# 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.
"""T5 Style dataset."""
import collections
import numpy as np
import torch
from megatron import get_tokenizer
from megatron.data.dataset_utils import (
create_masked_lm_predictions,
get_samples_mapping
)
class T5Dataset(torch.utils.data.Dataset):
def __init__(self, name, indexed_dataset, data_prefix,
num_epochs, max_num_samples, masked_lm_prob,
max_seq_length, max_seq_length_dec,
short_seq_prob, seed):
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.max_seq_length_dec = max_seq_length_dec
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length - 2, # account for added tokens
short_seq_prob,
self.seed,
self.name,
False)
# Vocab stuff.
tokenizer = get_tokenizer()
self.vocab_id_list = list(tokenizer.inv_vocab.keys())
self.vocab_id_to_token_dict = tokenizer.inv_vocab
self.cls_id = tokenizer.cls
self.sep_id = tokenizer.sep
self.mask_id = tokenizer.mask
self.pad_id = tokenizer.pad
self.bos_id = tokenizer.bos_token_id
self.eos_id = tokenizer.eos_token_id
self.sentinel_tokens = tokenizer.additional_special_tokens_ids
assert len(self.sentinel_tokens) > 0, "Provide the argument --vocab-extra-ids 100 to the script"
def __len__(self):
return self.samples_mapping.shape[0]
def __getitem__(self, idx):
start_index, end_index, seq_length = self.samples_mapping[idx]
sample = []
for index in range(start_index, end_index):
sample.append(self.indexed_dataset[index])
# Note that this rng state should be numpy and not python since
# python randint is inclusive whereas the numpy one is exclusive.
np_rng = np.random.RandomState(seed=(self.seed + idx))
return build_training_sample(sample, seq_length,
self.max_seq_length, # needed for padding
self.max_seq_length_dec,
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,
self.bos_id, self.eos_id,
self.sentinel_tokens)
def build_training_sample(sample, target_seq_length,
max_seq_length, max_seq_length_dec,
vocab_id_list, vocab_id_to_token_dict,
cls_id, sep_id, mask_id, pad_id,
masked_lm_prob, np_rng, bos_id=None,
eos_id=None, sentinel_tokens=None):
"""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 opper bound whereas the numpy one is exclusive.
bos_id: start of decoder example id
eos_id: end of generation id
sentinel_tokens: unique value to be substituted for every replaced span
"""
assert target_seq_length <= max_seq_length
# flatten sentences into one list
tokens = [token for sentence in sample for token in sentence]
# Truncate to `target_sequence_length`.
max_num_tokens = target_seq_length
truncated = len(tokens) > max_num_tokens
tokens = tokens[:max_num_tokens]
# Masking.
max_predictions_per_seq = masked_lm_prob * max_num_tokens
(tokens, masked_positions, masked_labels, _, masked_spans) = create_masked_lm_predictions(
tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob,
cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng,
max_ngrams=10, geometric_dist=True, masking_style="t5")
# Padding.
tokens_enc, tokens_dec_in, labels, enc_mask, \
dec_mask, enc_dec_mask, loss_mask \
= pad_and_convert_to_numpy(tokens, masked_positions,
masked_labels, pad_id, max_seq_length,
max_seq_length_dec, masked_spans,
bos_id, eos_id, sentinel_tokens)
train_sample = {
'text_enc': tokens_enc,
'text_dec': tokens_dec_in,
'labels': labels,
'loss_mask': loss_mask,
'truncated': int(truncated),
'enc_mask': enc_mask,
'dec_mask': dec_mask,
'enc_dec_mask': enc_dec_mask,
}
return train_sample
def pad_and_convert_to_numpy(tokens, masked_positions,
masked_labels, pad_id,
max_seq_length, max_seq_length_dec,
masked_spans=None, bos_id=None,
eos_id=None, sentinel_tokens=None):
"""Pad sequences and convert them to numpy."""
sentinel_tokens = collections.deque(sentinel_tokens)
t5_input = []
(t5_decoder_in, t5_decoder_out) = ([bos_id], [])
(start_index, end_index) = (0, None)
for span in masked_spans:
flag = sentinel_tokens.popleft()
# Append the same tokens in decoder input and output
t5_decoder_in.append(flag)
t5_decoder_in.extend(span.label)
t5_decoder_out.append(flag)
t5_decoder_out.extend(span.label)
end_index = span.index[0]
t5_input.extend(tokens[start_index: end_index])
t5_input.append(flag)
# the next start index is the token after the last span token
start_index = span.index[-1] + 1
# Add <eos> token to the t5_decoder_out
t5_decoder_out.append(eos_id)
# Add the remaining tokens to the t5 input
t5_input.extend(tokens[start_index:])
# assert (len(t5_input) - len(masked_spans)) + \
# (len(t5_decoder_in) - (len(masked_spans) + 1)) == len(tokens)
# Some checks.
# Encoder-side padding mask.
num_tokens = len(t5_input)
padding_length = max_seq_length - num_tokens
assert padding_length >= 0
assert len(masked_positions) == len(masked_labels)
# Tokens..
filler = [pad_id] * padding_length
tokens_enc = np.array(t5_input + filler, dtype=np.int64)
# Decoder-side padding mask.
num_tokens_dec = len(t5_decoder_in)
padding_length_dec = max_seq_length_dec - num_tokens_dec
assert padding_length_dec >= 0
filler_dec = [pad_id] * padding_length_dec
tokens_dec_in = np.array(t5_decoder_in + filler_dec, dtype=np.int64)
# Create attention masks
enc_mask = make_attention_mask(tokens_enc, tokens_enc)
enc_dec_mask = make_attention_mask(tokens_dec_in, tokens_enc)
dec_mask = make_attention_mask(tokens_dec_in, tokens_dec_in)
dec_mask = dec_mask * make_history_mask(tokens_dec_in)
# Labels mask.
labels = t5_decoder_out + ([-1] * padding_length_dec)
labels = np.array(labels, dtype=np.int64)
# Loss mask
loss_mask = ([1] * num_tokens_dec) + ([0] * padding_length_dec)
loss_mask = np.array(loss_mask, dtype=np.int64)
return tokens_enc, tokens_dec_in, labels, enc_mask, \
dec_mask, enc_dec_mask, loss_mask
def make_attention_mask(source_block, target_block):
"""
Returns a 2-dimensional (2-D) attention mask
:param source_block: 1-D array
:param target_block: 1-D array
"""
mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1)
mask = mask.astype(np.int64)
# (source_length, target_length)
return mask
def make_attention_mask_3d(source_block, target_block):
"""
Returns a 3-dimensional (3-D) attention mask
:param source_block: 1-D array
:param target_block: 1-D array
"""
mask = (target_block[:, None, :] >= 1) * (source_block[:, :, None] >= 1)
# (batch, source_length, target_length)
# mask = mask.astype(np.int64)
return mask
def make_history_mask(block):
length = block.shape[0]
arange = np.arange(length)
history_mask = (arange[None, ] <= arange[:, None])
history_mask = history_mask.astype(np.int64)
return history_mask
def make_history_mask_3d(block):
batch, length = block.shape
arange = torch.arange(length, device=block.device)
history_mask = (arange[None, ] <= arange[:, None])[None, ]
history_mask = history_mask.expand(batch, length, length)
return history_mask