added new gpt2 dataloder

megatron/data/new_gpt2_dataset.py

+# coding=utf-8
+# Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""GPT2 Style dataset."""
+
+import os
+import time
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+def print_rank_0(message):
+    print(message)
+
+
+class GPT2Dataset(Dataset):
+
+    def __init__(self, name, data_prefix,
+                 documents, indexed_dataset,
+                 num_samples, seq_length, seed):
+
+        self.name = name
+        self.data_prefix = data_prefix
+        self.num_samples = num_samples
+        self.seq_length = seq_length
+        self.seed = seed
+        self.indexed_dataset = indexed_dataset
+
+        # Checks
+        assert np.min(documents) >= 0
+        assert np.max(documents) < indexed_dataset.sizes.shape[0]
+
+        # Build index mappings.
+        self.num_epochs, self.doc_idx, self.sample_idx, self.shuffle_idx \
+            = _build_index_mappings(self.name, self.data_prefix, documents,
+                                    self.indexed_dataset.sizes,
+                                    self.num_samples, self.seq_length,
+                                    self.seed)
+
+
+    def __len__(self):
+        return self.sample_idx.shape[0]
+
+
+    def __getitem__(self, idx):
+        # Get the shuffled index.
+        idx = self.shuffle_idx[idx]
+        # Start and end documents and offsets.
+        doc_index_f = self.sample_idx[idx][0]
+        doc_index_l = self.sample_idx[idx+1][0]
+        offset_f = self.sample_idx[idx][1]
+        offset_l = self.sample_idx[idx+1][1]
+        # If we are within the same document, just extract the chunk.
+        if doc_index_f == doc_index_l:
+            sample = self.indexed_dataset.get(self.doc_idx[doc_index_f],
+                                              offset=offset_f,
+                                              length=offset_l - offset_f + 1)
+        else:
+            # Otherwise, get the rest of the initial document.
+            sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f],
+                                                    offset=offset_f)]
+            # Loop over all in between documents and add the entire document.
+            for i in range(doc_index_f+1, doc_index_l):
+                sample_list.append(self.indexed_dataset.get(self.doc_idx[i]))
+            # And finally add the relevant portion of last document.
+            sample_list.append(self.indexed_dataset.get(
+                self.doc_idx[doc_index_l],
+                length=offset_l+1))
+            sample = np.concatenate(sample_list)
+
+        return sample
+
+
+
+def _build_index_mappings(name, data_prefix, documents, sizes,
+                          num_samples, seq_length, seed):
+    """doc-idx, sample-idx, and shuffle-idx."""
+    # Number of tokens in each epoch and number of required epochs.
+    tokens_per_epoch = _num_tokens(documents, sizes)
+    num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples)
+    # rng state
+    np_rng = np.random.RandomState(seed=seed)
+    
+    # Filename of the index mappings.
+    _filename = data_prefix
+    _filename += '_{}_indexmap'.format(name)
+    _filename += '_{}ns'.format(num_samples)
+    _filename += '_{}sl'.format(seq_length)
+    _filename += '_{}s'.format(seed)
+    doc_idx_filename = _filename + '_doc_idx.npy'
+    sample_idx_filename = _filename + '_sample_idx.npy'
+    shuffle_idx_filename = _filename + '_shuffle_idx.npy'
+
+    # Build the indexed mapping if not exist.
+    if True: #torch.distributed.get_rank() == 0:
+        if (not os.path.isfile(doc_idx_filename)) or \
+           (not os.path.isfile(sample_idx_filename)) or \
+           (not os.path.isfile(shuffle_idx_filename)):
+            
+            print_rank_0(' > WARNING: could not find index map files, building '
+                         'the indices on rank 0 ...')
+            # doc-idx.
+            start_time = time.time()
+            doc_idx = _build_doc_idx(documents, num_epochs, np_rng)
+            np.save(doc_idx_filename, doc_idx, allow_pickle=True)
+            print_rank_0(' > elasped time to build and save doc-idx mapping '
+                         '(seconds): {:4f}'.format(time.time() - start_time))
+            # sample-idx.
+            start_time = time.time()
+            sample_idx = _build_sample_idx(sizes, doc_idx, seq_length,
+                                           num_epochs, tokens_per_epoch)
+            np.save(sample_idx_filename, sample_idx, allow_pickle=True)
+            print_rank_0(' > elasped time to build and save sample-idx mapping '
+                         '(seconds): {:4f}'.format(time.time() - start_time))
+            # shuffle-idx.
+            start_time = time.time()
+            shuffle_idx = _build_shuffle_idx(sample_idx.shape[0], np_rng)
+            np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True)
+            print_rank_0(' > elasped time to build and save shuffle-idx mapping'
+                         ' (seconds): {:4f}'.format(time.time() - start_time))
+
+    # This should be a barrier but nccl barrier assumes
+    # device_index=rank which is not the case for model
+    # parallel case
+    counts = torch.cuda.LongTensor([1])
+    #torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
+    #assert counts[0].item() == torch.distributed.get_world_size(
+    #    group=mpu.get_data_parallel_group())
+
+    # Load mappings.
