mirror of https://github.com/hpcaitech/ColossalAI
237 lines
9.4 KiB
Python
237 lines
9.4 KiB
Python
# coding=utf-8
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# Copyright (c) 2020, 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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"""BERT Style dataset."""
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import os
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import time
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.logging import get_dist_logger
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from ..tokenizer import get_tokenizer
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from .dataset_utils import (
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create_masked_lm_predictions,
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create_tokens_and_tokentypes,
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get_a_and_b_segments,
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pad_and_convert_to_numpy,
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truncate_segments,
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)
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try:
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from . import helpers
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except:
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print("helper is not built, ignore this message if you are using synthetic data.")
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class BertDataset(Dataset):
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def __init__(self, name, indexed_dataset, data_prefix, num_epochs, max_num_samples, masked_lm_prob, max_seq_length,
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short_seq_prob, seed, binary_head):
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# Params to store.
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self.name = name
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self.seed = seed
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self.masked_lm_prob = masked_lm_prob
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self.max_seq_length = max_seq_length
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self.binary_head = binary_head
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# Dataset.
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self.indexed_dataset = indexed_dataset
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# Build the samples mapping.
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self.samples_mapping = get_samples_mapping_(
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self.indexed_dataset,
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data_prefix,
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num_epochs,
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max_num_samples,
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self.max_seq_length - 3, # account for added tokens,
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short_seq_prob,
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self.seed,
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self.name,
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self.binary_head)
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# Vocab stuff.
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tokenizer = get_tokenizer()
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self.vocab_id_list = list(tokenizer.inv_vocab.keys())
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self.vocab_id_to_token_dict = tokenizer.inv_vocab
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self.cls_id = tokenizer.cls
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self.sep_id = tokenizer.sep
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self.mask_id = tokenizer.mask
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self.pad_id = tokenizer.pad
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def __len__(self):
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return self.samples_mapping.shape[0]
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def __getitem__(self, idx):
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start_idx, end_idx, seq_length = self.samples_mapping[idx]
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sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]
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# Note that this rng state should be numpy and not python since
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# python randint is inclusive whereas the numpy one is exclusive.
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# We % 2**32 since numpy requires the seed to be between 0 and 2**32 - 1
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np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
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return build_training_sample(
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sample,
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seq_length,
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self.max_seq_length, # needed for padding
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self.vocab_id_list,
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self.vocab_id_to_token_dict,
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self.cls_id,
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self.sep_id,
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self.mask_id,
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self.pad_id,
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self.masked_lm_prob,
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np_rng,
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self.binary_head)
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def get_samples_mapping_(indexed_dataset, data_prefix, num_epochs, max_num_samples, max_seq_length, short_seq_prob,
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seed, name, binary_head):
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logger = get_dist_logger()
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if not num_epochs:
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if not max_num_samples:
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raise ValueError("Need to specify either max_num_samples "
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"or num_epochs")
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num_epochs = np.iinfo(np.int32).max - 1
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if not max_num_samples:
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max_num_samples = np.iinfo(np.int64).max - 1
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# Filename of the index mapping
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indexmap_filename = data_prefix
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indexmap_filename += '_{}_indexmap'.format(name)
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if num_epochs != (np.iinfo(np.int32).max - 1):
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indexmap_filename += '_{}ep'.format(num_epochs)
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if max_num_samples != (np.iinfo(np.int64).max - 1):
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indexmap_filename += '_{}mns'.format(max_num_samples)
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indexmap_filename += '_{}msl'.format(max_seq_length)
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indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob)
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indexmap_filename += '_{}s'.format(seed)
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indexmap_filename += '.npy'
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# Build the indexed mapping if not exist.
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if torch.distributed.get_rank() == 0 and \
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not os.path.isfile(indexmap_filename):
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print(' > WARNING: could not find index map file {}, building '
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'the indices on rank 0 ...'.format(indexmap_filename))
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# Make sure the types match the helpers input types.
