deepseek_ocr_server_8708_20260204_172022.log

INFO 02-04 17:20:27 [__init__.py:240] Automatically detected platform rocm.
/home/lst/DeepSeek-OCR-vllm/deepseek_ocr_server.py:472: DeprecationWarning: 
        on_event is deprecated, use lifespan event handlers instead.

        Read more about it in the
        [FastAPI docs for Lifespan Events](https://fastapi.tiangolo.com/advanced/events/).
        
  @app.on_event("shutdown")
[INFO] 加载模型: /home/lst/deepseek_ocr
INFO 02-04 17:20:32 [config.py:460] Overriding HF config with {'architectures': ['DeepseekOCRForCausalLM']}
INFO 02-04 17:20:32 [config.py:721] This model supports multiple tasks: {'reward', 'embed', 'score', 'classify', 'generate'}. Defaulting to 'generate'.
INFO 02-04 17:20:32 [llm_engine.py:244] Initializing a V0 LLM engine (v0.8.5.post1) with config: model='/home/lst/deepseek_ocr', speculative_config=None, tokenizer='/home/lst/deepseek_ocr', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto,  device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=/home/lst/deepseek_ocr, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=True, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[24,16,8,4,2,1],"max_capture_size":24}, use_cached_outputs=False, 
INFO 02-04 17:20:33 [rocm.py:226] None is not supported in AMD GPUs.
INFO 02-04 17:20:33 [rocm.py:227] Using ROCmFlashAttention backend.
WARNING 02-04 17:20:33 [worker_base.py:41] VLLM_RANK0_NUMA is unset or set incorrectly, vllm will not bind to numa! VLLM_RANK0_NUMA = -1
INFO 02-04 17:20:33 [worker_base.py:653] ########## 45841 process(rank0) is running on CPU(s): {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31}
INFO 02-04 17:20:33 [worker_base.py:654] ########## 45841 process(rank0) is running on memnode(s): {0, 1, 2, 3}
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0204 17:20:33.298095 45841 ProcessGroupNCCL.cpp:881] [PG 0 Rank 0] ProcessGroupNCCL initialization options: size: 1, global rank: 0, TIMEOUT(ms): 600000, USE_HIGH_PRIORITY_STREAM: 0, SPLIT_FROM: 0, SPLIT_COLOR: 0, PG Name: 0
I0204 17:20:33.298182 45841 ProcessGroupNCCL.cpp:890] [PG 0 Rank 0] ProcessGroupNCCL environments: NCCL version: 2.18.3, TORCH_NCCL_ASYNC_ERROR_HANDLING: 3, TORCH_NCCL_DUMP_ON_TIMEOUT: 0, TORCH_NCCL_WAIT_TIMEOUT_DUMP_MILSEC: 60000, TORCH_NCCL_DESYNC_DEBUG: 0, TORCH_NCCL_ENABLE_TIMING: 0, TORCH_NCCL_BLOCKING_WAIT: 0, TORCH_DISTRIBUTED_DEBUG: OFF, TORCH_NCCL_ENABLE_MONITORING: 1, TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC: 600, TORCH_NCCL_TRACE_BUFFER_SIZE: 0, TORCH_NCCL_COORD_CHECK_MILSEC: 1000, TORCH_NCCL_NAN_CHECK: 0
I0204 17:20:33.298664 45841 ProcessGroupNCCL.cpp:881] [PG 1 Rank 0] ProcessGroupNCCL initialization options: size: 1, global rank: 0, TIMEOUT(ms): 600000, USE_HIGH_PRIORITY_STREAM: 0, SPLIT_FROM: 0x5590ffe7c9d0, SPLIT_COLOR: 3389850942126204093, PG Name: 1
