Megatron-LM adjusted to Polish texts

LICENSE

+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+------------- LICENSE FOR huggingface(transformer) repository --------------
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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, args):
+
+    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)
+    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
+    drop_last = distributed
+
+    if distributed:
+        batch_sampler = data_utils.samplers.DistributedBatchSampler(sampler,
+                                                                    batch_size,
+                                                                    drop_last,
+                                                                    rank,
+                                                                    world_size)
+    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 = {
+        'path': args.train_data,
+        'seq_length': seq_length,
+        'lazy': args.lazy_loader,
+        '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,
+        'parallel_group': mpu.get_data_parallel_group()}
+
+    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)
+        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)
+        args.do_valid = True
+    else:
+        args.do_valid = False
+    if test is not None:
+        test = make_data_loader(test, eval_batch_size, 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)

data_utils/__init__.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.
+"""utils for creating datasets"""
+import os
+import math
+
+import torch
+
+from .samplers import DistributedBatchSampler
+from .datasets import json_dataset, csv_dataset, split_ds, ConcatDataset, SplitDataset, bert_sentencepair_dataset, GPT2Dataset
+from .lazy_loader import exists_lazy, make_lazy, lazy_array_loader
+from .tokenization import Tokenization, CommandToken, Tokenizer, CharacterLevelTokenizer, BertWordPieceTokenizer, GPT2BPETokenizer, make_tokenizer
+from . import corpora
+
+TRAIN_DATA = 0
+VAL_DATA = 1
+TEST_DATA = 2
+
+def should_split(split):
+    """
+    given split proportions checks if should split
+    Examples:
+    >>> should_split([10,0,0]) 
+    False
+    >>> should_split([1,.1,.2])
+    True
+    """
+    return max(split)/sum(split) != 1.
+
+def get_ext(path):
+    """gets path extension"""
+    return os.path.splitext(path)[1]
+
+def get_dataset(path, **kwargs):
+    """gets dataset object based on keyword args and file at `path`"""
+    if supported_corpus(path):
+        return corpora.NAMED_CORPORA[path](**kwargs)
+    ext = get_ext(path)
+    if '.json' in ext:
+        text = json_dataset(path, **kwargs)
+    elif ext in ['.csv', '.tsv']:
+        text = csv_dataset(path, **kwargs)
+    else:
+        raise NotImplementedError('data file type %s is not supported'%(ext))
+    return text
+
+def supported_corpus(corpus_name):
+    """checks if corpus name is defined in `corpora.py`"""
+    return corpus_name in corpora.NAMED_CORPORA
+
+def make_dataset(path, seq_length, text_key, label_key, lazy=False, process_fn=None, split=[1.],
+                delim=',', loose=False, binarize_sent=False, drop_unlabeled=False, tokenizer=None,
+                tokenizer_type='CharacterLevelTokenizer', tokenizer_model_path=None, vocab_size=None,
+                model_type='bpe', pad_token=0, character_converage=1.0, non_binary_cols=None,
+                 parallel_group=None, **kwargs):
+    """function to create datasets+tokenizers for common options"""
+    if isinstance(process_fn, str):
+        process_fn = eval(process_fn)
+    if non_binary_cols is not None:
+        # multilabel dataset support (only for csvs)
+        label_key = non_binary_cols
+    def get_dataset_from_path(path_):
+        if lazy:
+            # get lazily loaded dataset
+            named_corpora = False
+            if supported_corpus(path_):
+                named_corpora = True
+                name = path_
+                path_ = corpora.NAMED_CORPORA[path_].PATH
+            if torch.distributed.get_rank() == 0 and not exists_lazy(path_, data_type='data'):
+                # create cached version of dataset for lazy loading if it doesn't exist
+                text = get_dataset(name if named_corpora else path_, text_key=text_key, label_key=label_key, binarize_sent=binarize_sent,
+                    delim=delim, drop_unlabeled=drop_unlabeled, loose_json=loose)
+                make_lazy(path_, text.X, data_type='data')
+            # 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=parallel_group)
+            assert counts[0].item() == torch.distributed.get_world_size(
+                group=parallel_group)
+
+            text = lazy_array_loader(path_, data_type='data', map_fn=process_fn)
+        else:
+            # get dataset
+            text = get_dataset(path_, text_key=text_key, label_key=label_key, binarize_sent=binarize_sent,
+                    delim=delim, drop_unlabeled=drop_unlabeled, loose_json=loose, preprocess_fn=process_fn)
+        return text
+    # get one or multiple datasets and concatenate
+    if isinstance(path, str):
+        path = [path]
+    datasets = [get_dataset_from_path(p) for p in path]
+    if len(datasets) == 1:
+        ds = datasets[0]
+    else:
+        ds = ConcatDataset(datasets)
+    # make tokenizer for dataset
+    if tokenizer is None:
+        tokenizer = make_tokenizer(tokenizer_type, ds, tokenizer_model_path, vocab_size, model_type, 
+                                    pad_token, character_converage, **kwargs)
+
+    ds_type = ''
+    if 'ds_type' in kwargs:
+        ds_type = kwargs['ds_type']
+    ds.SetTokenizer(tokenizer)
+    # Split dataset into train/val/test (and wrap bert dataset)
+    if should_split(split):
+        ds = split_ds(ds, split)
+        if 'bert' in ds_type.lower():
+            presplit_sentences = kwargs['presplit_sentences'] if 'presplit_sentences' in kwargs else False
+            dstype = bert_sentencepair_dataset
+            ds = [dstype(d, max_seq_len=seq_length, presplit_sentences=presplit_sentences)  if d is not None else None  for d in ds]
+        elif ds_type.lower() == 'gpt2':
+            ds = [GPT2Dataset(d, max_seq_len=seq_length) if d is not None else None for d in ds]
+    else:
+        if 'bert' in ds_type.lower():
+            presplit_sentences = kwargs['presplit_sentences'] if 'presplit_sentences' in kwargs else False
+            dstype = bert_sentencepair_dataset
+            ds = dstype(ds, max_seq_len=seq_length, presplit_sentences=presplit_sentences)
+        elif ds_type.lower() == 'gpt2':
+            ds = GPT2Dataset(ds, max_seq_len=seq_length)
+    return ds, tokenizer