added new gpt2 dataloder

# coding=utf-8

# Copyright (c) 2019, 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.

"""GPT2 Style dataset."""

import os

import time

import numpy as np

import torch

from torch.utils.data import Dataset

def print_rank_0(message):

    print(message)

class GPT2Dataset(Dataset):

    def __init__(self, name, data_prefix,

                 documents, indexed_dataset,

                 num_samples, seq_length, seed):

        self.name = name

        self.data_prefix = data_prefix

        self.num_samples = num_samples

        self.seq_length = seq_length

        self.seed = seed

        self.indexed_dataset = indexed_dataset

        # Checks

        assert np.min(documents) >= 0

        assert np.max(documents) < indexed_dataset.sizes.shape[0]

        # Build index mappings.

        self.num_epochs, self.doc_idx, self.sample_idx, self.shuffle_idx \

            = _build_index_mappings(self.name, self.data_prefix, documents,

                                    self.indexed_dataset.sizes,

                                    self.num_samples, self.seq_length,

                                    self.seed)

    def __len__(self):

        return self.sample_idx.shape[0]

    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 sample

def _build_index_mappings(name, data_prefix, documents, sizes,

                          num_samples, seq_length, seed):

    """doc-idx, sample-idx, and shuffle-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 True: #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 ...')

            # doc-idx.

            start_time = time.time()

            doc_idx = _build_doc_idx(documents, num_epochs, np_rng)

            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()

            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()

            shuffle_idx = _build_shuffle_idx(sample_idx.shape[0], 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())

    #assert counts[0].item() == torch.distributed.get_world_size(

    #    group=mpu.get_data_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)

    print_rank_0(' > loading sample-idx mapping from {}'.format(

        sample_idx_filename))

    sample_idx = np.load(sample_idx_filename, allow_pickle=True)

    print_rank_0(' > loading shuffle-idx mapping from {}'.format(

        shuffle_idx_filename))

    shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True)

    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]))

    return num_epochs, 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):

    """Build an array with length = number-of-epochs * number-of-dcuments.

    Each index is mapped to a corresponding document."""

    doc_idx = np.mgrid[0:num_epochs, 0:len(documents)][1]

    doc_idx[:] = documents

    doc_idx = doc_idx.reshape(-1)

    np_rng.shuffle(doc_idx)

    return doc_idx

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 [..., 0] 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(size, np_rng):

    """Build the range [0, size) and shuffle."""

    dtype_ = np.uint32

    if size >= (np.iinfo(np.uint32).max - 1):

        dtype_ = np.int64

    shuffle_idx = np.arange(start=0, stop=size, step=1, dtype=dtype_)

    #np_rng.shuffle(shuffle_idx)

    return shuffle_idx

class IndexedDataset:

    def __init__(self, num_docs, min_doc_length, max_doc_length, seq_length):

        self.seq_length = seq_length

        assert min_doc_length > 0

        self.tokens = []

        self.sizes = np.zeros(num_docs, dtype=np.int32)

        for i in range(num_docs):

            size = np.random.randint(low=min_doc_length, high=max_doc_length,

                                     size=1, dtype=np.uint32)[0]

            tokens_ = np.random.randint(low=1, high=60000,

                                        size=size, dtype=np.uint32)

            tokens_[-1] = 0

            self.sizes[i] = size

            self.tokens.append(tokens_)

        self.tokens_flat = None

    def get(self, doc_idx, offset=None, length=None):

        if length is None:

            if offset is None:

                return self.tokens[doc_idx]

            else:

                return self.tokens[doc_idx][offset:]

        if offset is None:

            return self.tokens[doc_idx][0:length]

        return self.tokens[doc_idx][offset:(offset+length)]

    def get_sample(self, index):

        start = index * self.seq_length

        end = start + self.seq_length + 1

        return self.tokens_flat[start:end]

    def build_tokens_flat(self, doc_idx):

        self.tokens_flat = np.concatenate([self.tokens[i] for i in doc_idx])

def test(seed, data_prefix, seq_length, num_samples,

         num_docs, min_doc_length, max_doc_length):

    print('testing for seed: {}, seq-length: {}, num-samples: {}, '

          'num-docs: {}, min-doc-length: {}, max-doc-length: {}'.format(

              seed, seq_length, num_samples,

              num_docs, min_doc_length, max_doc_length))

    np.random.seed(seed)

    indexed_dataset = IndexedDataset(num_docs, min_doc_length,

                                     max_doc_length, seq_length)

    indices = np.random.randint(indexed_dataset.sizes.shape[0]-2, size=2)

    documents = np.arange(np.min(indices), np.max(indices)+1)

    dataset = GPT2Dataset('gpt2', data_prefix, documents, indexed_dataset,

                          num_samples, seq_length, seed)

    print(' > number of epochs:', dataset.num_epochs)

    indexed_dataset.build_tokens_flat(dataset.doc_idx)

    for idx in range(num_samples):

        a = dataset[idx]

        b = indexed_dataset.get_sample(idx)

        assert np.sum(a - b) == 0

    print('passed')

if __name__ == '__main__':

    print('gpt2 dataset ...')

    import random

    data_prefix = 'junk/'

    for seed in range(1234, 1240):

        random.seed(seed)

        num_docs = random.randint(1, 999)

        min_doc_length = random.randint(1, 99)

        max_doc_length = random.randint(100, 9999)

        num_samples = random.randint(num_docs, 100*num_docs)

        seq_length = random.randint(min_doc_length, max_doc_length)

        test(seed, data_prefix, seq_length, num_samples,

             num_docs, min_doc_length, max_doc_length)

    exit()

    '''

    num_docs = 5

    min_doc_length = 2

    max_doc_length = 10

    num_samples = 9

    seq_length = 4

    seed = 1234

    np.random.seed(seed)

    indexed_dataset = IndexedDataset(num_docs, min_doc_length,

                                     max_doc_length, seq_length)

    print('> indexed dataset:')

    for s in indexed_dataset.tokens:

        print('   {}'.format(s))

    documents = np.array([1,2,3], dtype=np.int32)

    dataset = GPT2Dataset('gpt2', documents, indexed_dataset,

                          num_samples, seq_length, seed)

    indexed_dataset.build_tokens_flat(dataset.doc_idx)

    print(indexed_dataset.get_sample(6))

    print(dataset[6])

    '''    

    '''

    myds = MyDataset(ds, num_samples, seq_length)

    num_docs = myds._num_docs()

    print('> number of document: {}'.format(num_docs))

    tokens_per_epoch = myds._num_tokens()

    print('> number of tokens: {}'.format(tokens_per_epoch))

    num_epochs = myds._num_epochs(tokens_per_epoch)

    print('> number of epochs: {}'.format(num_epochs))

    doc_idx = myds._build_doc_idx(num_docs, num_epochs)

    print('> doc_idx: {}'.format(doc_idx))

    ds.build_tokens_flat(doc_idx)

    sample_idx =myds._build_sample_idx(num_epochs, tokens_per_epoch, doc_idx)

    for s in sample_idx:

        print(s)

    print(ds.tokens_flat)

    print(myds.get_sample(8))

    print(ds.get_sample(8))

    '''