| INFO 06-27 03:10:00 [__init__.py:239] Automatically detected platform cuda. | |
| INFO 06-27 03:10:02 [config.py:209] Replacing legacy 'type' key with 'rope_type' | |
| INFO 06-27 03:10:08 [config.py:717] This model supports multiple tasks: {'embed', 'classify', 'score', 'generate', 'reward'}. Defaulting to 'generate'. | |
| INFO 06-27 03:10:09 [config.py:1770] Defaulting to use mp for distributed inference | |
| INFO 06-27 03:10:09 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. | |
| INFO 06-27 03:10:10 [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 03:10:10 [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 03:10:10 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_8f74b1a6'), local_subscribe_addr='ipc:///tmp/d6a3a804-af20-451f-bbde-88cafad6d09e', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 03:10:10 [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 0x14d5646ffcd0> | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:10 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b833bb60'), local_subscribe_addr='ipc:///tmp/9b12d81b-f5a0-4a07-abff-2a89c6e7efc3', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 03:10:10 [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 0x14d552c40a30> | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:10 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_df1b06e4'), local_subscribe_addr='ipc:///tmp/5fbbbe13-7f22-462d-806c-b109618e0050', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 03:10:10 [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 0x14d5646ffc10> | |
| WARNING 06-27 03:10:10 [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 0x14d5646ff940> | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:10 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_fd88f513'), local_subscribe_addr='ipc:///tmp/5519fa92-0b12-4661-87d3-60cc8720f215', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:10 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b47742a0'), local_subscribe_addr='ipc:///tmp/df1ac424-4f25-4f30-8aa8-4fa48b068a28', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:12 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:12 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:12 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:12 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:12 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:12 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:12 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:12 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m WARNING 06-27 03:10:13 [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=3455001)[0;0m WARNING 06-27 03:10:13 [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=3 pid=3455004)[0;0m WARNING 06-27 03:10:13 [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=3455002)[0;0m WARNING 06-27 03:10:13 [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=3455001)[0;0m INFO 06-27 03:10:13 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_c686f274'), local_subscribe_addr='ipc:///tmp/6acb8797-fe95-4e20-b79c-c3db8cabe649', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:13 [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=1 pid=3455002)[0;0m INFO 06-27 03:10:13 [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=3455003)[0;0m INFO 06-27 03:10:13 [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=3455001)[0;0m INFO 06-27 03:10:13 [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=3 pid=3455004)[0;0m INFO 06-27 03:10:13 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:13 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:13 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:13 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m WARNING 06-27 03:10:13 [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=3455004)[0;0m WARNING 06-27 03:10:13 [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=3455001)[0;0m WARNING 06-27 03:10:13 [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=3455002)[0;0m WARNING 06-27 03:10:13 [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=2 pid=3455003)[0;0m INFO 06-27 03:10:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:14 [loader.py:458] Loading weights took 0.69 seconds | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:14 [loader.py:458] Loading weights took 0.70 seconds | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:14 [loader.py:458] Loading weights took 0.70 seconds | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:14 [loader.py:458] Loading weights took 0.72 seconds | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.892547 seconds | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.893978 seconds | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.888170 seconds | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:15 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.959422 seconds | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:20 [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=3455004)[0;0m INFO 06-27 03:10:20 [backends.py:430] Dynamo bytecode transform time: 5.52 s | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:20 [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=3455003)[0;0m INFO 06-27 03:10:20 [backends.py:430] Dynamo bytecode transform time: 5.59 s | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:20 [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=3455001)[0;0m INFO 06-27 03:10:20 [backends.py:430] Dynamo bytecode transform time: 5.64 s | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:20 [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=3455002)[0;0m INFO 06-27 03:10:20 [backends.py:430] Dynamo bytecode transform time: 5.79 s | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.333 s | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.369 s | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.354 s | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:26 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.428 s | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:31 [monitor.py:33] torch.compile takes 5.59 s in total | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:31 [monitor.py:33] torch.compile takes 5.52 s in total | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:31 [monitor.py:33] torch.compile takes 5.79 s in total | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:31 [monitor.py:33] torch.compile takes 5.64 s in total | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens | |
| INFO 06-27 03:10:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x | |
| [1;36m(VllmWorker rank=2 pid=3455003)[0;0m INFO 06-27 03:10:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=0 pid=3455001)[0;0m INFO 06-27 03:10:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=3 pid=3455004)[0;0m INFO 06-27 03:10:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=1 pid=3455002)[0;0m INFO 06-27 03:10:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| INFO 06-27 03:10:59 [core.py:159] init engine (profile, create kv cache, warmup model) took 44.01 seconds | |
| INFO 06-27 03:10:59 [core_client.py:439] Core engine process 0 ready. | |
| INFO 06-27 03:23:34 [importing.py:53] Triton module has been replaced with a placeholder. | |
| INFO 06-27 03:23:34 [__init__.py:239] Automatically detected platform cuda. | |
| | Task |Version| Metric |Value | |Stderr| | |
| |------------------|------:|---------------------|-----:|---|-----:| | |
| |all | |sem |0.5022|± |0.0273| | |
| | | |math_pass@1:1_samples|0.7850|± |0.0407| | |
| |mm\|arc_challenge\|0| 0|sem |0.6142|± |0.0250| | |
| |mm\|arc_easy\|0 | 0|sem |0.6283|± |0.0157| | |
| |mm\|commonsenseqa\|0| 0|sem |0.4938|± |0.0280| | |
| |mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.7450|± |0.0206| | |
| |mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8250|± |0.0608| | |
| |mm\|truthfulqa\|0 | 0|sem |0.2727|± |0.0407| | |