1047 lines
47 KiB
Python
1047 lines
47 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/vllm/worker/gpu_worker.py
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#
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import copy
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import gc
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import logging
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from types import NoneType
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import torch
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import torch.nn as nn
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import torch_npu
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from torch_npu.op_plugin.atb._atb_ops import _register_atb_extensions
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from torch_npu.profiler import dynamic_profile as dp
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from vllm.config import CUDAGraphMode, VllmConfig, set_current_vllm_config
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from vllm.distributed import ensure_model_parallel_initialized, get_pcp_group, init_distributed_environment
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from vllm.distributed.ec_transfer import ensure_ec_transfer_initialized
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from vllm.distributed.kv_transfer import (
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ensure_kv_transfer_initialized,
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ensure_kv_transfer_shutdown,
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get_kv_transfer_group,
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has_kv_transfer_group,
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)
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from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorHandshakeMetadata
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from vllm.distributed.parallel_state import Handle, get_pp_group, get_tp_group
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from vllm.logger import logger
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from vllm.lora.request import LoRARequest
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import SupportedTask
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from vllm.utils.mem_constants import GiB_bytes
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from vllm.utils.mem_utils import MemorySnapshot, format_gib, memory_profiling
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from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
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from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
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from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput, DraftTokenIds, ModelRunnerOutput
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from vllm.v1.utils import report_usage_stats
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from vllm.v1.worker.gpu_worker import AsyncIntermediateTensors
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from vllm.v1.worker.worker_base import CompilationTimes, WorkerBase
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from vllm.v1.worker.workspace import init_workspace_manager
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ascend_config import get_ascend_config, init_ascend_config
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from vllm_ascend.batch_invariant import init_batch_invariance
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from vllm_ascend.cpu_binding import bind_cpus
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from vllm_ascend.device_allocator.camem import CaMemAllocator
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from vllm_ascend.device_allocator.sleep_mem_optimized import SleepWakeupManager
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from vllm_ascend.distributed.parallel_state import init_ascend_model_parallel
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from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton
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from vllm_ascend.profiler.torch_npu_profiler import TorchNPUProfilerWrapper
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from vllm_ascend.utils import (
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AscendDeviceType,
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check_ascend_device_type,
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enable_sp,
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get_ascend_device_type,
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register_ascend_customop,
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setup_ascend_local_comm_res,
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vllm_version_is,
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)
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from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
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torch._dynamo.trace_rules.clear_lru_cache() # noqa: E402
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from torch._dynamo.variables import TorchInGraphFunctionVariable # noqa: E402
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from vllm.utils.torch_utils import set_random_seed # noqa: E402
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torch_non_c_binding_in_graph_functions_npu = dict.fromkeys(
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["torch.npu.current_stream"],
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TorchInGraphFunctionVariable,
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) # noqa: E402
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torch_non_c_binding_in_graph_functions_npu["torch.npu.stream"] = TorchInGraphFunctionVariable # noqa: E402
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torch._dynamo.trace_rules.torch_name_rule_map.append(torch_non_c_binding_in_graph_functions_npu) # noqa: E402
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class NPUWorker(WorkerBase):
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def __init__(
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self,
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vllm_config: VllmConfig,
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local_rank: int,
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rank: int,
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distributed_init_method: str,
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is_driver_worker: bool = False,
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# Additional parameters for compatibility with vllm
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**kwargs,
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):
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"""Initialize the worker for Ascend."""
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if not envs_ascend.COMPILE_CUSTOM_KERNELS:
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logger.warning(
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"COMPILE_CUSTOM_KERNELS is set to False. "
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"In most scenarios, without custom kernels, vllm-ascend will not function correctly."
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)
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# register patch for vllm
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from vllm_ascend.utils import adapt_patch
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adapt_patch()
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# Register ops when worker init.
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from vllm_ascend import ops
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ops.register_dummy_fusion_op()
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if get_ascend_device_type() != AscendDeviceType.A5:
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_register_atb_extensions()
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register_ascend_customop(vllm_config)
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# init ascend config and soc version
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init_ascend_config(vllm_config)
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from vllm_ascend.logger import configure_ascend_file_logging
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configure_ascend_file_logging()
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check_ascend_device_type()
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super().__init__(
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vllm_config=vllm_config,
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local_rank=local_rank,
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rank=rank,
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distributed_init_method=distributed_init_method,
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is_driver_worker=is_driver_worker,
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)
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if self.cache_config.cache_dtype == "auto":
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self.cache_dtype = self.model_config.dtype
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else:
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self.cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[self.cache_config.cache_dtype]
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# Profiler is lazily initialized on first profile(is_start=True) call (RFC #6954)
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self.profiler_config = vllm_config.profiler_config
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self.profiler: TorchNPUProfilerWrapper | None = None
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self.npugraph_memory_bytes = 0
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if vllm_config.model_config and vllm_config.model_config.enable_sleep_mode:
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# Buffers saved before sleep
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self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
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self.sleep_wakeup_manager = SleepWakeupManager(vllm_config, self, lambda: getattr(self, "model_runner", None))
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# Weight transfer engine is created in `load_model` once the model
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# is available, since the engine needs a reference to the model.
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self.weight_transfer_engine = None
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self._weight_update_active = False
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self._is_checkpoint_format = True
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# FixMe: this is a patch to fix the issue cause by https://github.com/vllm-project/vllm/commit/de94289a98d7ec52a5ef02719e01a1db8b505170
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from vllm.model_executor.layers.linear import WEIGHT_LOADER_V2_SUPPORTED
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if "UnquantizedLinearMethod" in WEIGHT_LOADER_V2_SUPPORTED:
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WEIGHT_LOADER_V2_SUPPORTED.remove("UnquantizedLinearMethod")
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self.use_v2_model_runner = self.vllm_config.use_v2_model_runner
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if self.use_v2_model_runner and vllm_version_is("0.23.0"):
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logger.warning("VLLM_USE_V2_MODEL_RUNNER is not supported on vllm 0.23.0; falling back to v1 model runner.")
