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Commit 898fcb94 authored by Mohammad's avatar Mohammad
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moved current gpt2 dataloader to old

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megatron/data/gpt2_dataset.py
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megatron/data/new_gpt2_dataset.py deleted 100644 → 0
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# 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 megatron import print_rank_0
from megatron import mpu
from megatron.data.bert_dataset import get_train_valid_test_split_
from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
train_valid_test_num_samples,
seq_length, seed, skip_warmup):
"""Build train, valid, and test datasets."""
# Indexed dataset.
indexed_dataset = get_indexed_dataset_(data_prefix,
data_impl,
skip_warmup)
total_num_of_documents = indexed_dataset.sizes.shape[0]
splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
# Print stats about the splits.
print_rank_0(' > dataset split:')
def print_split_stats(name, index):
print_rank_0(' {}:'.format(name))
print_rank_0(' document indices in [{}, {}) total of {} '
'documents'.format(splits[index], splits[index + 1],
splits[index + 1] - splits[index]))
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]:
documents = np.arange(start=splits[index], stop=splits[index+1],
step=1, dtype=np.int32)
dataset = GPT2Dataset(name, data_prefix,
documents, indexed_dataset,
train_valid_test_num_samples[index],
seq_length, seed)
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):
"""Build indexed dataset."""
print_rank_0(' > building dataset index ...')
start_time = time.time()
indexed_dataset = make_indexed_dataset(data_prefix,
data_impl,
skip_warmup)
print_rank_0(' > finished creating indexed dataset in {:4f} '
'seconds'.format(time.time() - start_time))
print_rank_0(' number of documents: {}'.format(
indexed_dataset.sizes.shape[0]))
return indexed_dataset
class GPT2Dataset(torch.utils.data.Dataset):
def __init__(self, name, data_prefix, documents, indexed_dataset,
num_samples, seq_length, seed):
self.name = name
self.indexed_dataset = indexed_dataset
# Checks
assert np.min(documents) >= 0
assert np.max(documents) < indexed_dataset.sizes.shape[0]
# Build index mappings.
self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
self.name, data_prefix, documents, self.indexed_dataset.sizes,
num_samples, seq_length, 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 {'text': np.array(sample, dtype=np.int64)}
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 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()
# Use C++ implementation for speed.
from megatron.data import helpers
assert doc_idx.dtype == np.int32
assert sizes.dtype == np.int32
sample_idx = helpers.build_sample_idx(sizes, doc_idx, seq_length,
num_epochs, tokens_per_epoch)
#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]))
print_rank_0(' total number of epochs: {}'.format(num_epochs))
return 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)
doc_idx = doc_idx.astype(np.int32)
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, 1245):
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)
'''
megatron/data/old_gpt2_dataset.py 0 → 100644
View file @ 898fcb94
# 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 dataset."""
import json
import os
import numpy as np
import torch
from torch.utils.data import Dataset
class GPT2Dataset(Dataset):
def __init__(self, data_path, sizes_filename, seq_length,
initial_seed, max_epochs=100):
# Input parameters.
self.data_path = data_path
self.sizes_filename = sizes_filename
self.seq_length = seq_length
self.initial_seed = initial_seed
self.max_epochs = max_epochs
# Shard stuff.
# Dictionary from shard nameto its size (number of element).
self.master_shard_size_dict = None
# Dictionary from shard name to modified size so it is
# divisible by self.seq_length.
self.shard_size_dict = None
# Long array (self.max_epochs * num-shards) populated
# randomly with shard names.
self.shards_name = None
# Start index of the data for a shard.
self.shards_start_index = None
self.build_shard_mappings_()
self.data_length = self.shards_start_index[-1]
# Data.
self.shards_data = [None]*self.shards_name.size
self.shards_sample_index = [None]*self.shards_name.size
def __len__(self):
return self.data_length
def __getitem__(self, idx):
# Find which shard we need.
shard_index = np.searchsorted(self.shards_start_index,
idx, side='right') - 1
# data index in the shard.
data_idx = idx - self.shards_start_index[shard_index]
# Load the shard if it is not in memory.
if self.shards_data[shard_index] is None:
print('global rank {} is building data for shard index {} ...'.
