File size: 3,740 Bytes
ee3e701 | 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 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
A .bin file corresponds to a Dataset instance here.
"""
import json
import mmap
import os
import threading
from pathlib import Path
import numpy as np
import torch
class JsonlDataset(torch.utils.data.Dataset):
"""
JSONL format is expected to roughly follow that of The Pile.
One-line-per-document of the form:
```
{
"tokens": List[int],
}
```
Note that only the "tokens" key is used.
"""
def __init__(self, path: str, dataset_type_id: int = 0, min_length=50):
self.path = path
self.threadlocal = threading.local()
resolved_path = Path(path).resolve()
self.resolved_path = resolved_path
self.meta = Path(f"{resolved_path}.meta")
self.type_id = dataset_type_id
# only build the cache in on the primary worker to prevent overloading nfs
assert os.path.exists(self.meta), f"The cache file:{self.meta} is not found for file:{self.path}"
try:
with open(self.meta, "rb") as f:
meta = np.load(f)
except Exception as e:
print(f"Cannot load file {self.meta}...")
raise e
self.offsets = meta[:, 0]
self.lengths = meta[:, -1]
if min_length > 0:
mask = self.lengths >= min_length
self.old_lengths = self.lengths.copy()
self.old_length = len(self.offsets)
self.offsets = self.offsets[mask]
self.lengths = self.lengths[mask]
def __getitem__(self, idx):
f = self._get_mmap()
position = self.offsets[idx]
f.seek(position)
item = f.readline().decode("utf-8")
try:
item = json.loads(item)
item["length"] = len(item["tokens"]) # add a length info
item["type_id"] = self.type_id
except Exception as err:
raise json.decoder.JSONDecodeError(
doc=self.path,
pos=position,
msg=(
f"Error while loading JSONL line in file {self.path} at byte "
f"{position}. Contents of line:\n{item}\n{err}"
),
)
return item
def get_dataset_name(self):
return str(self.resolved_path)
def _get_mmap(self):
if not hasattr(self.threadlocal, "handles"):
with open(self.path, "rb") as f:
mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
self.threadlocal.handles = [f, mm]
if self.path.endswith(".gz") or self.path.endswith(".bz") or self.path.endswith(".bz2"):
raise NotImplementedError(
"Compressed files are not supported because .seek() would require "
"rereading the entire file, making performance too slow."
)
return self.threadlocal.handles[-1]
def __setstate__(self, state):
self.__dict__ = state
self.threadlocal = threading.local()
def __getstate__(self):
d = {}
for i, v in self.__dict__.items():
if i != "threadlocal":
d[i] = v
return d
def __del__(self):
if hasattr(self.threadlocal, "handles"):
# cleanup files we opened on initialization
while self.threadlocal.handles:
self.threadlocal.handles.pop().close()
@staticmethod
def exists(path):
return os.path.exists(path)
def __len__(self):
# Virtual length of the dataset depends on the epoch number if the number of documents
# is not perfectly divisible by the data_subshard_count
return len(self.offsets)
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