+    start_time = time.time()
+    print_rank_0(' > loading doc-idx mapping from {}'.format(
+        doc_idx_filename))
+    doc_idx = np.load(doc_idx_filename, allow_pickle=True)
+    print_rank_0(' > loading sample-idx mapping from {}'.format(
+        sample_idx_filename))
+    sample_idx = np.load(sample_idx_filename, allow_pickle=True)
+    print_rank_0(' > loading shuffle-idx mapping from {}'.format(
+        shuffle_idx_filename))
+    shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True)
+    print_rank_0('    loaded indexed file in {:3.3f} seconds'.format(
+        time.time() - start_time))
+    print_rank_0('    total number of samples: {}'.format(
+        sample_idx.shape[0]))
+
+    return num_epochs, doc_idx, sample_idx, shuffle_idx
+
+
+def _num_tokens(documents, sizes):
+    """Total number of tokens in the dataset."""
+    return np.sum(sizes[documents])
+
+
+def _num_epochs(tokens_per_epoch, seq_length, num_samples):
+    """Based on number of samples and sequence lenght, calculate how many
+    epochs will be needed."""
+    num_epochs = 0
+    total_tokens = 0
+    while True:
+        num_epochs += 1
+        total_tokens += tokens_per_epoch
+        # -1 is because we need to retrieve seq_length + 1 token each time
+        # but the last token will overlap with the first token of the next
+        # sample except for the last sample.
+        if ((total_tokens - 1) // seq_length) >= num_samples:
+            return num_epochs
+
+
+def _build_doc_idx(documents, num_epochs, np_rng):
+    """Build an array with length = number-of-epochs * number-of-dcuments.
+    Each index is mapped to a corresponding document."""
+    doc_idx = np.mgrid[0:num_epochs, 0:len(documents)][1]
+    doc_idx[:] = documents
+    doc_idx = doc_idx.reshape(-1)
+    np_rng.shuffle(doc_idx)
+    return doc_idx
+
+
+def _build_sample_idx(sizes, doc_idx, seq_length,
+                      num_epochs, tokens_per_epoch):
+    """Sample index mapping is a 2D array with sizes
+    [number-of-samples + 1, 2] where [..., 0] contains
+    the index into `doc_idx` and [..., 0] is the
+    starting offset in that document."""
+
+    # Total number of samples. For -1 see comments in `_num_epochs`.
+    num_samples = (num_epochs * tokens_per_epoch - 1) // seq_length
+    sample_idx = np.zeros([num_samples + 1, 2], dtype=np.int32)
+
+    # Index into sample_idx.
+    sample_index = 0
+    # Index into doc_idx.
+    doc_idx_index = 0
+    # Begining offset for each document.
+    doc_offset = 0
+    # Start with first document and no offset.
+    sample_idx[sample_index][0] = doc_idx_index
+    sample_idx[sample_index][1] = doc_offset
+    sample_index += 1
+    while sample_index <= num_samples:
+        # Start with a fresh sequence.
+        remaining_seq_length = seq_length + 1
+        while remaining_seq_length != 0:
+            # Get the document length.
+            doc_id = doc_idx[doc_idx_index]
+            doc_length = sizes[doc_id] - doc_offset
+            # And add it to the current sequence.
+            remaining_seq_length -= doc_length
+            # If we have more than a full sequence, adjust offset and set
+            # remaining length to zero so we return from the while loop.
+            # Note that -1 here is for the same reason we have -1 in
+            # `_num_epochs` calculations.
+            if remaining_seq_length <= 0:
+                doc_offset += (remaining_seq_length + doc_length - 1)
+                remaining_seq_length = 0
+            else:
+                # Otherwise, start from the begining of the next document.
+                doc_idx_index += 1
+                doc_offset = 0
+        # Record the sequence.
+        sample_idx[sample_index][0] = doc_idx_index
+        sample_idx[sample_index][1] = doc_offset
+        sample_index += 1
+
+    return sample_idx
+
+
+def _build_shuffle_idx(size, np_rng):
+    """Build the range [0, size) and shuffle."""
