diff --git a/eval/0623_32k/logs/I-Phi4.log b/eval/0623_32k/logs/I-Phi4.log index d78d8e1ed8e5b19a2fd9fd3fb53b7391246876a2..dc659e51f35932b8f462c84dde356cacdd809139 100644 --- a/eval/0623_32k/logs/I-Phi4.log +++ b/eval/0623_32k/logs/I-Phi4.log @@ -1,11 +1,11 @@ -INFO 06-26 21:33:21 [importing.py:53] Triton module has been replaced with a placeholder. -INFO 06-26 21:33:21 [__init__.py:239] Automatically detected platform cuda. -INFO 06-26 22:19:23 [importing.py:53] Triton module has been replaced with a placeholder. -INFO 06-26 22:19:24 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 15:13:29 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 15:13:29 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 16:00:44 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 16:00:44 [__init__.py:239] Automatically detected platform cuda. | Task |Version| Metric |Value | |Stderr| 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replaced with a placeholder. +INFO 07-08 16:02:50 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 16:43:27 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 16:43:27 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|--------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.1892|± |0.0097| +| | |em |0.6406|± |0.0167| +| | |qem |0.6427|± |0.0167| +| | |pem |0.6412|± |0.0167| +| | |pqem |0.7188|± |0.0165| +|mm\|aime24_c\|0 | 3|math_pass@1:1_samples|0.0000|± |0.0000| +|mm\|arc_challenge_c\|0| 0|em |0.7978|± |0.0117| +| | |qem |0.7978|± |0.0117| +| | |pem |0.7978|± |0.0117| +| | |pqem |0.8439|± |0.0106| +|mm\|arc_easy_c\|0 | 0|em |0.9154|± |0.0057| +| | |qem |0.9154|± |0.0057| +| | |pem |0.9154|± |0.0057| +| | |pqem |0.9432|± |0.0048| +|mm\|commonsenseqa_c\|0| 0|em |0.6994|± |0.0131| +| | |qem |0.7084|± |0.0130| +| | |pem |0.6994|± |0.0131| +| | |pqem |0.7592|± |0.0122| +|mm\|gpqa_diamond_c\|0 | 1|em |0.3333|± |0.0336| +| | |qem |0.3333|± |0.0336| +| | |pem |0.3333|± |0.0336| +| | |pqem |0.4697|± |0.0356| +|mm\|gsm8k_c\|0 | 0|math_pass@1:1_samples|0.4276|± |0.0136| +|mm\|math_500_c\|0 | 3|math_pass@1:1_samples|0.1400|± |0.0155| +|mm\|truthfulqa_c\|0 | 0|em |0.4572|± |0.0195| +| | |qem |0.4587|± |0.0195| +| | |pem |0.4602|± |0.0195| +| | |pqem |0.5780|± |0.0193| + diff --git a/eval/0623_32k/logs/R-Phi4.log b/eval/0623_32k/logs/R-Phi4.log index b4dab954c25ad955545d5cfe51fb5e227030e897..ce38f945d01b1d3523a6374e1ff11b12c933a936 100644 --- a/eval/0623_32k/logs/R-Phi4.log +++ b/eval/0623_32k/logs/R-Phi4.log @@ -1,7 +1,7 @@ -INFO 06-25 03:35:59 [importing.py:53] Triton module has been replaced with a placeholder. -INFO 06-25 03:35:59 [__init__.py:239] Automatically detected platform cuda. -INFO 06-25 13:24:40 [importing.py:53] Triton module has been replaced with a placeholder. -INFO 06-25 13:24:40 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 13:51:35 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 13:51:35 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 15:03:11 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 15:03:11 [__init__.py:239] Automatically detected platform cuda. | Task |Version| Metric |Value | |Stderr| |------------------|------:|---------------------|-----:|---|-----:| |all | |sem |0.6602|± |0.0139| diff --git a/eval/0623_32k/outputs/._models_I-Phi4/2025-06-26T21-33-22.888531/outputs_mm|aime24|0_2025-06-26T21-33-22.888531.parquet b/eval/0623_32k/outputs/._models_I-Phi4/2025-06-26T21-33-22.888531/outputs_mm|aime24|0_2025-06-26T21-33-22.888531.parquet index 4d985cf4568405ab9382153fa8a5482264bbeebd..a5d8bf5884df24b5d0abbf32f562f855de6cd8bc 100644 --- a/eval/0623_32k/outputs/._models_I-Phi4/2025-06-26T21-33-22.888531/outputs_mm|aime24|0_2025-06-26T21-33-22.888531.parquet +++ 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0.01670906264245309, + "pem": 0.6412380582895972, + "pem_stderr": 0.016733080564131197, + "pqem": 0.7187861707517593, + "pqem_stderr": 0.016496786122759278 + } + }, + "versions": { + "mm|aime24_c|0": 3, + "mm|arc_challenge_c|0": 0, + "mm|arc_easy_c|0": 0, + "mm|commonsenseqa_c|0": 0, + "mm|gpqa_diamond_c|0": 1, + "mm|gsm8k_c|0": 0, + "mm|math_500_c|0": 3, + "mm|mmlu_pro_c|0": 0, + "mm|truthfulqa_c|0": 0 + } +} \ No newline at end of file diff --git a/eval/0623_32k/results/._models_R-Phi4/results_2025-06-23T01-52-10.258150.json b/eval/0623_32k/results/._models_R-Phi4/results_2025-06-23T01-52-10.258150.json index 9640db13044355282a1807310f8614409b3ea99c..2e5b71bf1ce104d130b97b84e06dcd0866a3e446 100644 --- a/eval/0623_32k/results/._models_R-Phi4/results_2025-06-23T01-52-10.258150.json +++ b/eval/0623_32k/results/._models_R-Phi4/results_2025-06-23T01-52-10.258150.json @@ -14,7 +14,7 @@ }, "mm|math_500|0": { "math_pass@1:1_samples": 0.89, - "math_pass@1:1_samples_stderr": 0.014006869199415625 + "math_pass@1:1_samples_stderr": 0.014006869199415621 }, "mm|arc_challenge|0": { "sem": 0.878839590443686, @@ -30,7 +30,7 @@ }, "mm|arc_easy|0": { "sem": 0.9452861952861953, - "sem_stderr": 0.004666575631131669 + "sem_stderr": 0.004666575631131668 }, "mm|mmlu_pro|0": { "sem": 0.546875, @@ -40,7 +40,7 @@ "sem": 0.6601657415196733, "sem_stderr": 0.013893408156259154, "math_pass@1:1_samples": 0.745887456827563, - "math_pass@1:1_samples_stderr": 0.03791158116728403 + "math_pass@1:1_samples_stderr": 0.03791158116728402 } }, "versions": { diff --git a/merge_bench2/logs/phi_darelinear_1.log b/merge_bench2/logs/phi_darelinear_1.log new file mode 100644 index 0000000000000000000000000000000000000000..79ca9fac0193f15353fb3950d83c3b404def18b7 --- /dev/null +++ b/merge_bench2/logs/phi_darelinear_1.log @@ -0,0 +1,101 @@ +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 +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 +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 +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 +(VllmWorker rank=1 pid=3913354) 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) +(VllmWorker rank=0 pid=3913353) 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) +(VllmWorker rank=2 pid=3913355) 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) +(VllmWorker rank=3 pid=3913356) 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) +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:35 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:35 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3913355) 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. +(VllmWorker rank=3 pid=3913356) 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. +(VllmWorker rank=1 pid=3913354) 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. +(VllmWorker rank=0 pid=3913353) 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. +(VllmWorker rank=0 pid=3913353) 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) +(VllmWorker rank=3 pid=3913356) 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 +(VllmWorker rank=1 pid=3913354) 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 +(VllmWorker rank=0 pid=3913353) 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 +(VllmWorker rank=2 pid=3913355) 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 +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3913356) 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. +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3913354) 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. +(VllmWorker rank=2 pid=3913355) 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. +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3913353) 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. +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_1... +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.13 seconds +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.31 seconds +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.31 seconds +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:41 [loader.py:458] Loading weights took 5.30 seconds +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.431536 seconds +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.569417 seconds +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.569807 seconds +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:41 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.570462 seconds +(VllmWorker rank=2 pid=3913355) 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 +(VllmWorker rank=0 pid=3913353) 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 +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s +(VllmWorker rank=1 pid=3913354) 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 +(VllmWorker rank=3 pid=3913356) 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 +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:49 [backends.py:430] Dynamo bytecode transform time: 7.58 s +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:46:54 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.02 s +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.28 s +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.32 s +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:47:15 [backends.py:148] Compiling a graph for general shape takes 25.40 s +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.90 s in total +(VllmWorker rank=0 pid=3913353) INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.60 s in total +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:47:37 [monitor.py:33] torch.compile takes 32.86 s in total +(VllmWorker rank=3 pid=3913356) 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 +(VllmWorker rank=1 pid=3913354) INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3913356) INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3913355) INFO 07-06 01:48:08 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3913353) 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| + diff --git a/merge_bench2/logs/phi_darelinear_3.log b/merge_bench2/logs/phi_darelinear_3.log new file mode 100644 index 0000000000000000000000000000000000000000..5ca46facbf2c8f955d627c3291f37aafff9832e0 --- /dev/null +++ b/merge_bench2/logs/phi_darelinear_3.log @@ -0,0 +1,101 @@ +INFO 07-06 01:49:17 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 01:49:19 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 01:49:19 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 01:49:25 [config.py:717] This model supports multiple tasks: {'generate', 'reward', 'classify', 'embed', 'score'}. Defaulting to 'generate'. +INFO 07-06 01:49:26 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 01:49:26 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 01:49:27 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_darelinear_3', speculative_config=None, tokenizer='./merged1/phi_darelinear_3', 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_3, 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 01:49:27 [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 01:49:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_c00e4e92'), local_subscribe_addr='ipc:///tmp/9a2051fe-f334-40fd-a278-5287f6399f4e', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:49:27 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a9a5cb11'), local_subscribe_addr='ipc:///tmp/16868dcf-a395-4e65-a290-fb125576f4d8', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:49:27 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 01:49:27 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_16b687e9'), local_subscribe_addr='ipc:///tmp/b6c103ea-b13e-4301-bc4d-2771b46e51c3', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:49:27 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_bb638763'), local_subscribe_addr='ipc:///tmp/926c8722-75e3-405a-92bc-10b1d1fa7e66', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e008e93b'), local_subscribe_addr='ipc:///tmp/87c4a958-40c9-49ad-a75d-3b107a3eb6ff', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:29 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:29 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:29 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:29 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:29 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:29 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:29 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:29 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3915505) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=2 pid=3915504) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=0 pid=3915502) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=1 pid=3915503) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:30 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_06f3312e'), local_subscribe_addr='ipc:///tmp/39034025-40d4-4b18-92dd-81dba79948c4', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:30 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:30 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:30 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:30 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:30 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:30 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3915504) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:30 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:30 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3915505) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=0 pid=3915502) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=1 pid=3915503) WARNING 07-06 01:49:30 [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. +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:30 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_3... +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:30 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_3... +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:30 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_3... +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:30 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_3... +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:34 [loader.py:458] Loading weights took 4.37 seconds +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:34 [loader.py:458] Loading weights took 4.43 seconds +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:34 [loader.py:458] Loading weights took 4.39 seconds +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:34 [loader.py:458] Loading weights took 4.40 seconds +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.555538 seconds +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.619168 seconds +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.618388 seconds +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.624664 seconds +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:40 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a9e72ffa9a/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:40 [backends.py:430] Dynamo bytecode transform time: 5.59 s +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:40 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a9e72ffa9a/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:40 [backends.py:430] Dynamo bytecode transform time: 5.60 s +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:40 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a9e72ffa9a/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:40 [backends.py:430] Dynamo bytecode transform time: 5.68 s +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:40 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a9e72ffa9a/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:40 [backends.py:430] Dynamo bytecode transform time: 5.70 s +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:49:45 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:49:45 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:49:45 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:49:45 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:50:06 [backends.py:148] Compiling a graph for general shape takes 25.40 s +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:50:07 [backends.py:148] Compiling a graph for general shape takes 25.42 s +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:50:07 [backends.py:148] Compiling a graph for general shape takes 25.83 s +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:50:07 [backends.py:148] Compiling a graph for general shape takes 26.05 s +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:50:29 [monitor.py:33] torch.compile takes 31.10 s in total +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:50:29 [monitor.py:33] torch.compile takes 31.53 s in total +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:50:29 [monitor.py:33] torch.compile takes 31.00 s in total +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:50:29 [monitor.py:33] torch.compile takes 31.63 s in total +INFO 07-06 01:50:30 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 01:50:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 01:50:30 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 01:50:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 01:50:30 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 01:50:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 01:50:30 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 01:50:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=2 pid=3915504) INFO 07-06 01:50:58 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3915502) INFO 07-06 01:50:58 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3915505) INFO 07-06 01:50:58 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3915503) INFO 07-06 01:50:58 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +INFO 07-06 01:50:58 [core.py:159] init engine (profile, create kv cache, warmup model) took 82.85 seconds +INFO 07-06 01:50:58 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 01:55:24 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 01:55:24 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8894|± |0.0184| +| | |math_pass@1:1_samples|0.9334|± |0.0266| +|mm\|arc_challenge\|0| 0|sem |0.9501|± |0.0112| +|mm\|arc_easy\|0 | 0|sem |0.9778|± |0.0048| +|mm\|commonsenseqa\|0| 0|sem |0.8281|± |0.0211| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9418|± |0.0111| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9250|± |0.0422| +|mm\|truthfulqa\|0 | 0|sem |0.8017|± |0.0364| + diff --git a/merge_bench2/logs/phi_darelinear_5.log b/merge_bench2/logs/phi_darelinear_5.log new file mode 100644 index 0000000000000000000000000000000000000000..62f6ac9fb0233093d11ca61813c57f67da84a95e --- /dev/null +++ b/merge_bench2/logs/phi_darelinear_5.log @@ -0,0 +1,101 @@ +INFO 07-06 01:55:23 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 01:55:25 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 01:55:25 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 01:55:32 [config.py:717] This model supports multiple tasks: {'score', 'embed', 'generate', 'classify', 'reward'}. Defaulting to 'generate'. +INFO 07-06 01:55:32 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 01:55:32 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 01:55:34 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_darelinear_5', speculative_config=None, tokenizer='./merged1/phi_darelinear_5', 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_5, 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 01:55:34 [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 01:55:34 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_2171845e'), local_subscribe_addr='ipc:///tmp/dba3e624-2ac5-4d20-8169-6a4f75252d0a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:55:34 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:34 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1b40b36f'), local_subscribe_addr='ipc:///tmp/1cdf240a-73da-4faf-a18c-dcb8e6e14a6e', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:55:34 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:34 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_58ea2b66'), local_subscribe_addr='ipc:///tmp/48ec31e2-cf6a-43da-8f28-41c977ee4357', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:55:34 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:34 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1dfc0477'), local_subscribe_addr='ipc:///tmp/94447305-b98c-4932-8e59-e1605c4caf30', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 01:55:34 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:34 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d9650a1e'), local_subscribe_addr='ipc:///tmp/705611bc-cd53-46b2-abb1-7aa321ee0a7b', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:36 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:36 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:36 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:36 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:36 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:36 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:36 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:36 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3919221) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=3 pid=3919222) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=0 pid=3919219) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=1 pid=3919220) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:36 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_24bcf7cc'), local_subscribe_addr='ipc:///tmp/c4c804a5-efd9-4de3-bf08-92b225ac40b7', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:36 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:36 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:36 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:36 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3919221) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=3 pid=3919222) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:36 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3919219) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=1 pid=3919220) WARNING 07-06 01:55:36 [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. +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_5... +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_5... +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_5... +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:36 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_5... +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:38 [loader.py:458] Loading weights took 1.56 seconds +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:38 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.751065 seconds +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:38 [loader.py:458] Loading weights took 1.88 seconds +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.069132 seconds +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:39 [loader.py:458] Loading weights took 2.19 seconds +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:39 [loader.py:458] Loading weights took 2.37 seconds +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.414548 seconds +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.606883 seconds +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a7e33e2aed/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:45 [backends.py:430] Dynamo bytecode transform time: 5.63 s +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a7e33e2aed/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:45 [backends.py:430] Dynamo bytecode transform time: 5.66 s +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a7e33e2aed/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:45 [backends.py:430] Dynamo bytecode transform time: 5.73 s +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a7e33e2aed/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:45 [backends.py:430] Dynamo bytecode transform time: 5.95 s +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:55:49 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:55:49 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:55:49 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:55:50 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:56:11 [backends.py:148] Compiling a graph for general shape takes 25.08 s +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:56:11 [backends.py:148] Compiling a graph for general shape takes 25.40 s +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:56:11 [backends.py:148] Compiling a graph for general shape takes 25.26 s +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:56:11 [backends.py:148] Compiling a graph for general shape takes 25.22 s +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:56:33 [monitor.py:33] torch.compile takes 31.17 s in total +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:56:33 [monitor.py:33] torch.compile takes 31.03 s in total +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:56:33 [monitor.py:33] torch.compile takes 30.74 s in total +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:56:33 [monitor.py:33] torch.compile takes 30.99 s in total +INFO 07-06 01:56:35 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 01:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 01:56:35 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 01:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 01:56:35 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 01:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 01:56:35 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 01:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=2 pid=3919221) INFO 07-06 01:57:05 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3919222) INFO 07-06 01:57:05 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3919219) INFO 07-06 01:57:05 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3919220) INFO 07-06 01:57:05 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +INFO 07-06 01:57:05 [core.py:159] init engine (profile, create kv cache, warmup model) took 85.49 seconds +INFO 07-06 01:57:05 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:07:57 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:07:58 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8642|± |0.0201| +| | |math_pass@1:1_samples|0.8954|± |0.0292| +|mm\|arc_challenge\|0| 0|sem |0.9265|± |0.0134| +|mm\|arc_easy\|0 | 0|sem |0.9578|± |0.0065| +|mm\|commonsenseqa\|0| 0|sem |0.7875|± |0.0229| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8658|± |0.0161| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9250|± |0.0422| +|mm\|truthfulqa\|0 | 0|sem |0.7851|± |0.0375| + diff --git a/merge_bench2/logs/phi_darelinear_7.log b/merge_bench2/logs/phi_darelinear_7.log new file mode 100644 index 0000000000000000000000000000000000000000..376e0af89dee367323d1900484ec2d4fd177a3d3 --- /dev/null +++ b/merge_bench2/logs/phi_darelinear_7.log @@ -0,0 +1,101 @@ +INFO 07-06 02:07:56 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:07:58 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:07:58 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:08:05 [config.py:717] This model supports multiple tasks: {'classify', 'generate', 'embed', 'reward', 'score'}. Defaulting to 'generate'. +INFO 07-06 02:08:05 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:08:05 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:08:07 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_darelinear_7', speculative_config=None, tokenizer='./merged1/phi_darelinear_7', 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_7, 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 02:08:07 [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 02:08:07 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_1bcb022b'), local_subscribe_addr='ipc:///tmp/ee948401-c9f8-41a2-a34e-44dd790f1865', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:08:07 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:07 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_bfa2d2a8'), local_subscribe_addr='ipc:///tmp/86d51acd-0efd-4dea-8ca3-c4f87555bace', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:08:07 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:07 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_79dfef4a'), local_subscribe_addr='ipc:///tmp/1c167278-2df4-417a-b744-d664c84373d5', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:08:07 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:08:07 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:07 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_79b3846e'), local_subscribe_addr='ipc:///tmp/ae256d6f-7965-455a-8523-b801fb96df48', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:07 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2861b1af'), local_subscribe_addr='ipc:///tmp/8e727f98-dd48-43de-8f1d-86cbb27336b8', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:19 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:19 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:19 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:19 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3923543) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=3 pid=3923544) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=1 pid=3923542) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=0 pid=3923541) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_f31c9e4e'), local_subscribe_addr='ipc:///tmp/051d2fb3-e6ad-4c1d-b095-62c5dea7f720', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:19 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:19 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:19 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:19 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3923543) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=3 pid=3923544) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3923542) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=0 pid=3923541) WARNING 07-06 02:08:19 [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. +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:19 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_7... +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:19 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_7... +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:19 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_7... +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:19 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_7... +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:21 [loader.py:458] Loading weights took 1.67 seconds +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:21 [loader.py:458] Loading weights took 2.03 seconds +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:21 [loader.py:458] Loading weights took 1.98 seconds +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:21 [loader.py:458] Loading weights took 2.04 seconds +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:21 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.882851 seconds +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:22 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.218165 seconds +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:22 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.216825 seconds +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:22 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.216819 seconds +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:27 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1a71ee31dc/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:27 [backends.py:430] Dynamo bytecode transform time: 5.57 s +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:27 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1a71ee31dc/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:27 [backends.py:430] Dynamo bytecode transform time: 5.58 s +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:27 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1a71ee31dc/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:27 [backends.py:430] Dynamo bytecode transform time: 5.63 s +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:27 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1a71ee31dc/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:27 [backends.py:430] Dynamo bytecode transform time: 5.69 s +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:32 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:32 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:32 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:32 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:08:53 [backends.py:148] Compiling a graph for general shape takes 24.72 s +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:08:53 [backends.py:148] Compiling a graph for general shape takes 24.79 s +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:08:53 [backends.py:148] Compiling a graph for general shape takes 24.92 s +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:08:53 [backends.py:148] Compiling a graph for general shape takes 24.81 s +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:09:15 [monitor.py:33] torch.compile takes 30.34 s in total +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:09:15 [monitor.py:33] torch.compile takes 30.37 s in total +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:09:15 [monitor.py:33] torch.compile takes 30.49 s in total +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:09:15 [monitor.py:33] torch.compile takes 30.50 s in total +INFO 07-06 02:09:16 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:09:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:09:16 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:09:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:09:16 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:09:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:09:16 