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fix multiproc executor determine kv cache memory & update Dockerfile

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This commit is contained in:
starkwj
2026-04-24 08:31:54 +00:00
parent e4d898b245
commit e17006077a
10 changed files with 145 additions and 153 deletions
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170
vllm_ascend/patch/platform/patch_core.py
View File

@@ -1,20 +1,103 @@
from logging import DEBUG
import os
import signal
import queue
import time
import vllm.envs as envs
from vllm.config import ParallelConfig, VllmConfig
from vllm.logger import logger
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.core.kv_cache_utils import (generate_scheduler_kv_cache_config,
get_kv_cache_configs)
from vllm.v1.engine.core import EngineCoreProc, EngineCore
from vllm.tracing import instrument
from vllm.tracing import maybe_init_worker_tracer
from vllm.transformers_utils.config import \
maybe_register_config_serialize_by_value
from vllm.utils.system_utils import decorate_logs, set_process_title
from vllm.v1.engine.core import EngineCoreProc, DPEngineCoreProc, EngineShutdownState
from vllm.v1.engine import EngineCoreRequestType
from vllm.v1.engine.utils import SignalCallback
import vllm_ascend.envs as envs_ascend
def run_engine_core(*args, dp_rank: int = 0, local_dp_rank: int = 0, **kwargs):
"""Launch EngineCore busy loop in background process."""
# Ensure we can serialize transformer config after spawning
maybe_register_config_serialize_by_value()
engine_core: EngineCoreProc | None = None
signal_callback: SignalCallback | None = None
try:
vllm_config: VllmConfig = kwargs["vllm_config"]
parallel_config: ParallelConfig = vllm_config.parallel_config
data_parallel = parallel_config.data_parallel_size > 1 or dp_rank > 0
if data_parallel:
parallel_config.data_parallel_rank_local = local_dp_rank
process_title = f"EngineCore_DP{dp_rank}"
else:
process_title = "EngineCore"
set_process_title(process_title)
maybe_init_worker_tracer("vllm.engine_core", "engine_core", process_title)
decorate_logs()
if data_parallel and vllm_config.kv_transfer_config is not None:
# modify the engine_id and append the local_dp_rank to it to ensure
# that the kv_transfer_config is unique for each DP rank.
vllm_config.kv_transfer_config.engine_id = (
f"{vllm_config.kv_transfer_config.engine_id}_dp{local_dp_rank}"
)
logger.debug(
"Setting kv_transfer_config.engine_id to %s",
vllm_config.kv_transfer_config.engine_id,
)
parallel_config.data_parallel_index = dp_rank
if data_parallel and vllm_config.model_config.is_moe:
# Set data parallel rank for this engine process.
parallel_config.data_parallel_rank = dp_rank
engine_core = DPEngineCoreProc(*args, **kwargs)
else:
# Non-MoE DP ranks are completely independent, so treat like DP=1.
# Note that parallel_config.data_parallel_index will still reflect
# the original DP rank.
parallel_config.data_parallel_size = 1
parallel_config.data_parallel_size_local = 1
parallel_config.data_parallel_rank = 0
engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
assert engine_core is not None
def wakeup_engine():
# Wakes up idle engine via input_queue when shutdown is requested
# Not safe in a signal handler - we may interrupt the main thread
# while it is holding the non-reentrant input_queue.mutex
engine_core.input_queue.put_nowait((EngineCoreRequestType.WAKEUP, None))
signal_callback = SignalCallback(wakeup_engine)
def signal_handler(signum, frame):
engine_core.shutdown_state = EngineShutdownState.REQUESTED
signal_callback.trigger()
signal.signal(signal.SIGTERM, signal_handler)
signal.signal(signal.SIGINT, signal_handler)
engine_core.run_busy_loop()
except SystemExit:
logger.debug("EngineCore exiting.")
raise
except Exception as e:
if engine_core is None:
logger.exception("EngineCore failed to start.")
else:
logger.exception("EngineCore encountered a fatal error.")
engine_core._send_engine_dead()
raise e
finally:
signal.signal(signal.SIGTERM, signal.SIG_DFL)
signal.signal(signal.SIGINT, signal.SIG_DFL)
if signal_callback is not None:
signal_callback.stop()
if engine_core is not None:
engine_core.shutdown()
def run_busy_loop(self):
"""Core busy loop of the EngineCore."""
while self._handle_shutdown():
@@ -77,75 +160,8 @@ def _process_input_queue(self):
self._handle_client_request(*req)
@instrument(span_name="Prepare model")
def _initialize_kv_caches(self, vllm_config: VllmConfig) -> KVCacheConfig:
start = time.time()
# Get all kv cache needed by the model
kv_cache_specs = self.model_executor.get_kv_cache_specs()
has_kv_cache = any(kv_cache_spec for kv_cache_spec in kv_cache_specs)
if has_kv_cache:
if envs_ascend.VLLM_ASCEND_ENABLE_VNPU:
# get available memory in idle offload mode
available_gpu_memory = (
self.model_executor.determine_available_memory_vnpu_offload_mode())
self.available_gpu_memory_for_kv_cache = \
available_gpu_memory[0]
elif envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH:
# NOTE(yongji): should already be set
# during _eep_scale_up_before_kv_init
assert self.available_gpu_memory_for_kv_cache > 0
available_gpu_memory = [self.available_gpu_memory_for_kv_cache] * len(
kv_cache_specs
)
else:
# Profiles the peak memory usage of the model to determine how
# much memory can be allocated for kv cache.
available_gpu_memory = self.model_executor.determine_available_memory()
self.available_gpu_memory_for_kv_cache = available_gpu_memory[0]
else:
# Attention free models don't need memory for kv cache
available_gpu_memory = [0] * len(kv_cache_specs)
assert len(kv_cache_specs) == len(available_gpu_memory)
# Track max_model_len before KV cache config to detect auto-fit changes
max_model_len_before = vllm_config.model_config.max_model_len
kv_cache_configs = get_kv_cache_configs(
vllm_config, kv_cache_specs, available_gpu_memory
)
# If auto-fit reduced max_model_len, sync the new value to workers.
# This is needed because workers were spawned before memory profiling
# and have the original (larger) max_model_len cached.
max_model_len_after = vllm_config.model_config.max_model_len
if max_model_len_after != max_model_len_before:
self.collective_rpc("update_max_model_len", args=(max_model_len_after,))
scheduler_kv_cache_config = generate_scheduler_kv_cache_config(kv_cache_configs)
vllm_config.cache_config.num_gpu_blocks = scheduler_kv_cache_config.num_blocks
kv_cache_groups = scheduler_kv_cache_config.kv_cache_groups
if kv_cache_groups:
vllm_config.cache_config.block_size = min(
g.kv_cache_spec.block_size for g in kv_cache_groups
)
vllm_config.validate_block_size()
# Initialize kv cache and warmup the execution
self.model_executor.initialize_from_config(kv_cache_configs)
elapsed = time.time() - start
logger.info_once(
"init engine (profile, create kv cache, warmup model) took %.2f seconds",
elapsed,
scope="local",
)
return scheduler_kv_cache_config
# to make multi-proc enginecore get patched
EngineCoreProc.run_engine_core = run_engine_core
EngineCoreProc.run_busy_loop = run_busy_loop
EngineCoreProc._process_input_queue = _process_input_queue
EngineCore._initialize_kv_caches = _initialize_kv_caches

