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from torch.utils.data import TensorDataset
import numpy as np
import logging
import os
import random
import torch
import time
from tqdm import tqdm
from _utils import *
logger = logging.getLogger(__name__)
def load_and_cache_gen_data(args, filename, pool, tokenizer, split_tag, only_src=False, is_sample=False):
# cache the data into args.cache_path except it is sampled
# only_src: control whether to return only source ids for bleu evaluating (dev/test)
# return: examples (Example object), data (TensorDataset)
data_tag = '_all' if args.data_num == -1 else '_%d' % args.data_num
cache_fn = '{}/{}.pt'.format(args.cache_path, split_tag + ('_src' if only_src else '') + data_tag)
examples = read_examples(filename, args.data_num, args.task)
if is_sample:
examples = random.sample(examples, min(5000, len(examples)))
if split_tag == 'train':
calc_stats(examples, tokenizer, is_tokenize=True)
else:
calc_stats(examples)
if os.path.exists(cache_fn) and not is_sample:
logger.info("Load cache data from %s", cache_fn)
data = torch.load(cache_fn)
else:
if is_sample:
logger.info("Sample 5k data for computing bleu from %s", filename)
else:
logger.info("Create cache data into %s", cache_fn)
tuple_examples = [(example, idx, tokenizer, args, split_tag) for idx, example in enumerate(examples)]
features = pool.map(convert_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples)))
all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long)
if split_tag == 'test' or only_src:
data = TensorDataset(all_source_ids)
else:
all_target_ids = torch.tensor([f.target_ids for f in features], dtype=torch.long)
data = TensorDataset(all_source_ids, all_target_ids)
if args.local_rank in [-1, 0] and not is_sample:
torch.save(data, cache_fn)
return examples, data
def load_and_cache_clone_data(args, filename, pool, tokenizer, split_tag, is_sample=False):
cache_fn = '{}/{}.pt'.format(args.cache_path, split_tag + '_all' if args.data_num == -1 else '_%d' % args.data_num)
examples = read_examples(filename, args.data_num, args.task)
if is_sample:
examples = random.sample(examples, int(len(examples) * 0.1))
calc_stats(examples, tokenizer, is_tokenize=True)
if os.path.exists(cache_fn):
logger.info("Load cache data from %s", cache_fn)
data = torch.load(cache_fn)
else:
if is_sample:
logger.info("Sample 10 percent of data from %s", filename)
elif args.data_num == -1:
logger.info("Create cache data into %s", cache_fn)
tuple_examples = [(example, idx, tokenizer, args) for idx, example in enumerate(examples)]
features = pool.map(convert_clone_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples)))
all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long)
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
data = TensorDataset(all_source_ids, all_labels)
if args.local_rank in [-1, 0] and args.data_num == -1:
torch.save(data, cache_fn)
return examples, data
def load_and_cache_defect_data(args, filename, pool, tokenizer, split_tag, is_sample=False):
cache_fn = os.path.join(args.cache_path, split_tag)
examples = read_examples(filename, args.data_num, args.task)
if is_sample:
examples = random.sample(examples, int(len(examples) * 0.1))
calc_stats(examples, tokenizer, is_tokenize=True)
if os.path.exists(cache_fn):
logger.info("Load cache data from %s", cache_fn)
data = torch.load(cache_fn)
else:
if is_sample:
logger.info("Sample 10 percent of data from %s", filename)
elif args.data_num == -1:
logger.info("Create cache data into %s", cache_fn)
tuple_examples = [(example, idx, tokenizer, args) for idx, example in enumerate(examples)]
features = pool.map(convert_defect_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples)))
# features = [convert_clone_examples_to_features(x) for x in tuple_examples]
all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long)
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
data = TensorDataset(all_source_ids, all_labels)
if args.local_rank in [-1, 0] and args.data_num == -1:
torch.save(data, cache_fn)
return examples, data
def load_and_cache_multi_gen_data(args, pool, tokenizer, split_tag, only_src=False, is_sample=False):
cache_fn = os.path.join(args.cache_path, split_tag)
if os.path.exists(cache_fn) and not is_sample:
logger.info("Load cache data from %s", cache_fn)
examples_data_dict = torch.load(cache_fn)
else:
examples_data_dict = {}
task_list = ['summarize', 'translate', 'refine', 'concode', 'defect']
for task in task_list:
if task == 'summarize':
sub_tasks = ['ruby', 'javascript', 'go', 'python', 'java', 'php']
elif task == 'translate':
sub_tasks = ['java-cs', 'cs-java']
elif task == 'refine':
sub_tasks = ['small', 'medium']
else:
sub_tasks = ['none']
args.task = task
for sub_task in sub_tasks:
args.sub_task = sub_task
if task == 'summarize':
args.max_source_length = 256
args.max_target_length = 128
elif task == 'translate':
args.max_source_length = 320
args.max_target_length = 256
elif task == 'refine':
if sub_task == 'small':
args.max_source_length = 130
args.max_target_length = 120
else:
args.max_source_length = 240
