執行設定: dflash
[spec_bench] === 設定: dflash ===
[spec_bench] speculative_config = {'method': 'dspark', 'num_speculative_tokens': 7, 'model': '/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/speculators/dflash_qwen3_4b', 'max_model_len': 4096, 'attention_backend': 'FLASHINFER', 'draft_sample_method': 'greedy'}
INFO 07-14 16:38:02 [api_utils.py:273] non-default args: {'max_model_len': 4096, 'tensor_parallel_size': 8, 'gpu_memory_utilization': 0.85, 'max_num_seqs': 64, 'speculative_config': {'method': 'dspark', 'num_speculative_tokens': 7, 'model': '/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/speculators/dflash_qwen3_4b', 'max_model_len': 4096, 'attention_backend': 'FLASHINFER', 'draft_sample_method': 'greedy'}, 'model': '/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/Qwen3-4B'}
INFO 07-14 16:38:03 [model.py:619] Resolved architecture: Qwen3ForCausalLM
INFO 07-14 16:38:03 [model.py:1776] Using max model len 4096
INFO 07-14 16:38:03 [model.py:619] Resolved architecture: Qwen3DSparkModel
INFO 07-14 16:38:03 [model.py:1776] Using max model len 4096
INFO 07-14 16:38:03 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=16384.
INFO 07-14 16:38:03 [vllm.py:1090] Asynchronous scheduling is enabled.
INFO 07-14 16:38:03 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
INFO 07-14 16:38:11 [compilation.py:312] Enabled custom fusions: allreduce_rms
WARNING 07-14 16:38:13 [system_utils.py:157] We must use the spawn multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: CUDA is initialized
(EngineCore pid=2717394) INFO 07-14 16:39:02 [core.py:114] Initializing a V1 LLM engine (v0.23.1rc1.dev1054+gee5a89f4d.d20260714) with config: model='/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/Qwen3-4B', speculative_config=SpeculativeConfig(method='dspark', model='/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/speculators/dflash_qwen3_4b', num_spec_tokens=7), tokenizer='/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/Qwen3-4B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=8, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=None, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False, jit_monitor_mode='warn', jit_monitor_verbose=False), seed=0, served_model_name=/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/llm_model/Qwen3-4B, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention_with_output', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::qwen_gdn_attention_core', 'vllm::gdn_attention_core_xpu', 'vllm::olmo_hybrid_gdn_full_forward', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::deepseek_v4_attention', 'vllm::hpc_rope_norm_forward', 'vllm::unified_kv_cache_update', 'vllm::unified_mla_kv_cache_update'], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [102, 16384], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'size_asserts': False, 'alignment_asserts': False, 'scalar_asserts': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': True, 'fuse_rope_kvcache_cat_mla': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']), enable_flashinfer_autotune=True, enable_cutedsl_warmup=True, moe_backend='auto', linear_backend='auto')
(EngineCore pid=2717394) WARNING 07-14 16:39:02 [multiproc_executor.py:1070] Reducing Torch parallelism from 96 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
(EngineCore pid=2717394) INFO 07-14 16:39:02 [multiproc_executor.py:140] DP group leader: node_rank=0, node_rank_within_dp=0, master_addr=127.0.0.1, mq_connect_ip=10.152.48.59 (local), world_size=8, local_world_size=8
(Worker pid=2717792) INFO 07-14 16:40:12 [parallel_state.py:1607] world_size=8 rank=0 local_rank=0 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
[W714 16:40:12.322625167 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
(Worker pid=2717797) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=5 local_rank=5 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
[W714 16:40:13.142249586 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
(Worker pid=2717793) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=1 local_rank=1 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
(Worker pid=2717794) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=2 local_rank=2 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
[W714 16:40:13.154184711 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
(Worker pid=2717799) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=7 local_rank=7 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
(Worker pid=2717796) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=4 local_rank=4 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
[W714 16:40:13.156045652 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
[W714 16:40:13.157470621 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
(Worker pid=2717798) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=6 local_rank=6 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
[W714 16:40:13.157787386 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
(Worker pid=2717795) INFO 07-14 16:40:13 [parallel_state.py:1607] world_size=8 rank=3 local_rank=3 distributed_init_method=tcp://127.0.0.1:53729 backend=nccl
[W714 16:40:13.159939835 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
[W714 16:40:13.161822908 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:53729 (errno: 97 - Address family not supported by protocol).
