mirror of https://github.com/hpcaitech/ColossalAI
570 lines
18 KiB
Python
570 lines
18 KiB
Python
# 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)
|