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from dataclasses import fields
from time import perf_counter
from tqdm.auto import tqdm
from transformers import AutoTokenizer
import torch.multiprocessing as mp
# Added imports for profiling
import torch
from contextlib import nullcontext
import torch.profiler as torch_profiler
from jetengine_ext.config import Config
from jetengine_ext.sampling_params import SamplingParams
from jetengine_ext.engine.sequence import Sequence, RunType
from jetengine_ext.engine.scheduler import Scheduler
from jetengine_ext.engine.model_runner import ModelRunner
class LLMEngine:
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}
config = Config(model, **config_kwargs)
self.ps = []
self.events = []
ctx = mp.get_context("spawn")
for i in range(1, config.tensor_parallel_size):
event = ctx.Event()
process = ctx.Process(target=ModelRunner, args=(config, i, event))
process.start()
self.ps.append(process)
self.events.append(event)
self.model_runner = ModelRunner(config, 0, self.events)
self.tokenizer = AutoTokenizer.from_pretrained(config.model, use_fast=True, trust_remote_code=True)
config.eos = self.tokenizer.eos_token_id
config.mask_token_id = self.tokenizer.mask_token_id if self.tokenizer.mask_token_id is not None else self.tokenizer.pad_token_id
assert config.mask_token_id is not None, "Model tokenizer must have a mask_token_id or pad_token_id"
self.config = config
self.scheduler = Scheduler(config)
atexit.register(self.exit)
def exit(self):
self.model_runner.call("exit")
del self.model_runner
for p in self.ps:
p.join()
def add_request(self, prompt: str | list[int], sampling_params: SamplingParams):
if isinstance(prompt, str):
prompt = self.tokenizer.encode(prompt)
seq = Sequence(prompt, self.config.mask_token_id, sampling_params)
seq.eos_token_id = self.tokenizer.eos_token_id
self.scheduler.add(seq)
def step(self):
scheduled_seqs, run_type = self.scheduler.schedule()
if scheduled_seqs is None:
return [], 0 # Nothing to run
logits = self.model_runner.call("run", scheduled_seqs, run_type)
self.scheduler.postprocess(scheduled_seqs, logits, run_type)
#finished_outputs = [(seq.seq_id, seq.completion_token_ids) for seq in scheduled_seqs if seq.is_finished]
finished_outputs = [
(seq.seq_id, seq.completion_token_ids, seq.first_unmask_steps)
for seq in scheduled_seqs
if seq.is_finished
]
# Throughput calculation needs to be adapted for block-wise generation
num_tokens = [self.scheduler.running[i].num_to_transfer if hasattr(self.scheduler.running[i], 'num_to_transfer') else 0 for i in range(len(self.scheduler.running))]
return finished_outputs, sum(num_tokens)
def is_finished(self):
return self.scheduler.is_finished()
def _clean_token_ids(self, token_ids):
# Accept tensors, numpy ints, etc.
try:
token_ids = list(token_ids)
except Exception:
token_ids = [token_ids]
vocab_size = getattr(self.tokenizer, "vocab_size", None)
special_ids = set(getattr(self.tokenizer, "all_special_ids", []) or [])
mask_id = getattr(self.config, "mask_token_id", None)
cleaned = []
for t in token_ids:
if t is None or t < 0 or t == mask_id or t >= vocab_size:
if t not in special_ids:
cleaned.append(0)
continue
cleaned.append(t)
return cleaned
def _safe_decode(self, token_ids):
ids = self._clean_token_ids(token_ids)
# skip_special_tokens can be True or False; doesn't affect the None issue
return self.tokenizer.decode(ids, skip_special_tokens=False)
def generate(
self,
prompts: list[str] | list[list[int]],
sampling_params: SamplingParams | list[SamplingParams],
use_tqdm: bool = True,
# New optional profiling controls
profile: bool = False,
profile_dir: str | None = None,
) -> list[str]:
