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(APIServer pid=2011301) INFO 02-02 06:57:51 [api_server.py:1351] vLLM API server version 0.13.0
(APIServer pid=2011301) INFO 02-02 06:57:51 [utils.py:253] non-default args: {'port': 9004, 'model': 'Elfsong/VLM_stage_2_iter_0003000', 'trust_remote_code': True, 'quantization': 'bitsandbytes', 'gpu_memory_utilization': 0.4}
(APIServer pid=2011301) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
(APIServer pid=2011301) INFO 02-02 06:57:52 [model.py:514] Resolved architecture: Qwen3ForCausalLM
(APIServer pid=2011301) INFO 02-02 06:57:52 [model.py:1661] Using max model len 40960
(APIServer pid=2011301) INFO 02-02 06:57:52 [scheduler.py:230] Chunked prefill is enabled with max_num_batched_tokens=8192.
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:05 [core.py:93] Initializing a V1 LLM engine (v0.13.0) with config: model='Elfsong/VLM_stage_2_iter_0003000', speculative_config=None, tokenizer='Elfsong/VLM_stage_2_iter_0003000', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=40960, download_dir=None, load_format=bitsandbytes, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=bitsandbytes, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, 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), seed=0, served_model_name=Elfsong/VLM_stage_2_iter_0003000, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::kda_attention', 'vllm::sparse_attn_indexer'], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [8192], 'inductor_compile_config': {'enable_auto_functionalized_v2': 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, 'eliminate_noops': True, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False}, 'local_cache_dir': None}
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:06 [parallel_state.py:1203] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://100.96.20.65:39845 backend=nccl
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:06 [parallel_state.py:1411] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:07 [gpu_model_runner.py:3562] Starting to load model Elfsong/VLM_stage_2_iter_0003000...
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:09 [cuda.py:351] Using FLASH_ATTN attention backend out of potential backends: ('FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION')
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:09 [bitsandbytes_loader.py:791] Loading weights with BitsAndBytes quantization. May take a while ...
(EngineCore_DP0 pid=2012375) Loading safetensors checkpoint shards: 0% Completed | 0/17 [00:00<?, ?it/s]
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(EngineCore_DP0 pid=2012375)
(EngineCore_DP0 pid=2012375) INFO 02-02 06:58:57 [gpu_model_runner.py:3659] Model loading took 19.4031 GiB memory and 48.278671 seconds
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:09 [backends.py:643] Using cache directory: /home/mingzhed/.cache/vllm/torch_compile_cache/c48ff69b02/rank_0_0/backbone for vLLM's torch.compile
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:09 [backends.py:703] Dynamo bytecode transform time: 12.18 s
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:19 [backends.py:226] Directly load the compiled graph(s) for compile range (1, 8192) from the cache, took 1.595 s
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:19 [monitor.py:34] torch.compile takes 13.77 s in total
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:20 [gpu_worker.py:375] Available KV cache memory: 10.30 GiB
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:20 [kv_cache_utils.py:1291] GPU KV cache size: 42,176 tokens
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:20 [kv_cache_utils.py:1296] Maximum concurrency for 40,960 tokens per request: 1.03x
(EngineCore_DP0 pid=2012375) Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 0%| | 0/51 [00:00<?, ?it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 2%|▏ | 1/51 [00:00<00:12, 3.95it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 4%|▍ | 2/51 [00:00<00:10, 4.80it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 6%|▌ | 3/51 [00:00<00:09, 5.12it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 8%|▊ | 4/51 [00:00<00:08, 5.32it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 10%|▉ | 5/51 [00:00<00:08, 5.33it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 12%|█▏ | 6/51 [00:01<00:08, 5.42it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 14%|█▎ | 7/51 [00:01<00:07, 5.54it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 16%|█▌ | 8/51 [00:01<00:07, 5.45it/s] Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 18%|█▊ | 9/51 [00:01<00:08, 5.07it/s] Capturing CUDA graphs (mixed 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(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:42 [gpu_model_runner.py:4587] Graph capturing finished in 22 secs, took 22.27 GiB
(EngineCore_DP0 pid=2012375) INFO 02-02 06:59:42 [core.py:259] init engine (profile, create kv cache, warmup model) took 45.49 seconds
(APIServer pid=2011301) INFO 02-02 06:59:45 [api_server.py:1099] Supported tasks: ['generate']
(APIServer pid=2011301) WARNING 02-02 06:59:45 [model.py:1487] Default sampling parameters have been overridden by the model's Hugging Face generation config recommended from the model creator. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`.
(APIServer pid=2011301) INFO 02-02 06:59:45 [serving_responses.py:201] Using default chat sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=2011301) INFO 02-02 06:59:45 [serving_chat.py:137] Using default chat sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=2011301) INFO 02-02 06:59:45 [serving_completion.py:77] Using default completion sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=2011301) INFO 02-02 06:59:45 [serving_chat.py:137] Using default chat sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=2011301) INFO 02-02 06:59:45 [api_server.py:1425] Starting vLLM API server 0 on http://0.0.0.0:9004
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:38] Available routes are:
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /openapi.json, Methods: GET, HEAD
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /docs, Methods: GET, HEAD
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /docs/oauth2-redirect, Methods: GET, HEAD
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /redoc, Methods: GET, HEAD
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /scale_elastic_ep, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /is_scaling_elastic_ep, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /tokenize, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /detokenize, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /inference/v1/generate, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /pause, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /resume, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /is_paused, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /metrics, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /health, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /load, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/models, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /version, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/responses, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/responses/{response_id}, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/responses/{response_id}/cancel, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/messages, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/chat/completions, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/completions, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/audio/transcriptions, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/audio/translations, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /ping, Methods: GET
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /ping, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /invocations, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /classify, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/embeddings, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /score, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/score, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /rerank, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v1/rerank, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /v2/rerank, Methods: POST
(APIServer pid=2011301) INFO 02-02 06:59:45 [launcher.py:46] Route: /pooling, Methods: POST
(APIServer pid=2011301) INFO: Started server process [2011301]
(APIServer pid=2011301) INFO: Waiting for application startup.
(APIServer pid=2011301) INFO: Application startup complete.
(APIServer pid=2011301) INFO 02-02 09:26:22 [launcher.py:110] Shutting down FastAPI HTTP server.
[rank0]:[W202 09:26:22.195426631 ProcessGroupNCCL.cpp:1524] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
(APIServer pid=2011301) INFO: Shutting down
(APIServer pid=2011301) INFO: Waiting for application shutdown.
(APIServer pid=2011301) INFO: Application shutdown complete.