EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

[V1] clean up V1 code (#505)

Browse Source 
Clean up V1 code:
1. remove useless code.
2. format code to be clear.

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-04-15 10:24:02 +08:00
committed by GitHub
parent f6af1d2471
commit c7f6584d75
2 changed files with 113 additions and 167 deletions
Download Patch File Download Diff File Expand all files Collapse all files

153
vllm_ascend/worker/worker_v1.py
View File

@@ -21,17 +21,15 @@ import gc
from typing import Dict, List, Optional
import torch
import torch.distributed
import torch.nn as nn
import torch_npu
from vllm import envs
from vllm.config import ParallelConfig, VllmConfig
from vllm.config import VllmConfig
from vllm.distributed import (ensure_model_parallel_initialized,
init_distributed_environment,
set_custom_all_reduce)
from vllm.logger import logger
from vllm.model_executor import set_random_seed
from vllm.platforms import current_platform
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
@@ -40,17 +38,22 @@ from vllm.v1.outputs import ModelRunnerOutput
from vllm.v1.utils import bind_kv_cache
from vllm.v1.worker.worker_base import WorkerBase
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
class NPUWorker(WorkerBase):
def __init__(self,
vllm_config: VllmConfig,
local_rank: int,
rank: int,
distributed_init_method: str,
is_driver_worker: bool = False):
def __init__(
self,
vllm_config: VllmConfig,
local_rank: int,
rank: int,
distributed_init_method: str,
is_driver_worker: bool = False,
# Additional parameters for compatibility with vllm
**kwargs):
"""Initialize the worker for Ascend."""
# Register ops when worker init.
from vllm_ascend import ops # noqa: F401
@@ -59,19 +62,6 @@ class NPUWorker(WorkerBase):
rank=rank,
distributed_init_method=distributed_init_method,
is_driver_worker=is_driver_worker)
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.cache_config = vllm_config.cache_config
self.lora_config = vllm_config.lora_config
self.load_config = vllm_config.load_config
self.parallel_config = vllm_config.parallel_config
self.scheduler_config = vllm_config.scheduler_config
self.device_config = vllm_config.device_config
self.speculative_config = vllm_config.speculative_config
self.prompt_adapter_config = vllm_config.prompt_adapter_config
self.observability_config = vllm_config.observability_config
if self.cache_config.cache_dtype == "auto":
self.cache_dtype = self.model_config.dtype
else:
@@ -82,53 +72,21 @@ class NPUWorker(WorkerBase):
# note: lazy import to avoid importing torch before initializing
from vllm.utils import init_cached_hf_modules
init_cached_hf_modules()
# Torch profiler. Enabled and configured through env vars:
# VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
if envs.VLLM_TORCH_PROFILER_DIR:
torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
logger.info("Profiling enabled. Traces will be saved to: %s",
torch_profiler_trace_dir)
experimental_config = torch_npu.profiler._ExperimentalConfig(
export_type=torch_npu.profiler.ExportType.Text,
profiler_level=torch_npu.profiler.ProfilerLevel.Level0,
msprof_tx=False,
aic_metrics=torch_npu.profiler.AiCMetrics.AiCoreNone,
l2_cache=False,
op_attr=False,
data_simplification=False,
record_op_args=False,
gc_detect_threshold=None,
)
self.profiler = torch_npu.profiler.profile(
activities=[
torch_npu.profiler.ProfilerActivity.CPU,
torch_npu.profiler.ProfilerActivity.NPU,
],
with_stack=True,
profile_memory=True,
with_modules=True,
experimental_config=experimental_config,
on_trace_ready=torch_npu.profiler.tensorboard_trace_handler(
torch_profiler_trace_dir))
else:
self.profiler = None
self.profiler = self._init_profiler()
def init_device(self):
if self.device_config.device.type == "npu":
self.device = torch.device(f"npu:{self.local_rank}")
current_platform.set_device(self.device)
current_platform.empty_cache()
self.init_npu_memory = current_platform.mem_get_info()[0]
NPUPlatform.set_device(self.device)
NPUPlatform.empty_cache()
self.init_npu_memory = NPUPlatform.mem_get_info()[0]
else:
raise RuntimeError(
f"Not support device type: {self.device_config.device}")
