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