File size: 15,428 Bytes
932bc69 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 | """
Offline Hidden States Generation Pipeline
This module generates hidden states and saves them to disk for offline training.
Usage::
speculators generate-offline-data \
--model meta-llama/Llama-3.1-8B-Instruct \
--preprocessed-data sharegpt \
--output ./training_data \
--max-samples 5000
"""
import asyncio
import logging
import os
import shutil
import sys
import time
from pathlib import Path
from typing import Annotated, Any
import openai
import typer
from datasets import load_from_disk
from safetensors.torch import load_file
from tqdm import tqdm
from speculators.data_generation.offline import (
check_hidden_states,
get_existing_hidden_state_indices,
get_indices_to_process,
)
from speculators.data_generation.vllm_client import (
DEFAULT_MAX_RETRIES,
DEFAULT_REQUEST_TIMEOUT,
generate_hidden_states_async,
wait_for_lock_async,
)
from speculators.train.data import build_client_item
from speculators.train.logger import setup_root_logger
logger = logging.getLogger(__name__)
class _FailureTracker:
"""Tracks consecutive sample failures across async workers.
When the number of consecutive failures (with no successes in between)
reaches ``threshold``, the tracker signals that the run should abort.
Because asyncio is single-threaded, no locking is needed.
"""
def __init__(self, threshold: int):
self.threshold = threshold
self._consecutive = 0
def record_success(self) -> None:
self._consecutive = 0
def record_failure(self) -> bool:
"""Record a failure. Returns True when the threshold is reached."""
self._consecutive += 1
return self._consecutive >= self.threshold
async def _worker( # noqa: C901
client,
model: str,
queue: "asyncio.Queue[dict[str, Any]]",
pbar: tqdm,
vllm_semaphore: asyncio.Semaphore,
write_semaphore: asyncio.Semaphore,
hidden_states_output_dir: Path,
validate_outputs: bool,
request_timeout: float | None,
max_retries: int,
fail_on_error: bool,
skipped_indices: list[int],
cancel_event: asyncio.Event,
failure_tracker: _FailureTracker | None,
stats: dict[str, Any],
):
"""Worker that pulls items from queue and sends them to the vLLM endpoint."""
while True:
item = await queue.get()
if item is None:
queue.task_done()
return
idx = item["idx"]
if cancel_event.is_set():
queue.task_done()
continue
target_hidden_states_path = hidden_states_output_dir / f"hs_{idx}.safetensors"
try:
async with vllm_semaphore:
t_vllm = time.perf_counter()
hidden_states_path = await generate_hidden_states_async(
client,
model,
item,
timeout=request_timeout,
max_retries=max_retries,
)
vllm_s = time.perf_counter() - t_vllm
lock_path = hidden_states_path + ".lock"
if Path(lock_path).exists(): # noqa: ASYNC240
await wait_for_lock_async(lock_path)
async with write_semaphore:
t_write = time.perf_counter()
await asyncio.to_thread(
shutil.move, hidden_states_path, target_hidden_states_path
)
write_s = time.perf_counter() - t_write
if validate_outputs:
def _load_and_check(
path=target_hidden_states_path,
tokens=item["input_ids"],
):
loaded = load_file(path)
check_hidden_states(loaded, tokens)
await asyncio.to_thread(_load_and_check)
except Exception as e:
if fail_on_error:
logger.exception(
"Fatal: sample %d aborted with --fail-on-error: %s", idx, e
)
logging.shutdown()
os._exit(1)
logger.warning("Skipping sample %d due to error: %s", idx, e)
skipped_indices.append(idx)
stats["errors"] += 1
if failure_tracker is not None and failure_tracker.record_failure():
cancel_event.set()
raise RuntimeError(
f"Aborting: {failure_tracker.threshold} consecutive samples "
"errored out. The vLLM server may be unreachable."
