mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
Sync v2.0 version of code to github repo
This commit is contained in:
Jiang-Jia-Jun
370
fastdeploy/engine/expert_service.py
Normal file
370
fastdeploy/engine/expert_service.py
Normal file
@@ -0,0 +1,370 @@
|
||||
"""
|
||||
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License"
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import signal
|
||||
import threading
|
||||
import time
|
||||
import traceback
|
||||
import weakref
|
||||
|
||||
import numpy as np
|
||||
|
||||
from fastdeploy.engine.resource_manager import ResourceManager
|
||||
from fastdeploy.inter_communicator import EngineWorkerQueue
|
||||
from fastdeploy.metrics.metrics import main_process_metrics
|
||||
from fastdeploy.output.token_processor import TokenProcessor
|
||||
from fastdeploy.splitwise.splitwise_connector import SplitwiseConnector
|
||||
from fastdeploy.utils import EngineError, console_logger, llm_logger
|
||||
|
||||
|
||||
class ExpertService(object):
|
||||
"""
|
||||
Engine class responsible for managing the Large Language Model (LLM) operations.
|
||||
|
||||
Attributes:
|
||||
cfg (Config): Configuration object containing all the parameters.
|
||||
local_data_parallel_id (int): Local data parallel ID.
|
||||
"""
|
||||
|
||||
def __init__(self, cfg, local_data_parallel_id):
|
||||
"""
|
||||
Initializes the LLMEngine with the provided configuration.
|
||||
|
||||
Args:
|
||||
cfg (Config): Config object containing all the configuration parameters.
|
||||
"""
|
||||
self.cfg = cfg
|
||||
start_pos = local_data_parallel_id * self.cfg.tensor_parallel_size
|
||||
end_pos = (local_data_parallel_id + 1) * self.cfg.tensor_parallel_size
|
||||
self.cfg.cache_config.rdma_comm_ports = self.cfg.cache_config.rdma_comm_ports[
|
||||
start_pos:end_pos]
|
||||
self.cfg.local_device_ids = self.cfg.device_ids.split(
|
||||
",")[start_pos:end_pos]
|
||||
self.cfg.parallel_config.local_data_parallel_id = local_data_parallel_id
|
||||
self.cfg.disaggregate_info = None
|
||||
|
||||
self.scheduler = cfg.scheduler_config.scheduler()
|
||||
|
||||
self.scheduler.reset_nodeid(
|
||||
f"{self.scheduler.infer.nodeid}_{str(local_data_parallel_id)}")
|
||||
|
||||
self.cfg.parallel_config.local_data_parallel_id = local_data_parallel_id
|
||||
|
||||
address = ('0.0.0.0', cfg.engine_worker_queue_port)
|
||||
self.engine_worker_queue = EngineWorkerQueue(
|
||||
address=address,
|
||||
is_server=False,
|
||||
client_id=0,
|
||||
num_client=cfg.tensor_parallel_size,
|
||||
local_data_parallel_id=local_data_parallel_id,
|
||||
)
|
||||
self.resource_manager = ResourceManager(cfg.max_num_seqs, cfg, \
|
||||
cfg.tensor_parallel_size, cfg.splitwise_role, local_data_parallel_id)
|
||||
|
||||
if len(self.cfg.cache_config.pd_comm_port) == 1:
|
||||
self.cfg.cache_config.pd_comm_port[0] = int(
|
||||
self.cfg.cache_config.pd_comm_port[0]) + local_data_parallel_id
|
||||
else:
|
||||
self.cfg.cache_config.pd_comm_port = [
|
||||
self.cfg.cache_config.pd_comm_port[local_data_parallel_id]
|
||||
]
|
||||
|
||||
self.split_connector = SplitwiseConnector(self.cfg, self.scheduler,
|
||||
self.engine_worker_queue,
|
||||
self.resource_manager)
|
||||
|
||||
self.token_processor = TokenProcessor(
|
||||
cfg=cfg,
|
||||
cached_generated_tokens=self.scheduler,
|
||||
engine_worker_queue=self.engine_worker_queue,
|
||||
split_connector=self.split_connector)
|
||||
self.token_processor.set_resource_manager(self.resource_manager)
|
||||
|
||||
self.partial_chunked_tokens = [0] * (
|
||||
self.cfg.max_num_partial_prefills + 1)
|
||||
for idx in range(1, self.cfg.max_num_partial_prefills + 1):
|
||||
self.partial_chunked_tokens[idx] = (self.cfg.max_num_batched_tokens // idx) \
|
||||
// self.cfg.cache_config.block_size * self.cfg.cache_config.block_size
|
||||
|
||||
self._finalizer = weakref.finalize(self, self._exit_sub_services)
|
||||
|
||||
def start(self, ipc_signal_suffix, local_data_parallel_id):
|
||||
"""
|
||||
Initializes the engine and starts its sub-services.