(Worker pid=2717792) INFO 07-14 16:40:18 [pynccl.py:113] vLLM is using nccl==2.28.9
(Worker pid=2717792) INFO 07-14 16:40:23 [cuda_communicator.py:264] Using ['CUSTOM', 'SYMM_MEM', 'PYNCCL'] all-reduce backends (in dispatch order) for group 'tp:0' out of potential backends: ['NCCL_SYMM_MEM', 'QUICK_REDUCE', 'FLASHINFER', 'AITER_CUSTOM', 'CUSTOM', 'SYMM_MEM', 'PYNCCL'].
(Worker pid=2717792) INFO 07-14 16:40:23 [parallel_state.py:1942] rank 0 in world size 8 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
(Worker pid=2717792) INFO 07-14 16:40:23 [gpu_worker.py:378] Using V2 Model Runner
(Worker_TP7 pid=2717799) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP4 pid=2717796) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP2 pid=2717794) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP6 pid=2717798) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP5 pid=2717797) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP3 pid=2717795) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP1 pid=2717793) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP0 pid=2717792) INFO 07-14 16:40:24 [model_runner.py:281] Loading model from scratch...
(Worker_TP0 pid=2717792) INFO 07-14 16:40:24 [cuda.py:476] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION'].
(Worker_TP0 pid=2717792) INFO 07-14 16:40:24 [flash_attn.py:772] Using FlashAttention version 3
(Worker_TP0 pid=2717792) INFO 07-14 16:40:24 [weight_utils.py:857] Filesystem type for checkpoints: FUSE.OFS. Checkpoint size: 7.49 GiB. Available RAM: 1411.31 GiB.
(Worker_TP0 pid=2717792) INFO 07-14 16:40:24 [weight_utils.py:880] Auto-prefetch is disabled because the filesystem (FUSE.OFS) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch.
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Loading safetensors checkpoint shards: 67% Completed | 2/3 [00:28<00:14, 14.27s/it]
(Worker_TP5 pid=2717797) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP5 pid=2717797) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
Loading safetensors checkpoint shards: 100% Completed | 3/3 [00:28<00:00, 7.92s/it]
Loading safetensors checkpoint shards: 100% Completed | 3/3 [00:28<00:00, 9.54s/it]
(Worker_TP0 pid=2717792)
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [default_loader.py:430] Loading weights took 28.64 seconds
(Worker_TP1 pid=2717793) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP2 pid=2717794) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP4 pid=2717796) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP7 pid=2717799) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP1 pid=2717793) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP3 pid=2717795) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP2 pid=2717794) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP4 pid=2717796) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP7 pid=2717799) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP3 pid=2717795) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP6 pid=2717798) INFO 07-14 16:40:53 [eagle3_utils.py:28] Using Eagle3 auxiliary layers from config: (2, 10, 18, 26, 34)
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [vllm.py:1090] Asynchronous scheduling is enabled.
(Worker_TP6 pid=2717798) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [kernel.py:292] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
(Worker_TP5 pid=2717797) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP2 pid=2717794) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP3 pid=2717795) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP1 pid=2717793) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP6 pid=2717798) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [compilation.py:312] Enabled custom fusions: allreduce_rms
(Worker_TP7 pid=2717799) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP4 pid=2717796) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [cuda.py:416] Using AttentionBackendEnum.FLASHINFER backend.
(Worker_TP0 pid=2717792) INFO 07-14 16:40:53 [weight_utils.py:857] Filesystem type for checkpoints: FUSE.OFS. Checkpoint size: 2.45 GiB. Available RAM: 1417.11 GiB.
