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# Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
# 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 hashlib
import numpy as np
import torch
from torch.utils.data import IterableDataset, get_worker_info
from litgpt.data_scheduler_utils import DataSchedulerTracker
from typing import Optional, Sequence, Any
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,
data_id=None,
return_data_id=False,
):
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
self._ds_fingerprint = None
self._data_id = data_id # This is human readble, correps to the full file list.
if return_data_id:
raise NotImplementedError("return_data_id is not implemented for PackedDataset")
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
total_num_files = len(self._filenames)
max_num_files = total_num_files // num_shards * num_shards
filenames = self._filenames[shard_id:max_num_files:num_shards]
self._ds_fingerprint = hashlib.shake_128(str(filenames).encode()).hexdigest(
4
) # This is not human readable, corresp to the file list _this_ process is using.
print(
f"Rank {self._process_rank}/{self._num_processes}, worker {worker_id} has {len(filenames)}/{total_num_files} files | "
f"identifier={self._data_id}:{self._ds_fingerprint}"
)
return PackedDatasetIterator(
filenames=filenames,
n_chunks=self._n_chunks,
block_size=self._block_size,
seed=self._seed,
shuffle=self._shuffle,
wrap=self._wrap,
data_id=self._data_id,
fingerprint=self._ds_fingerprint,
worker_id=worker_id,
process_rank=self._process_rank,
num_processes=self._num_processes,
)
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 = []
self._total_tokens_exact = 0
self._filler_sep_tokens = 0
def _write_chunk(self, skip_write=False):
filename = f"{self._prefix}_{self._counter:010d}.bin"
filename = os.path.join(self._outdir, filename)
# right before we write, we can compute the number of tokens being written
# and update the total number of tokens
last_non_sep_idx = np.argwhere((self._arr != self._sep_token)).squeeze()[-1]
tokens_in_chunk = last_non_sep_idx + 1 # +1 for zero-indexing
if skip_write:
self._arr.fill(self._sep_token)
self._idx = 0
return tokens_in_chunk # amount we are skipping
self._filler_sep_tokens += self._chunk_size - tokens_in_chunk
self._total_tokens_exact += tokens_in_chunk
# print(
# f"Chunk written with {tokens_in_chunk} tokens and {self._filler_sep_tokens} filler sep tokens"
# )
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_remainder(self):
self._write_chunk()
def skip_write_remainder(self):
return self._write_chunk(skip_write=True)
BlockIdxType = Sequence[int] | np.ndarray[Any, np.dtype[np.int64]]
class PackedDatasetIterator:
def __init__(
self,
filenames,
n_chunks,
block_size,
seed,
shuffle,
wrap,
data_id=None,
fingerprint=None,
worker_id=None,
process_rank=None,
num_processes=None,
):
self._data_id = data_id
self._ds_fingerprint = fingerprint
self._worker_id = worker_id
self._process_rank = process_rank
self._num_processes = num_processes
self._seed = seed
self._shuffle = shuffle
self._rng = np.random.default_rng(seed) # if shuffle else 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: Optional[np.dtype] = None
self._block_size = block_size
# self._n_blocks: Optional[int] = None
self._mmaps = []
self._buffers = []
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 fast_forward(self, block_idx):
"""Stub for eventual fast-forward"""
pass
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
# only print on the first 3 times we load chunks
if (self._file_idx * self._n_chunks) < (3 * self._n_chunks):
print(
f"({self._process_rank}/{self._num_processes}) will load {self._n_chunks} chunks: {self._filenames[self._file_idx:self._file_idx+self._n_chunks]}"
)
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)) # type: ignore
self._file_idx += self._n_chunks
n_all_blocks = self._n_chunks * self._n_blocks
self._block_idxs: BlockIdxType = self._rng.permutation(n_all_blocks) if self._shuffle else range(n_all_blocks)
# only print on the first 3 times we load chunks
if (self._file_idx * self._n_chunks) < (3 * self._n_chunks):
print(f"({self._process_rank}/{self._num_processes}) block read order: {self._block_idxs}")
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, data_scheduler_tracker=None, data_telemetry=False):
self._seed = seed
self._datasets = datasets
self._data_scheduler_tracker = data_scheduler_tracker
self._data_telemetry = data_telemetry
n_datasets = len(datasets)
if data_scheduler_tracker is None:
self._data_scheduler_tracker = DataSchedulerTracker([1 / n_datasets] * n_datasets)
def __iter__(self):
return CombinedDatasetIterator(self._datasets, self._seed, self._data_scheduler_tracker, self._data_telemetry)
class CombinedDatasetIterator:
def __init__(self, datasets, seed, data_scheduler_tracker, data_telemetry=False):
self._datasets = datasets
self._datasets_iterators = [iter(el) for el in datasets]
self._num_datasets = len(datasets)
self._data_scheduler_tracker = data_scheduler_tracker
self._rng = random.Random(seed)
self._iter_ct = 0
self._data_telemetry = data_telemetry
def __next__(self):
if sum(self._data_scheduler_tracker.weights) == 0:
if self._data_scheduler_tracker.base_id is not None:
# if all buckets have 0 weight, return the base dataset
self._data_scheduler_tracker.weights[self._data_scheduler_tracker.base_id] = 100
return self.__next__()
else:
# if all buckets have 0 weight and no base dataset, return empty
return torch.tensor([])
(dataset_idx,) = self._rng.choices(range(self._num_datasets), weights=self._data_scheduler_tracker.weights, k=1)
dataset = self._datasets_iterators[dataset_idx]
try:
curr_data = next(dataset)
self._data_scheduler_tracker.sample_count[dataset_idx] += 1
self._iter_ct += 1
# this is the very beginning of data telemetry
if self._data_telemetry and self._iter_ct < 5:
print(
f"Draw result i={self._iter_ct} for rank={dataset._process_rank}/{dataset._num_processes}, "
f"worker={dataset._worker_id} | {dataset._data_id}:{dataset._ds_fingerprint}"
)
elif self._data_telemetry and self._iter_ct == 5:
print("Data telemetry off ...")
return curr_data
except Exception as e: # which one? yea this is a problem.
self._data_scheduler_tracker.epoch_count[dataset_idx] += 1
self._datasets_iterators[dataset_idx] = iter(self._datasets[dataset_idx])
if (self._data_scheduler_tracker.max_epochs is not None) and (
self._data_scheduler_tracker.max_epochs[dataset_idx]
<= self._data_scheduler_tracker.epoch_count[dataset_idx]
):
# if exceeds max epoch
self._data_scheduler_tracker.weights[dataset_idx] = 0
return self.__next__()
else:
dataset = self._datasets_iterators[dataset_idx]
curr_data = next(dataset)
self._data_scheduler_tracker.sample_count[dataset_idx] += 1
return curr_data
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