# # 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 import gc from types import NoneType 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 CUDAGraphMode, VllmConfig, set_current_vllm_config from vllm.distributed import ensure_model_parallel_initialized, init_distributed_environment from vllm.distributed.ec_transfer import ensure_ec_transfer_initialized from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized, get_kv_transfer_group, has_kv_transfer_group 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.mem_constants import GiB_bytes from vllm.utils.mem_utils import MemorySnapshot, memory_profiling from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE from vllm.v1.core.sched.output import GrammarOutput, 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 from vllm.v1.worker.workspace import init_workspace_manager import vllm_ascend.envs as envs_ascend from vllm_ascend.ascend_config import get_ascend_config, init_ascend_config from vllm_ascend.batch_invariant import init_batch_invariance from vllm_ascend.cpu_binding import bind_cpus from vllm_ascend.device_allocator.camem import CaMemAllocator from vllm_ascend.distributed.parallel_state import init_ascend_model_parallel from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton from vllm_ascend.utils import ( AscendDeviceType, check_ascend_device_type, enable_sp, get_ascend_device_type, register_ascend_customop, ) from vllm_ascend.worker.model_runner_v1 import NPUModelRunner torch._dynamo.trace_rules.clear_lru_cache() # noqa: E402 from torch._dynamo.variables import TorchInGraphFunctionVariable # noqa: E402 from vllm.utils.torch_utils import set_random_seed # 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.""" if not envs_ascend.COMPILE_CUSTOM_KERNELS: logger.warning( "COMPILE_CUSTOM_KERNELS is set to False. " "In most scenarios, without custom kernels, vllm-ascend will not function correctly." ) # register patch for vllm from vllm_ascend.utils import adapt_patch adapt_patch() # Import _inductor for graph mode execution with triton # This lazy import avoids torch_npu re-initialization in patch from vllm.triton_utils import HAS_TRITON if HAS_TRITON: import torch_npu._inductor # noqa: F401 # Register ops when worker init. from vllm_ascend import ops ops.register_dummy_fusion_op() if get_ascend_device_type() != AscendDeviceType.A5: _register_atb_extensions() register_ascend_customop(vllm_config) # init ascend config and soc version init_ascend_config(vllm_config) check_ascend_device_type() super().__init__( vllm_config=vllm_config, local_rank=local_rank, rank=rank, distributed_init_method=distributed_init_method, is_driver_worker=is_driver_worker, ) 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] # Profiler is lazily initialized on first profile(is_start=True) call (RFC #6954) self.profiler_config = vllm_config.profiler_config self.profiler = None if vllm_config.model_config and vllm_config.model_config.enable_sleep_mode: # 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") self.use_v2_model_runner = envs_vllm.VLLM_USE_V2_MODEL_RUNNER ascend_compilation_config = get_ascend_config().ascend_compilation_config if ascend_compilation_config.enable_npugraph_ex and ascend_compilation_config.enable_static_kernel: # Prevent duplicate triggers, execute the exit logic only once shutdown_request = False def signal_handler(signum, frame): nonlocal shutdown_request if not shutdown_request: shutdown_request = True self.uninstall_static_kernel() raise SystemExit() # Either SIGTERM or SIGINT will terminate the worker import signal signal.signal(signal.SIGTERM, signal_handler) signal.signal(signal.SIGINT, signal_handler) def uninstall_static_kernel(self): import fcntl import os import subprocess ascend_home_path = os.environ["ASCEND_HOME_PATH"] static_kernel_dir_path = os.path.join(ascend_home_path, "opp/static_kernel") uninstall_script_path = os.path.join(static_kernel_dir_path, "ai_core/uninstall.sh") lock_file_path = os.path.join(static_kernel_dir_path, "uninstall.lock") if not os.path.exists(uninstall_script_path): return with open(lock_file_path, "w") as lock_fd: try: fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB) subprocess.Popen( ["bash", uninstall_script_path], stdin=subprocess.DEVNULL, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, start_new_session=True, ) except (BlockingIOError, OSError): return finally: try: fcntl.flock(lock_fd, fcntl.LOCK_UN) if os.path.exists(lock_file_path): os.remove(lock_file_path) except Exception: return def sleep(self, level: int = 1) -> None: free_bytes_before_sleep = torch.npu.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 = torch.npu.