suite.copy_backtrace(test)
error_msg = "ERROR: runtime failure during benchmark creation"
report.report_single_test(suite, test, test_list, failure_msg=error_msg)
if not test.diffDir == "":
diff_dir_bench = "{}/{}_{}".format(bench_dir, test.name, test.diffDir)
if os.path.isdir(diff_dir_bench):
shutil.rmtree(diff_dir_bench)
shutil.copytree(test.diffDir, diff_dir_bench)
else:
shutil.copy(test.diffDir, diff_dir_bench)
suite.log.log("new diffDir: {}_{}".format(test.name, test.diffDir))
else: # selfTest
if args.make_benchmarks is None:
suite.log.log("looking for selfTest success string: {} ...".format(test.stSuccessString))
try: of = open("{}.run.out".format(test.name), 'r')
except IOError:
suite.log.warn("no output file found")
out_lines = ['']
else:
out_lines = of.readlines()
# successful comparison is indicated by presence
# of success string
for line in out_lines:
if line.find(test.stSuccessString) >= 0:
test.compare_successful = True
break
of.close()
with open("{}.compare.out".format(test.name), 'w') as cf:
if test.compare_successful:
cf.write("SELF TEST SUCCESSFUL\n")
else:
cf.write("SELF TEST FAILED\n")
#----------------------------------------------------------------------
# do any requested visualization (2- and 3-d only) and analysis
#----------------------------------------------------------------------
if not test.selfTest:
if output_file != "":
if args.make_benchmarks is None:
# get any parameters for the summary table
job_info_file = "{}/job_info".format(output_file)
if os.path.isfile(job_info_file):
test.has_jobinfo = 1
try: jif = open(job_info_file, "r")
except:
suite.log.warn("unable to open the job_info file")
else:
job_file_lines = jif.readlines()
if suite.summary_job_info_field1 is not "":
for l in job_file_lines:
if l.find(suite.summary_job_info_field1) >= 0 and l.find(":") >= 0:
_tmp = l.split(":")[1]
idx = _tmp.rfind("/") + 1
test.job_info_field1 = _tmp[idx:]
break
if suite.summary_job_info_field2 is not "":
for l in job_file_lines:
if l.find(suite.summary_job_info_field2) >= 0 and l.find(":") >= 0:
_tmp = l.split(":")[1]
idx = _tmp.rfind("/") + 1
test.job_info_field2 = _tmp[idx:]
break
if suite.summary_job_info_field3 is not "":
for l in job_file_lines:
if l.find(suite.summary_job_info_field3) >= 0 and l.find(":") >= 0:
_tmp = l.split(":")[1]
idx = _tmp.rfind("/") + 1
test.job_info_field3 = _tmp[idx:]
break
# visualization
if test.doVis:
if test.dim == 1:
suite.log.log("Visualization not supported for dim = {}".format(test.dim))
else:
suite.log.log("doing the visualization...")
tool = suite.tools["fsnapshot{}d".format(test.dim)]
test_util.run('{} --palette {}/Palette -cname "{}" -p "{}"'.format(
tool, suite.compare_tool_dir, test.visVar, output_file))
# convert the .ppm files into .png files
ppm_file = test_util.get_recent_filename(output_dir, "", ".ppm")
if not ppm_file is None:
png_file = ppm_file.replace(".ppm", ".png")
test_util.run("convert {} {}".format(ppm_file, png_file))
test.png_file = png_file
# analysis
if not test.analysisRoutine == "":
suite.log.log("doing the analysis...")
if not test.extra_build_dir == "":
tool = "{}/{}".format(suite.repos[test.extra_build_dir].dir, test.analysisRoutine)
else:
tool = "{}/{}".format(suite.source_dir, test.analysisRoutine)
shutil.copy(tool, os.getcwd())
option = eval("suite.{}".format(test.analysisMainArgs))
test_util.run("{} {} {}".format(os.path.basename(test.analysisRoutine),
option, output_file))
else:
if test.doVis or test.analysisRoutine != "":
suite.log.warn("no output file. Skipping visualization")
#----------------------------------------------------------------------
# move the output files into the web directory
#----------------------------------------------------------------------
if args.make_benchmarks is None:
shutil.copy("{}.run.out".format(test.name), suite.full_web_dir)
if os.path.isfile("{}.err.out".format(test.name)):
shutil.copy("{}.err.out".format(test.name), suite.full_web_dir)
test.has_stderr = True
shutil.copy("{}.compare.out".format(test.name), suite.full_web_dir)
shutil.copy(test.inputFile, "{}/{}.{}".format(
suite.full_web_dir, test.name, test.inputFile) )
if test.has_jobinfo:
shutil.copy(job_info_file, "{}/{}.job_info".format(
suite.full_web_dir, test.name))
if suite.sourceTree == "C_Src" and test.probinFile != "":
shutil.copy(test.probinFile, "{}/{}.{}".format(
suite.full_web_dir, test.name, test.probinFile) )
for af in test.auxFiles:
# strip out any sub-directory under build dir for the aux file
# when copying
shutil.copy(os.path.basename(af),
"{}/{}.{}".format(suite.full_web_dir,
test.name, os.path.basename(af)) )
if not test.png_file is None:
try: shutil.copy(test.png_file, suite.full_web_dir)
except IOError:
# visualization was not successful. Reset image
test.png_file = None
if not test.analysisRoutine == "":
try: shutil.copy(test.analysisOutputImage, suite.full_web_dir)
except IOError:
# analysis was not successful. Reset the output image
test.analysisOutputImage = ""
# were any Backtrace files output (indicating a crash)
suite.copy_backtrace(test)
else:
shutil.copy("{}.status".format(test.name), suite.full_web_dir)
#----------------------------------------------------------------------
# archive (or delete) the output
#----------------------------------------------------------------------
suite.log.log("archiving the output...")
for pfile in os.listdir(output_dir):
if (os.path.isdir(pfile) and
(pfile.startswith("{}_plt".format(test.name)) or
pfile.startswith("{}_chk".format(test.name)) ) ):
if suite.purge_output == 1 and not pfile == output_file:
# delete the plt/chk file
if os.path.isdir(pfile):
try: shutil.rmtree(pfile)
except:
suite.log.warn("unable to remove {}".format(pfile))
else:
# tar it up
try:
tar = tarfile.open("{}.tgz".format(pfile), "w:gz")
tar.add("{}".format(pfile))
tar.close()
except:
suite.log.warn("unable to tar output file {}".format(pfile))
else:
try: shutil.rmtree(pfile)
except OSError:
suite.log.warn("unable to remove {}".format(pfile))
#----------------------------------------------------------------------
# write the report for this test
#----------------------------------------------------------------------
if args.make_benchmarks is None:
suite.log.log("creating problem test report ...")
report.report_single_test(suite, test, test_list)
#--------------------------------------------------------------------------
# write the report for this instance of the test suite
#--------------------------------------------------------------------------
suite.log.outdent()
suite.log.skip()
suite.log.bold("creating new test report...")
num_failed = report.report_this_test_run(suite, args.make_benchmarks, args.note,
update_time,
test_list, args.input_file[0])
# make sure that all of the files in the web directory are world readable
for file in os.listdir(suite.full_web_dir):
current_file = suite.full_web_dir + file
if os.path.isfile(current_file):
os.chmod(current_file, 0o644)
# reset the branch to what it was originally
suite.log.skip()
suite.log.bold("reverting git branches/hashes")
suite.log.indent()
for k in suite.repos:
if suite.repos[k].update or suite.repos[k].hash_wanted:
suite.repos[k].git_back()
suite.log.outdent()
# For temporary run, return now without creating suote report.
if args.do_temp_run:
return num_failed
# store an output file in the web directory that can be parsed easily by
# external program
name = "source"
if suite.sourceTree == "BoxLib": name = "BoxLib"
branch = suite.repos[name].branch_wanted.strip("\"")
with open("{}/suite.{}.status".format(suite.webTopDir, branch), "w") as f:
f.write("{}; num failed: {}; source hash: {}".format(
suite.repos[name].name, num_failed, suite.repos[name].hash_current))
#--------------------------------------------------------------------------
# generate the master report for all test instances
#--------------------------------------------------------------------------
suite.log.skip()
suite.log.bold("creating suite report...")
report.report_all_runs(suite, active_test_list)
def email_developers():
msg = email.message_from_string(suite.emailBody)
msg['From'] = suite.emailFrom
msg['To'] = ",".join(suite.emailTo)
msg['Subject'] = suite.emailSubject
server = smtplib.SMTP('localhost')
server.sendmail(suite.emailFrom, suite.emailTo, msg.as_string())
server.quit()
if num_failed > 0 and suite.sendEmailWhenFail and not args.send_no_email:
suite.log.skip()
suite.log.bold("sending email...")
