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data.py
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data.py
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#!/usr/bin/env python3
from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import torch
import tqdm
import torch.utils.data
logger = logging.getLogger(__name__)
class Seq2seqDatasetForBert2stage(torch.utils.data.Dataset):
def __init__(
self, features, max_source_len, max_target_len,
vocab_size, cls_id, sep_id, pad_id, mask_id,
random_prob, keep_prob, offset, num_training_instances,
span_len=1, span_prob=1.0):
self.features = features
self.max_source_len = max_source_len
self.max_target_len = max_target_len
self.offset = offset
if offset > 0:
logger.info(" **** Set offset %d in Seq2seqDatasetForBert **** ", offset)
self.cls_id = cls_id
self.sep_id = sep_id
self.pad_id = pad_id
self.random_prob = random_prob
self.keep_prob = keep_prob
self.mask_id = mask_id
self.vocab_size = vocab_size
self.num_training_instances = num_training_instances
self.span_len = span_len
self.span_prob = span_prob
def __len__(self):
return int(self.num_training_instances)
def __trunk(self, ids, max_len):
if len(ids) > max_len - 1:
ids = ids[:max_len - 1]
ids = ids + [self.sep_id]
return ids
def __pad(self, ids, max_len):
if len(ids) < max_len:
return ids + [self.pad_id] * (max_len - len(ids))
else:
assert len(ids) == max_len
return ids
def __getitem__(self, idx):
idx = (self.offset + idx) % len(self.features)
feature = self.features[idx]
source_ids = self.__trunk([self.cls_id] + feature["source_ids"], self.max_source_len)
target_ids = self.__trunk(feature["target_ids"], self.max_target_len)
target_kd_ids = self.__trunk(feature["target_kd_ids"], self.max_target_len)
pseudo_ids = []
for tk_id in target_kd_ids:
p = random.random()
if p < self.keep_prob:
pseudo_ids.append(tk_id)
elif p < self.keep_prob + self.random_prob:
pseudo_ids.append(random.randint(0, self.vocab_size - 1))
else:
pseudo_ids.append(self.mask_id)
num_source_tokens = len(source_ids)
num_target_tokens = len(target_kd_ids)
source_ids = self.__pad(source_ids, self.max_source_len)
target_ids = self.__pad(target_ids, self.max_target_len)
target_kd_ids = self.__pad(target_kd_ids, self.max_target_len)
pseudo_ids = self.__pad(pseudo_ids, self.max_target_len)
if self.span_len > 1:
span_ids = []
span_id = 1
while len(span_ids) < num_target_tokens:
p = random.random()
if p < self.span_prob:
span_len = random.randint(2, self.span_len)
span_len = min(span_len, num_target_tokens - len(span_ids))
else:
span_len = 1
span_ids.extend([span_id] * span_len)
span_id += 1
span_ids = self.__pad(span_ids, self.max_target_len)
return source_ids, target_ids, target_kd_ids, pseudo_ids, num_source_tokens, num_target_tokens, span_ids
else:
return source_ids, target_ids, target_kd_ids, pseudo_ids, num_source_tokens, num_target_tokens
class Seq2seqDatasetForBert(torch.utils.data.Dataset):
def __init__(
self, features, max_source_len, max_target_len,
vocab_size, cls_id, sep_id, pad_id, mask_id,
random_prob, keep_prob, offset, num_training_instances,
span_len=1, span_prob=1.0):
self.features = features
self.max_source_len = max_source_len
self.max_target_len = max_target_len
self.offset = offset
if offset > 0:
logger.info(" **** Set offset %d in Seq2seqDatasetForBert **** ", offset)
self.cls_id = cls_id
self.sep_id = sep_id
self.pad_id = pad_id
self.random_prob = random_prob
self.keep_prob = keep_prob
self.mask_id = mask_id
self.vocab_size = vocab_size
self.num_training_instances = num_training_instances
self.span_len = span_len
self.span_prob = span_prob
def __len__(self):
return int(self.num_training_instances)
