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c383594 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 | """Lightning DataModule + IterableDataset for HYDRA pretraining.
Replaces the custom threading/queue pipeline in prepare_nemotron.make_dataloader
with a standard multiprocessing DataLoader approach.
Design:
• IterableStreamDataset: each worker opens its own HF streams for the 7-way
blend, tokenizes with rustbpe, packs into (T+1,) rows via best-fit, and
yields one row per __next__.
• HydraDataModule: wraps the dataset with a standard DataLoader using
num_workers>=1, prefetch_factor=4, pin_memory=True. Lightning handles
device transfer.
• Val stream: deterministic seed 12345, weights match training blend.
The worker RNG is seeded per-worker so the weighted-sampling schedule is
independent across workers (else all workers request the same config at
the same step and prefetching serializes).
Env vars (all preserved from prepare_nemotron):
HYDRA_SEQ_LEN — sequence length T (default 512)
HYDRA_BATCH_SIZE — batch size B (default 1) — passed through
to DataLoader
HYDRA_STREAM_SHUFFLE_BUFFER — HF shuffle buffer (default 2048)
HYDRA_USE_FULL_BLEND — 7-way blend vs 5-way Nemotron phase
HYDRA_USE_NEMOTRON — enables streaming path (else shard path)
HYDRA_FACTUAL_INJECT_RATE — factual doc injection cadence
HYDRA_NEMOTRON_PHASE — phase1|phase2 (when not full blend)
HYDRA_DATA_NUM_WORKERS — DataLoader num_workers (default 2)
HYDRA_DATA_PREFETCH — DataLoader prefetch_factor (default 4)
HYDRA_DATA_BUFFER — doc_buffer size for best-fit packing
(default 1000)
"""
from __future__ import annotations
import os
import random
from typing import Iterator
import numpy as np
import torch
import lightning as L
from torch.utils.data import DataLoader, IterableDataset, get_worker_info
import prepare as _prepare
import prepare_nemotron as _p_nemo
from prepare_nemotron import (
FULL_BLEND_WEIGHTS,
PHASE1_WEIGHTS,
PHASE2_WEIGHTS,
_BLEND_REGISTRY,
_extract_text,
_open_stream,
)
# ---------------------------------------------------------------------------
# Worker-local weighted stream. A stripped version of prepare_nemotron's
# _WeightedStream that is constructed inside each worker. Adds worker sharding:
# when num_workers > 1 the RNG is seeded per-worker, so different workers
# sample different config sequences and pull disjoint shard assignments from
# HF's shuffle buffer.
# ---------------------------------------------------------------------------
class _WorkerWeightedStream:
def __init__(self, weights: dict[str, float], base_seed: int, worker_id: int):
self.configs = list(weights.keys())
self.weights = [weights[c] for c in self.configs]
self.base_seed = base_seed
self.worker_id = worker_id
# Each worker opens its own HF streams. _open_stream returns an iter()
# over a streaming dataset, with an internal shuffle buffer.
self.streams = {c: _open_stream(c, "train") for c in self.configs}
# Per-worker RNG so the config-choice trajectory is independent.
self.rng = random.Random(base_seed + worker_id * 7919)
self.epoch = 1
# Lazy-init factual docs (once per worker). The main-process version
# in prepare_nemotron._WeightedStream reads these on first __next__.
self._factual_docs: list[str] | None = None
self._factual_idx = 0
self._inject_counter = 0
inject_rate = int(os.environ.get("HYDRA_FACTUAL_INJECT_RATE", "50"))
self._inject_rate = inject_rate
if inject_rate > 0:
factual_path = os.path.join(
os.path.dirname(os.path.abspath(_p_nemo.__file__)),
"data", "factual", "facts.txt",
)
if os.path.exists(factual_path):
with open(factual_path) as fh:
self._factual_docs = fh.read().strip().split("\n")
def _reopen(self, config: str) -> None:
self.streams[config] = _open_stream(config, "train")
self.epoch += 1
def __iter__(self):
return self
def __next__(self) -> tuple[str, int]:
# Factual injection (preserves prepare_nemotron cadence).
if self._inject_rate > 0 and self._factual_docs:
self._inject_counter += 1
if self._inject_counter >= self._inject_rate:
self._inject_counter = 0
doc = self._factual_docs[self._factual_idx % len(self._factual_docs)]
self._factual_idx += 1
return doc, self.epoch
config = self.rng.choices(self.configs, weights=self.weights, k=1)[0]
try:
row = next(self.streams[config])
except StopIteration:
self._reopen(config)
row = next(self.streams[config])
return _extract_text(row), self.epoch
# ---------------------------------------------------------------------------
# IterableStreamDataset — yields (T+1,) packed rows. No threads. No queues.
# Lives inside each DataLoader worker. DataLoader's own multiprocessing stacks
# rows into batches of shape (B, T+1) and sends them to the main process.
# ---------------------------------------------------------------------------
class IterableStreamDataset(IterableDataset):
"""Streams docs, tokenizes, packs into (T+1,) rows via best-fit.