email_developers()
if suite.slack_post:
suite.slack_post_it("test complete, num failed = {}\n{}".format(num_failed, suite.emailBody))
return num_failed
def reg_test_gc(argv):
usage = """
./reg_test_gc [--before|-b 2000-00-00]
testfile.ini
"""
if len(sys.argv) == 1:
print usage
sys.exit(2)
try:
opts, next = getopt.getopt(argv[1:], "b:",
["before="])
except getopt.GetoptError:
print "invalid calling sequence"
print usage
sys.exit(2)
# defaults
gcdate = ""
for o, a in opts:
if o == "--before" or o == "-b" :
gcdate = a
try:
testFile = next[0]
except IndexError:
print "ERROR: a test file was not specified"
print usage
sys.exit(2)
if not gcdate:
print "ERROR: date was not specified"
print usage
sys.exit(2)
gcd = valid_date(gcdate)
if gcd == '':
print "ERROR: invalid date", gcdate
print usage
sys.exit(2)
workdir = os.getcwd()
print "loading ", testFile
args=test_util.get_args([testFile])
suite, testList = params.load_params(args)
activeTestList = [t.name for t in testList]
benchmarkTestList = [t for t in testList if not (t.compileTest or t.restartTest)]
benchmarkNotFound = {}
for t in benchmarkTestList:
benchmarkNotFound[t.name] = ''
### clean up the web dir
print "\ncleaning ", suite.webTopDir
os.chdir(suite.webTopDir)
validDirs = []
for d in os.listdir(suite.webTopDir):
if (d.startswith("20") and os.path.isdir(d)):
statusFile = d + '/' + d + '.status'
if (os.path.isfile(statusFile)):
validDirs.append(d)
validDirs.sort()
validDirs.reverse()
latestBMDate = {}
for d in validDirs:
bmtests = benchmarkNotFound.keys()
if d >= gcd and bmtests:
if isBenchmarkDir(d):
for t in bmtests:
if findBenchmark(d,t):
del benchmarkNotFound[t]
latestBMDate[t] = d
else:
if isBenchmarkDir(d) and bmtests:
found = False
for t in bmtests:
if findBenchmark(d,t):
found = True
del benchmarkNotFound[t]
latestBMDate[t] = d
if not found:
rmDir(d)
else:
rmDir(d)
### clean up the test dir
testDirs = os.path.join(suite.testTopDir,suite.suiteName+"-tests")
print "\ncleaning ", testDirs
os.chdir(testDirs)
validDirs = []
for d in os.listdir(testDirs):
if (d.startswith("20") and os.path.isdir(d)):
validDirs.append(d)
validDirs.sort()
validDirs.reverse()
for d in validDirs:
if d < gcd:
tests = [t for t in os.listdir(d) if os.path.isdir(os.path.join(d,t))]
found = False
for t in tests:
if t in latestBMDate.keys() and latestBMDate[t] == d:
found = True
break
if not found:
rmDir(d)
print "\ncreating suite report..."
report.report_all_runs(suite, activeTestList)
print "\nGarbage cleaning finished."
def test_suite(argv):
"""
the main test suite driver
"""
# parse the commandline arguments
args = test_util.get_args(arg_string=argv)
# read in the test information
suite, test_list = params.load_params(args)
active_test_list = [t.name for t in test_list]
test_list = suite.get_tests_to_run(test_list)
suite.log.skip()
suite.log.bold("running tests: ")
suite.log.indent()
for obj in test_list:
suite.log.log(obj.name)
suite.log.outdent()
if not args.complete_report_from_crash == "":
# make sure the web directory from the crash run exists
suite.full_web_dir = "{}/{}/".format(suite.webTopDir,
args.complete_report_from_crash)
if not os.path.isdir(suite.full_web_dir):
suite.log.fail("Crash directory does not exist")
suite.test_dir = args.complete_report_from_crash
# find all the tests that completed in that web directory
tests = []
test_file = ""
was_benchmark_run = 0
for sfile in os.listdir(suite.full_web_dir):
if os.path.isfile(sfile) and sfile.endswith(".status"):
index = string.rfind(sfile, ".status")
tests.append(sfile[:index])
with open(suite.full_web_dir + sfile, "r") as f:
for line in f:
if line.find("benchmarks updated") > 0:
was_benchmark_run = 1
if os.path.isfile(sfile) and sfile.endswith(".ini"):
test_file = sfile
# create the report for this test run
num_failed = report.report_this_test_run(
suite, was_benchmark_run, "recreated report after crash of suite",
"", tests, test_file)
# create the suite report
suite.log.bold("creating suite report...")
report.report_all_runs(suite, active_test_list)
suite.log.close_log()
sys.exit("done")
#--------------------------------------------------------------------------
# check bench dir and create output directories
#--------------------------------------------------------------------------
all_compile = all([t.compileTest == 1 for t in test_list])
if not all_compile:
bench_dir = suite.get_bench_dir()
if not args.copy_benchmarks is None:
last_run = suite.get_last_run()
suite.make_test_dirs()
if suite.slack_post:
msg = "> {} ({}) test suite started, id: {}\n> {}".format(
suite.suiteName, suite.sub_title, suite.test_dir, args.note)
suite.slack_post_it(msg)
if not args.copy_benchmarks is None:
old_full_test_dir = suite.testTopDir + suite.suiteName + "-tests/" + last_run
copy_benchmarks(old_full_test_dir, suite.full_web_dir, test_list,
bench_dir, suite.log)
# here, args.copy_benchmarks plays the role of make_benchmarks
num_failed = report.report_this_test_run(
suite, args.copy_benchmarks,
"copy_benchmarks used -- no new tests run", "", test_list,
args.input_file[0])
report.report_all_runs(suite, active_test_list)
if suite.slack_post:
msg = "> copied benchmarks\n> {}".format(args.copy_benchmarks)
suite.slack_post_it(msg)
sys.exit("done")
#--------------------------------------------------------------------------
# figure out what needs updating and do the git updates, save the
# current hash / HEAD, and make a ChangeLog
# --------------------------------------------------------------------------
now = time.localtime(time.time())
update_time = time.strftime("%Y-%m-%d %H:%M:%S %Z", now)
no_update = args.no_update.lower()
if not args.copy_benchmarks is None:
no_update = "all"
# the default is to update everything, unless we specified a hash
# when constructing the Repo object
if no_update == "none":
pass
elif no_update == "all":
for k in suite.repos:
suite.repos[k].update = False
else:
nouplist = [k.strip() for k in no_update.split(",")]
for repo in suite.repos.keys():
if repo.lower() in nouplist:
suite.repos[repo].update = False
os.chdir(suite.testTopDir)
for k in suite.repos:
suite.log.skip()
suite.log.bold("repo: {}".format(suite.repos[k].name))
suite.log.indent()
if suite.repos[k].update or suite.repos[k].hash_wanted:
suite.repos[k].git_update()
suite.repos[k].save_head()
if suite.repos[k].update:
suite.repos[k].make_changelog()
suite.log.outdent()
# keep track if we are running on any branch that is not the suite
# default
branches = [suite.repos[r].branch_wanted for r in suite.repos]
if not all(suite.default_branch == b for b in branches):
suite.log.warn("some git repos are not on the default branch")
bf = open("{}/branch.status".format(suite.full_web_dir), "w")
bf.write("branch different than suite default")
bf.close()
#--------------------------------------------------------------------------
# build the tools and do a make clean, only once per build directory
#--------------------------------------------------------------------------
suite.build_tools(test_list)
all_build_dirs = find_build_dirs(test_list)
suite.log.skip()
suite.log.bold("make clean in...")
for d, source_tree in all_build_dirs:
if not source_tree == "":
suite.log.log("{} in {}".format(d, source_tree))
os.chdir(suite.repos[source_tree].dir + d)
suite.make_realclean(repo=source_tree)
else:
suite.log.log("{}".format(d))
os.chdir(suite.source_dir + d)
if suite.sourceTree in ["AMReX", "amrex"]:
suite.make_realclean(repo="AMReX")
else:
suite.make_realclean()
os.chdir(suite.testTopDir)
#--------------------------------------------------------------------------
# Setup Cmake if needed
#--------------------------------------------------------------------------
if (suite.useCmake):
cmake_setup(suite)
#--------------------------------------------------------------------------
# main loop over tests
#--------------------------------------------------------------------------
for test in test_list:
suite.log.outdent() # just to make sure we have no indentation
suite.log.skip()
suite.log.bold("working on test: {}".format(test.name))
suite.log.indent()
if not args.make_benchmarks is None and (test.restartTest
or test.compileTest
or test.selfTest):
suite.log.warn("benchmarks not needed for test {}".format(
test.name))
continue
output_dir = suite.full_test_dir + test.name + '/'
os.mkdir(output_dir)
test.output_dir = output_dir
#----------------------------------------------------------------------
# compile the code
#----------------------------------------------------------------------
if not test.extra_build_dir == "":
bdir = suite.repos[test.extra_build_dir].dir + test.buildDir
else:
bdir = suite.source_dir + test.buildDir
# # For cmake builds, there is only one build dir
# if ( suite.useCmake ): bdir = suite.source_build_dir
os.chdir(bdir)
if test.reClean == 1:
# for one reason or another, multiple tests use different
# build options, make clean again to be safe
suite.log.log("re-making clean...")
if not test.extra_build_dir == "":
suite.make_realclean(repo=test.extra_build_dir)
else:
suite.make_realclean()
suite.log.log("building...")
coutfile = "{}/{}.make.out".format(output_dir, test.name)
if suite.sourceTree == "C_Src" or test.testSrcTree == "C_Src":
if (suite.useCmake):
comp_string, rc = suite.build_test_cmake(test=test,
outfile=coutfile)
else:
comp_string, rc = suite.build_c(test=test, outfile=coutfile)
executable = test_util.get_recent_filename(bdir, "", ".ex")
elif suite.sourceTree == "F_Src" or test.testSrcTree == "F_Src":
comp_string, rc = suite.build_f(test=test, outfile=coutfile)
executable = test_util.get_recent_filename(bdir, "main", ".exe")
test.comp_string = comp_string
# make return code is 0 if build was successful
if rc == 0: test.compile_successful = True
# copy the make.out into the web directory
shutil.copy("{}/{}.make.out".format(output_dir, test.name),
suite.full_web_dir)
if not test.compile_successful:
error_msg = "ERROR: compilation failed"
report.report_single_test(suite,
test,
test_list,
failure_msg=error_msg)
continue
if test.compileTest:
suite.log.log("creating problem test report ...")
report.report_single_test(suite, test, test_list)
continue
#----------------------------------------------------------------------
# copy the necessary files over to the run directory
#----------------------------------------------------------------------
suite.log.log("copying files to run directory...")