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self.use_v2_model_runner = False
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self._pp_send_work: list[Handle] = []
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ascend_compilation_config = get_ascend_config().ascend_compilation_config
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if ascend_compilation_config.enable_npugraph_ex and ascend_compilation_config.enable_static_kernel:
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# Prevent duplicate triggers, execute the exit logic only once
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shutdown_request = False
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def signal_handler(signum, frame):
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nonlocal shutdown_request
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if not shutdown_request:
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shutdown_request = True
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self.uninstall_static_kernel()
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raise SystemExit()
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# Either SIGTERM or SIGINT will terminate the worker
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import signal
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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def uninstall_static_kernel(self):
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import fcntl
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import os
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import subprocess
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ascend_home_path = os.environ["ASCEND_HOME_PATH"]
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static_kernel_dir_path = os.path.join(ascend_home_path, "opp/static_kernel")
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uninstall_script_path = os.path.join(static_kernel_dir_path, "ai_core/uninstall.sh")
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lock_file_path = os.path.join(static_kernel_dir_path, "uninstall.lock")
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if not os.path.exists(uninstall_script_path):
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return
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with open(lock_file_path, "w") as lock_fd:
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try:
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fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
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subprocess.Popen(
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["bash", uninstall_script_path],
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stdin=subprocess.DEVNULL,
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL,
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start_new_session=True,
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)
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except (BlockingIOError, OSError):
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return
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finally:
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try:
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fcntl.flock(lock_fd, fcntl.LOCK_UN)
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if os.path.exists(lock_file_path):
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os.remove(lock_file_path)
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except Exception:
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return
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def sleep(self, level: int = 1) -> None:
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free_bytes_before_sleep = torch.npu.mem_get_info()[0]
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# Save the buffers before level 2 sleep
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if level == 2:
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model = self.model_runner.model
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self._sleep_saved_buffers = {name: buffer.cpu().clone() for name, buffer in model.named_buffers()}
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cleanup_enabled = getattr(get_ascend_config(), "enable_sleep_mode_extra_cleanup", False)
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if cleanup_enabled:
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self.sleep_wakeup_manager.sleep()
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allocator = CaMemAllocator.get_instance()
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allocator.sleep(offload_tags=("weights",) if level == 1 else tuple())
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free_bytes_after_sleep, total = torch.npu.mem_get_info()
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freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
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used_bytes = total - free_bytes_after_sleep
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assert freed_bytes >= 0, "Memory usage increased after sleeping."
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logger.info(
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"Sleep mode (level=%s) freed %.2f GiB memory, %.2f GiB memory is still in use.",
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level,
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freed_bytes / GiB_bytes,
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used_bytes / GiB_bytes,
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)
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def wake_up(self, tags: list[str] | None = None) -> None:
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nz_mode = get_ascend_config().weight_nz_mode
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if nz_mode:
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raise ValueError(
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"FRACTAL_NZ mode is enabled. This may cause model parameter precision issues "
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"in the RL scenarios. Please set weight_nz_mode=0 via --additional-config."
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)
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allocator = CaMemAllocator.get_instance()
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allocator.wake_up(tags=tags)
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hidden_size = self.vllm_config.model_config.hf_text_config.hidden_size
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model = self.model_runner.model
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if self.vllm_config.quant_config is None and (tags is None or "weights" in tags):
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for name, param in model.named_parameters():
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if "w2_weight" in name and param.shape[2] == hidden_size:
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parts = name.split(".")
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param_name = parts[-1]
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parent_module = model.get_submodule(".".join(parts[:-1]))
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w2_data = param.transpose(1, 2)
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w2_data = torch.nn.Parameter(w2_data, requires_grad=False)
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setattr(parent_module, param_name, w2_data)
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elif "w13_weight" in name and param.shape[1] == hidden_size:
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parts = name.split(".")
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param_name = parts[-1]
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parent_module = model.get_submodule(".".join(parts[:-1]))
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w13_data = param.transpose(1, 2)
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w13_data = torch.nn.Parameter(w13_data, requires_grad=False)
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setattr(parent_module, param_name, w13_data)
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# Restore the buffers after level 2 sleep
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if len(self._sleep_saved_buffers):
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for name, buffer in model.named_buffers():
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if name in self._sleep_saved_buffers:
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buffer.data.copy_(self._sleep_saved_buffers[name].data)
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self._sleep_saved_buffers = {}
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cleanup_enabled = getattr(get_ascend_config(), "enable_sleep_mode_extra_cleanup", False)
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if cleanup_enabled:
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self.sleep_wakeup_manager.wakeup(tags)
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def _check_weight_transfer_engine(self) -> None:
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if self.weight_transfer_engine is None:
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raise RuntimeError(
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"Weight transfer not configured. Please set weight_transfer_config to enable weight transfer."
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)
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def init_weight_transfer_engine(self, init_info: dict) -> None:
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"""Initialize the HCCL weight transfer process group with the trainer."""
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self._check_weight_transfer_engine()
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assert self.weight_transfer_engine is not None
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typed_init_info = self.weight_transfer_engine.parse_init_info(init_info)
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self.weight_transfer_engine.init_transfer_engine(typed_init_info)
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def _check_nz_disabled(self) -> None:
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if envs_ascend.VLLM_ASCEND_ENABLE_NZ:
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raise ValueError(
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"FRACTAL_NZ mode is enabled. This may cause model parameter "
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"precision issues in the RL scenarios. Please set "
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"VLLM_ASCEND_ENABLE_NZ=0."
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)
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def start_weight_update(self, is_checkpoint_format: bool = True) -> None:
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"""Begin a new weight update; prepares the model for layerwise reload."""
