| INFO 07-06 01:46:21 [__init__.py:239] Automatically detected platform cuda. |
| INFO 07-06 01:46:23 [config.py:209] Replacing legacy 'type' key with 'rope_type' |
| INFO 07-06 01:46:23 [config.py:2968] Downcasting torch.float32 to torch.float16. |
| INFO 07-06 01:46:31 [config.py:717] This model supports multiple tasks: {'classify', 'embed', 'generate', 'score', 'reward'}. Defaulting to 'generate'. |
| INFO 07-06 01:46:31 [config.py:1770] Defaulting to use mp for distributed inference |
| INFO 07-06 01:46:31 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. |
| INFO 07-06 01:46:32 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_darelinear_1', speculative_config=None, tokenizer='./merged1/phi_darelinear_1', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, 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=./merged1/phi_darelinear_1, 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 07-06 01:46:32 [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 07-06 01:46:32 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_320f0274'), local_subscribe_addr='ipc:///tmp/ef542976-275d-44d2-a206-ec4a43253c55', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| WARNING 07-06 01:46:33 [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 0x1512d3fa3fd0> |
| WARNING 07-06 01:46:33 [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 0x1512d3fa3d30> |
| WARNING 07-06 01:46:33 [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 0x1512d3fa3310> |
| WARNING 07-06 01:46:33 [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 0x1512d2578f10> |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c7e9f694'), local_subscribe_addr='ipc:///tmp/3c0ff71a-fb9e-4e5e-ad20-d3788fe1fb79', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_aa664e66'), local_subscribe_addr='ipc:///tmp/e6acd350-ebca-4ef8-9cea-88a00b133b7f', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_3159a9e0'), local_subscribe_addr='ipc:///tmp/a4ee572b-6d0f-43f0-ab8c-2eacdc42b618', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_ed30c8fe'), local_subscribe_addr='ipc:///tmp/6b1abc0c-4920-4dda-9f5e-11f50e7f4ae6', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m WARNING 07-06 01:46:36 [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=3913356)[0;0m WARNING 07-06 01:46:36 [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=3913354)[0;0m WARNING 07-06 01:46:36 [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=3913353)[0;0m WARNING 07-06 01:46:36 [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=3913353)[0;0m INFO 07-06 01:46:36 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_d88251be'), local_subscribe_addr='ipc:///tmp/565c92f5-39f0-4734-bcde-ed8434943c8f', remote_subscribe_addr=None, remote_addr_ipv6=False) |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:36 [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=3913354)[0;0m INFO 07-06 01:46:36 [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=3913353)[0;0m INFO 07-06 01:46:36 [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=2 pid=3913355)[0;0m INFO 07-06 01:46:36 [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=3913356)[0;0m INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m WARNING 07-06 01:46:36 [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=3913355)[0;0m INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m WARNING 07-06 01:46:36 [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=3913355)[0;0m WARNING 07-06 01:46:36 [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=3913353)[0;0m INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m WARNING 07-06 01:46:36 [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=3913356)[0;0m INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.13 seconds |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.31 seconds |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.31 seconds |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.30 seconds |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.431536 seconds |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.569417 seconds |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.569807 seconds |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.570462 seconds |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:49 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/93042111ed/rank_2_0 for vLLM's torch.compile |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:49 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/93042111ed/rank_0_0 for vLLM's torch.compile |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:49 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/93042111ed/rank_1_0 for vLLM's torch.compile |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:49 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/93042111ed/rank_3_0 for vLLM's torch.compile |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.02 s |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.28 s |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.32 s |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.40 s |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.90 s in total |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.60 s in total |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.86 s in total |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.98 s in total |
| INFO 07-06 01:47:39 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens |
| INFO 07-06 01:47:39 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x |
| INFO 07-06 01:47:39 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens |
| INFO 07-06 01:47:39 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x |
| INFO 07-06 01:47:39 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens |
| INFO 07-06 01:47:39 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x |
| INFO 07-06 01:47:39 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens |
| INFO 07-06 01:47:39 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x |
| [1;36m(VllmWorker rank=1 pid=3913354)[0;0m INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB |
| [1;36m(VllmWorker rank=3 pid=3913356)[0;0m INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB |
| [1;36m(VllmWorker rank=2 pid=3913355)[0;0m INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB |
| [1;36m(VllmWorker rank=0 pid=3913353)[0;0m INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB |
| INFO 07-06 01:48:08 [core.py:159] init engine (profile, create kv cache, warmup model) took 86.39 seconds |
| INFO 07-06 01:48:08 [core_client.py:439] Core engine process 0 ready. |
| INFO 07-06 01:49:18 [importing.py:53] Triton module has been replaced with a placeholder. |
| INFO 07-06 01:49:18 [__init__.py:239] Automatically detected platform cuda. |
| | Task |Version| Metric |Value | |Stderr| |
| |------------------|------:|---------------------|-----:|---|-----:| |
| |all | |sem |0.8862|± |0.0187| |
| | | |math_pass@1:1_samples|0.9607|± |0.0178| |
| |mm\|arc_challenge\|0| 0|sem |0.9423|± |0.0120| |
| |mm\|arc_easy\|0 | 0|sem |0.9778|± |0.0048| |
| |mm\|commonsenseqa\|0| 0|sem |0.8313|± |0.0210| |
| |mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9463|± |0.0107| |
| |mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9750|± |0.0250| |
| |mm\|truthfulqa\|0 | 0|sem |0.7934|± |0.0370| |
|
|
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|