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:09:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=2 pid=3923543) INFO 07-06 02:09:51 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3923544) INFO 07-06 02:09:51 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3923542) INFO 07-06 02:09:51 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3923541) INFO 07-06 02:09:51 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 3.00 GiB +INFO 07-06 02:09:51 [core.py:159] init engine (profile, create kv cache, warmup model) took 89.50 seconds +INFO 07-06 02:09:52 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:21:17 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:21:17 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.2600|± |0.0241| +| | |math_pass@1:1_samples|0.7685|± |0.0450| +|mm\|arc_challenge\|0| 0|sem |0.3465|± |0.0244| +|mm\|arc_easy\|0 | 0|sem |0.3411|± |0.0154| +|mm\|commonsenseqa\|0| 0|sem |0.1375|± |0.0193| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8121|± |0.0185| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.7250|± |0.0715| +|mm\|truthfulqa\|0 | 0|sem |0.2149|± |0.0375| + diff --git a/merge_bench2/logs/phi_darelinear_9.log b/merge_bench2/logs/phi_darelinear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..8aa17976f56d7ac25d3541a6b6c4ec1c2d0dc38a --- /dev/null +++ b/merge_bench2/logs/phi_darelinear_9.log @@ -0,0 +1,101 @@ +INFO 07-06 02:21:16 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:21:17 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:21:17 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:21:24 [config.py:717] This model supports multiple tasks: {'score', 'classify', 'embed', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-06 02:21:24 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:21:24 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:21:26 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_darelinear_9', speculative_config=None, tokenizer='./merged1/phi_darelinear_9', 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_9, 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 02:21:26 [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 02:21:26 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_30f1a7b1'), local_subscribe_addr='ipc:///tmp/d1e803f4-0ebf-4e6c-bbfb-9ccad278781a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:21:26 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:26 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1476e0ab'), local_subscribe_addr='ipc:///tmp/53537ea2-5867-4e5e-9093-790836a402f1', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:21:26 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:21:26 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:26 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8e9eab81'), local_subscribe_addr='ipc:///tmp/8dde035c-f6ef-4844-ab48-311f0435aa49', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:21:26 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:26 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b13c31a5'), local_subscribe_addr='ipc:///tmp/2cde312f-120f-4e47-9fe3-61229001312c', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:26 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d1b62a6a'), local_subscribe_addr='ipc:///tmp/bcc5ee20-6ad0-4b8e-bfc1-22393c762b74', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:28 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:28 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:28 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:28 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:28 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:28 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:28 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:28 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3927761) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=2 pid=3927760) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=0 pid=3927757) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=1 pid=3927758) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:29 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_09c8b29c'), local_subscribe_addr='ipc:///tmp/ec6382d5-df44-4124-ab06-b0e8d98daf1d', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:29 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:29 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:29 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:29 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:29 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3927761) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=2 pid=3927760) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:29 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3927758) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:29 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:29 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3927757) WARNING 07-06 02:21:29 [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. +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:29 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_9... +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:29 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_9... +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:29 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_9... +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:29 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_darelinear_9... +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:31 [loader.py:458] Loading weights took 1.46 seconds +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.659963 seconds +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:31 [loader.py:458] Loading weights took 1.82 seconds +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:31 [loader.py:458] Loading weights took 1.81 seconds +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:31 [loader.py:458] Loading weights took 1.82 seconds +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.030502 seconds +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.037395 seconds +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.029022 seconds +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/966adedabd/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:37 [backends.py:430] Dynamo bytecode transform time: 5.69 s +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/966adedabd/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:37 [backends.py:430] Dynamo bytecode transform time: 5.71 s +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/966adedabd/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:37 [backends.py:430] Dynamo bytecode transform time: 5.72 s +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/966adedabd/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:37 [backends.py:430] Dynamo bytecode transform time: 5.80 s +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:21:41 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:21:41 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:21:41 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:21:42 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:22:02 [backends.py:148] Compiling a graph for general shape takes 24.80 s +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:22:03 [backends.py:148] Compiling a graph for general shape takes 24.91 s +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:22:03 [backends.py:148] Compiling a graph for general shape takes 24.97 s +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:22:03 [backends.py:148] Compiling a graph for general shape takes 25.22 s +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:22:25 [monitor.py:33] torch.compile takes 30.69 s in total +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:22:25 [monitor.py:33] torch.compile takes 31.02 s in total +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:22:25 [monitor.py:33] torch.compile takes 30.52 s in total +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:22:25 [monitor.py:33] torch.compile takes 30.61 s in total +INFO 07-06 02:22:26 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:22:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:22:26 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:22:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:22:26 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:22:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:22:26 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:22:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=0 pid=3927757) INFO 07-06 02:22:56 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3927758) INFO 07-06 02:22:56 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3927761) INFO 07-06 02:22:56 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3927760) INFO 07-06 02:22:56 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +INFO 07-06 02:22:56 [core.py:159] init engine (profile, create kv cache, warmup model) took 85.16 seconds +INFO 07-06 02:22:57 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:36:36 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:36:36 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.0911|± |0.0135| +| | |math_pass@1:1_samples|0.5582|± |0.0514| +|mm\|arc_challenge\|0| 0|sem |0.1470|± |0.0182| +|mm\|arc_easy\|0 | 0|sem |0.1616|± |0.0120| +|mm\|commonsenseqa\|0| 0|sem |0.0312|± |0.0097| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.5414|± |0.0236| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.5750|± |0.0792| +|mm\|truthfulqa\|0 | 0|sem |0.0248|± |0.0142| + diff --git a/merge_bench2/logs/phi_linear_1.log b/merge_bench2/logs/phi_linear_1.log new file mode 100644 index 0000000000000000000000000000000000000000..0a1c989143c192cd6cdff76a29bedd3feade522c --- /dev/null +++ b/merge_bench2/logs/phi_linear_1.log @@ -0,0 +1,101 @@ +INFO 07-06 02:36:35 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:36:37 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:36:37 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:36:44 [config.py:717] This model supports multiple tasks: {'score', 'generate', 'embed', 'classify', 'reward'}. Defaulting to 'generate'. +INFO 07-06 02:36:44 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:36:44 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:36:46 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_1', speculative_config=None, tokenizer='./merged1/phi_linear_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_linear_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 02:36:46 [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 02:36:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_bba99701'), local_subscribe_addr='ipc:///tmp/5b58d1e3-d352-4e22-97ce-b3481353fce1', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:36:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:36:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_dd1b80b4'), local_subscribe_addr='ipc:///tmp/5a17525a-ddc0-417a-aefd-3c288b1c7d73', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:36:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0a2f2346'), local_subscribe_addr='ipc:///tmp/9391ad30-1ca8-405f-93b7-49ff25e82cd7', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:36:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:36:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:36:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_fa41f380'), local_subscribe_addr='ipc:///tmp/88807445-2f9b-4d8d-8364-43855f0773f7', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:36:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2cfdcb03'), local_subscribe_addr='ipc:///tmp/8c1be914-f5f9-4792-afa1-d8746e587cd6', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:36:48 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:48 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:36:48 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:36:48 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:48 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:36:48 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:36:48 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:36:48 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3931836) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=2 pid=3931835) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=0 pid=3931833) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=1 pid=3931834) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:48 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_3b88a983'), local_subscribe_addr='ipc:///tmp/ee30ae32-937c-4998-8ca9-cb4910068838', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:36:48 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:36:48 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:36:48 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:36:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3931836) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:36:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:36:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3931835) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=1 pid=3931834) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:36:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_1... +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:36:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_1... +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:36:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_1... +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:48 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3931833) WARNING 07-06 02:36:48 [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. +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:36:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_1... +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:00 [loader.py:458] Loading weights took 11.14 seconds +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:00 [loader.py:458] Loading weights took 11.25 seconds +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:00 [loader.py:458] Loading weights took 11.26 seconds +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:00 [loader.py:458] Loading weights took 11.21 seconds +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:00 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.366286 seconds +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:00 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.453495 seconds +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:00 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.458498 seconds +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:00 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.460177 seconds +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/43ac6c7155/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:06 [backends.py:430] Dynamo bytecode transform time: 5.54 s +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/43ac6c7155/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:06 [backends.py:430] Dynamo bytecode transform time: 5.56 s +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/43ac6c7155/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:06 [backends.py:430] Dynamo bytecode transform time: 5.58 s +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/43ac6c7155/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:06 [backends.py:430] Dynamo bytecode transform time: 5.71 s +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:10 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:10 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:10 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:10 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:31 [backends.py:148] Compiling a graph for general shape takes 24.49 s +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:31 [backends.py:148] Compiling a graph for general shape takes 24.58 s +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:31 [backends.py:148] Compiling a graph for general shape takes 24.53 s +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:31 [backends.py:148] Compiling a graph for general shape takes 24.96 s +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:37:53 [monitor.py:33] torch.compile takes 30.66 s in total +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:37:53 [monitor.py:33] torch.compile takes 30.11 s in total +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:37:53 [monitor.py:33] torch.compile takes 30.02 s in total +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:37:53 [monitor.py:33] torch.compile takes 30.15 s in total +INFO 07-06 02:37:55 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:37:55 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:37:55 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:37:55 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:37:55 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:37:55 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:37:55 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:37:55 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=0 pid=3931833) INFO 07-06 02:38:25 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3931834) INFO 07-06 02:38:25 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3931836) INFO 07-06 02:38:25 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3931835) INFO 07-06 02:38:25 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +INFO 07-06 02:38:25 [core.py:159] init engine (profile, create kv cache, warmup model) took 85.17 seconds +INFO 07-06 02:38:26 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:39:29 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:39:29 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8849|± |0.0188| +| | |math_pass@1:1_samples|0.9732|± |0.0053| +|mm\|arc_challenge\|0| 0|sem |0.9423|± |0.0120| +|mm\|arc_easy\|0 | 0|sem |0.9736|± |0.0052| +|mm\|commonsenseqa\|0| 0|sem |0.8469|± |0.0202| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9463|± |0.0107| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |0.7769|± |0.0380| + diff --git a/merge_bench2/logs/phi_linear_3.log b/merge_bench2/logs/phi_linear_3.log new file mode 100644 index 0000000000000000000000000000000000000000..68f2613b702ad2a62a57cda4d12b286f6a5b1fd4 --- /dev/null +++ b/merge_bench2/logs/phi_linear_3.log @@ -0,0 +1,101 @@ +INFO 07-06 02:39:28 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:39:30 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:39:30 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:39:37 [config.py:717] This model supports multiple tasks: {'embed', 'reward', 'classify', 'generate', 'score'}. Defaulting to 'generate'. +INFO 07-06 02:39:37 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:39:37 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:39:38 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_3', speculative_config=None, tokenizer='./merged1/phi_linear_3', 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_linear_3, 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 02:39:38 [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 02:39:38 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_a5c01040'), local_subscribe_addr='ipc:///tmp/e28dd0a8-d7d9-484b-a9df-fd784df532e5', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:39:39 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:39:39 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:39 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_44c0b1be'), local_subscribe_addr='ipc:///tmp/f726f257-7494-4ae1-9440-c7a0d887e826', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:39 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_efeec022'), local_subscribe_addr='ipc:///tmp/21d65996-02e4-4e78-b9f6-32ff5b943186', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:39:39 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:39:39 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:39 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_bd8749ad'), local_subscribe_addr='ipc:///tmp/ff402d23-b98b-4c76-85b4-c4ac7cc322cd', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:39 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_f2ba94d4'), local_subscribe_addr='ipc:///tmp/e3551197-456e-47e4-85ac-fb4f426b53ca', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:41 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:41 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:41 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:41 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:41 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:41 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:41 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:41 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3934891) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=2 pid=3934890) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=0 pid=3934888) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=1 pid=3934889) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:41 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_9980edb2'), local_subscribe_addr='ipc:///tmp/59fc7118-f292-43a0-b4a6-2fb643396424', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:41 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:41 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:41 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:41 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:41 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=3934889) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:41 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:41 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3934890) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=3 pid=3934891) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:41 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3934888) WARNING 07-06 02:39:41 [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. +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:41 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_3... +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:41 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_3... +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:41 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_3... +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:41 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_3... +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:55 [loader.py:458] Loading weights took 13.34 seconds +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:55 [loader.py:458] Loading weights took 13.49 seconds +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:55 [loader.py:458] Loading weights took 13.46 seconds +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:55 [loader.py:458] Loading weights took 13.46 seconds +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:39:55 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.529307 seconds +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:39:55 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.679594 seconds +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:39:55 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.688507 seconds +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:39:55 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.676726 seconds +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:40:01 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/41d22a25e0/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:40:01 [backends.py:430] Dynamo bytecode transform time: 5.58 s +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:40:01 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/41d22a25e0/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:40:01 [backends.py:430] Dynamo bytecode transform time: 5.67 s +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:40:01 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/41d22a25e0/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:40:01 [backends.py:430] Dynamo bytecode transform time: 5.72 s +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:40:01 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/41d22a25e0/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:40:01 [backends.py:430] Dynamo bytecode transform time: 5.75 s +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:40:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:40:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:40:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:40:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:40:27 [backends.py:148] Compiling a graph for general shape takes 25.04 s +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:40:27 [backends.py:148] Compiling a graph for general shape takes 25.07 s +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:40:27 [backends.py:148] Compiling a graph for general shape takes 25.26 s +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:40:27 [backends.py:148] Compiling a graph for general shape takes 25.46 s +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:40:49 [monitor.py:33] torch.compile takes 30.74 s in total +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:40:49 [monitor.py:33] torch.compile takes 31.01 s in total +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:40:49 [monitor.py:33] torch.compile takes 31.18 s in total +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:40:49 [monitor.py:33] torch.compile takes 30.61 s in total +INFO 07-06 02:40:50 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:40:50 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:40:50 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:40:50 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:40:50 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:40:50 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:40:50 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:40:50 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=1 pid=3934889) INFO 07-06 02:41:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3934891) INFO 07-06 02:41:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3934888) INFO 07-06 02:41:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3934890) INFO 07-06 02:41:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 3.00 GiB +INFO 07-06 02:41:20 [core.py:159] init engine (profile, create kv cache, warmup model) took 85.05 seconds +INFO 07-06 02:41:21 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:42:33 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:42:33 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8920|± |0.0185| +| | |math_pass@1:1_samples|0.9504|± |0.0226| +|mm\|arc_challenge\|0| 0|sem |0.9291|± |0.0132| +|mm\|arc_easy\|0 | 0|sem |0.9768|± |0.0049| +|mm\|commonsenseqa\|0| 0|sem |0.8688|± |0.0189| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9508|± |0.0102| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9500|± |0.0349| +|mm\|truthfulqa\|0 | 0|sem |0.7934|± |0.0370| + diff --git a/merge_bench2/logs/phi_linear_5.log b/merge_bench2/logs/phi_linear_5.log new file mode 100644 index 0000000000000000000000000000000000000000..084688a10c61d001d131eff26322ff5e9180adc2 --- /dev/null +++ b/merge_bench2/logs/phi_linear_5.log @@ -0,0 +1,101 @@ +INFO 07-06 02:42:31 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:42:33 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:42:33 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:42:40 [config.py:717] This model supports multiple tasks: {'score', 'generate', 'classify', 'embed', 'reward'}. Defaulting to 'generate'. +INFO 07-06 02:42:40 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:42:40 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:42:42 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_5', speculative_config=None, tokenizer='./merged1/phi_linear_5', 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_linear_5, 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 02:42:42 [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 02:42:42 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_112ebc41'), local_subscribe_addr='ipc:///tmp/1882ecae-5424-4f82-b248-0f2d7e5fdc8f', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:42:42 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:42:42 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_370026e6'), local_subscribe_addr='ipc:///tmp/0571c96f-cafc-478b-a90d-095738c3a945', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:42:42 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:42 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_cee650a4'), local_subscribe_addr='ipc:///tmp/4e00ab79-208e-4b5e-8dde-dd04200008df', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:42:42 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:42:42 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:42:42 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b0d259b4'), local_subscribe_addr='ipc:///tmp/8b83fc84-07d7-4c46-a2bd-2b3f84ff642c', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:42:42 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1f29a7a3'), local_subscribe_addr='ipc:///tmp/ee9a8865-f497-4e80-87c1-e4529840ac14', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:42:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:42:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:42:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:42:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:42:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:42:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3937825) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=2 pid=3937824) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=0 pid=3937822) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=1 pid=3937823) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:54 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_7debc972'), local_subscribe_addr='ipc:///tmp/fd19e95c-cd17-47e2-8304-6822bab90a43', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:42:54 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:42:54 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:42:54 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:42:54 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:42:54 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:42:54 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3937825) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=2 pid=3937824) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=1 pid=3937823) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:42:54 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_5... +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:42:54 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_5... +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:42:54 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_5... +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:54 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:54 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3937822) WARNING 07-06 02:42:54 [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. +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:42:54 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_5... +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:43:07 [loader.py:458] Loading weights took 13.22 seconds +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:43:08 [loader.py:458] Loading weights took 13.30 seconds +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:43:08 [loader.py:458] Loading weights took 13.24 seconds +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:43:08 [loader.py:458] Loading weights took 13.25 seconds +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:43:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.413943 seconds +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:43:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.493882 seconds +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:43:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.502372 seconds +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:43:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.478218 seconds +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:43:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/5c84612a48/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:43:13 [backends.py:430] Dynamo bytecode transform time: 5.60 s +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:43:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/5c84612a48/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:43:13 [backends.py:430] Dynamo bytecode transform time: 5.61 s +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:43:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/5c84612a48/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:43:13 [backends.py:430] Dynamo bytecode transform time: 5.63 s +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:43:14 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/5c84612a48/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:43:14 [backends.py:430] Dynamo bytecode transform time: 5.98 s +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:43:18 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:43:18 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:43:18 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:43:18 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:43:39 [backends.py:148] Compiling a graph for general shape takes 25.13 s +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:43:39 [backends.py:148] Compiling a graph for general shape takes 25.14 s +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:43:39 [backends.py:148] Compiling a graph for general