def main():
parser = argparse.ArgumentParser()
parser.add_argument('mode',
help="preprocess/train/eval",
choices=["preprocess", "train", "eval"])
parser.add_argument('exp_config_file', help="jsonnet file for experiments")
parser.add_argument('--model_config_args',
help="optional overrides for model config args")
parser.add_argument('--logdir', help="optional override for logdir")
parser.add_argument('--ray', action='store_true')
args = parser.parse_args()
exp_config = json.loads(_jsonnet.evaluate_file(args.exp_config_file))
model_config_file = exp_config["model_config"]
if "model_config_args" in exp_config:
model_config_args = exp_config["model_config_args"]
if args.model_config_args is not None:
model_config_args_json = _jsonnet.evaluate_snippet(
"", args.model_config_args)
model_config_args.update(json.loads(model_config_args_json))
model_config_args = json.dumps(model_config_args)
elif args.model_config_args is not None:
model_config_args = _jsonnet.evaluate_snippet("",
args.model_config_args)
else:
model_config_args = None
logdir = args.logdir or exp_config["logdir"]
if args.mode == "preprocess":
preprocess_config = PreprocessConfig(model_config_file,
model_config_args)
preprocess.main(preprocess_config)
elif args.mode == "train":
mp.spawn(train_model,
nprocs=8,
args=(model_config_file, model_config_args, logdir, args.ray))
elif args.mode == "eval":
for step in exp_config["eval_steps"]:
infer_output_path = f"{exp_config['eval_output']}/{exp_config['eval_name']}-step{step}.infer"
infer_config = InferConfig(
model_config_file,
model_config_args,
logdir,
exp_config["eval_section"],
exp_config["eval_beam_size"],
infer_output_path,
step,
use_heuristic=exp_config["eval_use_heuristic"])
infer.main(infer_config)
eval_output_path = f"{exp_config['eval_output']}/{exp_config['eval_name']}-step{step}.eval"
eval_config = EvalConfig(model_config_file, model_config_args,
logdir, exp_config["eval_section"],
infer_output_path, eval_output_path)
eval.main(eval_config)
res_json = json.load(open(eval_output_path))
print(step, res_json['total_scores']['all']['exact'])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('mode',
help="preprocess/train/eval",
choices=["preprocess", "train", "eval"])
parser.add_argument('exp_config_file', help="jsonnet file for experiments")
parser.add_argument('--model_config_args',
help="optional overrides for model config args")
parser.add_argument('--logdir', help="optional override for logdir")
parser.add_argument('--model_name',
help="optional override for model_name")
parser.add_argument('--skip_infer', action='store_true', default=False)
parser.add_argument('--skip_eval', action='store_true', default=False)
parser.add_argument('--eval_name', help="optional override for eval name")
args = parser.parse_args()
exp_config = json.loads(_jsonnet.evaluate_file(args.exp_config_file))
model_config_file = exp_config["model_config"]
if "model_config_args" in exp_config:
model_config_args = exp_config["model_config_args"]
if args.model_config_args is not None:
model_config_args_json = _jsonnet.evaluate_snippet(
"", args.model_config_args)
model_config_args.update(json.loads(model_config_args_json))
model_config_args = json.dumps(model_config_args)
elif args.model_config_args is not None:
model_config_args = _jsonnet.evaluate_snippet("",
args.model_config_args)
else:
model_config_args = None
logdir = args.logdir or exp_config["logdir"]
if args.eval_name:
exp_config["eval_name"] = args.eval_name
if args.mode == "preprocess":
preprocess_config = PreprocessConfig(model_config_file,
model_config_args)
preprocess.main(preprocess_config)
elif args.mode == "train":
train_config = TrainConfig(model_config_file, model_config_args,
logdir)
train.main(train_config)
elif args.mode == "eval":
print(exp_config["eval_name"])
for step in exp_config["eval_steps"]:
print(step)
model = None
for section in exp_config["eval_sections"]:
infer_output_path = "{}/{}-step{}/{}.infer".format(
exp_config["eval_output"], exp_config["eval_name"], step,
section)
infer_config = InferConfig(
model_config_file,
model_config_args,
logdir,
section,
exp_config["eval_beam_size"],
infer_output_path,
step,
args.model_name,
use_heuristic=exp_config["eval_use_heuristic"],
output_history=True)
if not args.skip_infer:
model = infer.main(infer_config, model)
if not args.skip_eval:
eval_output_path = "{}/{}-step{}/{}.eval".format(
exp_config["eval_output"], exp_config["eval_name"],
step, section)
eval_config = EvalConfig(model_config_file,
model_config_args, logdir,
section, infer_output_path,
eval_output_path)
eval.main(eval_config)
print(section)
for infer_type in ['inferred_code']:
print(infer_type)
res_json = json.load(
open(
eval_output_path.replace(
'.eval', '_{}.eval'.format(infer_type))))
print('exact match: %.4f exec acc: %.4f,' %
(res_json['total_scores']['all']['exact'],
