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- # 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.
- """GPT style dataset."""
- import os
- import time
- import numpy as np
- import torch
- from megatron import mpu, print_rank_0
- from megatron.data.blendable_dataset import BlendableDataset
- from megatron.data.dataset_utils import get_datasets_weights_and_num_samples
- from megatron.data.dataset_utils import get_train_valid_test_split_
- from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
- def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
- train_valid_test_num_samples,
- seq_length, seed, skip_warmup):
- """Build train, valid, and test datasets."""
- # Single dataset.
- if len(data_prefix) == 1:
- return _build_train_valid_test_datasets(data_prefix[0],
- data_impl, splits_string,
- train_valid_test_num_samples,
- seq_length, seed, skip_warmup)
- # Blending dataset.
- # Parse the values.
- output = get_datasets_weights_and_num_samples(data_prefix,
- train_valid_test_num_samples)
- prefixes, weights, datasets_train_valid_test_num_samples = output
- # Build individual datasets.
- train_datasets = []
- valid_datasets = []
- test_datasets = []
- for i in range(len(prefixes)):
- train_ds, valid_ds, test_ds = _build_train_valid_test_datasets(
- prefixes[i], data_impl, splits_string,
- datasets_train_valid_test_num_samples[i],
- seq_length, seed, skip_warmup)
- if train_ds:
- train_datasets.append(train_ds)
- if valid_ds:
- valid_datasets.append(valid_ds)
- if test_ds:
- test_datasets.append(test_ds)
- # Blend.
- blending_train_dataset = None
- if train_datasets:
- blending_train_dataset = BlendableDataset(train_datasets, weights)
- blending_valid_dataset = None
- if valid_datasets:
- blending_valid_dataset = BlendableDataset(valid_datasets, weights)
- blending_test_dataset = None
- if test_datasets:
- blending_test_dataset = BlendableDataset(test_datasets, weights)
- return (blending_train_dataset, blending_valid_dataset,
- blending_test_dataset)
- def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
- train_valid_test_num_samples,
- seq_length, seed, skip_warmup):
- """Build train, valid, and test datasets."""
- # Indexed dataset.
- indexed_dataset = get_indexed_dataset_(data_prefix,
- data_impl,
- skip_warmup)
- total_num_of_documents = indexed_dataset.sizes.shape[0]
- splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
- # Print stats about the splits.
- print_rank_0(' > dataset split:')
- def print_split_stats(name, index):
- print_rank_0(' {}:'.format(name))
- print_rank_0(' document indices in [{}, {}) total of {} '
- 'documents'.format(splits[index], splits[index + 1],
- splits[index + 1] - splits[index]))
- print_split_stats('train', 0)
- print_split_stats('validation', 1)
- print_split_stats('test', 2)
- def build_dataset(index, name):
- dataset = None
- if splits[index + 1] > splits[index]:
- documents = np.arange(start=splits[index], stop=splits[index + 1],
- step=1, dtype=np.int32)
- dataset = GPTDataset(name, data_prefix,
- documents, indexed_dataset,
- train_valid_test_num_samples[index],
- seq_length, seed)
- return dataset
- train_dataset = build_dataset(0, 'train')
- valid_dataset = build_dataset(1, 'valid')
- test_dataset = build_dataset(2, 'test')
- return (train_dataset, valid_dataset, test_dataset)
- def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
- """Build indexed dataset."""
- print_rank_0(' > building dataset index ...')
- start_time = time.time()
- indexed_dataset = make_indexed_dataset(data_prefix,
- data_impl,
- skip_warmup)
- print_rank_0(' > finished creating indexed dataset in {:4f} '
- 'seconds'.format(time.time() - start_time))
- print_rank_0(' number of documents: {}'.format(
- indexed_dataset.sizes.shape[0]))
- return indexed_dataset
- class GPTDataset(torch.utils.data.Dataset):
- def __init__(self, name, data_prefix, documents, indexed_dataset,
- num_samples, seq_length, seed):
- self.name = name
- self.indexed_dataset = indexed_dataset
- # Checks
- assert np.min(documents) >= 0
- assert np.max(documents) < indexed_dataset.sizes.shape[0]
- # Build index mappings.