shape takes 25.20 s +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:43:40 [backends.py:148] Compiling a graph for general shape takes 25.04 s +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:44:02 [monitor.py:33] torch.compile takes 30.74 s in total +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:44:02 [monitor.py:33] torch.compile takes 30.76 s in total +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:44:02 [monitor.py:33] torch.compile takes 30.82 s in total +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:44:02 [monitor.py:33] torch.compile takes 31.02 s in total +INFO 07-06 02:44:03 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:44:03 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:44:03 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:44:03 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:44:03 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:44:03 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:44:03 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:44:03 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=1 pid=3937823) INFO 07-06 02:44:36 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3937825) INFO 07-06 02:44:36 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3937824) INFO 07-06 02:44:36 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3937822) INFO 07-06 02:44:36 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 3.00 GiB +INFO 07-06 02:44:36 [core.py:159] init engine (profile, create kv cache, warmup model) took 88.26 seconds +INFO 07-06 02:44:37 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:46:13 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:46:13 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8782|± |0.0191| +| | |math_pass@1:1_samples|0.9347|± |0.0239| +|mm\|arc_challenge\|0| 0|sem |0.9423|± |0.0120| +|mm\|arc_easy\|0 | 0|sem |0.9810|± |0.0044| +|mm\|commonsenseqa\|0| 0|sem |0.8375|± |0.0207| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9195|± |0.0129| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9500|± |0.0349| +|mm\|truthfulqa\|0 | 0|sem |0.7521|± |0.0394| + diff --git a/merge_bench2/logs/phi_linear_7.log b/merge_bench2/logs/phi_linear_7.log new file mode 100644 index 0000000000000000000000000000000000000000..ebe20edd3d56bad282ec81b3271a834639d516ed --- /dev/null +++ b/merge_bench2/logs/phi_linear_7.log @@ -0,0 +1,101 @@ +INFO 07-06 02:46:12 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:46:14 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:46:14 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:46:21 [config.py:717] This model supports multiple tasks: {'generate', 'embed', 'classify', 'score', 'reward'}. Defaulting to 'generate'. +INFO 07-06 02:46:21 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:46:21 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:46:22 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_7', speculative_config=None, tokenizer='./merged1/phi_linear_7', 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_linear_7, 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 02:46:22 [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 02:46:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_ce4c166f'), local_subscribe_addr='ipc:///tmp/9c8d640e-8d6e-4ee8-b0ef-db2baef5e786', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:46:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_16defaa0'), local_subscribe_addr='ipc:///tmp/935be012-6f66-4106-8bc9-e7e31a3aa51c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:46:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_ff4c5b18'), local_subscribe_addr='ipc:///tmp/31fd90f4-a7d7-4de2-ae6a-559c689da219', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:46:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:46:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:23 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b27dd7df'), local_subscribe_addr='ipc:///tmp/9d20279d-b32d-4cb7-8a6a-b30af9f5c77e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:23 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e1a03c9e'), local_subscribe_addr='ipc:///tmp/4b451eca-316d-4280-9087-2bf463e45cb4', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:24 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:24 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:24 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:24 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:24 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:24 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:24 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:24 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3940569) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=2 pid=3940568) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=1 pid=3940567) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=0 pid=3940566) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_6fbe7734'), local_subscribe_addr='ipc:///tmp/0c9267d0-be15-4f44-87c6-74ef0054aa0f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:25 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:25 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:25 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:25 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3940569) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=2 pid=3940568) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3940566) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=1 pid=3940567) WARNING 07-06 02:46:25 [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. +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:39 [loader.py:458] Loading weights took 13.74 seconds +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:39 [loader.py:458] Loading weights took 13.87 seconds +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:39 [loader.py:458] Loading weights took 13.86 seconds +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:39 [loader.py:458] Loading weights took 13.83 seconds +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.052956 seconds +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.052767 seconds +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.948024 seconds +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:39 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.053611 seconds +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:45 [backends.py:430] Dynamo bytecode transform time: 5.58 s +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:45 [backends.py:430] Dynamo bytecode transform time: 5.62 s +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:45 [backends.py:430] Dynamo bytecode transform time: 5.71 s +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:45 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:45 [backends.py:430] Dynamo bytecode transform time: 5.79 s +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:46:49 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:46:49 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:46:50 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:46:50 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:47:11 [backends.py:148] Compiling a graph for general shape takes 25.07 s +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:47:11 [backends.py:148] Compiling a graph for general shape takes 25.20 s +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:47:11 [backends.py:148] Compiling a graph for general shape takes 25.04 s +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:47:11 [backends.py:148] Compiling a graph for general shape takes 25.13 s +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:47:33 [monitor.py:33] torch.compile takes 30.75 s in total +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:47:33 [monitor.py:33] torch.compile takes 30.81 s in total +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:47:33 [monitor.py:33] torch.compile takes 30.92 s in total +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:47:33 [monitor.py:33] torch.compile takes 30.65 s in total +INFO 07-06 02:47:34 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:47:34 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:47:34 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:47:34 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:47:34 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:47:34 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:47:34 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:47:34 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=2 pid=3940568) INFO 07-06 02:48:05 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3940569) INFO 07-06 02:48:05 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3940567) INFO 07-06 02:48:05 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3940566) INFO 07-06 02:48:05 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 3.00 GiB +INFO 07-06 02:48:05 [core.py:159] init engine (profile, create kv cache, warmup model) took 85.74 seconds +INFO 07-06 02:48:05 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:49:54 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:49:54 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8754|± |0.0195| +| | |math_pass@1:1_samples|0.8782|± |0.0341| +|mm\|arc_challenge\|0| 0|sem |0.9396|± |0.0122| +|mm\|arc_easy\|0 | 0|sem |0.9662|± |0.0059| +|mm\|commonsenseqa\|0| 0|sem |0.8438|± |0.0203| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8814|± |0.0153| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8750|± |0.0530| +|mm\|truthfulqa\|0 | 0|sem |0.7521|± |0.0394| + diff --git a/merge_bench2/logs/phi_linear_9.log b/merge_bench2/logs/phi_linear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..bf5f6383f123e7f83cd33835e05faeb9c4da7bb6 --- /dev/null +++ b/merge_bench2/logs/phi_linear_9.log @@ -0,0 +1,101 @@ +INFO 07-06 02:49:53 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:49:55 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:49:55 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:50:02 [config.py:717] This model supports multiple tasks: {'embed', 'score', 'generate', 'reward', 'classify'}. Defaulting to 'generate'. +INFO 07-06 02:50:02 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:50:02 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:50:03 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_9', speculative_config=None, tokenizer='./merged1/phi_linear_9', 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_linear_9, 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 02:50:03 [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 02:50:03 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_b519e4fe'), local_subscribe_addr='ipc:///tmp/28925ff2-293f-492e-ad54-acab53b337fc', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:50:04 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:04 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c6ecda1f'), local_subscribe_addr='ipc:///tmp/c7dbebf6-19d4-49cd-8711-5d6dd4dae91c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:50:04 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:04 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_9464b9ea'), local_subscribe_addr='ipc:///tmp/40821cd8-5d98-4f24-9bf7-2b68bca621bf', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:50:04 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:50:04 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:04 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e28dc3de'), local_subscribe_addr='ipc:///tmp/1d96cd06-0467-4030-85e0-946e264263f4', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:04 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b327abc9'), local_subscribe_addr='ipc:///tmp/7275f8cb-e7de-4d42-aae8-a2da293e4203', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:05 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:05 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:05 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:05 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:05 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:05 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:05 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:05 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3943270) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=2 pid=3943269) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=1 pid=3943268) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=0 pid=3943267) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:06 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_c8444766'), local_subscribe_addr='ipc:///tmp/4b82c3c7-06a1-4ab0-b594-53220b4a55d5', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:06 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:06 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:06 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:06 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:06 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:06 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3943270) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=2 pid=3943269) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:06 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:06 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3943267) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=1 pid=3943268) WARNING 07-06 02:50:06 [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. +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:06 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:06 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:06 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:06 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:20 [loader.py:458] Loading weights took 13.90 seconds +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:20 [loader.py:458] Loading weights took 14.02 seconds +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:20 [loader.py:458] Loading weights took 13.98 seconds +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:20 [loader.py:458] Loading weights took 14.02 seconds +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:21 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.130689 seconds +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:21 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.203956 seconds +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:21 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.201688 seconds +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:21 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 14.201672 seconds +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:26 [backends.py:430] Dynamo bytecode transform time: 5.56 s +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:26 [backends.py:430] Dynamo bytecode transform time: 5.62 s +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:26 [backends.py:430] Dynamo bytecode transform time: 5.65 s +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:26 [backends.py:430] Dynamo bytecode transform time: 5.68 s +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:31 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:31 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:31 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:31 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:50:52 [backends.py:148] Compiling a graph for general shape takes 24.87 s +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:50:52 [backends.py:148] Compiling a graph for general shape takes 24.91 s +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:50:52 [backends.py:148] Compiling a graph for general shape takes 25.00 s +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:50:52 [backends.py:148] Compiling a graph for general shape takes 25.13 s +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:51:14 [monitor.py:33] torch.compile takes 30.81 s in total +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:51:14 [monitor.py:33] torch.compile takes 30.43 s in total +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:51:14 [monitor.py:33] torch.compile takes 30.56 s in total +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:51:14 [monitor.py:33] torch.compile takes 30.61 s in total +INFO 07-06 02:51:16 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 02:51:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 02:51:16 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:51:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:51:16 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 02:51:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 02:51:16 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 02:51:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=3 pid=3943270) INFO 07-06 02:51:53 [gpu_model_runner.py:1686] Graph capturing finished in 37 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3943269) INFO 07-06 02:51:53 [gpu_model_runner.py:1686] Graph capturing finished in 37 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3943267) INFO 07-06 02:51:53 [gpu_model_runner.py:1686] Graph capturing finished in 37 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3943268) INFO 07-06 02:51:53 [gpu_model_runner.py:1686] Graph capturing finished in 37 secs, took 3.00 GiB +INFO 07-06 02:51:53 [core.py:159] init engine (profile, create kv cache, warmup model) took 92.57 seconds +INFO 07-06 02:51:53 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 02:59:10 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 02:59:10 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.6572|± |0.0272| +| | |math_pass@1:1_samples|0.8719|± |0.0302| +|mm\|arc_challenge\|0| 0|sem |0.7507|± |0.0222| +|mm\|arc_easy\|0 | 0|sem |0.7772|± |0.0135| +|mm\|commonsenseqa\|0| 0|sem |0.5969|± |0.0275| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8188|± |0.0182| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9250|± |0.0422| +|mm\|truthfulqa\|0 | 0|sem |0.5041|± |0.0456| + diff --git a/merge_bench2/logs/phi_ties_1.log b/merge_bench2/logs/phi_ties_1.log new file mode 100644 index 0000000000000000000000000000000000000000..f74133ba626f6a61a2cda311e5a88bf928fa5208 --- /dev/null +++ b/merge_bench2/logs/phi_ties_1.log @@ -0,0 +1,101 @@ +INFO 07-06 02:59:09 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 02:59:10 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 02:59:10 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 02:59:17 [config.py:717] This model supports multiple tasks: {'classify', 'embed', 'score', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-06 02:59:17 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 02:59:17 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 02:59:19 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_1', speculative_config=None, tokenizer='./merged1/phi_ties_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_ties_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 02:59:19 [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 02:59:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_1c2919dd'), local_subscribe_addr='ipc:///tmp/7bf7a26b-deb0-4239-a469-ecff6222f327', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:59:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 02:59:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_525b7d48'), local_subscribe_addr='ipc:///tmp/9c0412be-a7a0-48c1-bea1-bd9804434b43', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:59:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2e63f1fc'), local_subscribe_addr='ipc:///tmp/52ccbd2f-912f-4611-a518-624b4eb315fe', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 02:59:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_bb7e1f2a'), local_subscribe_addr='ipc:///tmp/2d82e35a-7ad3-4f65-aa9b-3681d55e310c', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c133ff6d'), local_subscribe_addr='ipc:///tmp/32bc0b1f-e05e-4023-a81e-48f126850760', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:31 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:31 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:31 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:31 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:31 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:31 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:31 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:31 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3946092) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=3 pid=3946093) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=1 pid=3946089) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=0 pid=3946088) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:31 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_fb78652e'), local_subscribe_addr='ipc:///tmp/65c07cec-5c58-4524-afa2-da4fc7ac7056', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:31 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:31 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:31 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:31 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:31 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3946092) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:31 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3946093) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:31 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:31 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3946089) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=0 pid=3946088) WARNING 07-06 02:59:31 [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. +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:31 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:31 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:31 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:31 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:45 [loader.py:458] Loading weights took 13.18 seconds +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:45 [loader.py:458] Loading weights took 13.29 seconds +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:45 [loader.py:458] Loading weights took 13.33 seconds +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:45 [loader.py:458] Loading weights took 13.29 seconds +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:45 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.358423 seconds +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:45 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.510401 seconds +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:45 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.515920 seconds +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:45 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.504100 seconds +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:51 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:51 [backends.py:430] Dynamo bytecode transform time: 5.66 s +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:51 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:51 [backends.py:430] Dynamo bytecode transform time: 5.66 s +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:51 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:51 [backends.py:430] Dynamo bytecode transform time: 5.75 s +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:51 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:51 [backends.py:430] Dynamo bytecode transform time: 5.76 s +(VllmWorker rank=2 pid=3946092) INFO 07-06 02:59:55 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3946093) INFO 07-06 02:59:55 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3946088) INFO 07-06 02:59:56 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3946089) INFO 07-06 02:59:56 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3946093) INFO 07-06 03:00:17 [backends.py:148] Compiling a graph for general shape takes 25.01 s +(VllmWorker rank=2 pid=3946092) INFO 07-06 03:00:17 [backends.py:148] Compiling a graph for general shape takes 25.27 s +(VllmWorker rank=0 pid=3946088) INFO 07-06 03:00:17 [backends.py:148] Compiling a graph for general shape takes 25.64 s +(VllmWorker rank=1 pid=3946089) INFO 07-06 03:00:17 [backends.py:148] Compiling a graph for general shape takes 25.50 s +(VllmWorker rank=0 pid=3946088) INFO 07-06 03:00:39 [monitor.py:33] torch.compile takes 31.38 s in total +(VllmWorker rank=1 pid=3946089) INFO 07-06 03:00:39 [monitor.py:33] torch.compile takes 31.26 s in total +(VllmWorker rank=3 pid=3946093) INFO 07-06 03:00:39 [monitor.py:33] torch.compile takes 30.68 s in total +(VllmWorker rank=2 pid=3946092) INFO 07-06 03:00:39 [monitor.py:33] torch.compile takes 30.93 s in total +INFO 07-06 03:00:41 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 03:00:41 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 03:00:41 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:00:41 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:00:41 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:00:41 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:00:41 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 03:00:41 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=3 pid=3946093) INFO 07-06 03:01:12 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3946092) INFO 07-06 03:01:12 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3946089) INFO 07-06 03:01:12 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3946088) INFO 07-06 03:01:12 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 3.00 GiB +INFO 07-06 03:01:12 [core.py:159] init engine (profile, create kv cache, warmup model) took 87.18 seconds +INFO 07-06 03:01:13 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 03:04:35 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 03:04:35 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8768|± |0.0193| +| | |math_pass@1:1_samples|0.9312|± |0.0268| +|mm\|arc_challenge\|0| 0|sem |0.9318|± |0.0129| +|mm\|arc_easy\|0 | 0|sem |0.9789|± |0.0047| +|mm\|commonsenseqa\|0| 0|sem |0.8281|± |0.0211| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9374|± |0.0115| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9250|± |0.0422| +|mm\|truthfulqa\|0 | 0|sem |0.7686|± |0.0385| + diff --git a/merge_bench2/logs/phi_ties_3.log b/merge_bench2/logs/phi_ties_3.log new file mode 100644 index 0000000000000000000000000000000000000000..d50d623d88f8e0668feeaf02d02fadddf47f119a --- /dev/null +++ b/merge_bench2/logs/phi_ties_3.log @@ -0,0 +1,101 @@ +INFO 07-06 03:04:34 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 03:04:36 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 03:04:36 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 03:04:43 [config.py:717] This model supports multiple tasks: {'classify', 'score', 'embed', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-06 03:04:43 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 03:04:43 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 03:04:44 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_3', speculative_config=None, tokenizer='./merged1/phi_ties_3', 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_ties_3, 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 03:04:44 [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 03:04:44 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_d31d3e21'), local_subscribe_addr='ipc:///tmp/0afa905c-696e-49e8-a9f7-ac072d7260b3', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_7323f4c8'), local_subscribe_addr='ipc:///tmp/b5e77e84-7233-47ad-a3b6-8d6252a2b4cc', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a88afe59'), local_subscribe_addr='ipc:///tmp/26c773d3-abce-4d13-be0e-8fe86e22c3cd', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 03:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_cd7fc62f'), local_subscribe_addr='ipc:///tmp/3971f486-f21c-445a-a7c3-44978d3f6caf', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_19fec43b'), local_subscribe_addr='ipc:///tmp/143cc6f4-f292-4d83-8859-209200ba7f7e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:04:46 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:46 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:04:46 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:04:46 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:46 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:04:47 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:04:47 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:04:47 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3949381) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=3 pid=3949382) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=0 pid=3949379) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=1 pid=3949380) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:47 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_50ef9bc5'), local_subscribe_addr='ipc:///tmp/6f846a5d-944e-4426-82c3-f5468e0c1c5d', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:04:47 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:04:47 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:47 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:04:47 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:04:47 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:04:47 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3949381) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=3 pid=3949382) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:47 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:04:47 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3949379) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=1 pid=3949380) WARNING 07-06 03:04:47 [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. +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:04:47 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:04:47 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:04:47 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:04:47 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:00 [loader.py:458] Loading weights took 12.96 seconds +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:00 [loader.py:458] Loading weights took 13.03 seconds +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:00 [loader.py:458] Loading weights took 13.03 seconds +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:00 [loader.py:458] Loading weights took 12.99 seconds +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.192772 seconds +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.218936 seconds +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.219006 seconds +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.227179 seconds +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:06 [backends.py:430] Dynamo bytecode transform time: 5.57 s +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:06 [backends.py:430] Dynamo bytecode transform time: 5.63 s +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:06 [backends.py:430] Dynamo bytecode transform time: 5.72 s +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:06 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:06 [backends.py:430] Dynamo bytecode transform time: 5.73 s +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:11 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:11 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:11 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:11 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:32 [backends.py:148] Compiling a graph for general shape takes 25.47 s +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:32 [backends.py:148] Compiling a graph for general shape takes 25.39 s +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:34 [backends.py:148] Compiling a graph for general shape takes 26.36 s +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:34 [backends.py:148] Compiling a graph for general shape takes 26.38 s +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:05:56 [monitor.py:33] torch.compile takes 32.08 s in total +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:05:56 [monitor.py:33] torch.compile takes 32.10 s in total +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:05:56 [monitor.py:33] torch.compile takes 31.02 s in total +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:05:56 [monitor.py:33] torch.compile takes 31.04 s in total +INFO 07-06 03:05:58 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 03:05:58 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 03:05:58 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:05:58 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:05:58 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:05:58 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:05:58 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 03:05:58 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=3 pid=3949382) INFO 07-06 03:06:44 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3949381) INFO 07-06 03:06:44 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3949380) INFO 07-06 03:06:44 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3949379) INFO 07-06 03:06:44 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +INFO 07-06 03:06:44 [core.py:159] init engine (profile, create kv cache, warmup model) took 103.13 seconds +INFO 07-06 03:06:44 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 03:17:05 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 03:17:05 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.6482|± |0.0275| +| | |math_pass@1:1_samples|0.7766|± |0.0441| +|mm\|arc_challenge\|0| 0|sem |0.7480|± |0.0223| +|mm\|arc_easy\|0 | 0|sem |0.7223|± |0.0146| +|mm\|commonsenseqa\|0| 0|sem |0.5687|± |0.0277| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8031|± |0.0188| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.7500|± |0.0693| +|mm\|truthfulqa\|0 | 0|sem |0.5537|± |0.0454| + diff --git a/merge_bench2/logs/phi_ties_5.log b/merge_bench2/logs/phi_ties_5.log new file mode 100644 index 0000000000000000000000000000000000000000..1ec729a8cafdfc90ca33a17c97f84cfae8d54a56 --- /dev/null +++ b/merge_bench2/logs/phi_ties_5.log @@ -0,0 +1,101 @@ +INFO 07-06 03:17:04 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 03:17:06 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 03:17:06 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 03:17:13 [config.py:717] This model supports multiple tasks: {'generate', 'classify', 'embed', 'reward', 'score'}. Defaulting to 'generate'. +INFO 07-06 03:17:13 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 03:17:13 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 03:17:14 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_5', speculative_config=None, tokenizer='./merged1/phi_ties_5', 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_ties_5, 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 03:17:14 [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 03:17:14 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_1aeb2b19'), local_subscribe_addr='ipc:///tmp/4ce7181f-f696-469b-9d59-3a3ba0532656', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:17:14 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:14 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_9d74a4bd'), local_subscribe_addr='ipc:///tmp/073167ed-a886-4c5b-b4f4-c881a91c7360', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:17:14 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 03:17:14 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:14 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2d718e9c'), local_subscribe_addr='ipc:///tmp/ee8533ef-5a1b-4248-83cf-03546f8b65f5', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:17:14 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:14 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e0405c41'), local_subscribe_addr='ipc:///tmp/ac12da02-e53e-4cde-9df4-691838b52f65', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:15 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_6a234fc3'), local_subscribe_addr='ipc:///tmp/17ff2b28-39ba-4f76-a062-f0ff35d75651', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:16 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:16 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:16 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:16 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:16 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:16 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:16 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:16 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3953137) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=2 pid=3953135) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=0 pid=3953131) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=1 pid=3953132) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:17 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_50f8e6b5'), local_subscribe_addr='ipc:///tmp/888e46f6-53e6-4877-aa38-ce9312e98b27', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:17 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:17 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:17 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:17 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:17 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:17 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3953137) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=2 pid=3953135) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:17 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:17 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3953132) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=0 pid=3953131) WARNING 07-06 03:17:17 [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. +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:17 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:17 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:17 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:17 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:29 [loader.py:458] Loading weights took 12.38 seconds +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:29 [loader.py:458] Loading weights took 12.42 seconds +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:30 [loader.py:458] Loading weights took 12.39 seconds +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:30 [loader.py:458] Loading weights took 12.40 seconds +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.566000 seconds +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.600693 seconds +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.608823 seconds +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.609282 seconds +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:36 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:36 [backends.py:430] Dynamo bytecode transform time: 5.58 s +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:36 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:36 [backends.py:430] Dynamo bytecode transform time: 5.62 s +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:36 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:36 [backends.py:430] Dynamo bytecode transform time: 5.65 s +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:36 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:36 [backends.py:430] Dynamo bytecode transform time: 5.95 s +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:17:40 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:17:40 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:17:40 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:17:41 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:18:01 [backends.py:148] Compiling a graph for general shape takes 25.19 s +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:18:02 [backends.py:148] Compiling a graph for general shape takes 25.32 s +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:18:02 [backends.py:148] Compiling a graph for general shape takes 25.61 s +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:18:03 [backends.py:148] Compiling a graph for general shape takes 26.19 s +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:18:25 [monitor.py:33] torch.compile takes 31.23 s in total +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:18:25 [monitor.py:33] torch.compile takes 32.14 s in total +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:18:25 [monitor.py:33] torch.compile takes 30.77 s in total +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:18:25 [monitor.py:33] torch.compile takes 30.96 s in total +INFO 07-06 03:18:27 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 03:18:27 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 03:18:27 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:18:27 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:18:27 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:18:27 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:18:27 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 03:18:27 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=3 pid=3953137) INFO 07-06 03:19:11 [gpu_model_runner.py:1686] Graph capturing finished in 43 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3953135) INFO 07-06 03:19:11 [gpu_model_runner.py:1686] Graph capturing finished in 43 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3953132) INFO 07-06 03:19:11 [gpu_model_runner.py:1686] Graph capturing finished in 44 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3953131) INFO 07-06 03:19:11 [gpu_model_runner.py:1686] Graph capturing finished in 44 secs, took 3.00 GiB +INFO 07-06 03:19:11 [core.py:159] init engine (profile, create kv cache, warmup model) took 100.82 seconds +INFO 07-06 03:19:11 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 03:32:53 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 03:32:53 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.1473|± |0.0174| +| | |math_pass@1:1_samples|0.6414|± |0.0500| +|mm\|arc_challenge\|0| 0|sem |0.2310|± |0.0216| +|mm\|arc_easy\|0 | 0|sem |0.2397|± |0.0139| +|mm\|commonsenseqa\|0| 0|sem |0.0688|± |0.0142| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.6577|± |0.0225| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.6250|± |0.0775| +|mm\|truthfulqa\|0 | 0|sem |0.0496|± |0.0198| + diff --git a/merge_bench2/logs/phi_ties_7.log b/merge_bench2/logs/phi_ties_7.log new file mode 100644 index 0000000000000000000000000000000000000000..6a90ae41b8c6dcccea0371b74b3c4461eb6da244 --- /dev/null +++ b/merge_bench2/logs/phi_ties_7.log @@ -0,0 +1,101 @@ +INFO 07-06 03:32:52 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 03:32:54 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 03:32:54 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 03:33:01 [config.py:717] This model supports multiple tasks: {'classify', 'reward', 'score', 'embed', 'generate'}. Defaulting to 'generate'. +INFO 07-06 03:33:01 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 03:33:01 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 03:33:02 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_7', speculative_config=None, tokenizer='./merged1/phi_ties_7', 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_ties_7, 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 03:33:02 [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 07-06 03:33:02 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_ad4cd58b'), local_subscribe_addr='ipc:///tmp/67049ee1-4d29-4037-924e-033fe1a41620', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:33:03 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 03:33:03 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:03 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_9c043181'), local_subscribe_addr='ipc:///tmp/8f23a03f-3e7d-4981-9614-8d9cc0c37b87', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:03 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c297e158'), local_subscribe_addr='ipc:///tmp/fcd2a874-c38a-4726-9afb-aef406a66e18', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:33:03 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 03:33:03 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:03 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_7e5799f8'), local_subscribe_addr='ipc:///tmp/bdda0205-fcb1-434a-ac34-24ab76467748', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:03 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_23f73f8f'), local_subscribe_addr='ipc:///tmp/226ccf98-16b4-4ddd-b866-edb4e0d4b583', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:04 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:04 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:04 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:04 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:04 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:04 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:04 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:04 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3957274) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=3 pid=3957275) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=0 pid=3957272) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=1 pid=3957273) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_b4576b69'), local_subscribe_addr='ipc:///tmp/7df58fd2-3ac2-46df-8fff-6ab5cd32e45f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:05 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:05 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:05 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:05 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:05 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:05 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:05 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:05 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3957275) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=2 pid=3957274) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=0 pid=3957272) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=1 pid=3957273) WARNING 07-06 03:33:05 [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. +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:05 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:05 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:05 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:05 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:18 [loader.py:458] Loading weights took 12.74 seconds +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:18 [loader.py:458] Loading weights took 12.86 seconds +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:18 [loader.py:458] Loading weights took 12.80 seconds +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:18 [loader.py:458] Loading weights took 12.86 seconds +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:18 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.988445 seconds +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:18 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.050820 seconds +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:18 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.055578 seconds +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:18 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.045876 seconds +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:24 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:24 [backends.py:430] Dynamo bytecode transform time: 5.71 s +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:24 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:24 [backends.py:430] Dynamo bytecode transform time: 5.76 s +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:24 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:24 [backends.py:430] Dynamo bytecode transform time: 5.82 s +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:24 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:24 [backends.py:430] Dynamo bytecode transform time: 5.83 s +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:28 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:33:50 [backends.py:148] Compiling a graph for general shape takes 25.48 s +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:33:50 [backends.py:148] Compiling a graph for general shape takes 25.64 s +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:33:51 [backends.py:148] Compiling a graph for general shape takes 25.89 s +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:33:51 [backends.py:148] Compiling a graph for general shape takes 26.11 s +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:34:13 [monitor.py:33] torch.compile takes 31.30 s in total +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:34:13 [monitor.py:33] torch.compile takes 31.72 s in total +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:34:13 [monitor.py:33] torch.compile takes 31.35 s in total +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:34:13 [monitor.py:33] torch.compile takes 31.87 s in total +INFO 07-06 03:34:15 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 03:34:15 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 03:34:15 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:34:15 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:34:15 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:34:15 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:34:15 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 03:34:15 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=3 pid=3957275) INFO 07-06 03:35:01 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +(VllmWorker rank=2 pid=3957274) INFO 07-06 03:35:01 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3957272) INFO 07-06 03:35:01 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3957273) INFO 07-06 03:35:01 [gpu_model_runner.py:1686] Graph capturing finished in 46 secs, took 3.00 GiB +INFO 07-06 03:35:01 [core.py:159] init engine (profile, create kv cache, warmup model) took 103.07 seconds +INFO 07-06 03:35:02 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 03:49:02 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 03:49:03 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.0703|± |0.0127| +| | |math_pass@1:1_samples|0.4306|± |0.0513| +|mm\|arc_challenge\|0| 0|sem |0.0892|± |0.0146| +|mm\|arc_easy\|0 | 0|sem |0.1267|± |0.0108| +|mm\|commonsenseqa\|0| 0|sem |0.0406|± |0.0111| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.4362|± |0.0235| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.4250|± |0.0792| +|mm\|truthfulqa\|0 | 0|sem |0.0248|± |0.0142| + diff --git a/merge_bench2/logs/phi_ties_9.log b/merge_bench2/logs/phi_ties_9.log new file mode 100644 index 0000000000000000000000000000000000000000..a168e8ab0d3d1e5ebca5388f58f76f1082f731ce --- /dev/null +++ b/merge_bench2/logs/phi_ties_9.log @@ -0,0 +1,101 @@ +INFO 07-06 03:49:01 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 03:49:03 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 03:49:03 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-06 03:49:10 [config.py:717] This model supports multiple tasks: {'reward', 'classify', 'score', 'embed', 'generate'}. Defaulting to 'generate'. +INFO 07-06 03:49:10 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 03:49:10 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 03:49:12 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_9', speculative_config=None, tokenizer='./merged1/phi_ties_9', 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_ties_9, 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 03:49:12 [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 03:49:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_8eb9755e'), local_subscribe_addr='ipc:///tmp/4ca258b6-f8a8-44ca-bf8b-d992585ba541', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:49:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e5264b0b'), local_subscribe_addr='ipc:///tmp/66a38d90-bc49-471b-8d4c-771ce84e7fd0', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:49:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_7409db1c'), local_subscribe_addr='ipc:///tmp/cf8c1197-f495-4f69-b20a-a9b7000240c1', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 03:49:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 03:49:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_85e11c6f'), local_subscribe_addr='ipc:///tmp/10c6487b-1b6d-47c0-bd39-46e7077402ec', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_45e5b516'), local_subscribe_addr='ipc:///tmp/962acbba-247d-4cfc-9295-7c1ab9939ad4', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:14 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:14 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:14 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:14 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:14 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:14 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:14 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:14 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=3961375) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=2 pid=3961374) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=0 pid=3961372) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=1 pid=3961373) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:15 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_d8b12038'), local_subscribe_addr='ipc:///tmp/f44c0bc1-9cbd-4a4a-b0a6-2b1efdf756cc', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:15 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:15 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:15 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:15 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:15 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:15 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=3961374) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=3 pid=3961375) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:15 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:15 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=3961373) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=0 pid=3961372) WARNING 07-06 03:49:15 [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. +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:15 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:15 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:15 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:15 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:26 [loader.py:458] Loading weights took 11.58 seconds +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:26 [loader.py:458] Loading weights took 11.66 seconds +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:26 [loader.py:458] Loading weights took 11.63 seconds +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:26 [loader.py:458] Loading weights took 11.63 seconds +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:27 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.771326 seconds +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:27 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.845732 seconds +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:27 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.851462 seconds +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:27 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.840904 seconds +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:32 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:32 [backends.py:430] Dynamo bytecode transform time: 5.53 s +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:33 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:33 [backends.py:430] Dynamo bytecode transform time: 5.61 s +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:33 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:33 [backends.py:430] Dynamo bytecode transform time: 5.66 s +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:33 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:33 [backends.py:430] Dynamo bytecode transform time: 5.67 s +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:37 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:37 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:37 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:49:37 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:49:58 [backends.py:148] Compiling a graph for general shape takes 24.57 s +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:49:58 [backends.py:148] Compiling a graph for general shape takes 24.72 s +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:49:59 [backends.py:148] Compiling a graph for general shape takes 25.34 s +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:50:00 [backends.py:148] Compiling a graph for general shape takes 26.36 s +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:50:22 [monitor.py:33] torch.compile takes 30.38 s in total +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:50:22 [monitor.py:33] torch.compile takes 30.10 s in total +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:50:22 [monitor.py:33] torch.compile takes 30.95 s in total +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:50:22 [monitor.py:33] torch.compile takes 32.03 s in total +INFO 07-06 03:50:24 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 03:50:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 03:50:24 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:50:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:50:24 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 03:50:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 03:50:24 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 03:50:24 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=2 pid=3961374) INFO 07-06 03:51:06 [gpu_model_runner.py:1686] Graph capturing finished in 42 secs, took 3.00 GiB +(VllmWorker rank=3 pid=3961375) INFO 07-06 03:51:06 [gpu_model_runner.py:1686] Graph capturing finished in 42 secs, took 3.00 GiB +(VllmWorker rank=0 pid=3961372) INFO 07-06 03:51:07 [gpu_model_runner.py:1686] Graph capturing finished in 43 secs, took 3.00 GiB +(VllmWorker rank=1 pid=3961373) INFO 07-06 03:51:07 [gpu_model_runner.py:1686] Graph capturing finished in 43 secs, took 3.00 GiB +INFO 07-06 03:51:07 [core.py:159] init engine (profile, create kv cache, warmup model) took 99.76 seconds +INFO 07-06 03:51:07 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 04:04:58 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 04:04:58 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.0362|± |0.0094| +| | |math_pass@1:1_samples|0.3202|± |0.0485| +|mm\|arc_challenge\|0| 0|sem |0.0577|± |0.0120| +|mm\|arc_easy\|0 | 0|sem |0.0581|± |0.0076| +|mm\|commonsenseqa\|0| 0|sem 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newline at end of file diff --git a/merge_bench2/results/._merged1_phi_ties_9/results_2025-06-23T01-52-10.258150.json b/merge_bench2/results/._merged1_phi_ties_9/results_2025-06-23T01-52-10.258150.json new file mode 100644 index 0000000000000000000000000000000000000000..cd7490a89b4b305564715b2a020ec0c6e02eee25 --- /dev/null +++ b/merge_bench2/results/._merged1_phi_ties_9/results_2025-06-23T01-52-10.258150.json @@ -0,0 +1,53 @@ +{ + "results": { + "mm|commonsenseqa|0": { + "sem": 0.0125, + "sem_stderr": 0.00622054186271827 + }, + "mm|truthfulqa|0": { + "sem": 0.01652892561983471, + "sem_stderr": 0.011638914696571684 + }, + "mm|math_500|0": { + "math_pass@1:1_samples": 0.325, + "math_pass@1:1_samples_stderr": 0.07499999999999998 + }, + "mm|arc_challenge|0": { + "sem": 0.05774278215223097, + "sem_stderr": 0.011965801407338762 + }, + "mm|gsm8k|0": { + "math_pass@1:1_samples": 0.31543624161073824, + "math_pass@1:1_samples_stderr": 0.022003679833360552 + }, + "mm|arc_easy|0": { + "sem": 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}, + "mm|arc_challenge|0": { + "sem": 0.952755905511811, + "sem_stderr": 0.010883605491044059 + }, + "mm|gsm8k|0": { + "math_pass@1:1_samples": 0.9485458612975392, + "math_pass@1:1_samples_stderr": 0.010460968487095353 + }, + "mm|arc_easy|0": { + "sem": 0.9767687434002112, + "sem_stderr": 0.00489763774838021 + }, + "all": { + "sem": 0.9134374638808982, + "sem_stderr": 0.016776471117526833, + "math_pass@1:1_samples": 0.9742729306487696, + "math_pass@1:1_samples_stderr": 0.0052304842435476765 + } + }, + "versions": { + "mm|aime24|0": 3, + "mm|arc_challenge|0": 0, + "mm|arc_easy|0": 0, + "mm|commonsenseqa|0": 0, + "mm|gpqa_diamond|0": 2, + "mm|gsm8k|0": 0, + "mm|math_500|0": 3, + "mm|mmlu_pro|0": 0, + "mm|truthfulqa|0": 0 + }, + "size": { + "mm|commonsenseqa|0": 320, + "mm|truthfulqa|0": 121, + "mm|math_500|0": 40, + "mm|arc_challenge|0": 381, + "mm|gsm8k|0": 447, + "mm|arc_easy|0": 947 + } +} \ No newline at end of file diff --git a/merge_bench3/logs/I-Phi4.log b/merge_bench3/logs/I-Phi4.log new file mode 100644 index 0000000000000000000000000000000000000000..5804bfc6105b7db0bc3d841568e74e178bc0dd07 --- /dev/null +++ b/merge_bench3/logs/I-Phi4.log @@ -0,0 +1,100 @@ +INFO 07-06 23:53:45 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 23:53:47 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 23:53:54 [config.py:717] This model supports multiple tasks: {'reward', 'generate', 'embed', 'score', 'classify'}. Defaulting to 'generate'. +INFO 07-06 23:53:54 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 23:53:54 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 23:53:55 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./models/I-Phi4', speculative_config=None, tokenizer='./models/I-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/I-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 07-06 23:53:55 [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 23:53:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_f75827cc'), local_subscribe_addr='ipc:///tmp/202c9c7a-9a39-4a9d-ab34-89926cd66568', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 23:53:56 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:53:56 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_89988b93'), local_subscribe_addr='ipc:///tmp/026ae7cf-5ea3-48ca-bc8c-a6d1e5b80615', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 23:53:56 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:56 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_796ecefb'), local_subscribe_addr='ipc:///tmp/f94f90bd-cd7b-4970-9378-2db4ab20272d', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 23:53:56 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 23:53:56 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:53:56 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0e1a2d3e'), local_subscribe_addr='ipc:///tmp/df5f3dbe-9003-48b1-b8b4-8e243dcf3d59', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:53:56 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8373eb5b'), local_subscribe_addr='ipc:///tmp/8e1360f1-acda-402d-9857-1561b813da55', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:53:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:53:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:53:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:53:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:53:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:53:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4032590) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=0 pid=4032588) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=3 pid=4032591) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=1 pid=4032589) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:59 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_352db1db'), local_subscribe_addr='ipc:///tmp/cffec473-5c4d-4244-a9f6-91b56859d4c7', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:53:59 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:53:59 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:53:59 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:53:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:53:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:53:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4032591) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=2 pid=4032590) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=1 pid=4032589) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:53:59 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:53:59 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:53:59 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:59 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4032588) WARNING 07-06 23:53:59 [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. +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:53:59 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:05 [loader.py:458] Loading weights took 5.55 seconds +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:05 [loader.py:458] Loading weights took 5.62 seconds +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:05 [loader.py:458] Loading weights took 5.62 seconds +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:05 [loader.py:458] Loading weights took 5.57 seconds +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:05 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.804439 seconds +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:05 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.804694 seconds +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:05 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.771565 seconds +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:05 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 5.805106 seconds +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:11 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:11 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:11 [backends.py:430] Dynamo bytecode transform time: 5.66 s +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:11 [backends.py:430] Dynamo bytecode transform time: 5.66 s +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:11 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:11 [backends.py:430] Dynamo bytecode transform time: 5.70 s +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:11 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:11 [backends.py:430] Dynamo bytecode transform time: 5.77 s +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:37 [backends.py:148] Compiling a graph for general shape takes 24.92 s +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:37 [backends.py:148] Compiling a graph for general shape takes 25.05 s +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:37 [backends.py:148] Compiling a graph for general shape takes 24.92 s +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:37 [backends.py:148] Compiling a graph for general shape takes 25.31 s +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:54:59 [monitor.py:33] torch.compile takes 31.08 s in total +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:54:59 [monitor.py:33] torch.compile takes 30.62 s in total +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:54:59 [monitor.py:33] torch.compile takes 30.58 s in total +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:54:59 [monitor.py:33] torch.compile takes 30.72 s in total +INFO 07-06 23:55:00 [kv_cache_utils.py:634] GPU KV cache size: 1,999,536 tokens +INFO 07-06 23:55:00 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.34x +INFO 07-06 23:55:00 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 23:55:00 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 23:55:00 [kv_cache_utils.py:634] GPU KV cache size: 1,999,280 tokens +INFO 07-06 23:55:00 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.21x +INFO 07-06 23:55:00 [kv_cache_utils.py:634] GPU KV cache size: 2,000,560 tokens +INFO 07-06 23:55:00 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 976.84x +(VllmWorker rank=1 pid=4032589) INFO 07-06 23:55:27 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.96 GiB +(VllmWorker rank=0 pid=4032588) INFO 07-06 23:55:27 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.96 GiB +(VllmWorker rank=2 pid=4032590) INFO 07-06 23:55:27 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.96 GiB +(VllmWorker rank=3 pid=4032591) INFO 07-06 23:55:27 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.96 GiB +INFO 07-06 23:55:27 [core.py:159] init engine (profile, create kv cache, warmup model) took 81.91 seconds +INFO 07-06 23:55:28 [core_client.py:439] Core engine process 0 ready. +INFO 07-06 23:56:41 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-06 23:56:41 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8876|± |0.0186| +| | |math_pass@1:1_samples|0.9676|± |0.0058| +|mm\|arc_challenge\|0| 0|sem |0.9396|± |0.0122| +|mm\|arc_easy\|0 | 0|sem |0.9789|± |0.0047| +|mm\|commonsenseqa\|0| 0|sem |0.8469|± |0.0202| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9351|± |0.0117| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |0.7851|± |0.0375| + diff --git a/merge_bench3/logs/R-Phi4.log b/merge_bench3/logs/R-Phi4.log new file mode 100644 index 0000000000000000000000000000000000000000..fe933a4ea884b904f8b279fbcdfa8f543186554a --- /dev/null +++ b/merge_bench3/logs/R-Phi4.log @@ -0,0 +1,96 @@ +INFO 07-06 23:56:40 [__init__.py:239] Automatically detected platform cuda. +INFO 07-06 23:56:42 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-06 23:56:49 [config.py:717] This model supports multiple tasks: {'embed', 'score', 'generate', 'classify', 'reward'}. Defaulting to 'generate'. +INFO 07-06 23:56:49 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-06 23:56:49 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-06 23:56:51 [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 07-06 23:56:51 [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 23:56:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_acf80492'), local_subscribe_addr='ipc:///tmp/cc41639e-64f5-4ce2-a2f0-99d9a45ca2d2', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 23:56:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:56:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_64350fe2'), local_subscribe_addr='ipc:///tmp/b8a7a1ba-969f-45d3-ae20-ee46709fd8a6', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 23:56:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-06 23:56:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_33821744'), local_subscribe_addr='ipc:///tmp/ac826f96-b0a7-4224-998d-b9ad5335c186', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-06 23:56:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:56:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_65910fd5'), local_subscribe_addr='ipc:///tmp/f7afd5fc-0422-4717-8136-d5c8257d8192', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:56:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_84f87ef1'), local_subscribe_addr='ipc:///tmp/189e5138-c9fb-462c-a4cd-88df300416db', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:58 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:56:58 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:58 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:56:58 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:56:58 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:56:58 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:56:58 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:56:58 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4036080) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=3 pid=4036081) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=1 pid=4036079) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=0 pid=4036078) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:59 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_f5dae0d3'), local_subscribe_addr='ipc:///tmp/7e93337d-d244-42dd-bb64-fac98aa19f58', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:56:59 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:56:59 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:56:59 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:59 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:56:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:56:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4036081) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=2 pid=4036080) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:56:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4036078) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=1 pid=4036079) WARNING 07-06 23:56:59 [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. +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:56:59 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:56:59 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:56:59 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:56:59 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:04 [loader.py:458] Loading weights took 4.37 seconds +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:04 [loader.py:458] Loading weights took 4.40 seconds +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:04 [loader.py:458] Loading weights took 4.40 seconds +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:04 [loader.py:458] Loading weights took 4.44 seconds +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:04 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.554512 seconds +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:04 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.626640 seconds +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:04 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.618742 seconds +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:04 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 4.623726 seconds +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:10 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:10 [backends.py:430] Dynamo bytecode transform time: 5.58 s +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:10 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:10 [backends.py:430] Dynamo bytecode transform time: 5.65 s +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:10 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:10 [backends.py:430] Dynamo bytecode transform time: 5.65 s +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:10 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:10 [backends.py:430] Dynamo bytecode transform time: 5.73 s +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:15 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.438 s +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:15 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.635 s +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:15 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.600 s +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:15 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.663 s +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:21 [monitor.py:33] torch.compile takes 5.73 s in total +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:21 [monitor.py:33] torch.compile takes 5.65 s in total +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:21 [monitor.py:33] torch.compile takes 5.58 s in total +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:21 [monitor.py:33] torch.compile takes 5.65 s in total +INFO 07-06 23:57:22 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-06 23:57:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-06 23:57:22 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-06 23:57:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-06 23:57:22 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-06 23:57:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-06 23:57:22 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-06 23:57:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=3 pid=4036081) INFO 07-06 23:57:48 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +(VllmWorker rank=2 pid=4036080) INFO 07-06 23:57:48 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +(VllmWorker rank=0 pid=4036078) INFO 07-06 23:57:48 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +(VllmWorker rank=1 pid=4036079) INFO 07-06 23:57:48 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +INFO 07-06 23:57:48 [core.py:159] init engine (profile, create kv cache, warmup model) took 44.03 seconds +INFO 07-06 23:57:49 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 00:02:07 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:02:07 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8772|± |0.0194| +| | |math_pass@1:1_samples|0.9743|± |0.0052| +|mm\|arc_challenge\|0| 0|sem |0.9291|± |0.0132| +|mm\|arc_easy\|0 | 0|sem |0.9694|± |0.0056| +|mm\|commonsenseqa\|0| 0|sem |0.8500|± |0.0200| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9485|± |0.0105| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |0.7603|± |0.0390| + diff --git a/merge_bench3/logs/phi_darelinear_1.log b/merge_bench3/logs/phi_darelinear_1.log new file mode 100644 index 0000000000000000000000000000000000000000..9c1d7e85d23e5b4491ba109280f622987b5409a6 --- /dev/null +++ b/merge_bench3/logs/phi_darelinear_1.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:22 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:46:29 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:46:29 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8852|± |0.0187| +| | |math_pass@1:1_samples|0.9426|± |0.0233| +|mm\|arc_challenge\|0| 0|sem |0.9475|± |0.0114| +|mm\|arc_easy\|0 | 0|sem |0.9757|± |0.0050| +|mm\|commonsenseqa\|0| 0|sem |0.8406|± |0.0205| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9351|± |0.0117| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9500|± |0.0349| +|mm\|truthfulqa\|0 | 0|sem |0.7769|± |0.0380| + diff --git a/merge_bench3/logs/phi_darelinear_3.log b/merge_bench3/logs/phi_darelinear_3.log new file mode 100644 index 0000000000000000000000000000000000000000..dd0581a8a03ea238a5e890122df9543d7a23014f --- /dev/null +++ b/merge_bench3/logs/phi_darelinear_3.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:28 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:46:35 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:46:35 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8786|± |0.0192| +| | |math_pass@1:1_samples|0.9720|± |0.0054| +|mm\|arc_challenge\|0| 0|sem |0.9370|± |0.0125| +|mm\|arc_easy\|0 | 0|sem |0.9736|± |0.0052| +|mm\|commonsenseqa\|0| 0|sem |0.8187|± |0.0216| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9441|± |0.0109| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |0.7851|± |0.0375| + diff --git a/merge_bench3/logs/phi_darelinear_5.log b/merge_bench3/logs/phi_darelinear_5.log new file mode 100644 index 0000000000000000000000000000000000000000..70fa78e51ff73039ae6f355d624367574d1fb5af --- /dev/null +++ b/merge_bench3/logs/phi_darelinear_5.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:34 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:46:42 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:46:42 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8852|± |0.0187| +| | |math_pass@1:1_samples|0.9232|± |0.0294| +|mm\|arc_challenge\|0| 0|sem |0.9396|± |0.0122| +|mm\|arc_easy\|0 | 0|sem |0.9778|± |0.0048| +|mm\|commonsenseqa\|0| 0|sem |0.8219|± |0.0214| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9463|± |0.0107| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9000|± |0.0480| +|mm\|truthfulqa\|0 | 0|sem |0.8017|± |0.0364| + diff --git a/merge_bench3/logs/phi_darelinear_7.log b/merge_bench3/logs/phi_darelinear_7.log new file mode 100644 index 0000000000000000000000000000000000000000..dd996135b1f2c7dca6e2a6504f3bb8784db8ca05 --- /dev/null +++ b/merge_bench3/logs/phi_darelinear_7.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:40 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:46:48 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:46:48 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.7839|± |0.0239| +| | |math_pass@1:1_samples|0.8704|± |0.0345| +|mm\|arc_challenge\|0| 0|sem |0.8661|± |0.0175| +|mm\|arc_easy\|0 | 0|sem |0.9155|± |0.0090| +|mm\|commonsenseqa\|0| 0|sem |0.6844|± |0.0260| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8658|± |0.0161| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8750|± |0.0530| +|mm\|truthfulqa\|0 | 0|sem |0.6694|± |0.0429| + diff --git a/merge_bench3/logs/phi_darelinear_9.log b/merge_bench3/logs/phi_darelinear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..6712fd5a00c08cf9e0a9941dcc1c5efa8a936812 --- /dev/null +++ b/merge_bench3/logs/phi_darelinear_9.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:47 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:46:54 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:46:54 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.2015|± |0.0201| +| | |math_pass@1:1_samples|0.4606|± |0.0511| +|mm\|arc_challenge\|0| 0|sem |0.2467|± |0.0221| +|mm\|arc_easy\|0 | 0|sem |0.3432|± |0.0154| +|mm\|commonsenseqa\|0| 0|sem |0.1500|± |0.0200| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.5213|± |0.0237| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.4000|± |0.0784| +|mm\|truthfulqa\|0 | 0|sem |0.0661|± |0.0227| + diff --git a/merge_bench3/logs/phi_linear_1.log b/merge_bench3/logs/phi_linear_1.log new file mode 100644 index 0000000000000000000000000000000000000000..e1f29835da6ada358ac67ac47df820c5d249fcad --- /dev/null +++ b/merge_bench3/logs/phi_linear_1.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:53 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:47:00 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:47:00 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8858|± |0.0187| +| | |math_pass@1:1_samples|0.9595|± |0.0179| +|mm\|arc_challenge\|0| 0|sem |0.9449|± |0.0117| +|mm\|arc_easy\|0 | 0|sem |0.9810|± |0.0044| +|mm\|commonsenseqa\|0| 0|sem |0.8406|± |0.0205| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9441|± |0.0109| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9750|± |0.0250| +|mm\|truthfulqa\|0 | 0|sem |0.7769|± |0.0380| + diff --git a/merge_bench3/logs/phi_linear_3.log b/merge_bench3/logs/phi_linear_3.log new file mode 100644 index 0000000000000000000000000000000000000000..1d176d082c301cdf2d42d6adee2137b2137388ea --- /dev/null +++ b/merge_bench3/logs/phi_linear_3.log @@ -0,0 +1,14 @@ +INFO 07-07 00:46:59 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:47:06 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:47:06 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8834|± |0.0188| +| | |math_pass@1:1_samples|0.9584|± |0.0180| +|mm\|arc_challenge\|0| 0|sem |0.9318|± |0.0129| +|mm\|arc_easy\|0 | 0|sem |0.9863|± |0.0038| +|mm\|commonsenseqa\|0| 0|sem |0.8469|± |0.0202| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9418|± |0.0111| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9750|± |0.0250| +|mm\|truthfulqa\|0 | 0|sem |0.7686|± |0.0385| + diff --git a/merge_bench3/logs/phi_linear_5.log b/merge_bench3/logs/phi_linear_5.log new file mode 100644 index 0000000000000000000000000000000000000000..2bd49cf0b8fe81f9549037d804cb13048a86640b --- /dev/null +++ b/merge_bench3/logs/phi_linear_5.log @@ -0,0 +1,14 @@ +INFO 07-07 00:47:05 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:47:13 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:47:13 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8873|± |0.0188| +| | |math_pass@1:1_samples|0.9403|± |0.0235| +|mm\|arc_challenge\|0| 0|sem |0.9291|± |0.0132| +|mm\|arc_easy\|0 | 0|sem |0.9768|± |0.0049| +|mm\|commonsenseqa\|0| 0|sem |0.8500|± |0.0200| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9306|± |0.0120| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9500|± |0.0349| +|mm\|truthfulqa\|0 | 0|sem |0.7934|± |0.0370| + diff --git a/merge_bench3/logs/phi_linear_7.log b/merge_bench3/logs/phi_linear_7.log new file mode 100644 index 0000000000000000000000000000000000000000..fbb4128ee93b548744bda04b366228d1b5073054 --- /dev/null +++ b/merge_bench3/logs/phi_linear_7.log @@ -0,0 +1,97 @@ +INFO 07-07 00:47:11 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:47:13 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 00:47:13 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 00:47:20 [config.py:717] This model supports multiple tasks: {'classify', 'reward', 'embed', 'generate', 'score'}. Defaulting to 'generate'. +INFO 07-07 00:47:20 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 00:47:20 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 00:47:22 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_7', speculative_config=None, tokenizer='./merged1/phi_linear_7', 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_linear_7, 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 07-07 00:47:22 [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 07-07 00:47:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_8566a3d6'), local_subscribe_addr='ipc:///tmp/1273a10f-2388-49d5-b4c3-3f7d4b8dc539', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:47:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_4db68858'), local_subscribe_addr='ipc:///tmp/b41ed844-377e-49fa-9b53-77b2f2cdc8ec', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:47:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b61e5e10'), local_subscribe_addr='ipc:///tmp/3821bfbc-7c30-4a0d-af43-8fc058da5e24', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:47:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:47:22 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e108a397'), local_subscribe_addr='ipc:///tmp/e970d196-5ca3-4ce0-bb7e-bf339d4ccc25', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_34c134a9'), local_subscribe_addr='ipc:///tmp/665b2bc9-4d0b-4c99-9a76-fb1479cd7315', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:25 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:25 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:25 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:25 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:25 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:25 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:25 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:25 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4062167) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=3 pid=4062168) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=1 pid=4062166) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=0 pid=4062165) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_959315af'), local_subscribe_addr='ipc:///tmp/697cd960-09d8-4c74-8c9e-995091c8b87d', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:25 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:25 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:25 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:25 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4062168) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=2 pid=4062167) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4062166) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:25 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4062165) WARNING 07-07 00:47:25 [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. +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:25 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_7... +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:27 [loader.py:458] Loading weights took 1.64 seconds +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:27 [loader.py:458] Loading weights took 1.85 seconds +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:27 [loader.py:458] Loading weights took 1.91 seconds +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:28 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.866224 seconds +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:28 [loader.py:458] Loading weights took 2.03 seconds +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:28 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.107000 seconds +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:28 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.223928 seconds +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:28 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 2.072886 seconds +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:34 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:34 [backends.py:430] Dynamo bytecode transform time: 5.61 s +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:34 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:34 [backends.py:430] Dynamo bytecode transform time: 5.64 s +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:34 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:34 [backends.py:430] Dynamo bytecode transform time: 5.83 s +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:34 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/39e4f38180/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:34 [backends.py:430] Dynamo bytecode transform time: 5.94 s +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:39 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.615 s +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:39 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.647 s +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:39 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.614 s +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:39 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.609 s +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:47:45 [monitor.py:33] torch.compile takes 5.64 s in total +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:47:45 [monitor.py:33] torch.compile takes 5.61 s in total +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:47:45 [monitor.py:33] torch.compile takes 5.94 s in total +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:47:45 [monitor.py:33] torch.compile takes 5.83 s in total +INFO 07-07 00:47:46 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 00:47:46 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 00:47:46 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:47:46 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:47:46 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:47:46 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:47:46 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 00:47:46 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=2 pid=4062167) INFO 07-07 00:48:13 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4062168) INFO 07-07 00:48:13 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=0 pid=4062165) INFO 07-07 00:48:13 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=1 pid=4062166) INFO 07-07 00:48:13 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +INFO 07-07 00:48:13 [core.py:159] init engine (profile, create kv cache, warmup model) took 45.04 seconds +INFO 07-07 00:48:13 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 00:49:48 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:49:49 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8347|± |0.0219| +| | |math_pass@1:1_samples|0.9372|± |0.0196| +|mm\|arc_challenge\|0| 0|sem |0.8871|± |0.0162| +|mm\|arc_easy\|0 | 0|sem |0.9377|± |0.0079| +|mm\|commonsenseqa\|0| 0|sem |0.8031|± |0.0223| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8993|± |0.0142| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9750|± |0.0250| +|mm\|truthfulqa\|0 | 0|sem |0.7107|± |0.0414| + diff --git a/merge_bench3/logs/phi_linear_9.log b/merge_bench3/logs/phi_linear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..7f600e5d3c7362deb035548e231075f7ad61e18a --- /dev/null +++ b/merge_bench3/logs/phi_linear_9.log @@ -0,0 +1,14 @@ +INFO 07-07 00:49:48 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:49:55 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:49:55 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.5564|± |0.0285| +| | |math_pass@1:1_samples|0.8568|± |0.0367| +|mm\|arc_challenge\|0| 0|sem |0.5538|± |0.0255| +|mm\|arc_easy\|0 | 0|sem |0.6938|± |0.0150| +|mm\|commonsenseqa\|0| 0|sem |0.4656|± |0.0279| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8635|± |0.0163| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8500|± |0.0572| +|mm\|truthfulqa\|0 | 0|sem |0.5124|± |0.0456| + diff --git a/merge_bench3/logs/phi_ties_1.log b/merge_bench3/logs/phi_ties_1.log new file mode 100644 index 0000000000000000000000000000000000000000..f8e5420359b4056891661454b7788c5dd521a25a --- /dev/null +++ b/merge_bench3/logs/phi_ties_1.log @@ -0,0 +1,97 @@ +INFO 07-07 00:49:54 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:49:56 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 00:49:56 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 00:50:03 [config.py:717] This model supports multiple tasks: {'generate', 'classify', 'reward', 'embed', 'score'}. Defaulting to 'generate'. +INFO 07-07 00:50:03 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 00:50:03 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 00:50:04 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_1', speculative_config=None, tokenizer='./merged1/phi_ties_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_ties_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 07-07 00:50:04 [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 07-07 00:50:04 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_5fb26b87'), local_subscribe_addr='ipc:///tmp/86a46b5f-4ec0-41fa-8781-bdaad9d85662', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:50:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_ef8f209c'), local_subscribe_addr='ipc:///tmp/990828a6-3ac7-4369-b95c-4ce7379f9390', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:50:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_7ec0d566'), local_subscribe_addr='ipc:///tmp/df205a33-9aae-4d18-be54-d5e1315630eb', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:50:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:50:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_6fbb8518'), local_subscribe_addr='ipc:///tmp/a5366318-c2a5-4a9e-9934-20831a46fcf5', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_13e65c9f'), local_subscribe_addr='ipc:///tmp/cb55b7fb-d5e6-46e9-9f1d-552bb8974ddb', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:17 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:17 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:17 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:17 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:17 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:17 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:17 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:17 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4071268) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=3 pid=4071269) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=0 pid=4071265) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=1 pid=4071267) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_235eb2f4'), local_subscribe_addr='ipc:///tmp/cf032ef3-3747-4b3c-93d3-42de62f2013e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:18 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:18 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:18 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4071268) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=3 pid=4071269) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4071265) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:18 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4071267) WARNING 07-07 00:50:18 [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. +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_1... +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:30 [loader.py:458] Loading weights took 12.48 seconds +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:30 [loader.py:458] Loading weights took 12.45 seconds +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:30 [loader.py:458] Loading weights took 12.49 seconds +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:30 [loader.py:458] Loading weights took 12.45 seconds +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.689570 seconds +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.676910 seconds +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:30 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.683249 seconds +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.677000 seconds +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:36 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:36 [backends.py:430] Dynamo bytecode transform time: 5.84 s +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:36 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:36 [backends.py:430] Dynamo bytecode transform time: 5.94 s +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:37 [backends.py:430] Dynamo bytecode transform time: 6.02 s +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/89b1d99067/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:37 [backends.py:430] Dynamo bytecode transform time: 6.07 s +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.671 s +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 5.105 s +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 5.042 s +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 5.148 s +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:50:48 [monitor.py:33] torch.compile takes 6.07 s in total +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:50:48 [monitor.py:33] torch.compile takes 5.94 s in total +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:50:48 [monitor.py:33] torch.compile takes 6.02 s in total +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:50:48 [monitor.py:33] torch.compile takes 5.84 s in total +INFO 07-07 00:50:49 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 00:50:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 00:50:49 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:50:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:50:49 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:50:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:50:49 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 00:50:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=2 pid=4071268) INFO 07-07 00:51:16 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=0 pid=4071265) INFO 07-07 00:51:16 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=1 pid=4071267) INFO 07-07 00:51:16 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4071269) INFO 07-07 00:51:16 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +INFO 07-07 00:51:16 [core.py:159] init engine (profile, create kv cache, warmup model) took 45.43 seconds +INFO 07-07 00:51:16 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 00:52:32 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:52:32 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8810|± |0.0192| +| | |math_pass@1:1_samples|0.9573|± |0.0181| +|mm\|arc_challenge\|0| 0|sem |0.9318|± |0.0129| +|mm\|arc_easy\|0 | 0|sem |0.9736|± |0.0052| +|mm\|commonsenseqa\|0| 0|sem |0.8500|± |0.0200| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9396|± |0.0113| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9750|± |0.0250| +|mm\|truthfulqa\|0 | 0|sem |0.7686|± |0.0385| + diff --git a/merge_bench3/logs/phi_ties_3.log b/merge_bench3/logs/phi_ties_3.log new file mode 100644 index 0000000000000000000000000000000000000000..932bef98c08c0d2455962b2a47b145ade5d45e25 --- /dev/null +++ b/merge_bench3/logs/phi_ties_3.log @@ -0,0 +1,97 @@ +INFO 07-07 00:52:31 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:52:33 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 00:52:33 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 00:52:41 [config.py:717] This model supports multiple tasks: {'reward', 'classify', 'score', 'generate', 'embed'}. Defaulting to 'generate'. +INFO 07-07 00:52:41 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 00:52:41 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 00:52:42 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_3', speculative_config=None, tokenizer='./merged1/phi_ties_3', 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_ties_3, 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 07-07 00:52:42 [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 07-07 00:52:42 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_203539d7'), local_subscribe_addr='ipc:///tmp/62c97651-f465-4ac0-9194-548dc7a77252', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:52:43 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:52:43 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:43 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_839fb2f8'), local_subscribe_addr='ipc:///tmp/8149f539-1a1c-4645-b471-2f083ee3fc9b', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:52:43 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:52:43 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:43 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_32b0f362'), local_subscribe_addr='ipc:///tmp/712238d3-23f7-4a1f-be9c-41dcd141d9b2', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:43 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_784dfbde'), local_subscribe_addr='ipc:///tmp/81eda2d9-34cd-4627-9f51-8b6029671fbb', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:43 