import asyncio
import atexit
import concurrent.futures
import copy
import datetime
import faulthandler
import io
import json
import multiprocessing
import os
import pickle # nosec B403
import secrets
import signal
import time
import traceback
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor
from dataclasses import dataclass, field
from multiprocessing.shared_memory import SharedMemory
from pathlib import Path
from queue import Empty, Queue
from typing import (Any, Callable, Dict, Generator, List, Literal, NamedTuple,
Optional, Tuple, Union)
from weakref import WeakMethod
import numpy as np
import torch
import zmq
from ._utils import mpi_rank, mpi_world_size
from .bindings import executor as tllm
from .builder import ConfigEncoder, Engine, EngineConfig
from .llmapi.mpi_session import (MpiPoolSession, MpiSession,
external_mpi_comm_available, find_free_port,
need_spawn_mpi_workers)
from .llmapi.tracer import (VizTracer, enable_llm_tracer, get_tracer,
global_tracer, set_global_tracer)
from .llmapi.utils import (AsyncQueue, ManagedThread, _SyncQueue,
enable_llm_debug, print_colored)
from .lora_manager import LoraManager
from .prompt_adapter_manager import PromptAdapterManager
from .runtime import ModelConfig
from .runtime.model_runner import _engine_config_to_model_config
from .sampling_params import SamplingParams
unblock_corountine = True
if enable_llm_debug():
# Mainly enable more detailed logging from cpp runtime.
set_level("info")
@dataclass(slots=True)
class LoRARequest:
""" Request for a LoRA adapter. """
lora_name: str
lora_int_id: int
lora_path: str = ""
def __post_init__(self):
if not os.path.exists(self.lora_path):
raise ValueError(f"lora_path ({self.lora_path}) does not exist.")
@property
def adapter_id(self):
return self.lora_int_id
@property
def name(self):
return self.lora_name
@property
def path(self):
return self.lora_path
@dataclass(slots=True)
class PromptAdapterRequest:
"""
Request for a Prompt adapter.
"""
prompt_adapter_name: str
prompt_adapter_id: int
prompt_adapter_local_path: str = ""
def __post_init__(self):
if not os.path.exists(self.prompt_adapter_local_path):
raise RuntimeError(
f"prompt_adapter_local_path ({self.prompt_adapter_local_path}) does not exist."
)
@property
def adapter_id(self):
return self.prompt_adapter_id
@property
def name(self):
return self.prompt_adapter_name
@property
def local_path(self):
return self.prompt_adapter_local_path
class GenerationRequest:
def __init__(
self,
prompt_token_ids: Union[torch.Tensor, np.ndarray,
Union[List[int], List[List[int]]]],
sampling_params: SamplingParams,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
streaming: bool = False,
):
if isinstance(prompt_token_ids, list):
self.prompt_token_ids = prompt_token_ids
elif isinstance(prompt_token_ids, (torch.Tensor, np.ndarray)):
self.prompt_token_ids = prompt_token_ids.tolist()
else:
raise TypeError(
f"prompt_token_ids ({prompt_token_ids}) should be an instance of torch.Tensor, np.ndarray or list"
)
self.sampling_params = sampling_params
self.lora_request = lora_request
self.prompt_adapter_request = prompt_adapter_request
self.streaming = streaming
self.id: Optional[int] = None
def set_id(self, id):
assert self.id is None, f"Request ID is already set: {self.id}"
self.id = id
return self
@dataclass(slots=True)
class CompletionOutput:
"""The output data of one completion output of a request.
Args:
index (int): The index of the output in the request.
text (str): The generated output text. Defaults to "".
token_ids (List[int]): The token ids of the generated output text. Defaults to [].
cumulative_logprob (float, optional): The cumulative log probability of the generated output text. Defaults to None.
logprobs (List[float]): The log probabilities of the top probability words at each position if the logprobs are requested. Defaults to [].
finish_reason (Literal['stop', 'length'], optional): The reason why the sequence is finished. Defaults to None.
stop_reason (int, str, optional): The stop string or token id that caused the completion to stop, None if the completion finished for some other reason. Defaults to None.
generation_logits (torch.Tensor, optional): The logits on the generated output token ids. Defaults to None.
Properties:
length (int): The number of generated tokens.
token_ids_diff (List[int]): Newly generated token ids.
logprobs_diff (List[float]): Logprobs of newly generated tokens.
text_diff (str): Newly generated tokens.
"""
index: int
text: str = ""
token_ids: List[int] = field(default_factory=list)
cumulative_logprob: Optional[float] = None
logprobs: List[float] = field(default_factory=list)
finish_reason: Optional[Literal['stop', 'length']] = None
stop_reason: Optional[Union[int, str]] = None
generation_logits: Optional[torch.Tensor] = None
# hidden fields for tracking the diffs
_last_text_len: int = field(default=0, init=False, repr=False)
_last_token_ids_len: int = field(default=0, init=False, repr=False)
_last_logprobs_len: int = field(default=0, init=False, repr=False)
_incremental_states: Optional[dict] = field(default=None,
init=False,
repr=False)
@property
def length(self):
return len(self.token_ids)
@property
def text_diff(self) -> str:
return self.text[self._last_text_len:]
@property
def token_ids_diff(self) -> List[int]:
return self.token_ids[self._last_token_ids_len:]
@property
def logprobs_diff(self) -> List[float]:
return self.logprobs[self._last_logprobs_len:]
class CppExecutorError(RuntimeError):
def __init__(self, message: Optional[str] = None):
self.message = message
self.stack_trace = traceback.format_exc()
super().__init__(message)
def __str__(self):
return f"{self.message}\nStack trace:\n{self.stack_trace}"
[docs]
class RequestError(RuntimeError):
''' The error raised when the request is failed. '''
class GenerationResult:
'''
The result of a generation request. It can be used to wait for the completion of the request.
Args:
generation_request (GenerationRequest): The generation request object.
background_error_handler (Callable, optional): The error handler to process the errors from the background threads/processes. Defaults to None.