format(torch.distributed.get_rank(), shard_index))
self.build_dataset_(shard_index)
#assert self.shards_data[shard_index] is not None
# Start index.
start_index = self.shards_sample_index[shard_index][data_idx]
# Add one for label shift.
end_index = start_index + self.seq_length + 1
data = self.shards_data[shard_index][start_index:end_index]
return {'text': np.array(data, dtype=np.int64)}
def build_dataset_(self, shard_index):
# Garbage collect so we don't use a lot of memory.
# Leave the last one in case other threads have not catche up yet.
#for i in range(shard_index - 1):
for i in range(shard_index):
self.shards_data[i] = None
self.shards_sample_index[i] = None
# Read the shard.
filename = os.path.join(self.data_path, self.shards_name[shard_index])
print('loading {}'.format(filename))
data = np.load(filename, allow_pickle=True)
# Shuffle the data
rng = np.random.RandomState(self.initial_seed + shard_index)
rng.shuffle(data)
# Flatten.
data = np.hstack(data)
size = (data.shape[0] - 1) // self.seq_length
last_index = size * self.seq_length + 1
data = data[0:last_index]
self.shards_data[shard_index] = data
indices = np.arange(size) * self.seq_length
rng.shuffle(indices)
self.shards_sample_index[shard_index] = indices
def build_shard_mappings_(self):
# Load the sizes file.
sizes_filename = os.path.join(self.data_path, self.sizes_filename)
if torch.distributed.get_rank() == 0:
print(' > loading sizes from {}'.format(sizes_filename))
with open(sizes_filename, 'r') as f:
self.master_shard_size_dict = json.load(f)
if torch.distributed.get_rank() == 0:
print(' found {} shards'.format(len(self.master_shard_size_dict)))
# Adjust sizes to be a multiple of seq_length.
self.shard_size_dict = self.master_shard_size_dict.copy()
total_samples = 0
for shard in self.shard_size_dict:
size = self.shard_size_dict[shard]
size = ((size - 1) // self.seq_length) * self.seq_length
total_samples += size // self.seq_length
self.shard_size_dict[shard] = size
if torch.distributed.get_rank() == 0:
print(' found {} samples in the dataset'.format(total_samples))
# Build a list of shards.
shards_ = np.sort(np.array(list(self.shard_size_dict.keys())))
rng = np.random.RandomState(self.initial_seed)
self.shards_name = np.copy(shards_)
rng.shuffle(self.shards_name)
for i in range(1, self.max_epochs):
shards_c = np.copy(shards_)
rng.shuffle(shards_c)
self.shards_name = np.append(self.shards_name, shards_c)
# Build the global indexing.
self.shards_start_index = np.zeros(self.shards_name.size, dtype=np.int)
self.shards_start_index[0] = 0
for i in range(1, self.shards_name.size):
shard = str(self.shards_name[i-1])
size = self.shard_size_dict[shard]
self.shards_start_index[i] = self.shards_start_index[i-1] + \
size // self.seq_length
pretrain_gpt2.py
View file @ 898fcb94
......@@ -24,7 +24,7 @@ from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron import print_rank_0
from megatron.data.gpt2_dataset import GPT2Dataset
from megatron.data.gpt2_dataset import build_train_valid_test_datasets
from megatron.model import GPT2Model
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
......@@ -98,56 +98,53 @@ def forward_step(data_iterator, model):
return loss, {'lm loss': reduced_loss[0]}
def make_gpt2_dataloaders():
"""Build gpt2 dataloders."""
args = get_args()
# Input parameters.
input_data_sizes_file = args.input_data_sizes_file
seq_length = args.seq_length
initial_seed = args.seed
# Build the datasets.
def _build_dataset(name):
return GPT2Dataset(os.path.join(args.data_path, name),
args.input_data_sizes_file,
args.seq_length, args.seed)
train_ds = _build_dataset('train')
valid_ds = _build_dataset('valid')
test_ds = _build_dataset('test')
# Dataloaders
train = make_data_loader(train_ds)
valid = make_data_loader(valid_ds)
test = make_data_loader(test_ds)
args.do_train = False
args.do_valid = False
args.do_test = False
if train is not None:
args.do_train = True
if valid is not None:
args.do_valid = True
if test is not None:
args.do_test = True
return (train, valid, test)
def get_train_val_test_data():
"""Load the data on rank zero and boradcast number of tokens to all GPUS."""
args = get_args()
(train_data, val_data, test_data) = (None, None, None)
(train_data, valid_data, test_data) = (None, None, None)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
(train_data, val_data, test_data) = make_gpt2_dataloaders()
flags = torch.cuda.LongTensor([int(args.do_train),
int(args.do_valid),
int(args.do_test)])
print_rank_0('> building train, validation, and test datasets '
'for GPT2 ...')
