[tutorial] edited hands-on practices (#1899)

* Add handson to ColossalAI.

* Change names of handsons and edit sequence parallel example.

* Edit wrong folder name

* resolve conflict

* delete readme

examples/tutorial/sequence_parallel/data/datasets/Makefile

@@ -0,0 +1,9 @@
+CXXFLAGS += -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color
+CPPFLAGS += $(shell python3 -m pybind11 --includes)
+LIBNAME = helpers
+LIBEXT = $(shell python3-config --extension-suffix)
+
+default: $(LIBNAME)$(LIBEXT)
+
+%$(LIBEXT): %.cpp
+	$(CXX) $(CXXFLAGS) $(CPPFLAGS) $< -o $@

examples/tutorial/sequence_parallel/data/datasets/__init__.py

@@ -0,0 +1 @@
+from . import indexed_dataset

examples/tutorial/sequence_parallel/data/datasets/bert_dataset.py

@@ -0,0 +1,225 @@
+# 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.
+"""BERT Style dataset."""
+
+from colossalai.logging import get_dist_logger
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from ..tokenizer import get_tokenizer
+from .dataset_utils import (get_a_and_b_segments, truncate_segments, create_tokens_and_tokentypes,
+                            create_masked_lm_predictions, pad_and_convert_to_numpy)
+from colossalai.core import global_context as gpc
+from colossalai.context import ParallelMode
+import time
+import os
+from . import helpers
+
+
+class BertDataset(Dataset):
+
+    def __init__(self, name, indexed_dataset, data_prefix, num_epochs, max_num_samples, masked_lm_prob, max_seq_length,
+                 short_seq_prob, seed, binary_head):
+
+        # Params to store.
+        self.name = name
+        self.seed = seed
+        self.masked_lm_prob = masked_lm_prob
+        self.max_seq_length = max_seq_length
+        self.binary_head = binary_head
+
+        # Dataset.
+        self.indexed_dataset = indexed_dataset
+
+        # Build the samples mapping.
+        self.samples_mapping = get_samples_mapping_(
+            self.indexed_dataset,
+            data_prefix,
+            num_epochs,
+            max_num_samples,
+            self.max_seq_length - 3,    # account for added tokens,
+            short_seq_prob,
+            self.seed,
+            self.name,
+            self.binary_head)
+
+        # Vocab stuff.
+        tokenizer = get_tokenizer()
+        self.vocab_id_list = list(tokenizer.inv_vocab.keys())
+        self.vocab_id_to_token_dict = tokenizer.inv_vocab
+        self.cls_id = tokenizer.cls
+        self.sep_id = tokenizer.sep
+        self.mask_id = tokenizer.mask
+        self.pad_id = tokenizer.pad
+
+    def __len__(self):
+        return self.samples_mapping.shape[0]
+
+    def __getitem__(self, idx):
+        start_idx, end_idx, seq_length = self.samples_mapping[idx]
+        sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]
+        # Note that this rng state should be numpy and not python since
+        # python randint is inclusive whereas the numpy one is exclusive.
+        # We % 2**32 since numpy requires the seed to be between 0 and 2**32 - 1
+        np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
+        return build_training_sample(
+            sample,
+            seq_length,
+            self.max_seq_length,    # needed for padding
+            self.vocab_id_list,
+            self.vocab_id_to_token_dict,
+            self.cls_id,
+            self.sep_id,
+            self.mask_id,
+            self.pad_id,
+            self.masked_lm_prob,
+            np_rng,
+            self.binary_head)
+
+
+def get_samples_mapping_(indexed_dataset, data_prefix, num_epochs, max_num_samples, max_seq_length, short_seq_prob,
+                         seed, name, binary_head):
+    logger = get_dist_logger()
+    if not num_epochs:
+        if not max_num_samples:
+            raise ValueError("Need to specify either max_num_samples "
+                             "or num_epochs")
+        num_epochs = np.iinfo(np.int32).max - 1
+    if not max_num_samples:
+        max_num_samples = np.iinfo(np.int64).max - 1
+
+    # Filename of the index mapping
+    indexmap_filename = data_prefix
+    indexmap_filename += '_{}_indexmap'.format(name)
+    if num_epochs != (np.iinfo(np.int32).max - 1):
+        indexmap_filename += '_{}ep'.format(num_epochs)
+    if max_num_samples != (np.iinfo(np.int64).max - 1):
+        indexmap_filename += '_{}mns'.format(max_num_samples)
+    indexmap_filename += '_{}msl'.format(max_seq_length)
+    indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob)
+    indexmap_filename += '_{}s'.format(seed)
+    indexmap_filename += '.npy'
+
+    # Build the indexed mapping if not exist.
+    if torch.distributed.get_rank() == 0 and \
+       not os.path.isfile(indexmap_filename):
+        print(' > WARNING: could not find index map file {}, building '
+              'the indices on rank 0 ...'.format(indexmap_filename))
+
+        # Make sure the types match the helpers input types.
+        assert indexed_dataset.doc_idx.dtype == np.int64
+        assert indexed_dataset.sizes.dtype == np.int32
+
+        # Build samples mapping
+        verbose = torch.distributed.get_rank() == 0
+        start_time = time.time()
+        logger.info('\n > building samples index mapping for {} ...'.format(name), ranks=[0])
+        # First compile and then import.
+        samples_mapping = helpers.build_mapping(indexed_dataset.doc_idx, indexed_dataset.sizes, num_epochs,
+                                                max_num_samples, max_seq_length, short_seq_prob, seed, verbose,
+                                                2 if binary_head else 1)
+        logger.info('\n > done building samples index maping', ranks=[0])
+        np.save(indexmap_filename, samples_mapping, allow_pickle=True)
+        logger.info('\n > saved the index mapping in {}'.format(indexmap_filename), ranks=[0])
+        # Make sure all the ranks have built the mapping
+        logger.info('\n > elapsed time to build and save samples mapping '
+                    '(seconds): {:4f}'.format(time.time() - start_time),
+                    ranks=[0])
+    # 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=gpc.get_group(ParallelMode.DATA))
+    if gpc.is_initialized(ParallelMode.PIPELINE):
+        torch.distributed.all_reduce(counts, group=gpc.get_group(ParallelMode.PIPELINE))
+    assert counts[0].item() == (torch.distributed.get_world_size() //
+                                torch.distributed.get_world_size(group=gpc.get_group(ParallelMode.SEQUENCE)))
+
+    # Load indexed dataset.
+    start_time = time.time()
+    samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r')
+    logger.info('\n > loading indexed mapping from {}'.format(indexmap_filename) +
+                '\n    loaded indexed file in {:3.3f} seconds'.format(time.time() - start_time) +
+                '\n    total number of samples: {}'.format(samples_mapping.shape[0]),
+                ranks=[0])
+
+    return samples_mapping
+
+
+def build_training_sample(sample, target_seq_length, max_seq_length, vocab_id_list, vocab_id_to_token_dict, cls_id,
+                          sep_id, mask_id, pad_id, masked_lm_prob, np_rng, binary_head):
+    """Build training sample.
+
+    Arguments:
+        sample: A list of sentences in which each sentence is a list token ids.
+        target_seq_length: Desired sequence length.
+        max_seq_length: Maximum length of the sequence. All values are padded to
+            this length.
+        vocab_id_list: List of vocabulary ids. Used to pick a random id.
+        vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
+        cls_id: Start of example id.
+        sep_id: Separator id.
+        mask_id: Mask token id.
+        pad_id: Padding token id.
+        masked_lm_prob: Probability to mask tokens.
+        np_rng: Random number genenrator. Note that this rng state should be
+              numpy and not python since python randint is inclusive for
+              the opper bound whereas the numpy one is exclusive.
+    """
+
+    if binary_head:
+        # We assume that we have at least two sentences in the sample
+        assert len(sample) > 1
+    assert target_seq_length <= max_seq_length
+
+    # Divide sample into two segments (A and B).
+    if binary_head:
+        tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)
+    else:
+        tokens_a = []
+        for j in range(len(sample)):
+            tokens_a.extend(sample[j])
+        tokens_b = []
+        is_next_random = False
+
+    # Truncate to `target_sequence_length`.
+    max_num_tokens = target_seq_length
+    truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a), len(tokens_b), max_num_tokens, np_rng)
+
+    # Build tokens and toketypes.
+    tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id)
+
+    # Masking.
+    max_predictions_per_seq = masked_lm_prob * max_num_tokens
+    (tokens, masked_positions, masked_labels,
+     _) = create_masked_lm_predictions(tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob, cls_id, sep_id,
+                                       mask_id, max_predictions_per_seq, np_rng)
+
+    # Padding.
+    tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \
+        = pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
+                                   masked_labels, pad_id, max_seq_length)
+
+    train_sample = {
+        'text': tokens_np,
+        'types': tokentypes_np,
+        'labels': labels_np,
+        'is_random': int(is_next_random),
+        'loss_mask': loss_mask_np,
+        'padding_mask': padding_mask_np,
+        'truncated': int(truncated)
+    }
+    return train_sample

examples/tutorial/sequence_parallel/data/datasets/blendable_dataset.py

@@ -0,0 +1,62 @@
+# 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.
+
+"""Blendable dataset."""
+
+import time
+
+import numpy as np
+import torch
+
+
+class BlendableDataset(torch.utils.data.Dataset):
+
+    def __init__(self, datasets, weights):
+
+        self.datasets = datasets
+        num_datasets = len(datasets)
+        assert num_datasets == len(weights)
+
+        self.size = 0
+        for dataset in self.datasets:
+            self.size += len(dataset)
+
+        # Normalize weights.
+        weights = np.array(weights, dtype=np.float64)
+        sum_weights = np.sum(weights)
+        assert sum_weights > 0.0
+        weights /= sum_weights
+
+        # Build indices.
+        start_time = time.time()
+        assert num_datasets < 255
+        self.dataset_index = np.zeros(self.size, dtype=np.uint8)
+        self.dataset_sample_index = np.zeros(self.size, dtype=np.int64)
+
+        from . import helpers
+        helpers.build_blending_indices(self.dataset_index,
+                                       self.dataset_sample_index,
+                                       weights, num_datasets, self.size,
+                                       torch.distributed.get_rank() == 0)
+        print('> elapsed time for building blendable dataset indices: '
+              '{:.2f} (sec)'.format(time.time() - start_time))
+
+    def __len__(self):
+        return self.size
+
+    def __getitem__(self, idx):
+        dataset_idx = self.dataset_index[idx]
+        sample_idx = self.dataset_sample_index[idx]
+        return self.datasets[dataset_idx][sample_idx]