'''
def __init__(self,
generation_request: GenerationRequest,
background_error_handler: Optional[Callable] = None) -> None:
self._done = False
self._cancelled = False
self._generation_request = generation_request
if has_event_loop():
aqueue = AsyncQueue()
self.queue = aqueue.sync_q
self.aqueue = aqueue.async_q
else:
self.queue = Queue()
self.aqueue = None
# In Sampling mode, the Executor runtime will return best_of sequences
# in total, which the LLM API will select the n-best sequences among
# them based on their cumulative log probabilities.
self._outputs: List[CompletionOutput] = [
CompletionOutput(i)
for i in range(self._generation_request.sampling_params.best_of)
]
self.context_logits: Optional[torch.Tensor] = None
self._background_error_handler = None
if background_error_handler is not None:
self._background_error_handler = WeakMethod(
background_error_handler)
@property
def request_id(self) -> int:
return self._generation_request.id
@property
def prompt_token_ids(self) -> List[int]:
return self._generation_request.prompt_token_ids
@property
def finished(self) -> bool:
return self._done
@property
def streaming(self):
return self._generation_request.streaming
@property
def outputs(self) -> List[CompletionOutput]:
sampling_param = self._generation_request.sampling_params
if (sampling_param.use_beam_search
or sampling_param.n == sampling_param.best_of):
return self._outputs[:sampling_param.n]
# Pick the top-n outputs, sorted by cumulative log probs.
sorted_outputs = sorted(
self._outputs,
key=lambda x:
(x.cumulative_logprob
if x.cumulative_logprob is not None else float('-inf')),
reverse=True)
# Reindex the sequence.
for i, sorted_out in enumerate(sorted_outputs):
sorted_out.index = i
return sorted_outputs[:sampling_param.n]
def handle_sequence(self, response: "GenerationExecutor.Response",
sequence_index: int):
""" Handle a single sequence in the response. """
tensors = response.tensors
assert tensors is not None
beam_search = self._generation_request.sampling_params.use_beam_search
seq_idx = sequence_index
src_idx = sequence_index if beam_search else 0
output = self._outputs[seq_idx]
output._last_token_ids_len = len(output.token_ids)
output.token_ids.extend(tensors.output_token_ids[src_idx])
if tensors.cum_log_probs is not None:
output.cumulative_logprob = tensors.cum_log_probs[src_idx]
if tensors.log_probs is not None:
output._last_logprobs_len = len(output.logprobs)
output.logprobs = tensors.log_probs[src_idx]
assert len(output.logprobs) == output.length
if tensors.generation_logits is not None:
output.generation_logits = tensors.generation_logits[
src_idx, :output.length]
if self.finished:
if response.finish_reasons[src_idx] == tllm.FinishReason.END_ID:
output.finish_reason = 'stop'
elif response.finish_reasons[
src_idx] == tllm.FinishReason.STOP_WORDS:
output.finish_reason = 'stop'
sampling_params = self._generation_request.sampling_params
for stop_reason, stop_ids in sampling_params._get_stop_reasons_and_words(
):
if output.token_ids[-len(stop_ids):] == stop_ids:
output.stop_reason = stop_reason
if not sampling_params.include_stop_str_in_output:
output.token_ids = output.token_ids[:-len(stop_ids)]
break
elif response.finish_reasons[src_idx] == tllm.FinishReason.LENGTH:
output.finish_reason = 'length'
def handle_response(self, response: "GenerationExecutor.Response"):
self._done = response.is_final
if response.error:
if handler := self._background_error_handler():
handler(response.error)
tensors = response.tensors
# output_token_ids = (beams, tokens)
if self._generation_request.sampling_params.use_beam_search:
for beam_idx, _ in enumerate(tensors.output_token_ids):
self.handle_sequence(response, beam_idx)
else:
self.handle_sequence(response, response.sequence_index)
if tensors.context_logits is not None:
self.context_logits = tensors.context_logits
# Processing background errors here ASAF during generation.
if self._background_error_handler and (
handler := self._background_error_handler()):
handler()
def result_step(self, timeout: Optional[float] = None):
response = self.queue.get(timeout=timeout)
self.handle_response(response)
async def aresult_step(self):
assert self.aqueue is not None, "The asyncio event loop was not present during initialization, so async operations are not available."
response = await self.aqueue.get()
global_tracer().log_instant("result_step.get")
self.handle_response(response)
def result(self, timeout: Optional[float] = None) -> "GenerationResult":
while not self._done:
self.result_step(timeout)
return self
async def aresult(self) -> "GenerationResult":
while not self._done:
await self.aresult_step()
return self
def __await__(self):
return self.aresult().__await__()
def __iter__(self):
return self
def __next__(self):
if self._done:
raise StopIteration
self.result_step()
return self
def __aiter__(self):
return self
async def __anext__(self):
if self._done:
raise StopAsyncIteration
await self.aresult_step()
return self
def running(self) -> bool:
return not self._done
def cancelled(self) -> bool:
return self._cancelled
def cancel(self):
raise NotImplementedError
def done(self) -> bool:
return self._done
def exception(self, timeout: Optional[float] = None):
try:
self.result(timeout)
except RuntimeError as e:
return e
def _repr_fields(self):
return [
'request_id', 'prompt_token_ids', 'outputs', 'finished',
"context_logits"
]
def __repr__(self) -> str:
repr = []
for field in self._repr_fields():
value = getattr(self, field)
if isinstance(value, str):
repr.append(f"{field}={value!r}")
else:
repr.append(f"{field}={value}")
repr = ", ".join(repr)
repr = f"{self.__class__.__name__}({repr})"
return repr
def __hash__(self):
return hash(self.request_id)
[docs]
class NoStatsAvailable(Exception):
pass
class GenerationExecutor(ABC):
class ResponseTensors(NamedTuple):
output_token_ids: List[List[int]]
# context_logits is a tensor or a string denoting the path to the shared memory.
context_logits: Optional[torch.Tensor | str]
# generation_logits is a tensor or a string denoting the path to the shared memory.
generation_logits: Optional[torch.Tensor | str]
log_probs: Optional[list]
cum_log_probs: Optional[list]
class Response(NamedTuple):
""" The response from the cpp-executor to the Python main thread. """
client_id: int
tensors: Optional["GenerationExecutor.ResponseTensors"]
finish_reasons: Optional[List[tllm.FinishReason]]
is_final: Optional[bool]
sequence_index: Optional[int]