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self._check_weight_transfer_engine()
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if self._weight_update_active:
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raise RuntimeError(
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"start_weight_update called while a weight update is already active. Call finish_weight_update first."
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)
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self._check_nz_disabled()
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if is_checkpoint_format:
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from vllm.model_executor.model_loader.reload import initialize_layerwise_reload
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model = self.model_runner.model
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with torch.device(self.device):
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initialize_layerwise_reload(model)
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self._is_checkpoint_format = is_checkpoint_format
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self._weight_update_active = True
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def update_weights(self, update_info: dict) -> None:
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"""Receive a chunk of weights from the trainer and load them in place."""
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self._check_weight_transfer_engine()
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assert self.weight_transfer_engine is not None
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typed_update_info = self.weight_transfer_engine.parse_update_info(update_info)
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model = self.model_runner.model
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# state machine driven by start/finish.
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if not self._weight_update_active:
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raise RuntimeError("start_weight_update must be called before update_weights.")
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with torch.device(self.device):
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if self._is_checkpoint_format:
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self.weight_transfer_engine.receive_weights(
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typed_update_info,
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load_weights=model.load_weights,
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)
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else:
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def load_weights_direct(weights: list[tuple[str, torch.Tensor]]) -> None:
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with torch.no_grad():
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for name, weight in weights:
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param = model.get_parameter(name)
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param.copy_(weight)
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self.weight_transfer_engine.receive_weights(
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typed_update_info,
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load_weights=load_weights_direct,
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)
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# HCCL broadcast / packed paths are asynchronous.
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# Sync so the next step uses the new weights.
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torch.npu.synchronize()
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def finish_weight_update(self) -> None:
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"""Finish the current weight update; runs layerwise postprocessing."""
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self._check_weight_transfer_engine()
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if not self._weight_update_active:
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raise RuntimeError("start_weight_update must be called before finish_weight_update.")
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if self._is_checkpoint_format:
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from vllm.model_executor.model_loader.reload import finalize_layerwise_reload
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model = self.model_runner.model
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with torch.device(self.device):
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finalize_layerwise_reload(model, self.model_config)
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self._weight_update_active = False
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self._is_checkpoint_format = True
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def shutdown(self) -> None:
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if ensure_kv_transfer_shutdown is not None:
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ensure_kv_transfer_shutdown()
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if self.profiler is not None:
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self.profiler.shutdown()
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if weight_transfer_engine := getattr(self, "weight_transfer_engine", None):
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weight_transfer_engine.shutdown()
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if model_runner := getattr(self, "model_runner", None):
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shutdown_fn = getattr(model_runner, "shutdown", None)
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if callable(shutdown_fn):
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shutdown_fn()
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def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
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self.cache_config.num_gpu_blocks = num_gpu_blocks
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self.cache_config.num_cpu_blocks = num_cpu_blocks
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def _init_device(self):
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if not vllm_version_is("0.23.0"):
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# vLLM v0.24.0 (PR #45026) removed automatic per-process device
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# isolation for DP workers. Mirror gpu_worker.py::init_device:
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# shift self.local_rank by dp_local_rank * tp_pp_world_size so
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# that each DP group binds to a distinct set of NPUs.
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parallel_config = self.parallel_config
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if (
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parallel_config.distributed_executor_backend not in ("ray", "external_launcher")
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and parallel_config.data_parallel_backend != "ray"
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and parallel_config.nnodes_within_dp == 1
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# vllm-ascend: when the user pre-shards devices via
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# --device-ids (which becomes assigned_physical_gpu_ids),
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# each child process already binds to its own NPU(s); the
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# DP local_rank shift below would push local_rank past the
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# length of the per-rank device list and trip the assert
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# in this same method. Skip the shift in that case.
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and parallel_config.assigned_physical_gpu_ids is None
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):
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dp_local_rank = parallel_config.data_parallel_rank_local
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if dp_local_rank is None:
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dp_local_rank = parallel_config.data_parallel_index
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tp_pp_world_size = parallel_config.pipeline_parallel_size * parallel_config.tensor_parallel_size
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self.local_rank += dp_local_rank * tp_pp_world_size
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# Publish the logical-to-physical mapping for topology queries.
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assigned_physical_gpu_ids = parallel_config.assigned_physical_gpu_ids
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if assigned_physical_gpu_ids is not None:
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from vllm.platforms.interface import set_assigned_physical_gpu_ids
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set_assigned_physical_gpu_ids(assigned_physical_gpu_ids)
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assert self.local_rank < len(assigned_physical_gpu_ids), (
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f"local_rank {self.local_rank} is out of bounds for "
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f"assigned_physical_gpu_ids {assigned_physical_gpu_ids}"
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)
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|
if parallel_config.distributed_executor_backend not in ("ray", "external_launcher"):
|
|
assert parallel_config.local_world_size <= len(assigned_physical_gpu_ids), (
|
|
f"local_world_size ({parallel_config.local_world_size}) "
|
|
f"exceeds assigned_physical_gpu_ids count "
|
|
f"({len(assigned_physical_gpu_ids)})"
|
|
)
|
|
else:
|
|
visible_device_count = torch.npu.device_count() if torch.npu.is_available() else 0
|
|
assert self.local_rank < visible_device_count, (
|
|
f"DP adjusted local rank {self.local_rank} is out of bounds for {visible_device_count} devices."
