configure_data.py

# 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.

"""parses arguments and preps data loader"""

import copy
import torch
import data_utils

import mpu

class DataConfig:

    def __init__(self, defaults={}):
        super(DataConfig, self).__init__()
        self.defaults = defaults

    def apply(self, args):
        if torch.distributed.get_rank() == 0:
            print('configuring data')
        self.apply_defaults(args)
        return make_loaders(args)

    def set_defaults(self, **kwargs):
        for k, v in kwargs.items():
            self.defaults[k] = v

    def apply_defaults(self, args):
        for k, v in self.defaults.items():
            k = k.replace('-', '_')
            if not hasattr(args, k):
                setattr(args, k, v)


def make_data_loader(dataset, batch_size, num_iters, args):
    world_size = torch.distributed.get_world_size(
        group=mpu.get_data_parallel_group())
    rank = torch.distributed.get_rank(group=mpu.get_data_parallel_group())
    distributed = world_size > 1
    if args.transformer_xl:
        batch_sampler = data_utils.samplers.DistributedSequentialSampler(len(dataset),
                                                                         num_iters,
                                                                         batch_size,
                                                                         rank,
                                                                         world_size)
    else:
        shuffle = args.shuffle
        if shuffle:
            sampler = data_utils.samplers.RandomSampler(dataset, replacement=True, num_samples=batch_size*args.train_iters)
        else:
            sampler = torch.utils.data.SequentialSampler(dataset)
        drop_last = distributed
        # the GPUs in the same model parallel group receive the same data
        if distributed:
            batch_sampler = data_utils.samplers.DistributedBatchSampler(sampler,
                                                                        batch_size,
                                                                        drop_last,
                                                                        rank,
                                                                        world_size,
                                                                        gradient_accumulation_steps=args.gradient_accumulation_steps)
        else:
            batch_sampler = torch.utils.data.BatchSampler(sampler,
                                                          batch_size,
                                                          drop_last)
    data_loader = torch.utils.data.DataLoader(dataset,
                                              batch_sampler=batch_sampler,
                                              num_workers=args.num_workers,
                                              pin_memory=True)

    return data_loader


def make_tfrecord_loaders(args):
    """Load train/val/test dataset from shuffled TFRecords"""

    import data_utils.tf_dl
    data_set_args = {'batch_size': args.batch_size,
                     'max_seq_len': args.seq_length,
                     'max_preds_per_seq': args.max_preds_per_seq,
                     'train': True,
                     'num_workers': max(args.num_workers, 1),
                     'seed': args.seed + args.rank + 1,
                     'threaded_dl': args.num_workers > 0
                     }
    train = data_utils.tf_dl.TFRecordDataLoader(args.train_data,
                                                **data_set_args)
    data_set_args['train'] = False
    if args.eval_seq_length is not None:
        data_set_args['max_seq_len'] = args.eval_seq_length
    if args.eval_max_preds_per_seq is not None:
        data_set_args['max_preds_per_seq'] = args.eval_max_preds_per_seq
    valid = None
    if args.valid_data is not None:
        valid = data_utils.tf_dl.TFRecordDataLoader(args.valid_data,
                                                    **data_set_args)
    test = None
    if args.test_data is not None:
        test = data_utils.tf_dl.TFRecordDataLoader(args.test_data,
                                                   **data_set_args)
    tokenizer = data_utils.make_tokenizer(args.tokenizer_type,
                                          train,
                                          args.tokenizer_path,
                                          args.vocab_size,
                                          args.tokenizer_model_type,
                                          cache_dir=args.cache_dir)

    return (train, valid, test), tokenizer


def make_loaders(args):
    """makes training/val/test"""