data_parallel_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
global_batch_size = args.batch_size * data_parallel_size
# Number of train/valid/test samples.
train_iters = args.train_iters
eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
test_iters = args.eval_iters
train_val_test_num_samples = [train_iters * global_batch_size,
eval_iters * global_batch_size,
test_iters * global_batch_size]
print_rank_0(' > datasets target sizes (minimum size):')
print_rank_0(' train: {}'.format(train_val_test_num_samples[0]))
print_rank_0(' validation: {}'.format(train_val_test_num_samples[1]))
print_rank_0(' test: {}'.format(train_val_test_num_samples[2]))
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
data_prefix=args.data_path,
data_impl=args.data_impl,
splits_string=args.split,
train_valid_test_num_samples=train_val_test_num_samples,
seq_length=args.seq_length,
seed=args.seed,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating GPT2 datasets ...")
train_data = make_data_loader(train_ds)
valid_data = make_data_loader(valid_ds)
test_data = make_data_loader(test_ds)
do_train = train_data is not None and args.train_iters > 0
do_valid = valid_data is not None and args.eval_iters > 0
do_test = test_data is not None and args.eval_iters > 0
# Need to broadcast num_tokens and num_type_tokens.
flags = torch.cuda.LongTensor(
[int(do_train), int(do_valid), int(do_test)])
else:
flags = torch.cuda.LongTensor([0, 0, 0])
......@@ -159,7 +156,7 @@ def get_train_val_test_data():
args.do_valid = flags[1].item()
args.do_test = flags[2].item()
return train_data, val_data, test_data
return train_data, valid_data, test_data
if __name__ == "__main__":
......
pretrain_gpt2_old.py 0 → 100644
View file @ 898fcb94
# 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.
"""Pretrain GPT2"""
import os
import torch
from megatron import get_args
from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron import print_rank_0
from megatron.data.old_gpt2_dataset import GPT2Dataset
from megatron.model import GPT2Model
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import make_data_loader
from megatron.utils import reduce_losses
def model_provider():
"""Build the model."""
args = get_args()
print_rank_0('building GPT2 model ...')
model = GPT2Model(num_tokentypes=0, parallel_output=True)
return model
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss,
args.fp16)
return tokens, labels, loss_mask, attention_mask, position_ids
def forward_step(data_iterator, model):
"""Forward step."""
timers = get_timers()
# Get the batch.
timers('batch generator').start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch generator').stop()
# Forward model.
output = model(tokens, position_ids, attention_mask)
losses = mpu.vocab_parallel_cross_entropy(output.contiguous().float(),
labels)
loss_mask = loss_mask.view(-1)
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
reduced_loss = reduce_losses([loss])
return loss, {'lm loss': reduced_loss[0]}
def make_gpt2_dataloaders():
"""Build gpt2 dataloders."""
args = get_args()
# Input parameters.
input_data_sizes_file = args.input_data_sizes_file
seq_length = args.seq_length
initial_seed = args.seed
# Build the datasets.
def _build_dataset(name):
return GPT2Dataset(os.path.join(args.data_path, name),
args.input_data_sizes_file,
args.seq_length, args.seed)
train_ds = _build_dataset('train')
valid_ds = _build_dataset('valid')
test_ds = _build_dataset('test')
# Dataloaders
train = make_data_loader(train_ds)
valid = make_data_loader(valid_ds)
test = make_data_loader(test_ds)
args.do_train = False
args.do_valid = False
args.do_test = False
if train is not None:
args.do_train = True
if valid is not None:
args.do_valid = True
if test is not None:
args.do_test = True
return (train, valid, test)
def get_train_val_test_data():
"""Load the data on rank zero and boradcast number of tokens to all GPUS."""
args = get_args()
(train_data, val_data, test_data) = (None, None, None)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
(train_data, val_data, test_data) = make_gpt2_dataloaders()
flags = torch.cuda.LongTensor([int(args.do_train),
int(args.do_valid),
int(args.do_test)])
else:
flags = torch.cuda.LongTensor([0, 0, 0])
# Broadcast num tokens.
torch.distributed.broadcast(flags,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
args.do_train = flags[0].item()
args.do_valid = flags[1].item()
args.do_test = flags[2].item()
return train_data, val_data, test_data
if __name__ == "__main__":
pretrain(get_train_val_test_data, model_provider, forward_step,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})
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