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 1.77it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 1.77it/s]
(Worker_TP0 pid=2717792)
(Worker_TP0 pid=2717792) INFO 07-14 16:40:59 [default_loader.py:430] Loading weights took 5.66 seconds
(Worker_TP2 pid=2717794) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.339381 seconds
(Worker_TP3 pid=2717795) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.384071 seconds
(Worker_TP7 pid=2717799) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.395200 seconds
(Worker_TP0 pid=2717792) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.312437 seconds
(Worker_TP0 pid=2717792) INFO 07-14 16:41:00 [topk_topp_sampler.py:55] Using FlashInfer for top-p & top-k sampling.
(Worker_TP1 pid=2717793) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.446548 seconds
(Worker_TP5 pid=2717797) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.450890 seconds
(Worker_TP4 pid=2717796) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 36.462522 seconds
(Worker_TP6 pid=2717798) INFO 07-14 16:41:00 [model_runner.py:302] Model loading took 1.14 GiB and 37.255286 seconds
(Worker_TP0 pid=2717792) INFO 07-14 16:41:18 [backends.py:1089] Using cache directory: /home/luban/.cache/vllm/torch_compile_cache/12200ee1ef/rank_0_0/backbone for vLLM's torch.compile
(Worker_TP0 pid=2717792) INFO 07-14 16:41:18 [backends.py:1148] Dynamo bytecode transform time: 16.82 s
(Worker_TP0 pid=2717792) INFO 07-14 16:41:18 [flashinfer_all_reduce.py:119] Auto-selected flashinfer allreduce backend: mnnvl
(Worker_TP0 pid=2717792) /home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify device_id in init_process_group to mute this warning.
(Worker_TP0 pid=2717792) return func(*args, **kwargs)
[rank0]:[W714 16:41:18.061585182 ProcessGroupNCCL.cpp:5188] Guessing device ID based on global rank. This can cause a hang if rank to GPU mapping is heterogeneous. You can specify device_id in init_process_group()
(Worker_TP0 pid=2717792) INFO 07-14 16:41:19 [flashinfer_all_reduce.py:168] Initialized FlashInfer Allreduce norm fusion workspace with backend=mnnvl
(Worker_TP0 pid=2717792) /home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify device_id in init_process_group to mute this warning.
(Worker_TP0 pid=2717792) return func(*args, **kwargs)
(Worker_TP0 pid=2717792) INFO 07-14 16:41:20 [flashinfer_all_reduce.py:216] Initialized FlashInfer Allreduce norm quantization fusion workspace with backend=trtllm
(Worker_TP0 pid=2717792) INFO 07-14 16:41:39 [backends.py:378] Cache the graph of compile range (1, 102) for later use
(Worker_TP0 pid=2717792) INFO 07-14 16:41:45 [backends.py:378] Cache the graph of compile range (103, 16384) for later use
(EngineCore pid=2717394) INFO 07-14 16:42:02 [shm_broadcast.py:705] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization).
(Worker_TP0 pid=2717792) INFO 07-14 16:42:02 [backends.py:393] Compiling a graph for compile range (1, 102) takes 23.34 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:04 [backends.py:393] Compiling a graph for compile range (103, 16384) takes 24.77 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:08 [decorators.py:708] saved AOT compiled function to /home/luban/.cache/vllm/torch_compile_cache/torch_aot_compile/9eab0c9ebc88ae1cf38579697056e651d35fa14dbc047f42e5fb3cbf579b93a1/rank_0_0/model
(Worker_TP0 pid=2717792) INFO 07-14 16:42:08 [monitor.py:53] torch.compile took 67.24 s in total