|
||||
If `api_server_pid` is defined, will launch a thread
|
||||
to keep getting request from zmq_server.
|
||||
"""
|
||||
# assert not self.is_started, "The engine is already started."
|
||||
start_time = time.time()
|
||||
|
||||
llm_logger.info(f"start expert service {local_data_parallel_id}")
|
||||
|
||||
self.cache_manager_processes = self.resource_manager.cache_manager.launch_cache_manager(
|
||||
self.cfg.cache_config, self.cfg.tensor_parallel_size,
|
||||
self.cfg.local_device_ids, self.cfg.engine_worker_queue_port,
|
||||
f"{local_data_parallel_id}_{ipc_signal_suffix}")
|
||||
|
||||
self.insert_task_to_worker_thread = threading.Thread(
|
||||
target=self._insert_task_to_worker, args=())
|
||||
self.insert_task_to_worker_thread.daemon = True
|
||||
self.insert_task_to_worker_thread.start()
|
||||
|
||||
# Start TokenProcessor thread
|
||||
os.environ["INFERENCE_MSG_QUEUE_ID"] = str(
|
||||
local_data_parallel_id + int(self.cfg.engine_worker_queue_port))
|
||||
|
||||
self.token_processor.run()
|
||||
|
||||
self.split_mode_get_tasks()
|
||||
|
||||
self.cfg.init_cache_info()
|
||||
|
||||
role = self.cfg.splitwise_role
|
||||
host_ip = self.cfg.host_ip
|
||||
disaggregate = self.cfg.disaggregate_info
|
||||
self.scheduler.start(role, host_ip, disaggregate)
|
||||
self.cfg.print()
|
||||
|
||||
console_logger.info(
|
||||
"Worker processes are launched with {} seconds.".format(
|
||||
time.time() - start_time))
|
||||
return True
|
||||
|
||||
def _insert_task_to_worker(self):
|
||||
"""
|
||||
Insert task to engine thread, monitor scheduler request queue.
|
||||
if the engine has resource, insert task to engine
|
||||
"""
|
||||
current_id = -1
|
||||
while True:
|
||||
try:
|
||||
if self.resource_manager.available_batch() == 0:
|
||||
time.sleep(0.001)
|
||||
continue
|
||||
if self.engine_worker_queue.num_tasks() > 0:
|
||||
time.sleep(0.001)
|
||||
continue
|
||||
if len(self.split_connector.current_request_ids) > 0:
|
||||
time.sleep(0.001)
|
||||
continue
|
||||
|
||||
num_prefill_batch = min(
|
||||
int(self.resource_manager.available_batch()),
|
||||
self.cfg.max_prefill_batch)
|
||||
|
||||
self.resource_manager.check_and_free_block_tables()
|
||||
tasks = self.scheduler.get_requests(
|
||||
available_blocks=self.resource_manager.available_block_num(
|
||||
),
|
||||
block_size=self.cfg.cache_config.block_size,
|
||||
reserved_output_blocks=self.cfg.cache_config.