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_6f552cca'), local_subscribe_addr='ipc:///tmp/42f9795c-d9b8-442e-b74a-fc410d7b96ad', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:45 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:45 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:45 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:45 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:45 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:45 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:45 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:45 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4074926) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=0 pid=4074925) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=3 pid=4074928) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=2 pid=4074927) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_78d5f69f'), local_subscribe_addr='ipc:///tmp/2fca3d3e-4784-40f2-985d-4345d52bee88', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:46 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:46 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:46 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:46 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:46 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:46 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4074925) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=1 pid=4074926) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:46 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:46 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4074928) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=2 pid=4074927) WARNING 07-07 00:52:46 [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. +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:46 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:46 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:46 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:46 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_3... +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:59 [loader.py:458] Loading weights took 12.90 seconds +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:59 [loader.py:458] Loading weights took 12.93 seconds +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:59 [loader.py:458] Loading weights took 12.92 seconds +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:59 [loader.py:458] Loading weights took 12.93 seconds +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:52:59 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.134090 seconds +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:52:59 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.110200 seconds +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:52:59 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.155652 seconds +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:52:59 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 13.135957 seconds +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:53:05 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:53:05 [backends.py:430] Dynamo bytecode transform time: 5.59 s +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:53:05 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:53:05 [backends.py:430] Dynamo bytecode transform time: 5.64 s +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:53:05 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:53:05 [backends.py:430] Dynamo bytecode transform time: 5.80 s +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:53:05 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/01f46e37be/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:53:05 [backends.py:430] Dynamo bytecode transform time: 5.91 s +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:53:10 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.509 s +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:53:10 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.499 s +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:53:10 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.483 s +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:53:10 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.515 s +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:53:16 [monitor.py:33] torch.compile takes 5.59 s in total +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:53:16 [monitor.py:33] torch.compile takes 5.64 s in total +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:53:16 [monitor.py:33] torch.compile takes 5.80 s in total +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:53:16 [monitor.py:33] torch.compile takes 5.91 s in total +INFO 07-07 00:53:17 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 00:53:17 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 00:53:17 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:53:17 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:53:17 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:53:17 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:53:17 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 00:53:17 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=1 pid=4074926) INFO 07-07 00:53:42 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 3.00 GiB +(VllmWorker rank=0 pid=4074925) INFO 07-07 00:53:42 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 3.00 GiB +(VllmWorker rank=2 pid=4074927) INFO 07-07 00:53:42 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4074928) INFO 07-07 00:53:42 [gpu_model_runner.py:1686] Graph capturing finished in 25 secs, took 3.00 GiB +INFO 07-07 00:53:43 [core.py:159] init engine (profile, create kv cache, warmup model) took 43.37 seconds +INFO 07-07 00:53:43 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 01:04:35 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 01:04:35 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.8419|± |0.0214| +| | |math_pass@1:1_samples|0.9030|± |0.0309| +|mm\|arc_challenge\|0| 0|sem |0.9055|± |0.0150| +|mm\|arc_easy\|0 | 0|sem |0.9578|± |0.0065| +|mm\|commonsenseqa\|0| 0|sem |0.7937|± |0.0227| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9060|± |0.0138| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9000|± |0.0480| +|mm\|truthfulqa\|0 | 0|sem |0.7107|± |0.0414| + diff --git a/merge_bench3/logs/phi_ties_5.log b/merge_bench3/logs/phi_ties_5.log new file mode 100644 index 0000000000000000000000000000000000000000..474b4b9c1518266f5e42f3c948fea34eadf9c4ed --- /dev/null +++ b/merge_bench3/logs/phi_ties_5.log @@ -0,0 +1,97 @@ +INFO 07-07 01:04:34 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 01:04:36 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 01:04:36 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 01:04:43 [config.py:717] This model supports multiple tasks: {'reward', 'classify', 'embed', 'score', 'generate'}. Defaulting to 'generate'. +INFO 07-07 01:04:43 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 01:04:43 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 01:04:44 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_5', speculative_config=None, tokenizer='./merged1/phi_ties_5', 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_ties_5, 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 07-07 01:04:44 [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 07-07 01:04:44 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_a863e06a'), local_subscribe_addr='ipc:///tmp/8a0bb5f2-2c7d-4518-a909-b526e60ea225', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2c754f84'), local_subscribe_addr='ipc:///tmp/efbadbf7-40d7-4738-975f-19e285202a7a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0ba45290'), local_subscribe_addr='ipc:///tmp/9d73a627-6bfa-43a0-8ce6-ca38e166dcdc', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 01:04:45 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_9da4dcf0'), local_subscribe_addr='ipc:///tmp/7eaf9a63-b690-4c6a-9f37-ba04fa5f8f32', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:45 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2958c554'), local_subscribe_addr='ipc:///tmp/0c810171-d986-45f3-b0b3-f80ccae0daae', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:47 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:47 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:47 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:47 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:47 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:47 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:47 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:47 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4080172) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=3 pid=4080173) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=0 pid=4080170) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=1 pid=4080171) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:48 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_2a31c042'), local_subscribe_addr='ipc:///tmp/ff3211ff-55ff-4498-bd03-7de3a03444ea', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:48 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:48 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:48 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:48 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4080172) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:48 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4080173) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=0 pid=4080170) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=1 pid=4080171) WARNING 07-07 01:04:48 [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. +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:48 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_5... +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:57 [loader.py:458] Loading weights took 9.33 seconds +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:57 [loader.py:458] Loading weights took 9.33 seconds +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:57 [loader.py:458] Loading weights took 9.25 seconds +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:57 [loader.py:458] Loading weights took 9.33 seconds +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:04:58 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 9.519868 seconds +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:04:58 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 9.521195 seconds +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:04:58 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 9.526701 seconds +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:04:58 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 9.565087 seconds +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:05:03 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:05:03 [backends.py:430] Dynamo bytecode transform time: 5.55 s +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:05:03 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:05:03 [backends.py:430] Dynamo bytecode transform time: 5.60 s +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:05:03 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:05:03 [backends.py:430] Dynamo bytecode transform time: 5.63 s +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:05:03 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/e1e28f285d/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:05:03 [backends.py:430] Dynamo bytecode transform time: 5.67 s +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:05:08 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.406 s +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:05:08 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.513 s +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:05:08 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.475 s +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:05:08 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.511 s +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:05:14 [monitor.py:33] torch.compile takes 5.60 s in total +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:05:14 [monitor.py:33] torch.compile takes 5.63 s in total +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:05:14 [monitor.py:33] torch.compile takes 5.67 s in total +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:05:14 [monitor.py:33] torch.compile takes 5.55 s in total +INFO 07-07 01:05:16 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 01:05:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 01:05:16 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 01:05:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 01:05:16 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 01:05:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 01:05:16 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 01:05:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=0 pid=4080170) INFO 07-07 01:05:41 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4080173) INFO 07-07 01:05:41 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=2 pid=4080172) INFO 07-07 01:05:41 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=1 pid=4080171) INFO 07-07 01:05:41 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +INFO 07-07 01:05:41 [core.py:159] init engine (profile, create kv cache, warmup model) took 43.70 seconds +INFO 07-07 01:05:42 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 01:19:06 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 01:19:06 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.4200|± |0.0270| +| | |math_pass@1:1_samples|0.7348|± |0.0467| +|mm\|arc_challenge\|0| 0|sem |0.5328|± |0.0256| +|mm\|arc_easy\|0 | 0|sem |0.5797|± |0.0160| +|mm\|commonsenseqa\|0| 0|sem |0.2781|± |0.0251| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.7696|± |0.0199| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.7000|± |0.0734| +|mm\|truthfulqa\|0 | 0|sem |0.2893|± |0.0414| + diff --git a/merge_bench3/logs/phi_ties_7.log b/merge_bench3/logs/phi_ties_7.log new file mode 100644 index 0000000000000000000000000000000000000000..2fbebb6bbed5b7e31aab63d521cb37f4d441adae --- /dev/null +++ b/merge_bench3/logs/phi_ties_7.log @@ -0,0 +1,97 @@ +INFO 07-07 01:19:05 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 01:19:06 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 01:19:06 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 01:19:13 [config.py:717] This model supports multiple tasks: {'score', 'embed', 'classify', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-07 01:19:14 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 01:19:14 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 01:19:15 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_7', speculative_config=None, tokenizer='./merged1/phi_ties_7', 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_ties_7, 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 07-07 01:19:15 [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 07-07 01:19:15 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_99e4fa40'), local_subscribe_addr='ipc:///tmp/052dc5a5-acd4-4ec2-8ad2-a0416237b4b3', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:19:15 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:15 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d2f7b68e'), local_subscribe_addr='ipc:///tmp/ee5b1900-fa83-4b9a-8e9f-8bf7f002676c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:19:15 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 01:19:15 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 01:19:15 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:15 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_fa2a2464'), local_subscribe_addr='ipc:///tmp/a40dda21-2e5e-48f3-aaf6-efd4d8df1b43', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:15 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0a214792'), local_subscribe_addr='ipc:///tmp/6644406c-2c91-4eef-b49e-2b0175a6f433', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:16 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_9dcad41a'), local_subscribe_addr='ipc:///tmp/57f0445f-726b-4216-92e2-124f19e29d1a', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4085431) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=3 pid=4085432) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=1 pid=4085430) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=0 pid=4085429) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_13301893'), local_subscribe_addr='ipc:///tmp/8357aec9-c065-4ed8-a5bb-effab80a7008', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:18 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:18 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:18 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:18 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4085431) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4085430) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=0 pid=4085429) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:18 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4085432) WARNING 07-07 01:19:18 [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. +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:18 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_7... +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:31 [loader.py:458] Loading weights took 12.50 seconds +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:31 [loader.py:458] Loading weights took 12.56 seconds +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:31 [loader.py:458] Loading weights took 12.50 seconds +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:31 [loader.py:458] Loading weights took 12.58 seconds +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.746890 seconds +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.749077 seconds +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:31 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.762192 seconds +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:32 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 12.739415 seconds +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:37 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/efefaeffc9/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:37 [backends.py:430] Dynamo bytecode transform time: 5.73 s +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:37 [backends.py:430] Dynamo bytecode transform time: 5.74 s +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:37 [backends.py:430] Dynamo bytecode transform time: 5.74 s +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:37 [backends.py:430] Dynamo bytecode transform time: 5.74 s +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.432 s +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.432 s +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.448 s +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:42 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.460 s +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:19:48 [monitor.py:33] torch.compile takes 5.74 s in total +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:19:48 [monitor.py:33] torch.compile takes 5.73 s in total +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:19:48 [monitor.py:33] torch.compile takes 5.74 s in total +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:19:48 [monitor.py:33] torch.compile takes 5.74 s in total +INFO 07-07 01:19:49 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 01:19:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 01:19:49 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 01:19:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 01:19:49 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 01:19:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 01:19:49 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 01:19:49 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=2 pid=4085431) INFO 07-07 01:20:16 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4085432) INFO 07-07 01:20:16 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=1 pid=4085430) INFO 07-07 01:20:16 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +(VllmWorker rank=0 pid=4085429) INFO 07-07 01:20:16 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 3.00 GiB +INFO 07-07 01:20:17 [core.py:159] init engine (profile, create kv cache, warmup model) took 45.01 seconds +INFO 07-07 01:20:17 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 01:33:43 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 01:33:43 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.1370|± |0.0178| +| | |math_pass@1:1_samples|0.3829|± |0.0493| +|mm\|arc_challenge\|0| 0|sem |0.1654|± |0.0191| +|mm\|arc_easy\|0 | 0|sem |0.2323|± |0.0137| +|mm\|commonsenseqa\|0| 0|sem |0.0844|± |0.0156| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.4407|± |0.0235| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.3250|± |0.0750| +|mm\|truthfulqa\|0 | 0|sem |0.0661|± |0.0227| + diff --git a/merge_bench3/logs/phi_ties_9.log b/merge_bench3/logs/phi_ties_9.log new file mode 100644 index 0000000000000000000000000000000000000000..44e5ae4df695c90a1bdc23162878c8f1078e4e76 --- /dev/null +++ b/merge_bench3/logs/phi_ties_9.log @@ -0,0 +1,97 @@ +INFO 07-07 01:33:42 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 01:33:44 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 01:33:44 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 01:33:51 [config.py:717] This model supports multiple tasks: {'classify', 'reward', 'embed', 'generate', 'score'}. Defaulting to 'generate'. +INFO 07-07 01:33:51 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 01:33:51 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 01:33:53 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_ties_9', speculative_config=None, tokenizer='./merged1/phi_ties_9', 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_ties_9, 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 07-07 01:33:53 [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 07-07 01:33:53 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_9ff580df'), local_subscribe_addr='ipc:///tmp/c934b328-dcd7-4ff5-a7be-202a9f24440b', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:33:53 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:53 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c33efc7b'), local_subscribe_addr='ipc:///tmp/79a6225b-dc49-44f7-a9ee-8e9083012fd7', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:33:53 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 01:33:53 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:33:53 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_f81a2a7d'), local_subscribe_addr='ipc:///tmp/8d89ea27-efc7-44b2-b72a-abf569165a4c', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:33:53 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_09a2f652'), local_subscribe_addr='ipc:///tmp/debbb09b-5c9e-43ad-915e-9fc404e0724f', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 01:33:53 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:33:53 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_37ec0f4a'), local_subscribe_addr='ipc:///tmp/f8aac241-0278-49a8-9f6d-ea9db38f82a2', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:55 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:55 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:33:55 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:33:55 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:33:55 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:33:55 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:33:55 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:33:55 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4090768) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=3 pid=4090769) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=1 pid=4090767) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=0 pid=4090766) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:56 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_66f144f7'), local_subscribe_addr='ipc:///tmp/9ad60a4b-54f3-45b2-96d8-12bffe9ce29d', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:33:56 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:33:56 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:33:56 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:56 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:33:56 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:33:56 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4090769) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=2 pid=4090768) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:33:56 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:56 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4090767) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=0 pid=4090766) WARNING 07-07 01:33:56 [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. +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:33:56 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:33:56 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:33:56 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:33:56 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_ties_9... +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:34:08 [loader.py:458] Loading weights took 11.63 seconds +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:34:08 [loader.py:458] Loading weights took 11.63 seconds +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:34:08 [loader.py:458] Loading weights took 11.61 seconds +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:34:08 [loader.py:458] Loading weights took 11.61 seconds +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:34:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.836297 seconds +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:34:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.837632 seconds +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:34:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.832518 seconds +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:34:08 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 11.858424 seconds +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:34:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:34:13 [backends.py:430] Dynamo bytecode transform time: 5.50 s +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:34:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:34:13 [backends.py:430] Dynamo bytecode transform time: 5.51 s +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:34:14 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:34:14 [backends.py:430] Dynamo bytecode transform time: 5.65 s +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:34:14 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1779764463/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:34:14 [backends.py:430] Dynamo bytecode transform time: 5.75 s +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:34:19 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.436 s +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:34:19 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.484 s +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:34:19 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.586 s +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:34:19 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.515 s +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:34:25 [monitor.py:33] torch.compile takes 5.51 s in total +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:34:25 [monitor.py:33] torch.compile takes 5.75 s in total +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:34:25 [monitor.py:33] torch.compile takes 5.65 s in total +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:34:25 [monitor.py:33] torch.compile takes 5.50 s in total +INFO 07-07 01:34:26 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 01:34:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 01:34:26 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 01:34:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 01:34:26 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 01:34:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 01:34:26 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 01:34:26 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=0 pid=4090766) INFO 07-07 01:35:00 [gpu_model_runner.py:1686] Graph capturing finished in 34 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4090769) INFO 07-07 01:35:00 [gpu_model_runner.py:1686] Graph capturing finished in 34 secs, took 3.00 GiB +(VllmWorker rank=2 pid=4090768) INFO 07-07 01:35:00 [gpu_model_runner.py:1686] Graph capturing finished in 34 secs, took 3.00 GiB +(VllmWorker rank=1 pid=4090767) INFO 07-07 01:35:00 [gpu_model_runner.py:1686] Graph capturing finished in 34 secs, took 3.00 GiB +INFO 07-07 01:35:00 [core.py:159] init engine (profile, create kv cache, warmup model) took 52.39 seconds +INFO 07-07 01:35:01 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 01:48:29 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 01:48:29 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |sem |0.0607|± |0.0113| +| | |math_pass@1:1_samples|0.2094|± |0.0406| +|mm\|arc_challenge\|0| 0|sem |0.0787|± |0.0138| +|mm\|arc_easy\|0 | 0|sem |0.1193|± |0.0105| +|mm\|commonsenseqa\|0| 0|sem |0.0281|± |0.0093| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.2438|± |0.0203| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.1750|± |0.0608| +|mm\|truthfulqa\|0 | 0|sem |0.0165|± |0.0116| + diff --git a/merge_bench3/logs/show_results.log b/merge_bench3/logs/show_results.log new file mode 100644 index 0000000000000000000000000000000000000000..4c1fa9d58ea3df472e950fb1278d8650608adbcf --- /dev/null +++ b/merge_bench3/logs/show_results.log @@ -0,0 +1,105 @@ +| Task |Version| Metric | Model |Value | |Stderr| +|------------------|------:|---------------------|--------------------------|-----:|---|-----:| +|mm\|arc_challenge\|0| 0|sem |._merged1_phi_darelinear_1|0.9475|± |0.0114| +| | | |._merged1_phi_darelinear_3|0.9370|± |0.0125| +| | | |._merged1_phi_darelinear_5|0.9396|± |0.0122| +| | | |._merged1_phi_darelinear_7|0.8661|± |0.0175| +| | | |._merged1_phi_darelinear_9|0.2467|± |0.0221| +| | | |._merged1_phi_linear_1 |0.9449|± |0.0117| +| | | |._merged1_phi_linear_3 |0.9318|± |0.0129| +| | | |._merged1_phi_linear_5 |0.9291|± |0.0132| +| | | |._merged1_phi_linear_7 |0.8871|± |0.0162| +| | | |._merged1_phi_linear_9 |0.5538|± |0.0255| +| | | |._merged1_phi_ties_1 |0.9318|± |0.0129| +| | | |._merged1_phi_ties_3 |0.9055|± |0.0150| +| | | |._merged1_phi_ties_5 |0.5328|± |0.0256| +| | | |._merged1_phi_ties_7 |0.1654|± |0.0191| +| | | |._merged1_phi_ties_9 |0.0787|± |0.0138| +| | | |._models_I-Phi4 |0.9396|± |0.0122| +| | | |._models_R-Phi4 |0.9291|± |0.0132| +|mm\|arc_easy\|0 | 0|sem |._merged1_phi_darelinear_1|0.9757|± |0.0050| +| | | |._merged1_phi_darelinear_3|0.9736|± |0.0052| +| | | |._merged1_phi_darelinear_5|0.9778|± |0.0048| +| | | |._merged1_phi_darelinear_7|0.9155|± |0.0090| +| | | |._merged1_phi_darelinear_9|0.3432|± |0.0154| +| | | |._merged1_phi_linear_1 |0.9810|± |0.0044| +| | | |._merged1_phi_linear_3 |0.9863|± |0.0038| +| | | |._merged1_phi_linear_5 |0.9768|± |0.0049| +| | | |._merged1_phi_linear_7 |0.9377|± |0.0079| +| | | |._merged1_phi_linear_9 |0.6938|± |0.0150| +| | | |._merged1_phi_ties_1 |0.9736|± |0.0052| +| | | |._merged1_phi_ties_3 |0.9578|± |0.0065| +| | | |._merged1_phi_ties_5 |0.5797|± |0.0160| +| | | |._merged1_phi_ties_7 |0.2323|± |0.0137| +| | | |._merged1_phi_ties_9 |0.1193|± |0.0105| +| | | |._models_I-Phi4 |0.9789|± |0.0047| +| | | |._models_R-Phi4 |0.9694|± |0.0056| +|mm\|commonsenseqa\|0| 0|sem |._merged1_phi_darelinear_1|0.8406|± |0.0205| +| | | |._merged1_phi_darelinear_3|0.8187|± |0.0216| +| | | |._merged1_phi_darelinear_5|0.8219|± |0.0214| +| | | |._merged1_phi_darelinear_7|0.6844|± |0.0260| +| | | |._merged1_phi_darelinear_9|0.1500|± |0.0200| +| | | |._merged1_phi_linear_1 |0.8406|± |0.0205| +| | | |._merged1_phi_linear_3 |0.8469|± |0.0202| +| | | |._merged1_phi_linear_5 |0.8500|± |0.0200| +| | | |._merged1_phi_linear_7 |0.8031|± |0.0223| +| | | |._merged1_phi_linear_9 |0.4656|± |0.0279| +| | | |._merged1_phi_ties_1 |0.8500|± |0.0200| +| | | |._merged1_phi_ties_3 |0.7937|± |0.0227| +| | | |._merged1_phi_ties_5 |0.2781|± |0.0251| +| | | |._merged1_phi_ties_7 |0.0844|± |0.0156| +| | | |._merged1_phi_ties_9 |0.0281|± |0.0093| +| | | |._models_I-Phi4 |0.8469|± |0.0202| +| | | |._models_R-Phi4 |0.8500|± |0.0200| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|._merged1_phi_darelinear_1|0.9351|± |0.0117| +| | | |._merged1_phi_darelinear_3|0.9441|± |0.0109| +| | | |._merged1_phi_darelinear_5|0.9463|± |0.0107| +| | | |._merged1_phi_darelinear_7|0.8658|± |0.0161| +| | | |._merged1_phi_darelinear_9|0.5213|± |0.0237| +| | | |._merged1_phi_linear_1 |0.9441|± |0.0109| +| | | |._merged1_phi_linear_3 |0.9418|± |0.0111| +| | | |._merged1_phi_linear_5 |0.9306|± |0.0120| +| | | |._merged1_phi_linear_7 |0.8993|± |0.0142| +| | | |._merged1_phi_linear_9 |0.8635|± |0.0163| +| | | |._merged1_phi_ties_1 |0.9396|± |0.0113| +| | | |._merged1_phi_ties_3 |0.9060|± |0.0138| +| | | |._merged1_phi_ties_5 |0.7696|± |0.0199| +| | | |._merged1_phi_ties_7 |0.4407|± |0.0235| +| | | |._merged1_phi_ties_9 |0.2438|± |0.0203| +| | | |._models_I-Phi4 |0.9351|± |0.0117| +| | | |._models_R-Phi4 |0.9485|± |0.0105| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|._merged1_phi_darelinear_1|0.9500|± |0.0349| +| | | |._merged1_phi_darelinear_3|1.0000|± |0.0000| +| | | |._merged1_phi_darelinear_5|0.9000|± |0.0480| +| | | |._merged1_phi_darelinear_7|0.8750|± |0.0530| +| | | |._merged1_phi_darelinear_9|0.4000|± |0.0784| +| | | |._merged1_phi_linear_1 |0.9750|± |0.0250| +| | | |._merged1_phi_linear_3 |0.9750|± |0.0250| +| | | |._merged1_phi_linear_5 |0.9500|± |0.0349| +| | | |._merged1_phi_linear_7 |0.9750|± |0.0250| +| | | |._merged1_phi_linear_9 |0.8500|± |0.0572| +| | | |._merged1_phi_ties_1 |0.9750|± |0.0250| +| | | |._merged1_phi_ties_3 |0.9000|± |0.0480| +| | | |._merged1_phi_ties_5 |0.7000|± |0.0734| +| | | |._merged1_phi_ties_7 |0.3250|± |0.0750| +| | | |._merged1_phi_ties_9 |0.1750|± |0.0608| +| | | |._models_I-Phi4 |1.0000|± |0.0000| +| | | |._models_R-Phi4 |1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |._merged1_phi_darelinear_1|0.7769|± |0.0380| +| | | |._merged1_phi_darelinear_3|0.7851|± |0.0375| +| | | |._merged1_phi_darelinear_5|0.8017|± |0.0364| +| | | |._merged1_phi_darelinear_7|0.6694|± |0.0429| +| | | |._merged1_phi_darelinear_9|0.0661|± |0.0227| +| | | |._merged1_phi_linear_1 |0.7769|± |0.0380| +| | | |._merged1_phi_linear_3 |0.7686|± |0.0385| +| | | |._merged1_phi_linear_5 |0.7934|± |0.0370| +| | | |._merged1_phi_linear_7 |0.7107|± |0.0414| +| | | |._merged1_phi_linear_9 |0.5124|± |0.0456| +| | | |._merged1_phi_ties_1 |0.7686|± |0.0385| +| | | |._merged1_phi_ties_3 |0.7107|± |0.0414| +| | | |._merged1_phi_ties_5 |0.2893|± |0.0414| +| | | |._merged1_phi_ties_7 |0.0661|± |0.0227| +| | | |._merged1_phi_ties_9 |0.0165|± |0.0116| +| | | |._models_I-Phi4 |0.7851|± |0.0375| +| | | |._models_R-Phi4 |0.7603|± |0.0390| + diff --git a/merge_bench3/outputs/._merged1_phi_darelinear_1/2025-06-23T01-52-10.258150/outputs_mm|arc_challenge|0_2025-06-23T01-52-10.258150.parquet b/merge_bench3/outputs/._merged1_phi_darelinear_1/2025-06-23T01-52-10.258150/outputs_mm|arc_challenge|0_2025-06-23T01-52-10.258150.parquet new file mode 100644 index 0000000000000000000000000000000000000000..0619bb90395e759197a8cb3937cf9f17c66e865e --- /dev/null +++ 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}, + "all": { + "sem": 0.8772103541791888, + "sem_stderr": 0.019431518630421234, + "math_pass@1:1_samples": 0.9742729306487696, + "math_pass@1:1_samples_stderr": 0.0052304842435476825 + } + }, + "versions": { + "mm|aime24|0": 3, + "mm|arc_challenge|0": 0, + "mm|arc_easy|0": 0, + "mm|commonsenseqa|0": 0, + "mm|gpqa_diamond|0": 2, + "mm|gsm8k|0": 0, + "mm|math_500|0": 3, + "mm|mmlu_pro|0": 0, + "mm|truthfulqa|0": 0 + }, + "size": { + "mm|commonsenseqa|0": 320, + "mm|truthfulqa|0": 121, + "mm|math_500|0": 40, + "mm|arc_challenge|0": 381, + "mm|gsm8k|0": 447, + "mm|arc_easy|0": 947 + } +} \ No newline at end of file diff --git a/merge_bench4/logs/I-Phi4.log b/merge_bench4/logs/I-Phi4.log new file mode 100644 index 0000000000000000000000000000000000000000..2c26f2336e1707f659f597a71b56643a703506a1 --- /dev/null +++ b/merge_bench4/logs/I-Phi4.log @@ -0,0 +1,96 @@ +INFO 07-07 00:13:42 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:13:43 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 00:13:50 [config.py:717] This model supports multiple tasks: {'score', 'classify', 'generate', 'reward', 'embed'}. Defaulting to 'generate'. +INFO 07-07 00:13:50 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 00:13:50 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 00:13:52 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./models/I-Phi4', speculative_config=None, tokenizer='./models/I-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/I-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 07-07 00:13:52 [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 07-07 00:13:52 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_7450a6c6'), local_subscribe_addr='ipc:///tmp/7155aae4-72ba-4b04-95ea-253acd863399', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:13:52 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:13:52 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_94bf8963'), local_subscribe_addr='ipc:///tmp/31daa2b8-2aa3-4688-ba90-b7ca40f01eb0', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:13:52 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:13:52 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:13:52 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0c6075db'), local_subscribe_addr='ipc:///tmp/f1a3183d-b9c3-4279-9e4c-3711f4812280', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:13:52 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:13:52 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_ee537eb1'), local_subscribe_addr='ipc:///tmp/8bf0d090-f092-4d6e-bf63-029508e129d7', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:13:52 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_87515bf4'), local_subscribe_addr='ipc:///tmp/46ed3f01-2c29-40ae-9586-1a2f2867ba56', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:13:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:13:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:13:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:13:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:13:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:13:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:13:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:13:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4045451) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=2 pid=4045450) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=0 pid=4045448) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=1 pid=4045449) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:00 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_125a9834'), local_subscribe_addr='ipc:///tmp/0082b0ba-6e62-4fdb-84cc-552fd986c46e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:00 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:00 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:00 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:00 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:00 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:00 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:00 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4045450) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=3 pid=4045451) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:00 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4045448) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=1 pid=4045449) WARNING 07-07 00:14:00 [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. +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:00 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:00 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:00 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:00 [gpu_model_runner.py:1329] Starting to load model ./models/I-Phi4... +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:01 [loader.py:458] Loading weights took 0.75 seconds +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:01 [loader.py:458] Loading weights took 0.76 seconds +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:01 [loader.py:458] Loading weights took 0.77 seconds +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:01 [loader.py:458] Loading weights took 0.86 seconds +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.943534 seconds +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 0.938025 seconds +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.007339 seconds +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:01 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.098361 seconds +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:07 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:07 [backends.py:430] Dynamo bytecode transform time: 5.60 s +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:07 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:07 [backends.py:430] Dynamo bytecode transform time: 5.63 s +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:07 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:07 [backends.py:430] Dynamo bytecode transform time: 5.71 s +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:07 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6d0cdcb90e/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:07 [backends.py:430] Dynamo bytecode transform time: 5.87 s +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:12 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.352 s +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:12 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.392 s +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:12 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.384 s +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:12 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.411 s +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:18 [monitor.py:33] torch.compile takes 5.71 s in total +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:18 [monitor.py:33] torch.compile takes 5.63 s in total +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:18 [monitor.py:33] torch.compile takes 5.60 s in total +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:18 [monitor.py:33] torch.compile takes 5.87 s in total +INFO 07-07 00:14:19 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 00:14:19 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 00:14:19 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:14:19 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:14:19 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:14:19 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:14:19 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 00:14:19 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=2 pid=4045450) INFO 07-07 00:14:42 [gpu_model_runner.py:1686] Graph capturing finished in 23 secs, took 2.96 GiB +(VllmWorker rank=3 pid=4045451) INFO 07-07 00:14:42 [gpu_model_runner.py:1686] Graph capturing finished in 23 secs, took 2.96 GiB +(VllmWorker rank=1 pid=4045449) INFO 07-07 00:14:42 [gpu_model_runner.py:1686] Graph capturing finished in 23 secs, took 2.96 GiB +(VllmWorker rank=0 pid=4045448) INFO 07-07 00:14:42 [gpu_model_runner.py:1686] Graph capturing finished in 23 secs, took 2.96 GiB +INFO 07-07 00:14:42 [core.py:159] init engine (profile, create kv cache, warmup model) took 41.23 seconds +INFO 07-07 00:14:43 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 00:15:46 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:15:46 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.9698|± |0.0056| +| | |sem |0.8770|± |0.0192| +|mm\|arc_challenge\|0| 0|sem |0.9449|± |0.0117| +|mm\|arc_easy\|0 | 0|sem |0.9799|± |0.0046| +|mm\|commonsenseqa\|0| 0|sem |0.8313|± |0.0210| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9396|± |0.0113| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |0.7521|± |0.0394| + diff --git a/merge_bench4/logs/R-Phi4.log b/merge_bench4/logs/R-Phi4.log new file mode 100644 index 0000000000000000000000000000000000000000..38eb2b270d511d91024e53343ed540aac6d213cc --- /dev/null +++ b/merge_bench4/logs/R-Phi4.log @@ -0,0 +1,96 @@ +INFO 07-07 00:15:45 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:15:46 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 00:15:53 [config.py:717] This model supports multiple tasks: {'score', 'classify', 'embed', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-07 00:15:53 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 00:15:53 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 00:15:55 [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 07-07 00:15:55 [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 07-07 00:15:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_8ae0fc6b'), local_subscribe_addr='ipc:///tmp/4b30adc6-eafd-49f2-affb-4362897a1f6b', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:15:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:15:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e95ffa00'), local_subscribe_addr='ipc:///tmp/ec5247d5-ca91-440d-9965-407085696567', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:15:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:15:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:15:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:15:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_95be3810'), local_subscribe_addr='ipc:///tmp/8635562c-267a-4543-ae6b-859d88472c01', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:15:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_9414c6c9'), local_subscribe_addr='ipc:///tmp/392acbdc-bc3e-4332-8bc4-63a270f8af01', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:15:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_4a5c838d'), local_subscribe_addr='ipc:///tmp/b7d76535-aa48-4fbc-bf8d-c127ba5b36ea', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4048018) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=2 pid=4048017) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=0 pid=4048015) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=1 pid=4048016) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:13 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_6a321450'), local_subscribe_addr='ipc:///tmp/ea659b39-5c29-4073-89d2-f0875b7fe4ee', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:13 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:13 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:13 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:13 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4048018) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=2 pid=4048017) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4048015) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=1 pid=4048016) WARNING 07-07 00:16:13 [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. +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:13 [gpu_model_runner.py:1329] Starting to load model ./models/R-Phi4... +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:14 [loader.py:458] Loading weights took 0.80 seconds +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:14 [loader.py:458] Loading weights took 0.80 seconds +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:14 [loader.py:458] Loading weights took 0.75 seconds +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:14 [loader.py:458] Loading weights took 0.77 seconds +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.004454 seconds +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.004367 seconds +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.009081 seconds +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:14 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.030746 seconds +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:20 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:20 [backends.py:430] Dynamo bytecode transform time: 5.52 s +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:20 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:20 [backends.py:430] Dynamo bytecode transform time: 5.56 s +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:20 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:20 [backends.py:430] Dynamo bytecode transform time: 5.61 s +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:20 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/bc6735f00d/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:20 [backends.py:430] Dynamo bytecode transform time: 5.69 s +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.467 s +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.446 s +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.436 s +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:25 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.436 s +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:31 [monitor.py:33] torch.compile takes 5.56 s in total +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:31 [monitor.py:33] torch.compile takes 5.61 s in total +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:31 [monitor.py:33] torch.compile takes 5.69 s in total +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:31 [monitor.py:33] torch.compile takes 5.52 s in total +INFO 07-07 00:16:32 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 00:16:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 00:16:32 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:16:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:16:32 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:16:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:16:32 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 00:16:32 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=3 pid=4048018) INFO 07-07 00:16:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +(VllmWorker rank=2 pid=4048017) INFO 07-07 00:16:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +(VllmWorker rank=0 pid=4048015) INFO 07-07 00:16:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +(VllmWorker rank=1 pid=4048016) INFO 07-07 00:16:58 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 2.96 GiB +INFO 07-07 00:16:58 [core.py:159] init engine (profile, create kv cache, warmup model) took 44.02 seconds +INFO 07-07 00:16:59 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 00:20:45 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:20:45 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.9470|± |0.0229| +| | |sem |0.8286|± |0.0222| +|mm\|arc_challenge\|0| 0|sem |0.8871|± |0.0162| +|mm\|arc_easy\|0 | 0|sem |0.9261|± |0.0085| +|mm\|commonsenseqa\|0| 0|sem |0.7906|± |0.0228| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.9441|± |0.0109| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9500|± |0.0349| +|mm\|truthfulqa\|0 | 0|sem |0.7107|± |0.0414| + diff --git a/merge_bench4/logs/phi_linear_9.log b/merge_bench4/logs/phi_linear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..b607f18d8780f14618d3e38f601926169219fd69 --- /dev/null +++ b/merge_bench4/logs/phi_linear_9.log @@ -0,0 +1,97 @@ +INFO 07-07 00:08:19 [__init__.py:239] Automatically detected platform cuda. +INFO 07-07 00:08:21 [config.py:209] Replacing legacy 'type' key with 'rope_type' +INFO 07-07 00:08:21 [config.py:2968] Downcasting torch.float32 to torch.float16. +INFO 07-07 00:08:28 [config.py:717] This model supports multiple tasks: {'generate', 'classify', 'score', 'reward', 'embed'}. Defaulting to 'generate'. +INFO 07-07 00:08:28 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-07 00:08:28 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-07 00:08:29 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged1/phi_linear_9', speculative_config=None, tokenizer='./merged1/phi_linear_9', 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_linear_9, 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 07-07 00:08:29 [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 07-07 00:08:29 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_b47effa6'), local_subscribe_addr='ipc:///tmp/947b9baa-eef0-4c27-aed5-34270c083d92', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:08:29 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:29 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_01f6dea0'), local_subscribe_addr='ipc:///tmp/0d092c78-a16c-4455-8186-5ab3acb13cf3', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-07 00:08:29 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:08:29 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-07 00:08:29 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:29 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_27c8e66c'), local_subscribe_addr='ipc:///tmp/f65cdba4-f53d-4256-8656-7d1a816f9b37', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:30 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_4b83337a'), local_subscribe_addr='ipc:///tmp/15d5657b-cd37-4835-af56-8dd3277fe73e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:30 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_50058d84'), local_subscribe_addr='ipc:///tmp/f762c34f-a2f9-448a-97db-1db7cc9db8c8', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:32 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:32 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:32 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:32 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:32 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:32 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:32 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:32 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=4042132) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=2 pid=4042134) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=3 pid=4042135) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=1 pid=4042133) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_4ca06e85'), local_subscribe_addr='ipc:///tmp/9aa2a8d5-9ec6-4389-9943-55ed1afa709f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:33 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=4042134) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:33 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:33 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:33 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=4042135) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=1 pid=4042133) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=0 pid=4042132) WARNING 07-07 00:08:33 [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. +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:33 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:33 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:33 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:33 [gpu_model_runner.py:1329] Starting to load model ./merged1/phi_linear_9... +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:35 [loader.py:458] Loading weights took 1.51 seconds +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:35 [loader.py:458] Loading weights took 1.51 seconds +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:35 [loader.py:458] Loading weights took 1.71 seconds +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.687102 seconds +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.699885 seconds +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:35 [loader.py:458] Loading weights took 1.78 seconds +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.911037 seconds +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:35 [gpu_model_runner.py:1347] Model loading took 1.8196 GiB and 1.998182 seconds +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:41 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:41 [backends.py:430] Dynamo bytecode transform time: 5.78 s +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:41 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:41 [backends.py:430] Dynamo bytecode transform time: 5.78 s +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:41 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:41 [backends.py:430] Dynamo bytecode transform time: 5.79 s +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:41 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/fe3a6231bf/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:41 [backends.py:430] Dynamo bytecode transform time: 5.90 s +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:46 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.410 s +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:46 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.430 s +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:46 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.417 s +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:46 [backends.py:118] Directly load the compiled graph(s) for shape None from the cache, took 4.486 s +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:08:52 [monitor.py:33] torch.compile takes 5.90 s in total +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:08:52 [monitor.py:33] torch.compile takes 5.78 s in total +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:08:52 [monitor.py:33] torch.compile takes 5.79 s in total +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:08:52 [monitor.py:33] torch.compile takes 5.78 s in total +INFO 07-07 00:08:53 [kv_cache_utils.py:634] GPU KV cache size: 2,007,088 tokens +INFO 07-07 00:08:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.02x +INFO 07-07 00:08:53 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:08:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:08:53 [kv_cache_utils.py:634] GPU KV cache size: 2,006,832 tokens +INFO 07-07 00:08:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 979.90x +INFO 07-07 00:08:53 [kv_cache_utils.py:634] GPU KV cache size: 2,008,112 tokens +INFO 07-07 00:08:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 980.52x +(VllmWorker rank=2 pid=4042134) INFO 07-07 00:09:19 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=3 pid=4042135) INFO 07-07 00:09:19 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=0 pid=4042132) INFO 07-07 00:09:19 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +(VllmWorker rank=1 pid=4042133) INFO 07-07 00:09:19 [gpu_model_runner.py:1686] Graph capturing finished in 26 secs, took 3.00 GiB +INFO 07-07 00:09:19 [core.py:159] init engine (profile, create kv cache, warmup model) took 43.82 seconds +INFO 07-07 00:09:19 [core_client.py:439] Core engine process 0 ready. +INFO 07-07 00:13:43 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-07 00:13:43 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.8659|± |0.0348| +| | |sem |0.4659|± |0.0286| +|mm\|arc_challenge\|0| 0|sem |0.4724|± |0.0256| +|mm\|arc_easy\|0 | 0|sem |0.5512|± |0.0162| +|mm\|commonsenseqa\|0| 0|sem |0.3937|± |0.0274| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8568|± |0.0166| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8750|± |0.0530| +|mm\|truthfulqa\|0 | 0|sem |0.4463|± |0.0454| + diff --git a/merge_bench4/logs/show_results.log b/merge_bench4/logs/show_results.log new file mode 100644 index 0000000000000000000000000000000000000000..5fe75fdf3eb1096bd77f55c612c638458e1e2dc8 --- /dev/null +++ b/merge_bench4/logs/show_results.log @@ -0,0 +1,21 @@ +| Task |Version| Metric | Model |Value | |Stderr| +|------------------|------:|---------------------|----------------------|-----:|---|-----:| +|mm\|arc_challenge\|0| 0|sem |._merged1_phi_linear_9|0.4724|± |0.0256| +| | | |._models_I-Phi4 |0.9449|± |0.0117| +| | | |._models_R-Phi4 |0.8871|± |0.0162| +|mm\|arc_easy\|0 | 0|sem |._merged1_phi_linear_9|0.5512|± |0.0162| +| | | |._models_I-Phi4 |0.9799|± |0.0046| +| | | |._models_R-Phi4 |0.9261|± |0.0085| +|mm\|commonsenseqa\|0| 0|sem |._merged1_phi_linear_9|0.3937|± |0.0274| +| | | |._models_I-Phi4 |0.8313|± |0.0210| +| | | |._models_R-Phi4 |0.7906|± |0.0228| +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|._merged1_phi_linear_9|0.8568|± |0.0166| +| | | |._models_I-Phi4 |0.9396|± |0.0113| +| | | |._models_R-Phi4 |0.9441|± |0.0109| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|._merged1_phi_linear_9|0.8750|± |0.0530| +| | | |._models_I-Phi4 |1.0000|± |0.0000| +| | | |._models_R-Phi4 |0.9500|± |0.0349| +|mm\|truthfulqa\|0 | 0|sem |._merged1_phi_linear_9|0.4463|± |0.0454| +| | | |._models_I-Phi4 |0.7521|± |0.0394| +| | | |._models_R-Phi4 |0.7107|± |0.0414| + diff --git a/merge_bench4/outputs/._merged1_phi_linear_9/2025-06-26T21-33-22.888531/outputs_mm|arc_challenge|0_2025-06-26T21-33-22.888531.parquet b/merge_bench4/outputs/._merged1_phi_linear_9/2025-06-26T21-33-22.888531/outputs_mm|arc_challenge|0_2025-06-26T21-33-22.888531.parquet new file mode 100644 index 0000000000000000000000000000000000000000..2f3a092c578a4f9ee325f22901b8ce5ad2c35076 --- /dev/null +++ b/merge_bench4/outputs/._merged1_phi_linear_9/2025-06-26T21-33-22.888531/outputs_mm|arc_challenge|0_2025-06-26T21-33-22.888531.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2596154314c5a9353b3c8f15093874b5cf81dc42303dd26ebcc659183e84ea9 +size 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"mm|arc_challenge|0": 381 + } +} \ No newline at end of file diff --git a/merge_llama/logs/llama_dare_linear_1.log b/merge_llama/logs/llama_dare_linear_1.log new file mode 100644 index 0000000000000000000000000000000000000000..528d5bff145acf0aad1437e5c4f15a36ac407c4c --- /dev/null +++ b/merge_llama/logs/llama_dare_linear_1.log @@ -0,0 +1,100 @@ +INFO 07-08 21:57:07 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 21:57:17 [config.py:717] This model supports multiple tasks: {'embed', 'score', 'generate', 'reward', 'classify'}. Defaulting to 'generate'. +INFO 07-08 21:57:17 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 21:57:17 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 21:57:18 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_dare_linear_1', speculative_config=None, tokenizer='./merged2/llama_dare_linear_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=./merged2/llama_dare_linear_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 07-08 21:57:18 [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 07-08 21:57:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_efa86c3a'), local_subscribe_addr='ipc:///tmp/427f2a48-5877-4905-9925-0c2b4d17785e', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 21:57:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0f005f40'), local_subscribe_addr='ipc:///tmp/80d4e5ea-4c66-4c15-b8ef-f7c70aaa4da2', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 21:57:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 21:57:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 21:57:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8e721de3'), local_subscribe_addr='ipc:///tmp/320e5e0d-55dd-4357-b9de-5cde0f6c36f7', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a0b7a43c'), local_subscribe_addr='ipc:///tmp/cfc24e0a-21eb-417d-b7d7-18d8486c08b9', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_51583d04'), local_subscribe_addr='ipc:///tmp/b246fe40-1357-44f1-a6a9-35733606dbe4', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=464442) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=2 pid=464441) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=1 pid=464440) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=0 pid=464439) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_ed261c14'), local_subscribe_addr='ipc:///tmp/5fb92e36-f741-4d3d-b123-c5965ff43fe9', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:27 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:27 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:27 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:27 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=464442) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=1 pid=464440) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=2 pid=464441) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=0 pid=464439) WARNING 07-08 21:57:27 [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. +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_1... +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_1... +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_1... +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_1... +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:42 [loader.py:458] Loading weights took 15.47 seconds +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:42 [loader.py:458] Loading weights took 15.49 seconds +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:42 [loader.py:458] Loading weights took 15.48 seconds +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:42 [loader.py:458] Loading weights took 15.50 seconds +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:43 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.799646 seconds +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:43 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.797310 seconds +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:43 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.818653 seconds +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:43 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.782109 seconds +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:50 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c50dca8b5e/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:50 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c50dca8b5e/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:50 [backends.py:430] Dynamo bytecode transform time: 7.03 s +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:50 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c50dca8b5e/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:50 [backends.py:430] Dynamo bytecode transform time: 7.03 s +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:50 [backends.py:430] Dynamo bytecode transform time: 7.03 s +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:50 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c50dca8b5e/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:50 [backends.py:430] Dynamo bytecode transform time: 7.03 s +(VllmWorker rank=1 pid=464440) INFO 07-08 21:57:53 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=464439) INFO 07-08 21:57:53 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=464441) INFO 07-08 21:57:53 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=464442) INFO 07-08 21:57:53 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=464441) INFO 07-08 21:58:15 [backends.py:148] Compiling a graph for general shape takes 24.81 s +(VllmWorker rank=1 pid=464440) INFO 07-08 21:58:15 [backends.py:148] Compiling a graph for general shape takes 24.71 s +(VllmWorker rank=3 pid=464442) INFO 07-08 21:58:15 [backends.py:148] Compiling a graph for general shape takes 24.88 s +(VllmWorker rank=0 pid=464439) INFO 07-08 21:58:16 [backends.py:148] Compiling a graph for general shape takes 25.29 s +(VllmWorker rank=3 pid=464442) INFO 07-08 21:58:29 [monitor.py:33] torch.compile takes 31.91 s in total +(VllmWorker rank=0 