Each worker gets its own instance (via fork/spawn) and initializes its
own HF streams + rustbpe tokenizer + factual injector. The tokenizer
pickled blob is small (~1 MB) and thread-safe per tiktoken docs.
"""
def __init__(
self,
split: str,
seq_len: int,
*,
base_seed: int = 0,
doc_buffer_size: int = 1000,
tokenizer_batch: int = 128,
):
super().__init__()
assert split in ("train", "val"), split
self.split = split
self.seq_len = seq_len
self.row_capacity = seq_len + 1
self.base_seed = base_seed
self.doc_buffer_size = doc_buffer_size
self.tokenizer_batch = tokenizer_batch
def _pick_weights(self) -> dict[str, float]:
if self.split == "val":
if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1":
return FULL_BLEND_WEIGHTS
return {"Nemotron-Pretraining-Multiple-Choice": 1.0}
if os.environ.get("HYDRA_USE_FULL_BLEND", "0") == "1":
return FULL_BLEND_WEIGHTS
phase = os.environ.get("HYDRA_NEMOTRON_PHASE", "phase1").strip().lower()
return PHASE2_WEIGHTS if phase == "phase2" else PHASE1_WEIGHTS
def __iter__(self) -> Iterator[torch.Tensor]:
info = get_worker_info()
worker_id = 0 if info is None else info.id
# Each worker builds its own tokenizer instance. tiktoken's Encoding
# object is pickleable and the underlying C++ BPE is thread-safe;
# per-worker instantiation avoids cross-process sharing headaches.
tokenizer = _prepare.Tokenizer.from_directory()
bos = tokenizer.get_bos_token_id()
# Each worker gets its own weighted HF stream. Seed offset ensures
# disjoint config-choice trajectories; HF's own shuffle buffer handles
# shard randomization.
val_seed = 12345 # deterministic val
seed = val_seed if self.split == "val" else self.base_seed
stream = _WorkerWeightedStream(
self._pick_weights(), base_seed=seed, worker_id=worker_id,
)
row_capacity = self.row_capacity
doc_buffer: list[list[int]] = []
doc_batch_size = self.tokenizer_batch
def refill_buffer() -> None:
# Collect doc_batch_size text strings, then batch-tokenize.
texts: list[str] = []
for _ in range(doc_batch_size):
text, _epoch = next(stream)
if text:
texts.append(text)
if texts:
token_lists = tokenizer.encode(texts, prepend=bos)
doc_buffer.extend(token_lists)
while True:
pos = 0
row = torch.empty(row_capacity, dtype=torch.long)
while pos < row_capacity:
while len(doc_buffer) < self.doc_buffer_size:
refill_buffer()
remaining = row_capacity - pos
# Best-fit packing: largest doc that fully fits.
best_idx = -1
best_len = 0
for i, doc in enumerate(doc_buffer):
dlen = len(doc)
if dlen <= remaining and dlen > best_len:
best_idx = i
best_len = dlen
if best_idx >= 0:
doc = doc_buffer.pop(best_idx)
row[pos : pos + len(doc)] = torch.tensor(doc, dtype=torch.long)
pos += len(doc)
else:
# No doc fits remaining space — crop shortest to fill.
shortest_idx = min(
range(len(doc_buffer)),
key=lambda i: len(doc_buffer[i]),
)
doc = doc_buffer.pop(shortest_idx)
row[pos : pos + remaining] = torch.tensor(
doc[:remaining], dtype=torch.long,
)
pos += remaining
yield row
# ---------------------------------------------------------------------------
# LightningDataModule
# ---------------------------------------------------------------------------
class HydraDataModule(L.LightningDataModule):
def __init__(
self,
batch_size: int | None = None,
seq_len: int | None = None,
num_workers: int | None = None,
prefetch_factor: int | None = None,
):
super().__init__()
self.batch_size = batch_size or int(os.environ.get("HYDRA_BATCH_SIZE", "1"))
self.seq_len = seq_len or int(os.environ.get("HYDRA_SEQ_LEN", "512"))
self.num_workers = (
num_workers
if num_workers is not None
else int(os.environ.get("HYDRA_DATA_NUM_WORKERS", "2"))
)
self.prefetch_factor = (
prefetch_factor
if prefetch_factor is not None
else int(os.environ.get("HYDRA_DATA_PREFETCH", "4"))
)
self.doc_buffer = int(os.environ.get("HYDRA_DATA_BUFFER", "1000"))
def _make_loader(self, split: str, seed: int) -> DataLoader:
dataset = IterableStreamDataset(
split=split,
seq_len=self.seq_len,
base_seed=seed,
doc_buffer_size=self.doc_buffer,
)
# num_workers=0 → main-process iteration (useful for debugging). With
# IterableDataset the DataLoader batches the rows into (B, T+1) via
# default torch.stack-collate.
kw: dict = dict(
dataset=dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=True,
drop_last=True,
)
if self.num_workers > 0:
kw["prefetch_factor"] = self.prefetch_factor
kw["persistent_workers"] = True
return DataLoader(**kw)
def train_dataloader(self) -> DataLoader:
return self._make_loader("train", seed=0)
def val_dataloader(self) -> DataLoader:
return self._make_loader("val", seed=12345)
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