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# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Dataloaders to train DistilBERT
adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
"""
from typing import List
import math
from itertools import chain
from collections import Counter
import numpy as np
import torch
from utils import logger
class Dataset:
def __init__(self,
params,
data):
self.params = params
self.tokens_per_batch = params.tokens_per_batch
self.batch_size = params.batch_size
self.shuffle = params.shuffle
self.group_by_size = params.group_by_size
self.token_ids = np.array(data)
self.lengths = np.uint16([len(t) for t in data])
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.check()
self.print_statistics()
def __len__(self):
return len(self.lengths)
def check(self):
"""
Some sanity checks
"""
assert len(self.token_ids) == len(self.lengths)
def remove_long_sequences(self):
"""
Sequences that are too long are splitted by chunk of max_position_embeddings.
"""
indices = self.lengths >= self.params.max_position_embeddings
logger.info(f'Splitting {sum(indices)} too long sequences.')
def divide_chunks(l, n):
return [l[i:i + n] for i in range(0, len(l), n)]
new_tok_ids = []
new_lengths = []
cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
max_len = self.params.max_position_embeddings
for seq_, len_ in zip(self.token_ids, self.lengths):
if len_ <= max_len:
new_tok_ids.append(seq_)
new_lengths.append(len_)
else:
sub_seqs = []
for sub_s in divide_chunks(seq_, max_len-2):
if sub_s[0] != cls_id:
sub_s = np.insert(sub_s, 0, cls_id)
if sub_s[-1] != sep_id:
sub_s = np.insert(sub_s, len(sub_s), sep_id)
assert len(sub_s) <= max_len
sub_seqs.append(sub_s)
new_tok_ids.extend(sub_seqs)
new_lengths.extend([len(l) for l in sub_seqs])
self.token_ids = np.array(new_tok_ids)
self.lengths = np.array(new_lengths)
def remove_empty_sequences(self):
"""
Too short sequences are simply removed. This could be tunedd.
"""
init_size = len(self)
indices = self.lengths > 11
self.token_ids = self.token_ids[indices]
self.lengths = self.lengths[indices]
new_size = len(self)
logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')
def print_statistics(self):
"""
Print some statistics on the corpus. Only the master process.
"""
if not self.params.is_master:
return
logger.info(f'{len(self)} sequences')
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unkown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unkown} unknown tokens (covering {100*nb_unkown/data_len:.2f}% of the data)')
def select_data(self, a: int, b: int):
"""
Select a subportion of the data.
"""
n_sequences = len(self)
assert 0 <= a < b <= n_sequences, ValueError(f'`0 <= a < b <= n_sequences` is not met with a={a} and b={b}')
logger.info(f'Selecting sequences from {a} to {b} (excluded).')
self.token_ids = self.token_ids[a:b]
self.lengths = self.lengths[a:b]
self.check()
def split(self):
"""
Distributed training: split the data accross the processes.
"""
assert self.params.n_gpu > 1
logger.info('Splitting the data accross the processuses.')
n_seq = len(self)
n_seq_per_procesus = n_seq // self.params.world_size
a = n_seq_per_procesus * self.params.global_rank
b = a + n_seq_per_procesus
self.select_data(a=a, b=b)
def batch_sequences(self,
token_ids: List[List[int]],
lengths: List[int]):
"""
Do the padding and transform into torch.tensor.
"""
assert len(token_ids) == len(lengths)
# Max for paddings
max_seq_len_ = max(lengths)
# Pad token ids
pad_idx = self.params.special_tok_ids['pad_token']
tk_ = [list(t.astype(int)) + [pad_idx]*(max_seq_len_-len(t)) for t in token_ids]
assert len(tk_) == len(token_ids)
assert all(len(t) == max_seq_len_ for t in tk_)
tk_t = torch.tensor(tk_) # (bs, max_seq_len_)
lg_t = torch.tensor(lengths.astype(int)) # (bs)
return tk_t, lg_t
def get_batches_iterator(self,
batches):
"""
Return an iterator over batches.
"""
for sequences_ids in batches:
token_ids, lengths = self.batch_sequences(self.token_ids[sequences_ids],
self.lengths[sequences_ids])
yield (token_ids, lengths)
def get_iterator(self,
seed: int = None):
"""
Return a data iterator.
"""
rng = np.random.RandomState(seed)
n_sequences = len(self)
indices = np.arange(n_sequences)
if self.group_by_size:
indices = indices[np.argsort(self.lengths[indices], kind='mergesort')]
if self.tokens_per_batch == -1:
batches = np.array_split(indices, math.ceil(len(indices) * 1. / self.batch_size))
else:
assert self.tokens_per_batch > 0
batch_ids = np.cumsum(self.lengths[indices]) // self.tokens_per_batch
_, bounds = np.unique(batch_ids, return_index=True)
batches = [indices[bounds[i]:bounds[i + 1]] for i in range(len(bounds) - 1)]
if bounds[-1] < len(indices):
batches.append(indices[bounds[-1]:])
if self.shuffle:
rng.shuffle(batches)
assert n_sequences == sum([len(x) for x in batches])
assert self.lengths[indices].sum() == sum([self.lengths[x].sum() for x in batches])
return self.get_batches_iterator(batches=batches)
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