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: list[str] | None = None) -> None: if envs_ascend.VLLM_ASCEND_ENABLE_NZ: raise ValueError( "FRACTAL_NZ mode is enabled. This may cause model parameter precision issues " "in the RL scenarios. Please set VLLM_ASCEND_ENABLE_NZ=0." ) allocator = CaMemAllocator.get_instance() allocator.wake_up(tags=tags) hidden_size = self.vllm_config.model_config.hf_text_config.hidden_size model = self.model_runner.model if tags is None or "weights" in tags: for name, param in model.named_parameters(): if "w2_weight" in name and param.shape[2] == hidden_size: parts = name.split(".") param_name = parts[-1] parent_module = model.get_submodule(".".join(parts[:-1])) w2_data = param.transpose(1, 2) w2_data = torch.nn.Parameter(w2_data, requires_grad=False) setattr(parent_module, param_name, w2_data) elif "w13_weight" in name and param.shape[1] == hidden_size: parts = name.split(".") param_name = parts[-1] parent_module = model.get_submodule(".".join(parts[:-1])) w13_data = param.transpose(1, 2) w13_data = torch.nn.Parameter(w13_data, requires_grad=False) setattr(parent_module, param_name, w13_data) # Restore the buffers after level 2 sleep if len(self._sleep_saved_buffers): 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}") torch.npu.set_device(device) gc.collect() torch.npu.empty_cache() # take current memory snapshot self.init_snapshot = MemorySnapshot() 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() # binding cpu if get_ascend_config().enable_cpu_binding: try: bind_cpus(self.local_rank) except Exception as e: logger.warning(f"Bind cpus failed in rank{self.local_rank}: {e} Skip binding cpu.") 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) @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 # 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() free_memory, total_memory = torch.npu.mem_get_info() torch_memory = torch.npu.memory_reserved() non_torch_memory_before_empty_cache = total_memory - free_memory - torch_memory self.non_torch_memory = profile_result.non_torch_increase self.peak_activation_memory = profile_result.torch_peak_increase non_torch_memory_cleared_by_empty_cache = non_torch_memory_before_empty_cache - self.non_torch_memory 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 - non_torch_memory_cleared_by_empty_cache ) 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 execute_model( self, scheduler_output: "SchedulerOutput", ) -> ModelRunnerOutput | AsyncModelRunnerOutput | None: # 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: # 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() intermediate_tensors = IntermediateTensors( get_pp_group().recv_tensor_dict(all_gather_group=all_gather_group) ) 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() get_pp_group().send_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() def compile_or_warm_up_model(self) -> float: # 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) 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. if get_ascend_device_type() != AscendDeviceType.A5: self._warm_up_atb() # 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 self.vllm_config.compilation_config.compilation_time 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_connector_handshake_metadata(self) -> dict | 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 return {self.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 %d", 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) 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 = self._create_profiler(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.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 _create_profiler(self, trace_name: str): """Create torch_npu profiler with trace naming for unique files per worker (RFC #6954).""" profiler_config = self.profiler_config if profiler_config.profiler != "torch": raise RuntimeError(f"Unrecognized profiler: {profiler_config.profiler}") if not profiler_config.torch_profiler_dir: raise RuntimeError("torch_profiler_dir cannot be empty.") if envs_ascend.MSMONITOR_USE_DAEMON: raise RuntimeError("MSMONITOR_USE_DAEMON and torch profiler cannot be both enabled at the same time.") 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=True, 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=False, profile_memory=profiler_config.torch_profiler_with_memory, # NOTE: torch_npu.profiler.with_modules is equivalent to torch.profiler.with_stack. # The with_stack option in torch_npu.profiler introduces significant time overhead. with_modules=profiler_config.torch_profiler_with_stack, experimental_config=experimental_config, on_trace_ready=torch_npu.profiler.tensorboard_trace_handler( profiler_config.torch_profiler_dir, worker_name=trace_name, ), ) 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.info("check_health Start!") 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.info("check_health success!") else: logger.info(f"query NPU card {self.local_rank} fail: {result.stderr}") except subprocess.TimeoutExpired: logger.info(f"query NPU card {self.local_rank} timeout.") except FileNotFoundError: logger.info("npu-smi tool not found.") except Exception as e: logger.info(f"query NPU card {self.local_rank} fail: {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