- self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
- self.name, data_prefix, documents, self.indexed_dataset.sizes,
- num_samples, seq_length, seed)
- def __len__(self):
- # -1 is due to data structure used to retieve the index:
- # sample i --> [sample_idx[i], sample_idx[i+1])
- return self.sample_idx.shape[0] - 1
- def __getitem__(self, idx):
- # Get the shuffled index.
- idx = self.shuffle_idx[idx]
- # Start and end documents and offsets.
- doc_index_f = self.sample_idx[idx][0]
- doc_index_l = self.sample_idx[idx + 1][0]
- offset_f = self.sample_idx[idx][1]
- offset_l = self.sample_idx[idx + 1][1]
- # If we are within the same document, just extract the chunk.
- if doc_index_f == doc_index_l:
- sample = self.indexed_dataset.get(self.doc_idx[doc_index_f],
- offset=offset_f,
- length=offset_l - offset_f + 1)
- else:
- # Otherwise, get the rest of the initial document.
- sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f],
- offset=offset_f)]
- # Loop over all in between documents and add the entire document.
- for i in range(doc_index_f + 1, doc_index_l):
- sample_list.append(self.indexed_dataset.get(self.doc_idx[i]))
- # And finally add the relevant portion of last document.
- sample_list.append(self.indexed_dataset.get(
- self.doc_idx[doc_index_l],
- length=offset_l + 1))
- sample = np.concatenate(sample_list)
- return {'text': np.array(sample, dtype=np.int64)}
- def _build_index_mappings(name, data_prefix, documents, sizes,
- num_samples, seq_length, seed):
- """Build doc-idx, sample-idx, and shuffle-idx.
- doc-idx: is an array (ordered) of documents to be used in training.
- sample-idx: is the start document index and document offset for each
- training sample.
- shuffle-idx: maps the sample index into a random index into sample-idx.
- """
- # Number of tokens in each epoch and number of required epochs.
- tokens_per_epoch = _num_tokens(documents, sizes)
- num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples)
- # rng state
- np_rng = np.random.RandomState(seed=seed)
- # Filename of the index mappings.
- _filename = data_prefix
- _filename += '_{}_indexmap'.format(name)
- _filename += '_{}ns'.format(num_samples)
- _filename += '_{}sl'.format(seq_length)
- _filename += '_{}s'.format(seed)
- doc_idx_filename = _filename + '_doc_idx.npy'
- sample_idx_filename = _filename + '_sample_idx.npy'
- shuffle_idx_filename = _filename + '_shuffle_idx.npy'
- # Build the indexed mapping if not exist.
- if torch.distributed.get_rank() == 0:
- if (not os.path.isfile(doc_idx_filename)) or \
- (not os.path.isfile(sample_idx_filename)) or \
- (not os.path.isfile(shuffle_idx_filename)):
- print_rank_0(' > WARNING: could not find index map files, building '
- 'the indices on rank 0 ...')
- # For the last epoch, decide whether include the entire epoch
- # in the global shuffle or not.
- # If we need only one epoch, then separating last epoch does
- # not mean anything.
- if num_epochs == 1:
- separate_last_epoch = False
- print(' > only one epoch required, setting '
- 'separate_last_epoch to False', flush=True)
- else:
- # Get the number of samples for the last epoch
- num_samples_from_epochs_minus_one = (
- (num_epochs - 1) * tokens_per_epoch - 1) // seq_length
- last_epoch_num_samples = num_samples - \
- num_samples_from_epochs_minus_one
- assert last_epoch_num_samples >= 0, \
- 'last epoch number of samples should be non-negative.'
