# # Copyright (c) 2025 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. # Adapted from vllm-project/vllm/vllm/worker/gpu_worker.py # import copy from typing import Optional, Union import torch import torch.nn as nn import torch_npu import vllm.envs as envs_vllm from torch_npu.op_plugin.atb._atb_ops import _register_atb_extensions from torch_npu.profiler import dynamic_profile as dp from vllm.config import VllmConfig from vllm.distributed import (ensure_model_parallel_initialized, init_distributed_environment) from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized from vllm.distributed.parallel_state import get_pp_group, get_tp_group from vllm.logger import logger from vllm.lora.request import LoRARequest from vllm.sequence import IntermediateTensors from vllm.tasks import SupportedTask 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 from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput, DraftTokenIds, ModelRunnerOutput) from vllm.v1.worker.worker_base import WorkerBase import vllm_npu.envs as envs_ascend from vllm_npu.ascend_config import get_ascend_config, init_ascend_config from vllm_npu.cpu_binding import bind_cpus from vllm_npu.device_allocator.camem import CaMemAllocator from vllm_npu.distributed.parallel_state import init_ascend_model_parallel from vllm_npu.platform import NPUPlatform from vllm_npu.utils import (init_ascend_soc_version, is_enable_nz, register_ascend_customop, sleep_mode_enabled, try_register_lib) from vllm_npu.worker.model_runner_v1 import NPUModelRunner torch._dynamo.trace_rules.clear_lru_cache() # noqa: E402 from torch._dynamo.variables import TorchInGraphFunctionVariable # noqa: E402 torch_non_c_binding_in_graph_functions_npu = dict.fromkeys( ["torch.npu.current_stream"], TorchInGraphFunctionVariable, ) # noqa: E402 torch_non_c_binding_in_graph_functions_npu[ "torch.npu.stream"] = TorchInGraphFunctionVariable # noqa: E402 torch._dynamo.trace_rules.torch_name_rule_map.append( torch_non_c_binding_in_graph_functions_npu) # noqa: E402 class NPUWorker(WorkerBase): 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 patch for vllm from vllm_npu.utils import adapt_patch adapt_patch() is_enable_nz(vllm_config=vllm_config) # Register ops when worker init. from vllm_npu import ops ops.register_dummy_fusion_op() _register_atb_extensions() register_ascend_customop(vllm_config) # init ascend config and soc version init_ascend_config(vllm_config) init_ascend_soc_version() use_sparse = False if vllm_config.model_config is not None: use_sparse = hasattr(vllm_config.model_config.hf_config, "index_topk") if use_sparse: # Direct import instead of using try_register_lib to ensure proper error handling when # custom_ops is necessary but not available (e.g., in DeepSeek v3.2 deployments) # yapf: disable import custom_ops # type: ignore # noqa # yapf: enable logger.info( "custom_ops module loaded successfully. Custom operators like " "torch.ops.custom.npu_sparse_flash_attention are now available." ) super().__init__(vllm_config=vllm_config, local_rank=local_rank, rank=rank, distributed_init_method=distributed_init_method, is_driver_worker=is_driver_worker) # binding cpu if get_ascend_config().enable_cpu_binding: try: bind_cpus(self.local_rank, ratio=1.0) except RuntimeError as e: logger.error(f"{e} in {self.local_rank}") except ValueError as e: logger.error(f"{e} in {self.local_rank}") except Exception: logger.info("Skip binding cpu.") # Try to import mindie_turbo to accelerate vLLM inference. try_register_lib( "mindie_turbo", "MindIE Turbo is installed. vLLM inference will be accelerated with MindIE Turbo." ) if self.cache_config.cache_dtype == "auto": self.cache_dtype = self.model_config.dtype else: self.cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[ self.cache_config.cache_dtype] if self.model_config.trust_remote_code: # note: lazy import to avoid importing torch before initializing from vllm.utils import init_cached_hf_modules init_cached_hf_modules() self.profiler = self._init_profiler() if sleep_mode_enabled(): # Buffers saved before sleep self._sleep_saved_buffers: dict[str, torch.Tensor] = {} # FixMe: this is a patch to fix the issue cause by https://github.com/vllm-project/vllm/commit/de94289a98d7ec52a5ef02719e01a1db8b505170 from vllm.model_executor.layers.linear import \ WEIGHT_LOADER_V2_SUPPORTED if "UnquantizedLinearMethod" in WEIGHT_LOADER_V2_SUPPORTED: WEIGHT_LOADER_V2_SUPPORTED.remove("UnquantizedLinearMethod") def sleep(self, level: int = 1) -> None: if not sleep_mode_enabled(): raise ValueError( "Sleep mode is not enabled. Please compile vllm-ascend with COMPILE_CUSTOM_KERNELS=1." ) free_bytes_before_sleep = NPUPlatform.mem_get_info()[0] # Save the buffers before level 2 sleep if level == 2: model = self.model_runner.model self._sleep_saved_buffers = { name: buffer.cpu().clone() for name, buffer in model.named_buffers() } allocator = CaMemAllocator.get_instance() allocator.sleep(offload_tags=("weights", ) if level == 1 else tuple()) free_bytes_after_sleep, total = NPUPlatform.mem_get_info() freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep used_bytes = total - free_bytes_after_sleep assert freed_bytes >= 0, "Memory usage increased after sleeping." logger.info( "Sleep mode freed %.2f GiB memory, " "%.2f GiB memory is still in use.", freed_bytes / GiB_bytes, used_bytes / GiB_bytes) def wake_up(self, tags: Optional[list[str]] = None) -> None: if not sleep_mode_enabled(): raise ValueError( "Sleep mode is not enabled. Please compile vllm-ascend with COMPILE_CUSTOM_KERNELS=1." ) if is_enable_nz(): raise ValueError( "FRACTAL_NZ mode is enabled. This may cause model parameter precision issues " "in the RL scenarios. Please set vllm_npu_ENABLE_NZ=0.") allocator = CaMemAllocator.get_instance() allocator.wake_up(tags=tags) # Restore the buffers after level 2 sleep if len(self._sleep_saved_buffers): model = self.model_runner.model for name, buffer in model.named_buffers(): if name in self._sleep_saved_buffers: buffer.data.copy_(self._sleep_saved_buffers[name].data) self._sleep_saved_buffers = {} def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks def _init_device(self): 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. NPUPlatform.seed_everything(self.model_config.seed) return device def init_device(self): device = self._init_device() # Init ModelRunner here, so that we have access to 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 # cache blocks that can be allocated with the remaining free memory. NPUPlatform.clear_npu_memory() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. _, total_npu_memory = NPUPlatform.mem_get_info() self.model_runner.profile_run() # Calculate the number of blocks that can be allocated with the # profiled peak memory. free_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. assert self.init_npu_memory > free_npu_memory, ( "Error in memory profiling. " f"Initial free memory {self.init_npu_memory}, current free memory" f" {free_npu_memory}. This happens when the NPU memory was " "not properly cleaned up before initializing the vLLM instance.") # Get the peak memory allocation recorded by torch peak_memory = torch_npu.npu.memory_stats()["allocated_bytes.all.peak"] # TODO: don`t need impl this func after empty_cache in # Worker.determine_num_available_blocks() unified` NPUPlatform.empty_cache() torch_allocated_bytes = torch_npu.npu.memory_stats( )["allocated_bytes.all.current"] total_allocated_bytes = torch_npu.npu.mem_get_info( )[1] - torch_npu.npu.mem_get_info()[0] non_torch_allocations = total_allocated_bytes - torch_allocated_bytes if non_torch_allocations > 0: peak_memory += non_torch_allocations available_kv_cache_memory = int( total_npu_memory * self.cache_config.gpu_memory_utilization - peak_memory) available_kv_cache_memory = int(max(available_kv_cache_memory, 0)) logger.info( f"Available memory: {available_kv_cache_memory}, total memory: {total_npu_memory}" ) return available_kv_cache_memory def execute_model( self, scheduler_output: "SchedulerOutput", ) -> Optional[Union[ModelRunnerOutput, AsyncModelRunnerOutput]]: # enable msMonitor to monitor the performance of vllm-ascend if envs_ascend.MSMONITOR_USE_DAEMON: dp.step() intermediate_tensors = None forward_pass = scheduler_output.total_num_scheduled_tokens > 0 if forward_pass and not get_pp_group().is_first_rank: intermediate_tensors = IntermediateTensors( get_pp_group().recv_tensor_dict( all_gather_group=get_tp_group())) output = self.model_runner.execute_model(scheduler_output, intermediate_tensors) if isinstance(output, (ModelRunnerOutput, AsyncModelRunnerOutput)): 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 get_pp_group().send_tensor_dict(output.tensors, all_gather_group=get_tp_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 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: self.model_runner.load_model() def compile_or_warm_up_model(self) -> None: # Note: need to adapt for graph mode. self.model_runner.eplb_warmup() warmup_sizes = (self.vllm_config.compilation_config.compile_sizes or []).copy() if not self.model_config.enforce_eager: warmup_sizes = [ x for x in warmup_sizes if x not in self.vllm_config.compilation_config.cudagraph_capture_sizes ] 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) if not self.model_config.enforce_eager: self.model_runner.capture_model() # Call ATB matmul to warm up; otherwise, the first operation (ReshapeAndCache) # may cause performance degradation at runtime. self._warm_up_atb() # Reset the seed to ensure that the random state is not affected by # the model initialization and profiling. NPUPlatform.seed_everything(self.model_config.seed) 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() def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: return self.model_runner.get_kv_cache_spec() def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None: """Allocate NPU KV cache with the specified 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) def profile(self, is_start: bool = True): if self.profiler is None: raise RuntimeError("Profiler is not enabled.") if is_start: self.profiler.start() else: 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 execute_dummy_batch(self) -> None: 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_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) init_ascend_model_parallel(self.parallel_config) ensure_kv_transfer_initialized(self.vllm_config) def _init_profiler(self): # Torch profiler. Enabled and configured through env vars: # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace if envs_vllm.VLLM_TORCH_PROFILER_DIR: if envs_ascend.MSMONITOR_USE_DAEMON: raise RuntimeError( "MSMONITOR_USE_DAEMON and VLLM_TORCH_PROFILER_DIR cannot be both set at the same time." ) torch_profiler_trace_dir = envs_vllm.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.Level1, 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, ) return torch_npu.profiler.profile( activities=[ torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU, ], with_stack=envs_vllm.VLLM_TORCH_PROFILER_WITH_STACK, profile_memory=envs_vllm.\ VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY, with_modules=False, experimental_config=experimental_config, on_trace_ready=torch_npu.profiler.tensorboard_trace_handler( torch_profiler_trace_dir)) else: return None 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) -> Optional[DraftTokenIds]: return self.model_runner.take_draft_token_ids()