(Worker_TP0 pid=2717792) INFO 07-14 16:42:09 [monitor.py:81] Initial profiling/warmup run took 1.07 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:11 [backends.py:1089] Using cache directory: /home/luban/.cache/vllm/torch_compile_cache/12200ee1ef/rank_0_0/dspark_head for vLLM's torch.compile
(Worker_TP0 pid=2717792) INFO 07-14 16:42:11 [backends.py:1148] Dynamo bytecode transform time: 1.32 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:24 [backends.py:393] Compiling a graph for compile range (1, 102) takes 6.66 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:25 [backends.py:393] Compiling a graph for compile range (103, 16384) takes 7.17 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:25 [decorators.py:708] saved AOT compiled function to /home/luban/.cache/vllm/torch_compile_cache/torch_aot_compile/ac2a6de3c1a4ddbaaf74459a41ffbf20fcda51a627446a0fddc648cb5ed043a5/rank_0_0/model
(Worker_TP0 pid=2717792) INFO 07-14 16:42:25 [monitor.py:53] torch.compile took 16.14 s in total
(Worker_TP0 pid=2717792) INFO 07-14 16:42:27 [monitor.py:81] Initial profiling/warmup run took 1.13 s
(Worker_TP0 pid=2717792) INFO 07-14 16:42:33 [gpu_worker.py:560] Available KV cache memory: 77.87 GiB
(EngineCore pid=2717394) INFO 07-14 16:42:33 [kv_cache_utils.py:2173] GPU KV cache size: 3,983,232 tokens
(EngineCore pid=2717394) INFO 07-14 16:42:33 [kv_cache_utils.py:2174] Maximum concurrency for 4,096 tokens per request: 972.47x
(Worker_TP0 pid=2717792) INFO 07-14 16:42:33 [flashinfer.py:794] FlashInfer resolved query dtypes: prefill=torch.bfloat16, decode=torch.bfloat16, decode_backend=xqa, kv_cache_dtype=torch.bfloat16, arch=sm90
(Worker_TP7 pid=2717799) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP5 pid=2717797) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP4 pid=2717796) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP0 pid=2717792) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP3 pid=2717795) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP2 pid=2717794) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP1 pid=2717793) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP6 pid=2717798) WARNING 07-14 16:42:33 [compilation.py:1409] CUDAGraphMode.FULL_AND_PIECEWISE is not supported with spec-decode for attention backend FlashInferBackend (support: AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE); setting cudagraph_mode=PIECEWISE
(Worker_TP1 pid=2717793) 2026-07-14 16:42:34,053 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP7 pid=2717799) 2026-07-14 16:42:34,056 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP0 pid=2717792) 2026-07-14 16:42:34,056 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP2 pid=2717794) 2026-07-14 16:42:34,056 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP5 pid=2717797) 2026-07-14 16:42:34,057 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP4 pid=2717796) 2026-07-14 16:42:34,057 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP3 pid=2717795) 2026-07-14 16:42:34,057 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
(Worker_TP6 pid=2717798) 2026-07-14 16:42:34,058 - INFO - autotuner.py:651 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_api.cpp:697): invalid configuration argument
(EngineCore pid=2717394) ERROR 07-14 16:42:34 [multiproc_executor.py:284] Worker proc VllmWorker-5 died unexpectedly (exit code: None), shutting down executor.
(EngineCore pid=2717394) INFO 07-14 16:42:34 [multiproc_executor.py:429] [shutdown] Executor: waiting for worker exit count=8
(EngineCore pid=2717394) INFO 07-14 16:42:36 [multiproc_executor.py:436] [shutdown] Executor: all workers exited gracefully
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] EngineCore failed to start.