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assert indexed_dataset.doc_idx.dtype == np.int64
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assert indexed_dataset.sizes.dtype == np.int32
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# Build samples mapping
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verbose = torch.distributed.get_rank() == 0
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start_time = time.time()
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logger.info('\n > building samples index mapping for {} ...'.format(name), ranks=[0])
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# First compile and then import.
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samples_mapping = helpers.build_mapping(indexed_dataset.doc_idx, indexed_dataset.sizes, num_epochs,
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max_num_samples, max_seq_length, short_seq_prob, seed, verbose,
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2 if binary_head else 1)
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logger.info('\n > done building samples index maping', ranks=[0])
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np.save(indexmap_filename, samples_mapping, allow_pickle=True)
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logger.info('\n > saved the index mapping in {}'.format(indexmap_filename), ranks=[0])
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# Make sure all the ranks have built the mapping
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logger.info('\n > elapsed time to build and save samples mapping '
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'(seconds): {:4f}'.format(time.time() - start_time),
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ranks=[0])
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# This should be a barrier but nccl barrier assumes
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# device_index=rank which is not the case for model
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# parallel case
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counts = torch.cuda.LongTensor([1])
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torch.distributed.all_reduce(counts, group=gpc.get_group(ParallelMode.DATA))
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if gpc.is_initialized(ParallelMode.PIPELINE):
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torch.distributed.all_reduce(counts, group=gpc.get_group(ParallelMode.PIPELINE))
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assert counts[0].item() == (torch.distributed.get_world_size() //
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torch.distributed.get_world_size(group=gpc.get_group(ParallelMode.SEQUENCE)))
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# Load indexed dataset.
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start_time = time.time()
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samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r')
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logger.info('\n > loading indexed mapping from {}'.format(indexmap_filename) +
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'\n loaded indexed file in {:3.3f} seconds'.format(time.time() - start_time) +
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'\n total number of samples: {}'.format(samples_mapping.shape[0]),
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ranks=[0])
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return samples_mapping
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def build_training_sample(sample, target_seq_length, max_seq_length, vocab_id_list, vocab_id_to_token_dict, cls_id,
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sep_id, mask_id, pad_id, masked_lm_prob, np_rng, binary_head):
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"""Build training sample.
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Arguments:
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sample: A list of sentences in which each sentence is a list token ids.
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target_seq_length: Desired sequence length.
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max_seq_length: Maximum length of the sequence. All values are padded to
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this length.
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vocab_id_list: List of vocabulary ids. Used to pick a random id.
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vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
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cls_id: Start of example id.
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sep_id: Separator id.
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mask_id: Mask token id.
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pad_id: Padding token id.
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masked_lm_prob: Probability to mask tokens.
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np_rng: Random number genenrator. Note that this rng state should be
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numpy and not python since python randint is inclusive for
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the opper bound whereas the numpy one is exclusive.
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"""
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if binary_head:
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# We assume that we have at least two sentences in the sample
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assert len(sample) > 1
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assert target_seq_length <= max_seq_length
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# Divide sample into two segments (A and B).
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if binary_head:
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tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)
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else:
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tokens_a = []
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for j in range(len(sample)):
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tokens_a.extend(sample[j])
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tokens_b = []
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is_next_random = False
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# Truncate to `target_sequence_length`.
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max_num_tokens = target_seq_length
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truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a), len(tokens_b), max_num_tokens, np_rng)
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# Build tokens and toketypes.
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tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id)
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# Masking.
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max_predictions_per_seq = masked_lm_prob * max_num_tokens
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(tokens, masked_positions, masked_labels,
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_) = create_masked_lm_predictions(tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob, cls_id, sep_id,
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mask_id, max_predictions_per_seq, np_rng)
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# Padding.
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tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \
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= pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
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masked_labels, pad_id, max_seq_length)
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train_sample = {
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'text': tokens_np,
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'types': tokentypes_np,
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'labels': labels_np,
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'is_random': int(is_next_random),
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'loss_mask': loss_mask_np,
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'padding_mask': padding_mask_np,
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'truncated': int(truncated)
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}
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return train_sample
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