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[Misc] Clean up uesless code for LLM initialize (#1373)

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This PR aims to clean up the useless code for LLM setup. It helps to
make the code more clear.
1. remove useless `self.xxx` property
2. change `set_random_seed` to `seed_everything`
3. remove `set_custom_all_reduce`, it's only used for cuda

This is just a code clean. no change for any code logic.

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-06-25 16:20:14 +08:00
committed by GitHub
parent 0060886a37
commit ca884ef86d
2 changed files with 72 additions and 126 deletions
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27
vllm_ascend/worker/worker_v1.py
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@@ -26,12 +26,10 @@ from torch_npu.op_plugin.atb._atb_ops import _register_atb_extensions
from vllm import envs
from vllm.config import VllmConfig
from vllm.distributed import (ensure_model_parallel_initialized,
init_distributed_environment,
set_custom_all_reduce)
init_distributed_environment)
from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized
from vllm.logger import logger
from vllm.lora.request import LoRARequest
from vllm.model_executor import set_random_seed
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, GiB_bytes
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
@@ -93,7 +91,6 @@ class NPUWorker(WorkerBase):
self.profiler = self._init_profiler()
def sleep(self, level: int = 1) -> None:
NPUPlatform.set_device(self.device)
free_bytes_before_sleep = NPUPlatform.mem_get_info()[0]
allocator = CaMemAllocator.get_instance()
allocator.sleep(offload_tags=("weights", ) if level == 1 else tuple())
@@ -116,22 +113,18 @@ class NPUWorker(WorkerBase):
self.cache_config.num_cpu_blocks = num_cpu_blocks
def init_device(self):
if self.device_config.device.type == "npu":
self.device = torch.device(f"npu:{self.local_rank}")
NPUPlatform.set_device(self.device)
NPUPlatform.empty_cache()
self.init_npu_memory = NPUPlatform.mem_get_info()[0]
else:
info = f"Not support device type: {self.device_config.device}"
logger.error(info)
raise RuntimeError(info)
device = torch.device(f"npu:{self.local_rank}")
NPUPlatform.set_device(device)
NPUPlatform.empty_cache()
self.init_npu_memory = NPUPlatform.mem_get_info()[0]
# Initialize the distributed environment.
self._init_worker_distributed_environment()
# Set random seed.
set_random_seed(self.model_config.seed)
NPUPlatform.seed_everything(self.model_config.seed)
# Init ModelRunner here, so that we have access to self.device.
self.model_runner = NPUModelRunner(self.vllm_config, self.device)
self.model_runner = NPUModelRunner(self.vllm_config, device)
def determine_available_memory(self) -> int:
# Profile the memory usage of the model and get the maximum number of
@@ -205,7 +198,7 @@ class NPUWorker(WorkerBase):
self.model_runner.capture_model()
# Reset the seed to ensure that the random state is not affected by
# the model initialization and profiling.
set_random_seed(self.model_config.seed)
NPUPlatform.seed_everything(self.model_config.seed)
def get_model(self) -> nn.Module:
return self.model_runner.get_model()
@@ -261,8 +254,6 @@ class NPUWorker(WorkerBase):
def _init_worker_distributed_environment(self) -> None:
"""Initialize the distributed environment."""
parallel_config = self.vllm_config.parallel_config
set_custom_all_reduce(
not self.parallel_config.disable_custom_all_reduce)
init_distributed_environment(self.parallel_config.world_size,
self.rank, self.distributed_init_method,
self.local_rank, "hccl")