needed_files = []
if executable is not None:
needed_files.append((executable, "move"))
needed_files.append((test.inputFile, "copy"))
# strip out any sub-directory from the build dir
test.inputFile = os.path.basename(test.inputFile)
if test.probinFile != "":
needed_files.append((test.probinFile, "copy"))
# strip out any sub-directory from the build dir
test.probinFile = os.path.basename(test.probinFile)
for auxf in test.auxFiles:
needed_files.append((auxf, "copy"))
# if any copy/move fail, we move onto the next test
skip_to_next_test = 0
for nfile, action in needed_files:
if action == "copy":
act = shutil.copy
elif action == "move":
act = shutil.move
else:
suite.log.fail("invalid action")
try:
act(nfile, output_dir)
except IOError:
error_msg = "ERROR: unable to {} file {}".format(action, nfile)
report.report_single_test(suite,
test,
test_list,
failure_msg=error_msg)
skip_to_next_test = 1
break
if skip_to_next_test: continue
skip_to_next_test = 0
for lfile in test.linkFiles:
if not os.path.exists(lfile):
error_msg = "ERROR: link file {} does not exist".format(lfile)
report.report_single_test(suite,
test,
test_list,
failure_msg=error_msg)
skip_to_next_test = 1
break
else:
link_source = os.path.abspath(lfile)
link_name = os.path.join(output_dir, os.path.basename(lfile))
try:
os.symlink(link_source, link_name)
except IOError:
error_msg = "ERROR: unable to symlink link file: {}".format(
lfile)
report.report_single_test(suite,
test,
test_list,
failure_msg=error_msg)
skip_to_next_test = 1
break
if skip_to_next_test: continue
#----------------------------------------------------------------------
# run the test
#----------------------------------------------------------------------
suite.log.log("running the test...")
os.chdir(output_dir)
test.wall_time = time.time()
if suite.sourceTree == "C_Src" or test.testSrcTree == "C_Src":
base_cmd = "./{} {} amr.plot_file={}_plt amr.check_file={}_chk".format(
executable, test.inputFile, test.name, test.name)
# keep around the checkpoint files only for the restart runs
if test.restartTest:
base_cmd += " amr.checkpoint_files_output=1 amr.check_int=%d" % \
(test.restartFileNum)
else:
base_cmd += " amr.checkpoint_files_output=0"
base_cmd += " {} {}".format(suite.globalAddToExecString,
test.runtime_params)
elif suite.sourceTree == "F_Src" or test.testSrcTree == "F_Src":
base_cmd = "./{} {} --plot_base_name {}_plt --check_base_name {}_chk ".format(
executable, test.inputFile, test.name, test.name)
# keep around the checkpoint files only for the restart runs
if not test.restartTest: base_cmd += " --chk_int 0 "
base_cmd += "{} {}".format(suite.globalAddToExecString,
test.runtime_params)
if args.with_valgrind:
base_cmd = "valgrind " + args.valgrind_options + " " + base_cmd
if test.customRunCmd is not None:
base_cmd = test.customRunCmd
suite.run_test(test, base_cmd)
# if it is a restart test, then rename the final output file and
# restart the test
if test.restartTest:
skip_restart = False
last_file = test.get_last_plotfile(output_dir=output_dir)
if last_file == "":
error_msg = "ERROR: test did not produce output. Restart test not possible"
skip_restart = True
if len(test.find_backtrace()) > 0:
error_msg = "ERROR: test produced backtraces. Restart test not possible"
skip_restart = True
if skip_restart:
# copy what we can
test.wall_time = time.time() - test.wall_time
shutil.copy("{}.run.out".format(test.name), suite.full_web_dir)
if os.path.isfile("{}.err.out".format(test.name)):
shutil.copy("{}.err.out".format(test.name),
suite.full_web_dir)
test.has_stderr = True
suite.copy_backtrace(test)
report.report_single_test(suite,
test,
test_list,
failure_msg=error_msg)
continue
orig_last_file = "orig_{}".format(last_file)
shutil.move(last_file, orig_last_file)
if test.diffDir:
orig_diff_dir = "orig_{}".format(test.diffDir)
shutil.move(test.diffDir, orig_diff_dir)
# get the file number to restart from
restart_file = "%s_chk%5.5d" % (test.name, test.restartFileNum)
suite.log.log("restarting from {} ... ".format(restart_file))
if suite.sourceTree == "C_Src" or test.testSrcTree == "C_Src":
base_cmd = "./{} {} amr.plot_file={}_plt amr.check_file={}_chk amr.checkpoint_files_output=0 amr.restart={}".format(
executable, test.inputFile, test.name, test.name,
restart_file)
elif suite.sourceTree == "F_Src" or test.testSrcTree == "F_Src":
base_cmd = "./{} {} --plot_base_name {}_plt --check_base_name {}_chk --chk_int 0 --restart {} {}".format(
executable, test.inputFile, test.name, test.name,
test.restartFileNum, suite.globalAddToExecString)
suite.run_test(test, base_cmd)
test.wall_time = time.time() - test.wall_time
#----------------------------------------------------------------------
# do the comparison
#----------------------------------------------------------------------
if not test.selfTest:
if test.outputFile == "":
if test.compareFile == "":
compare_file = test.get_last_plotfile(
output_dir=output_dir)
else:
# we specified the name of the file we want to
# compare to -- make sure it exists
compare_file = test.compareFile
if not os.path.isdir(compare_file):
compare_file = ""
output_file = compare_file
else:
output_file = test.outputFile
compare_file = test.name + '_' + output_file
# get the number of levels for reporting
prog = "{} -l {}".format(suite.tools["fboxinfo"], output_file)
stdout0, stderr0, rc = test_util.run(prog)
test.nlevels = stdout0.rstrip('\n')
if not type(params.convert_type(test.nlevels)) is int:
test.nlevels = ""
if args.make_benchmarks is None:
suite.log.log("doing the comparison...")
suite.log.indent()
suite.log.log("comparison file: {}".format(output_file))
test.compare_file_used = output_file
if not test.restartTest:
bench_file = bench_dir + compare_file
else:
bench_file = orig_last_file
# see if it exists
# note, with AMReX, the plotfiles are actually directories
if not os.path.isdir(bench_file):
suite.log.warn("no corresponding benchmark found")
bench_file = ""
with open("{}.compare.out".format(test.name), 'w') as cf:
cf.write("WARNING: no corresponding benchmark found\n")
cf.write(" unable to do a comparison\n")
else:
if not compare_file == "":
suite.log.log("benchmark file: {}".format(bench_file))
command = "{} -n 0 {} {}".format(
suite.tools["fcompare"], bench_file, output_file)
sout, serr, ierr = test_util.run(
command,
outfile="{}.compare.out".format(test.name),
store_command=True)
if ierr == 0:
test.compare_successful = True
if test.compareParticles:
for ptype in test.particleTypes.strip().split():
command = "{} {} {} {}".format(
suite.tools["particle_compare"],
bench_file, output_file, ptype)
sout, serr, ierr = test_util.run(
command,
outfile="{}.compare.out".format(test.name),
store_command=True)
test.compare_successful = test.compare_successful and not ierr
else:
suite.log.warn("unable to do a comparison")
with open("{}.compare.out".format(test.name),
'w') as cf:
cf.write(
"WARNING: run did not produce any output\n")
cf.write(" unable to do a comparison\n")
suite.log.outdent()
if not test.diffDir == "":
if not test.restartTest:
diff_dir_bench = bench_dir + '/' + test.name + '_' + test.diffDir
else:
diff_dir_bench = orig_diff_dir
suite.log.log("doing the diff...")
suite.log.log("diff dir: {}".format(test.diffDir))
command = "diff {} -r {} {}".format(
test.diffOpts, diff_dir_bench, test.diffDir)
outfile = "{}.compare.out".format(test.name)
sout, serr, diff_status = test_util.run(command,
outfile=outfile,
store_command=True)
if diff_status == 0:
diff_successful = True
with open("{}.compare.out".format(test.name),
'a') as cf:
cf.write("\ndiff was SUCCESSFUL\n")
else:
diff_successful = False
test.compare_successful = test.compare_successful and diff_successful
else: # make_benchmarks
suite.log.log(
"storing output of {} as the new benchmark...".format(
test.name))
suite.log.indent()
suite.log.warn("new benchmark file: {}".format(compare_file))
suite.log.outdent()
if not compare_file == "":
if not output_file == compare_file:
source_file = output_file
else:
source_file = compare_file
try:
shutil.rmtree("{}/{}".format(bench_dir, compare_file))
except:
pass
shutil.copytree(source_file,
"{}/{}".format(bench_dir, compare_file))
with open("{}.status".format(test.name), 'w') as cf:
cf.write("benchmarks updated. New file: {}\n".format(
compare_file))
else:
with open("{}.status".format(test.name), 'w') as cf:
cf.write("benchmarks failed")
# copy what we can
shutil.copy("{}.run.out".format(test.name),
suite.full_web_dir)
if os.path.isfile("{}.err.out".format(test.name)):
shutil.copy("{}.err.out".format(test.name),
suite.full_web_dir)
test.has_stderr = True
suite.copy_backtrace(test)
error_msg = "ERROR: runtime failure during benchmark creation"
report.report_single_test(suite,
test,
test_list,
failure_msg=error_msg)
if not test.diffDir == "":
diff_dir_bench = "{}/{}_{}".format(bench_dir, test.name,
test.diffDir)
if os.path.isdir(diff_dir_bench):
shutil.rmtree(diff_dir_bench)
shutil.copytree(test.diffDir, diff_dir_bench)
else:
if os.path.isdir(test.diffDir):
shutil.copytree(test.diffDir, diff_dir_bench)
else:
shutil.copy(test.diffDir, diff_dir_bench)
suite.log.log("new diffDir: {}_{}".format(
test.name, test.diffDir))
else: # selfTest
if args.make_benchmarks is None:
suite.log.log(
"looking for selfTest success string: {} ...".format(
test.stSuccessString))
try:
of = open("{}.run.out".format(test.name), 'r')
except IOError:
suite.log.warn("no output file found")
out_lines = ['']
else:
out_lines = of.readlines()
# successful comparison is indicated by presence
# of success string
for line in out_lines:
if line.find(test.stSuccessString) >= 0:
test.compare_successful = True
break
of.close()
with open("{}.compare.out".format(test.name), 'w') as cf:
if test.compare_successful:
cf.write("SELF TEST SUCCESSFUL\n")
else:
cf.write("SELF TEST FAILED\n")
#----------------------------------------------------------------------
# do any requested visualization (2- and 3-d only) and analysis
#----------------------------------------------------------------------
if not test.selfTest:
if output_file != "":
if args.make_benchmarks is None:
# get any parameters for the summary table
job_info_file = "{}/job_info".format(output_file)
if os.path.isfile(job_info_file):
test.has_jobinfo = 1
try:
jif = open(job_info_file, "r")
except:
suite.log.warn("unable to open the job_info file")
else:
job_file_lines = jif.readlines()
jif.close()
if suite.summary_job_info_field1 is not "":
for l in job_file_lines:
if l.startswith(suite.summary_job_info_field1.
