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import os
import signal
import time
import torch.multiprocessing as mp
from dataclasses import fields
from time import perf_counter
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from diffulex.config import Config
from diffulex.distributed.parallel_state import get_world_size
from diffulex.engine.model_runner import AutoModelRunner
from diffulex.engine.request import AutoReq
from diffulex.engine.scheduler import AutoScheduler, DataParallelScheduler, SchedulerBase
from diffulex.logger import get_logger
from diffulex.mixin.async_serving.engine import DiffulexAsyncEngineMixin
from diffulex.profiling import TorchProfileSession, record_function
from diffulex.sampling_params import SamplingParams
from diffulex.utils.output import GenerationOutputs
logger = get_logger(__name__)
def _set_parent_death_signal(sig: int = signal.SIGTERM) -> None:
if os.name != "posix":
return
try:
import ctypes
libc = ctypes.CDLL("libc.so.6", use_errno=True)
pr_set_pdeathsig = 1
libc.prctl(pr_set_pdeathsig, sig)
except Exception:
logger.debug("Failed to set parent-death signal for worker process.", exc_info=True)
def _run_model_runner_worker(config: Config, rank: int, event) -> None:
_set_parent_death_signal()
if os.getppid() == 1:
raise SystemExit("Diffulex worker parent exited before worker initialization.")
AutoModelRunner.from_config(config, rank, event)
class DiffulexEngine(DiffulexAsyncEngineMixin):
def __init__(self, model, **kwargs):
config_fields = {field.name for field in fields(Config)}
config_kwargs = {k: v for k, v in kwargs.items() if k in config_fields}
self.config = config = Config(model, **config_kwargs)
self.model_parallel_world_size = get_world_size(
config.tensor_parallel_size,
config.expert_parallel_size,
dp_size=config.data_parallel_size,
)
if len(config.device_ids) < self.model_parallel_world_size:
raise ValueError(
"Not enough CUDA devices for the requested topology, "
f"need {self.model_parallel_world_size}, got device_ids={config.device_ids}."
)
self.ps = []
self.events = []
ctx = mp.get_context("spawn")
for i in range(1, self.model_parallel_world_size):
event = ctx.Event()
process = ctx.Process(target=_run_model_runner_worker, args=(config, i, event))
process.start()
self.ps.append(process)
self.events.append(event)
self._exited = False
self.profile_session = TorchProfileSession("engine")
atexit.register(self.exit)
self._install_signal_handlers()
try:
self.tokenizer = AutoTokenizer.from_pretrained(config.model, use_fast=True, trust_remote_code=True)
config.tokenizer_vocab_size = len(self.tokenizer)
config.eos = self.tokenizer.eos_token_id
if (
getattr(self.tokenizer, "mask_token_id", None) is not None
and config.mask_token_id != self.tokenizer.mask_token_id
):
logger.warning(
"Overriding mask_token_id from %s to tokenizer mask_token_id %s.",
config.mask_token_id,
self.tokenizer.mask_token_id,
)
config.mask_token_id = self.tokenizer.mask_token_id
self.model_runner = AutoModelRunner.from_config(config, 0, self.events)
self.scheduler: SchedulerBase | DataParallelScheduler = AutoScheduler.from_config(config)
except BaseException:
self.exit()
raise
def _install_signal_handlers(self) -> None:
if getattr(self, "_signal_handlers_installed", False):
return
self._signal_handlers_installed = True
self._previous_signal_handlers = {}
for sig in (signal.SIGINT, signal.SIGTERM):
try:
previous = signal.getsignal(sig)
self._previous_signal_handlers[sig] = previous
def handler(signum, frame, *, _previous=previous):
self.exit()
if callable(_previous):
_previous(signum, frame)
else:
raise SystemExit(128 + signum)
signal.signal(sig, handler)
except Exception:
logger.debug("Failed to install signal handler for %s.", sig, exc_info=True)
@staticmethod
def _join_or_stop_process(process, *, timeout: float = 5.0) -> None:
try:
process.join(timeout=timeout)
except Exception:
logger.debug("Failed to join worker process %s.", getattr(process, "pid", None), exc_info=True)
if not process.is_alive():
return
logger.warning("Terminating stale worker process pid=%s.", process.pid)
try:
process.terminate()
except Exception:
