| INFO 06-27 02:42:43 [__init__.py:239] Automatically detected platform cuda. | |
| INFO 06-27 02:42:45 [config.py:209] Replacing legacy 'type' key with 'rope_type' | |
| INFO 06-27 02:42:52 [config.py:717] This model supports multiple tasks: {'classify', 'reward', 'generate', 'score', 'embed'}. Defaulting to 'generate'. | |
| INFO 06-27 02:42:52 [config.py:1770] Defaulting to use mp for distributed inference | |
| INFO 06-27 02:42:52 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. | |
| INFO 06-27 02:42:54 [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 02:42:54 [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 02:42:54 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_f75c7f2e'), local_subscribe_addr='ipc:///tmp/128f64f5-39ca-4318-b6d6-702afc25e764', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 02:42:54 [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 0x14ea69747d60> | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:42:54 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8c6a899a'), local_subscribe_addr='ipc:///tmp/bfe1aa7b-3caf-4823-aee3-3b040821364d', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 02:42:54 [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 0x14ea5bcd0ac0> | |
| WARNING 06-27 02:42:54 [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 0x14ea69747ca0> | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:42:54 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_91075db3'), local_subscribe_addr='ipc:///tmp/fb40d5e0-49c2-414a-b62e-75f1e168124f', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| WARNING 06-27 02:42:54 [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 0x14ea697479d0> | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:42:54 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_56dad22d'), local_subscribe_addr='ipc:///tmp/bdc6a620-d195-4169-96e6-e1afc95097d1', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:54 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_bbd791fe'), local_subscribe_addr='ipc:///tmp/8bc2120a-e48f-4551-be13-deeee6692171', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:42:56 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:42:56 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:42:56 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:42:56 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:42:56 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:56 [utils.py:1055] Found nccl from library libnccl.so.2 | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:42:56 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:56 [pynccl.py:69] vLLM is using nccl==2.21.5 | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m WARNING 06-27 02:42:56 [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=3438993)[0;0m WARNING 06-27 02:42:56 [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=3438992)[0;0m WARNING 06-27 02:42:56 [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=3438991)[0;0m WARNING 06-27 02:42:56 [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=3438991)[0;0m INFO 06-27 02:42:56 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_835e5bd0'), local_subscribe_addr='ipc:///tmp/53797ae2-24e6-41c5-a70a-50496a13bf1a', remote_subscribe_addr=None, remote_addr_ipv6=False) | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:42:56 [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=0 pid=3438991)[0;0m INFO 06-27 02:42:56 [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=3438992)[0;0m INFO 06-27 02:42:56 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m WARNING 06-27 02:42:56 [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=3438991)[0;0m INFO 06-27 02:42:56 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:56 [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=0 pid=3438991)[0;0m WARNING 06-27 02:42:56 [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=3438993)[0;0m INFO 06-27 02:42:56 [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=3 pid=3438994)[0;0m INFO 06-27 02:42:56 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m WARNING 06-27 02:42:56 [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=3438993)[0;0m INFO 06-27 02:42:56 [cuda.py:221] Using Flash Attention backend on V1 engine. | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m WARNING 06-27 02:42:56 [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=3438992)[0;0m INFO 06-27 02:42:56 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:42:56 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:56 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:42:56 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:42:57 [loader.py:458] Loading weights took 0.67 seconds | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:42:57 [loader.py:458] Loading weights took 0.66 seconds | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:57 [loader.py:458] Loading weights took 0.69 seconds | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:42:57 [loader.py:458] Loading weights took 0.69 seconds | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:42:57 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.881809 seconds | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:42:57 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.906570 seconds | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:42:58 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.904886 seconds | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:42:58 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.923940 seconds | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:43:03 [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=3438993)[0;0m INFO 06-27 02:43:03 [backends.py:430] Dynamo bytecode transform time: 5.62 s | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:43:04 [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=3438994)[0;0m INFO 06-27 02:43:04 [backends.py:430] Dynamo bytecode transform time: 5.69 s | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:43:04 [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=3438992)[0;0m INFO 06-27 02:43:04 [backends.py:430] Dynamo bytecode transform time: 5.76 s | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:43:04 [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=3438991)[0;0m INFO 06-27 02:43:04 [backends.py:430] Dynamo bytecode transform time: 5.94 s | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:43:09 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.426 s | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:43:09 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.417 s | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:43:09 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.500 s | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:43:09 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.542 s | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:43:15 [monitor.py:33] torch.compile takes 5.69 s in total | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:43:15 [monitor.py:33] torch.compile takes 5.94 s in total | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:43:15 [monitor.py:33] torch.compile takes 5.62 s in total | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:43:15 [monitor.py:33] torch.compile takes 5.76 s in total | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens | |
| INFO 06-27 02:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x | |
| [1;36m(VllmWorker rank=3 pid=3438994)[0;0m INFO 06-27 02:43:42 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=2 pid=3438993)[0;0m INFO 06-27 02:43:42 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=1 pid=3438992)[0;0m INFO 06-27 02:43:42 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| [1;36m(VllmWorker rank=0 pid=3438991)[0;0m INFO 06-27 02:43:42 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB | |
| INFO 06-27 02:43:42 [core.py:159] init engine (profile, create kv cache, warmup model) took 44.34 seconds | |
| INFO 06-27 02:43:43 [core_client.py:439] Core engine process 0 ready. | |
| INFO 06-27 02:56:22 [importing.py:53] Triton module has been replaced with a placeholder. | |
| INFO 06-27 02:56:22 [__init__.py:239] Automatically detected platform cuda. | |
| | Task |Version| Metric |Value | |Stderr| | |
| |------------------|------:|---------------------|-----:|---|-----:| | |
| |all | |sem |0.5228|± |0.0278| | |
| | | |math_pass@1:1_samples|0.7669|± |0.0425| | |
| |mm\|arc_challenge\|0| 0|sem |0.6168|± |0.0249| | |
| |mm\|arc_easy\|0 | 0|sem |0.6241|± |0.0157| | |
| |mm\|commonsenseqa\|0| 0|sem |0.5281|± |0.0280| | |
| |mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.7338|± |0.0209| | |
| |mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8000|± |0.0641| | |
| |mm\|truthfulqa\|0 | 0|sem |0.3223|± |0.0427| | |