|
|
)
|
|
|
|
visible_device_index = current_platform.logical_device_id_to_visible_device_id(self.local_rank)
|
|
device = torch.device(f"{current_platform.device_type}:{visible_device_index}")
|
|
else:
|
|
device = torch.device(f"npu:{self.local_rank}")
|
|
|
|
torch.npu.set_device(device)
|
|
|
|
# Import _inductor for graph mode execution with triton
|
|
# This lazy import avoids torch_npu re-initialization in patch
|
|
# Note that this should be imported after torch.npu.set_device
|
|
# to avoid repeated set_device in extra processes
|
|
from vllm.triton_utils import HAS_TRITON
|
|
|
|
if HAS_TRITON:
|
|
import torch_npu._inductor # noqa: F401
|
|
|
|
gc.collect()
|
|
torch.npu.empty_cache()
|
|
|
|
if get_ascend_device_type() == AscendDeviceType.A5:
|
|
setup_ascend_local_comm_res(self.local_rank, self.vllm_config.kv_transfer_config)
|
|
|
|
# take current memory snapshot
|
|
if vllm_version_is("0.23.0"):
|
|
self.init_snapshot = MemorySnapshot()
|
|
else:
|
|
self.init_snapshot = MemorySnapshot(device=device)
|
|
self.requested_memory = self.init_snapshot.total_memory * self.cache_config.gpu_memory_utilization
|
|
if self.init_snapshot.free_memory < self.requested_memory:
|
|
GiB = lambda b: round(b / GiB_bytes, 2)
|
|
raise ValueError(
|
|
f"Free memory on device "
|
|
f"({GiB(self.init_snapshot.free_memory)}/"
|
|
f"{GiB(self.init_snapshot.total_memory)} GiB) on startup "
|
|
f"is less than desired GPU memory utilization "
|
|
f"({self.cache_config.gpu_memory_utilization}, "
|
|
f"{GiB(self.requested_memory)} GiB). Decrease GPU memory "
|
|
f"utilization or reduce GPU memory used by other processes."
|
|
)
|
|
|
|
if (
|
|
self.parallel_config.data_parallel_size > 1
|
|
and self.parallel_config.data_parallel_size_local > 0
|
|
and self.parallel_config.distributed_executor_backend not in ["ray", "external_launcher"]
|
|
and self.vllm_config.parallel_config.data_parallel_backend != "ray"
|
|
and self.vllm_config.parallel_config.nnodes_within_dp == 1
|
|
):
|
|
visible_device_count = torch.npu.device_count() if torch.npu.is_available() else 0
|
|
assert self.parallel_config.local_world_size <= visible_device_count, (
|
|
f"local_world_size ({self.parallel_config.local_world_size}) must "
|
|
f"be less than or equal to the number of visible devices "
|
|
f"({visible_device_count})."
|
|
)
|
|
|
|
# Initialize the distributed environment.
|
|
self._init_worker_distributed_environment()
|
|
# Set random seed.
|
|
set_random_seed(self.model_config.seed)
|
|
# Initialize device properties used by triton kernels.
|
|
init_device_properties_triton()
|
|
|
|
return device
|
|
|
|
def init_device(self):
|
|
# NOTE: KEEP device the member of `NPUWorker`, as it will be checked
|
|
# in ray scenario. see https://github.com/vllm-project/vllm/pull/26845
|
|
# for more details
|
|
self.device = self._init_device()
|
|
# Initialize workspace manager
|
|
num_ubatches = 1
|
|
init_workspace_manager(self.device, num_ubatches)
|
|
# Init ModelRunner here, so that we have access to self.device.
|
|
if self.use_v2_model_runner:
|
|
logger.warning("npu model runner v2 is in developing, some features doesn't work for now.")
|
|
from vllm_ascend.worker.v2.model_runner import NPUModelRunner as NPUModelRunnerV2
|
|
|
|
self.model_runner = NPUModelRunnerV2(self.vllm_config, self.device)
|
|
else:
|
|
self.model_runner = NPUModelRunner(self.vllm_config, self.device)
|
|
|
|
if self.rank == 0:
|
|
# If usage stat is enabled, collect relevant info.
|
|
report_usage_stats(self.vllm_config)
|
|
|
|
@torch.inference_mode()
|
|
def determine_available_memory(self) -> int:
|
|
"""Profiles the peak memory usage of the model to determine how much
|
|
memory can be used for KV cache without OOMs.
|
|
|
|
The engine will first conduct a profiling of the existing memory usage.
|
|
Then, it calculates the free memory that can be used for KV cache in
|
|
bytes.
|
|
"""
|
|
GiB = lambda b: b / GiB_bytes
|
|
|
|
# Fast path: user has explicitly specified KV cache size via
|
|
# --kv-cache-memory. Still run profile_run() to compile the model,
|
|
# but skip the memory profiling calculation entirely.
|
|
if kv_cache_memory_bytes := self.cache_config.kv_cache_memory_bytes:
|
|
self.model_runner.profile_run()
|
|
logger.info(
|
|
"Initial free memory %.2f GiB, reserved %.2f GiB for KV Cache "
|
|
"as specified by kv_cache_memory_bytes, skipping memory profiling. "
|
|
"This does not respect the gpu_memory_utilization config. "
|
|
"Only use kv_cache_memory_bytes when you want manual control of "
|
|
"KV cache memory size. If OOM'ed, check the difference of initial "
|
|
"free memory between the current run and the previous run where "
|
|
"kv_cache_memory_bytes is suggested and update it correspondingly.",
|
|
GiB(self.init_snapshot.free_memory),
|
|
GiB(kv_cache_memory_bytes),
|
|
)
|
|
return kv_cache_memory_bytes
|
|
|
|
# Execute a forward pass with dummy inputs to profile the memory usage
|
|
# of the model.
|
|
with memory_profiling(
|
|
self.init_snapshot,
|
|
weights_memory=int(self.model_runner.model_memory_usage),
|
|
) as profile_result:
|
|
self.model_runner.profile_run()