# ... (This method remains largely the same, but the progress bar will update differently) ...
# The logic inside the `while not self.is_finished()` loop correctly calls `self.step()`
# and collects outputs.
if use_tqdm:
pbar = tqdm(total=len(prompts), desc="Generating", dynamic_ncols=True)
if not isinstance(sampling_params, list):
sampling_params = [sampling_params] * len(prompts)
for prompt, sp in zip(prompts, sampling_params):
self.add_request(prompt, sp)
outputs = {}
total_generated_tokens = 0
start_time = perf_counter()
# Setup profiler context
activities = [torch_profiler.ProfilerActivity.CPU]
if torch.cuda.is_available():
activities.append(torch_profiler.ProfilerActivity.CUDA)
trace_dir = profile_dir or "profiler_traces"
prof_ctx = (
torch_profiler.profile(
activities=activities,
record_shapes=True,
profile_memory=True,
on_trace_ready=torch_profiler.tensorboard_trace_handler(trace_dir),
)
if profile else nullcontext()
)
with prof_ctx as prof:
while not self.is_finished():
output, num_processed = self.step()
if profile:
prof.step()
total_generated_tokens += num_processed
throughput = total_generated_tokens / (perf_counter() - start_time)
if use_tqdm:
pbar.set_postfix({"Throughput": f"{int(throughput)} tok/s"})
#for seq_id, token_ids in output:
# outputs[seq_id] = token_ids
for seq_id, token_ids, unmask_times in output:
outputs[seq_id] = {"token_ids": token_ids, "unmask_times": unmask_times}
if use_tqdm:
pbar.update(1)
#outputs = [outputs[seq_id] for seq_id in sorted(outputs)]
#outputs = [{"text": self.tokenizer.decode(token_ids), "token_ids": token_ids} for token_ids in outputs]
outputs = [outputs[seq_id] for seq_id in sorted(outputs)]
outputs = [
{
"text": self._safe_decode(item["token_ids"]),
"token_ids": self._clean_token_ids(item["token_ids"]),
"first_unmask_times": item["unmask_times"], # 与 token_ids 等长
}
for item in outputs
]
if use_tqdm:
pbar.close()
return outputs
def generate_streaming(
self,
prompts: list[str] | list[list[int]],
sampling_params: SamplingParams | list[SamplingParams],
max_active: int | None = None,
use_tqdm: bool = True,
# New optional profiling controls
profile: bool = False,
profile_dir: str | None = None,
) -> list[str]:
"""
Stream prompts through the engine while keeping up to `max_active` sequences running.
As sequences finish, new prompts are added from the pending list to maximize GPU utilization.
"""
total = len(prompts)
if not isinstance(sampling_params, list):
sampling_params = [sampling_params] * total
if max_active is None:
max_active = getattr(self.scheduler, "max_num_seqs", 32)
if use_tqdm:
pbar = tqdm(total=total, desc="Generating", dynamic_ncols=True)
outputs: dict[int, list[int]] = {}
pending_idx = 0
# Prime initial requests up to capacity
initial = min(max_active, total)
for i in range(initial):
self.add_request(prompts[i], sampling_params[i])
pending_idx = initial
total_generated_tokens = 0
start_time = perf_counter()
# Setup profiler context
activities = [torch_profiler.ProfilerActivity.CPU]
if torch.cuda.is_available():
activities.append(torch_profiler.ProfilerActivity.CUDA)
trace_dir = profile_dir or "profiler_traces"
prof_ctx = (
torch_profiler.profile(
activities=activities,
record_shapes=True,
profile_memory=True,
on_trace_ready=torch_profiler.tensorboard_trace_handler(trace_dir),
)
if profile else nullcontext()
)
with prof_ctx as prof:
while not self.is_finished() or pending_idx < total:
# Top up to capacity before each step
running = getattr(self.scheduler, "running", [])
deficit = max_active - len(running)
while deficit > 0 and pending_idx < total:
self.add_request(prompts[pending_idx], sampling_params[pending_idx])
pending_idx += 1
deficit -= 1
output, num_processed = self.step()
if profile:
prof.step()
total_generated_tokens += num_processed
if use_tqdm:
throughput = total_generated_tokens / (perf_counter() - start_time + 1e-6)
pbar.set_postfix({"Throughput": f"{int(throughput)} tok/s"})
pbar.update(len(output))
#for seq_id, token_ids in output:
# outputs[seq_id] = token_ids
for seq_id, token_ids, unmask_times in output:
outputs[seq_id] = {"token_ids": token_ids, "unmask_times": unmask_times}
#outputs_list = [outputs[seq_id] for seq_id in sorted(outputs)]
#results = [{"text": self.tokenizer.decode(token_ids), "token_ids": token_ids} for token_ids in outputs_list]
outputs_list = [outputs[seq_id] for seq_id in sorted(outputs)]
results = [
{
"text": self._safe_decode(item["token_ids"]),
"token_ids": self._clean_token_ids(item["token_ids"]),
"first_unmask_times": item["unmask_times"],
}
for item in outputs_list
]
if use_tqdm:
pbar.close()
return results |