| INFO 06-27 02:56:21 [__init__.py:239] Automatically detected platform cuda. | |
| INFO 06-27 02:56:23 [config.py:209] Replacing legacy 'type' key with 'rope_type' | |
| INFO 06-27 02:56:30 [config.py:717] This model supports multiple tasks: {'classify', 'generate', 'reward', 'embed', 'score'}. Defaulting to 'generate'. | |
| INFO 06-27 02:56:30 [config.py:1770] Defaulting to use mp for distributed inference | |
| INFO 06-27 02:56:30 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. | |
| INFO 06-27 02:56:32 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./models/R-Phi4', speculative_config=None, tokenizer='./models/R-Phi4', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=2048, download_dir=None, load_format=auto, tensor_parallel_size=4, 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='auto', 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=./models/R-Phi4, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"level":3,"custom_ops":["none"],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output"],"use_inductor":true,"compile_sizes":[],"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":512} | |
| WARNING 06-27 02:56:32 [multiproc_worker_utils.py:306] Reducing Torch parallelism from 128 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. | |
| INFO 06-27 02:56:32 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_033fa3da'), local_subscribe_addr='ipc:///tmp/a1051549-2dec-4688-9494-0718440d0c2a', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 02:56:32 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x14d51a9dbd60> | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:32 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_6383bb93'), local_subscribe_addr='ipc:///tmp/94832504-1a54-45c4-a0b8-da03a934d306', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 02:56:32 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x14d518db4ac0> | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:32 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_4b8e0cc5'), local_subscribe_addr='ipc:///tmp/9db3c5c5-8593-4669-b9ec-1b2dfcd93261', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 02:56:32 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x14d51a9db9d0> | |
| WARNING 06-27 02:56:32 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x14d51a9dbca0> | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:32 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b6e0d56f'), local_subscribe_addr='ipc:///tmp/3ebc8f6d-94f2-4041-b265-1596b1a7e5c3', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:32 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_43b80aeb'), local_subscribe_addr='ipc:///tmp/33b51447-7fe1-46a4-a147-ba820a37718b', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:34 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:34 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:34 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:34 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:34 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:34 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:34 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:34 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m WARNING 06-27 02:56:34 [custom_all_reduce.py:136] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly. | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m WARNING 06-27 02:56:34 [custom_all_reduce.py:136] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly. | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m WARNING 06-27 02:56:34 [custom_all_reduce.py:136] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly. | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m WARNING 06-27 02:56:34 [custom_all_reduce.py:136] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly. | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:34 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_4f17e707'), local_subscribe_addr='ipc:///tmp/cc1369ad-3d5f-4a77-a9bf-bfb609385bef', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:34 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:34 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:34 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:34 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:34 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:34 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m WARNING 06-27 02:56:34 [topk_topp_sampler.py:69] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer. | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m WARNING 06-27 02:56:34 [topk_topp_sampler.py:69] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer. | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:34 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:34 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m WARNING 06-27 02:56:34 [topk_topp_sampler.py:69] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer. | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m WARNING 06-27 02:56:34 [topk_topp_sampler.py:69] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer. | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:34 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:34 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:34 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:34 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:35 [loader.py:458] Loading weights took 0.69 seconds | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:35 [loader.py:458] Loading weights took 0.69 seconds | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:35 [loader.py:458] Loading weights took 0.67 seconds | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:35 [loader.py:458] Loading weights took 0.70 seconds | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:36 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.878122 seconds | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:36 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.878944 seconds | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:36 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.937358 seconds | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:36 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.907204 seconds | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:41 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_1_0 for vLLM's torch.compile | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:41 [backends.py:430] Dynamo bytecode transform time: 5.68 s | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:42 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_0_0 for vLLM's torch.compile | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:42 [backends.py:430] Dynamo bytecode transform time: 5.80 s | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:42 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_3_0 for vLLM's torch.compile | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:42 [backends.py:430] Dynamo bytecode transform time: 5.82 s | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:42 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_2_0 for vLLM's torch.compile | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:42 [backends.py:430] Dynamo bytecode transform time: 5.88 s | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:47 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.405 s | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:47 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.381 s | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:47 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.368 s | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:47 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.486 s | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:56:52 [monitor.py:33] torch.compile takes 5.82 s in total | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:56:52 [monitor.py:33] torch.compile takes 5.68 s in total | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:56:52 [monitor.py:33] torch.compile takes 5.80 s in total | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:56:52 [monitor.py:33] torch.compile takes 5.88 s in total | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens | |
| INFO 06-27 02:56:54 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x | |
| [1;36m(VllmWorker rank=3 pid=3447778)[0;0m INFO 06-27 02:57:20 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=2 pid=3447776)[0;0m INFO 06-27 02:57:20 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=0 pid=3447772)[0;0m INFO 06-27 02:57:20 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=1 pid=3447773)[0;0m INFO 06-27 02:57:20 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| INFO 06-27 02:57:20 [core.py:159] init engine (profile, create kv cache, warmup model) took 44.10 seconds | |
| INFO 06-27 02:57:20 [core_client.py:439] Core engine process 0 ready. | |
| INFO 06-27 03:10:01 [importing.py:53] Triton module has been replaced with a placeholder. | |
| INFO 06-27 03:10:01 [__init__.py:239] Automatically detected platform cuda. | |
| | Task |Version| Metric |Value | |Stderr| | |
| |------------------|------:|---------------------|-----:|---|-----:| | |
| |all | |sem |0.5275|± |0.0280| | |
| | | |math_pass@1:1_samples|0.7814|± |0.0421| | |
| |mm\|arc_challenge\|0| 0|sem |0.6115|± |0.0250| | |
| |mm\|arc_easy\|0 | 0|sem |0.6315|± |0.0157| | |
| |mm\|commonsenseqa\|0| 0|sem |0.5281|± |0.0280| | |
| |mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.7629|± |0.0201| | |
| |mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8000|± |0.0641| | |
| |mm\|truthfulqa\|0 | 0|sem |0.3388|± |0.0432| | |