strip()) and l.find(":") >= 0:
_tmp = l.split(":")[1]
idx = _tmp.rfind("/") + 1
test.job_info_field1 = _tmp[idx:]
break
if suite.summary_job_info_field2 is not "":
for l in job_file_lines:
if l.startswith(suite.summary_job_info_field2.
strip()) and l.find(":") >= 0:
_tmp = l.split(":")[1]
idx = _tmp.rfind("/") + 1
test.job_info_field2 = _tmp[idx:]
break
if suite.summary_job_info_field3 is not "":
for l in job_file_lines:
if l.startswith(suite.summary_job_info_field3.
strip()) and l.find(":") >= 0:
_tmp = l.split(":")[1]
idx = _tmp.rfind("/") + 1
test.job_info_field3 = _tmp[idx:]
break
# visualization
if test.doVis:
if test.dim == 1:
suite.log.log(
"Visualization not supported for dim = {}".
format(test.dim))
else:
suite.log.log("doing the visualization...")
tool = suite.tools["fsnapshot{}d".format(test.dim)]
test_util.run(
'{} --palette {}/Palette -cname "{}" -p "{}"'.
format(tool, suite.f_compare_tool_dir,
test.visVar, output_file))
# convert the .ppm files into .png files
ppm_file = test_util.get_recent_filename(
output_dir, "", ".ppm")
if not ppm_file is None:
png_file = ppm_file.replace(".ppm", ".png")
test_util.run("convert {} {}".format(
ppm_file, png_file))
test.png_file = png_file
# analysis
if not test.analysisRoutine == "":
suite.log.log("doing the analysis...")
if not test.extra_build_dir == "":
tool = "{}/{}".format(
suite.repos[test.extra_build_dir].dir,
test.analysisRoutine)
else:
tool = "{}/{}".format(suite.source_dir,
test.analysisRoutine)
shutil.copy(tool, os.getcwd())
if test.analysisMainArgs == "":
option = ""
else:
option = eval("suite.{}".format(
test.analysisMainArgs))
cmd_name = os.path.basename(test.analysisRoutine)
cmd_string = "./{} {} {}".format(
cmd_name, option, output_file)
outfile = "{}.analysis.out".format(test.name)
_, _, rc = test_util.run(cmd_string,
outfile=outfile,
store_command=True)
if rc == 0:
analysis_successful = True
else:
analysis_successful = False
suite.log.warn("analysis failed...")
test.compare_successful = test.compare_successful and analysis_successful
else:
if test.doVis or test.analysisRoutine != "":
suite.log.warn("no output file. Skipping visualization")
#----------------------------------------------------------------------
# move the output files into the web directory
#----------------------------------------------------------------------
if args.make_benchmarks is None:
shutil.copy("{}.run.out".format(test.name), suite.full_web_dir)
if os.path.isfile("{}.err.out".format(test.name)):
shutil.copy("{}.err.out".format(test.name), suite.full_web_dir)
test.has_stderr = True
shutil.copy("{}.compare.out".format(test.name), suite.full_web_dir)
try:
shutil.copy("{}.analysis.out".format(test.name),
suite.full_web_dir)
except:
pass
shutil.copy(
test.inputFile, "{}/{}.{}".format(suite.full_web_dir,
test.name, test.inputFile))
if test.has_jobinfo:
shutil.copy(
job_info_file,
"{}/{}.job_info".format(suite.full_web_dir, test.name))
if suite.sourceTree == "C_Src" and test.probinFile != "":
shutil.copy(
test.probinFile,
"{}/{}.{}".format(suite.full_web_dir, test.name,
test.probinFile))
for af in test.auxFiles:
# strip out any sub-directory under build dir for the aux file
# when copying
shutil.copy(
os.path.basename(af),
"{}/{}.{}".format(suite.full_web_dir, test.name,
os.path.basename(af)))
if not test.png_file is None:
try:
shutil.copy(test.png_file, suite.full_web_dir)
except IOError:
# visualization was not successful. Reset image
test.png_file = None
if not test.analysisRoutine == "":
try:
shutil.copy(test.analysisOutputImage, suite.full_web_dir)
except IOError:
# analysis was not successful. Reset the output image
test.analysisOutputImage = ""
# were any Backtrace files output (indicating a crash)
suite.copy_backtrace(test)
else:
shutil.copy("{}.status".format(test.name), suite.full_web_dir)
#----------------------------------------------------------------------
# archive (or delete) the output
#----------------------------------------------------------------------
suite.log.log("archiving the output...")
for pfile in os.listdir(output_dir):
if (os.path.isdir(pfile)
and (pfile.startswith("{}_plt".format(test.name))
or pfile.startswith("{}_chk".format(test.name)))):
if suite.purge_output == 1 and not pfile == output_file:
# delete the plt/chk file
if os.path.isdir(pfile):
try:
shutil.rmtree(pfile)
except:
suite.log.warn("unable to remove {}".format(pfile))
else:
# tar it up
try:
tar = tarfile.open("{}.tgz".format(pfile), "w:gz")
tar.add("{}".format(pfile))
tar.close()
except:
suite.log.warn(
"unable to tar output file {}".format(pfile))
else:
try:
shutil.rmtree(pfile)
except OSError:
suite.log.warn("unable to remove {}".format(pfile))
#----------------------------------------------------------------------
# write the report for this test
#----------------------------------------------------------------------
if args.make_benchmarks is None:
suite.log.log("creating problem test report ...")
report.report_single_test(suite, test, test_list)
#--------------------------------------------------------------------------
# Clean Cmake build and install directories if needed
#--------------------------------------------------------------------------
if (suite.useCmake):
suite.cmake_clean("AMReX", suite.amrex_dir)
suite.cmake_clean(suite.suiteName, suite.source_dir)
#--------------------------------------------------------------------------
# write the report for this instance of the test suite
#--------------------------------------------------------------------------
suite.log.outdent()
suite.log.skip()
suite.log.bold("creating new test report...")
num_failed = report.report_this_test_run(suite, args.make_benchmarks,
args.note, update_time, test_list,
args.input_file[0])
# make sure that all of the files in the web directory are world readable
for file in os.listdir(suite.full_web_dir):
current_file = suite.full_web_dir + file
if os.path.isfile(current_file):
os.chmod(current_file, 0o644)
# reset the branch to what it was originally
suite.log.skip()
suite.log.bold("reverting git branches/hashes")
suite.log.indent()
for k in suite.repos:
if suite.repos[k].update or suite.repos[k].hash_wanted:
suite.repos[k].git_back()
suite.log.outdent()
# For temporary run, return now without creating suote report.
if args.do_temp_run:
return num_failed
# store an output file in the web directory that can be parsed easily by
# external program
name = "source"
if suite.sourceTree in ["AMReX", "amrex"]: name = "AMReX"
branch = ''
if suite.repos[name].branch_wanted:
branch = suite.repos[name].branch_wanted.strip("\"")
with open("{}/suite.{}.status".format(suite.webTopDir, branch), "w") as f:
f.write("{}; num failed: {}; source hash: {}".format(
suite.repos[name].name, num_failed,
suite.repos[name].hash_current))
#--------------------------------------------------------------------------
# generate the master report for all test instances
#--------------------------------------------------------------------------
suite.log.skip()
suite.log.bold("creating suite report...")
report.report_all_runs(suite, active_test_list)
def email_developers():
msg = email.message_from_string(suite.emailBody)
msg['From'] = suite.emailFrom
msg['To'] = ",".join(suite.emailTo)
msg['Subject'] = suite.emailSubject
server = smtplib.SMTP('localhost')
server.sendmail(suite.emailFrom, suite.emailTo, msg.as_string())
server.quit()
if num_failed > 0 and suite.sendEmailWhenFail and not args.send_no_email:
suite.log.skip()
suite.log.bold("sending email...")