    if args.use_tfrecords:
        return make_tfrecord_loaders(args)
    world_size = torch.distributed.get_world_size(
        group=mpu.get_data_parallel_group())
    batch_size = args.batch_size * world_size
    eval_batch_size = batch_size
    if args.eval_batch_size is not None:
        eval_batch_size = args.eval_batch_size * world_size
    seq_length = args.seq_length
    if seq_length < 0:
        seq_length = seq_length * world_size
    eval_seq_length = args.eval_seq_length
    if eval_seq_length is not None and eval_seq_length < 0:
        eval_seq_length = eval_seq_length * world_size
    split = get_split(args)
    data_set_args = {
        'local_rank': args.local_rank,
        'path': args.train_data,
        'seq_length': seq_length,
        'mem_length': args.mem_length,
        'lazy': args.lazy_loader,
        'xl_style': args.xl_dataset,
        'delim': args.delim,
        'text_key': args.text_key,
        'label_key': 'label',
        'non_binary_cols': None,
        'ds_type': args.data_set_type,
        'split': split,
        'loose': args.loose_json,
        'tokenizer_type': args.tokenizer_type,
        'tokenizer_model_path': args.tokenizer_path,
        'vocab_size': args.vocab_size,
        'model_type': args.tokenizer_model_type,
        'cache_dir': args.cache_dir,
        'max_preds_per_seq': args.max_preds_per_seq,
        'presplit_sentences': args.presplit_sentences,
        'sample_one_document': args.sample_one_document,
        'pre_tokenize': not args.not_pre_tokenize
    }

    eval_set_args = copy.copy(data_set_args)
    eval_set_args['split'] = [1.]
    # if optional eval args were set then replace their
    # equivalent values in the arg dict
    if eval_seq_length:
        eval_set_args['seq_length'] = eval_seq_length
    if args.eval_max_preds_per_seq:
        eval_set_args['max_preds_per_seq'] = args.eval_max_preds_per_seq
    if args.eval_text_key is not None:
        eval_set_args['text_key'] = args.eval_text_key

    # make datasets splits and tokenizer
    train = None
    valid = None
    test = None

    if args.train_data is not None:
        train, tokenizer = data_utils.make_dataset(**data_set_args)
        if data_utils.should_split(split):
            train, valid, test = train
        eval_set_args['tokenizer'] = tokenizer

    # make training and val dataset if necessary
    if valid is None and args.valid_data is not None:
        eval_set_args['path'] = args.valid_data
        valid, tokenizer = data_utils.make_dataset(**eval_set_args)
        eval_set_args['tokenizer'] = tokenizer
    if test is None and args.test_data is not None:
        eval_set_args['path'] = args.test_data
        test, tokenizer = data_utils.make_dataset(**eval_set_args)

    # wrap datasets with data loader
    if train is not None and args.batch_size > 0:
        train = make_data_loader(train, batch_size, args.train_iters, args)
        args.do_train = True
    else:
        args.do_train = False
    eval_batch_size = eval_batch_size if eval_batch_size != 0 else batch_size
    if valid is not None:
        valid = make_data_loader(valid, eval_batch_size, args.train_iters, args)
        args.do_valid = True
    else:
        args.do_valid = False
    if test is not None:
        test = make_data_loader(test, eval_batch_size, len(test) // eval_batch_size + 1, args)
        args.do_test = True
    else:
        args.do_test = False

    return (train, valid, test), tokenizer

def get_split(args):
    """
    Get dataset splits from comma separated string list
    """
    splits = []
    if args.split.find(',') != -1:
        splits = [float(s) for s in args.split.split(',')]
    elif args.split.find('/') != -1:
        splits = [float(s) for s in args.split.split('/')]
    else:
        splits = [float(args.split)]
    split_total = sum(splits)
    if split_total < 1.:
        splits.append(1-split_total)
    while len(splits) < 3:
        splits.append(0.)
    splits = splits[:3]
    if args.valid_data is not None:
        splits[1] = 0.
    if args.test_data is not None:
        splits[2] = 0.
    final_sum = sum(splits)
    return [s/final_sum for s in splits]


def configure_data():

    """add cmdline flags for configuring datasets"""
    # These are options that are used by data_utils, but are either
    # deprecated or not meant to be exposed to the command line user.
    # These options are intneded to be set in code by specific scripts.
    defaults = {
        'world_size': 1,
        'rank': -1,
        'persist_state': 0,
        'lazy': False,
        'transpose': False,
        'data_set_type': 'supervised',
        'seq_length': 256,
        'eval_seq_length': 256,
        'samples_per_shard': 100
    }

    return DataConfig(defaults=defaults)