def __trunk(self, ids, max_len):
if len(ids) > max_len - 1:
ids = ids[:max_len - 1]
ids = ids + [self.sep_id]
return ids
def __pad(self, ids, max_len):
if len(ids) < max_len:
return ids + [self.pad_id] * (max_len - len(ids))
else:
assert len(ids) == max_len
return ids
def __getitem__(self, idx):
idx = (self.offset + idx) % len(self.features)
feature = self.features[idx]
source_ids = self.__trunk([self.cls_id] + feature["source_ids"], self.max_source_len)
target_ids = self.__trunk(feature["target_ids"], self.max_target_len)
pseudo_ids = []
for tk_id in target_ids:
p = random.random()
if p < self.keep_prob:
pseudo_ids.append(tk_id)
elif p < self.keep_prob + self.random_prob:
pseudo_ids.append(random.randint(0, self.vocab_size - 1))
else:
pseudo_ids.append(self.mask_id)
num_source_tokens = len(source_ids)
num_target_tokens = len(target_ids)
source_ids = self.__pad(source_ids, self.max_source_len)
target_ids = self.__pad(target_ids, self.max_target_len)
pseudo_ids = self.__pad(pseudo_ids, self.max_target_len)
if self.span_len > 1:
span_ids = []
span_id = 1
while len(span_ids) < num_target_tokens:
p = random.random()
if p < self.span_prob:
span_len = random.randint(2, self.span_len)
span_len = min(span_len, num_target_tokens - len(span_ids))
else:
span_len = 1
span_ids.extend([span_id] * span_len)
span_id += 1
span_ids = self.__pad(span_ids, self.max_target_len)
return source_ids, target_ids, pseudo_ids, num_source_tokens, num_target_tokens, span_ids
else:
return source_ids, target_ids, pseudo_ids, num_source_tokens, num_target_tokens
def batch_list_to_batch_tensors(batch):
batch_tensors = []
for x in zip(*batch):
if isinstance(x[0], torch.Tensor):
batch_tensors.append(torch.stack(x))
else:
batch_tensors.append(torch.tensor(x, dtype=torch.long))
return batch_tensors
def get_max_epoch_model(output_dir):
fn_model_list = glob.glob(os.path.join(output_dir, "model.*.bin"))
fn_optim_list = glob.glob(os.path.join(output_dir, "optim.*.bin"))
if (not fn_model_list) or (not fn_optim_list):
return None
os.path.basename(output_dir)
both_set = set([int(os.path.basename(fn).split('.')[1]) for fn in fn_model_list]
) & set([int(os.path.basename(fn).split('.')[1]) for fn in fn_optim_list])
if both_set:
return max(both_set)
else:
return None
def load_and_cache_examples(
example_file, tokenizer, local_rank, cached_features_file, shuffle=True):
# Make sure only the first process in distributed training process the dataset, and the others will use the cache
if local_rank not in [-1, 0]:
torch.distributed.barrier()
if cached_features_file is not None and os.path.exists(cached_features_file):
logger.info("Loading features from cached file %s", cached_features_file)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", example_file)
examples = []
with open(example_file, mode="r", encoding="utf-8") as reader:
for line in reader:
examples.append(json.loads(line))
features = []
for example in tqdm.tqdm(examples):
if isinstance(example["src"], list):
source_tokens = example["src"]
target_tokens = example["tgt"]
else:
source_tokens = tokenizer.tokenize(example["src"])
target_tokens = tokenizer.tokenize(example["tgt"])
features.append({
"source_ids": tokenizer.convert_tokens_to_ids(source_tokens),
"target_ids": tokenizer.convert_tokens_to_ids(target_tokens),
})
if shuffle:
random.shuffle(features)
if local_rank in [-1, 0] and cached_features_file is not None:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(features, cached_features_file)
# Make sure only the first process in distributed training process the dataset, and the others will use the cache
if local_rank == 0:
torch.distributed.barrier()
return features