|
|
|
|
# Record torch peak INSIDE the context and BEFORE graph capture,
|
|
# so that graph pool allocations don't inflate the activation peak.
|
|
# The memory_profiling context will also compute torch_peak_increase
|
|
# on exit, but we override it below with this pre-graph value.
|
|
profile_torch_peak = torch.npu.memory_stats(self.device).get("allocated_bytes.all.peak", 0)
|
|
|
|
# Override torch_peak_increase with the pre-graph-capture value to
|
|
# avoid double-counting graph pool memory as activation memory.
|
|
profile_result.torch_peak_increase = profile_torch_peak - profile_result.before_profile.torch_peak
|
|
profile_result.non_kv_cache_memory = (
|
|
profile_result.non_torch_increase + profile_result.torch_peak_increase + profile_result.weights_memory
|
|
)
|
|
|
|
# Save per-category memory for use in compile_or_warm_up_model() (step 5).
|
|
self.peak_activation_memory = profile_result.torch_peak_increase
|
|
self.non_torch_memory = profile_result.non_torch_increase
|
|
|
|
free_gpu_memory = profile_result.after_profile.free_memory
|
|
assert self.init_snapshot.free_memory > free_gpu_memory, (
|
|
"Error in memory profiling. "
|
|
f"Initial free memory {GiB(self.init_snapshot.free_memory)} GiB, "
|
|
f"current free memory {GiB(free_gpu_memory)} GiB. "
|
|
"This happens when other processes sharing the same container "
|
|
"release GPU memory while vLLM is profiling during initialization. "
|
|
"To fix this, ensure consistent GPU memory allocation or "
|
|
"isolate vLLM in its own container."
|
|
)
|
|
self.available_kv_cache_memory_bytes = self.requested_memory - profile_result.non_kv_cache_memory
|
|
|
|
logger.debug(profile_result)
|
|
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 log_memory_stats(self) -> None:
|
|
"""Profiles the torch reserved memory, torch allocated memory in execute_model()."""
|
|
if not logger.isEnabledFor(logging.DEBUG):
|
|
return
|
|
self.torch_reserved = torch.npu.memory_reserved()
|
|
self.torch_allocated = torch.npu.memory_allocated()
|
|
logger.debug(
|
|
"torch reserved memory: %.2f GiB, torch allocated memory: %.2f GiB",
|
|
self.torch_reserved / GiB_bytes,
|
|
self.torch_allocated / GiB_bytes,
|
|
)
|
|
|
|
def execute_model(
|
|
self,
|
|
scheduler_output: "SchedulerOutput",
|
|
) -> ModelRunnerOutput | AsyncModelRunnerOutput | None:
|
|
self.log_memory_stats()
|
|
# enable msMonitor to monitor the performance of vllm-ascend
|
|
if get_ascend_config().msmonitor_use_daemon:
|
|
dp.step()
|
|
|
|
if self._pp_send_work:
|
|
for handle in self._pp_send_work:
|
|
handle.wait()
|
|
self._pp_send_work = []
|
|
|
|
intermediate_tensors = None
|
|
forward_pass = scheduler_output.total_num_scheduled_tokens > 0
|
|
if forward_pass and not get_pp_group().is_first_rank:
|
|
# If flashcomm1 is used, this all_gather_group parameter needs to be removed, otherwise
|
|
# it will conflict with the all-gather operation in flashcomm1.
|
|
if enable_sp():
|
|
all_gather_group = None
|
|
else:
|
|
all_gather_group = get_tp_group()
|
|
tensor_dict, comm_handles, comm_postprocess = get_pp_group().irecv_tensor_dict(
|
|
all_gather_group=all_gather_group
|
|
)
|
|
assert tensor_dict is not None
|
|
intermediate_tensors = AsyncIntermediateTensors(
|
|
tensor_dict,
|
|
comm_handles=comm_handles,
|
|
comm_postprocess=comm_postprocess,
|
|
)
|
|
|
|
if self.profiler is not None:
|
|
self.profiler.step()
|
|
|
|
output = self.model_runner.execute_model(scheduler_output, intermediate_tensors)
|
|
if isinstance(output, (ModelRunnerOutput, AsyncModelRunnerOutput, NoneType)):
|
|
return output
|
|
|
|
assert isinstance(output, IntermediateTensors)
|
|
parallel_config = self.vllm_config.parallel_config
|
|
assert parallel_config.distributed_executor_backend != ("external_launcher") and not get_pp_group().is_last_rank
|
|
# If flashcomm1 is used, this all_gather_group parameter needs to be removed, otherwise
|
|
# it will conflict with the all-gather operation in flashcomm1.
|
|
if enable_sp():
|
|
all_gather_group = None
|
|
else:
|
|
all_gather_group = get_tp_group()
|
|
self._pp_send_work = get_pp_group().isend_tensor_dict(
|
|
output.tensors,
|
|
all_gather_group=all_gather_group,
|
|
)
|
|
|
|
kv_connector_output = output.kv_connector_output
|
|
if not kv_connector_output:
|
|
return None
|
|
|
|
# In case of PP with kv transfer, we need to pass through the
|
|
# kv_connector_output
|
|
if not kv_connector_output.finished_sending and not kv_connector_output.finished_recving:
|
|
return EMPTY_MODEL_RUNNER_OUTPUT
|
|
output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
|
|
output.kv_connector_output = kv_connector_output
|
|
return output
|
|
|
|
@torch.inference_mode()
|
|
def sample_tokens(self, grammar_output: "GrammarOutput") -> ModelRunnerOutput | AsyncModelRunnerOutput:
|
|
return self.model_runner.sample_tokens(grammar_output)
|
|
|
|
def load_model(self) -> None:
|
|
if self.vllm_config.model_config.enable_sleep_mode:
|
|
allocator = CaMemAllocator.get_instance()
|
|
assert allocator.get_current_usage() == 0, "Sleep mode can only be used for one instance per process."