examples/tutorial/sequence_parallel/data/datasets/builder.py

@@ -0,0 +1,152 @@
+from .blendable_dataset import BlendableDataset
+from .dataset_utils import get_datasets_weights_and_num_samples, get_indexed_dataset_, get_train_valid_test_split_
+from .bert_dataset import BertDataset
+from colossalai.logging import get_dist_logger
+
+DSET_TYPE_BERT = 'standard_bert'
+DSET_TYPE_ICT = 'ict'
+DSET_TYPE_T5 = 't5'
+
+DSET_TYPES = [DSET_TYPE_BERT, DSET_TYPE_ICT, DSET_TYPE_T5]
+
+
+def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
+                                     train_valid_test_num_samples,
+                                     max_seq_length, masked_lm_prob,
+                                     short_seq_prob, seed, skip_warmup,
+                                     binary_head,
+                                     dataset_type='standard_bert'):
+
+    if dataset_type not in DSET_TYPES:
+        raise ValueError("Invalid dataset_type: ", dataset_type)
+
+    # Indexed dataset.
+    indexed_dataset = get_indexed_dataset_(data_prefix,
+                                           data_impl,
+                                           skip_warmup)
+
+    # Get start and end indices of train/valid/train into doc-idx
+    # Note that doc-idx is designed to be num-docs + 1 so we can
+    # easily iterate over it.
+    total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1
+    splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
+
+    logger = get_dist_logger()
+
+    # Print stats about the splits.
+    logger.info('\n > dataset split:', ranks=[0])
+
+    def print_split_stats(name, index):
+        start_index = indexed_dataset.doc_idx[splits[index]]
+        end_index = indexed_dataset.doc_idx[splits[index + 1]]
+        logger.info('\n    {}:'.format(name) +
+                    '\n     document indices in [{}, {}) total of {} documents'.format(
+                        splits[index], splits[index + 1],
+                        splits[index + 1] - splits[index]) +
+                    '\n     sentence indices in [{}, {}) total of {} sentences'.format(
+                        start_index, end_index,
+                        end_index - start_index),
+                    ranks=[0])
+    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]:
+            # Get the pointer to the original doc-idx so we can set it later.
+            doc_idx_ptr = indexed_dataset.get_doc_idx()
+            # Slice the doc-idx
+            start_index = splits[index]
+            # Add +1 so we can index into the dataset to get the upper bound.
+            end_index = splits[index + 1] + 1
+            # New doc_idx view.
+            indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index])
+            # Build the dataset accordingly.
+            kwargs = dict(
+                name=name,
+                data_prefix=data_prefix,
+                num_epochs=None,
+                max_num_samples=train_valid_test_num_samples[index],
+                max_seq_length=max_seq_length,
+                seed=seed,
+            )
+
+            if dataset_type != DSET_TYPE_BERT:
+                raise NotImplementedError("Only BERT dataset is supported")
+            else:
+                dataset = BertDataset(
+                    indexed_dataset=indexed_dataset,
+                    masked_lm_prob=masked_lm_prob,
+                    short_seq_prob=short_seq_prob,
+                    binary_head=binary_head,
+                    **kwargs
+                )
+
+            # Set the original pointer so dataset remains the main dataset.
+            indexed_dataset.set_doc_idx(doc_idx_ptr)
+            # Checks.
+            assert indexed_dataset.doc_idx[0] == 0
+            assert indexed_dataset.doc_idx.shape[0] == \
+                (total_num_of_documents + 1)
+        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 build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
+                                    train_valid_test_num_samples,
+                                    max_seq_length, masked_lm_prob,
+                                    short_seq_prob, seed, skip_warmup,
+                                    binary_head,
+                                    dataset_type='standard_bert'):
+
+    if len(data_prefix) == 1:
+        return _build_train_valid_test_datasets(data_prefix[0],
+                                                data_impl, splits_string,
+                                                train_valid_test_num_samples,
+                                                max_seq_length, masked_lm_prob,
+                                                short_seq_prob, seed,
+                                                skip_warmup,
+                                                binary_head,
+                                                dataset_type=dataset_type)
+    # 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],
+            max_seq_length, masked_lm_prob, short_seq_prob,
+            seed, skip_warmup, binary_head, dataset_type=dataset_type)
+        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)

examples/tutorial/sequence_parallel/data/datasets/data_samplers.py

@@ -0,0 +1,153 @@
+# 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.
+"""Dataloaders."""
+
+import torch
+import random
+from colossalai.core import global_context as gpc
+from colossalai.context import ParallelMode
+
+
+def build_pretraining_data_loader(dataset, consumed_samples, micro_batch_size, dataloader_type='single', num_workers=0):
+    """Build dataloader given an input dataset."""
+
+    if dataset is None:
+        return None
+
+    # Megatron sampler
+    if dataloader_type == 'single':
+        batch_sampler = MegatronPretrainingSampler(total_samples=len(dataset),
+                                                   consumed_samples=consumed_samples,
+                                                   micro_batch_size=micro_batch_size,
+                                                   data_parallel_rank=gpc.get_local_rank(ParallelMode.DATA),
+                                                   data_parallel_size=gpc.get_world_size(ParallelMode.DATA))
+    elif dataloader_type == 'cyclic':
+        batch_sampler = MegatronPretrainingRandomSampler(total_samples=len(dataset),
+                                                         consumed_samples=consumed_samples,
+                                                         micro_batch_size=micro_batch_size,
+                                                         data_parallel_rank=gpc.get_local_rank(ParallelMode.DATA),
+                                                         data_parallel_size=gpc.get_world_size(ParallelMode.DATA))
+    else:
+        raise Exception('{} dataloader type is not supported.'.format(dataloader_type))
+
+    # Torch dataloader.
+    return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True)
+
+
+class MegatronPretrainingSampler:
+
+    def __init__(self,
+                 total_samples,
+                 consumed_samples,
+                 micro_batch_size,
+                 data_parallel_rank,
+                 data_parallel_size,
+                 drop_last=True):
+        # Keep a copy of input params for later use.
+        self.total_samples = total_samples
+        self.consumed_samples = consumed_samples
+        self.micro_batch_size = micro_batch_size
+        self.data_parallel_rank = data_parallel_rank
+        self.micro_batch_times_data_parallel_size = \
+            self.micro_batch_size * data_parallel_size
+        self.drop_last = drop_last
+
+        # Sanity checks.
+        assert self.total_samples > 0, \
+            'no sample to consume: {}'.format(self.total_samples)
+        assert self.consumed_samples < self.total_samples, \
+            'no samples left to consume: {}, {}'.format(self.consumed_samples,
+                                                        self.total_samples)
+        assert self.micro_batch_size > 0
+        assert data_parallel_size > 0
+        assert self.data_parallel_rank < data_parallel_size, \
+            'data_parallel_rank should be smaller than data size: {}, ' \
+            '{}'.format(self.data_parallel_rank, data_parallel_size)
+
+    def __len__(self):
+        return self.total_samples
+
+    def get_start_end_idx(self):
+        start_idx = self.data_parallel_rank * self.micro_batch_size
+        end_idx = start_idx + self.micro_batch_size
+        return start_idx, end_idx
+
+    def __iter__(self):
+        batch = []
+        # Last batch will be dropped if drop_last is not set False
+        for idx in range(self.consumed_samples, self.total_samples):
+            batch.append(idx)
+            if len(batch) == self.micro_batch_times_data_parallel_size:
+                start_idx, end_idx = self.get_start_end_idx()
+                yield batch[start_idx:end_idx]
+                batch = []
+
+        # Check the last partial batch and see drop_last is set
+        if len(batch) > 0 and not self.drop_last:
+            start_idx, end_idx = self.get_start_end_idx()
+            yield batch[start_idx:end_idx]
+
+
+class MegatronPretrainingRandomSampler:
+
+    def __init__(self, total_samples, consumed_samples, micro_batch_size, data_parallel_rank, data_parallel_size):
+        # Keep a copy of input params for later use.
+        self.total_samples = total_samples
+        self.consumed_samples = consumed_samples
+        self.micro_batch_size = micro_batch_size
+        self.data_parallel_rank = data_parallel_rank
+        self.data_parallel_size = data_parallel_size
+        self.micro_batch_times_data_parallel_size = \
+            self.micro_batch_size * data_parallel_size
+        self.last_batch_size = \
+            self.total_samples % self.micro_batch_times_data_parallel_size
+
+        # Sanity checks.
+        assert self.total_samples > 0, \
+            'no sample to consume: {}'.format(self.total_samples)
+        assert self.micro_batch_size > 0
+        assert data_parallel_size > 0
+        assert self.data_parallel_rank < data_parallel_size, \
+            'data_parallel_rank should be smaller than data size: {}, ' \
+            '{}'.format(self.data_parallel_rank, data_parallel_size)
+
+    def __len__(self):
+        return self.total_samples
+
+    def __iter__(self):
+        active_total_samples = self.total_samples - self.last_batch_size
+        self.epoch = self.consumed_samples // active_total_samples
+        current_epoch_samples = self.consumed_samples % active_total_samples
+        assert current_epoch_samples % self.micro_batch_times_data_parallel_size == 0
+
+        # data sharding and random sampling
+        bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) \
+            * self.micro_batch_size
+        bucket_offset = current_epoch_samples // self.data_parallel_size
+        start_idx = self.data_parallel_rank * bucket_size
+
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        random_idx = torch.randperm(bucket_size, generator=g).tolist()
+        idx_range = [start_idx + x for x in random_idx[bucket_offset:]]
+
+        batch = []
+        # Last batch if not complete will be dropped.
+        for idx in idx_range:
+            batch.append(idx)
+            if len(batch) == self.micro_batch_size:
+                self.consumed_samples += self.micro_batch_times_data_parallel_size
+                yield batch
+                batch = []

