186
vllm_ascend/device_allocator/sleep_mem_optimized.py
Normal file
186
vllm_ascend/device_allocator/sleep_mem_optimized.py
Normal file
@@ -0,0 +1,186 @@
|
||||
#
|
||||
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||||
# Copyright 2023 The vLLM team.
|
||||
#
|
||||
# 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.
|
||||
# This file is a part of the vllm-ascend project.
|
||||
#
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable, MutableMapping
|
||||
from dataclasses import fields
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
from vllm.config import VllmConfig, set_current_vllm_config
|
||||
from vllm.distributed.parallel_state import _groups
|
||||
from vllm.logger import logger
|
||||
from vllm.utils.mem_constants import GiB_bytes
|
||||
|
||||
from vllm_ascend.compilation import acl_graph
|
||||
|
||||
|
||||
class SleepWakeupManager:
|
||||
def __init__(self, vllm_config: VllmConfig, worker: Any, model_runner_getter: Callable[[], Any]):
|
||||
self.acl_graph = AclGraphSleepWakeupManager(vllm_config, model_runner_getter)
|
||||
self.hccl = HcclSleepWakeupManager(vllm_config, worker)
|
||||
self._model_runner_getter = model_runner_getter
|
||||
|
||||
@staticmethod
|
||||
def _measure_memory_released(cleanup: Callable[[], None]) -> int:
|
||||
free_bytes_before_cleanup = torch.npu.mem_get_info()[0]
|
||||
cleanup()
|
||||
free_bytes_after_cleanup = torch.npu.mem_get_info()[0]
|
||||
return max(free_bytes_after_cleanup - free_bytes_before_cleanup, 0)
|
||||
|
||||
def sleep(self) -> None:
|
||||
model_runner = self._model_runner_getter()
|
||||
free_bytes_before_cleanup = torch.npu.mem_get_info()[0]
|
||||
if model_runner.use_aclgraph:
|
||||
self.acl_graph.sleep()
|
||||
self.hccl.sleep()
|
||||
free_bytes_after_cleanup = torch.npu.mem_get_info()[0]
|
||||
free_mem = free_bytes_after_cleanup - free_bytes_before_cleanup
|
||||
logger.info(
|
||||
"Sleep mode released HCCL and attention workspace memory: %.3f GiB.",
|
||||
free_mem / GiB_bytes,
|
||||
)
|
||||
|
||||
def wakeup(self, tags: list[str] | None = None) -> None:
|
||||
self.hccl.wakeup()
|
||||
model_runner = self._model_runner_getter()
|
||||
if model_runner.use_aclgraph:
|
||||
self.acl_graph.wakeup(tags)
|
||||
|
||||
|
||||
class AclGraphSleepWakeupManager:
|
||||
def __init__(self, vllm_config: VllmConfig, model_runner_getter: Callable[[], Any]):
|
||||
self.vllm_config = vllm_config
|
||||
self._model_runner_getter = model_runner_getter
|
||||
|
||||
@staticmethod
|
||||
def clear_attention_workspaces(params) -> None:
|
||||
if params is None:
|
||||
return
|
||||
for num_tokens in params.workspaces:
|
||||
params.workspaces[num_tokens] = None
|
||||
|
||||
@classmethod
|
||||
def clear_all_attention_workspaces(cls) -> None:
|
||||
cls.clear_attention_workspaces(acl_graph._graph_params)
|
||||
cls.clear_attention_workspaces(acl_graph._draft_graph_params)
|
||||
cls.clear_attention_workspaces(acl_graph._draft_graph_prefill_params)
|
||||
|
||||
@staticmethod
|
||||
def reset_graph_params(params) -> None:
|
||||
if params is None:
|
||||
return
|
||||
for graph_field in fields(params):
|
||||
attr_dict = getattr(params, graph_field.name, None)
|
||||
if not isinstance(attr_dict, MutableMapping):
|
||||
continue
|
||||
for num_tokens, value in attr_dict.items():
|
||||
if isinstance(value, list):
|
||||
attr_dict[num_tokens] = []
|
||||
|
||||
@classmethod