- num_samples_per_epoch = (tokens_per_epoch - 1) // seq_length
- assert last_epoch_num_samples < (num_samples_per_epoch + 1), \
- 'last epoch number of samples exceeded max value.'
- # If we have less than 80% of the samples for the last epoch,
- # seperate out the epoch and treat it differently.
- # Note: the 80% number is just based on common sense and can
- # be adjusted if needed.
- separate_last_epoch = (last_epoch_num_samples <
- int(0.80 * num_samples_per_epoch))
- if separate_last_epoch:
- string = ' > last epoch number of samples ({}) is smaller '\
- 'than 80% of number of samples per epoch ({}), '\
- 'setting separate_last_epoch to True'
- else:
- string = ' > last epoch number of samples ({}) is larger '\
- 'than 80% of number of samples per epoch ({}), '\
- 'setting separate_last_epoch to False'
- print(string.format(last_epoch_num_samples,
- num_samples_per_epoch), flush=True)
- # doc-idx.
- start_time = time.time()
- doc_idx = _build_doc_idx(documents, num_epochs, np_rng,
- separate_last_epoch)
- np.save(doc_idx_filename, doc_idx, allow_pickle=True)
- print_rank_0(' > elasped time to build and save doc-idx mapping '
- '(seconds): {:4f}'.format(time.time() - start_time))
- # sample-idx.
- start_time = time.time()
- # Use C++ implementation for speed.
- # First compile and then import.
- from megatron.data import helpers
- assert doc_idx.dtype == np.int32
- assert sizes.dtype == np.int32
- sample_idx = helpers.build_sample_idx(sizes, doc_idx, seq_length,
- num_epochs, tokens_per_epoch)
- # sample_idx = _build_sample_idx(sizes, doc_idx, seq_length,
- # num_epochs, tokens_per_epoch)
- np.save(sample_idx_filename, sample_idx, allow_pickle=True)
- print_rank_0(' > elasped time to build and save sample-idx mapping '
- '(seconds): {:4f}'.format(time.time() - start_time))
- # shuffle-idx.
- start_time = time.time()
- # -1 is due to data structure used to retieve the index:
- # sample i --> [sample_idx[i], sample_idx[i+1])
- if separate_last_epoch:
- num_samples_ = num_samples_from_epochs_minus_one
- else:
- num_samples_ = sample_idx.shape[0] - 1
- shuffle_idx = _build_shuffle_idx(num_samples_,
- sample_idx.shape[0] - 1, np_rng)
- np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True)
- print_rank_0(' > elasped time to build and save shuffle-idx mapping'
- ' (seconds): {:4f}'.format(time.time() - start_time))
- # This should be a barrier but nccl barrier assumes
- # device_index=rank which is not the case for model
- # parallel case
- counts = torch.cuda.LongTensor([1])
- torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
- torch.distributed.all_reduce(counts, group=mpu.get_pipeline_model_parallel_group())
- assert counts[0].item() == (
- torch.distributed.get_world_size() //
- torch.distributed.get_world_size(group=mpu.get_tensor_model_parallel_group()))
- # Load mappings.
- start_time = time.time()
- print_rank_0(' > loading doc-idx mapping from {}'.format(
- doc_idx_filename))
- doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode='r')
- print_rank_0(' > loading sample-idx mapping from {}'.format(
- sample_idx_filename))
- sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode='r')
- print_rank_0(' > loading shuffle-idx mapping from {}'.format(
- shuffle_idx_filename))
- shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode='r')
- print_rank_0(' loaded indexed file in {:3.3f} seconds'.format(
- time.time() - start_time))
- print_rank_0(' total number of samples: {}'.format(
- sample_idx.shape[0]))
- print_rank_0(' total number of epochs: {}'.format(num_epochs))
- return doc_idx, sample_idx, shuffle_idx
- def _num_tokens(documents, sizes):
- """Total number of tokens in the dataset."""
- return np.sum(sizes[documents])
- def _num_epochs(tokens_per_epoch, seq_length, num_samples):
- """Based on number of samples and sequence lenght, calculate how many
- epochs will be needed."""