examples/tutorial/sequence_parallel/data/datasets/dataset_utils.py

@@ -0,0 +1,592 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors, and NVIDIA.
+#
+# 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.
+
+
+# Most of the code here has been copied from:
+#   https://github.com/google-research/albert/blob/master/create_pretraining_data.py
+# with some modifications.
+
+import math
+import time
+import collections
+from colossalai.logging import get_dist_logger
+import numpy as np
+from .blendable_dataset import BlendableDataset
+from .indexed_dataset import make_dataset as make_indexed_dataset
+
+DSET_TYPE_STD = 'standard_bert'
+DSET_TYPE_ICT = 'ict'
+
+DSET_TYPES = [DSET_TYPE_ICT, DSET_TYPE_STD]
+
+
+def get_datasets_weights_and_num_samples(data_prefix,
+                                         train_valid_test_num_samples):
+
+    # The data prefix should be in the format of:
+    #   weight-1, data-prefix-1, weight-2, data-prefix-2, ..
+    assert len(data_prefix) % 2 == 0
+    num_datasets = len(data_prefix) // 2
+    weights = [0]*num_datasets
+    prefixes = [0]*num_datasets
+    for i in range(num_datasets):
+        weights[i] = float(data_prefix[2*i])
+        prefixes[i] = (data_prefix[2*i+1]).strip()
+    # Normalize weights
+    weight_sum = 0.0
+    for weight in weights:
+        weight_sum += weight
+    assert weight_sum > 0.0
+    weights = [weight / weight_sum for weight in weights]
+
+    # Add 0.5% (the 1.005 factor) so in case the bleding dataset does
+    # not uniformly distribute the number of samples, we still have
+    # samples left to feed to the network.
+    datasets_train_valid_test_num_samples = []
+    for weight in weights:
+        datasets_train_valid_test_num_samples.append(
+            [int(math.ceil(val * weight * 1.005))
+             for val in train_valid_test_num_samples])
+
+    return prefixes, weights, datasets_train_valid_test_num_samples
+
+
+def compile_helper():
+    """Compile helper function ar runtime. Make sure this
+    is invoked on a single process."""
+    import os
+    import subprocess
+    path = os.path.abspath(os.path.dirname(__file__))
+    ret = subprocess.run(['make', '-C', path])
+    if ret.returncode != 0:
+        print("Making C++ dataset helpers module failed, exiting.")
+        import sys
+        sys.exit(1)
+
+
+def get_a_and_b_segments(sample, np_rng):
+    """Divide sample into a and b segments."""
+
+    # Number of sentences in the sample.
+    n_sentences = len(sample)
+    # Make sure we always have two sentences.
+    assert n_sentences > 1, 'make sure each sample has at least two sentences.'
+
+    # First part:
+    # `a_end` is how many sentences go into the `A`.
+    a_end = 1
+    if n_sentences >= 3:
+        # Note that randin in numpy is exclusive.
+        a_end = np_rng.randint(1, n_sentences)
+    tokens_a = []
+    for j in range(a_end):
+        tokens_a.extend(sample[j])
+
+    # Second part:
+    tokens_b = []
+    for j in range(a_end, n_sentences):
+        tokens_b.extend(sample[j])
+
+    # Random next:
+    is_next_random = False
+    if np_rng.random() < 0.5:
+        is_next_random = True
+        tokens_a, tokens_b = tokens_b, tokens_a
+
+    return tokens_a, tokens_b, is_next_random
+
+
+def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng):
+    """Truncates a pair of sequences to a maximum sequence length."""
+    #print(len_a, len_b, max_num_tokens)
+    assert len_a > 0
+    if len_a + len_b <= max_num_tokens:
+        return False
+    while len_a + len_b > max_num_tokens:
+        if len_a > len_b:
+            len_a -= 1
+            tokens = tokens_a
+        else:
+            len_b -= 1
+            tokens = tokens_b
+        if np_rng.random() < 0.5:
+            del tokens[0]
+        else:
+            tokens.pop()
+    return True
+
+
+def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id):
+    """Merge segments A and B, add [CLS] and [SEP] and build tokentypes."""
+
+    tokens = []
+    tokentypes = []
+    # [CLS].
+    tokens.append(cls_id)
+    tokentypes.append(0)
+    # Segment A.
+    for token in tokens_a:
+        tokens.append(token)
+        tokentypes.append(0)
+    # [SEP].
+    tokens.append(sep_id)
+    tokentypes.append(0)
+    # Segment B.
+    for token in tokens_b:
+        tokens.append(token)
+        tokentypes.append(1)
+    if tokens_b:
+        # [SEP].
+        tokens.append(sep_id)
+        tokentypes.append(1)
+
+    return tokens, tokentypes
+
+
+MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
+                                          ["index", "label"])
+
+
+def is_start_piece(piece):
+    """Check if the current word piece is the starting piece (BERT)."""
+    # When a word has been split into
+    # WordPieces, the first token does not have any marker and any subsequence
+    # tokens are prefixed with ##. So whenever we see the ## token, we
+    # append it to the previous set of word indexes.
+    return not piece.startswith("##")
+
+
+def create_masked_lm_predictions(tokens,
+                                 vocab_id_list, vocab_id_to_token_dict,
+                                 masked_lm_prob,
+                                 cls_id, sep_id, mask_id,
+                                 max_predictions_per_seq,
+                                 np_rng,
+                                 max_ngrams=3,
+                                 do_whole_word_mask=True,
+                                 favor_longer_ngram=False,
+                                 do_permutation=False):
+    """Creates the predictions for the masked LM objective.
+    Note: Tokens here are vocab ids and not text tokens."""
+
+    cand_indexes = []
+    # Note(mingdachen): We create a list for recording if the piece is
+    # the starting piece of current token, where 1 means true, so that
+    # on-the-fly whole word masking is possible.
+    token_boundary = [0] * len(tokens)
+
+    for (i, token) in enumerate(tokens):
+        if token == cls_id or token == sep_id:
+            token_boundary[i] = 1
+            continue
+        # Whole Word Masking means that if we mask all of the wordpieces
+        # corresponding to an original word.
+        #
+        # Note that Whole Word Masking does *not* change the training code
+        # at all -- we still predict each WordPiece independently, softmaxed
+        # over the entire vocabulary.
+        if (do_whole_word_mask and len(cand_indexes) >= 1 and
+                not is_start_piece(vocab_id_to_token_dict[token])):
+            cand_indexes[-1].append(i)
+        else:
+            cand_indexes.append([i])
+            if is_start_piece(vocab_id_to_token_dict[token]):
+                token_boundary[i] = 1
+
+    output_tokens = list(tokens)
+
+    masked_lm_positions = []
+    masked_lm_labels = []
+
+    if masked_lm_prob == 0:
+        return (output_tokens, masked_lm_positions,
+                masked_lm_labels, token_boundary)
+
+    num_to_predict = min(max_predictions_per_seq,
+                         max(1, int(round(len(tokens) * masked_lm_prob))))
+
+    # Note(mingdachen):
+    # By default, we set the probabilities to favor shorter ngram sequences.
+    ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64)
+    pvals = 1. / np.arange(1, max_ngrams + 1)
+    pvals /= pvals.sum(keepdims=True)
+
+    if favor_longer_ngram:
+        pvals = pvals[::-1]
+
+    ngram_indexes = []
+    for idx in range(len(cand_indexes)):
+        ngram_index = []
+        for n in ngrams:
+            ngram_index.append(cand_indexes[idx:idx + n])
+        ngram_indexes.append(ngram_index)
+
+    np_rng.shuffle(ngram_indexes)
+
+    masked_lms = []
+    covered_indexes = set()
+    for cand_index_set in ngram_indexes:
+        if len(masked_lms) >= num_to_predict:
+            break
+        if not cand_index_set:
+            continue
+        # Note(mingdachen):
+        # Skip current piece if they are covered in lm masking or previous ngrams.
+        for index_set in cand_index_set[0]:
+            for index in index_set:
+                if index in covered_indexes:
+                    continue
+
+        n = np_rng.choice(ngrams[:len(cand_index_set)],
+                          p=pvals[:len(cand_index_set)] /
+                          pvals[:len(cand_index_set)].sum(keepdims=True))
+        index_set = sum(cand_index_set[n - 1], [])
+        n -= 1
+        # Note(mingdachen):
+        # Repeatedly looking for a candidate that does not exceed the
+        # maximum number of predictions by trying shorter ngrams.
+        while len(masked_lms) + len(index_set) > num_to_predict:
+            if n == 0:
+                break
+            index_set = sum(cand_index_set[n - 1], [])
+            n -= 1
+        # If adding a whole-word mask would exceed the maximum number of
+        # predictions, then just skip this candidate.
+        if len(masked_lms) + len(index_set) > num_to_predict:
+            continue
+        is_any_index_covered = False
+        for index in index_set:
+            if index in covered_indexes:
+                is_any_index_covered = True
+                break
+        if is_any_index_covered:
+            continue
+        for index in index_set:
+            covered_indexes.add(index)
+
+            masked_token = None
+            # 80% of the time, replace with [MASK]
+            if np_rng.random() < 0.8:
+                masked_token = mask_id
+            else:
+                # 10% of the time, keep original
+                if np_rng.random() < 0.5:
+                    masked_token = tokens[index]
+                # 10% of the time, replace with random word
+                else:
+                    masked_token = vocab_id_list[np_rng.randint(0, len(vocab_id_list))]
+
+            output_tokens[index] = masked_token
+
+            masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
+    assert len(masked_lms) <= num_to_predict
+
+    np_rng.shuffle(ngram_indexes)
+
+    select_indexes = set()
+    if do_permutation:
+        for cand_index_set in ngram_indexes:
+            if len(select_indexes) >= num_to_predict:
+                break
+            if not cand_index_set:
+                continue
+            # Note(mingdachen):
+            # Skip current piece if they are covered in lm masking or previous ngrams.
+            for index_set in cand_index_set[0]:
+                for index in index_set:
+                    if index in covered_indexes or index in select_indexes:
+                        continue
+
+            n = np.random.choice(ngrams[:len(cand_index_set)],
+                                 p=pvals[:len(cand_index_set)] /
+                                 pvals[:len(cand_index_set)].sum(keepdims=True))
+            index_set = sum(cand_index_set[n - 1], [])
+            n -= 1
+
+            while len(select_indexes) + len(index_set) > num_to_predict:
+                if n == 0:
+                    break
+                index_set = sum(cand_index_set[n - 1], [])
+                n -= 1
+            # If adding a whole-word mask would exceed the maximum number of
+            # predictions, then just skip this candidate.
+            if len(select_indexes) + len(index_set) > num_to_predict:
+                continue
+            is_any_index_covered = False
+            for index in index_set:
+                if index in covered_indexes or index in select_indexes:
+                    is_any_index_covered = True
+                    break
+            if is_any_index_covered:
+                continue
+            for index in index_set:
+                select_indexes.add(index)
+        assert len(select_indexes) <= num_to_predict
+
+        select_indexes = sorted(select_indexes)
+        permute_indexes = list(select_indexes)
+        np_rng.shuffle(permute_indexes)
+        orig_token = list(output_tokens)
+
+        for src_i, tgt_i in zip(select_indexes, permute_indexes):
+            output_tokens[src_i] = orig_token[tgt_i]
+            masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i]))
+
+    masked_lms = sorted(masked_lms, key=lambda x: x.index)
+
+    for p in masked_lms:
+        masked_lm_positions.append(p.index)
+        masked_lm_labels.append(p.label)
+
+    return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary)
+
+
+def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
+                             masked_labels, pad_id, max_seq_length):
+    """Pad sequences and convert them to numpy."""
+
+    # Some checks.
+    num_tokens = len(tokens)
+    padding_length = max_seq_length - num_tokens
+    assert padding_length >= 0
+    assert len(tokentypes) == num_tokens
+    assert len(masked_positions) == len(masked_labels)
+
+    # Tokens and token types.
+    filler = [pad_id] * padding_length
+    tokens_np = np.array(tokens + filler, dtype=np.int64)
+    tokentypes_np = np.array(tokentypes + filler, dtype=np.int64)
+
+    # Padding mask.
+    padding_mask_np = np.array([1] * num_tokens + [0] * padding_length,
+                               dtype=np.int64)
+
+    # Lables and loss mask.
+    labels = [-1] * max_seq_length
+    loss_mask = [0] * max_seq_length
+    for i in range(len(masked_positions)):
+        assert masked_positions[i] < num_tokens
+        labels[masked_positions[i]] = masked_labels[i]
+        loss_mask[masked_positions[i]] = 1
+    labels_np = np.array(labels, dtype=np.int64)
+    loss_mask_np = np.array(loss_mask, dtype=np.int64)
+
+    return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np
+
+
+def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
+                                    train_valid_test_num_samples,
+                                    max_seq_length, masked_lm_prob,
+                                    short_seq_prob, seed, skip_warmup,
+                                    binary_head,
+                                    dataset_type='standard_bert'):
+
+    if len(data_prefix) == 1:
+        return _build_train_valid_test_datasets(data_prefix[0],
+                                                data_impl, splits_string,
+                                                train_valid_test_num_samples,
+                                                max_seq_length, masked_lm_prob,
+                                                short_seq_prob, seed,
+                                                skip_warmup,
+                                                binary_head,
+                                                dataset_type=dataset_type)
+    # 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],
+            max_seq_length, masked_lm_prob, short_seq_prob,
+            seed, skip_warmup, binary_head, dataset_type=dataset_type)
+        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,
+                                     max_seq_length, masked_lm_prob,
+                                     short_seq_prob, seed, skip_warmup,
+                                     binary_head,
+                                     dataset_type='standard_bert'):
+    logger = get_dist_logger()
+
+    if dataset_type not in DSET_TYPES:
+        raise ValueError("Invalid dataset_type: ", dataset_type)
+
+    # Indexed dataset.
+    indexed_dataset = get_indexed_dataset_(data_prefix,
+                                           data_impl,
+                                           skip_warmup)
+
+    if dataset_type == DSET_TYPE_ICT:
+        args = get_args()
+        title_dataset = get_indexed_dataset_(args.titles_data_path,
+                                             data_impl,
+                                             skip_warmup)
+
+    # Get start and end indices of train/valid/train into doc-idx
+    # Note that doc-idx is designed to be num-docs + 1 so we can
+    # easily iterate over it.
+    total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1
+    splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
+
+    # Print stats about the splits.
+    logger.info('\n > dataset split:')
+
+    def print_split_stats(name, index):
+        start_index = indexed_dataset.doc_idx[splits[index]]
+        end_index = indexed_dataset.doc_idx[splits[index + 1]]
+        logger.info('\n    {}:'.format(name) +
+                    '\n     document indices in [{}, {}) total of {} documents'.format(
+                        splits[index],
+                        splits[index + 1],
+                        splits[index + 1] - splits[index]) +
+                    '\n     sentence indices in [{}, {}) total of {} sentences'.format(
+                        start_index,
+                        end_index,
+                        end_index - start_index),
+                    ranks=[0])
+    print_split_stats('train', 0)
+    print_split_stats('validation', 1)
+    print_split_stats('test', 2)
+
+    def build_dataset(index, name):
+        from .bert_dataset import BertDataset
+        dataset = None
+        if splits[index + 1] > splits[index]:
+            # Get the pointer to the original doc-idx so we can set it later.
+            doc_idx_ptr = indexed_dataset.get_doc_idx()
+            # Slice the doc-idx
+            start_index = splits[index]
+            # Add +1 so we can index into the dataset to get the upper bound.
+            end_index = splits[index + 1] + 1
+            # New doc_idx view.
+            indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index])
+            # Build the dataset accordingly.
+            kwargs = dict(
+                name=name,
+                data_prefix=data_prefix,
+                num_epochs=None,
+                max_num_samples=train_valid_test_num_samples[index],
+                max_seq_length=max_seq_length,
+                seed=seed,
+                binary_head=binary_head
+            )
+
+            if dataset_type == DSET_TYPE_ICT:
+                args = get_args()
+                dataset = ICTDataset(
+                    block_dataset=indexed_dataset,
+                    title_dataset=title_dataset,
+                    query_in_block_prob=args.query_in_block_prob,
+                    use_one_sent_docs=args.use_one_sent_docs,
+                    **kwargs
+                )
+            else:
+                dataset = BertDataset(
+                    indexed_dataset=indexed_dataset,
+                    masked_lm_prob=masked_lm_prob,
+                    short_seq_prob=short_seq_prob,
+                    **kwargs
+                )
+
+            # Set the original pointer so dataset remains the main dataset.
+            indexed_dataset.set_doc_idx(doc_idx_ptr)
+            # Checks.
+            assert indexed_dataset.doc_idx[0] == 0
+            assert indexed_dataset.doc_idx.shape[0] == \
+                (total_num_of_documents + 1)
+        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):
+    logger = get_dist_logger()
+    start_time = time.time()
+    indexed_dataset = make_indexed_dataset(data_prefix,
+                                           data_impl,
+                                           skip_warmup)
+    assert indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1]
+    logger.info('\n > building dataset index ...', ranks=[0])
+    logger.info('\n > finished creating indexed dataset in {:4f} '
+                'seconds'.format(time.time() - start_time), ranks=[0])
+    logger.info('\n > indexed dataset stats:' +
+                '\n    number of documents: {}'.format(
+                    indexed_dataset.doc_idx.shape[0] - 1) +
+                '\n    number of sentences: {}'.format(
+                    indexed_dataset.sizes.shape[0]),
+                ranks=[0]
+                )
+
+    return indexed_dataset
+
+
+def get_train_valid_test_split_(splits_string, size):
+    """ Get dataset splits from comma or '/' separated string list."""
+
+    splits = []
+    if splits_string.find(',') != -1:
+        splits = [float(s) for s in splits_string.split(',')]
+    elif splits_string.find('/') != -1:
+        splits = [float(s) for s in splits_string.split('/')]
+    else:
+        splits = [float(splits_string)]
+    while len(splits) < 3:
+        splits.append(0.)
+    splits = splits[:3]
+    splits_sum = sum(splits)
+    assert splits_sum > 0.0
+    splits = [split / splits_sum for split in splits]
+    splits_index = [0]
+    for index, split in enumerate(splits):
+        splits_index.append(splits_index[index] +
+                            int(round(split * float(size))))
+    diff = splits_index[-1] - size
+    for index in range(1, len(splits_index)):
+        splits_index[index] -= diff
+    assert len(splits_index) == 4
+    assert splits_index[-1] == size
+    return splits_index