email_developers()
if suite.slack_post:
suite.slack_post_it("> test complete, num failed = {}\n{}".format(
num_failed, suite.emailBody))
return num_failed
def quench_single_inverse_power(coord_file_name, foldpath, sub_fold_name,
optimizer, opt_param_dict):
"""
figures out the minimum correspoding to a set of particle coords
Parameters
----------
coord_file_name: string
name of the path to the coordinates
foldername: str
folder definining the run
sub_fold_name:
name of subfolder where the run data is stored
optimizer: optimizer
quench
opt_param_dict: dict
dictionary of parameters for the optimizer
"""
sysparams = load_params(foldpath)
# path to quench coords
quench_coords_path = (foldpath + "/" + sub_fold_name + "/" + "ensemble/" +
coord_file_name)
quench_coords = np.loadtxt(quench_coords_path)
radii = get_hs_radii(foldpath, sub_fold_name)
box_length = get_box_length(radii, sysparams.ndim.value,
sysparams.phi.value)
boxv = [box_length] * sysparams.ndim.value
ncellx_scale = get_ncellsx_scale(radii, boxv)
potential = InversePower(
sysparams.power.value,
sysparams.eps.value,
use_cell_lists=False,
ndim=sysparams.ndim.value,
radii=radii * 1.0,
boxvec=boxv,
)
boxv = [box_length] * sysparams.ndim.value
# ncellx_scale = get_ncellsx_scale(radii, boxv)
print(potential.getEnergy(quench_coords))
ret = optimizer(quench_coords, potential, **opt_param_dict)
try:
ret = optimizer(quench_coords, potential, **opt_param_dict)
except:
print("exception occured")
# if exception occurs, treat as failure. this is for rattlers
# not enough failures occur that it would make a difference to not just assume this never happens
# but we shoudl switch this out
# but jic
return (quench_coords, False, 0, 0, 0, 0, 0)
# This exists because some runs don't have hessian evaluations
try:
ret["nhev"]
except:
ret["nhev"] = 0
# mixed optimizer statistics
try:
ret["n_phase_1"]
except:
ret["n_phase_1"] = 0
# mixed optimizer statistics
try:
ret["n_phase_2"]
except:
ret["n_phase_2"] = 0
print(ret.coords - quench_coords)
print(opt_param_dict)
results = (
ret.coords,
ret.success,
ret.nfev,
ret.nsteps,
ret.nhev,
ret.n_phase_1,
ret.n_phase_2,
)
print(quench_coords_path)
return results
def googlenet_train(train_batch_size=32, val_batch_size=50, image_size=(3, 224, 224), n_epochs=60):
#mean_path = '/home/2T/caffe/data/ilsvrc12/imagenet_mean.binaryproto'
train_lmdb_path = '/home/2T/imagenet/ilsvrc2012/lmdb/ilsvrc12_train_lmdb'
val_lmdb_path = '/home/2T/imagenet/ilsvrc2012/lmdb/ilsvrc12_val_lmdb'
train_input_shape = (train_batch_size,) + image_size
val_input_shape = (val_batch_size,) + image_size
trainning_model = googlenet(train_input_shape)
validating_model = googlenet(val_input_shape)
#####read lmdb
train_lmdb_iterator = read_lmdb(train_batch_size, train_lmdb_path)
train_data_size = train_lmdb_iterator.total_number
n_train_batches = train_data_size / train_batch_size
print('n_train_batches = '+ str(n_train_batches))
val_lmdb_iterator = read_lmdb(val_batch_size, val_lmdb_path)
val_data_size = val_lmdb_iterator.total_number
n_val_batches = val_data_size / val_batch_size
print('n_val_batches = '+ str(n_val_batches))
## COMPILE FUNCTIONS ##
(train_model, train_error,
train_shared_x, train_shared_y, shared_lr) = compile_train_model(trainning_model, batch_size=train_batch_size)
(val_model, val_shared_x, val_shared_y) = compile_val_model(validating_model, batch_size=val_batch_size)
all_costs = []
all_errors = []
####load net state
net_params = load_net_state()
if net_params:
load_params(model.params, net_params['model_params'])
train_lmdb_iterator.set_cursor(net_params['minibatch_index'])
all_errors = net_params['all_errors']
all_costs = net_params['all_costs']
epoch = net_params['epoch']
minibatch_index = net_params['minibatch_index']
else:
all_costs = []
all_errors = []
epoch = 0
minibatch_index = 0
print('... training')
while(epoch < n_epochs):
while(minibatch_index < n_train_batches):
####training
#print(minibatch_index)
iter = epoch * n_train_batches + minibatch_index
print('training @ epoch = %d : iter = %d : totoal_batches = %d' %(epoch, iter, n_train_batches))
begin_time = time.time()
train_data, train_label = train_lmdb_iterator.next()
train_shared_x.set_value(train_data)
train_shared_y.set_value(train_label)
set_learning_rate(shared_lr, iter)
#begin_time = time.time()
cost_ij = train_model()
error_ij = train_error()
all_costs.append(cost_ij)
all_errors.append(error_ij)
print('train_error: %f %%' %(error_ij*100))
print('trian_cost: %f' %(cost_ij))
end_time = time.time()
print('Time per iteration: %f' % (end_time - begin_time))
if math.isnan(cost_ij):
nan_params = get_params(model.params)
common_save(nan_params, './nan_params')
sys.exit(0)
###validation
if (iter+1) % (4*n_train_batches) == 0:
validation_erorrs = []
validating_model.set_dropout_off()
for validation_index in xrange(0, n_val_batches):
#print('validation_index = %d : total_batches = %d' %(validation_index, n_val_batches))
val_data, val_label = val_lmdb_iterator.next()
val_shared_x.set_value(val_data)
val_shared_y.set_value(val_label)
cost, errors, errors_top_5 = val_model()
validation_erorrs.append(errors_top_5)
validating_model.set_dropout_on()
this_validation_error = np.mean(validation_erorrs)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_error * 100.))
###save params every epoch
if (iter+1) % n_train_batches == 0:
net_params['model_params'] = get_params(model.params)
net_params['minibatch_index'] = minibatch_index
net_params['all_costs'] = all_costs
net_params['all_errors'] = all_errors
net_params['epoch'] = epoch
save_net_state(net_params)
save_figure(all_costs, all_errors)
minibatch_index += 1
if minibatch_index == n_train_batches:
minibatch_index = 0
epoch = epoch + 1
from params import load_params, load_secondary_params
from pele.potentials import InversePower
# change according to which minima you want to compare
m1_arg = 148
m2_arg = 159
m3_arg = 194
m4_arg = 195
foldnameInversePower = "ndim=2phi=0.9seed=0n_part=16r1=1.0r2=1.4rstd1=0.05rstd2=0.06999999999999999use_cell_lists=0power=2.5eps=1.0"
minima_database_path = (BASE_DIRECTORY + "/" + foldnameInversePower + "/" +
MINIMA_DATABASE_NAME)
th = np.load(minima_database_path)
foldpath = BASE_DIRECTORY + "/" + foldnameInversePower
sysparams = load_params(foldpath)
(hs_radii, initial_coords, box_length) = load_secondary_params(foldpath)
ctol = 1e-3
ndim = 2
potential = InversePower(
sysparams.power.value,
sysparams.eps.value,
use_cell_lists=False,
ndim=sysparams.ndim.value,
radii=hs_radii * 1.0,
boxvec=[box_length, box_length],
)
minima_container = CheckSameMinimum(
ctol,
ndim,
boxl=box_length,
apply_mask_l2 = theano.function(
inputs=[],
updates={model.models_stack[2].w : model.models_stack[2].w * mask_l2_theano}
)
apply_mask = [apply_mask_l0, apply_mask_l1, apply_mask_l2]
# deeper layers are fully connected.
# pdb.set_trace()
"""
print "Done."
#############
# PRE-TRAIN #
#############
"""
for i in range(len(hid_layer_sizes)):
print "\n\nPre-training layer %d:" % i
trainer = GraddescentMinibatch(
varin=model.varin, data=train_x,
cost=model.models_stack[i].cost(),
params=model.models_stack[i].params_private,
supervised=False,
batchsize=batchsize, learningrate=pretrain_lr, momentum=momentum,
rng=npy_rng
)
init_lr = trainer.learningrate
prev_cost = numpy.inf
epc_cost = 0.
patience = 0
def map_binary_inversepower(
foldername,
particle_coords,
optimizer,
parameter_dict,
random_coord_0=0,
random_coord_1=-1,
z=0,
):
"""
Finds whether a point defined by particle_coord
on the meshgrid correspond to a minimum or not for a 2d
case.
"""
foldpath = BASE_DIRECTORY + "/" + foldername
# import params
sysparams = load_params(foldpath)
(hs_radii, initial_coords, box_length) = load_secondary_params(foldpath)
assert sysparams.ndim.value == 2
minimum_coords = np.loadtxt(foldpath + "/coords_of_minimum.txt", delimiter=",")
quench_coords = initial_coords.copy()
if len(quench_coords) == 16:
quench_coords = (
quench_coords
+ particle_coords[0] * VEC_8_0
+ particle_coords[1] * VEC_8_1
+ z * VEC_8_2
)
elif len(quench_coords) == 32:
quench_coords = (
quench_coords
+ particle_coords[0] * VEC_16_0
+ particle_coords[1] * VEC_16_1
+ z * VEC_16_2
)
elif len(quench_coords) == 64:
quench_coords = (
quench_coords
+ particle_coords[0] * VEC_16_0
+ particle_coords[1] * VEC_16_1
+ z * VEC_16_2
)
else:
raise Exception("error other random coords have not been generated")
print(quench_coords, "quench")
# print(quench_coords, 'quench coords')
# box length
box_length = float(box_length)
boxv = [box_length] * sysparams.ndim.value
ncellx_scale = get_ncellsx_scale(hs_radii, boxv)
potential = InversePower(
sysparams.power.value,
sysparams.eps.value,
use_cell_lists=False,
ndim=sysparams.ndim.value,
radii=hs_radii * 1.0,
boxvec=boxv,
)
# ret = quench_mixed_optimizer(potential,
# quench_coords, # make sure right coords are being passed
# T=10,
# step=1,
# nsteps=100000,
# conv_tol=1e-8,
# tol=1e-6, rtol=1e-4, atol=1e-4)
# ret = quench_steepest(
# potential,
# quench_coords, # make sure right coords are being passed
# nsteps=2000000,
# stepsize=5e-3, # for steepest descent step size should be small
# tol=1e-4)
# ret = quench_cvode_opt(potential, quench_coords, tol=1e-6, rtol=1e-4, atol=1e-4)
try:
ret = optimizer(quench_coords, potential, **parameter_dict)
except:
print(quench_coords, "failed here")
print(initial_coords, "coords")
print(len(quench_coords))
raise Exception("failure")
# ret = lbfgs_cpp(quench_coords, potential, tol=1e-8, M=1)
# This exists because some runs don't have hessian evaluations
try:
ret["nhev"]
except:
ret["nhev"] = 0
coordarg = 0
results = (ret.coords, ret.success, coordarg, ret.nfev, ret.nsteps, ret.nhev)
# the reason the potential is being passed is because quench coords needs the potential to figure out what to do
return results
def map_pointset_loop_xy(
foldname,
pointset,
optimizer,
parameter_dict,