I0204 17:20:33.298677 45841 ProcessGroupNCCL.cpp:890] [PG 1 Rank 0] ProcessGroupNCCL environments: NCCL version: 2.18.3, TORCH_NCCL_ASYNC_ERROR_HANDLING: 3, TORCH_NCCL_DUMP_ON_TIMEOUT: 0, TORCH_NCCL_WAIT_TIMEOUT_DUMP_MILSEC: 60000, TORCH_NCCL_DESYNC_DEBUG: 0, TORCH_NCCL_ENABLE_TIMING: 0, TORCH_NCCL_BLOCKING_WAIT: 0, TORCH_DISTRIBUTED_DEBUG: OFF, TORCH_NCCL_ENABLE_MONITORING: 1, TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC: 600, TORCH_NCCL_TRACE_BUFFER_SIZE: 0, TORCH_NCCL_COORD_CHECK_MILSEC: 1000, TORCH_NCCL_NAN_CHECK: 0
I0204 17:20:33.306862 45841 ProcessGroupNCCL.cpp:881] [PG 3 Rank 0] ProcessGroupNCCL initialization options: size: 1, global rank: 0, TIMEOUT(ms): 600000, USE_HIGH_PRIORITY_STREAM: 0, SPLIT_FROM: 0x5590ffe7c9d0, SPLIT_COLOR: 3389850942126204093, PG Name: 3
I0204 17:20:33.306901 45841 ProcessGroupNCCL.cpp:890] [PG 3 Rank 0] ProcessGroupNCCL environments: NCCL version: 2.18.3, TORCH_NCCL_ASYNC_ERROR_HANDLING: 3, TORCH_NCCL_DUMP_ON_TIMEOUT: 0, TORCH_NCCL_WAIT_TIMEOUT_DUMP_MILSEC: 60000, TORCH_NCCL_DESYNC_DEBUG: 0, TORCH_NCCL_ENABLE_TIMING: 0, TORCH_NCCL_BLOCKING_WAIT: 0, TORCH_DISTRIBUTED_DEBUG: OFF, TORCH_NCCL_ENABLE_MONITORING: 1, TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC: 600, TORCH_NCCL_TRACE_BUFFER_SIZE: 0, TORCH_NCCL_COORD_CHECK_MILSEC: 1000, TORCH_NCCL_NAN_CHECK: 0
I0204 17:20:33.308358 45841 ProcessGroupNCCL.cpp:881] [PG 5 Rank 0] ProcessGroupNCCL initialization options: size: 1, global rank: 0, TIMEOUT(ms): 600000, USE_HIGH_PRIORITY_STREAM: 0, SPLIT_FROM: 0x5590ffe7c9d0, SPLIT_COLOR: 3389850942126204093, PG Name: 5
I0204 17:20:33.308387 45841 ProcessGroupNCCL.cpp:890] [PG 5 Rank 0] ProcessGroupNCCL environments: NCCL version: 2.18.3, TORCH_NCCL_ASYNC_ERROR_HANDLING: 3, TORCH_NCCL_DUMP_ON_TIMEOUT: 0, TORCH_NCCL_WAIT_TIMEOUT_DUMP_MILSEC: 60000, TORCH_NCCL_DESYNC_DEBUG: 0, TORCH_NCCL_ENABLE_TIMING: 0, TORCH_NCCL_BLOCKING_WAIT: 0, TORCH_DISTRIBUTED_DEBUG: OFF, TORCH_NCCL_ENABLE_MONITORING: 1, TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC: 600, TORCH_NCCL_TRACE_BUFFER_SIZE: 0, TORCH_NCCL_COORD_CHECK_MILSEC: 1000, TORCH_NCCL_NAN_CHECK: 0
I0204 17:20:33.309455 45841 ProcessGroupNCCL.cpp:881] [PG 7 Rank 0] ProcessGroupNCCL initialization options: size: 1, global rank: 0, TIMEOUT(ms): 600000, USE_HIGH_PRIORITY_STREAM: 0, SPLIT_FROM: 0x5590ffe7c9d0, SPLIT_COLOR: 3389850942126204093, PG Name: 7
I0204 17:20:33.309473 45841 ProcessGroupNCCL.cpp:890] [PG 7 Rank 0] ProcessGroupNCCL environments: NCCL version: 2.18.3, TORCH_NCCL_ASYNC_ERROR_HANDLING: 3, TORCH_NCCL_DUMP_ON_TIMEOUT: 0, TORCH_NCCL_WAIT_TIMEOUT_DUMP_MILSEC: 60000, TORCH_NCCL_DESYNC_DEBUG: 0, TORCH_NCCL_ENABLE_TIMING: 0, TORCH_NCCL_BLOCKING_WAIT: 0, TORCH_DISTRIBUTED_DEBUG: OFF, TORCH_NCCL_ENABLE_MONITORING: 1, TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC: 600, TORCH_NCCL_TRACE_BUFFER_SIZE: 0, TORCH_NCCL_COORD_CHECK_MILSEC: 1000, TORCH_NCCL_NAN_CHECK: 0
INFO 02-04 17:20:33 [parallel_state.py:1004] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0
INFO 02-04 17:20:33 [model_runner.py:1133] Starting to load model /home/lst/deepseek_ocr...
INFO 02-04 17:20:34 [config.py:3627] cudagraph sizes specified by model runner [1, 2, 4, 8, 16, 24] is overridden by config [1, 2, 4, 8, 16, 24]

Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]

Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  7.08it/s]

Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  7.06it/s]

INFO 02-04 17:20:36 [loader.py:460] Loading weights took 2.12 seconds
INFO 02-04 17:20:37 [model_runner.py:1165] Model loading took 6.2319 GiB and 3.572350 seconds
Some kwargs in processor config are unused and will not have any effect: normalize, sft_format, image_token, mask_prompt, ignore_id, downsample_ratio, patch_size, candidate_resolutions, image_mean, pad_token, add_special_token, image_std. 
/home/lst/DeepSeek-OCR-vllm/deepencoder/sam_vary_sdpa.py:310: UserWarning: 1Torch was not compiled with memory efficient attention. (Triggered internally at /home/pytorch/aten/src/ATen/native/transformers/hip/sdp_utils.cpp:627.)
  x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
WARNING 02-04 17:20:51 [fused_moe.py:882] Using default MoE config. Performance might be sub-optimal! Config file not found at /usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=896,device_name=K100_AI.json
INFO 02-04 17:20:51 [worker.py:287] Memory profiling takes 14.59 seconds
INFO 02-04 17:20:51 [worker.py:287] the current vLLM instance can use total_gpu_memory (63.98GiB) x gpu_memory_utilization (0.90) = 57.59GiB
INFO 02-04 17:20:51 [worker.py:287] model weights take 6.23GiB; non_torch_memory takes 1.55GiB; PyTorch activation peak memory takes 1.60GiB; the rest of the memory reserved for KV Cache is 48.20GiB.
INFO 02-04 17:20:52 [executor_base.py:112] # rocm blocks: 13162, # CPU blocks: 1092
INFO 02-04 17:20:52 [executor_base.py:117] Maximum concurrency for 8192 tokens per request: 102.83x
INFO 02-04 17:20:54 [model_runner.py:1523] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.

Capturing CUDA graph shapes:   0%|          | 0/6 [00:00<?, ?it/s]
Capturing CUDA graph shapes:  17%|█▋        | 1/6 [00:00<00:03,  1.46it/s]
Capturing CUDA graph shapes:  33%|███▎      | 2/6 [00:01<00:02,  1.51it/s]
Capturing CUDA graph shapes:  50%|█████     | 3/6 [00:02<00:02,  1.48it/s]
Capturing CUDA graph shapes:  67%|██████▋   | 4/6 [00:02<00:01,  1.62it/s]
Capturing CUDA graph shapes:  83%|████████▎ | 5/6 [00:03<00:00,  1.72it/s]
Capturing CUDA graph shapes: 100%|██████████| 6/6 [00:03<00:00,  1.84it/s]
Capturing CUDA graph shapes: 100%|██████████| 6/6 [00:03<00:00,  1.70it/s]
INFO 02-04 17:20:57 [model_runner.py:1752] Graph capturing finished in 4 secs, took 0.12 GiB
INFO 02-04 17:20:57 [llm_engine.py:447] init engine (profile, create kv cache, warmup model) took 20.50 seconds
[SUCCESS] 模型加载完成
[INFO] 线程池配置:
   - CPU 线程池: 2 线程
   - GPU 线程池: 1 线程

[INFO] 服务启动: http://0.0.0.0:8708
[INFO] 接口文档: http://0.0.0.0:8708/docs

INFO:     Started server process [45841]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8708 (Press CTRL+C to quit)