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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import numpy as np
import torch
from megatron import get_args, get_tokenizer
from megatron.data.bert_dataset import build_training_sample
class BertEmbeddingDataset(torch.utils.data.Dataset):
'''Dataset to convert a text dataset to Bert tokens.'''
def __init__(self, text_dataset, max_seq_length):
super().__init__()
args = get_args()
# Dataset, tokenizer.
self.text_dataset = text_dataset
self.bert_tokenizer = get_tokenizer()
# Params to store.
self.max_seq_length = max_seq_length
self.seed = args.seed
self.masked_lm_prob = args.mask_prob
# Vocab stuff.
self.vocab_id_list = list(self.bert_tokenizer.inv_vocab.keys())
self.vocab_id_to_token_dict = self.bert_tokenizer.inv_vocab
self.cls_id = self.bert_tokenizer.cls
self.sep_id = self.bert_tokenizer.sep
self.mask_id = self.bert_tokenizer.mask
self.pad_id = self.bert_tokenizer.pad
def __len__(self):
return len(self.text_dataset)
def __getitem__(self, idx):
# Text.
text_sample = self.text_dataset[idx]
text = text_sample["text"]
text = text.replace("<|endoftext|>", "")
# Bert/Wordpiece tokens (+truncate).
bert_token_ids = self.bert_tokenizer.tokenize(text)
bert_token_ids = bert_token_ids[:self.max_seq_length - 2] # cls+sep.
if not bert_token_ids:
bert_token_ids = [ self.bert_tokenizer.pad_id ] # hack when empty seq
# 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 requres the seed to be between 0 and 2**32 - 1
np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
# Build sample.
sample = build_training_sample([bert_token_ids],
len(bert_token_ids),
len(bert_token_ids) + 2, # for cls+sep
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,
binary_head=False)
sample["seq_length"] = len(sample["text"])
return sample
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