|
||||
enc_dec_block_num,
|
||||
max_num_batched_tokens=self.cfg.max_num_batched_tokens,
|
||||
batch=num_prefill_batch)
|
||||
|
||||
if len(tasks) == 0:
|
||||
time.sleep(0.001)
|
||||
continue
|
||||
|
||||
if self.cfg.splitwise_role != "mixed":
|
||||
llm_logger.info("Inserting splitwise tasks")
|
||||
self.split_connector.send_splitwise_tasks(
|
||||
tasks, current_id)
|
||||
|
||||
current_id = (current_id + 1) % 100003
|
||||
|
||||
self.insert_tasks(tasks, current_id)
|
||||
|
||||
main_process_metrics.num_requests_waiting.dec(len(tasks))
|
||||
main_process_metrics.num_requests_running.inc(len(tasks))
|
||||
except Exception as e:
|
||||
err_msg = "Error happend while insert task to engine: {}, {}.".format(
|
||||
e, str(traceback.format_exc()))
|
||||
llm_logger.error(err_msg)
|
||||
|
||||
def split_mode_get_tasks(self):
|
||||
"""
|
||||
Split mode get tasks
|
||||
"""
|
||||
waiting_requests = []
|
||||
|
||||
def receiver_loop():
|
||||
while True:
|
||||
try:
|
||||
if len(waiting_requests) > 0:
|
||||
for task in waiting_requests:
|
||||
if self.resource_manager.is_resource_sufficient(
|
||||
task.prompt_token_ids_len):
|
||||
self.insert_tasks([task])
|
||||
waiting_requests.remove(task)
|
||||
else:
|
||||
break
|
||||
if not self.engine_worker_queue.disaggregate_queue_empty():
|
||||
items = self.engine_worker_queue.get_disaggregated_tasks(
|
||||
)
|
||||
for item in items:
|
||||
role = item[0]
|
||||
tasks = item[1]
|
||||
if role == "prefill":
|
||||
llm_logger.info("get prefill tasks")
|
||||
for task in tasks:
|
||||
task.max_tokens = task.min_tokens = 2
|
||||
self.insert_tasks(tasks)
|
||||
elif role == "decode":
|
||||
llm_logger.info(f"get decode tasks {tasks}")
|
||||
if hasattr(tasks[0], 'finished'):
|
||||
if not isinstance(tasks, list):
|
||||
tasks = [tasks]
|
||||
for task in tasks:
|
||||
task.finished = False
|
||||
# self.scheduler.put_results(tasks)
|
||||
|
||||
self.insert_tasks(tasks, allocated=True)
|
||||
else:
|
||||
if len(waiting_requests):
|
||||
for task in tasks:
|
||||
waiting_requests.append(task)
|
||||
else:
|
||||
for task in tasks:
|
||||
if not self.resource_manager.is_resource_sufficient(
|
||||
task.prompt_token_ids_len):
|
||||
waiting_requests.append(task)
|
||||
else:
|
||||
self.insert_tasks([task])
|
||||
|
||||
else:
|
||||
time.sleep(0.001)
|
||||
continue
|
||||
except Exception as e:
|
||||
llm_logger.error(f"get decode tasks error: {e}")
|
||||
|
||||
threading.Thread(target=receiver_loop, daemon=True).start()
|
||||
|
||||
def insert_tasks(self, tasks, current_id=-1, allocated=False):
|
||||
"""
|
||||
Insert tasks to engine.
|
||||
"""
|
||||
if allocated:
|
||||
current_tasks = []
|
||||
for task in tasks:
|
||||
cur_task_idx = self.resource_manager.req_dict[task.request_id]
|
||||
del self.resource_manager.req_dict[task.request_id]
|
||||
cur_task = self.resource_manager.tasks_list[cur_task_idx]
|
||||
if task.error_code != 200:
|
||||
self.resource_manager.stop_flags[cur_task_idx] = True
|
||||
self.resource_manager.tasks_list[cur_task_idx] = None
|
||||
self.resource_manager._recycle_block_tables(cur_task)
|
||||
if task.request_id in self.token_processor.tokens_counter:
|
||||
del self.token_processor.tokens_counter[
|
||||
task.request_id]
|
||||
self.scheduler.put_results([task])
|
||||
llm_logger.warning(
|
||||
f"{task.request_id} prefill failed with msg:{task.error_msg}, recycle resource."