- num_epochs = 0
- total_tokens = 0
- while True:
- num_epochs += 1
- total_tokens += tokens_per_epoch
- # -1 is because we need to retrieve seq_length + 1 token each time
- # but the last token will overlap with the first token of the next
- # sample except for the last sample.
- if ((total_tokens - 1) // seq_length) >= num_samples:
- return num_epochs
- def _build_doc_idx(documents, num_epochs, np_rng, separate_last_epoch):
- """Build an array with length = number-of-epochs * number-of-dcuments.
- Each index is mapped to a corresponding document."""
- if not separate_last_epoch or num_epochs == 1:
- doc_idx = np.mgrid[0:num_epochs, 0:len(documents)][1]
- doc_idx[:] = documents
- doc_idx = doc_idx.reshape(-1)
- doc_idx = doc_idx.astype(np.int32)
- np_rng.shuffle(doc_idx)
- return doc_idx
- doc_idx_first = _build_doc_idx(documents, num_epochs-1, np_rng, False)
- doc_idx_last = _build_doc_idx(documents, 1, np_rng, False)
- return np.concatenate((doc_idx_first, doc_idx_last))
- def _build_sample_idx(sizes, doc_idx, seq_length,
- num_epochs, tokens_per_epoch):
- """Sample index mapping is a 2D array with sizes
- [number-of-samples + 1, 2] where [..., 0] contains
- the index into `doc_idx` and [..., 1] is the
- starting offset in that document."""
- # Total number of samples. For -1 see comments in `_num_epochs`.
- num_samples = (num_epochs * tokens_per_epoch - 1) // seq_length
- sample_idx = np.zeros([num_samples + 1, 2], dtype=np.int32)
- # Index into sample_idx.
- sample_index = 0
- # Index into doc_idx.
- doc_idx_index = 0
- # Begining offset for each document.
- doc_offset = 0
- # Start with first document and no offset.
- sample_idx[sample_index][0] = doc_idx_index
- sample_idx[sample_index][1] = doc_offset
- sample_index += 1
- while sample_index <= num_samples:
- # Start with a fresh sequence.
- remaining_seq_length = seq_length + 1
- while remaining_seq_length != 0:
- # Get the document length.
- doc_id = doc_idx[doc_idx_index]
- doc_length = sizes[doc_id] - doc_offset
- # And add it to the current sequence.
- remaining_seq_length -= doc_length
- # If we have more than a full sequence, adjust offset and set
- # remaining length to zero so we return from the while loop.
- # Note that -1 here is for the same reason we have -1 in
- # `_num_epochs` calculations.
- if remaining_seq_length <= 0:
- doc_offset += (remaining_seq_length + doc_length - 1)
- remaining_seq_length = 0
- else:
- # Otherwise, start from the begining of the next document.
- doc_idx_index += 1
- doc_offset = 0
- # Record the sequence.
- sample_idx[sample_index][0] = doc_idx_index
- sample_idx[sample_index][1] = doc_offset
- sample_index += 1
- return sample_idx
- def _build_shuffle_idx(num_samples, total_size, np_rng):
- """Build the range [0, size) and shuffle."""
- print(' > building shuffle index with split [0, {}) and [{}, {}) '
- '...'.format(num_samples, num_samples, total_size), flush=True)
-
- dtype_ = np.uint32
- if total_size >= (np.iinfo(np.uint32).max - 1):
- dtype_ = np.int64
- shuffle_idx_first = np.arange(start=0, stop=num_samples,
- step=1, dtype=dtype_)
- np_rng.shuffle(shuffle_idx_first)
- if num_samples == total_size:
- return shuffle_idx_first
- shuffle_idx_last = np.arange(start=num_samples, stop=total_size,
- step=1, dtype=dtype_)
- np_rng.shuffle(shuffle_idx_last)
- return np.concatenate((shuffle_idx_first, shuffle_idx_last))
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