examples/tutorial/sequence_parallel/data/datasets/helpers.cpp

@@ -0,0 +1,717 @@
+/*
+ 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.
+ */
+
+
+/* Helper methods for fast index mapping builds */
+
+#include <algorithm>
+#include <iostream>
+#include <limits>
+#include <math.h>
+#include <stdexcept>
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <random>
+
+namespace py = pybind11;
+using namespace std;
+
+const int32_t LONG_SENTENCE_LEN = 512;
+
+
+void build_blending_indices(py::array_t<uint8_t>& dataset_index,
+			    py::array_t<int64_t>& dataset_sample_index,
+			    const py::array_t<double>& weights,
+			    const int32_t num_datasets,
+			    const int64_t size, const bool verbose) {
+  /* Given multiple datasets and a weighting array, build samples
+   such that it follows those wieghts.*/
+
+  if (verbose) {
+    std::cout << "> building indices for blendable datasets ..." << std::endl;
+  }
+
+  // Get the pointer access without the checks.
+  auto dataset_index_ptr = dataset_index.mutable_unchecked<1>();
+  auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>();
+  auto weights_ptr = weights.unchecked<1>();
+
+  // Initialize buffer for number of samples used for each dataset.
+  int64_t current_samples[num_datasets];
+  for(int64_t i = 0; i < num_datasets; ++i) {
+    current_samples[i] = 0;
+  }
+
+  // For each sample:
+  for(int64_t sample_idx = 0; sample_idx < size; ++sample_idx) {
+
+    // Determine where the max error in sampling is happening.
+    auto sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
+    int64_t max_error_index = 0;
+    double max_error = weights_ptr[0] * sample_idx_double -
+      static_cast<double>(current_samples[0]);
+    for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx) {
+      double error = weights_ptr[dataset_idx] * sample_idx_double -
+	static_cast<double>(current_samples[dataset_idx]);
+      if (error > max_error) {
+	max_error = error;
+	max_error_index = dataset_idx;
+      }
+    }
+
+    // Populate the indices.
+    dataset_index_ptr[sample_idx] = static_cast<uint8_t>(max_error_index);
+    dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index];
+
+    // Update the total samples.
+    current_samples[max_error_index] += 1;
+    
+  }
+
+  // print info
+  if (verbose) {
+    std::cout << " > sample ratios:" << std::endl;
+    for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) {
+      auto ratio = static_cast<double>(current_samples[dataset_idx]) /
+	static_cast<double>(size);
+      std::cout << "   dataset " << dataset_idx << ", input: " <<
+	weights_ptr[dataset_idx] << ", achieved: " << ratio << std::endl; 
+    }
+  }
+
+}
+
+
+py::array build_sample_idx(const py::array_t<int32_t>& sizes_,
+			   const py::array_t<int32_t>& doc_idx_,
+			   const int32_t seq_length,
+			   const int32_t num_epochs,
+			   const int64_t tokens_per_epoch) {
+    /* Sample index (sample_idx) is used for gpt2 like dataset for which
+       the documents are flattened and the samples are built based on this
+       1-D flatten array. It 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.*/
+
+    // Consistency checks.
+    assert(seq_length > 1);
+    assert(num_epochs > 0);
+    assert(tokens_per_epoch > 1);
+
+    // Remove bound checks.
+    auto sizes = sizes_.unchecked<1>();
+    auto doc_idx = doc_idx_.unchecked<1>();
+
+    // Mapping and it's length (1D).
+    int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length;
+    int32_t* sample_idx = new int32_t[2*(num_samples+1)];
+
+    cout << "    using:" << endl << std::flush;
+    cout << "     number of documents:       " <<
+      doc_idx_.shape(0) / num_epochs << endl << std::flush;
+    cout << "     number of epochs:          " << num_epochs <<
+      endl << std::flush;
+    cout << "     sequence length:           " << seq_length <<
+      endl << std::flush;
+    cout << "     total number of samples:   " << num_samples <<
+      endl << std::flush;
+
+    // Index into sample_idx.
+    int64_t sample_index = 0;
+    // Index into doc_idx.
+    int64_t doc_idx_index = 0;
+    // Begining offset for each document.
+    int32_t doc_offset = 0;
+    // Start with first document and no offset.
+    sample_idx[2 * sample_index] = doc_idx_index;
+    sample_idx[2 * sample_index + 1] = doc_offset;
+    ++sample_index;
+
+    while (sample_index <= num_samples) {
+        // Start with a fresh sequence.
+      int32_t remaining_seq_length = seq_length + 1;
+      while (remaining_seq_length != 0) {
+            // Get the document length.
+	auto doc_id = doc_idx[doc_idx_index];
+	auto 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;
+	  doc_offset = 0;
+	}
+      }
+      // Record the sequence.
+      sample_idx[2 * sample_index] = doc_idx_index;
+      sample_idx[2 * sample_index + 1] = doc_offset;
+      ++sample_index;
+    }
+
+    // Method to deallocate memory.
+    py::capsule free_when_done(sample_idx, [](void *mem_) {
+	int32_t *mem = reinterpret_cast<int32_t*>(mem_);
+	delete[] mem;
+      });
+
+    // Return the numpy array.
+    const auto byte_size = sizeof(int32_t);
+    return py::array(std::vector<int64_t>{num_samples+1, 2}, // shape
+                     {2*byte_size, byte_size}, // C-style contiguous strides
+                     sample_idx, // the data pointer
+                     free_when_done); // numpy array references
+    
+}
+
+
+inline int32_t get_target_sample_len(const int32_t short_seq_ratio,
+				     const int32_t max_length,
+				     std::mt19937& rand32_gen) {
+    /* Training sample length. */
+    if (short_seq_ratio == 0) {
+      return max_length;
+    }
+    const auto random_number = rand32_gen();
+    if ((random_number % short_seq_ratio) == 0) {
+      return 2 + random_number % (max_length - 1);
+    }
+    return max_length;
+}
+
+
+template<typename DocIdx>
+py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
+                             const py::array_t<int32_t>& sizes_,
+                             const int32_t num_epochs,
+                             const uint64_t max_num_samples,
+                             const int32_t max_seq_length,
+                             const double short_seq_prob,
+                             const int32_t seed,
+			     const bool verbose,
+			     const int32_t min_num_sent) {
+    /* Build a mapping of (start-index, end-index, sequence-length) where
+       start and end index are the indices of the sentences in the sample
+       and sequence-length is the target sequence length.
+    */
+
+    // Consistency checks.
+    assert(num_epochs > 0);
+    assert(max_seq_length > 1);
+    assert(short_seq_prob >= 0.0);
+    assert(short_seq_prob <= 1.0);
+    assert(seed > 0);
+
+    // Remove bound checks.
+    auto docs = docs_.unchecked<1>();
+    auto sizes = sizes_.unchecked<1>();
+
+    // For efficiency, convert probability to ratio. Note: rand() generates int.
+    int32_t short_seq_ratio = 0;
+    if (short_seq_prob > 0) {
+      short_seq_ratio = static_cast<int32_t>(round(1.0 / short_seq_prob));
+    }
+
+    if (verbose) {
+        const auto sent_start_index = docs[0];
+	const auto sent_end_index = docs[docs_.shape(0) - 1];
+	const auto num_sentences = sent_end_index - sent_start_index;
+	cout << "    using:" << endl << std::flush;
+	cout << "     number of documents:            " << docs_.shape(0) - 1 <<
+	  endl << std::flush;
+	cout << "     sentences range:                [" << sent_start_index <<
+	", " << sent_end_index << ")" << endl << std::flush;
+	cout << "     total number of sentences:      " << num_sentences <<
+	  endl << std::flush;
+	cout << "     number of epochs:               " << num_epochs <<
+	  endl << std::flush;
+	cout << "     maximum number of samples:      " << max_num_samples <<
+	  endl << std::flush;
+	cout << "     maximum sequence length:        " << max_seq_length <<
+	  endl << std::flush;
+	cout << "     short sequence probability:     " << short_seq_prob <<
+	endl << std::flush;
+	cout << "     short sequence ration (1/prob): " << short_seq_ratio <<
+	  endl << std::flush;
+	cout << "     seed:                           " << seed << endl <<
+	  std::flush;
+    }
+
+    // Mapping and it's length (1D).
+    int64_t num_samples = -1;
+    DocIdx* maps = NULL;
+
+    // Perform two iterations, in the first iteration get the size
+    // and allocate memory and in the second iteration populate the map.
+    bool second = false;
+    for (int32_t iteration=0; iteration<2; ++iteration) {
+
+        // Set the seed so both iterations produce the same results.
+        std::mt19937 rand32_gen(seed);
+
+        // Set the flag on second iteration.
+        second = (iteration == 1);
+
+        // Counters:
+        uint64_t empty_docs = 0;
+        uint64_t one_sent_docs = 0;
+	uint64_t long_sent_docs = 0;
+
+        // Current map index.
+        uint64_t map_index = 0;
+
+        // For each epoch:
+        for (int32_t epoch=0; epoch<num_epochs; ++epoch) {
+            if (map_index >= max_num_samples) {
+	        if (verbose && (!second)) {
+		  cout << "    reached " << max_num_samples << " samples after "
+		       << epoch << " epochs ..." << endl << std::flush;
+		}
+                break;
+            }
+            // For each document:
+            for (int32_t doc=0; doc<(docs.shape(0) - 1); ++doc) {
+
+                // Document sentences are in [sent_index_first, sent_index_last)
+                const auto sent_index_first = docs[doc];
+                const auto sent_index_last = docs[doc + 1];
+
+                // At the begining of the document previous index is the
+		// start index.
+                auto prev_start_index = sent_index_first;
+
+                // Remaining documents.
+                auto num_remain_sent = sent_index_last - sent_index_first;
+
+                // Some bookkeeping
+                if ((epoch == 0) && (!second)) {
+                    if (num_remain_sent == 0) {
+		        ++empty_docs;
+                    }
+                    if (num_remain_sent == 1) {
+		        ++one_sent_docs;
+                    }
+                }
+
+		// Detect documents with long sentences.
+		bool contains_long_sentence = false;
+		if (num_remain_sent > 1) {
+		    for (auto sent_index=sent_index_first;
+			 sent_index < sent_index_last; ++sent_index) {
+		        if (sizes[sent_index] > LONG_SENTENCE_LEN){
+			    if ((epoch == 0) && (!second)) {
+			        ++long_sent_docs;
+			    }
+			    contains_long_sentence = true;
+			    break;
+			}
+		    }
+		}
+
+                // If we have more than two sentences.
+                if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) {
+
+                    // Set values.
+                    auto seq_len = int32_t{0};
+                    auto num_sent = int32_t{0};
+                    auto target_seq_len = get_target_sample_len(short_seq_ratio,
+								max_seq_length,
+								rand32_gen);
+
+                    // Loop through sentences.
+                    for (auto sent_index=sent_index_first;
+                         sent_index < sent_index_last; ++sent_index) {
+
+		        // Add the size and number of sentences.
+		        seq_len += sizes[sent_index];
+		        ++num_sent;
+			--num_remain_sent;
+
+			// If we have reached the target length.
+			// and if not only one sentence is left in the document.
+			// and if we have at least two sentneces.
+			// and if we have reached end of the document.
+			if (((seq_len >= target_seq_len) &&
+			     (num_remain_sent > 1) &&
+			     (num_sent >= min_num_sent) ) || (num_remain_sent == 0)) {
+
+			    // Check for overflow.
+			    if ((3 * map_index + 2) >
+				std::numeric_limits<int64_t>::max()) {
+			        cout << "number of samples exceeded maximum "
+				     << "allowed by type int64: "
+				     << std::numeric_limits<int64_t>::max()
+				     << endl;
+				throw std::overflow_error("Number of samples");
+			    }
+
+			    // Populate the map.
+			    if (second) {
+			        const auto map_index_0 = 3 * map_index;
+				maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
+				maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
+				maps[map_index_0 + 2] = static_cast<DocIdx>(target_seq_len);
+			    }
+
+			    // Update indices / counters.
+			    ++map_index;
+			    prev_start_index = sent_index + 1;
+			    target_seq_len = get_target_sample_len(short_seq_ratio,
+								   max_seq_length,
+								   rand32_gen);
+			    seq_len = 0;
+			    num_sent = 0;
+			}
+
+                    } // for (auto sent_index=sent_index_first; ...
+                } // if (num_remain_sent > 1) {
+            } // for (int doc=0; doc < num_docs; ++doc) {
+        } // for (int epoch=0; epoch < num_epochs; ++epoch) {
+
+        if (!second) {
+	    if (verbose) {
+	        cout << "   number of empty documents: " << empty_docs <<
+		  endl << std::flush;
+		cout << "   number of documents with one sentence: " <<
+		  one_sent_docs << endl << std::flush;
+		cout << "   number of documents with long sentences: " <<
+		  long_sent_docs << endl << std::flush;
+		cout << "   will create mapping for " << map_index <<
+		  " samples" << endl << std::flush;
+	    }
+	    assert(maps == NULL);