|
||||
def reset_all_graph_params(cls) -> None:
|
||||
cls.reset_graph_params(acl_graph._graph_params)
|
||||
cls.reset_graph_params(acl_graph._draft_graph_params)
|
||||
cls.reset_graph_params(acl_graph._draft_graph_prefill_params)
|
||||
for wrapper in list(acl_graph._acl_graph_wrappers):
|
||||
wrapper.concrete_aclgraph_entries.clear()
|
||||
wrapper.first_run_finished = False
|
||||
|
||||
@staticmethod
|
||||
def reset_model_runner_graph_manager(model_runner: Any) -> None:
|
||||
manager = getattr(model_runner, "cudagraph_manager", None)
|
||||
if manager is None:
|
||||
return
|
||||
if hasattr(manager, "graphs"):
|
||||
manager.graphs.clear()
|
||||
if hasattr(manager, "_graphs_captured"):
|
||||
manager._graphs_captured = False
|
||||
|
||||
def sleep(self) -> None:
|
||||
self.clear_all_attention_workspaces()
|
||||
self.reset_all_graph_params()
|
||||
self.reset_model_runner_graph_manager(self._model_runner_getter())
|
||||
|
||||
def wakeup(self, tags: list[str] | None = None) -> None:
|
||||
if tags is not None and "kv_cache" not in tags:
|
||||
# Level-2 wakeup restores weights before external weight loading;
|
||||
# recapture graphs only after KV cache is restored.
|
||||
return
|
||||
model_runner = self._model_runner_getter()
|
||||
with set_current_vllm_config(self.vllm_config):
|
||||
model_runner.capture_model()
|
||||
|
||||
|
||||
class HcclSleepWakeupManager:
|
||||
def __init__(self, vllm_config: VllmConfig, worker: Any):
|
||||
self.vllm_config = vllm_config
|
||||
self.worker = worker
|
||||
|
||||
@staticmethod
|
||||
def iter_alive_group_coordinators():
|
||||
seen: set[int] = set()
|
||||
for group_ref in list(_groups.values()):
|
||||
group = group_ref()
|
||||
if group is None or id(group) in seen:
|
||||
continue
|
||||
seen.add(id(group))
|
||||
yield group
|
||||
|
||||
@classmethod
|
||||
def destroy_hccl(cls) -> int:
|
||||
num_destroyed = 0
|
||||
for group in cls.iter_alive_group_coordinators():
|
||||
if group.destroy_hccl():
|
||||
num_destroyed += 1
|
||||
return num_destroyed
|
||||
|
||||
@classmethod
|
||||
def restore_hccl(cls) -> int:
|
||||
num_restored = 0
|
||||
for group in cls.iter_alive_group_coordinators():
|
||||
if group.restore_hccl():
|
||||
num_restored += 1
|
||||
return num_restored
|
||||
|
||||
@staticmethod
|
||||
def refresh_moe_hccl_groups() -> None:
|
||||
from vllm_ascend.ops.fused_moe.moe_comm_method import _MoECommMethods
|
||||
|
||||
for comm_method in _MoECommMethods.values():
|
||||
dispatcher = getattr(comm_method, "token_dispatcher", None)
|
||||
refresh_fn = getattr(dispatcher, "refresh_hccl_group", None)
|
||||
if callable(refresh_fn):
|
||||
refresh_fn()
|
||||
|
||||
def sleep(self) -> None:
|
||||
if torch.distributed.is_available() and torch.distributed.is_initialized():
|
||||
for handle in getattr(self.worker, "_pp_send_work", []):
|
||||
handle.wait()
|
||||
self.worker._pp_send_work = []
|
||||
torch.npu.synchronize()
|
||||
num_destroyed = self.destroy_hccl()
|
||||
if num_destroyed > 0:
|
||||
logger.info("Destroyed %d HCCL process groups for sleep mode.", num_destroyed)
|
||||
|
||||
def wakeup(self) -> None:
|
||||
with set_current_vllm_config(self.vllm_config):
|
||||
num_restored = self.restore_hccl()
|
||||
self.refresh_moe_hccl_groups()
|
||||
logger.info("Restored %d HCCL process groups after sleep mode.", num_restored)
|
||||
Reference in New Issue
Block a user