|
|
context = allocator.use_memory_pool(tag="weights")
|
|
else:
|
|
from contextlib import nullcontext
|
|
|
|
context = nullcontext() # type: ignore
|
|
|
|
with context, set_current_vllm_config(self.vllm_config):
|
|
self.model_runner.load_model()
|
|
|
|
if self.vllm_config.weight_transfer_config is not None:
|
|
from vllm.distributed.weight_transfer.factory import (
|
|
WeightTransferEngineFactory,
|
|
)
|
|
|
|
# main: create_engine takes (config, parallel_config, model)
|
|
self.weight_transfer_engine = WeightTransferEngineFactory.create_engine(
|
|
self.vllm_config.weight_transfer_config,
|
|
self.vllm_config.parallel_config,
|
|
self.model_runner.get_model(),
|
|
)
|
|
|
|
def compile_or_warm_up_model(self) -> CompilationTimes:
|
|
# Note: need to adapt for graph mode.
|
|
warmup_sizes = (self.vllm_config.compilation_config.compile_sizes or []).copy()
|
|
if not self.model_config.enforce_eager:
|
|
cg_capture_sizes: list[int] = []
|
|
if self.vllm_config.compilation_config.cudagraph_mode != CUDAGraphMode.NONE:
|
|
cg_sizes = self.vllm_config.compilation_config.cudagraph_capture_sizes
|
|
cg_capture_sizes = [] if cg_sizes is None else cg_sizes
|
|
warmup_sizes = [x for x in warmup_sizes if x not in cg_capture_sizes]
|
|
|
|
compile_ranges = self.vllm_config.compilation_config.get_compile_ranges()
|
|
# For each compile_range, if none of the batch sizes
|
|
# in warmup_sizes or cudagraph_capture_sizes are in the range,
|
|
# add the end of the range to ensure compilation/warmup.
|
|
all_sizes = set(cg_capture_sizes)
|
|
all_sizes.update([x for x in warmup_sizes if isinstance(x, int)])
|
|
for compile_range in compile_ranges:
|
|
if not any(x in compile_range for x in all_sizes):
|
|
warmup_sizes.append(compile_range.end)
|
|
|
|
for size in sorted(warmup_sizes, reverse=True):
|
|
logger.info("Compile and warming up model for size %d", size)
|
|
self.model_runner._dummy_run(size)
|
|
|
|
npugraph_memory_bytes = 0
|
|
if not self.model_config.enforce_eager:
|
|
npugraph_memory_bytes = self.model_runner.capture_model()
|
|
|
|
# Suggest an optimal --kv-cache-memory value for future runs.
|
|
# Only emitted when we ran full profiling (kv_cache_memory_bytes was not
|
|
# pre-specified) so that peak_activation_memory etc. are available.
|
|
# non_kv_memory already includes NPU graph memory, so the suggestion
|
|
# accounts for all measured memory categories. A 150 MiB buffer is kept
|
|
# because memory_profiling may slightly underestimate non-torch
|
|
# allocations (ACL context, HCCL buffers, driver layer, etc.).
|
|
if self.cache_config.kv_cache_memory_bytes is None and hasattr(self, "peak_activation_memory"):
|
|
redundancy_buffer = 150 * (1 << 20) # 150 MiB safety margin
|
|
non_kv_memory = (
|
|
self.model_runner.model_memory_usage
|
|
+ self.peak_activation_memory
|
|
+ self.non_torch_memory
|
|
+ npugraph_memory_bytes
|
|
)
|
|
self.npugraph_memory_bytes = npugraph_memory_bytes
|
|
suggested_to_requested = int(self.requested_memory) - non_kv_memory - redundancy_buffer
|
|
suggested_to_gpu_limit = int(self.init_snapshot.free_memory) - non_kv_memory - redundancy_buffer
|
|
msg = (
|
|
f"Free memory on device "
|
|
f"({format_gib(self.init_snapshot.free_memory)}/"
|
|
f"{format_gib(self.init_snapshot.total_memory)} GiB) on startup. "
|
|
f"Desired GPU memory utilization is "
|
|
f"({self.cache_config.gpu_memory_utilization}, "
|
|
f"{format_gib(self.requested_memory)} GiB). "
|
|
f"Actual usage: {format_gib(self.model_runner.model_memory_usage)} GiB "
|
|
f"for weights, {format_gib(self.peak_activation_memory)} GiB for peak "
|
|
f"activation, {format_gib(self.non_torch_memory)} GiB for non-torch "
|
|
f"memory, {format_gib(npugraph_memory_bytes)} GiB for NPU graph memory. "
|
|
f"Replace gpu_memory_utilization with "
|
|
f"`--kv-cache-memory={suggested_to_requested}` "
|
|
f"({format_gib(suggested_to_requested)} GiB) to fit into requested "
|
|
f"memory, or `--kv-cache-memory={suggested_to_gpu_limit}` "
|
|
f"({format_gib(suggested_to_gpu_limit)} GiB) to fully utilize NPU "
|
|
f"free memory. Current KV cache memory: "
|
|
f"{format_gib(self.available_kv_cache_memory_bytes)} GiB."
