Spaces:
Running on Zero
Running on Zero
File size: 4,881 Bytes
8125804 | 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 | import os
import json
from pathlib import Path
import torch
class ByteCorpusDataset:
def __init__(
self,
data: bytes,
split: str = "train",
seq_len: int = 256,
device: str = "cpu",
) -> None:
assert split in ("train", "val")
self.seq_len = seq_len
self.device = device
tensor = torch.tensor(list(data), dtype=torch.long)
n = int(0.9 * len(tensor))
self.data = tensor[:n] if split == "train" else tensor[n:]
@property
def vocab_size(self) -> int:
return 256
def get_batch(self, batch_size: int) -> tuple[torch.Tensor, torch.Tensor]:
max_start = len(self.data) - self.seq_len - 1
if max_start <= 0:
raise ValueError(
f"Corpus too small for seq_len={self.seq_len}. "
f"Need more than {self.seq_len + 1} bytes, got {len(self.data)}."
)
starts = torch.randint(0, max_start, (batch_size,))
x = torch.stack([self.data[s: s + self.seq_len] for s in starts])
y = torch.stack([self.data[s + 1: s + self.seq_len + 1] for s in starts])
return x.to(self.device), y.to(self.device)
def __len__(self) -> int:
return len(self.data)
def _normalize_repo_name(repo_id: str) -> str:
return repo_id.replace("/", "__").replace("-", "_")
def _stream_the_stack_text(
repo_id: str,
lang: str,
target_bytes: int,
) -> bytes:
try:
from huggingface_hub import hf_hub_download
json_path = hf_hub_download(repo_id, f"data/{lang}/data.json", repo_type="dataset")
chunks: list[bytes] = []
total = 0
with open(json_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
sample = json.loads(line)
text = sample.get("content") or ""
if not isinstance(text, str) or not text.strip():
continue
encoded = text.encode("utf-8", errors="ignore") + b"\n\n"
chunks.append(encoded)
total += len(encoded)
if total >= target_bytes:
break
if total > 0:
return b"".join(chunks)
except Exception:
pass
try:
from datasets import load_dataset
except ImportError as exc:
raise ImportError(
"datasets package is required for The Stack loading. "
"Install with `pip install datasets`."
) from exc
try:
ds = load_dataset(repo_id, lang, split="train", streaming=True)
except Exception:
data_dir = f"data/{lang}"
try:
ds = load_dataset(repo_id, data_dir=data_dir, split="train", streaming=True)
except Exception as exc:
raise RuntimeError(
f"Failed to load repo={repo_id} lang={lang}. "
"If this is a gated dataset, accept the Hugging Face terms first or "
"switch to a public Stack-family subset such as `bigcode/the-stack-smol-xs`."
) from exc
chunks: list[bytes] = []
total = 0
for sample in ds:
text = sample.get("content") or sample.get("text") or ""
if not isinstance(text, str) or not text.strip():
continue
encoded = text.encode("utf-8", errors="ignore") + b"\n\n"
chunks.append(encoded)
total += len(encoded)
if total >= target_bytes:
break
if total == 0:
raise RuntimeError(f"No text content collected from repo={repo_id} lang={lang}.")
return b"".join(chunks)
def load_the_stack_text(
data_dir: str = "data_cache",
repo_id: str = "bigcode/the-stack-smol-xs",
lang: str = "python",
target_bytes: int = 8_000_000,
) -> bytes:
Path(data_dir).mkdir(parents=True, exist_ok=True)
cache_name = f"{_normalize_repo_name(repo_id)}_{lang}_{target_bytes}.bin"
cache_path = os.path.join(data_dir, cache_name)
if os.path.exists(cache_path):
return Path(cache_path).read_bytes()
data = _stream_the_stack_text(repo_id=repo_id, lang=lang, target_bytes=target_bytes)
Path(cache_path).write_bytes(data)
return data
def load_the_stack(
seq_len: int = 256,
device: str = "cpu",
data_dir: str = "data_cache",
repo_id: str = "bigcode/the-stack-smol-xs",
lang: str = "python",
target_bytes: int = 8_000_000,
) -> tuple[ByteCorpusDataset, ByteCorpusDataset]:
data = load_the_stack_text(
data_dir=data_dir,
repo_id=repo_id,
lang=lang,
target_bytes=target_bytes,
)
train = ByteCorpusDataset(data=data, split="train", seq_len=seq_len, device=device)
val = ByteCorpusDataset(data=data, split="val", seq_len=seq_len, device=device)
return train, val
|