ctol=1e-2,
ndim=2,
use_minima_database=True,
minima_database_path=None,
coord_arg_0=0,
coord_arg_1=1,
z=0,
):
""" Checks a bunch of points if they match to a minimum by using a for loop
"""
is_same_minimum_list = []
resultlist = []
foldpath = BASE_DIRECTORY + "/" + foldname
sysparams = load_params(foldpath)
(hs_radii, initial_coords, box_length) = load_secondary_params(foldpath)
minimum_coords = np.loadtxt(foldpath + "/coords_of_minimum.txt", delimiter=",")
# Initialize CheckSameMinimum
potential = InversePower(
sysparams.power.value,
sysparams.eps.value,
use_cell_lists=False,
ndim=sysparams.ndim.value,
radii=hs_radii * 1.0,
boxvec=[box_length, box_length],
)
minima_container = CheckSameMinimum(
ctol,
ndim,
boxl=box_length,
minimalist_max_len=200000,
minima_database_location=minima_database_path,
update_database=True,
rattler_check=True,
potential=potential,
hs_radii=hs_radii,
)
if use_minima_database == True:
try:
minima_container.minimalist = [
minima_container.box_reshape_coords(x)
for x in np.load(minima_database_path)
]
except:
print("warning no minima data found. generating")
minima_container.minimalist = [
# minima_container.box_reshape_coords(minimum_coords)
]
nfevlist = []
nstepslist = []
nhevlist = []
for index, point in enumerate(pointset):
res = map_binary_inversepower(
foldname,
point,
optimizer,
parameter_dict,
random_coord_0=coord_arg_0,
random_coord_1=coord_arg_1,
z=z,
)
minima_container.add_minimum(res[0], point, res[2])
# print(index)
# print(minima_container.nrattlermin, 'nrattlermin')
# print(minima_container.nfluidstates, 'nfluidstates')
nfevlist.append(res[3])
nstepslist.append(res[4])
nhevlist.append(res[5])
# print(np.average(nfevlist), 'number of function evaluations')
# print(np.average(nstepslist), 'number of steps')
# print(np.average(nstepslist), 'number of steps')
# print(np.average(nhevlist), "number of hessian evaluations")
# print(minima_container.orderparamlist)
foldpathdata = foldpath + "/" + QUENCH_FOLDER_NAME + "/z_data_30_l6/" + str(z)
os.makedirs(foldpathdata, exist_ok=True)
minima_container.dump_map(foldpathdata)
run_diagnostics = {}
run_diagnostics["nfev"] = float(np.average(nfevlist))
run_diagnostics["nhev"] = float(np.average(nhevlist))
run_diagnostics["nsteps"] = float(np.average(nstepslist))
# print(minima_container.initial_coords_list)
# print(minima_container.orderparamlist)
# print(minima_container.orderparamlist)
return run_diagnostics, is_same_minimum_list, resultlist
coord_arg_0=coord_arg_0,
coord_arg_1=coord_arg_1,
z=z,
)
# print(res)
# # boollist = np.loadtxt(BASE_DIRECTORY + '/' + foldnameInversePower + '/' + 'quench_results_fire.txt')
# np.savetxt(BASE_DIRECTORY + '/' + foldnameInversePower + '/' + 'quench_results_mxopt.txt', boollist)
# boollistreshaped = np.reshape(boollist, (nmesh, nmesh))
# print(boollistreshaped)
# print(boollist)
# plt.imshow(boollistreshaped)
# plt.show()
# # print(reslist)
sysparams = load_params(data_location)
# save all parameters from run
run_diagnostics = res[0]
run_diagnostics["identification tolerance"] = identification_tolerance
run_diagnostics["nmesh"] = nmesh
run_diagnostics["ndim"] = sysparams.ndim.value
run_diagnostics["nparticles"] = sysparams.n_part.value
run_diagnostics["run data location"] = data_location + "/" + QUENCH_FOLDER_NAME
opt_name = parameter_dict["name"].replace(" ", "_")
# write run data to two different locations
write_run_data_to_file(
parameter_dict,
run_diagnostics,
folder_location=data_location,
name=opt_name + ".yaml",
map1.shelter_policy({"school":0.00,"shop":0.05,"entertainment":0.0,"home":1,"hospital":0.00})
'''
print('\n'+"Day "+str(map1.date)+" statistics:")
if len(stats)==0:
stats=simulate_one_day(map1)
else:
stats= np.vstack((stats,simulate_one_day(map1)))
if map1.date>=10:
map1.quarantine_policy({"school":0.0,"shop":0.0,"entertainment":0.0,"home":0.0,"hospital":0},detect_prob=0.15)
print("R0 values",get_R0(stats))
end_time=time.time()
print("Simulation time:",end_time-start_time)
plot_stats(stats)
print("peak value",(np.argmax(stats[:,0]),np.max(stats[:,0])))
if __name__ == "__main__":
Jkantor_param=load_params()
print("parameters",Jkantor_param)
simulate()
'''
map1.print_people()
'''
model_ft.models_stack[-1].params)
)
def return_grad(test_params, input_x, truth_y):
tmp = get_params(model_ft.models_stack[-1])
set_params(model_ft.models_stack[-1], test_params)
result = numpy.concatenate([numpy.array(i).flatten() for i in fun_grad(input_x, truth_y)])
set_params(model_ft.models_stack[-1], tmp)
return result
p, g, numlinesearches = minimize(
get_params(model_ft.models_stack[-1]), return_cost, return_grad,
(train_x.get_value(), train_y.get_value()), logreg_epc, verbose=False
)
set_params(model_ft.models_stack[-1], p)
save_params(model_ft, 'ZLIN_4000_1000_4000_10_normhid_nolinb_cae1_dtagmt2_dropout.npy')
load_params(model_ft, 'ZLIN_4000_1000_4000_10_normhid_nolinb_cae1_dtagmt2_dropout.npy')
print "***error rate: train: %f, test: %f" % (
train_set_error_rate(), test_set_error_rate()
)
#############
# FINE-TUNE #
#############
"""
print "\n\n... fine-tuning the whole network"
truth = T.lmatrix('truth')
trainer = GraddescentMinibatch(
varin=model_ft.varin, data=train_x,
truth=model_ft.models_stack[-1].vartruth, truth_data=train_y,
supervised=True,
def quench_single_mxopt_inverse_power_julia(
coord_file_name, foldpath, sub_fold_name, optimizer, opt_param_dict
):
"""
quenches a single system through mxopt
Parameters
----------
coord_file_name: string
name of the path to the coordinates
foldername: str
folder definining the run
sub_fold_name:
name of subfolder where the run data is stored
optimizer: optimizer
quench
opt_param_dict: dict
dictionary of parameters for the optimizer
"""
sysparams = load_params(foldpath)
# path to quench coords
quench_coords_path = (
foldpath + "/" + sub_fold_name + "/" + "ensemble/" + coord_file_name
)
quench_coords = np.loadtxt(quench_coords_path)
radii = get_hs_radii(foldpath, sub_fold_name)
box_length = get_box_length(radii, sysparams.ndim.value, sysparams.phi.value)
box_length
boxv = np.array([box_length] * sysparams.ndim.value)
ncellx_scale = get_ncellsx_scale(radii, boxv)
# potential = InversePower(sysparams.power.value,
# sysparams.eps.value,
# use_cell_lists=False,
# ndim=sysparams.ndim.value,
# radii=radii * 1.0,
# boxvec=boxv)
mxd = Main.Mixed_Descent(
pele_wrapped_python_pot,
Main.solver,
Main.nls,
coords,
opt_param_dict["T"],
opt_param_dict["rtol"],
opt_param_dict["conv_tol"],
opt_param_dict["tol"],
)
pot = Main.pot.InversePower(
sysparams.power.value,
sysparams.eps.value,
radii,
ndim=sysparams.ndim.value,
boxvec=boxv,
use_cell_lists=False,
ncellx_scale=cell_scale,
)
ppot = Main.PythonPotential(pot)
try:
Main.run_b(mxd, 10000)
except:
print("exception occured")
# if exception occurs, treat as failure. this is for rattlers
# not enough failures occur that it would make a difference to not just assume this never happens
# but we shoudl switch this out
# but jic
return (quench_coords, False, 0, 0, 0, 0, 0)
results = (mxd.optimizer.x0, mxd.converged, 0, mxd.iter_number, 0, 0, 0)
return results
def reg_test_gc(argv):
usage = """
./reg_test_gc [--before|-b 2000-00-00]
testfile.ini
"""
if len(sys.argv) == 1:
print usage
sys.exit(2)
try:
opts, next = getopt.getopt(argv[1:], "b:", ["before="])
except getopt.GetoptError:
print "invalid calling sequence"
print usage
sys.exit(2)
# defaults
gcdate = ""
for o, a in opts:
if o == "--before" or o == "-b":
gcdate = a
try:
testFile = next[0]
except IndexError:
print "ERROR: a test file was not specified"
print usage
sys.exit(2)
if not gcdate:
print "ERROR: date was not specified"
print usage
sys.exit(2)
gcd = valid_date(gcdate)
if gcd == '':
print "ERROR: invalid date", gcdate
print usage
sys.exit(2)
workdir = os.getcwd()
print "loading ", testFile
args = test_util.get_args([testFile])
suite, testList = params.load_params(args)
activeTestList = [t.name for t in testList]
benchmarkTestList = [
t for t in testList if not (t.compileTest or t.restartTest)
]
benchmarkNotFound = {}
for t in benchmarkTestList:
benchmarkNotFound[t.name] = ''
### clean up the web dir
print "\ncleaning ", suite.webTopDir
os.chdir(suite.webTopDir)
validDirs = []
for d in os.listdir(suite.webTopDir):
if (d.startswith("20") and os.path.isdir(d)):
statusFile = d + '/' + d + '.status'
if (os.path.isfile(statusFile)):
validDirs.append(d)
validDirs.sort()
validDirs.reverse()
latestBMDate = {}
for d in validDirs:
bmtests = benchmarkNotFound.keys()
if d >= gcd and bmtests:
if isBenchmarkDir(d):
for t in bmtests:
if findBenchmark(d, t):
del benchmarkNotFound[t]
latestBMDate[t] = d
else:
if isBenchmarkDir(d) and bmtests:
found = False
for t in bmtests:
if findBenchmark(d, t):
found = True
del benchmarkNotFound[t]
latestBMDate[t] = d
if not found:
rmDir(d)
else:
rmDir(d)
### clean up the test dir
testDirs = os.path.join(suite.testTopDir, suite.suiteName + "-tests")
print "\ncleaning ", testDirs
os.chdir(testDirs)
validDirs = []
for d in os.listdir(testDirs):
if (d.startswith("20") and os.path.isdir(d)):
validDirs.append(d)
validDirs.sort()
validDirs.reverse()
for d in validDirs:
if d < gcd:
tests = [
t for t in os.listdir(d) if os.path.isdir(os.path.join(d, t))
]
found = False
for t in tests:
if t in latestBMDate.keys() and latestBMDate[t] == d:
found = True
break
if not found:
rmDir(d)
print "\ncreating suite report..."
report.report_all_runs(suite, activeTestList)
print "\nGarbage cleaning finished."
def train(train_data, dicts, save_to, save_frequency, valid_data, valid_frequency, patience,
encoder, decoder, params_dtype, dim_emb, dim_rnn, n_words_source, n_words_target, maxlen,
decay_c, alpha_c, clip_c, dropout, l_rate, epochs, batch_size, optimizer, devices, characters,
resume_training, log_file, display_frequency):
"""
Trains a Neural Machine Translation model with the specified parameters.