|
||||
)
|
||||
continue
|
||||
llm_logger.info(f"{cur_task_idx} {task.request_id}")
|
||||
cur_task.prompt_token_ids[0] = task.outputs.token_ids[0]
|
||||
self.token_processor.tokens_counter[task.request_id] = 1
|
||||
current_tasks.append(cur_task)
|
||||
self.engine_worker_queue.put_tasks(
|
||||
(current_tasks, self.resource_manager.real_bsz))
|
||||
return True
|
||||
|
||||
self.resource_manager.check_and_free_block_tables()
|
||||
|
||||
if not isinstance(tasks, list):
|
||||
tasks = [tasks]
|
||||
|
||||
for item in tasks:
|
||||
item.schedule_start_time = time.time()
|
||||
|
||||
available_batch = np.sum(self.resource_manager.stop_flags)
|
||||
if len(tasks) > available_batch:
|
||||
llm_logger.error(
|
||||
"Inserting batch:{} exceeds the available batch:{}.".format(
|
||||
len(tasks), available_batch))
|
||||
llm_logger.error("The exceeded part will be ignored!")
|
||||
tasks = tasks[:available_batch]
|
||||
|
||||
req_ids = [t.request_id for t in tasks]
|
||||
|
||||
tasks = self.resource_manager.allocate_resources_for_new_tasks(tasks)
|
||||
|
||||
if not tasks:
|
||||
error_msg = f"The request required resources is exceed the limit, request id={req_ids}."
|
||||
llm_logger.error(error_msg)
|
||||
raise EngineError(error_msg, error_code=500)
|
||||
return False
|
||||
|
||||
self.token_processor.number_of_tasks += len(tasks)
|
||||
|
||||
is_decode = False
|
||||
is_prefill = False
|
||||
for i in range(len(tasks)):
|
||||
if tasks[i].disaggregate_info is not None:
|
||||
if tasks[i].disaggregate_info["role"] == "decode":
|
||||
is_decode = True
|
||||
else:
|
||||
is_prefill = True
|
||||
self.token_processor.number_of_input_tokens += tasks[
|
||||
i].prompt_token_ids_len
|
||||
|
||||
self.split_connector.send_cache_infos(tasks, current_id)
|
||||
for task in tasks:
|
||||
task.infer_start_time = time.time()
|
||||
if not is_decode:
|
||||
llm_logger.info(f"Tasks are sent to engine, req_ids={req_ids}")
|
||||
if not is_prefill:
|
||||
if not self.cfg.enable_mm:
|
||||
self.update_requests_chunk_size(tasks)
|
||||
else:
|
||||
self.update_mm_requests_chunk_size(tasks)
|
||||
self.engine_worker_queue.put_tasks(
|
||||
(tasks, self.resource_manager.real_bsz))
|
||||
return True
|
||||
|
||||
def _exit_sub_services(self):
|
||||
"""
|
||||
exit sub services
|
||||
"""
|
||||
|
||||
if hasattr(self, "cache_manager_processes"):
|
||||
self.resource_manager.cache_manager.shm_cache_task_flag_broadcast.clear(
|
||||
)
|
||||
self.resource_manager.cache_manager.cache_ready_signal.clear()
|
||||
for p in self.cache_manager_processes:
|
||||
llm_logger.info(f"Killing cache manager process {p.pid}")
|
||||
try:
|
||||
os.killpg(p.pid, signal.SIGTERM)
|
||||
except:
|
||||
pass
|
||||
|
||||
if hasattr(self, "zmq_server") and self.zmq_server is not None:
|
||||
self.zmq_server.close()
|
||||
|
||||
|
||||
def start_expert_service(cfg, local_data_parallel_id, ipc_signal_suffix):
|
||||
"""
|
||||
Start expert service
|
||||
"""
|
||||
expert_service = ExpertService(cfg, local_data_parallel_id)
|
||||
try:
|
||||
expert_service.start(ipc_signal_suffix, local_data_parallel_id)
|
||||
expert_service.split_connector.start_receiver()
|
||||
except Exception as e:
|
||||
llm_logger.exception(f"Expert service failed to start: {e}")
|
||||
Reference in New Issue
Block a user