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] Traceback (most recent call last):
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 1209, in run_engine_core
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] return func(*args, **kwargs)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 975, in init
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] super().init(
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 133, in init
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] kv_cache_config = self._initialize_kv_caches(vllm_config)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] return func(*args, **kwargs)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 321, in _initialize_kv_caches
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] self.model_executor.initialize_from_config(kv_cache_configs)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/abstract.py", line 124, in initialize_from_config
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] compilation_times: list[CompilationTimes] = self.collective_rpc(
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 405, in collective_rpc
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] return future if non_block else future.result()
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 91, in result
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] return super().result()
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/concurrent/futures/_base.py", line 449, in result
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] return self.__get_result()
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] raise self._exception
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 95, in _wait_for_response
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] response = self.aggregate(self.get_response())
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 390, in get_response
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] status, result = mq.dequeue(timeout=dequeue_timeout)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/distributed/device_communicators/shm_broadcast.py", line 779, in dequeue
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] with self.acquire_read(timeout, indefinite) as buf:
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/contextlib.py", line 137, in enter
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] return next(self.gen)
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] ^^^^^^^^^^^^^^
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/distributed/device_communicators/shm_broadcast.py", line 701, in acquire_read
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] raise RuntimeError("cancelled")
(EngineCore pid=2717394) ERROR 07-14 16:42:36 [core.py:1240] RuntimeError: cancelled
(EngineCore pid=2717394) Process EngineCore:
(EngineCore pid=2717394) Traceback (most recent call last):
(EngineCore pid=2717394) File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=2717394) self.run()
(EngineCore pid=2717394) File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/multiprocessing/process.py", line 108, in run
(EngineCore pid=2717394) self._target(*self._args, **self._kwargs)
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 1244, in run_engine_core
(EngineCore pid=2717394) raise e
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 1209, in run_engine_core
(EngineCore pid=2717394) engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=2717394) return func(*args, **kwargs)
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 975, in init
(EngineCore pid=2717394) super().init(
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 133, in init
(EngineCore pid=2717394) kv_cache_config = self._initialize_kv_caches(vllm_config)
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=2717394) return func(*args, **kwargs)
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core.py", line 321, in _initialize_kv_caches
(EngineCore pid=2717394) self.model_executor.initialize_from_config(kv_cache_configs)
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/abstract.py", line 124, in initialize_from_config
(EngineCore pid=2717394) compilation_times: list[CompilationTimes] = self.collective_rpc(
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 405, in collective_rpc
(EngineCore pid=2717394) return future if non_block else future.result()
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 91, in result
(EngineCore pid=2717394) return super().result()
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/concurrent/futures/_base.py", line 449, in result
(EngineCore pid=2717394) return self.__get_result()
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=2717394) raise self._exception
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 95, in _wait_for_response
(EngineCore pid=2717394) response = self.aggregate(self.get_response())
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/executor/multiproc_executor.py", line 390, in get_response
(EngineCore pid=2717394) status, result = mq.dequeue(timeout=dequeue_timeout)
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/distributed/device_communicators/shm_broadcast.py", line 779, in dequeue
(EngineCore pid=2717394) with self.acquire_read(timeout, indefinite) as buf:
(EngineCore pid=2717394) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/contextlib.py", line 137, in enter
(EngineCore pid=2717394) return next(self.gen)
(EngineCore pid=2717394) ^^^^^^^^^^^^^^
(EngineCore pid=2717394) File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/distributed/device_communicators/shm_broadcast.py", line 701, in acquire_read
(EngineCore pid=2717394) raise RuntimeError("cancelled")
(EngineCore pid=2717394) RuntimeError: cancelled
Traceback (most recent call last):
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/spec_bench/spec_bench.py", line 350, in
main()
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/spec_bench/spec_bench.py", line 286, in main
llm = LLM(**llm_kwargs)
^^^^^^^^^^^^^^^^^
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/entrypoints/llm.py", line 349, in init
self.llm_engine = LLMEngine.from_engine_args(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/llm_engine.py", line 179, in from_engine_args
return cls(
^^^^
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/llm_engine.py", line 105, in init
self.engine_core = EngineCoreClient.make_client(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core_client.py", line 103, in make_client
return SyncMPClient(vllm_config, executor_class, log_stats)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core_client.py", line 786, in init
super().init(
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/core_client.py", line 573, in init
with launch_core_engines(
^^^^^^^^^^^^^^^^^^^^
File "/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/contextlib.py", line 144, in exit
next(self.gen)
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/utils.py", line 1213, in launch_core_engines
wait_for_engine_startup(
File "/nfs/dataset-ofs-heterogeneous-computing/cuihangbin/vllm_0.25/vllm/vllm/v1/engine/utils.py", line 1272, in wait_for_engine_startup
raise RuntimeError(
RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}
/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/multiprocessing/resource_tracker.py:279: UserWarning: resource_tracker: There appear to be 8 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
/home/luban/miniconda3/envs/vllm_0.25/lib/python3.12/multiprocessing/resource_tracker.py:279: UserWarning: resource_tracker: There appear to be 9 leaked shared_memory objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
!!! 設定 dflash 執行失敗 (exit=1), 略過。
Your current environment
How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
Before submitting a new issue...