Provides asynchronous optimization algorithms, see option --optimizer.
Training (and validation) data must provide tokenization which can be recovered
with `str.split`. Not necessary for character based models.
"""
if log_file:
f_handler = logging.FileHandler(log_file)
f_handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s %(module)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S"))
logging.getLogger().addHandler(f_handler)
logging.info("logging to {}".format(log_file))
logging.info("loading dictionaries from {}, {}".format(*dicts))
with open(dicts[0], 'r') as f1, open(dicts[1], 'r') as f2:
dictionaries = [json.load(f1), json.load(f2)]
logging.info("preparing training data streams from {}, {}".format(*train_data))
train_data_iter = TextIterator(train_data[0], train_data[1], dictionaries[0], dictionaries[1],
n_words_source=n_words_source, n_words_target=n_words_target,
batch_size=batch_size, maxlen=maxlen, raw_characters=characters)
valid_data_iter = None
if valid_data:
logging.info("preparing validation data streams from {}, {}".format(*valid_data))
valid_data_iter = TextIterator(valid_data[0], valid_data[1], dictionaries[0], dictionaries[1],
n_words_source=n_words_source, n_words_target=n_words_target,
batch_size=batch_size, maxlen=maxlen, raw_characters=characters)
logging.info("initializing weights")
if resume_training:
params = load_params(resume_training[0])
# check if model options saved in json format match the current settings
with open(resume_training[1], "r") as f:
resume_options = json.load(f)
if not all([encoder == resume_options["encoder"],
decoder == resume_options["decoder"],
dim_emb == resume_options["dim_emb"],
dim_rnn == resume_options["dim_rnn"],
n_words_source == resume_options["n_words_source"],
n_words_target == resume_options["n_words_target"],
maxlen == resume_options["maxlen"],
decay_c == resume_options["decay_c"],
alpha_c == resume_options["alpha_c"],
dropout == resume_options["dropout"],
characters == resume_options["characters"]]):
raise ValueError("Option mismatch!")
else:
params = init_params(n_words_source, n_words_target, dim_emb, dim_rnn, dtype=params_dtype)
if optimizer in ["hogwild", "async_agrad", "async_da"]:
logging.info("selected parallelizable optimizing algorithm {}, handing over to async-train".format(optimizer))
# saving, validation and logging is taken care of by async_train.train_params # TODO: set noise to 0 if used
trained_params = train_params(params, build_model, data=train_data_iter,
devices=devices.split(","), update_scheme=optimizer,
num_epochs=epochs, l_rate=l_rate, log_level=30, log_file=log_file,
valid_data=valid_data_iter, valid_freq=valid_frequency, patience=patience,
save_to=save_to, save_freq=save_frequency,
dim_emb=dim_emb, dim_rnn=dim_rnn, encoder=encoder, decoder=decoder,
n_words_target=n_words_target, n_words_source=n_words_source, maxlen=maxlen,
params_dtype=params_dtype, dropout=dropout,
decay_c=decay_c, alpha_c=alpha_c, clip_c=clip_c,
display_freq=display_frequency, characters=characters)
elif optimizer in ["sgd", "adagrad", "adadelta", "adam", "rmsprop"]:
logging.info("selected sequential optimizer {}".format(optimizer))
dir_path = os.path.dirname(save_to)
if dir_path and not os.path.exists(dir_path):
os.makedirs(dir_path)
save_file = save_params(params, save_to, epoch_update=(0, 0))
logging.info("update {}, saving current model parameters to {}".format(0, save_file))
train_options_file = os.path.splitext(save_to)[0] + ".json"
logging.info("saving training options to {}".format(train_options_file))
train_options = {"devices": devices,
"optimizer": optimizer,
"l_rate": l_rate,
"save_to": save_to,
"patience": patience,
"dim_emb": dim_emb,
"dim_rnn": dim_rnn,
"encoder": encoder,
"decoder": decoder,
"dropout": dropout,
"n_words_target": n_words_target,
"n_words_source": n_words_source,
"maxlen": maxlen,
"decay_c": decay_c,
"alpha_c": alpha_c,
"clip_c": clip_c,
"characters": characters}
with open(train_options_file, "w") as f:
json.dump(train_options, f, indent=4)
# get callable optimizer function
import seq_optimizers
opt_func = getattr(seq_optimizers, optimizer)
import theano
import theano.tensor as T
import theano.sandbox.cuda
theano.sandbox.cuda.use(devices.split(",")[0])
tparams = OrderedDict()
for param_name, param in params.items():
tparams[param_name] = theano.shared(param)
def pull_from_tparams(param_dict):
params = OrderedDict()
for param_name, param in param_dict.items():
params[param_name] = param.value()
return params
logging.info("building model")
inputs, cost, _ = build_model(tparams, dim_emb=dim_emb, encoder=encoder, decoder=decoder,
dropout=dropout, n_words_target=n_words_target,
decay_c=decay_c, alpha_c=alpha_c)
grads = T.grad(cost, wrt=list(tparams.values()))
if clip_c > 0.:
grads_squard_sum = 0.
for g in grads:
grads_squard_sum += (g**2).sum()
grads = [T.switch(grads_squard_sum > (clip_c**2),
g / T.sqrt(grads_squard_sum) * clip_c,
g)
for g in grads]
logging.info("compiling model")
learning_rate = T.scalar("learning_rate")
f_grad_shared, f_update = opt_func(learning_rate, tparams, grads, inputs, cost)
update_idx = 0
early_stop = False
best_params = params
best_valid_error = np.inf
patience_left = patience
logging.info("starting training")
for epoch_idx in range(1, epochs+1):
for train_batch in train_data_iter:
logging.debug("processing next data sample")
cost = f_grad_shared(*train_batch)
f_update(l_rate)
update_idx += 1
if update_idx % display_frequency == 0:
logging.info("epoch {} update {}, cost of last processed batch: {}"
.format(epoch_idx, update_idx, cost))
if update_idx % valid_frequency == 0 and valid_data:
# TODO: set noise to 0 if used
cur_valid_error = np.mean([f_grad_shared(*d) for d in valid_data_iter])
if cur_valid_error < best_valid_error:
best_params = pull_from_tparams(tparams)
best_valid_error = cur_valid_error
patience_left = patience
else:
patience_left -= 1
if patience_left == 0:
early_stop = True
break
if update_idx % save_frequency == 0:
save_file = save_params(pull_from_tparams(tparams), save_to, epoch_update=(epoch_idx, update_idx))
logging.info("epoch {}, update {} saved to {}".format(epoch_idx, update_idx, save_file))
if early_stop:
break
save_file = save_params(best_params, save_to)
logging.info("saved best parameters to {}".format(save_file))
logging.info("done!")
def map_binary_inversepower_mxopt_jl(
foldername,
particle_coords,
optimizer,
opt_param_dict,
random_coord_0=0,
random_coord_1=-1,
z=0,
):
"""
Finds whether a point defined by particle_coord
on the meshgrid correspond to a minimum or not for a 2d
case.
"""
foldpath = BASE_DIRECTORY + "/" + foldername
# import params
sysparams = load_params(foldpath)
(hs_radii, initial_coords, box_length) = load_secondary_params(foldpath)
assert sysparams.ndim.value == 2
minimum_coords = np.loadtxt(foldpath + "/coords_of_minimum.txt",
delimiter=",")
minimum_coords = np.loadtxt(foldpath + "/coords_of_minimum.txt",
delimiter=",")
quench_coords = initial_coords.copy()
quench_coords = (quench_coords + particle_coords[0] * VEC_16_0 +
particle_coords[1] * VEC_16_1 + z * VEC_16_2)
# quench_coords = quench_coords + \
# particle_coords[0]*VEC_8_0 + particle_coords[1]*VEC_8_1 + z*VEC_8_2
# print(quench_coords, 'quench coords')
# box length
box_length = float(box_length)
boxv = [box_length] * sysparams.ndim.value
ncellx_scale = get_ncellsx_scale(hs_radii, boxv)
print(hs_radii, "hs_radii")
print(quench_coords, "quench_coords")
print(boxv)
potential = Main.pot.InversePower(
sysparams.power.value,
sysparams.eps.value,
use_cell_lists=False,
ndim=sysparams.ndim.value,
radii=hs_radii * 1.0,
boxvec=boxv,
)
ppot = Main.PythonPotential(potential)
nls = Main.NewtonLinesearch(ppot, quench_coords, opt_param_dict["tol"])
mxd = Main.Mixed_Descent(
ppot,
Main.CVODE_BDF(),
nls,
quench_coords,
opt_param_dict["T"],
opt_param_dict["rtol"],
opt_param_dict["conv_tol"],
opt_param_dict["tol"],
)
try:
Main.run_b(mxd, 10000)
except:
print(quench_coords, "failed here")
print(initial_coords, "coords")
print(len(quench_coords))
# ret = lbfgs_cpp(quench_coords, potential, tol=1e-8, M=1)
# This exists because some runs don't have hessian evaluations
coordarg = 0
print(mxd.converged, "converged")
results = (
mxd.optimizer.x0,
mxd.converged,
0,
mxd.n_g_evals,
mxd.iter_number,
mxd.n_h_evals,
)
# the reason the potential is being passed is because quench coords needs the potential to figure out what to do
return results
def train_mnist():
nv, nhs = 28*28, [500] #, 200, 200, 200]
batch_size = 20
train_size = 10000
n_epochs = 15
learning_rate = 0.001
decay = 1.