# There are two types of errors:
# 1. str for the errors from the cpp-executor.await_responses, this will be dispatched to the user's
# generate_async as a per-request error, and won't stop the whole service.
# 2. Exception for the errors from the background threads/processes, this will be processed in the main thread,
# and stop the whole service.
error: Optional[str | Exception]
def __init__(self):
self._stats = None
self.stats_queue = None
atexit.register(self.shutdown)
# This is used to capture the exceptions from the threads.
self._error_queue = Queue()
# A flag to avoid calling shutdown() recursively. This happens when the background threads raise errors.
self.doing_shutdown = False
self._last_client_id: int = 1
@abstractmethod
def submit(self, request: GenerationRequest) -> GenerationResult:
pass
def generate_async(
self,
prompt_token_ids: List[int],
sampling_params: SamplingParams,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
streaming: bool = False,
) -> GenerationResult:
"""Generate output for the given prompt token ids in the asynchronous mode.
Asynchronous generation accepts single prompt only.
"""
assert isinstance(prompt_token_ids[0], int)
assert isinstance(sampling_params, SamplingParams)
result = self.submit(
GenerationRequest(prompt_token_ids,
sampling_params=sampling_params,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request,
streaming=streaming))
return result
def generate(
self,
prompt_token_ids: Union[List[int], List[List[int]]],
sampling_params: Union[SamplingParams, List[SamplingParams]],
lora_request: Optional[Union[LoRARequest, List[LoRARequest]]] = None,
prompt_adapter_request: Optional[Union[
PromptAdapterRequest, List[PromptAdapterRequest]]] = None,
) -> Union[GenerationResult, List[GenerationResult]]:
"""Generate output for the given prompt token ids in the synchronous mode.
Synchronous generation accepts either single prompt or batched prompts.
"""
unbatched = isinstance(prompt_token_ids[0], int)
if unbatched:
prompt_token_ids = [prompt_token_ids]
futures = []
for i, p in enumerate(prompt_token_ids):
if isinstance(sampling_params, list):
sp = sampling_params[i]
else:
sp = sampling_params
if isinstance(lora_request, list):
lora_req = lora_request[i]
else:
lora_req = lora_request
if isinstance(prompt_adapter_request, list):
pa_req = prompt_adapter_request[i]
else:
pa_req = prompt_adapter_request
future = self.generate_async(p,
sampling_params=sp,
lora_request=lora_req,
prompt_adapter_request=pa_req,
streaming=False)
futures.append(future)
for future in futures:
future.result()
if unbatched:
futures = futures[0]
return futures
def _get_next_client_id(self):
# (self._last_client_id + 1) % UINT64_MAX
self._last_client_id = (self._last_client_id + 1) & ((1 << 64) - 1)
return self._last_client_id
def _handle_background_error(self, error: Optional[Exception | str] = None):
""" Process the errors from the threads or processes.
NOTE: This should be called in the main thread.
"""
if error is not None:
# For details please refer to the comment of `GenerationResult.error`
if isinstance(error, Exception):
# Serious error from background thread or process
if enable_llm_debug():
print_colored(
f"Got background error: {repr(error)}, will shutdown the LLM instance\n",
"red")
self.shutdown()
raise error
elif isinstance(error, str):
if enable_llm_debug():
print_colored(f"Got per-request error: {repr(error)}\n",
"red")
print_colored(str(traceback.extract_stack()) + "\n", "red")
# A per-request error, can be captured and ignored
raise RequestError(error)
# Here we raise the first error in the queue. This method will be called repeatedly and user can choose to catch
# more than one error.
if not self._error_queue.empty():
e = self._error_queue.get()
self._error_queue.task_done()
self.shutdown()
# We can catch some exceptions here.
raise e
@abstractmethod
def shutdown(self):
pass
def create_stats_queue(self):
# Stats queue is created during first submission to ensure event loop exists if it is needed.
if not self._stats:
if has_event_loop():
self._stats = AsyncQueue()
self.stats_queue = self._stats.sync_q
self.stats_aqueue = self._stats.async_q
else:
self._stats = Queue()
self.stats_queue = self._stats
self.stats_aqueue = None
def get_stats(self, timeout=None) -> str:
''' Get the stats from the runtime.
Exceptions:
NoStatsAvailable: If the stats are not available.
Returns:
str: The stats in JSON format.
Known issue:
The `get_stats` cannot mix with `aget_stats` in the same Executor instance.
'''
assert self.stats_queue, "The stats queue is not created. It is likely that `get_stats` and `aget_stats` methods" \
" are mixed."
try:
res = self.stats_queue.get(timeout=timeout)
except Empty:
raise NoStatsAvailable
return res
async def aget_stats(self, timeout=None) -> Optional[str]:
''' Get the stats from the runtime.
Exceptions:
NoStatsAvailable: If the stats are not available.
Returns:
str: The stats in JSON format.
Known issue:
The `aget_stats` cannot mix with `get_stats` in the same Executor instance.