+	    assert(num_samples < 0);
+            maps = new DocIdx[3*map_index];
+            num_samples = static_cast<int64_t>(map_index);
+        }
+
+    } // for (int iteration=0; iteration < 2; ++iteration) {
+
+    // Shuffle.
+    // We need a 64 bit random number generator as we might have more
+    // than 2 billion samples.
+    std::mt19937_64 rand64_gen(seed + 1);
+    for (auto i=(num_samples - 1); i > 0; --i) {
+      const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
+      const auto i0 = 3 * i;
+      const auto j0 = 3 * j;
+      // Swap values.
+      swap(maps[i0], maps[j0]);
+      swap(maps[i0 + 1], maps[j0 + 1]);
+      swap(maps[i0 + 2], maps[j0 + 2]);
+    }
+
+    // Method to deallocate memory.
+    py::capsule free_when_done(maps, [](void *mem_) {
+            DocIdx *mem = reinterpret_cast<DocIdx*>(mem_);
+	    delete[] mem;
+        });
+
+    // Return the numpy array.
+    const auto byte_size = sizeof(DocIdx);
+    return py::array(std::vector<int64_t>{num_samples, 3}, // shape
+                     {3*byte_size, byte_size}, // C-style contiguous strides
+                     maps, // the data pointer
+                     free_when_done); // numpy array references
+
+}
+
+
+py::array build_mapping(const py::array_t<int64_t>& docs_,
+                        const py::array_t<int>& sizes_,
+                        const int num_epochs,
+                        const uint64_t max_num_samples,
+                        const int max_seq_length,
+                        const double short_seq_prob,
+                        const int seed,
+			const bool verbose,
+			const int32_t min_num_sent) {
+
+    if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
+        if (verbose) {
+	   cout << "    using uint64 for data mapping..." << endl << std::flush;
+	}
+	return build_mapping_impl<uint64_t>(docs_, sizes_, num_epochs,
+					    max_num_samples, max_seq_length,
+					    short_seq_prob, seed, verbose,
+					    min_num_sent);
+    } else {
+       if (verbose) {
+	   cout << "    using uint32 for data mapping..." << endl << std::flush;
+       }
+       return build_mapping_impl<uint32_t>(docs_, sizes_, num_epochs,
+					   max_num_samples, max_seq_length,
+					   short_seq_prob, seed, verbose,
+					   min_num_sent);
+    }
+}
+
+template<typename DocIdx>
+py::array build_blocks_mapping_impl(const py::array_t<int64_t>& docs_,
+                                    const py::array_t<int32_t>& sizes_,
+                                    const py::array_t<int32_t>& titles_sizes_,
+                                    const int32_t num_epochs,
+                                    const uint64_t max_num_samples,
+                                    const int32_t max_seq_length,
+                                    const int32_t seed,
+                                    const bool verbose,
+                                    const bool use_one_sent_blocks) {
+    /* Build a mapping of (start-index, end-index, sequence-length) where
+       start and end index are the indices of the sentences in the sample
+       and sequence-length is the target sequence length.
+    */
+
+    // Consistency checks.
+    assert(num_epochs > 0);
+    assert(max_seq_length > 1);
+    assert(seed > 0);
+
+    // Remove bound checks.
+    auto docs = docs_.unchecked<1>();
+    auto sizes = sizes_.unchecked<1>();
+    auto titles_sizes = titles_sizes_.unchecked<1>();
+
+    if (verbose) {
+        const auto sent_start_index = docs[0];
+        const auto sent_end_index = docs[docs_.shape(0) - 1];
+        const auto num_sentences = sent_end_index - sent_start_index;
+        cout << "    using:" << endl << std::flush;
+        cout << "     number of documents:            " << docs_.shape(0) - 1 <<
+          endl << std::flush;
+        cout << "     sentences range:                [" << sent_start_index <<
+        ", " << sent_end_index << ")" << endl << std::flush;
+        cout << "     total number of sentences:      " << num_sentences <<
+          endl << std::flush;
+        cout << "     number of epochs:               " << num_epochs <<
+          endl << std::flush;
+        cout << "     maximum number of samples:      " << max_num_samples <<
+          endl << std::flush;
+        cout << "     maximum sequence length:        " << max_seq_length <<
+          endl << std::flush;
+        cout << "     seed:                           " << seed << endl <<
+          std::flush;
+    }
+
+    // Mapping and its length (1D).
+    int64_t num_samples = -1;
+    DocIdx* maps = NULL;
+
+    // Acceptable number of sentences per block.
+    int min_num_sent = 2;
+    if (use_one_sent_blocks) {
+        min_num_sent = 1;
+    }
+
+    // Perform two iterations, in the first iteration get the size
+    // and allocate memory and in the second iteration populate the map.
+    bool second = false;
+    for (int32_t iteration=0; iteration<2; ++iteration) {
+
+        // Set the flag on second iteration.
+        second = (iteration == 1);
+
+        // Current map index.
+        uint64_t map_index = 0;
+
+        uint64_t empty_docs = 0;
+        uint64_t one_sent_docs = 0;
+        uint64_t long_sent_docs = 0;
+        // For each epoch:
+        for (int32_t epoch=0; epoch<num_epochs; ++epoch) {
+            // assign every block a unique id
+            int32_t block_id = 0;
+
+            if (map_index >= max_num_samples) {
+                if (verbose && (!second)) {
+                cout << "    reached " << max_num_samples << " samples after "
+                     << epoch << " epochs ..." << endl << std::flush;
+                }
+                break;
+            }
+            // For each document:
+            for (int32_t doc=0; doc<(docs.shape(0) - 1); ++doc) {
+
+                // Document sentences are in [sent_index_first, sent_index_last)
+                const auto sent_index_first = docs[doc];
+                const auto sent_index_last = docs[doc + 1];
+                const auto target_seq_len = max_seq_length - titles_sizes[doc];
+
+                // At the begining of the document previous index is the
+                // start index.
+                auto prev_start_index = sent_index_first;
+
+                // Remaining documents.
+                auto num_remain_sent = sent_index_last - sent_index_first;
+
+                // Some bookkeeping
+                if ((epoch == 0) && (!second)) {
+                    if (num_remain_sent == 0) {
+		                ++empty_docs;
+                    }
+                    if (num_remain_sent == 1) {
+		                ++one_sent_docs;
+                    }
+                }
+                // Detect documents with long sentences.
+                bool contains_long_sentence = false;
+                if (num_remain_sent >= min_num_sent) {
+                    for (auto sent_index=sent_index_first;
+                    sent_index < sent_index_last; ++sent_index) {
+                        if (sizes[sent_index] > LONG_SENTENCE_LEN){
+                            if ((epoch == 0) && (!second)) {
+                                ++long_sent_docs;
+                            }
+                            contains_long_sentence = true;
+                            break;
+                        }
+                    }
+                }
+                // If we have enough sentences and no long sentences.
+                if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) {
+
+                    // Set values.
+                    auto seq_len = int32_t{0};
+                    auto num_sent = int32_t{0};
+
+                    // Loop through sentences.
+                    for (auto sent_index=sent_index_first;
+                         sent_index < sent_index_last; ++sent_index) {
+
+                            // Add the size and number of sentences.
+                            seq_len += sizes[sent_index];
+                            ++num_sent;
+                            --num_remain_sent;
+
+                        // If we have reached the target length.
+                        // and there are an acceptable number of sentences left
+                        // and if we have at least the minimum number of sentences.
+                        // or if we have reached end of the document.
+                        if (((seq_len >= target_seq_len) &&
+                             (num_remain_sent >= min_num_sent) &&
+                             (num_sent >= min_num_sent) ) || (num_remain_sent == 0)) {
+
+                            // Populate the map.
+                            if (second) {
+                                const auto map_index_0 = 4 * map_index;
+                                // Each sample has 4 items: the starting sentence index, ending sentence index,
+                                // the index of the document from which the block comes (used for fetching titles)
+                                // and the unique id of the block (used for creating block indexes)
+
+                                maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
+                                maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
+                                maps[map_index_0 + 2] = static_cast<DocIdx>(doc);
+                                maps[map_index_0 + 3] = static_cast<DocIdx>(block_id);
+                            }
+
+                            // Update indices / counters.
+                            ++map_index;
+                            ++block_id;
+                            prev_start_index = sent_index + 1;
+                            seq_len = 0;
+                            num_sent = 0;
+                        }
+                    } // for (auto sent_index=sent_index_first; ...
+                } // if (num_remain_sent > 1) {
+            } // for (int doc=0; doc < num_docs; ++doc) {
+        } // for (int epoch=0; epoch < num_epochs; ++epoch) {
+
+        if (!second) {
+            if (verbose) {
+	        cout << "   number of empty documents: " << empty_docs <<
+              endl << std::flush;
+            cout << "   number of documents with one sentence: " <<
+              one_sent_docs << endl << std::flush;
+            cout << "   number of documents with long sentences: " <<
+              long_sent_docs << endl << std::flush;
+            cout << "   will create mapping for " << map_index <<
+              " samples" << endl << std::flush;
+            }
+            assert(maps == NULL);
+            assert(num_samples < 0);
+            maps = new DocIdx[4*map_index];
+            num_samples = static_cast<int64_t>(map_index);
+        }
+
+    } // for (int iteration=0; iteration < 2; ++iteration) {
+
+    // Shuffle.
+    // We need a 64 bit random number generator as we might have more
+    // than 2 billion samples.
+    std::mt19937_64 rand64_gen(seed + 1);
+    for (auto i=(num_samples - 1); i > 0; --i) {
+        const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
+        const auto i0 = 4 * i;
+        const auto j0 = 4 * j;
+        // Swap values.
+        swap(maps[i0], maps[j0]);
+        swap(maps[i0 + 1], maps[j0 + 1]);
+        swap(maps[i0 + 2], maps[j0 + 2]);
+        swap(maps[i0 + 3], maps[j0 + 3]);
+    }
+
+    // Method to deallocate memory.
+    py::capsule free_when_done(maps, [](void *mem_) {
+            DocIdx *mem = reinterpret_cast<DocIdx*>(mem_);
+	    delete[] mem;
+        });
+
+    // Return the numpy array.
+    const auto byte_size = sizeof(DocIdx);
+    return py::array(std::vector<int64_t>{num_samples, 4}, // shape
+                     {4*byte_size, byte_size}, // C-style contiguous strides
+                     maps, // the data pointer
+                     free_when_done); // numpy array references
+
+}
+
+py::array build_blocks_mapping(const py::array_t<int64_t>& docs_,
+                               const py::array_t<int>& sizes_,
+                               const py::array_t<int>& titles_sizes_,
+                               const int num_epochs,
+                               const uint64_t max_num_samples,
+                               const int max_seq_length,
+                               const int seed,
+                    const bool verbose,
+                    const bool use_one_sent_blocks) {
+
+    if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
+        if (verbose) {
+	   cout << "    using uint64 for data mapping..." << endl << std::flush;
+	}
+	return build_blocks_mapping_impl<uint64_t>(docs_, sizes_, titles_sizes_,
+	                    num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks);
+    } else {
+       if (verbose) {
+	   cout << "    using uint32 for data mapping..." << endl << std::flush;
+       }
+       return build_blocks_mapping_impl<uint32_t>(docs_, sizes_, titles_sizes_,
+                        num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks);
+    }
+}
+
+PYBIND11_MODULE(helpers, m) {
+    m.def("build_mapping", &build_mapping);
+    m.def("build_blocks_mapping", &build_blocks_mapping);
+    m.def("build_sample_idx", &build_sample_idx);
+    m.def("build_blending_indices", &build_blending_indices);
+}

