File size: 6,840 Bytes
11c11f8 | 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 | from __future__ import annotations
import os
import torch
from torch.utils.data import Dataset
class SequenceTokenDataset(Dataset):
def __init__(self, chunks: torch.Tensor):
self.chunks = chunks
def __len__(self) -> int:
return self.chunks.size(0)
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
chunk = self.chunks[idx]
return {"input_ids": chunk, "labels": chunk}
class PreTokenizedDataset(Dataset):
def __init__(self, ids: torch.Tensor, seq_len: int):
n = ids.numel() // (seq_len + 1)
self.chunks = ids[: n * (seq_len + 1)].view(n, seq_len + 1)
self.seq_len = seq_len
def __len__(self) -> int:
return self.chunks.size(0)
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
chunk = self.chunks[idx]
return {"input_ids": chunk[:-1], "labels": chunk[1:]}
class GrowLengthDataset(Dataset):
def __init__(self, all_ids: torch.Tensor, seq_len: int = 16):
self.all_ids = all_ids
self._seq_len = 0
self._n = 0
self.set_seq_len(seq_len)
def set_seq_len(self, seq_len: int) -> None:
self._seq_len = int(seq_len)
self._n = self.all_ids.numel() // (self._seq_len + 1)
@property
def seq_len(self) -> int:
return self._seq_len
def __len__(self) -> int:
return self._n
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
start = idx * (self._seq_len + 1)
chunk = self.all_ids[start : start + self._seq_len + 1]
return {"input_ids": chunk[:-1], "labels": chunk[1:]}
def matches_category_filter(example: dict, filters: list[str]) -> bool:
category = example.get("category", "") or ""
if not category:
return False
category_lower = category.lower()
return any(f.lower() in category_lower for f in filters)
def format_dataset_example(ex: dict, tok, text_column: str = "auto", include_reasoning: bool = False) -> str:
if text_column == "auto":
for candidate in ("messages", "text", "content", "conversation"):
if candidate in ex:
text_column = candidate
break
else:
text_column = ""
if text_column == "messages" and "messages" in ex:
messages = ex["messages"]
if include_reasoning and isinstance(messages, list):
rewritten = []
for message in messages:
if isinstance(message, dict) and message.get("role") == "assistant" and "reasoning" in message:
rewritten.append(
{
"role": "assistant",
"content": (
f"<|thinking|>\n{message['reasoning']}\n<|/thinking|>\n"
f"{message.get('content', '')}"
),
}
)
else:
rewritten.append(message)
messages = rewritten
return tok.apply_chat_template(messages)
if text_column and text_column in ex:
value = ex[text_column]
if isinstance(value, str):
return value
if isinstance(value, list) and value and isinstance(value[0], dict):
return tok.apply_chat_template(value)
return str(value)
return str(ex)
def build_token_buffer(
dataset_name: str,
split: str,
text_column: str,
max_tokens: int,
cache_dir: str,
*,
dataset_config: str | None = None,
category_filter: str | None = None,
include_reasoning: bool = False,
):
from datasets import load_dataset
from chimera import ChimeraTokenizer
cache_name = f"{dataset_name.replace('/', '_')}_{split}_{max_tokens}.pt"
cache_path = os.path.join(cache_dir, cache_name)
os.makedirs(cache_dir, exist_ok=True)
if os.path.exists(cache_path):
print(f"[DATA] Cache hit: {cache_path}")
return torch.load(cache_path, weights_only=True)
print(f"[DATA] Streaming {dataset_name} ({split})...")
load_kwargs = {"split": split, "streaming": True}
if dataset_config:
load_kwargs["name"] = dataset_config
ds = load_dataset(dataset_name, **load_kwargs)
tok = ChimeraTokenizer(pretrained="o200k_base")
filters = [c.strip() for c in category_filter.split(",") if c.strip()] if category_filter else None
if filters:
print(f"[DATA] Filtering categories: {filters}")
buf = torch.empty(max_tokens, dtype=torch.long)
idx = processed = skipped = 0
for ex in ds:
if filters and not matches_category_filter(ex, filters):
skipped += 1
continue
text = format_dataset_example(ex, tok, text_column, include_reasoning)
if not text or not text.strip():
skipped += 1
continue
ids = tok.encode(text, add_special_tokens=False)
ids.append(tok.eos_token_id)
n = min(len(ids), max_tokens - idx)
if n <= 0:
break
buf[idx : idx + n] = torch.tensor(ids[:n], dtype=torch.long)
idx += n
processed += 1
if processed % 5000 == 0:
print(f" {processed:,} docs {idx:,}/{max_tokens} tokens")
token_buf = buf[:idx].contiguous()
torch.save(token_buf, cache_path)
print(f"[DATA] Processed {processed:,} examples, skipped {skipped:,}.")
print(f"[DATA] {idx:,} tokens -> {cache_path}")
return token_buf
def build_sequence_dataset(
seq_len: int,
*,
max_samples=None,
max_tokens=None,
split: str = "train",
dataset_name: str = "roneneldan/TinyStories",
dataset_config: str | None = None,
text_column: str = "auto",
category_filter: str | None = None,
include_reasoning: bool = False,
cache_dir: str = "./cache",
):
token_budget = int(max_tokens) if max_tokens is not None else None
if token_budget is None and max_samples is not None:
token_budget = int(max_samples) * (seq_len + 1)
if token_budget is None or token_budget <= 0:
token_budget = max(500_000, (int(max_samples) if max_samples else 10000) * (seq_len + 1))
token_buffer = build_token_buffer(
dataset_name,
split,
text_column,
token_budget,
cache_dir,
dataset_config=dataset_config,
category_filter=category_filter,
include_reasoning=include_reasoning,
)
if token_buffer.numel() == 0:
raise ValueError("No data matched filters.")
n = token_buffer.numel() // (seq_len + 1)
if max_samples:
n = min(n, max_samples)
chunks = token_buffer[: n * (seq_len + 1)].view(n, seq_len + 1)
print(f"[DATA] {n:,} chunks × {seq_len} tokens = {n * seq_len:,} total")
return SequenceTokenDataset(chunks)
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