) from e
else:
stats["ok"] += 1
stats["total_vllm_s"] += vllm_s
stats["total_write_s"] += write_s
logger.debug(
"Sample %d: vLLM %.0f ms, write %.0f ms",
idx,
vllm_s * 1000,
write_s * 1000,
)
if failure_tracker is not None:
failure_tracker.record_success()
finally:
elapsed = time.perf_counter() - stats["start_time"]
postfix = {"ok": stats["ok"], "err": stats["errors"]}
if elapsed > 0 and stats["ok"] > 0:
postfix["rps"] = f"{stats['ok'] / elapsed:.1f}"
postfix["vllm"] = f"{stats['total_vllm_s'] / stats['ok'] * 1000:.0f}ms"
postfix["write"] = (
f"{stats['total_write_s'] / stats['ok'] * 1000:.0f}ms"
)
pbar.set_postfix(postfix, refresh=False)
pbar.update(1)
queue.task_done()
async def _feed_queue(to_process, dataset, queue, cancel_event):
"""Feed dataset items into the worker queue, respecting cancellation."""
for i in to_process:
if cancel_event.is_set():
break
dataset_item = dataset[i]
client_item = build_client_item(dataset_item) | {"idx": i}
while not cancel_event.is_set():
try:
queue.put_nowait(client_item)
break
except asyncio.QueueFull:
await asyncio.sleep(0.1)
async def _shutdown_workers(workers, queue, cancel_event):
"""Shut down workers and propagate the first real exception."""
logger.info("Waiting for remaining file saves to complete...")
if cancel_event.is_set():
for w in workers:
if not w.done():
w.cancel()
else:
for _ in range(len(workers)):
await queue.put(None)
results = await asyncio.gather(*workers, return_exceptions=True)
for result in results:
if isinstance(result, Exception) and not isinstance(
result, asyncio.CancelledError
):
raise result
async def _generate_and_save_hidden_states(
model: str | None,
endpoint: str,
preprocessed_data: str,
output: str | None,
max_samples: int | None,
concurrency: int,
validate_outputs: bool,
request_timeout: float,
max_retries: int,
fail_on_error: bool,
max_consecutive_errors: int | None,
world_size: int,
rank: int,
):
dataset = load_from_disk(preprocessed_data)
if output is None:
hidden_states_dir = Path(preprocessed_data) / "hidden_states"
else:
hidden_states_dir = Path(output)
hidden_states_dir.mkdir(parents=True, exist_ok=True)
existing_file_indices = get_existing_hidden_state_indices(hidden_states_dir)
num_samples = len(dataset)
to_process = get_indices_to_process(
num_samples,
max_samples,
existing_file_indices,
world_size,
rank,
)
if not to_process:
return
logger.info(f"Processing {len(to_process)} samples")
queue: asyncio.Queue = asyncio.Queue(maxsize=concurrency * 4)
vllm_semaphore = asyncio.Semaphore(concurrency)
write_semaphore = asyncio.Semaphore(concurrency)
skipped_indices: list[int] = []
cancel_event = asyncio.Event()
stats: dict[str, Any] = {
"ok": 0,
"errors": 0,
"total_vllm_s": 0.0,
"total_write_s": 0.0,
"start_time": time.perf_counter(),
}
max_consec = max_consecutive_errors
if max_consec is None:
max_consec = concurrency
failure_tracker = _FailureTracker(max_consec) if not fail_on_error else None
async with openai.AsyncOpenAI(
base_url=endpoint, api_key="EMPTY", max_retries=0
) as client:
list_models = await client.models.list()
if not list_models.data:
raise RuntimeError(
"No models found on the vLLM server. "
"Make sure the server is fully loaded."
)
model_id = list_models.data[0].id
if model and model != model_id:
raise ValueError(
f"An explicit model name was passed ({model}) which doesn't match"
f" found model_id {model_id}."
"Please make sure --endpoint is set to the correct vllm instance."