examples/tutorial/sequence_parallel/data/datasets/ict_dataset.py

@@ -0,0 +1,156 @@
+import itertools
+import random
+
+import numpy as np
+from torch.utils.data import Dataset
+
+from megatron import get_tokenizer
+from megatron import get_args
+from megatron.data.dataset_utils import get_indexed_dataset_
+from megatron.data.realm_dataset_utils import get_block_samples_mapping
+
+def make_attention_mask(source_block, target_block):
+    """
+    Returns a 2-dimensional (2-D) attention mask
+    :param source_block: 1-D array
+    :param target_block: 1-D array
+    """
+    mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1)
+    mask = mask.astype(np.int64)
+    # (source_length, target_length)
+    return mask
+
+def get_ict_dataset(use_titles=True, query_in_block_prob=1):
+    """Get a dataset which uses block samples mappings to get ICT/block indexing data (via get_block())
+    rather than for training, since it is only built with a single epoch sample mapping.
+    """
+    args = get_args()
+    block_dataset = get_indexed_dataset_(args.data_path, 'mmap', True)
+    titles_dataset = get_indexed_dataset_(args.titles_data_path, 'mmap', True)
+
+    kwargs = dict(
+        name='full',
+        block_dataset=block_dataset,
+        title_dataset=titles_dataset,
+        data_prefix=args.data_path,
+        num_epochs=1,
+        max_num_samples=None,
+        max_seq_length=args.seq_length,
+        seed=1,
+        query_in_block_prob=query_in_block_prob,
+        use_titles=use_titles,
+        use_one_sent_docs=args.use_one_sent_docs
+    )
+    dataset = ICTDataset(**kwargs)
+    return dataset
+
+
+class ICTDataset(Dataset):
+    """Dataset containing sentences and their blocks for an inverse cloze task."""
+    def __init__(self, name, block_dataset, title_dataset, data_prefix,
+                 num_epochs, max_num_samples, max_seq_length, query_in_block_prob,
+                 seed, use_titles=True, use_one_sent_docs=False, binary_head=False):
+        self.name = name
+        self.seed = seed
+        self.max_seq_length = max_seq_length
+        self.query_in_block_prob = query_in_block_prob
+        self.block_dataset = block_dataset
+        self.title_dataset = title_dataset
+        self.rng = random.Random(self.seed)
+        self.use_titles = use_titles
+        self.use_one_sent_docs = use_one_sent_docs
+
+        self.samples_mapping = get_block_samples_mapping(
+            block_dataset, title_dataset, data_prefix, num_epochs,
+            max_num_samples, max_seq_length, seed, name, use_one_sent_docs)
+        self.tokenizer = get_tokenizer()
+        self.vocab_id_list = list(self.tokenizer.inv_vocab.keys())
+        self.vocab_id_to_token_list = self.tokenizer.inv_vocab
+        self.cls_id = self.tokenizer.cls
+        self.sep_id = self.tokenizer.sep
+        self.mask_id = self.tokenizer.mask
+        self.pad_id = self.tokenizer.pad
+
+    def __len__(self):
+        return len(self.samples_mapping)
+
+    def __getitem__(self, idx):
+        """Get an ICT example of a pseudo-query and the block of text from which it was extracted"""
+        sample_data = self.samples_mapping[idx]
+        start_idx, end_idx, doc_idx, block_idx = sample_data.as_tuple()
+
+        if self.use_titles:
+            title = self.title_dataset[int(doc_idx)]
+            title_pad_offset = 3 + len(title)
+        else:
+            title = None
+            title_pad_offset = 2
+        block = [self.block_dataset[i] for i in range(start_idx, end_idx)]
+        assert len(block) > 1 or self.use_one_sent_docs or self.query_in_block_prob == 1
+
+        # randint() is inclusive for Python rng
+        rand_sent_idx = self.rng.randint(0, len(block) - 1)
+
+        # keep the query in the context query_in_block_prob fraction of the time.
+        if self.rng.random() < self.query_in_block_prob:
+            query = block[rand_sent_idx].copy()
+        else:
+            query = block.pop(rand_sent_idx)
+
+        # still need to truncate because blocks are concluded when
+        # the sentence lengths have exceeded max_seq_length.
+        query = query[:self.max_seq_length - 2]
+        block = list(itertools.chain(*block))[:self.max_seq_length - title_pad_offset]
+
+        query_tokens, query_pad_mask = self.concat_and_pad_tokens(query)
+        context_tokens, context_pad_mask = self.concat_and_pad_tokens(block, title)
+
+        query_mask = make_attention_mask(query_tokens, query_tokens)
+        context_mask = make_attention_mask(context_tokens, context_tokens)
+
+        block_data = sample_data.as_array()
+
+        sample = {
+            'query_tokens': query_tokens,
+            'query_mask': query_mask,
+            'query_pad_mask': query_pad_mask,
+            'context_tokens': context_tokens,
+            'context_mask': context_mask,
+            'context_pad_mask': context_pad_mask,
+            'block_data': block_data,
+        }
+
+        return sample
+
+    def get_block(self, start_idx, end_idx, doc_idx):
+        """Get the IDs for an evidence block plus the title of the corresponding document"""
+        block = [self.block_dataset[i] for i in range(start_idx, end_idx)]
+        title = self.title_dataset[int(doc_idx)]
+
+        block = list(itertools.chain(*block))[:self.max_seq_length - (3 + len(title))]
+        block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title)
+
+        return block_tokens, block_pad_mask
+
+    def get_null_block(self):
+        """Get empty block and title - used in REALM pretraining"""
+        block, title = [], []
+        block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title)
+
+        return block_tokens, block_pad_mask
+
+    def concat_and_pad_tokens(self, tokens, title=None):
+        """Concat with special tokens and pad sequence to self.max_seq_length"""
+        tokens = list(tokens)
+        if title is None:
+            tokens = [self.cls_id] + tokens + [self.sep_id]
+        else:
+            title = list(title)
+            tokens = [self.cls_id] + title + [self.sep_id] + tokens + [self.sep_id]
+        assert len(tokens) <= self.max_seq_length
+
+        num_pad = self.max_seq_length - len(tokens)
+        pad_mask = [1] * len(tokens) + [0] * num_pad
+        tokens += [self.pad_id] * num_pad
+
+        return np.array(tokens), np.array(pad_mask)

