| INFO 06-29 03:05:52 [__init__.py:239] Automatically detected platform cuda. |
| INFO 06-29 03:05:53 [config.py:209] Replacing legacy 'type' key with 'rope_type' |
| INFO 06-29 03:06:00 [config.py:717] This model supports multiple tasks: {'generate', 'embed', 'score', 'reward', 'classify'}. Defaulting to 'generate'. |
| INFO 06-29 03:06:00 [config.py:1770] Defaulting to use mp for distributed inference |
| INFO 06-29 03:06:00 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. |
| INFO 06-29 03:06:02 [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 06-29 03:06:02 [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 06-29 03:06:02 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_ba3201d8'), local_subscribe_addr='ipc:///tmp/79f94962-df31-4714-a5ba-66e8dabc65f0', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| WARNING 06-29 03:06:02 [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 0x15300d83bd30> |
| WARNING 06-29 03:06:02 [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 0x153007db8b50> |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:02 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a98cd5c8'), local_subscribe_addr='ipc:///tmp/1665beca-a759-4a74-8a35-328b73af7aab', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| WARNING 06-29 03:06:02 [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 0x15300d83bdf0> |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:02 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_997a3d0d'), local_subscribe_addr='ipc:///tmp/5fa1bf18-a627-4f65-a698-951a2e2be934', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| WARNING 06-29 03:06:02 [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 0x15300d83baf0> |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:02 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_64e56576'), local_subscribe_addr='ipc:///tmp/add31029-1e82-4cb0-91a5-1096740d59b3', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:02 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0123b614'), local_subscribe_addr='ipc:///tmp/ec760e8b-edf8-4c14-85ba-8192fe5f2fc9', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:04 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:04 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:04 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:04 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:04 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:04 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:04 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:04 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m WARNING 06-29 03:06:05 [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=3667355)[0;0m WARNING 06-29 03:06:05 [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=3667353)[0;0m WARNING 06-29 03:06:05 [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=3667352)[0;0m WARNING 06-29 03:06:05 [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=3667352)[0;0m INFO 06-29 03:06:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_e9e897c3'), local_subscribe_addr='ipc:///tmp/e92f1b57-1c65-4d47-b989-9bc2763f34ca', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:05 [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=3667352)[0;0m INFO 06-29 03:06:05 [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=3667355)[0;0m INFO 06-29 03:06:05 [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=3667354)[0;0m INFO 06-29 03:06:05 [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=3667355)[0;0m INFO 06-29 03:06:05 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:05 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m WARNING 06-29 03:06:05 [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=3667354)[0;0m WARNING 06-29 03:06:05 [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=3667352)[0;0m INFO 06-29 03:06:05 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:05 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m WARNING 06-29 03:06:05 [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=3667353)[0;0m WARNING 06-29 03:06:05 [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=3667355)[0;0m INFO 06-29 03:06:05 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:05 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:05 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:05 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:06 [loader.py:458] Loading weights took 0.68 seconds |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:06 [loader.py:458] Loading weights took 0.68 seconds |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:06 [loader.py:458] Loading weights took 0.70 seconds |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:06 [loader.py:458] Loading weights took 0.75 seconds |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:06 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.869035 seconds |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:06 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.869307 seconds |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:06 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.963994 seconds |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:06 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.924952 seconds |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:12 [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=3667353)[0;0m INFO 06-29 03:06:12 [backends.py:430] Dynamo bytecode transform time: 5.86 s |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:12 [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=3667354)[0;0m INFO 06-29 03:06:12 [backends.py:430] Dynamo bytecode transform time: 5.97 s |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:12 [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=3667352)[0;0m INFO 06-29 03:06:12 [backends.py:430] Dynamo bytecode transform time: 6.03 s |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:12 [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=3667355)[0;0m INFO 06-29 03:06:12 [backends.py:430] Dynamo bytecode transform time: 6.06 s |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:17 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.370 s |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:17 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.428 s |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:17 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.399 s |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:17 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.410 s |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:23 [monitor.py:33] torch.compile takes 5.86 s in total |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:23 [monitor.py:33] torch.compile takes 6.03 s in total |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:23 [monitor.py:33] torch.compile takes 5.97 s in total |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:23 [monitor.py:33] torch.compile takes 6.06 s in total |
| INFO 06-29 03:06:24 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens |
| INFO 06-29 03:06:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x |
| INFO 06-29 03:06:24 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens |
| INFO 06-29 03:06:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x |
| INFO 06-29 03:06:24 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens |
| INFO 06-29 03:06:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x |
| INFO 06-29 03:06:24 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens |
| INFO 06-29 03:06:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x |
| [1;36m(VllmWorker rank=3 pid=3667355)[0;0m INFO 06-29 03:06:50 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 2.96 GiB |
| [1;36m(VllmWorker rank=2 pid=3667354)[0;0m INFO 06-29 03:06:50 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 2.96 GiB |
| [1;36m(VllmWorker rank=1 pid=3667353)[0;0m INFO 06-29 03:06:50 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 2.96 GiB |
| [1;36m(VllmWorker rank=0 pid=3667352)[0;0m INFO 06-29 03:06:50 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 2.96 GiB |
| INFO 06-29 03:06:50 [core.py:159] init engine (profile, create kv cache, warmup model) took 43.64 seconds |
| INFO 06-29 03:06:50 [core_client.py:439] Core engine process 0 ready. |
| INFO 06-29 03:19:33 [importing.py:53] Triton module has been replaced with a placeholder. |
| INFO 06-29 03:19:33 [__init__.py:239] Automatically detected platform cuda. |
| | Task |Version| Metric |Value | |Stderr| |
| |------------------|------:|---------------------|-----:|---|-----:| |
| |all | |sem |0.5172|± |0.0278| |
| | | |math_pass@1:1_samples|0.7669|± |0.0425| |
| |mm\|arc_challenge\|0| 0|sem |0.6247|± |0.0248| |
| |mm\|arc_easy\|0 | 0|sem |0.6146|± |0.0158| |
| |mm\|commonsenseqa\|0| 0|sem |0.5156|± |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 |0.3140|± |0.0424| |
|
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