File size: 7,893 Bytes
60616b8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 |
# Very loosely inspired by indexed_dataset in Fairseq, Megatron
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/data/indexed_dataset.py
import os
import random
import struct
import numpy as np
import torch
from torch.utils.data import IterableDataset, get_worker_info
dtypes = {1: np.uint8, 2: np.int8, 3: np.int16, 4: np.int32, 5: np.int64, 6: np.float32, 7: np.float64, 8: np.uint16}
def code(dtype):
for k in dtypes:
if dtypes[k] == dtype:
return k
raise ValueError(dtype)
HDR_MAGIC = b"LITPKDS"
HDR_SIZE = 24 # bytes
class PackedDataset(IterableDataset):
def __init__(
self, filenames, n_chunks, block_size, seed=12345, shuffle=True, wrap=False, num_processes=1, process_rank=0
):
self._filenames = filenames
self._n_chunks = n_chunks
self._block_size = block_size
self._seed = seed
self._shuffle = shuffle
self._wrap = wrap
self._num_processes = num_processes
self._process_rank = process_rank
def __iter__(self):
worker_info = get_worker_info()
num_workers = worker_info.num_workers if worker_info is not None else 1
worker_id = worker_info.id if worker_info is not None else 0
num_shards = num_workers * self._num_processes
shard_id = self._process_rank * num_workers + worker_id
max_num_files = len(self._filenames) // num_shards * num_shards
filenames = self._filenames[shard_id:max_num_files:num_shards]
return PackedDatasetIterator(
filenames=filenames,
n_chunks=self._n_chunks,
block_size=self._block_size,
seed=self._seed,
shuffle=self._shuffle,
wrap=self._wrap,
)
class PackedDatasetBuilder(object):
def __init__(self, outdir, prefix, chunk_size, sep_token, dtype="auto", vocab_size=None):
if dtype == "auto":
if vocab_size is None:
raise ValueError("vocab_size cannot be None when dtype='auto'")
if vocab_size is not None and vocab_size < 65500:
self._dtype = np.uint16
else:
self._dtype = np.int32
else:
self._dtype = dtype
self._counter = 0
self._chunk_size = chunk_size
self._outdir = outdir
self._prefix = prefix
self._sep_token = sep_token
self._arr = np.zeros(self._chunk_size, dtype=self._dtype)
self._arr.fill(self._sep_token)
self._idx = 0
self._version = 1
self._filenames = []
def _write_chunk(self):
filename = f"{self._prefix}_{self._counter:010d}.bin"
filename = os.path.join(self._outdir, filename)
with open(filename, "wb") as f:
f.write(HDR_MAGIC)
f.write(struct.pack("<Q", self._version))
f.write(struct.pack("<B", code(self._dtype)))
f.write(struct.pack("<Q", self._chunk_size))
f.write(self._arr.tobytes(order="C"))
self._filenames.append(filename)
self._counter += 1
self._arr.fill(self._sep_token)
self._idx = 0
@property
def dtype(self):
return self._dtype
@property
def filenames(self):
return self._filenames.copy()
def add_array(self, arr):
while self._idx + arr.shape[0] > self._chunk_size:
part_len = self._chunk_size - self._idx
self._arr[self._idx : self._idx + part_len] = arr[:part_len]
self._write_chunk()
arr = arr[part_len:]
arr_len = arr.shape[0]
self._arr[self._idx : self._idx + arr_len] = arr
self._idx += arr_len
def write_reminder(self):
self._write_chunk()
class PackedDatasetIterator:
def __init__(self, filenames, n_chunks, block_size, seed, shuffle, wrap):
self._seed = seed
self._shuffle = shuffle
self._rng = np.random.default_rng(seed) if shuffle else None
self._block_idxs = None
self._wrap = wrap
# TODO: instead of filenames, we could have a single text stream
# (or text file) with the sequence of all files to be
# fetched/loaded.
self._filenames = filenames
self._file_idx = 0
self._n_chunks = n_chunks
self._dtype = None
self._block_size = block_size
self._n_blocks = None
self._mmaps = []
self._buffers = []
self._block_idxs = []
self._curr_idx = 0
self._load_n_chunks()
def _read_header(self, path):
with open(path, "rb") as f:
magic = f.read(len(HDR_MAGIC))
assert magic == HDR_MAGIC, "File doesn't match expected format."
version = struct.unpack("<Q", f.read(8))
assert version == (1,)
(dtype_code,) = struct.unpack("<B", f.read(1))
dtype = dtypes[dtype_code]
(chunk_size,) = struct.unpack("<Q", f.read(8))
return dtype, chunk_size
def _close_mmaps(self):
for mmap in self._mmaps:
mmap._mmap.close()
def _load_n_chunks(self):
self._close_mmaps()
self._mmaps = []
self._buffers = []
if self._n_chunks > len(self._filenames[self._file_idx :]):
if not self._wrap:
raise StopIteration
self._file_idx = 0
for i in range(self._n_chunks):
filename = self._filenames[self._file_idx + i]
if self._dtype is None:
self._dtype, self._chunk_size = self._read_header(filename)
self._n_blocks = self._chunk_size // self._block_size
# TODO: check header matches with previous files
mmap = np.memmap(filename, mode="r", order="C", offset=HDR_SIZE)
self._mmaps.append(mmap)
self._buffers.append(memoryview(mmap))
self._file_idx += self._n_chunks
n_all_blocks = self._n_chunks * self._n_blocks
self._block_idxs = self._rng.permutation(n_all_blocks) if self._shuffle else range(n_all_blocks)
self._curr_idx = 0
def __del__(self):
self._close_mmaps()
del self._mmaps
del self._buffers
def __iter__(self):
return self
def __next__(self):
if self._curr_idx >= len(self._block_idxs):
self._load_n_chunks()
# TODO: trigger fetching next next n_chunks if remote
block_idx = self._block_idxs[self._curr_idx]
chunk_id = block_idx // self._n_blocks
buffer = self._buffers[chunk_id]
elem_id = (block_idx % self._n_blocks) * self._block_size
offset = np.dtype(self._dtype).itemsize * elem_id
arr = np.frombuffer(buffer, dtype=self._dtype, count=self._block_size, offset=offset)
self._curr_idx += 1
return torch.from_numpy(arr.astype(np.int64))
class CombinedDataset(IterableDataset):
def __init__(self, datasets, seed, weights=None):
self._seed = seed
self._datasets = datasets
self._weights = weights
n_datasets = len(datasets)
if weights is None:
self._weights = [1 / n_datasets] * n_datasets
def __iter__(self):
return CombinedDatasetIterator(self._datasets, self._seed, self._weights)
class CombinedDatasetIterator:
def __init__(self, datasets, seed, weights):
self._datasets = [iter(el) for el in datasets]
self._weights = weights
self._rng = random.Random(seed)
def __next__(self):
(dataset,) = self._rng.choices(self._datasets, weights=self._weights, k=1)
return next(dataset) |