examples/tutorial/sequence_parallel/data/datasets/indexed_dataset.py

@@ -0,0 +1,569 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+# copied from fairseq/fairseq/data/indexed_dataset.py
+# Removed IndexedRawTextDataset since it relied on Fairseq dictionary
+# other slight modifications to remove fairseq dependencies
+# Added document index to index file and made it accessible.
+#    An empty sentence no longer separates documents.
+
+from functools import lru_cache
+import os
+import shutil
+import struct
+from itertools import accumulate
+
+import numpy as np
+import torch
+
+
+def __best_fitting_dtype(vocab_size=None):
+    if vocab_size is not None and vocab_size < 65500:
+        return np.uint16
+    else:
+        return np.int32
+
+
+def get_available_dataset_impl():
+    return ['lazy', 'cached', 'mmap']
+
+
+def infer_dataset_impl(path):
+    if IndexedDataset.exists(path):
+        with open(index_file_path(path), 'rb') as f:
+            magic = f.read(8)
+            if magic == IndexedDataset._HDR_MAGIC:
+                return 'cached'
+            elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
+                return 'mmap'
+            else:
+                return None
+    else:
+        print(f"Dataset does not exist: {path}")
+        print("Path should be a basename that both .idx and .bin can be appended to get full filenames.")
+        return None
+
+
+def make_builder(out_file, impl, vocab_size=None):
+    if impl == 'mmap':
+        return MMapIndexedDatasetBuilder(out_file, dtype=__best_fitting_dtype(vocab_size))
+    else:
+        return IndexedDatasetBuilder(out_file)
+
+
+def make_dataset(path, impl, skip_warmup=False):
+    if not IndexedDataset.exists(path):
+        print(f"Dataset does not exist: {path}")
+        print("Path should be a basename that both .idx and .bin can be appended to get full filenames.")
+        return None
+    if impl == 'infer':
+        impl = infer_dataset_impl(path)
+    if impl == 'lazy' and IndexedDataset.exists(path):
+        return IndexedDataset(path)
+    elif impl == 'cached' and IndexedDataset.exists(path):
+        return IndexedCachedDataset(path)
+    elif impl == 'mmap' and MMapIndexedDataset.exists(path):
+        return MMapIndexedDataset(path, skip_warmup)
+    print(f"Unknown dataset implementation: {impl}")
+    return None
+
+
+def dataset_exists(path, impl):
+    if impl == 'mmap':
+        return MMapIndexedDataset.exists(path)
+    else:
+        return IndexedDataset.exists(path)
+
+
+def read_longs(f, n):
+    a = np.empty(n, dtype=np.int64)
+    f.readinto(a)
+    return a
+
+
+def write_longs(f, a):
+    f.write(np.array(a, dtype=np.int64))
+
+
+dtypes = {
+    1: np.uint8,
+    2: np.int8,
+    3: np.int16,
+    4: np.int32,
+    5: np.int64,
+    6: np.float,
+    7: np.double,
+    8: np.uint16
+}
+
+
+def code(dtype):
+    for k in dtypes.keys():
+        if dtypes[k] == dtype:
+            return k
+    raise ValueError(dtype)
+
+
+def index_file_path(prefix_path):
+    return prefix_path + '.idx'
+
+
+def data_file_path(prefix_path):
+    return prefix_path + '.bin'
+
+
+def create_doc_idx(sizes):
+    doc_idx = [0]
+    for i, s in enumerate(sizes):
+        if s == 0:
+            doc_idx.append(i + 1)
+    return doc_idx
+
+
+class IndexedDataset(torch.utils.data.Dataset):
+    """Loader for IndexedDataset"""
+    _HDR_MAGIC = b'TNTIDX\x00\x00'
+
+    def __init__(self, path):
+        super().__init__()
+        self.path = path
+        self.data_file = None
+        self.read_index(path)
+
+    def read_index(self, path):
+        with open(index_file_path(path), 'rb') as f:
+            magic = f.read(8)
+            assert magic == self._HDR_MAGIC, (
+                'Index file doesn\'t match expected format. '
+                'Make sure that --dataset-impl is configured properly.'
+            )
+            version = f.read(8)
+            assert struct.unpack('<Q', version) == (1,)
+            code, self.element_size = struct.unpack('<QQ', f.read(16))
+            self.dtype = dtypes[code]
+            self._len, self.s = struct.unpack('<QQ', f.read(16))
+            self.doc_count = struct.unpack('<Q', f.read(8))
+            self.dim_offsets = read_longs(f, self._len + 1)
+            self.data_offsets = read_longs(f, self._len + 1)
+            self.sizes = read_longs(f, self.s)
+            self.doc_idx = read_longs(f, self.doc_count)
+
+    def read_data(self, path):
+        self.data_file = open(data_file_path(path), 'rb', buffering=0)
+
+    def check_index(self, i):
+        if i < 0 or i >= self._len:
+            raise IndexError('index out of range')
+
+    def __del__(self):
+        if self.data_file:
+            self.data_file.close()
+
+    # @lru_cache(maxsize=8)
+    def __getitem__(self, idx):
+        if not self.data_file:
+            self.read_data(self.path)
+        if isinstance(idx, int):
+            i = idx
+            self.check_index(i)
+            tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]]
+            a = np.empty(tensor_size, dtype=self.dtype)
+            self.data_file.seek(self.data_offsets[i] * self.element_size)
+            self.data_file.readinto(a)
+            return a
+        elif isinstance(idx, slice):
+            start, stop, step = idx.indices(len(self))
+            if step != 1:
+                raise ValueError("Slices into indexed_dataset must be contiguous")
+            sizes = self.sizes[self.dim_offsets[start]:self.dim_offsets[stop]]
+            size = sum(sizes)
+            a = np.empty(size, dtype=self.dtype)
+            self.data_file.seek(self.data_offsets[start] * self.element_size)
+            self.data_file.readinto(a)
+            offsets = list(accumulate(sizes))
+            sents = np.split(a, offsets[:-1])
+            return sents
+
+    def __len__(self):
+        return self._len
+
+    def num_tokens(self, index):
+        return self.sizes[index]
+
+    def size(self, index):
+        return self.sizes[index]
+
+    @staticmethod
+    def exists(path):
+        return (
+            os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))
+        )
+
+    @property
+    def supports_prefetch(self):
+        return False  # avoid prefetching to save memory
+
+
+class IndexedCachedDataset(IndexedDataset):
+
+    def __init__(self, path):
+        super().__init__(path)
+        self.cache = None
+        self.cache_index = {}
+
+    @property
+    def supports_prefetch(self):
+        return True
+
+    def prefetch(self, indices):
+        if all(i in self.cache_index for i in indices):
+            return
+        if not self.data_file:
+            self.read_data(self.path)
+        indices = sorted(set(indices))
+        total_size = 0
+        for i in indices:
+            total_size += self.data_offsets[i + 1] - self.data_offsets[i]
+        self.cache = np.empty(total_size, dtype=self.dtype)
+        ptx = 0
+        self.cache_index.clear()
+        for i in indices:
+            self.cache_index[i] = ptx
+            size = self.data_offsets[i + 1] - self.data_offsets[i]
+            a = self.cache[ptx: ptx + size]
+            self.data_file.seek(self.data_offsets[i] * self.element_size)
+            self.data_file.readinto(a)
+            ptx += size
+        if self.data_file:
+            # close and delete data file after prefetch so we can pickle
+            self.data_file.close()
+            self.data_file = None
+
+    # @lru_cache(maxsize=8)
+    def __getitem__(self, idx):
+        if isinstance(idx, int):
+            i = idx
+            self.check_index(i)
+            tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]]
+            a = np.empty(tensor_size, dtype=self.dtype)
+            ptx = self.cache_index[i]
+            np.copyto(a, self.cache[ptx: ptx + a.size])
+            return a
+        elif isinstance(idx, slice):
+            # Hack just to make this work, can optimizer later if necessary
+            sents = []
+            for i in range(*idx.indices(len(self))):
+                sents.append(self[i])
+            return sents
+
+
+class IndexedDatasetBuilder(object):
+    element_sizes = {
+        np.uint8: 1,
+        np.int8: 1,
+        np.int16: 2,
+        np.int32: 4,
+        np.int64: 8,
+        np.float: 4,
+        np.double: 8
+    }
+
+    def __init__(self, out_file, dtype=np.int32):
+        self.out_file = open(out_file, 'wb')
+        self.dtype = dtype
+        self.data_offsets = [0]
+        self.dim_offsets = [0]
+        self.sizes = []
+        self.element_size = self.element_sizes[self.dtype]
+        self.doc_idx = [0]
+
+    def add_item(self, tensor):
+        bytes = self.out_file.write(np.array(tensor.numpy(), dtype=self.dtype))
+        self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
+        for s in tensor.size():
+            self.sizes.append(s)
+        self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size()))
+
+    def end_document(self):
+        self.doc_idx.append(len(self.sizes))
+
+    def merge_file_(self, another_file):
+        index = IndexedDataset(another_file)
+        assert index.dtype == self.dtype
+
+        begin = self.data_offsets[-1]
+        for offset in index.data_offsets[1:]:
+            self.data_offsets.append(begin + offset)
+        self.sizes.extend(index.sizes)
+        begin = self.dim_offsets[-1]
+        for dim_offset in index.dim_offsets[1:]:
+            self.dim_offsets.append(begin + dim_offset)
+
+        with open(data_file_path(another_file), 'rb') as f:
+            while True:
+                data = f.read(1024)
+                if data:
+                    self.out_file.write(data)
+                else:
+                    break
+
+    def finalize(self, index_file):
+        self.out_file.close()
+        index = open(index_file, 'wb')
+        index.write(b'TNTIDX\x00\x00')
+        index.write(struct.pack('<Q', 1))
+        index.write(struct.pack('<QQ', code(self.dtype), self.element_size))
+        index.write(struct.pack('<QQ', len(self.data_offsets) - 1, len(self.sizes)))
+        index.write(struct.pack('<Q', len(self.doc_idx)))
+        write_longs(index, self.dim_offsets)
+        write_longs(index, self.data_offsets)
+        write_longs(index, self.sizes)
+        write_longs(index, self.doc_idx)
+        index.close()
+
+
+def _warmup_mmap_file(path):
+    with open(path, 'rb') as stream:
+        while stream.read(100 * 1024 * 1024):
+            pass
+
+
+class MMapIndexedDataset(torch.utils.data.Dataset):
+    class Index(object):
+        _HDR_MAGIC = b'MMIDIDX\x00\x00'
+
+        @classmethod
+        def writer(cls, path, dtype):
+            class _Writer(object):
+                def __enter__(self):
+                    self._file = open(path, 'wb')
+
+                    self._file.write(cls._HDR_MAGIC)
+                    self._file.write(struct.pack('<Q', 1))
+                    self._file.write(struct.pack('<B', code(dtype)))
+
+                    return self
+
+                @staticmethod
+                def _get_pointers(sizes):
+                    dtype_size = dtype().itemsize
+                    address = 0
+                    pointers = []
+
+                    for size in sizes:
+                        pointers.append(address)
+                        address += size * dtype_size
+
+                    return pointers
+
+                def write(self, sizes, doc_idx):
+                    pointers = self._get_pointers(sizes)
+
+                    self._file.write(struct.pack('<Q', len(sizes)))
+                    self._file.write(struct.pack('<Q', len(doc_idx)))
+
+                    sizes = np.array(sizes, dtype=np.int32)
+                    self._file.write(sizes.tobytes(order='C'))
+                    del sizes
+
+                    pointers = np.array(pointers, dtype=np.int64)
+                    self._file.write(pointers.tobytes(order='C'))
+                    del pointers
+
+                    doc_idx = np.array(doc_idx, dtype=np.int64)
+                    self._file.write(doc_idx.tobytes(order='C'))
+
+                def __exit__(self, exc_type, exc_val, exc_tb):
+                    self._file.close()
+
+            return _Writer()
+
+        def __init__(self, path, skip_warmup=False):
+            with open(path, 'rb') as stream:
+                magic_test = stream.read(9)
+                assert self._HDR_MAGIC == magic_test, (
+                    'Index file doesn\'t match expected format. '
+                    'Make sure that --dataset-impl is configured properly.'
+                )
+                version = struct.unpack('<Q', stream.read(8))
+                assert (1,) == version
+
+                dtype_code, = struct.unpack('<B', stream.read(1))
+                self._dtype = dtypes[dtype_code]
+                self._dtype_size = self._dtype().itemsize
+
+                self._len = struct.unpack('<Q', stream.read(8))[0]
+                self._doc_count = struct.unpack('<Q', stream.read(8))[0]
+                offset = stream.tell()
+
+            if not skip_warmup:
+                print("    warming up index mmap file...")
+                _warmup_mmap_file(path)
+
+            self._bin_buffer_mmap = np.memmap(path, mode='r', order='C')
+            self._bin_buffer = memoryview(self._bin_buffer_mmap)
+            print("    reading sizes...")
+            self._sizes = np.frombuffer(
+                self._bin_buffer,
+                dtype=np.int32,
+                count=self._len,
+                offset=offset)
+            print("    reading pointers...")
+            self._pointers = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._len,
+                                           offset=offset + self._sizes.nbytes)
+            print("    reading document index...")
+            self._doc_idx = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._doc_count,
+                                          offset=offset + self._sizes.nbytes + self._pointers.nbytes)
+
+        def __del__(self):
+            self._bin_buffer_mmap._mmap.close()
+            del self._bin_buffer_mmap
+
+        @property
+        def dtype(self):
+            return self._dtype
+
+        @property
+        def sizes(self):
+            return self._sizes
+
+        @property
+        def doc_idx(self):
+            return self._doc_idx
+
+        @lru_cache(maxsize=8)
+        def __getitem__(self, i):
+            return self._pointers[i], self._sizes[i]
+
+        def __len__(self):
+            return self._len
+
+    def __init__(self, path, skip_warmup=False):
+        super().__init__()
+
+        self._path = None
+        self._index = None
+        self._bin_buffer = None
+
+        self._do_init(path, skip_warmup)
+
+    def __getstate__(self):
+        return self._path
+
+    def __setstate__(self, state):
+        self._do_init(state)
+
+    def _do_init(self, path, skip_warmup):
+        self._path = path
+        self._index = self.Index(index_file_path(self._path), skip_warmup)
+
+        if not skip_warmup:
+            print("    warming up data mmap file...")
+            _warmup_mmap_file(data_file_path(self._path))
+        print("    creating numpy buffer of mmap...")
+        self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode='r', order='C')
+        print("    creating memory view of numpy buffer...")
+        self._bin_buffer = memoryview(self._bin_buffer_mmap)
+
+    def __del__(self):
+        self._bin_buffer_mmap._mmap.close()
+        del self._bin_buffer_mmap
+        del self._index
+
+    def __len__(self):
+        return len(self._index)
+
+    # @lru_cache(maxsize=8)
+    def __getitem__(self, idx):
+        if isinstance(idx, int):
+            ptr, size = self._index[idx]
+            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
+                                     count=size, offset=ptr)
+            return np_array
+        elif isinstance(idx, slice):
+            start, stop, step = idx.indices(len(self))
+            if step != 1:
+                raise ValueError("Slices into indexed_dataset must be contiguous")
+            ptr = self._index._pointers[start]
+            sizes = self._index._sizes[idx]
+            offsets = list(accumulate(sizes))
+            total_size = sum(sizes)
+            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
+                                     count=total_size, offset=ptr)
+            sents = np.split(np_array, offsets[:-1])
+            return sents
+
+    def get(self, idx, offset=0, length=None):
+        """ Retrieves a single item from the dataset with the option to only
+        return a portion of the item.
+
+        get(idx) is the same as [idx] but get() does not support slicing.
+        """
+        ptr, size = self._index[idx]
+        if length is None:
+            length = size - offset
+        ptr += offset * np.dtype(self._index.dtype).itemsize
+        np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
+                                 count=length, offset=ptr)
+        return np_array
+
+    @property
+    def sizes(self):
+        return self._index.sizes
+
+    @property
+    def doc_idx(self):
+        return self._index.doc_idx
+
+    def get_doc_idx(self):
+        return self._index._doc_idx
+
+    def set_doc_idx(self, doc_idx_):
+        self._index._doc_idx = doc_idx_
+
+    @property
+    def supports_prefetch(self):
+        return False
+
+    @staticmethod
+    def exists(path):
+        return (
+            os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))
+        )
+
+
+class MMapIndexedDatasetBuilder(object):
+    def __init__(self, out_file, dtype=np.int64):
+        self._data_file = open(out_file, 'wb')
+        self._dtype = dtype
+        self._sizes = []
+        self._doc_idx = [0]
+
+    def add_item(self, tensor):
+        np_array = np.array(tensor.numpy(), dtype=self._dtype)
+        self._data_file.write(np_array.tobytes(order='C'))
+        self._sizes.append(np_array.size)
+
+    def end_document(self):
+        self._doc_idx.append(len(self._sizes))
+
+    def merge_file_(self, another_file):
+        # Concatenate index
+        index = MMapIndexedDataset.Index(index_file_path(another_file))
+        assert index.dtype == self._dtype
+
+        for size in index.sizes:
+            self._sizes.append(size)
+
+        # Concatenate data
+        with open(data_file_path(another_file), 'rb') as f:
+            shutil.copyfileobj(f, self._data_file)
+
+    def finalize(self, index_file):
+        self._data_file.close()
+
+        with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index:
+            index.write(self._sizes, self._doc_idx)