|
|
)
|
|
logger.info(msg)
|
|
|
|
# Call ATB matmul to warm up; otherwise, the first operation (ReshapeAndCache)
|
|
# may cause performance degradation at runtime.
|
|
if get_ascend_device_type() != AscendDeviceType.A5:
|
|
self._warm_up_atb()
|
|
# Bind after warmup so hot allocations are already materialized on the
|
|
# worker process before migratepages/taskset run.
|
|
if get_ascend_config().enable_cpu_binding:
|
|
try:
|
|
bind_cpus(self.local_rank)
|
|
except Exception as e:
|
|
logger.warning("Bind cpus failed in rank%s: %s Skip binding cpu.", self.local_rank, e)
|
|
# 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)
|
|
return CompilationTimes(
|
|
language_model=self.vllm_config.compilation_config.compilation_time,
|
|
# `encoder_compilation_time` was added after v0.19.1 (vLLM #39240); fall
|
|
# back to 0.0 so the older release still constructs CompilationTimes.
|
|
encoder=getattr(
|
|
self.vllm_config.compilation_config,
|
|
"encoder_compilation_time",
|
|
0.0,
|
|
),
|
|
)
|
|
|
|
def _warm_up_atb(self):
|
|
x = torch.rand((2, 4), dtype=torch.float16).npu()
|
|
weight = torch.rand((2, 4), dtype=torch.float16).npu()
|
|
c = torch.rand((4, 4), dtype=torch.float32).npu()
|
|
torch_npu._npu_matmul_add_fp32(x, weight, c)
|
|
|
|
def get_model(self) -> nn.Module:
|
|
return self.model_runner.get_model()
|
|
|
|
@torch.inference_mode()
|
|
def profile_prefill_latency(self, num_tokens: int) -> float:
|
|
"""
|
|
Profile prefill latency for a given number of tokens.
|
|
|
|
This runs a real model forward pass and measures the execution time.
|
|
Used for profiling-based dynamic chunk sizing.
|
|
|
|
In PP (Pipeline Parallelism) mode:
|
|
- All workers execute the forward pass to stay synchronized
|
|
- Only the timing from PP0 (first rank) is meaningful for scheduling
|
|
- PP0 includes all the pipeline stages' latency when using async scheduling
|
|
|
|
Args:
|
|
num_tokens: Number of tokens to profile
|
|
|
|
Returns:
|
|
Latency in milliseconds
|
|
"""
|
|
import time
|
|
|
|
# Clamp to valid range
|
|
num_tokens = min(num_tokens, self.scheduler_config.max_num_batched_tokens)
|
|
num_tokens = max(num_tokens, 1)
|
|
|
|
# Synchronize all devices before timing
|
|
# This ensures clean measurement in PP/TP scenarios
|
|
torch.npu.synchronize()
|
|
|
|
# In PP mode, we still run on all ranks to keep them synchronized
|
|
# but only the first rank's timing is used for scheduling decisions
|
|
is_first_pp_rank = get_pp_group().is_first_rank
|
|
|
|
start = time.perf_counter()
|
|
|
|
# Run real model forward with force_attention=True
|
|
# This ensures attention is actually executed, not skipped.
|
|
# Without force_attention, attn_metadata may be None and attention
|
|
# won't run, making profiling results inaccurate.
|
|
# _dummy_run handles PP internally (intermediate tensors, etc.)
|
|
self.model_runner._dummy_run(
|
|
num_tokens=num_tokens,
|
|
force_attention=True, # Critical: ensure attention is executed
|
|
profile_cpp=True,
|
|
)
|
|
|
|
# Synchronize after forward to ensure NPU operations complete
|
|
torch.npu.synchronize()
|
|
|
|
latency_ms = (time.perf_counter() - start) * 1000
|
|
|
|
# Log for debugging in PP mode
|
|
if not is_first_pp_rank:
|
|
if logger.isEnabledFor(logging.DEBUG):
|
|
logger.debug(
|
|
"[ProfilingChunk] PP rank %s: profiled %s tokens, latency=%.2f ms (not used)",
|
|
get_pp_group().rank_in_group,
|
|
num_tokens,
|
|
latency_ms,
|
|
)
|
|
|
|
return latency_ms
|
|
|
|
def get_kv_connector_handshake_metadata(
|
|
self,
|
|
) -> dict[tuple[int, ...], KVConnectorHandshakeMetadata] | None:
|
|
"""Get KV connector metadata from this worker if available."""
|
|
if not has_kv_transfer_group():
|
|
return None
|
|
|
|
connector = get_kv_transfer_group()
|
|
|
|
# Return None for connectors that don't need to exchange handshake
|
|
# metadata across workers.
|
|
if (metadata := connector.get_handshake_metadata()) is None:
|
|
return None
|
|
tp_rank = get_tp_group().rank_in_group
|
|
pp_rank = get_pp_group().rank_in_group
|
|
pcp_size = get_pcp_group().world_size
|
|
if pcp_size > 1:
|
|
pcp_rank = get_pcp_group().rank_in_group
|
|
return {(pp_rank, pcp_rank, tp_rank): metadata}
|
|
return {(pp_rank, tp_rank): metadata}
|
|
|
|
def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
|
|
return self.model_runner.get_kv_cache_spec()
|
|
|
|
def update_max_model_len(self, max_model_len: int) -> None:
|
|
"""Update max_model_len after auto-fit to NPU memory.
|
|
|
|
This is called when max_model_len=-1 is used and the engine
|
|
automatically determines the maximum context length that fits
|
|
in GPU memory. Workers need to update their cached max_model_len
|
|
to match the engine's decision.
|
|
"""
|
|
self.model_config.max_model_len = max_model_len
|
|
if self.model_runner is not None:
|
|
self.model_runner.update_max_model_len(max_model_len)
|
|
logger.debug("Updated max_model_len to %s", max_model_len)
|
|
|
|
def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
|
|
"""Allocate NPU KV cache with the specified kv_cache_config."""
