2019-11-27 17:00:18 +01:00
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# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import glob
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import logging
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import os
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import re
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import numpy as np
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import sacremoses
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import torch
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import utils
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from utils.vocabulary import OpenAIVocab
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from utils.vocabulary import Vocab
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class LMOrderedIterator(object):
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def __init__(self, data, bsz, bptt, device='cpu', mem_len=None, ext_len=None, warmup=True):
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2019-11-27 17:00:18 +01:00
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"""
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data -- LongTensor -- the LongTensor is strictly ordered
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"""
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self.bsz = bsz
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self.bptt = bptt
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self.ext_len = ext_len if ext_len is not None else 0
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self.mem_len = mem_len
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self.warmup = warmup
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self.device = device
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# Work out how cleanly we can divide the dataset into bsz parts.
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n_step = data.size(0) // bsz
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# Trim off any extra elements that wouldn't cleanly fit (remainders).
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data = data[:n_step * bsz]
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# Evenly divide the data across the bsz batches.
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self.data = data.view(bsz, -1).t().contiguous()
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if mem_len and warmup:
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self.warmup_batches = (mem_len + bptt - 1) // bptt
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self.warmup_elems = self.warmup_batches * bptt
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warmup_data = self.data.roll((self.warmup_elems, 1), (0, 1))[:self.warmup_elems]
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self.data = torch.cat((warmup_data, self.data))
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# Partition data for DistributedDataParallel
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world_size = utils.distributed.get_world_size()
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rank = utils.distributed.get_rank()
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self.data = self.data.chunk(world_size, dim=1)[rank]
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# Number of mini-batches
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self.n_batch = (self.data.size(0) + self.bptt - 1) // self.bptt
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def roll(self):
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for i in range(self.data.size(1)):
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row = self.data[:, i]
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shift = torch.randint(0, self.data.size(0), (1,))
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row = torch.cat((row[shift:], row[:shift]))
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self.data[:, i] = row
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def get_batch(self, i, bptt=None):
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if bptt is None:
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bptt = self.bptt
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seq_len = min(bptt, self.data.size(0) - 1 - i)
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end_idx = i + seq_len
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beg_idx = max(0, i - self.ext_len)
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data = self.data[beg_idx:end_idx].to(self.device)
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target = self.data[i+1:i+1+seq_len].to(self.device)
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if self.mem_len and self.warmup:
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warm = i >= self.warmup_elems
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else:
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warm = True
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return data, target, seq_len, warm
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def get_fixlen_iter(self, start=0):
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for i in range(start, self.data.size(0) - 1, self.bptt):
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yield self.get_batch(i)
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def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3):
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max_len = self.bptt + max_deviation * std
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i = start
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while True:
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bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.
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bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std))))
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data, target, seq_len = self.get_batch(i, bptt)
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i += seq_len
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yield data, target, seq_len
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if i >= self.data.size(0) - 2:
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break
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def __iter__(self):
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return self.get_fixlen_iter()
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class LMShuffledIterator(object):
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def __init__(self, data, bsz, bptt, device='cpu', ext_len=None, shuffle=False):
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"""
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data -- list[LongTensor] -- there is no order among the LongTensors
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"""
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self.data = data
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self.bsz = bsz
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self.bptt = bptt
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self.ext_len = ext_len if ext_len is not None else 0
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self.device = device
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self.shuffle = shuffle
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def get_sent_stream(self):
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# index iterator
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epoch_indices = np.random.permutation(len(self.data)) if self.shuffle \
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else np.array(range(len(self.data)))
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# sentence iterator
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for idx in epoch_indices:
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yield self.data[idx]
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def stream_iterator(self, sent_stream):
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# streams for each data in the batch
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streams = [None] * self.bsz
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data = torch.LongTensor(self.bptt, self.bsz)
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target = torch.LongTensor(self.bptt, self.bsz)
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n_retain = 0
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while True:
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# data : [n_retain+bptt x bsz]
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# target : [bptt x bsz]
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data[n_retain:].fill_(-1)
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target.fill_(-1)
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valid_batch = True
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for i in range(self.bsz):
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n_filled = 0
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try:
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while n_filled < self.bptt:
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if streams[i] is None or len(streams[i]) <= 1:
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streams[i] = next(sent_stream)
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# number of new tokens to fill in
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n_new = min(len(streams[i]) - 1, self.bptt - n_filled)
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# first n_retain tokens are retained from last batch
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data[n_retain+n_filled:n_retain+n_filled+n_new, i] = \
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streams[i][:n_new]
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target[n_filled:n_filled+n_new, i] = \
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streams[i][1:n_new+1]
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streams[i] = streams[i][n_new:]
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n_filled += n_new
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except StopIteration:
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valid_batch = False
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break
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if not valid_batch:
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return
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data = data.to(self.device)
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target = target.to(self.device)
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yield data, target, self.bptt
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n_retain = min(data.size(0), self.ext_len)
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if n_retain > 0:
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data[:n_retain] = data[-n_retain:]
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data.resize_(n_retain + self.bptt, data.size(1))
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def __iter__(self):
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# sent_stream is an iterator
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sent_stream = self.get_sent_stream()
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for batch in self.stream_iterator(sent_stream):