args.max_target_length = 240
elif task == 'concode':
args.max_source_length = 320
args.max_target_length = 150
elif task == 'defect':
args.max_source_length = 512
args.max_target_length = 3 # as do not need to add lang ids
filename = get_filenames(args.data_dir, args.task, args.sub_task, split_tag)
examples = read_examples(filename, args.data_num, args.task)
if is_sample:
examples = random.sample(examples, min(5000, len(examples)))
if split_tag == 'train':
calc_stats(examples, tokenizer, is_tokenize=True)
else:
calc_stats(examples)
tuple_examples = [(example, idx, tokenizer, args, split_tag) for idx, example in enumerate(examples)]
if args.data_num == -1:
features = pool.map(convert_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples)))
else:
features = [convert_examples_to_features(x) for x in tuple_examples]
all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long)
if only_src:
data = TensorDataset(all_source_ids)
else:
all_target_ids = torch.tensor([f.target_ids for f in features], dtype=torch.long)
data = TensorDataset(all_source_ids, all_target_ids)
examples_data_dict['{}_{}'.format(task, sub_task) if sub_task != 'none' else task] = (examples, data)
if args.local_rank in [-1, 0] and not is_sample:
torch.save(examples_data_dict, cache_fn)
logger.info("Save data into %s", cache_fn)
return examples_data_dict
def get_filenames(data_root, task, sub_task, split=''):
if task == 'concode':
data_dir = '{}/{}'.format(data_root, task)
train_fn = '{}/train.json'.format(data_dir)
dev_fn = '{}/dev.json'.format(data_dir)
test_fn = '{}/test.json'.format(data_dir)
elif task == 'summarize':
data_dir = '{}/{}/{}'.format(data_root, task, sub_task)
train_fn = '{}/train.jsonl'.format(data_dir)
dev_fn = '{}/valid.jsonl'.format(data_dir)
test_fn = '{}/test.jsonl'.format(data_dir)
elif task == 'refine':
data_dir = '{}/{}/{}'.format(data_root, task, sub_task)
train_fn = '{}/train.buggy-fixed.buggy,{}/train.buggy-fixed.fixed'.format(data_dir, data_dir)
dev_fn = '{}/valid.buggy-fixed.buggy,{}/valid.buggy-fixed.fixed'.format(data_dir, data_dir)
test_fn = '{}/test.buggy-fixed.buggy,{}/test.buggy-fixed.fixed'.format(data_dir, data_dir)
elif task == 'translate':
data_dir = '{}/{}'.format(data_root, task)
if sub_task == 'cs-java':
train_fn = '{}/train.java-cs.txt.cs,{}/train.java-cs.txt.java'.format(data_dir, data_dir)
dev_fn = '{}/valid.java-cs.txt.cs,{}/valid.java-cs.txt.java'.format(data_dir, data_dir)
test_fn = '{}/test.java-cs.txt.cs,{}/test.java-cs.txt.java'.format(data_dir, data_dir)
else:
train_fn = '{}/train.java-cs.txt.java,{}/train.java-cs.txt.cs'.format(data_dir, data_dir)
dev_fn = '{}/valid.java-cs.txt.java,{}/valid.java-cs.txt.cs'.format(data_dir, data_dir)
test_fn = '{}/test.java-cs.txt.java,{}/test.java-cs.txt.cs'.format(data_dir, data_dir)
elif task == 'clone':
data_dir = '{}/{}'.format(data_root, task)
train_fn = '{}/train.txt'.format(data_dir)
dev_fn = '{}/valid.txt'.format(data_dir)
test_fn = '{}/test.txt'.format(data_dir)
elif task == 'defect':
data_dir = '{}/{}'.format(data_root, task)
train_fn = '{}/train.jsonl'.format(data_dir)
dev_fn = '{}/valid.jsonl'.format(data_dir)
test_fn = '{}/test.jsonl'.format(data_dir)
if split == 'train':
return train_fn
elif split == 'dev':
return dev_fn
elif split == 'test':
return test_fn
else:
return train_fn, dev_fn, test_fn
def read_examples(filename, data_num, task):
read_example_dict = {
'summarize': read_summarize_examples,
'refine': read_refine_examples,
'translate': read_translate_examples,
'concode': read_concode_examples,
'clone': read_clone_examples,
'defect': read_defect_examples,
}
return read_example_dict[task](filename, data_num)
def calc_stats(examples, tokenizer=None, is_tokenize=False):
avg_src_len = []
avg_trg_len = []
avg_src_len_tokenize = []
avg_trg_len_tokenize = []
for ex in examples:
if is_tokenize:
avg_src_len.append(len(ex.source.split()))
avg_trg_len.append(len(str(ex.target).split()))
avg_src_len_tokenize.append(len(tokenizer.tokenize(ex.source)))
avg_trg_len_tokenize.append(len(tokenizer.tokenize(str(ex.target))))
else:
avg_src_len.append(len(ex.source.split()))
avg_trg_len.append(len(str(ex.target).split()))
if is_tokenize:
logger.info("Read %d examples, avg src len: %d, avg trg len: %d, max src len: %d, max trg len: %d",
len(examples), np.mean(avg_src_len), np.mean(avg_trg_len), max(avg_src_len), max(avg_trg_len))
logger.info("[TOKENIZE] avg src len: %d, avg trg len: %d, max src len: %d, max trg len: %d",
np.mean(avg_src_len_tokenize), np.mean(avg_trg_len_tokenize), max(avg_src_len_tokenize),
max(avg_trg_len_tokenize))
else:
logger.info("Read %d examples, avg src len: %d, avg trg len: %d, max src len: %d, max trg len: %d",
len(examples), np.mean(avg_src_len), np.mean(avg_trg_len), max(avg_src_len), max(avg_trg_len))
def get_elapse_time(t0):
elapse_time = time.time() - t0
if elapse_time > 3600:
hour = int(elapse_time // 3600)
minute = int((elapse_time % 3600) // 60)
return "{}h{}m".format(hour, minute)
else:
minute = int((elapse_time % 3600) // 60)
return "{}m".format(minute)
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