+    dtype_ = np.uint32
+    if size >= (np.iinfo(np.uint32).max - 1):
+        dtype_ = np.int64
+    shuffle_idx = np.arange(start=0, stop=size, step=1, dtype=dtype_)
+    #np_rng.shuffle(shuffle_idx)
+    return shuffle_idx
+
+
+
+class IndexedDataset:
+
+    def __init__(self, num_docs, min_doc_length, max_doc_length, seq_length):
+
+        self.seq_length = seq_length
+        assert min_doc_length > 0
+
+        self.tokens = []
+        self.sizes = np.zeros(num_docs, dtype=np.int32)
+        for i in range(num_docs):
+            size = np.random.randint(low=min_doc_length, high=max_doc_length,
+                                     size=1, dtype=np.uint32)[0]
+            tokens_ = np.random.randint(low=1, high=60000,
+                                        size=size, dtype=np.uint32)
+            tokens_[-1] = 0
+            self.sizes[i] = size
+            self.tokens.append(tokens_)
+
+        self.tokens_flat = None
+
+    def get(self, doc_idx, offset=None, length=None):
+        if length is None:
+            if offset is None:
+                return self.tokens[doc_idx]
+            else:
+                return self.tokens[doc_idx][offset:]
+        if offset is None:
+            return self.tokens[doc_idx][0:length]
+        return self.tokens[doc_idx][offset:(offset+length)]
+
+    def get_sample(self, index):
+        start = index * self.seq_length
+        end = start + self.seq_length + 1
+        return self.tokens_flat[start:end]
+
+    def build_tokens_flat(self, doc_idx):
+        self.tokens_flat = np.concatenate([self.tokens[i] for i in doc_idx])
+
+
+def test(seed, data_prefix, seq_length, num_samples,
+         num_docs, min_doc_length, max_doc_length):
+
+    print('testing for seed: {}, seq-length: {}, num-samples: {}, '
+          'num-docs: {}, min-doc-length: {}, max-doc-length: {}'.format(
+              seed, seq_length, num_samples,
+              num_docs, min_doc_length, max_doc_length))
+    np.random.seed(seed)
+
+    indexed_dataset = IndexedDataset(num_docs, min_doc_length,
+                                     max_doc_length, seq_length)
+    indices = np.random.randint(indexed_dataset.sizes.shape[0]-2, size=2)
+    documents = np.arange(np.min(indices), np.max(indices)+1)
+    dataset = GPT2Dataset('gpt2', data_prefix, documents, indexed_dataset,
+                          num_samples, seq_length, seed)
+
+    print(' > number of epochs:', dataset.num_epochs)
+    indexed_dataset.build_tokens_flat(dataset.doc_idx)
+
+    for idx in range(num_samples):
+        a = dataset[idx]
+        b = indexed_dataset.get_sample(idx)
+        assert np.sum(a - b) == 0
+
+    print('passed')
+    
+
+if __name__ == '__main__':
+
+    print('gpt2 dataset ...')
+
+
+    import random
+    data_prefix = 'junk/'
+    for seed in range(1234, 1240):
+        random.seed(seed)
+        num_docs = random.randint(1, 999)
+        min_doc_length = random.randint(1, 99)
+        max_doc_length = random.randint(100, 9999)
+        num_samples = random.randint(num_docs, 100*num_docs)
+        seq_length = random.randint(min_doc_length, max_doc_length)
+
+        test(seed, data_prefix, seq_length, num_samples,
+             num_docs, min_doc_length, max_doc_length)
+    exit()
+
+    '''
+
+    num_docs = 5
+    min_doc_length = 2
+    max_doc_length = 10
+    num_samples = 9
+    seq_length = 4
+    seed = 1234
+    
+    np.random.seed(seed)
+    indexed_dataset = IndexedDataset(num_docs, min_doc_length,
+                                     max_doc_length, seq_length)
+    print('> indexed dataset:')
+    for s in indexed_dataset.tokens:
+        print('   {}'.format(s))
+
+    documents = np.array([1,2,3], dtype=np.int32)
+
+    dataset = GPT2Dataset('gpt2', documents, indexed_dataset,
+                          num_samples, seq_length, seed)
+    indexed_dataset.build_tokens_flat(dataset.doc_idx)
+
+    print(indexed_dataset.get_sample(6))
+    print(dataset[6])
+    '''    
+    '''
+    myds = MyDataset(ds, num_samples, seq_length)
+    num_docs = myds._num_docs()
+    print('> number of document: {}'.format(num_docs))
+    tokens_per_epoch = myds._num_tokens()
+    print('> number of tokens: {}'.format(tokens_per_epoch))
+    num_epochs = myds._num_epochs(tokens_per_epoch)
+    print('> number of epochs: {}'.format(num_epochs))
+    doc_idx = myds._build_doc_idx(num_docs, num_epochs)
+    print('> doc_idx: {}'.format(doc_idx))
+
+    ds.build_tokens_flat(doc_idx)
+    sample_idx =myds._build_sample_idx(num_epochs, tokens_per_epoch, doc_idx)
+
+    for s in sample_idx:
+        print(s)
+        
+    print(ds.tokens_flat)
+    print(myds.get_sample(8))
+    print(ds.get_sample(8))
+    '''
+    
+