L2_reg = 0.01
L1_reg = 0.000
momi, momf, momsw = 0.5, 0.9, 10
k=1
sample_every = None
LOAD_PARAMS = False
data_path = '/export/mlrg/ebuchman/datasets/mnistX_binary_lowres.pkl'
data_path = '/export/mlrg/ebuchman/datasets/mnist_binary.pkl'
data_path = '/mnt/data/datasets/mnist_binary.pkl'
print 'opening data'
f = open(data_path)
d = pickle.load(f)
f.close()
if len(d) == 3:
tr, val, ts = d
X = tr[0]
else:
X = d
print X.shape
X0 = X
optimizer = 'cd'
n_layers = len(nhs)
layer_params = []
X = X[:100]
# X = X[random.sample(xrange(len(X0)), train_size)]
w, bh, bv, t = test_rbm(X)
print 'took', t
f = open('data/results/mnist_cd_deeplearning_ref.pkl')
pickle.dump([None, [w, bh, bv]], d)
f.close()
quit()
# train each layer of DBN
for layer in xrange(n_layers):
if layer == 0:
nv, nh = nv, nhs[0]
else:
nv, nh = nhs[layer-1], nhs[layer]
if LOAD_PARAMS: # this needs to be fixed...
param_init = load_params(nv, nh, k, 10, learning_rate)
else:
theta_init = random_theta(nv, nh, k=k)
param_init = split_theta(theta_init, nv, nh, k=k)
param_init[0] = param_init[0].reshape(nv*nh)
# fit rbm
params_fit = _train(X, optimizer, param_init, nv, nh, batch_size, n_epochs, learning_rate, decay, momi, momf, momsw, L1_reg, L2_reg, k)
layer_params.append(params_fit)
# sample_and_save(param_first_layer, nh, n_epochs, learning_rate, k, optimizer)
# get data for next layer by propogating mnist up to current layer
if k == 1:
X = X0[random.sample(xrange(len(X0)), train_size)]
for i in xrange(layer+1):
W, bh, bv = layer_params[i]
mean = False if i == layer else True
X = sample_h_given_v_np(X, W, bh, nh, mean=mean)
elif k == 2:
X = X0[random.sample(xrange(len(X0)), train_size)]
for i in xrange(layer+1):
W, Wh, bh, bv = layer_params[i]
mean = False if i == layer else True
X = sample_h_given_v_2wise_np(X, W, Wh, bh, nh, mean = mean)
#save_name = "mnist_
#params_fit = split_theta(model.mpf.theta.get_value(), nv, nh, k=k)
deep_samples(layer_params, nsamps=50, opt=optimizer)
def map_binary_inversepower(
foldername,
particle_coords,
optimizer,
parameter_dict,
random_coord_0=0,
random_coord_1=-1,
z=0,
index=None,
mesh_length=None,
):
"""
Finds whether a point defined by particle_coord
on the meshgrid correspond to a minimum or not for a 2d
case.
"""
foldpath = BASE_DIRECTORY + "/" + foldername
# import params
sysparams = load_params(foldpath)
(hs_radii, initial_coords, box_length) = load_secondary_params(foldpath)
assert sysparams.ndim.value == 2
quench_coords = initial_coords.copy()
print("initial_coords", initial_coords)
print(particle_coords[0], particle_coords[1],
"vector coefficients as passed")
print("z value")
quench_coords = (quench_coords + particle_coords[0] * VEC_16_0 +
particle_coords[1] * VEC_16_1 + z * VEC_16_2)
# TODO: save this as a unit meshgrid
# print(quench_coords, 'quench coords')
# box length
box_length = float(box_length)
boxv = [box_length] * sysparams.ndim.value
ncellx_scale = get_ncellsx_scale(hs_radii, boxv)
print(hs_radii)
potential = InversePower(
sysparams.power.value,
sysparams.eps.value,
use_cell_lists=False,
ndim=sysparams.ndim.value,
radii=hs_radii * 1.0,
boxvec=boxv,
)
# save potential parameters
# writing this out since Enum to dict conversion does not give the mapping we want
potential_params_fname = "potential_params.yaml"
potential_param_dict = {
"ndim": sysparams.ndim.value,
"phi": sysparams.phi.value,
"seed": 0,
"n_part": sysparams.n_part.value,
"r1": sysparams.r1.value,
"r2": sysparams.r2.value,
"rstd1": sysparams.rstd1.value,
"rstd2": sysparams.rstd2.value,
"use_cell_lists": int(False),
"power": sysparams.power.value,
"eps": sysparams.eps.value,
}
param_str = generate_param_str_from_dict(potential_param_dict)
# make directory to emulate structure by refactored code
emulation_folder = COMPARE_FOLDER_NAME + "/" + param_str
# add README to emulation folder
os.makedirs(emulation_folder, exist_ok=True)
with open(COMPARE_FOLDER_NAME + "/README.org", "w") as f:
f.write(" \#+AUTHOR Praharsh Suryadevara .\n")
f.write("* Read First.")
f.write(
"This dirctory has been auto-generated by old code before refactoring. Do not change.\n"
)
# emulates path of the refactored code
ensemble_folder_path = emulation_folder + "/ensemble/random_plane"
os.makedirs(ensemble_folder_path, exist_ok=True)
# 0 to emulate the addition of seed
sec_param_folder_path = emulation_folder + "/sec_params/0"
os.makedirs(sec_param_folder_path, exist_ok=True)
old_code_results_folder_path = emulation_folder + "/old_data"
os.makedirs(old_code_results_folder_path, exist_ok=True)
with open(emulation_folder + "/params.yaml", "w") as param_file:
yaml.dump(potential_param_dict, param_file)
opt_param_fname = "minima_finder_params.yaml"
with open(emulation_folder + "/" + opt_param_fname, "w") as param_file:
yaml.dump(parameter_dict, param_file)
initial_coords_fname = str(index) + ".txt"
np.savetxt(ensemble_folder_path + "/" + initial_coords_fname,
quench_coords,
delimiter=",")
mesh_coords_fname = str(index) + "_mesh.txt"
np.savetxt(ensemble_folder_path + "/" + mesh_coords_fname,
particle_coords,
delimiter=",")
if mesh_length is not None:
# 2 *[particle_coords[0], particle_coords[1]]/mesh_length -0.5 form our unit mesh_grid
# centered around [0, 0]
mesh_coords = (2 * np.array([particle_coords[0], particle_coords[1]]) /
mesh_length - 0.5)
mesh_coords_fname = str(index) + "_mesh.txt"
np.savetxt(ensemble_folder_path + "/" + mesh_coords_fname,
mesh_coords,
delimiter=",")
np.savetxt(
sec_param_folder_path + "/" + "initial_coords.txt",
initial_coords,
delimiter=",",
)
np.savetxt(
sec_param_folder_path + "/" + "box_length.txt",
np.array([box_length]),
delimiter=",",
)
np.savetxt(sec_param_folder_path + "/" + "hs_radii.txt",
hs_radii,
delimiter=",")
# ret = quench_mixed_optimizer(potential,
# quench_coords, # make sure right coords are being passed
# T=10,
# step=1,
# nsteps=100000,
# conv_tol=1e-8,
# tol=1e-6, rtol=1e-4, atol=1e-4)
# ret = quench_steepest(
# potential,
# quench_coords, # make sure right coords are being passed
# nsteps=2000000,
# stepsize=5e-3, # for steepest descent step size should be small
# tol=1e-4)
# ret = quench_cvode_opt(potential, quench_coords, tol=1e-6, rtol=1e-4, atol=1e-4)
try:
ret = optimizer(quench_coords, potential, **parameter_dict)
except:
raise Exception("failure")
# ret = lbfgs_cpp(quench_coords, potential, tol=1e-8, M=1)
# This exists because some runs don't have hessian evaluations
try:
ret["nhev"]
except:
ret["nhev"] = 0
coordarg = 0
final_coords = ret["coords"]
final_coords_fname = str(index) + "_coords.txt"
np.savetxt(
old_code_results_folder_path + "/" + final_coords_fname,
final_coords,
delimiter=",",
)
energy, grad, hess = potential.getEnergyGradientHessian(final_coords)
hessian_fname = str(index) + "_hessian.txt"
np.savetxt(old_code_results_folder_path + "/" + hessian_fname,
hess,
delimiter=",")
grad_fname = str(index) + "_grad.txt"
np.savetxt(old_code_results_folder_path + "/" + grad_fname,
grad,
delimiter=",")
last_step_fname = str(index) + "_step.txt"
# np.savetxt(old_code_results_folder_path + '/' +
# last_step_fname, ret['step'], delimiter=',')
# check that the minimum hessian eigenvalue is positive
# print out the eigenvector corresponding to it
eigvals, eigvecs = np.linalg.eigh(hess)
print(eigvals[1])
print(eigvecs[:, 1])
# step_in_eigvec_basis = np.matmul(eigvecs.T, ret['step'])
# step_in_eigvec_basis_normalized = step_in_eigvec_basis/np.linalg.norm(step_in_eigvec_basis)
# -1e-15 is to avoid numerical issues
if np.min(eigvals) < -1e-3:
print("minimum Eigenvalue: ", np.min(eigvals))
print("Eigenvalues: ", eigvals)
raise Exception("negative eigenvalue")
# save heuristics as a dict
results = (
ret.coords,
ret.success,
coordarg,
ret.nfev,
ret.nsteps,
ret.nhev,
eigvecs,
)
# simplified dictionary since we're only using the success variable
res_dict = {
"success": bool(ret.success),
"nsteps": ret.nsteps,
"energy": ret.energy,
"nfev": ret.nfev,
}
# print("-------------------------------------------------steppp", step_in_eigvec_basis_normalized)
# print("eigenvalues", eigvals)
yaml_fname = str(index) + ".yaml"
# save heuristics as yaml
with open(old_code_results_folder_path + "/" + yaml_fname, "w") as f:
yaml.dump(res_dict, f)
print(ret.nsteps, "nsteps")
# the reason the potential is being passed is because quench coords needs the potential to figure out what to do
return results