biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[core] clean up executor class hierarchy between v1 and v0 (#12171)

Browse Source 
Signed-off-by: youkaichao <youkaichao@gmail.com>
This commit is contained in:
youkaichao
2025-01-18 14:35:15 +08:00
committed by GitHub
parent 02798ecabe
commit 6d0e3d3724
6 changed files with 61 additions and 798 deletions
Download Patch File Download Diff File Expand all files Collapse all files

50
vllm/v1/executor/multiproc_executor.py
View File

@@ -25,8 +25,6 @@ from vllm.logger import init_logger
from vllm.utils import (get_distributed_init_method, get_mp_context,
get_open_port, get_open_zmq_ipc_path, zmq_socket_ctx)
from vllm.v1.executor.abstract import Executor
from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
from vllm.v1.outputs import ModelRunnerOutput
from vllm.worker.worker_base import WorkerWrapperBase
logger = init_logger(__name__)
@@ -37,7 +35,7 @@ POLLING_TIMEOUT_S = POLLING_TIMEOUT_MS // 1000
class MultiprocExecutor(Executor):
def __init__(self, vllm_config: VllmConfig) -> None:
def _init_executor(self) -> None:
# Call self.shutdown at exit to clean up
# and ensure workers will be terminated.
self._finalizer = weakref.finalize(self, self.shutdown)
@@ -55,9 +53,6 @@ class MultiprocExecutor(Executor):
signal.signal(signal.SIGUSR1, sigusr1_handler)
self.vllm_config = vllm_config
self.parallel_config = vllm_config.parallel_config
self.world_size = self.parallel_config.world_size
tensor_parallel_size = self.parallel_config.tensor_parallel_size
assert self.world_size == tensor_parallel_size, (
@@ -82,7 +77,8 @@ class MultiprocExecutor(Executor):
# Create workers
self.workers: List[WorkerProcHandle] = []
for rank in range(self.world_size):
worker = WorkerProc.make_worker_process(vllm_config, rank, rank,
worker = WorkerProc.make_worker_process(self.vllm_config, rank,
rank,
distributed_init_method,
scheduler_output_handle)
self.workers.append(worker)
@@ -93,34 +89,6 @@ class MultiprocExecutor(Executor):
for w in self.workers:
w.worker_response_mq.wait_until_ready()
def initialize(self, kv_cache_config: KVCacheConfig) -> None:
"""
Initialize the KV caches and begin the model execution loop of the
underlying workers.
"""
self.collective_rpc("initialize_cache", args=(kv_cache_config, ))
self.collective_rpc("compile_or_warm_up_model")
def determine_available_memory(self) -> int:
"""
Determine the available memory (in bytes) for KV cache by invoking the
underlying worker.
"""
memory_sizes = self.collective_rpc("determine_available_memory")
# Since we use a shared centralized controller, we take the minimum
# memory size across all workers to make sure all the memory
# operators can be applied to all workers.
return min(memory_sizes)
def get_kv_cache_spec(self) -> KVCacheSpec:
"""
Get all kv cache needed by the model by invoking the underlying worker.
"""
kv_cache_specs = self.collective_rpc("get_kv_cache_spec")
assert all(s == kv_cache_specs[0] for s in kv_cache_specs)
return kv_cache_specs[0]
def collective_rpc(self,
method: Union[str, Callable],
timeout: Optional[float] = None,
@@ -172,18 +140,6 @@ class MultiprocExecutor(Executor):
# Re-raise any other exceptions
raise e
def execute_model(
self,
scheduler_output,
) -> ModelRunnerOutput:
model_output = self.collective_rpc("execute_model",
args=(scheduler_output, ))[0]
return model_output
def profile(self, is_start: bool = True):
self.collective_rpc("profile", args=(is_start, ))
return
def _ensure_worker_termination(self):
"""Ensure that all worker processes are terminated. Assumes workers have
received termination requests. Waits for processing, then sends