info = f"Not support device type: {self.device_config.device}"
logger.error(info)
raise RuntimeError(info)
# Initialize the distributed environment.
init_worker_distributed_environment(self.parallel_config, self.rank,
self.distributed_init_method,
self.local_rank)
self._init_worker_distributed_environment()
# Set random seed.
set_random_seed(self.model_config.seed)
@@ -140,14 +98,15 @@ class NPUWorker(WorkerBase):
kv_cache_spec = self.model_runner.get_kv_cache_spec()
for layer_name, layer_spec in kv_cache_spec.items():
if isinstance(layer_spec, FullAttentionSpec):
dtype = layer_spec.dtype
# Use an empty tensor instead of `None`` to force Dynamo to pass
# it by reference, rather by specializing on the value ``None``.
tpu_k_cache = torch.tensor([], dtype=dtype, device=self.device)
tpu_v_cache = torch.tensor([], dtype=dtype, device=self.device)
kv_caches[layer_name] = (tpu_k_cache, tpu_v_cache)
npu_k_cache = torch.tensor([],
dtype=layer_spec.dtype,
device=self.device)
npu_v_cache = torch.tensor([],
dtype=layer_spec.dtype,
device=self.device)
kv_caches[layer_name] = (npu_k_cache, npu_v_cache)
else:
raise NotImplementedError
@@ -159,7 +118,7 @@ class NPUWorker(WorkerBase):
# Profile the memory usage of the model and get the maximum number of
# cache blocks that can be allocated with the remaining free memory.
current_platform.empty_cache()
NPUPlatform.empty_cache()
# Execute a forward pass with dummy inputs to profile the memory usage
# of the model.
@@ -167,7 +126,7 @@ class NPUWorker(WorkerBase):
# Calculate the number of blocks that can be allocated with the
# profiled peak memory.
free_npu_memory, total_npu_memory = current_platform.mem_get_info()
free_npu_memory, total_npu_memory = NPUPlatform.mem_get_info()
# NOTE(woosuk): Here we assume that the other processes using the same
# GPU did not change their memory usage during the profiling.
peak_memory = self.init_npu_memory - free_npu_memory
@@ -180,7 +139,7 @@ class NPUWorker(WorkerBase):
gc.collect()
# TODO: don`t need impl this func after empty_cache in
# Worker.determine_num_available_blocks() unified`
current_platform.empty_cache()
NPUPlatform.empty_cache()
usable_memory_size = total_npu_memory * self.cache_config.gpu_memory_utilization - peak_memory
npu_kv_cache_bytes = max(usable_memory_size, 0)
logger.info(
@@ -228,17 +187,47 @@ class NPUWorker(WorkerBase):
else:
self.profiler.stop()
def _init_worker_distributed_environment(self) -> None:
"""Initialize the distributed environment."""
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")
ensure_model_parallel_initialized(
self.parallel_config.tensor_parallel_size,
self.parallel_config.pipeline_parallel_size)
def init_worker_distributed_environment(
parallel_config: ParallelConfig,
rank: int,
distributed_init_method: Optional[str] = None,
local_rank: int = -1) -> None:
"""Initialize the distributed environment."""
set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
def _init_profiler(self):
# Torch profiler. Enabled and configured through env vars:
# VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
if envs.VLLM_TORCH_PROFILER_DIR:
torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
logger.info("Profiling enabled. Traces will be saved to: %s",
torch_profiler_trace_dir)
init_distributed_environment(parallel_config.world_size, rank,
distributed_init_method, local_rank, "hccl")
experimental_config = torch_npu.profiler._ExperimentalConfig(
export_type=torch_npu.profiler.ExportType.Text,
profiler_level=torch_npu.profiler.ProfilerLevel.Level0,
msprof_tx=False,
aic_metrics=torch_npu.profiler.AiCMetrics.AiCoreNone,
l2_cache=False,
op_attr=False,
data_simplification=False,
record_op_args=False,
gc_detect_threshold=None,
)
ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
parallel_config.pipeline_parallel_size)
return torch_npu.profiler.profile(
activities=[
torch_npu.profiler.ProfilerActivity.CPU,
torch_npu.profiler.ProfilerActivity.NPU,
],
with_stack=True,
profile_memory=True,
with_modules=True,
experimental_config=experimental_config,
on_trace_ready=torch_npu.profiler.tensorboard_trace_handler(
torch_profiler_trace_dir))
else:
return None