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yield batch
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class LMMultiFileIterator(LMShuffledIterator):
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def __init__(self, paths, vocab, bsz, bptt, device='cpu', ext_len=None,
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shuffle=False):
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self.paths = paths
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self.vocab = vocab
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self.bsz = bsz
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self.bptt = bptt
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self.ext_len = ext_len if ext_len is not None else 0
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self.device = device
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self.shuffle = shuffle
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def get_sent_stream(self, path):
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sents = self.vocab.encode_file(path, add_double_eos=True)
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if self.shuffle:
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np.random.shuffle(sents)
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sent_stream = iter(sents)
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return sent_stream
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def __iter__(self):
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if self.shuffle:
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np.random.shuffle(self.paths)
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for path in self.paths:
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# sent_stream is an iterator
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sent_stream = self.get_sent_stream(path)
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for batch in self.stream_iterator(sent_stream):
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yield batch
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class Corpus(object):
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def __init__(self, path, dataset, vocab, *args, **kwargs):
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self.dataset = dataset
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if vocab == 'word':
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self.vocab = Vocab(*args, **kwargs)
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elif vocab == 'bpe':
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self.vocab = OpenAIVocab()
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else:
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raise RuntimeError('Unsupported vocab')
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if self.dataset in ['ptb', 'wt2', 'enwik8', 'text8']:
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self.vocab.count_file(os.path.join(path, 'train.txt'))
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self.vocab.count_file(os.path.join(path, 'valid.txt'))
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self.vocab.count_file(os.path.join(path, 'test.txt'))
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elif self.dataset == 'wt103':
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self.vocab.count_file(os.path.join(path, 'train.txt'))
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elif self.dataset == 'lm1b':
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train_path_pattern = os.path.join(
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path, '1-billion-word-language-modeling-benchmark-r13output',
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'training-monolingual.tokenized.shuffled', 'news.en-*')
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train_paths = glob.glob(train_path_pattern)
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# the vocab will load from file when build_vocab() is called
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self.vocab.build_vocab()
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if self.dataset in ['ptb', 'wt2', 'wt103']:
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self.train = self.vocab.encode_file(
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os.path.join(path, 'train.txt'), ordered=True)
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self.valid = self.vocab.encode_file(
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os.path.join(path, 'valid.txt'), ordered=True)
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self.test = self.vocab.encode_file(
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os.path.join(path, 'test.txt'), ordered=True)
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elif self.dataset in ['enwik8', 'text8']:
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self.train = self.vocab.encode_file(
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os.path.join(path, 'train.txt'), ordered=True, add_eos=False)
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self.valid = self.vocab.encode_file(
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os.path.join(path, 'valid.txt'), ordered=True, add_eos=False)
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self.test = self.vocab.encode_file(
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os.path.join(path, 'test.txt'), ordered=True, add_eos=False)
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elif self.dataset == 'lm1b':
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self.train = train_paths
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self.valid = self.vocab.encode_file(
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os.path.join(path, 'valid.txt'), ordered=False, add_double_eos=True)
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self.test = self.vocab.encode_file(
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os.path.join(path, 'test.txt'), ordered=False, add_double_eos=True)
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def get_iterator(self, split, *args, **kwargs):
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if split == 'train':
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if self.dataset in ['ptb', 'wt2', 'wt103', 'enwik8', 'text8']:
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data_iter = LMOrderedIterator(self.train, *args, **kwargs)
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elif self.dataset == 'lm1b':
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kwargs['shuffle'] = True
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data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs)
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elif split in ['valid', 'test']:
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data = self.valid if split == 'valid' else self.test
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if self.dataset in ['ptb', 'wt2', 'wt103', 'enwik8', 'text8']:
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data_iter = LMOrderedIterator(data, *args, **kwargs)
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elif self.dataset == 'lm1b':
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data_iter = LMShuffledIterator(data, *args, **kwargs)
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return data_iter
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def get_lm_corpus(datadir, dataset, vocab):
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if vocab == 'word':
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fn = os.path.join(datadir, 'cache.pt')
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elif vocab == 'bpe':
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fn = os.path.join(datadir, 'cache.pt.bpe')
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else:
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raise RuntimeError('Unsupported vocab')
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if os.path.exists(fn):
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logging.info('Loading cached dataset...')
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corpus = torch.load(fn)
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else:
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logging.info('Producing dataset {}...'.format(dataset))
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kwargs = {}
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if dataset in ['wt103', 'wt2']:
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kwargs['special'] = ['<eos>']
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kwargs['lower_case'] = False
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elif dataset == 'ptb':
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kwargs['special'] = ['<eos>']
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kwargs['lower_case'] = True
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elif dataset == 'lm1b':
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kwargs['special'] = []
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kwargs['lower_case'] = False
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kwargs['vocab_file'] = os.path.join(datadir, '1b_word_vocab.txt')
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elif dataset in ['enwik8', 'text8']:
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pass
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corpus = Corpus(datadir, dataset, vocab, **kwargs)
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with utils.distributed.sync_workers() as rank:
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if rank == 0:
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torch.save(corpus, fn)
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return corpus
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def tokenize_raw(text, lang='en'):
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mt = sacremoses.MosesTokenizer(lang)
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text = mt.tokenize(text, return_str=True)
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text = re.sub(r'"', '"', text)
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text = re.sub(r''', "'", text)
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text = re.sub(r'(\d)\.(\d)', r'\1 @.@ \2', text)
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text = re.sub(r'(\d),(\d)', r'\1 @,@ \2', text)
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text = re.sub(r'(\w)-(\w)', r'\1 @-@ \2', text)
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return text
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='unit test')
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parser.add_argument('--datadir', type=str, default='../data/text8',
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help='location of the data corpus')
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parser.add_argument('--dataset', type=str, default='text8',
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choices=['ptb', 'wt2', 'wt103', 'lm1b', 'enwik8', 'text8'],
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help='dataset name')
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO)
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corpus = get_lm_corpus(args.datadir, args.dataset, vocab='word')
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logging.info('Vocab size : {}'.format(len(corpus.vocab.idx2sym)))
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