'''
self.create_stats_queue()
assert self.stats_aqueue is not None
if not has_event_loop():
raise NoStatsAvailable
try:
res = await self.stats_aqueue.get(timeout=timeout)
except asyncio.TimeoutError:
raise NoStatsAvailable
return res
@staticmethod
def create(
engine: Union[Path, Engine],
executor_config: tllm.ExecutorConfig = tllm.ExecutorConfig(1),
model_world_size: int = 1,
world_size: int = 0,
mpi_session: Optional[MpiSession] = None,
reuse_mpi_comm: bool = False,
enable_processes_for_single_gpu: bool = False
) -> Union["ExecutorBindingsProxy", "ExecutorBindingsWorker"]:
if world_size == 0:
world_size = mpi_world_size()
if world_size > 1 and world_size < model_world_size:
raise RuntimeError(
"Cannot instantiate Generator for engine built "
f"for {model_world_size} ranks, while currently running "
f"on {world_size} ranks.")
worker_kwargs = {
"engine": engine,
"executor_config": executor_config,
}
# The case where the Python main process is launched by mpirun
mpirun_launch = external_mpi_comm_available(model_world_size)
# The case where the Python main process utilizes mpi4py to spawn MPI workers
spawn_workers = need_spawn_mpi_workers(model_world_size)
if spawn_workers or (mpirun_launch and reuse_mpi_comm):
if reuse_mpi_comm:
assert mpi_session is not None, "reuse_mpi_comm requires an external MPI session"
return ExecutorBindingsProxy(worker_kwargs,
model_world_size=model_world_size,
mpi_session=mpi_session)
# For single-gpu case:
# Partition the workload to multiple process for performance. While this requires uses to protect their entrypoint
# to `if __name__ == "__main__":`.
try:
if enable_processes_for_single_gpu:
ctx = multiprocessing.get_context("fork")
mpi_session = ProcessPoolExecutorSession(n_workers=1,
mp_context=ctx)
return ExecutorBindingsProxy(worker_kwargs,
model_world_size=model_world_size,
mpi_session=mpi_session)
finally:
# If the user's entrypoint is not protected by `if __name__ == "__main__":`, it will fall back to the traditional
# single process way.
return ExecutorBindingsWorker(engine=engine,
executor_config=executor_config)
class ProcessPoolExecutorSession(MpiSession):
# This process pool is introduced for better recoverable exceptions handling.
# It replaces MpiPoolExecutor for single-gpu case.
def __init__(self, n_workers: int, **kwargs):
self.n_workers = n_workers
self.mpi_pool = ProcessPoolExecutor(max_workers=self.n_workers,
**kwargs)
def submit(self, task: Callable, *args,
**kwargs) -> List[concurrent.futures.Future]:
return [
self.mpi_pool.submit(task, *args, **kwargs)
for i in range(self.n_workers)
]
def submit_sync(self, task: Callable, *args, **kwargs) -> List[Any]:
futures = [
self.mpi_pool.submit(task, *args, **kwargs)
for i in range(self.n_workers)
]
return [future.result() for future in futures]
def shutdown(self):
self.mpi_pool.shutdown(wait=False)
class ExecutorBindingsWorker(GenerationExecutor):
class WorkerExit(GeneratorExit):
pass
def __init__(
self,
engine: Union[Path, Engine],
executor_config: tllm.ExecutorConfig = tllm.ExecutorConfig(1)
) -> None:
super().__init__()
self.engine = None
self.result_queue = None
self.rank = mpi_rank()
# mapping: client_id -> GenerationResult
self._results: Dict[int, GenerationResult] = {}
if isinstance(engine, list):
engine = engine[self.rank]
def _create_engine():
if isinstance(engine, Engine):
return tllm.Executor(engine.engine,
json.dumps(engine.config.to_dict(),
cls=ConfigEncoder),
tllm.ModelType.DECODER_ONLY,
executor_config=executor_config,
managed_weights=engine.managed_weights)
use_default_executor = True
# PIVOT_TO_PYTHON_START
use_default_executor = not hasattr(executor_config, "backend")
# PIVOT_TO_PYTHON_END
if use_default_executor:
return tllm.Executor(engine, tllm.ModelType.DECODER_ONLY,
executor_config)
# PIVOT_TO_PYTHON_START
from tensorrt_llm.pyexecutor.backend_registries.backend_registry import \
unique_create_executor
return unique_create_executor(engine,
tllm.ModelType.DECODER_ONLY,
executor_config=executor_config,
device_id=self.rank %
torch.cuda.device_count())
# PIVOT_TO_PYTHON_END
self.engine = _create_engine()
self._lora_manager: Optional[LoraManager] = None
self._prompt_adapter_manager: Optional[PromptAdapterManager] = None
self._runtime_model_config: Optional[ModelConfig] = None
if self.rank == 0 and isinstance(self.engine, tllm.Executor):
if isinstance(engine, Engine):
engine_config = engine.config
else:
engine_config = EngineConfig.from_json_file(
f"{engine}/config.json")
self._runtime_model_config = _engine_config_to_model_config(
engine_config)
if engine_config.build_config.plugin_config.lora_plugin:
self._lora_manager = LoraManager()
if engine_config.build_config.max_prompt_embedding_table_size > 0:
self._prompt_adapter_manager = PromptAdapterManager()
self.await_response_thread = ManagedThread(
self.await_response_task,
error_queue=self._error_queue,
name="await_response_thread")
self.dispatch_stats_thread = ManagedThread(
self.dispatch_stats_task,
error_queue=self._error_queue,
name="dispatch_stats_thread")
def create_stats_queue(self):
# Stats queue is created during first submission to ensure event loop exists if it is needed.
if not self._stats:
if has_event_loop():
self._stats = AsyncQueue()
self.stats_queue = self._stats.sync_q
self.stats_aqueue = self._stats.async_q
else:
self._stats = Queue()
self.stats_queue = self._stats
self.stats_aqueue = None
def set_result_queue(self, queue):
"""In multi-gpu mode, result_queue will be set here to communicate between the proxy and the worker 0 process."""
self.result_queue = queue
def set_stats_queue(self, queue):
"""In multi-gpu mode, stats_queue will be set here to communicate between the proxy and the worker 0 process."""
self._stats = queue
self.stats_queue = self._stats
self.stats_aqueue = None
def return_queue(self, client_id: int):
""" If a centralized result queue is registered (used for communication with the proxy)
send the message there.
Otherwise, push the result directly in the GenerationResult queue.