res_json['total_scores']['all']['exec']))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('mode',
help="preprocess/train/eval",
choices=["preprocess", "train", "eval", "finetune"])
parser.add_argument('exp_config_file', help="jsonnet file for experiments")
parser.add_argument('--model_config_args',
help="optional overrides for model config args")
parser.add_argument('--logdir', help="optional override for logdir")
args = parser.parse_args()
exp_config = json.loads(_jsonnet.evaluate_file(args.exp_config_file))
model_config_file = exp_config["model_config"]
if "model_config_args" in exp_config:
model_config_args = exp_config["model_config_args"]
if args.model_config_args is not None:
model_config_args_json = _jsonnet.evaluate_snippet(
"", args.model_config_args)
model_config_args.update(json.loads(model_config_args_json))
model_config_args = json.dumps(model_config_args)
elif args.model_config_args is not None:
model_config_args = _jsonnet.evaluate_snippet("",
args.model_config_args)
else:
model_config_args = None
logdir = args.logdir or exp_config["logdir"]
if args.mode == "preprocess":
preprocess_config = PreprocessConfig(model_config_file,
model_config_args)
preprocess.main(preprocess_config)
elif args.mode == "train":
train_config = TrainConfig(model_config_file, model_config_args,
logdir)
train.main(train_config)
elif args.mode == "finetune":
finetunedir = exp_config["finetunedir"]
#TODO: FIX STEPS TO MAKE IT INTO CONFIG FILE
step = exp_config['eval_steps'][-1]
infer_output_path = f"{exp_config['finetunedir']}/infer/"
fine_tune_config = FineTuneConfig(
model_config_file,
model_config_args,
logdir,
finetunedir,
infer_output_path,
exp_config["eval_beam_size"],
use_heuristic=exp_config["eval_use_heuristic"])
finetune.main(fine_tune_config)
elif args.mode == "eval":
for step in exp_config["eval_steps"]:
infer_output_path = f"{exp_config['eval_output']}/{exp_config['eval_name']}-step{step}.infer"
infer_config = InferConfig(
model_config_file,
model_config_args,
logdir,
exp_config["eval_section"],
exp_config["eval_beam_size"],
infer_output_path,
step,
use_heuristic=exp_config["eval_use_heuristic"])
infer.main(infer_config)
eval_output_path = f"{exp_config['eval_output']}/{exp_config['eval_name']}-step{step}.eval"
eval_config = EvalConfig(model_config_file, model_config_args,
logdir, exp_config["eval_section"],
infer_output_path, eval_output_path)
eval.main(eval_config)
res_json = json.load(open(eval_output_path))
print(step, res_json['total_scores']['all']['exact'])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('mode', help="preprocess/train/eval", choices=["preprocess", "train", "eval"])
parser.add_argument('exp_config_file', help="jsonnet file for experiments")
parser.add_argument('--use_scheduler', help='whether to use lr_scheduler for training.')
parser.add_argument('--model_config_args', help="optional overrides for model config args")
parser.add_argument('--logdir', help="optional override for logdir")
args = parser.parse_args()
exp_config = json.loads(_jsonnet.evaluate_file(args.exp_config_file))
model_config_file = exp_config["model_config"]
if "model_config_args" in exp_config:
model_config_args = exp_config["model_config_args"]
if args.model_config_args is not None:
model_config_args_json = _jsonnet.evaluate_snippet("", args.model_config_args)
model_config_args.update(json.loads(model_config_args_json))
model_config_args = json.dumps(model_config_args)
elif args.model_config_args is not None:
model_config_args = _jsonnet.evaluate_snippet("", args.model_config_args)
else:
model_config_args = None
logdir = args.logdir or exp_config["logdir"]
trainset = exp_config["trainset"]
valset = exp_config["valset"]
if args.mode == "preprocess":
preprocess_config = PreprocessConfig(model_config_file, model_config_args)
preprocess.main(preprocess_config)
elif args.mode == "train":
train_config = TrainConfig(model_config_file,
model_config_args, logdir, trainset, valset)
if args.use_scheduler is None or args.use_scheduler == 'True':
train.main(train_config)
elif args.use_scheduler == 'False':
train_noscheduler.main(train_config)
else:
raise AssertionError
elif args.mode == "eval":
for step in exp_config["eval_steps"]:
infer_output_path = f"{exp_config['eval_output']}/{exp_config['eval_name']}-step{step}.infer"
infer_config = InferConfig(
model_config_file,
model_config_args,
logdir,
exp_config["eval_section"],
exp_config["eval_beam_size"],
infer_output_path,
step,
use_heuristic=exp_config["eval_use_heuristic"]
)
infer.main(infer_config)
eval_output_path = f"{exp_config['eval_output']}/{exp_config['eval_name']}-step{step}.eval"
eval_config = EvalConfig(
model_config_file,
model_config_args,
logdir,
exp_config["eval_section"],
infer_output_path,
eval_output_path
)
eval.main(eval_config)
res_json = json.load(open(eval_output_path))
print(step, res_json['total_scores']['all']['exact'])