)
with tqdm(total=len(to_process)) as pbar:
workers = [
asyncio.create_task(
_worker(
client,
model_id,
queue,
pbar,
vllm_semaphore,
write_semaphore,
hidden_states_dir,
validate_outputs,
request_timeout,
max_retries,
fail_on_error,
skipped_indices,
cancel_event,
failure_tracker,
stats,
)
)
for _ in range(concurrency * 2)
]
await _feed_queue(to_process, dataset, queue, cancel_event)
await _shutdown_workers(workers, queue, cancel_event)
elapsed = time.perf_counter() - stats["start_time"]
if stats["ok"] > 0:
logger.info(
"Timing: %.1fs elapsed, %.1f samples/s, "
"avg vLLM request %.0f ms, avg file write %.0f ms",
elapsed,
stats["ok"] / elapsed if elapsed > 0 else 0,
stats["total_vllm_s"] / stats["ok"] * 1000,
stats["total_write_s"] / stats["ok"] * 1000,
)
num_saved = len(to_process) - len(skipped_indices)
logger.info(f"Saved {num_saved} new data points to {hidden_states_dir}")
if skipped_indices:
logger.warning(
f"Skipped {len(skipped_indices)} samples due to errors: {skipped_indices}"
)
def generate_offline_data(
model: Annotated[
str | None,
typer.Option(
help=(
"HuggingFace model ID or local path for target model "
"(default auto select). For verification purposes only."
),
),
] = None,
endpoint: Annotated[
str,
typer.Option(
help=(
"The address of the vLLM instance to use for hidden states "
"generation. The instance must be configured for hidden states "
"extraction."
),
),
] = "http://localhost:8000/v1",
preprocessed_data: Annotated[
str,
typer.Option(
help="Path to preprocessed dataset (produced by prepare-data)",
),
] = "./output",
max_samples: Annotated[
int | None,
typer.Option(help="Maximum number of samples to process"),
] = None,
output: Annotated[
str | None,
typer.Option(
help=(
"Directory to save generated hidden states files "
"(default: {preprocessed-data}/hidden_states)"
),
),
] = None,
concurrency: Annotated[
int,
typer.Option(
help=(
"Number of active vLLM requests at a time. "
"Note: number of async workers set to 2*concurrency"
),
),
] = 32,
validate_outputs: Annotated[
bool,
typer.Option(
"--validate-outputs",
help=(
"Load generated safetensor files and check output token ids "
"match prompt tokens and hidden states seq_len matches num tokens"
),
),
] = False,
request_timeout: Annotated[
float,
typer.Option(
help="Timeout in seconds for each individual vLLM request",
),
] = DEFAULT_REQUEST_TIMEOUT,
max_retries: Annotated[
int,
typer.Option(
help="Maximum number of retry attempts per request on failure",
),
] = DEFAULT_MAX_RETRIES,
fail_on_error: Annotated[
bool,
typer.Option(
"--fail-on-error",
help=(
"Abort when a request fails after all retries. "
"By default, failed samples are skipped."
),
),
] = False,
max_consecutive_errors: Annotated[
int | None,
typer.Option(
help=(
"Abort after this many consecutive sample failures (each sample "
"already retried --max-retries times). Prevents silently churning "
"through the entire dataset when the server is down. "
"Ignored when --fail-on-error is set. "
"(default: value of --concurrency)"
),
),
] = None,
world_size: Annotated[
int,
typer.Option(
help=(
"World size for multi-node data generation offline. "
"This is the number of nodes (not GPUs)."
),
),
] = 1,
rank: Annotated[
int,
typer.Option(
help=(
"Rank for multi-node data generation offline. "
"This is the node index, not a GPU index. "
"Must be in range [0, world_size)."
),
),
] = 0,
) -> None:
"""Generate hidden states offline from a vLLM server.
Connects to a running vLLM instance, sends preprocessed samples, and saves
the extracted hidden states to disk for offline training.
"""
if concurrency < 1:
raise typer.BadParameter("--concurrency must be >= 1")
if rank < 0 or rank >= world_size:
raise typer.BadParameter("--rank must be in range [0, world_size)")
setup_root_logger()
logger.info("EAGLE Offline Data Generation")
try:
asyncio.run(
_generate_and_save_hidden_states(
model=model,
endpoint=endpoint,
preprocessed_data=preprocessed_data,
output=output,
max_samples=max_samples,
concurrency=concurrency,
validate_outputs=validate_outputs,
request_timeout=request_timeout,
max_retries=max_retries,
fail_on_error=fail_on_error,
max_consecutive_errors=max_consecutive_errors,
world_size=world_size,
rank=rank,
)
)
except KeyboardInterrupt:
sys.exit(130)
except Exception:
logger.exception("Data generation failed")
sys.exit(1)
logger.info("Data generation complete!")
|