"""
if self.result_queue is not None:
return self.result_queue
return self._results[client_id].queue
def start_awaiter_thread(self):
if self.engine.can_enqueue_requests(
) and not self.await_response_thread.is_alive():
self.await_response_thread.start()
def start_stats_thread(self):
if self.engine.can_enqueue_requests(
) and not self.dispatch_stats_thread.is_alive():
self.dispatch_stats_thread.start()
def _engine_response_callback(self, response: tllm.Response):
return response
def await_response_task(self) -> bool:
# Get responses and place in queue.
async_events = []
event_loop = None
for response in self.engine.await_responses(timeout=datetime.timedelta(
milliseconds=100)):
response = self._engine_response_callback(response)
if response is None:
continue
client_id = response.client_id
assert client_id is not None
if response.has_error():
# This error will be dispatched to the user's generate_async for the corresponding request. It won't
# stop the whole service.
rsp = self.Response(
client_id,
tensors=None,
# Note: error Response only has one finish reason.
# Since the error will be raised in the main thread, so the finish reason is not actually used.
finish_reasons=[tllm.FinishReason.NOT_FINISHED],
is_final=True,
sequence_index=None,
error=response.error_msg)
else:
tensors = self.ResponseTensors(
output_token_ids=response.result.output_token_ids,
context_logits=response.result.context_logits,
generation_logits=response.result.generation_logits,
log_probs=response.result.log_probs,
cum_log_probs=response.result.cum_log_probs,
)
rsp = self.Response(
client_id,
tensors,
finish_reasons=response.result.finish_reasons,
is_final=response.result.is_final,
sequence_index=response.result.sequence_index,
error=None)
queue = self.return_queue(client_id)
if self._has_background_error():
rsp = self._create_error_response(client_id)
# For AsyncQueue.sync_q, we will batch the events to avoid too many event notifications, thus put without
# wait here.
if isinstance(queue, _SyncQueue):
global_tracer().log_instant("worker-rsp.put")
queue.put_nowait(rsp)
async_events.append(queue.event)
# all the loops are identical
event_loop = queue.loop if event_loop is None else event_loop
else:
global_tracer().log_instant("worker-rsp.put")
queue.put(rsp) # This could be IPC
# Eliminate the finished GenerationRequest instances timely, which may take considerable memory.
if rsp.is_final:
self._results.pop(client_id)
if async_events:
_SyncQueue.notify_events(event_loop, async_events)
return True # success
def _has_background_error(self) -> bool:
return not self._error_queue.empty()
def _create_error_response(self, client_id) -> GenerationExecutor.Response:
bck_error = self._error_queue.get_nowait()
assert isinstance(bck_error, Exception)
return GenerationExecutor.Response(client_id,
tensors=None,
finish_reasons=None,
is_final=None,
sequence_index=None,
error=bck_error)
stats_count = 0
def dispatch_stats_task(self) -> bool:
time.sleep(0.1)
# Get stats and place in queue.
for stats in self.engine.get_latest_iteration_stats():
self.stats_count += 1
while hasattr(self.stats_queue, "full") and self.stats_queue.full():
self.stats_queue.get()
try:
self.stats_queue.put(stats.to_json_str())
except AsyncQueue.EventLoopShutdownError:
# This happens in the last stats loop while the generate workflow is stopped.
pass
except Exception as e:
raise e
return True # success
def start(self):
self.create_stats_queue()
self.start_awaiter_thread()
self.start_stats_thread()
def _load_lora_adapter(self, lora_request: LoRARequest):
self._lora_manager.load_from_ckpt(
[lora_request.path],
model_config=self._runtime_model_config,
runtime_mapping=None,
uids=[str(lora_request.adapter_id)])
def _load_prompt_adapter(self,
prompt_adapter_request: PromptAdapterRequest):
self._prompt_adapter_manager.load_from_ckpt(
[prompt_adapter_request.local_path],
model_config=self._runtime_model_config,
uids=[str(prompt_adapter_request.adapter_id)])
def _enqueue_request(self, request: GenerationRequest) -> int:
assert request.id is not None
if self._lora_manager is not None and request.lora_request is not None:
self._load_lora_adapter(request.lora_request)
uid = str(request.lora_request.adapter_id)
lora_config = tllm.LoraConfig(
task_id=request.lora_request.adapter_id,
weights=self._lora_manager.cpp_lora_weights[uid],
config=self._lora_manager.cpp_lora_config[uid])
else:
lora_config = None
prompt_token_ids = copy.deepcopy(request.prompt_token_ids)
if request.prompt_adapter_request is not None:
self._load_prompt_adapter(request.prompt_adapter_request)
uid = str(request.prompt_adapter_request.adapter_id)
prompt_tuning_config = tllm.PromptTuningConfig(
self._prompt_adapter_manager.uid_to_weights[uid])
vocab_size = self._runtime_model_config.vocab_size
pa_length = prompt_tuning_config.embedding_table.size(0)
prompt_token_ids = list(range(
vocab_size, vocab_size + pa_length)) + prompt_token_ids
else:
prompt_tuning_config = None
assert request.id is not None
try:
executor_request = tllm.Request(
client_id=request.id,
input_token_ids=prompt_token_ids,
max_tokens=request.sampling_params.max_tokens,
max_new_tokens=request.sampling_params.max_new_tokens,
streaming=request.streaming,
sampling_config=request.sampling_params._get_sampling_config(),
end_id=-1 if request.sampling_params.ignore_eos else
request.sampling_params.end_id,
pad_id=request.sampling_params.pad_id,
output_config=request.sampling_params._get_output_config(),
bad_words=request.sampling_params._get_bad_words(),
stop_words=request.sampling_params._get_stop_words(),
embedding_bias=request.sampling_params.embedding_bias,
external_draft_tokens_config=request.sampling_params.
external_draft_tokens_config,
lora_config=lora_config,
prompt_tuning_config=prompt_tuning_config,
logits_post_processor_name=request.sampling_params.
logits_post_processor_name,
)
req_id = self.engine.enqueue_request(executor_request)
return req_id
except Exception as e:
raise RequestError(str(e))
def submit(self, request: GenerationRequest) -> GenerationResult:
""" Low-level API to the executor. Return a "future" GenerationResult which can be waited. """
self.start()
if self.rank != 0:
raise RuntimeError(
"Only rank 0 can submit requests.\n"
"To fix this, ensure that the llm.generate(...) method is "
"guarded with the `if __name__ == '__main__':` block.")