|
|
ensure_kv_transfer_initialized(self.vllm_config, kv_cache_config)
|
|
if self.vllm_config.model_config.enable_sleep_mode:
|
|
allocator = CaMemAllocator.get_instance()
|
|
context = allocator.use_memory_pool(tag="kv_cache")
|
|
else:
|
|
from contextlib import nullcontext
|
|
|
|
context = nullcontext() # type: ignore
|
|
with context:
|
|
self.model_runner.initialize_kv_cache(kv_cache_config)
|
|
|
|
# Restrict to mamba and full attn hybrid models (e.g. Qwen3.x).
|
|
#
|
|
# When eagle3 is enabled with num_speculative_tokens>1, mamba blocks may be reallocated to full blocks if
|
|
# the target and draft models share the same kv cache tensor (e.g. unaligned full attn layers with
|
|
# different num_kv_heads and head_size). In addition, for performance reasons, the current mtp/eagle path
|
|
# does not update seq_lens_cpu with num_rejected_tokens for step>1, since it would require d2h sync. As a
|
|
# result, seq_lens_cpu can become stale and some blocks will be unintentionally used.
|
|
#
|
|
# If an uncleared mamba block is later reused, the stale state combined with the incorrect seq_lens_cpu may
|
|
# lead to NaNs and reduced acceptance rate.
|
|
if (
|
|
kv_cache_config.needs_kv_cache_zeroing
|
|
and hasattr(self.model_runner, "_init_kv_zero_meta")
|
|
and self.vllm_config is not None
|
|
and self.vllm_config.speculative_config is not None
|
|
and self.vllm_config.speculative_config.method == "eagle3"
|
|
and self.vllm_config.speculative_config.num_speculative_tokens > 1
|
|
):
|
|
self.model_runner._init_kv_zero_meta()
|
|
|
|
def profile(self, is_start: bool = True, profile_prefix: str | None = None):
|
|
# Check if profiling is enabled (RFC #6954 - align with upstream vLLM)
|
|
if self.profiler_config is None or self.profiler_config.profiler is None:
|
|
raise RuntimeError(
|
|
"Profiling is not enabled. Please set --profiler-config to enable "
|
|
"profiling. Example: "
|
|
"'--profiler-config.profiler=torch --profiler-config.torch_profiler_dir"
|
|
"=YOUR_DIR_PATH_TO_DUMP_TRACE'"
|
|
)
|
|
|
|
if is_start:
|
|
from vllm.distributed.utils import get_worker_rank_suffix
|
|
|
|
rank_suffix = get_worker_rank_suffix(global_rank=self.rank)
|
|
trace_name = f"{profile_prefix}_{rank_suffix}" if profile_prefix else rank_suffix
|
|
|
|
if self.profiler is None:
|
|
self.profiler = TorchNPUProfilerWrapper(self.profiler_config, trace_name)
|
|
logger.debug("Starting torch profiler with trace name: %s", trace_name)
|
|
self.profiler.start() # type: ignore[attr-defined]
|
|
else:
|
|
# Profiler already initialized. Restart profiling but keep
|
|
# the original trace name from the first initialization.
|
|
self.profiler.start()
|
|
else:
|
|
if self.profiler is None:
|
|
logger.warning("Profiler was not started, nothing to stop.")
|
|
return
|
|
self.profiler.stop()
|
|
|
|
def add_lora(self, lora_request: LoRARequest) -> bool:
|
|
return self.model_runner.add_lora(lora_request)
|
|
|
|
def remove_lora(self, lora_id: int) -> bool:
|
|
return self.model_runner.remove_lora(lora_id)
|
|
|
|
def list_loras(self) -> set[int]:
|
|
return self.model_runner.list_loras()
|
|
|
|
def pin_lora(self, lora_id: int) -> bool:
|
|
return self.model_runner.pin_lora(lora_id)
|
|
|
|
def reset_encoder_cache(self) -> None:
|
|
self.model_runner.reset_encoder_cache()
|
|
|
|
def execute_dummy_batch(self) -> None:
|
|
self.log_memory_stats()
|
|
self.model_runner._dummy_run(num_tokens=self.model_runner.decode_token_per_req, uniform_decode=True)
|
|
|
|
def _init_worker_distributed_environment(self) -> None:
|
|
"""Initialize the distributed environment."""
|
|
init_batch_invariance()
|
|
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,
|
|
self.parallel_config.prefill_context_parallel_size,
|
|
self.parallel_config.decode_context_parallel_size,
|
|
)
|
|
init_ascend_model_parallel(self.parallel_config)
|
|
ensure_ec_transfer_initialized(self.vllm_config)
|
|
|
|
def get_supported_pooling_tasks(self):
|
|
return self.model_runner.get_supported_pooling_tasks()
|
|
|
|
def get_supported_tasks(self) -> "tuple[SupportedTask, ...]":
|
|
return self.model_runner.get_supported_tasks()
|
|
|
|
def take_draft_token_ids(self) -> DraftTokenIds | None:
|
|
return self.model_runner.take_draft_token_ids()
|
|
|
|
def check_health(self) -> None:
|
|
import subprocess
|
|
|
|
logger.debug("check_health starting for rank %s...", self.local_rank)
|
|
try:
|
|
result = subprocess.run(
|
|
["npu-smi", "info", "-i", str(self.local_rank), "-t", "health"],
|
|
capture_output=True,
|
|
text=True,
|
|
timeout=10,
|
|
)
|
|
|
|
if result.returncode == 0:
|
|
parse_text_output(result.stdout)
|
|
logger.debug("check_health success for rank %s.", self.local_rank)
|
|
else:
|
|
logger.warning("query NPU card %s fail: %s", self.local_rank, result.stderr)
|
|
except subprocess.TimeoutExpired:
|
|
logger.warning("query NPU card %s timeout.", self.local_rank)
|
|
except FileNotFoundError:
|
|
logger.warning("npu-smi tool not found.")
|
|
except Exception as e:
|
|
logger.error("query NPU card %s fail: %s", self.local_rank, e)
|
|
return
|
|
|
|
|
|
def parse_text_output(output) -> None:
|
|
lines = output.strip().split("\n")
|
|
for i, line in enumerate(lines):
|
|
line = line.strip()
|
|
if "Health" in line:
|
|
if line.split(":")[-1].strip() != "OK":
|
|
raise RuntimeError("NPU card health status is not OK")
|
|
return
|