logger.debug("Failed to terminate worker process %s.", process.pid, exc_info=True)
try:
process.join(timeout=timeout)
except Exception:
logger.debug("Failed to join terminated worker process %s.", process.pid, exc_info=True)
if not process.is_alive():
return
logger.warning("Killing stale worker process pid=%s.", process.pid)
try:
process.kill()
except Exception:
logger.debug("Failed to kill worker process %s.", process.pid, exc_info=True)
try:
process.join(timeout=timeout)
except Exception:
logger.debug("Failed to join killed worker process %s.", process.pid, exc_info=True)
def exit(self):
if getattr(self, "_exited", False):
return
self._exited = True
if hasattr(self, "profile_session"):
self.profile_session.stop()
if hasattr(self, "model_runner") and self.model_runner is not None:
try:
self.model_runner.call("exit")
except Exception:
pass
try:
del self.model_runner
except Exception:
pass
for p in getattr(self, "ps", []):
self._join_or_stop_process(p)
time.sleep(0)
def add_request(self, prompt: str | list[int], sampling_params: SamplingParams):
if isinstance(prompt, str):
with record_function("diffulex.engine.tokenizer_encode"):
prompt = self.tokenizer.encode(prompt)
with record_function("diffulex.engine.add_request"):
req = AutoReq.create(self.config, prompt, sampling_params)
req.page_size = self.config.kv_cache_page_size
with record_function("diffulex.engine.scheduler_add"):
self.scheduler.add(req)
return req.req_id
def step(self):
self.profile_session.start()
with record_function("diffulex.engine.scheduler_schedule"):
reqs, is_prefill = self.scheduler.schedule()
with record_function("diffulex.engine.prepare_reqs_for_execution"):
self._prepare_reqs_for_execution(reqs)
try:
with record_function("diffulex.engine.model_runner_run"):
sample_output = self.model_runner.call("run", reqs)
finally:
with record_function("diffulex.engine.clear_execution_prepared"):
self._clear_execution_prepared(reqs)
with record_function("diffulex.engine.scheduler_postprocess"):
self.scheduler.postprocess(reqs, sample_output)
finished_req_ids = [req.req_id for req in reqs if (req.is_completed or req.is_finished)]
if finished_req_ids:
with record_function("diffulex.engine.evict_sampler_state"):
self.model_runner.call("evict_sampler_state", finished_req_ids)
self.profile_session.step()
return reqs, is_prefill
@staticmethod
def _prepare_reqs_for_execution(reqs):
for req in reqs:
step_fn = getattr(req, "step", None)
if callable(step_fn):
step_fn()
mark_fn = getattr(req, "mark_execution_prepared", None)
if callable(mark_fn):
mark_fn()
@staticmethod
def _clear_execution_prepared(reqs):
for req in reqs:
clear_fn = getattr(req, "clear_execution_prepared", None)
if callable(clear_fn):
clear_fn()
def is_finished(self):
return self.scheduler.is_finished()
def abort_request(self, req_id: int) -> bool:
return self.scheduler.abort_request(req_id)
def generate(
self,
prompts: list[str] | list[list[int]],
sampling_params: SamplingParams | list[SamplingParams],
use_tqdm: bool = True,
) -> list[str]:
with record_function("diffulex.engine.generate"):
if use_tqdm:
pbar = tqdm(total=len(prompts), desc="Diffulex Generating", dynamic_ncols=True)
if not isinstance(sampling_params, list):
sampling_params = [sampling_params] * len(prompts)
req_id_to_prompt_id = {}
for prompt_id, (prompt, sp) in tqdm(
enumerate(zip(prompts, sampling_params)),
total=len(prompts),
desc="Adding Requests to Scheduler",
dynamic_ncols=True,
):
req_id = self.add_request(prompt, sp)
req_id_to_prompt_id[req_id] = prompt_id
step = 0
outputs = GenerationOutputs(len(prompts))
while not self.is_finished():
step += 1
start = perf_counter()
reqs, is_prefill = self.step()
step_time = perf_counter() - start
with record_function("diffulex.engine.record_outputs"):
outputs.record_step(reqs, step_time, req_id_to_prompt_id)
if use_tqdm:
pbar.set_postfix(outputs.fast_postfix())
for req in reqs:
if (req.is_completed or req.is_finished) and use_tqdm:
pbar.update(1)
if use_tqdm:
pbar.close()
outputs.log_summary()
with record_function("diffulex.engine.convert_outputs_to_text"):
outputs.convert_to_text(self.tokenizer)
return outputs
__all__ = ["DiffulexEngine"]
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