client_id = request.id if request.id is not None else self._get_next_client_id(
)
if request.id is None:
request.set_id(client_id)
self._enqueue_request(request)
result = GenerationResult(
request, background_error_handler=self._handle_background_error)
self._results[client_id] = result
self._handle_background_error()
return result
def shutdown(self):
if enable_llm_debug():
try:
print_colored('Proxy.shutdown...\n', "yellow")
print(traceback.extract_stack())
except ValueError:
pass
if self.doing_shutdown:
return
else:
self.doing_shutdown = True
if self.engine is not None:
if self.engine.can_enqueue_requests():
if self.await_response_thread.is_alive():
self.await_response_thread.stop()
self.await_response_thread.join()
if self.dispatch_stats_thread.is_alive():
self.dispatch_stats_thread.stop()
self.dispatch_stats_thread.join()
self.engine.shutdown()
self.engine = None
# Check if there are any errors from the threads before shutdown.
self._handle_background_error()
def block_subordinates(self):
if self.rank != 0:
if isinstance(self.engine, tllm.Executor):
self.shutdown()
raise self.WorkerExit(
"block_subordinates() should be used in a `with ExecutorBindingsWorker() as ...:` block"
)
# PIVOT_TO_PYTHON_START
from tensorrt_llm.pyexecutor.py_executor import PyExecutor
if isinstance(self.engine, PyExecutor):
self.engine.wait_shutdown()
# PIVOT_TO_PYTHON_END
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback) -> bool:
self.shutdown()
return exc_type is None or exc_type == ExecutorBindingsWorker.WorkerExit
def __del__(self):
self.shutdown()
def wait_first_completed(
self, futures: List[GenerationResult]
) -> Generator[GenerationResult, None, None]:
wait_set = set(futures)
# clear already-finished requests
for f in futures:
if f._done:
wait_set.pop(f)
yield f
# wait remaining active requests
while len(wait_set) > 0:
fut = wait_set.pop()
if fut.request_id not in self._results:
yield fut
else:
wait_set.add(fut)
class ZeroMqQueue:
''' A Queue-like container for IPC using ZeroMQ. '''
def __init__(self,
address: Optional[Tuple[str, int, str]] = None,
*,
is_server: bool):
# NOTE: The port could be occupied by other processes if run in parallel.
address = address or ('localhost', find_free_port(),
secrets.token_bytes(512))
self.host_port, self.authkey = (address[0], address[1]), address[2]
self.is_server = is_server
self.context = zmq.Context()
self.poller = None
self.socket = None
@property
def address(self):
return (self.host_port[0], self.host_port[1], self.authkey)
def setup(self):
self.socket = self.context.socket(
zmq.PAIR) # PAIR for bidir communication
if self.is_server:
self.socket.bind(f'tcp://{self.host_port[0]}:{self.host_port[1]}')
else:
self.socket.connect(
f'tcp://{self.host_port[0]}:{self.host_port[1]}')
self.poller = zmq.Poller()
self.poller.register(self.socket, zmq.POLLIN)
def poll(self, timeout: int) -> bool:
"""
Parameters:
timeout (int): Timeout in seconds
"""
if self.socket is None:
self.setup()
events = dict(self.poller.poll(timeout=timeout * 1000))
if self.socket in events and events[self.socket] == zmq.POLLIN:
return True
else:
return False
def put(self, obj: Any):
if self.socket is None:
self.setup()
if isinstance(obj, GenerationExecutor.Response):
tensors = self._store_tensors_in_shmm(obj.tensors)
obj = GenerationExecutor.Response(client_id=obj.client_id,
tensors=tensors,
finish_reasons=obj.finish_reasons,
is_final=obj.is_final,
error=obj.error)
message = pickle.dumps(obj) # nosec B301
self.socket.send(message)
def get(self) -> Any:
if self.socket is None:
self.setup()
message = self.socket.recv()
obj = pickle.loads(message) # nosec B301
if isinstance(obj, GenerationExecutor.Response):
tensors = self._load_tensors_from_shmm(obj.tensors)
obj = GenerationExecutor.Response(client_id=obj.client_id,
tensors=tensors,
finish_reasons=obj.finish_reasons,
is_final=obj.is_final,
error=obj.error)
return obj
def close(self):
if self.socket:
self.socket.close()
self.socket = None
if self.context:
self.context.term()
self.context = None
def _store_tensors_in_shmm(
self, tensors: GenerationExecutor.ResponseTensors
) -> GenerationExecutor.ResponseTensors:
if tensors is None:
return tensors
# The tensors are huge and cannot be transferred through socket directly. We need to store them in shared memory,
# and replace the tensors with the shared memory path.
def store_tensor(tensor: Optional[torch.Tensor]) -> Optional[str]:
if tensor is None:
return None
# NOTE: We create random shmm here rather than two specific shmm for context and generation logit, since the
# shmm may not be read timely by the IpcQueue.get() in the other side, so there might be multiple alive shmm
# for logits.
# A known issue: the shmm instance may leak if the IpcQueue.get() thread is stopped before the IpcQueue.put()
# thread. This is not a big issue since the shmm will be automatically cleaned up when the process exits.
shm = SharedMemory(create=True, size=tensor.nbytes + 2048)
torch.save(tensor, shm._mmap)
shm.close()
return shm.name
return GenerationExecutor.ResponseTensors(
output_token_ids=tensors.output_token_ids,
context_logits=store_tensor(tensors.context_logits),
generation_logits=store_tensor(tensors.generation_logits),
log_probs=tensors.log_probs,
cum_log_probs=tensors.cum_log_probs,
)
def _load_tensors_from_shmm(
self, tensors: GenerationExecutor.ResponseTensors
) -> GenerationExecutor.ResponseTensors:
if tensors is None:
return tensors
def load_tensor(tensor: Optional[str]) -> Optional[torch.Tensor]:
if tensor is None or isinstance(tensor, torch.Tensor):
return tensor
shm = SharedMemory(name=tensor, create=False)
tensor = torch.load(io.BytesIO(shm.buf))
shm.close()
shm.unlink()
return tensor
return GenerationExecutor.ResponseTensors(
output_token_ids=tensors.output_token_ids,
context_logits=load_tensor(tensors.context_logits),
generation_logits=load_tensor(tensors.generation_logits),
log_probs=tensors.log_probs,
cum_log_probs=tensors.cum_log_probs,
)
def __del__(self):
self.close()
IpcQueue = ZeroMqQueue
class FusedIpcQueue:
''' A Queue-like container for IPC with optional message batched. '''
def __init__(self,
address: Optional[Tuple[str, int, str]] = None,
*,
is_server: bool,
fuse_message=False,
fuse_size=100000,
error_queue=None,
queue_cls=ZeroMqQueue):
self.queue = queue_cls(address=address, is_server=is_server)
self.fuse_message = fuse_message
self.error_queue = error_queue
self.fuse_size = fuse_size
self._message_counter = 0
self._obj_counter = 0
self._send_thread = None
self.sending_queue = Queue() if fuse_message else None
def setup_sender(self):
if not self.fuse_message or self._send_thread is not None:
return
def send_task():
while True:
qsize = self.sending_queue.qsize()
if qsize > 0:
qsize = min(self.fuse_size, qsize)
self._obj_counter += qsize
message = [
self.sending_queue.get_nowait() for _ in range(qsize)
]
self.queue.put(message)
self._message_counter += 1
else:
time.sleep(0.001)
self._send_thread = ManagedThread(send_task,
name="fused_send_thread",
error_queue=self.error_queue)
self._send_thread.start()
def put(self, obj: Any):
self.setup_sender()
if self.fuse_message:
self.sending_queue.put_nowait(self._prepare_message(obj))
else:
self.queue.put(self._prepare_message(obj))
def get(self) -> Any:
obj = self.queue.get()
if isinstance(obj, list):
return [self._process_message(o) for o in obj]
return self._process_message(obj)
def _prepare_message(self, obj: Any) -> Any:
if isinstance(obj, GenerationExecutor.Response):
tensors = self.queue._store_tensors_in_shmm(obj.tensors)
return GenerationExecutor.Response(
client_id=obj.client_id,
tensors=tensors,
finish_reasons=obj.finish_reasons,
is_final=obj.is_final,
sequence_index=obj.sequence_index,
error=obj.error)
return obj
def _process_message(self, obj: Any) -> Any:
if isinstance(obj, GenerationExecutor.Response):
tensors = self.queue._load_tensors_from_shmm(obj.tensors)
return GenerationExecutor.Response(
client_id=obj.client_id,
tensors=tensors,
finish_reasons=obj.finish_reasons,
is_final=obj.is_final,
sequence_index=obj.sequence_index,
error=obj.error)
return obj
@property
def address(self) -> Tuple[str, int, bytes]:
return self.queue.address
def __del__(self):
self.close()
def print_fuse_stats(self):
if self._message_counter > 0:
print_colored(
f"IPCQueue: {self._message_counter} messages, {self._obj_counter} objects sent, average: {self._obj_counter/self._message_counter}.\n",
"green")
def close(self):
self.queue.close()
if self._send_thread is not None:
self._send_thread.stop()
self._send_thread.join()
self._send_thread = None
if enable_llm_debug():
self.print_fuse_stats()
class ExecutorBindingsProxy(GenerationExecutor):
READY_SIGNAL = b"READY"
def __init__(self,
workers_kwargs,
model_world_size: int = 1,
mpi_session: Optional[MpiSession] = None,
*,
worker_cls: type = ExecutorBindingsWorker) -> None:
super().__init__()
self.workers_started = False
self.worker_cls = worker_cls
self.request_queue = IpcQueue(is_server=True)
self.request_error_queue = IpcQueue(is_server=True)
self.result_queue = FusedIpcQueue(is_server=True, fuse_message=True)
self.mp_stats_queue = FusedIpcQueue(is_server=True, fuse_message=True)
self._results: Dict[int, GenerationResult] = {}
if mpi_session is None:
self.mpi_session = MpiPoolSession(n_workers=model_world_size)
else:
self.mpi_session = mpi_session
self.model_world_size = model_world_size
self.workers_kwargs = workers_kwargs
self.workers_kwargs.update({
"request_queue_addr":
self.request_queue.address,
"request_error_queue_addr":
self.request_error_queue.address,
"result_queue_addr":
self.result_queue.address,
"stats_queue_addr":
self.mp_stats_queue.address,
})
self.dispatch_result_thread: Optional[ManagedThread] = None
self.dispatch_stats_thread: Optional[ManagedThread] = None
self._start_executor_workers()
@staticmethod
def workers_main(engine: Union[Path, Engine],
request_queue_addr: Tuple[str, int, bytes],
request_error_queue_addr: Tuple[str, int, bytes],
result_queue_addr: Tuple[str, int, bytes],
stats_queue_addr: Tuple[str, int, bytes],
executor_config: tllm.ExecutorConfig = tllm.ExecutorConfig(
1),
worker_cls: type = ExecutorBindingsWorker,
tracer_init_kwargs=None) -> None:
result_queue = None
if tracer_init_kwargs is not None and mpi_rank() == 0:
tracer = VizTracer(**tracer_init_kwargs)
tracer.register_exit()
tracer.start()
set_global_tracer(tracer)
if mpi_rank() == 0:
request_queue = IpcQueue(request_queue_addr, is_server=False)
request_error_queue = IpcQueue(request_error_queue_addr,
is_server=False)
result_queue = FusedIpcQueue(result_queue_addr,
is_server=False,
fuse_message=True)
mp_stats_queue = FusedIpcQueue(stats_queue_addr,
is_server=False,
fuse_message=True)
def notify_proxy_threads_to_quit():
# Signal the dispatcher thread in the proxy to quit
result_queue.put(None)
# Signal the stats thread in the proxy to quit
mp_stats_queue.put(None)