examples/tutorial/sequence_parallel/data/datasets/test/test_indexed_dataset.py

@@ -0,0 +1,125 @@
+# This file isn't really a formal automated test, it's just a place to
+# put some code used during development and manual testing of
+# indexed_dataset.
+
+from megatron.data import indexed_dataset
+from megatron.tokenizer import build_tokenizer
+import argparse
+import os
+import sys
+
+import torch
+
+script_dir = os.path.dirname(os.path.realpath(__file__))
+sys.path.append(os.path.join(script_dir, "../../../"))
+
+
+def test_indexed_dataset(args):
+    ds = indexed_dataset.make_dataset(args.data, args.dataset_impl)
+    tokenizer = build_tokenizer(args)
+    print(len(ds.doc_idx))
+    print(len(ds))
+    print(ds.doc_idx[-1])
+    if ds.supports_prefetch:
+        # just prefetch the whole thing in test (so assume it is small)
+        ds.prefetch(range(len(ds)))
+    if args.count > len(ds.doc_idx) - 1:
+        args.count = len(ds.doc_idx) - 1
+
+    for i in range(args.count):
+        start = ds.doc_idx[i]
+        end = ds.doc_idx[i + 1]
+        ids = ds[start:end]
+        print(f"Document {i}:")
+        print("--------------")
+        for s in ids:
+            assert len(s) > 0
+            l = s.data.tolist()
+            text = tokenizer.detokenize(l)
+            print(text)
+            print("---")
+
+
+def test_indexed_dataset_get(args):
+    ds = indexed_dataset.make_dataset(args.data, args.dataset_impl)
+    tokenizer = build_tokenizer(args)
+    size = ds.sizes[0]
+    print(f"size: {size}")
+    full = ds.get(0)
+    print(full)
+    # print(tokenizer.detokenize(full.data.tolist()))
+    print("---")
+    end = ds.get(0, offset=size - 10)
+    print(end)
+    # print(tokenizer.detokenize(end.data.tolist()))
+
+    start = ds.get(0, length=10)
+    print(start)
+    # print(tokenizer.detokenize(start.data.tolist()))
+
+    part = ds.get(0, offset=2, length=8)
+    print(part)
+    # print(tokenizer.detokenize(part.data.tolist()))
+
+# def test_albert_dataset(args):
+#     # tokenizer = FullBertTokenizer(args.vocab, do_lower_case=True)
+#     # idataset = indexed_dataset.make_dataset(args.data, args.dataset_impl)
+#     # ds = AlbertDataset(idataset, tokenizer)
+#     ds = AlbertDataset.from_paths(args.vocab, args.data, args.dataset_impl,
+#                                   args.epochs, args.max_num_samples,
+#                                   args.masked_lm_prob, args.seq_length,
+#                                   args.short_seq_prob, args.seed)
+#     truncated = 0
+#     total = 0
+#     for i, s in enumerate(ds):
+#         ids = s['text']
+#         tokens = ds.tokenizer.convert_ids_to_tokens(ids)
+#         print(tokens)
+#         if i >= args.count-1:
+#             exit()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, help='prefix to data files')
+    parser.add_argument('--dataset-impl', type=str, default='infer',
+                        choices=['lazy', 'cached', 'mmap', 'infer'])
+    parser.add_argument('--count', type=int, default=10,
+                        help='Number of samples/documents to print')
+
+    group = parser.add_argument_group(title='tokenizer')
+    group.add_argument('--tokenizer-type', type=str, required=True,
+                       choices=['BertWordPieceLowerCase',
+                                'GPT2BPETokenizer'],
+                       help='What type of tokenizer to use.')
+    group.add_argument('--vocab-file', type=str, default=None,
+                       help='Path to the vocab file')
+    group.add_argument('--merge-file', type=str, default=None,
+                       help='Path to the BPE merge file (if necessary).')
+
+    parser.add_argument('--epochs', type=int, default=5,
+                        help='Number of epochs to plan for')
+    parser.add_argument('--max-num-samples', type=int, default=None,
+                        help='Maximum number of samples to plan for')
+    parser.add_argument('--masked-lm-prob', type=float, default=0.15,
+                        help='probability of masking tokens')
+    parser.add_argument('--seq-length', type=int, default=512,
+                        help='maximum sequence length')
+    parser.add_argument('--short-seq-prob', type=float, default=0.1,
+                        help='probability of creating a short sequence')
+    parser.add_argument('--seed', type=int, default=1234,
+                        help='random seed')
+    args = parser.parse_args()
+    args.rank = 0
+    args.make_vocab_size_divisible_by = 128
+    args.tensor_model_parallel_size = 1
+
+    if args.dataset_impl == "infer":
+        args.dataset_impl = indexed_dataset.infer_dataset_impl(args.data)
+
+#    test_albert_dataset(args)
+    test_indexed_dataset_get(args)
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/sequence_parallel/data/datasets/test/test_preprocess_data.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+IMPL=cached
+python ../preprocess_data.py \
+       --input test_samples.json \
+       --vocab vocab.txt \
+       --dataset-impl ${IMPL} \
+       --output-prefix test_samples_${IMPL} \
+       --workers 1 \
+       --log-interval 2