5
vllm_ascend/patch/platform/patch_executor.py
View File

@@ -42,11 +42,6 @@ def reload_vram(self) -> bool:
time.sleep(0.001)
def determine_available_memory_vnpu_offload_mode(self) -> int:
return self.collective_rpc("determine_available_memory_vnpu_offload_mode")
Executor.is_offloaded = is_offloaded
Executor.offload_vram = offload_vram
Executor.reload_vram = reload_vram
Executor.determine_available_memory_vnpu_offload_mode = determine_available_memory_vnpu_offload_mode

41
vllm_ascend/worker/worker.py
View File

@@ -336,6 +336,25 @@ class NPUWorker(WorkerBase):
bytes.
"""
GiB = lambda b: b / GiB_bytes
if envs_ascend.VLLM_ASCEND_ENABLE_VNPU:
allocator = CaMemAllocator.get_instance()
free, total = allocator.get_pool_mem_info()
if self.cache_config.gpu_memory_utilization <= 0.9:
logger.warning(
"GPU memory utilization is set to %.2f. For VNPU mode, it is recommended to set gpu_memory_utilization to a larger value",
self.cache_config.gpu_memory_utilization,
)
available_kv_cache_memory = int(
total * self.cache_config.gpu_memory_utilization - (total - free)
)
available_kv_cache_memory = int(max(available_kv_cache_memory, 0))
self.available_kv_cache_memory_bytes = available_kv_cache_memory
logger.info_once(
"Available KV cache memory: %.2f GiB",
GiB(self.available_kv_cache_memory_bytes),
scope="local",
)
return int(self.available_kv_cache_memory_bytes)
# Execute a forward pass with dummy inputs to profile the memory usage
# of the model.
@@ -363,28 +382,6 @@ class NPUWorker(WorkerBase):
return int(self.available_kv_cache_memory_bytes)
@torch.inference_mode()
def determine_available_memory_vnpu_offload_mode(self) -> int:
GiB = lambda b: b / GiB_bytes
allocator = CaMemAllocator.get_instance()
free, total = allocator.get_pool_mem_info()
if self.cache_config.gpu_memory_utilization <= 0.9:
logger.warning(
"GPU memory utilization is set to %.2f. For VNPU mode, it is recommended to set gpu_memory_utilization to a larger value",
self.cache_config.gpu_memory_utilization,
)
available_kv_cache_memory = int(
total * self.cache_config.gpu_memory_utilization - (total - free)
)
available_kv_cache_memory = int(max(available_kv_cache_memory, 0))
self.available_kv_cache_memory_bytes = available_kv_cache_memory
logger.info_once(
"Available KV cache memory: %.2f GiB",
GiB(self.available_kv_cache_memory_bytes),
scope="local",
)
return int(self.available_kv_cache_memory_bytes)
def execute_model(
self,
scheduler_output: "SchedulerOutput",