# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""Roberta Style dataset."""
import numpy as np
import oneflow as flow
from libai.data.structures import DistTensorData, Instance
from ..data_utils import create_masked_lm_predictions, get_samples_mapping
from .bert_dataset import pad_and_convert_to_numpy
[docs]class RobertaDataset(flow.utils.data.Dataset):
"""Dataset containing sentence for RoBERTa training.
Each index corresponds to a randomly selected sentence.
Args:
name: Name of dataset for clarification.
tokenizer: Tokenizer to use.
data_prefix: Path to the training dataset.
indexed_dataset: Indexed dataset to use.
max_seq_length: Maximum length of the sequence. All values are padded to
this length. Defaults to 512.
mask_lm_prob: Probability to mask tokens. Defaults to 0.15.
short_seq_prob: Probability of producing a short sequence. Defaults to 0.0.
max_predictions_per_seq: Maximum number of mask tokens in each sentence. Defaults to None.
seed: Seed for random number generator for reproducibility. Defaults to 1234.
"""
def __init__(
self,
name,
tokenizer,
indexed_dataset,
data_prefix,
max_num_samples,
mask_lm_prob,
max_seq_length,
short_seq_prob=0.0,
seed=1234,
masking_style="bert",
):
super().__init__()
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = mask_lm_prob
self.max_seq_length = max_seq_length
self.masking_style = masking_style
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
self.indexed_dataset,
data_prefix,
None,
max_num_samples,
self.max_seq_length - 2, # account for added tokens
short_seq_prob,
self.seed,
self.name,
binary_head=False,
)
# Vocab stuff.
self.tokenizer = tokenizer
self.vocab_id_list = list(tokenizer.get_vocab().values())
self.vocab_id_to_token_dict = {v: k for k, v in tokenizer.get_vocab().items()}
self.cls_id = tokenizer.cls_token_id
self.sep_id = tokenizer.sep_token_id
self.mask_id = tokenizer.mask_token_id
self.pad_id = tokenizer.pad_token_id
def __len__(self):
return self.samples_mapping.shape[0]
def __getitem__(self, idx):
start_idx, end_idx, seq_length = self.samples_mapping[idx]
sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]
# Note that this rng state should be numpy and not python since
# python randint is inclusive whereas the numpy one is exclusive.
# We % 2**32 since numpy requires the seed to be between 0 and 2**32 - 1
np_rng = np.random.RandomState(seed=((self.seed + idx) % 2 ** 32))
return build_training_sample(
self.tokenizer,
sample,
seq_length,
self.max_seq_length, # needed for padding
self.vocab_id_list,
self.vocab_id_to_token_dict,
self.cls_id,
self.sep_id,
self.mask_id,
self.pad_id,
self.masked_lm_prob,
np_rng,
masking_style=self.masking_style,
)
def build_training_sample(
tokenizer,
sample,
target_seq_length,
max_seq_length,
vocab_id_list,
vocab_id_to_token_dict,
cls_id,
sep_id,
mask_id,
pad_id,
masked_lm_prob,
np_rng,
masking_style="bert",
):
"""Build training sample.
Arguments:
sample: A list of sentences in which each sentence is a list token ids.
target_seq_length: Desired sequence length.
max_seq_length: Maximum length of the sequence. All values are padded to
this length.
vocab_id_list: List of vocabulary ids. Used to pick a random id.
vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
cls_id: Start of example id.
sep_id: Separator id.
mask_id: Mask token id.
pad_id: Padding token id.
masked_lm_prob: Probability to mask tokens.
np_rng: Random number genenrator. Note that this rng state should be
numpy and not python since python randint is inclusive for
the upper bound whereas the numpy one is exclusive.
"""
assert target_seq_length <= max_seq_length
tokens = []
for j in range(len(sample)):
tokens.extend(sample[j])
max_num_tokens = target_seq_length
truncate_segments(tokens, len(tokens), max_num_tokens, np_rng)
# create tokens and tokentypes
tokens, tokentypes = create_tokens_and_tokentypes(tokens, cls_id, sep_id)
# Masking
max_predictions_per_seq = masked_lm_prob * max_num_tokens
(tokens, masked_positions, masked_labels, _, _) = create_masked_lm_predictions(
tokenizer,
tokens,
vocab_id_list,
vocab_id_to_token_dict,
masked_lm_prob,
cls_id,
sep_id,
mask_id,
max_predictions_per_seq,
np_rng,
masking_style=masking_style,
)
# Padding.
tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np = pad_and_convert_to_numpy(
tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length
)
train_sample = Instance(
input_ids=DistTensorData(flow.tensor(tokens_np)),
attention_mask=DistTensorData(flow.tensor(padding_mask_np)),
tokentype_ids=DistTensorData(flow.tensor(tokentypes_np)),
lm_labels=DistTensorData(flow.tensor(labels_np), placement_idx=-1),
loss_mask=DistTensorData(flow.tensor(loss_mask_np), placement_idx=-1),
)
return train_sample
def truncate_segments(tokens, len_tokens, max_num_tokens, np_rng):
"""Truncates a sequences to a maximum sequence length."""
assert len_tokens > 0
if len_tokens <= max_num_tokens:
return False
while len_tokens > max_num_tokens:
if np_rng.random() < 0.5:
del tokens[0]
else:
tokens.pop()
len_tokens -= 1
return True
def create_tokens_and_tokentypes(tokens, cls_id, sep_id):
"""Add [CLS] and [SEP] and build tokentypes."""
# [CLS].
tokens.insert(0, cls_id)
# [SPE].
tokens.append(sep_id)
tokentypes = [0] * len(tokens)
return tokens, tokentypes