# Error handling in the Worker/MPI process
# 1. During Executor initialization, the errors will be captured and send back via request_error_queue.
# 2. During execution, the errors will be captured by ManagedThreads
# a) For per-request error, the error will be send back via result_queue, and eventually raised in
# handle_response() in the main thread.
# b) For system error, the error will be raised in the MPI process and handled by future.done_callback,
# that will propagate the error to the error_queue in the main thread.
try:
executor = worker_cls(engine, executor_config)
except Exception as e:
if mpi_rank() == 0:
request_error_queue.put(e)
return
with executor:
try:
executor.block_subordinates()
if mpi_rank() == 0:
executor.set_result_queue(result_queue)
executor.set_stats_queue(mp_stats_queue)
request_error_queue.put(ExecutorBindingsProxy.READY_SIGNAL)
while (req := request_queue.get()) is not None:
try:
result = executor.submit(req)
request_error_queue.put(None) # None means success
except RequestError as e:
request_error_queue.put(e)
notify_proxy_threads_to_quit()
except ExecutorBindingsWorker.WorkerExit as e:
# This will capture by the with-statement and exit normally.
raise e
except Exception as e: # other critical errors
if mpi_rank() == 0:
notify_proxy_threads_to_quit()
err = CppExecutorError(f"Failed during generation: {e}")
if mpi_rank() == 0:
request_error_queue.put(err)
def dispatch_result_task(self) -> bool:
if (res := self.result_queue.get()) is None:
return False # shutdown the thread
async_events = []
event_loop = None
def process_res(res):
client_id = res.client_id
nonlocal event_loop
nonlocal async_events
queue = self._results[client_id].queue
if isinstance(queue, _SyncQueue):
queue.put_nowait(res)
async_events.append(queue.event)
# all the loops are identical
event_loop = queue.loop if event_loop is None else event_loop
else:
queue.put(res)
if res.is_final:
self._results.pop(client_id)
res = res if isinstance(res, list) else [res]
for i in res:
global_tracer().log_instant("IPC.get")
if i is None:
return False
process_res(i)
if async_events:
_SyncQueue.notify_events(event_loop, async_events)
return True # success
def dispatch_stats_task(self) -> bool:
# get-stats is not urgent, so we can sleep a bit
time.sleep(0.1)
try:
stats = self.mp_stats_queue.get()
except:
return False
if stats is None:
return False
stats = stats if isinstance(stats, list) else [stats]
while self.stats_queue.full():
self.stats_queue.get()
try:
for s in stats:
if s is None:
return False
self.stats_queue.put(s)
except AsyncQueue.EventLoopShutdownError:
# This happens in the last stats loop while the generate workflow is stopped.
pass
except Exception as e:
raise e
return True # success
def _start_dispatch_threads(self):
if self.dispatch_result_thread is None:
self.dispatch_result_thread = ManagedThread(
self.dispatch_result_task,
error_queue=self._error_queue,
name="proxy_dispatch_result_thread")
self.dispatch_stats_thread = ManagedThread(
self.dispatch_stats_task,
error_queue=self._error_queue,
name="proxy_dispatch_stats_thread")
self.dispatch_result_thread.start()
self.create_stats_queue()
self.dispatch_stats_thread.start()
self._handle_background_error()
def _start_executor_workers(self):
def mpi_done_callback(future: concurrent.futures.Future):
# This is called when the MPI worker is done, so future.exception() will not block.
if future.exception() is not None:
self._error_queue.put_nowait(future.exception())
tracer_init_kwargs = get_tracer().init_kwargs if enable_llm_tracer(
) else None
self.mpi_futures = self.mpi_session.submit(
ExecutorBindingsProxy.workers_main,
**self.workers_kwargs,
worker_cls=self.worker_cls,
tracer_init_kwargs=tracer_init_kwargs)
for fut in self.mpi_futures:
fut.add_done_callback(mpi_done_callback)
self.workers_started = True
while not self.request_error_queue.poll(1):
self._handle_background_error()
ready_signal = self.request_error_queue.get()
if ready_signal != ExecutorBindingsProxy.READY_SIGNAL:
raise ready_signal
def shutdown(self):
if enable_llm_debug():
try:
print_colored('Proxy.shutdown...\n', "yellow")
print_colored(str(traceback.format_exc()) + "\n", "yellow")
except ValueError:
pass
if not self.workers_started:
return
if self.doing_shutdown:
return
else:
self.doing_shutdown = True
# step1: notify the workers to quit
if all(not f.done() for f in self.mpi_futures):
self.request_queue.put(None)
for f in self.mpi_futures:
try:
f.result()
except:
# The errors are already captured in mpi_done_callback, ignored here
pass
# step2: notify the background threads to quit
if self.dispatch_result_thread is not None and self.dispatch_result_thread.is_alive(
):
self.dispatch_result_thread.stop()
self.dispatch_result_thread.join()
if self.dispatch_stats_thread is not None and self.dispatch_stats_thread.is_alive(
):
self.dispatch_stats_thread.stop()
self.dispatch_stats_thread.join()
# step3: finish all remaining work
# close all the sockets
self.request_queue.close()
self.request_error_queue.close()
self.result_queue.close()
self.mp_stats_queue.close()
self.workers_started = False
self.mpi_session.shutdown()
# Process the errors in-case error during shutting down the threads
self._handle_background_error()
def submit(self, request: GenerationRequest) -> GenerationResult:
"""
Low-level API to the executor. Return a "future" GenerationResult which can be waited.
Forwards the request to the workers through the request queue.
"""
self._start_dispatch_threads()
request.set_id(self._get_next_client_id())
result = GenerationResult(
request, background_error_handler=self._handle_background_error)
self._results[request.id] = result
self.request_queue.put(request)
error = self.request_error_queue.get()
if isinstance(error, Exception):
raise error
self._handle_background_error()
return result
def __del__(self):
self.shutdown()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.shutdown()
return False # propagate the exception
if enable_llm_debug():
print_colored("LLM debug mode enabled.\n", "yellow")
# This will dump all the alive threads when the process is interrupted by SIGINT.
faulthandler.register(signal.SIGINT, all_threads=True)
def has_event_loop() -> bool:
try:
asyncio.get_running_loop()
except RuntimeError:
return False
return True