pid=464439) INFO 07-08 21:58:29 [monitor.py:33] torch.compile takes 32.32 s in total +(VllmWorker rank=1 pid=464440) INFO 07-08 21:58:29 [monitor.py:33] torch.compile takes 31.74 s in total +(VllmWorker rank=2 pid=464441) INFO 07-08 21:58:29 [monitor.py:33] torch.compile takes 31.84 s in total +INFO 07-08 21:58:30 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 21:58:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 21:58:30 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 21:58:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 21:58:30 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 21:58:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 21:58:30 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 21:58:30 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=464442) INFO 07-08 21:59:02 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 2.44 GiB +(VllmWorker rank=2 pid=464441) INFO 07-08 21:59:02 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 2.44 GiB +(VllmWorker rank=0 pid=464439) INFO 07-08 21:59:02 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 2.44 GiB +(VllmWorker rank=1 pid=464440) INFO 07-08 21:59:02 [gpu_model_runner.py:1686] Graph capturing finished in 32 secs, took 2.44 GiB +INFO 07-08 21:59:02 [core.py:159] init engine (profile, create kv cache, warmup model) took 79.81 seconds +INFO 07-08 21:59:03 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 22:10:10 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 22:10:10 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value| |Stderr| +|------------------|------:|---------------------|----:|---|-----:| +|all | |math_pass@1:1_samples| 0|± | 0| +| | |sem | 0|± | 0| +|mm\|arc_challenge\|0| 0|sem | 0|± | 0| +|mm\|arc_easy\|0 | 0|sem | 0|± | 0| +|mm\|commonsenseqa\|0| 0|sem | 0|± | 0| +|mm\|gpqa_diamond\|0 | 2|sem | 0| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples| 0|± | 0| +|mm\|math_500\|0 | 3|math_pass@1:1_samples| 0|± | 0| +|mm\|truthfulqa\|0 | 0|sem | 0|± | 0| + diff --git a/merge_llama/logs/llama_dare_linear_3.log b/merge_llama/logs/llama_dare_linear_3.log new file mode 100644 index 0000000000000000000000000000000000000000..d3886d6abe61eb29192e6fdf00e19c1f961255a3 --- /dev/null +++ b/merge_llama/logs/llama_dare_linear_3.log @@ -0,0 +1,100 @@ +INFO 07-08 22:10:09 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 22:10:17 [config.py:717] This model supports multiple tasks: {'generate', 'score', 'embed', 'classify', 'reward'}. Defaulting to 'generate'. +INFO 07-08 22:10:17 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 22:10:17 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 22:10:19 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_dare_linear_3', speculative_config=None, tokenizer='./merged2/llama_dare_linear_3', 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=./merged2/llama_dare_linear_3, 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 07-08 22:10:19 [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 07-08 22:10:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_1555f65d'), local_subscribe_addr='ipc:///tmp/6e528593-82c3-4270-944f-a35f93f9de59', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:10:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_219c95bf'), local_subscribe_addr='ipc:///tmp/efd0d5d1-e040-42c6-b9a2-313bba811e51', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:10:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8726dac0'), local_subscribe_addr='ipc:///tmp/8d3af5b5-b3b4-4297-9863-eb3f56a06ad9', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:10:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 22:10:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1223b949'), local_subscribe_addr='ipc:///tmp/6bc83f99-8054-4666-96eb-c76bef750739', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b6481fc7'), local_subscribe_addr='ipc:///tmp/73405b47-4cb4-488c-847e-40e382f91067', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:21 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:21 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:21 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:21 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:22 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:22 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:22 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:22 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=469317) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=3 pid=469318) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=1 pid=469316) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=0 pid=469315) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:22 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_ccf24d71'), local_subscribe_addr='ipc:///tmp/a6ef8cea-1071-4abf-8588-0ba47c52e58b', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:22 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:22 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:22 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:22 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:22 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:22 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=469318) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:22 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:22 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=469317) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=1 pid=469316) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=0 pid=469315) WARNING 07-08 22:10:22 [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. +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:22 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_3... +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:22 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_3... +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:22 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_3... +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:22 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_3... +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:41 [loader.py:458] Loading weights took 18.51 seconds +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:41 [loader.py:458] Loading weights took 18.59 seconds +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:41 [loader.py:458] Loading weights took 18.53 seconds +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:41 [loader.py:458] Loading weights took 18.53 seconds +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 18.743890 seconds +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 18.822089 seconds +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 18.814975 seconds +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 18.813055 seconds +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/7161fb40be/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:48 [backends.py:430] Dynamo bytecode transform time: 6.46 s +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/7161fb40be/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:48 [backends.py:430] Dynamo bytecode transform time: 6.49 s +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/7161fb40be/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:48 [backends.py:430] Dynamo bytecode transform time: 6.54 s +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/7161fb40be/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:48 [backends.py:430] Dynamo bytecode transform time: 6.57 s +(VllmWorker rank=0 pid=469315) INFO 07-08 22:10:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=469318) INFO 07-08 22:10:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=469316) INFO 07-08 22:10:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=469317) INFO 07-08 22:10:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=469316) INFO 07-08 22:11:13 [backends.py:148] Compiling a graph for general shape takes 24.65 s +(VllmWorker rank=2 pid=469317) INFO 07-08 22:11:13 [backends.py:148] Compiling a graph for general shape takes 24.86 s +(VllmWorker rank=3 pid=469318) INFO 07-08 22:11:13 [backends.py:148] Compiling a graph for general shape takes 25.04 s +(VllmWorker rank=0 pid=469315) INFO 07-08 22:11:14 [backends.py:148] Compiling a graph for general shape takes 25.18 s +(VllmWorker rank=1 pid=469316) INFO 07-08 22:11:27 [monitor.py:33] torch.compile takes 31.19 s in total +(VllmWorker rank=2 pid=469317) INFO 07-08 22:11:27 [monitor.py:33] torch.compile takes 31.43 s in total +(VllmWorker rank=3 pid=469318) INFO 07-08 22:11:27 [monitor.py:33] torch.compile takes 31.52 s in total +(VllmWorker rank=0 pid=469315) INFO 07-08 22:11:27 [monitor.py:33] torch.compile takes 31.64 s in total +INFO 07-08 22:11:28 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 22:11:28 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 22:11:28 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:11:28 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:11:28 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:11:28 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:11:28 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 22:11:28 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=2 pid=469317) INFO 07-08 22:11:58 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 2.44 GiB +(VllmWorker rank=1 pid=469316) INFO 07-08 22:11:58 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 2.44 GiB +(VllmWorker rank=3 pid=469318) INFO 07-08 22:11:58 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 2.44 GiB +(VllmWorker rank=0 pid=469315) INFO 07-08 22:11:58 [gpu_model_runner.py:1686] Graph capturing finished in 29 secs, took 2.44 GiB +INFO 07-08 22:11:58 [core.py:159] init engine (profile, create kv cache, warmup model) took 76.56 seconds +INFO 07-08 22:11:58 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 22:23:01 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 22:23:01 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value| |Stderr| +|------------------|------:|---------------------|----:|---|-----:| +|all | |math_pass@1:1_samples| 0|± | 0| +| | |sem | 0|± | 0| +|mm\|arc_challenge\|0| 0|sem | 0|± | 0| +|mm\|arc_easy\|0 | 0|sem | 0|± | 0| +|mm\|commonsenseqa\|0| 0|sem | 0|± | 0| +|mm\|gpqa_diamond\|0 | 2|sem | 0| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples| 0|± | 0| +|mm\|math_500\|0 | 3|math_pass@1:1_samples| 0|± | 0| +|mm\|truthfulqa\|0 | 0|sem | 0|± | 0| + diff --git a/merge_llama/logs/llama_dare_linear_5.log b/merge_llama/logs/llama_dare_linear_5.log new file mode 100644 index 0000000000000000000000000000000000000000..7f7f9925f5f0fcff599cfd9fc2fbf1b2f9d9832d --- /dev/null +++ b/merge_llama/logs/llama_dare_linear_5.log @@ -0,0 +1,100 @@ +INFO 07-08 22:23:00 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 22:23:09 [config.py:717] This model supports multiple tasks: {'classify', 'score', 'generate', 'reward', 'embed'}. Defaulting to 'generate'. +INFO 07-08 22:23:09 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 22:23:09 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 22:23:11 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_dare_linear_5', speculative_config=None, tokenizer='./merged2/llama_dare_linear_5', 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=./merged2/llama_dare_linear_5, 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 07-08 22:23:11 [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 07-08 22:23:11 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_e50cf4a7'), local_subscribe_addr='ipc:///tmp/d46eed58-9637-42f3-a623-ff2e80a2a95a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:23:11 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 22:23:11 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 22:23:11 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:11 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_5006b0bf'), local_subscribe_addr='ipc:///tmp/af3a3e8f-8905-4c77-b9a0-acd320d8995b', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:11 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c0cb8e5d'), local_subscribe_addr='ipc:///tmp/f9fe5cab-8925-4085-a1b5-e0ffa80568e8', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:23:11 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:11 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_7c261c90'), local_subscribe_addr='ipc:///tmp/61907bbf-e308-41fd-847f-343f4dd24474', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:11 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_015a357e'), local_subscribe_addr='ipc:///tmp/80ded898-8c80-4451-b2c7-e1408a8739a9', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:18 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:18 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=476078) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=2 pid=476077) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=0 pid=476075) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=1 pid=476076) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_8e902508'), local_subscribe_addr='ipc:///tmp/d8f42dfb-3e52-4cd7-b7ba-671fc5da2e8e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:19 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:19 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:19 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:19 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=476077) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=3 pid=476078) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:19 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=476076) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=0 pid=476075) WARNING 07-08 22:23:19 [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. +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:19 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_5... +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:19 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_5... +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:19 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_5... +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:19 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_5... +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:36 [loader.py:458] Loading weights took 17.29 seconds +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:36 [loader.py:458] Loading weights took 17.40 seconds +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:36 [loader.py:458] Loading weights took 17.40 seconds +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:37 [loader.py:458] Loading weights took 17.37 seconds +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 17.619191 seconds +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 17.616676 seconds +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 17.631825 seconds +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 17.556969 seconds +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f39d1a17b9/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:43 [backends.py:430] Dynamo bytecode transform time: 6.39 s +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f39d1a17b9/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:43 [backends.py:430] Dynamo bytecode transform time: 6.45 s +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f39d1a17b9/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:43 [backends.py:430] Dynamo bytecode transform time: 6.50 s +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f39d1a17b9/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:43 [backends.py:430] Dynamo bytecode transform time: 6.55 s +(VllmWorker rank=3 pid=476078) INFO 07-08 22:23:47 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=476077) INFO 07-08 22:23:47 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=476075) INFO 07-08 22:23:47 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=476076) INFO 07-08 22:23:47 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=476078) INFO 07-08 22:24:08 [backends.py:148] Compiling a graph for general shape takes 24.31 s +(VllmWorker rank=2 pid=476077) INFO 07-08 22:24:08 [backends.py:148] Compiling a graph for general shape takes 24.38 s +(VllmWorker rank=0 pid=476075) INFO 07-08 22:24:09 [backends.py:148] Compiling a graph for general shape takes 25.14 s +(VllmWorker rank=1 pid=476076) INFO 07-08 22:24:10 [backends.py:148] Compiling a graph for general shape takes 25.49 s +(VllmWorker rank=0 pid=476075) INFO 07-08 22:24:23 [monitor.py:33] torch.compile takes 31.69 s in total +(VllmWorker rank=1 pid=476076) INFO 07-08 22:24:23 [monitor.py:33] torch.compile takes 31.99 s in total +(VllmWorker rank=3 pid=476078) INFO 07-08 22:24:23 [monitor.py:33] torch.compile takes 30.70 s in total +(VllmWorker rank=2 pid=476077) INFO 07-08 22:24:23 [monitor.py:33] torch.compile takes 30.83 s in total +INFO 07-08 22:24:25 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 22:24:25 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 22:24:25 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:24:25 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:24:25 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:24:25 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:24:25 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 22:24:25 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=476078) INFO 07-08 22:25:05 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=2 pid=476077) INFO 07-08 22:25:05 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=1 pid=476076) INFO 07-08 22:25:05 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=0 pid=476075) INFO 07-08 22:25:05 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +INFO 07-08 22:25:05 [core.py:159] init engine (profile, create kv cache, warmup model) took 88.22 seconds +INFO 07-08 22:25:05 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 22:36:30 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 22:36:30 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value| |Stderr| +|------------------|------:|---------------------|----:|---|-----:| +|all | |math_pass@1:1_samples| 0|± | 0| +| | |sem | 0|± | 0| +|mm\|arc_challenge\|0| 0|sem | 0|± | 0| +|mm\|arc_easy\|0 | 0|sem | 0|± | 0| +|mm\|commonsenseqa\|0| 0|sem | 0|± | 0| +|mm\|gpqa_diamond\|0 | 2|sem | 0| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples| 0|± | 0| +|mm\|math_500\|0 | 3|math_pass@1:1_samples| 0|± | 0| +|mm\|truthfulqa\|0 | 0|sem | 0|± | 0| + diff --git a/merge_llama/logs/llama_dare_linear_7.log b/merge_llama/logs/llama_dare_linear_7.log new file mode 100644 index 0000000000000000000000000000000000000000..0a820e36d14c9d4dafd806011317ab9817a293c1 --- /dev/null +++ b/merge_llama/logs/llama_dare_linear_7.log @@ -0,0 +1,100 @@ +INFO 07-08 22:36:29 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 22:36:39 [config.py:717] This model supports multiple tasks: {'generate', 'score', 'embed', 'reward', 'classify'}. Defaulting to 'generate'. +INFO 07-08 22:36:39 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 22:36:39 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 22:36:40 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_dare_linear_7', speculative_config=None, tokenizer='./merged2/llama_dare_linear_7', 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=./merged2/llama_dare_linear_7, 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 07-08 22:36:40 [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 07-08 22:36:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_25695ef2'), local_subscribe_addr='ipc:///tmp/e21d0519-544d-4ccf-b895-9703412415c5', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:36:40 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_99f2f2b7'), local_subscribe_addr='ipc:///tmp/847f7cc7-4f2f-4b7a-80f3-b149b5c62813', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:36:40 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 22:36:41 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:41 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0af9a286'), local_subscribe_addr='ipc:///tmp/0cd726b6-374d-49ec-89d9-d3c02a4e6949', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:36:41 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:41 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_87b5682e'), local_subscribe_addr='ipc:///tmp/f6619b64-b4b7-44c0-b963-6c664c27e353', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:41 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b3f062fa'), local_subscribe_addr='ipc:///tmp/41b644d0-a138-43a8-ac2f-eba8f3399b4f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:48 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:48 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:48 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:48 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:49 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:49 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:49 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:49 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=482152) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=1 pid=482153) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=2 pid=482154) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=3 pid=482155) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:49 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_4ed93110'), local_subscribe_addr='ipc:///tmp/dc24726f-99f5-4074-8be5-4f77646d9894', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:49 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:49 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:49 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:49 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:49 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:49 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=482152) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:49 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=482153) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:49 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=482154) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=3 pid=482155) WARNING 07-08 22:36:49 [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. +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:49 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_7... +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:49 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_7... +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:49 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_7... +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:49 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_7... +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:53 [loader.py:458] Loading weights took 3.24 seconds +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:53 [loader.py:458] Loading weights took 3.42 seconds +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:53 [loader.py:458] Loading weights took 3.41 seconds +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:53 [loader.py:458] Loading weights took 3.43 seconds +(VllmWorker rank=3 pid=482155) INFO 07-08 22:36:53 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 3.471464 seconds +(VllmWorker rank=0 pid=482152) INFO 07-08 22:36:53 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 3.660119 seconds +(VllmWorker rank=2 pid=482154) INFO 07-08 22:36:53 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 3.668374 seconds +(VllmWorker rank=1 pid=482153) INFO 07-08 22:36:53 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 3.667559 seconds +(VllmWorker rank=2 pid=482154) INFO 07-08 22:37:00 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/50735dc362/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=482154) INFO 07-08 22:37:00 [backends.py:430] Dynamo bytecode transform time: 6.44 s +(VllmWorker rank=1 pid=482153) INFO 07-08 22:37:00 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/50735dc362/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=482153) INFO 07-08 22:37:00 [backends.py:430] Dynamo bytecode transform time: 6.48 s +(VllmWorker rank=3 pid=482155) INFO 07-08 22:37:00 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/50735dc362/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=482155) INFO 07-08 22:37:00 [backends.py:430] Dynamo bytecode transform time: 6.51 s +(VllmWorker rank=0 pid=482152) INFO 07-08 22:37:00 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/50735dc362/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=482152) INFO 07-08 22:37:00 [backends.py:430] Dynamo bytecode transform time: 6.52 s +(VllmWorker rank=2 pid=482154) INFO 07-08 22:37:03 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=482152) INFO 07-08 22:37:03 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=482155) INFO 07-08 22:37:03 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=482153) INFO 07-08 22:37:03 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=482152) INFO 07-08 22:37:25 [backends.py:148] Compiling a graph for general shape takes 24.50 s +(VllmWorker rank=2 pid=482154) INFO 07-08 22:37:25 [backends.py:148] Compiling a graph for general shape takes 24.75 s +(VllmWorker rank=3 pid=482155) INFO 07-08 22:37:25 [backends.py:148] Compiling a graph for general shape takes 24.75 s +(VllmWorker rank=1 pid=482153) INFO 07-08 22:37:25 [backends.py:148] Compiling a graph for general shape takes 24.68 s +(VllmWorker rank=0 pid=482152) INFO 07-08 22:37:38 [monitor.py:33] torch.compile takes 31.01 s in total +(VllmWorker rank=2 pid=482154) INFO 07-08 22:37:38 [monitor.py:33] torch.compile takes 31.19 s in total +(VllmWorker rank=1 pid=482153) INFO 07-08 22:37:38 [monitor.py:33] torch.compile takes 31.17 s in total +(VllmWorker rank=3 pid=482155) INFO 07-08 22:37:38 [monitor.py:33] torch.compile takes 31.26 s in total +INFO 07-08 22:37:40 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 22:37:40 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 22:37:40 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:37:40 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:37:40 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:37:40 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:37:40 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 22:37:40 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=1 pid=482153) INFO 07-08 22:38:10 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=2 pid=482154) INFO 07-08 22:38:10 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=3 pid=482155) INFO 07-08 22:38:10 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=0 pid=482152) INFO 07-08 22:38:10 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +INFO 07-08 22:38:10 [core.py:159] init engine (profile, create kv cache, warmup model) took 76.46 seconds +INFO 07-08 22:38:10 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 22:49:16 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 22:49:16 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value| |Stderr| +|------------------|------:|---------------------|----:|---|-----:| +|all | |math_pass@1:1_samples| 0|± | 0| +| | |sem | 0|± | 0| +|mm\|arc_challenge\|0| 0|sem | 0|± | 0| +|mm\|arc_easy\|0 | 0|sem | 0|± | 0| +|mm\|commonsenseqa\|0| 0|sem | 0|± | 0| +|mm\|gpqa_diamond\|0 | 2|sem | 0| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples| 0|± | 0| +|mm\|math_500\|0 | 3|math_pass@1:1_samples| 0|± | 0| +|mm\|truthfulqa\|0 | 0|sem | 0|± | 0| + diff --git a/merge_llama/logs/llama_dare_linear_9.log b/merge_llama/logs/llama_dare_linear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..06ef5d7a51e25d35d014ed536edc547b81d344ce --- /dev/null +++ b/merge_llama/logs/llama_dare_linear_9.log @@ -0,0 +1,100 @@ +INFO 07-08 22:49:15 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 22:49:23 [config.py:717] This model supports multiple tasks: {'embed', 'generate', 'reward', 'score', 'classify'}. Defaulting to 'generate'. +INFO 07-08 22:49:24 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 22:49:24 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 22:49:25 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_dare_linear_9', speculative_config=None, tokenizer='./merged2/llama_dare_linear_9', 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=./merged2/llama_dare_linear_9, 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 07-08 22:49:25 [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 07-08 22:49:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_45047748'), local_subscribe_addr='ipc:///tmp/acb35501-de94-4e6a-bf5a-facba48a843a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:49:25 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_00b1d97e'), local_subscribe_addr='ipc:///tmp/17961952-3607-4af0-9578-764639f60284', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:49:25 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_5a6e9ace'), local_subscribe_addr='ipc:///tmp/d4080b03-4e53-44df-a4bc-8ab739572723', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 22:49:25 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 22:49:25 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_eba67524'), local_subscribe_addr='ipc:///tmp/b695e098-02a6-4729-aa3b-4d3fcba2f0ee', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:25 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_130fd0a5'), local_subscribe_addr='ipc:///tmp/821ccdc1-a3f0-48b2-9e01-bd5ffb8a5157', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:33 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:33 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:33 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:33 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:33 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:33 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:33 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:33 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=488003) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=3 pid=488004) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=0 pid=488001) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=1 pid=488002) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_8eaba29c'), local_subscribe_addr='ipc:///tmp/a6eadacd-e079-4cb3-b942-6a246a8aa25b', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:33 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:33 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:33 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:33 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=488004) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=488003) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:33 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=488001) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=1 pid=488002) WARNING 07-08 22:49:33 [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. +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:33 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_9... +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:33 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_9... +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:33 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_9... +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:33 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_dare_linear_9... +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:49 [loader.py:458] Loading weights took 15.64 seconds +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:49 [loader.py:458] Loading weights took 15.67 seconds +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:49 [loader.py:458] Loading weights took 15.65 seconds +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:49 [loader.py:458] Loading weights took 15.66 seconds +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:50 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.899282 seconds +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:50 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.858337 seconds +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:50 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.919920 seconds +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:50 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.933426 seconds +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:56 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9bbc9acd0/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:56 [backends.py:430] Dynamo bytecode transform time: 6.36 s +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:56 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9bbc9acd0/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:56 [backends.py:430] Dynamo bytecode transform time: 6.36 s +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:56 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9bbc9acd0/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:56 [backends.py:430] Dynamo bytecode transform time: 6.38 s +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:56 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9bbc9acd0/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:56 [backends.py:430] Dynamo bytecode transform time: 6.46 s +(VllmWorker rank=2 pid=488003) INFO 07-08 22:49:59 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=488004) INFO 07-08 22:49:59 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=488002) INFO 07-08 22:49:59 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=488001) INFO 07-08 22:49:59 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=488002) INFO 07-08 22:50:21 [backends.py:148] Compiling a graph for general shape takes 24.07 s +(VllmWorker rank=2 pid=488003) INFO 07-08 22:50:21 [backends.py:148] Compiling a graph for general shape takes 24.34 s +(VllmWorker rank=3 pid=488004) INFO 07-08 22:50:21 [backends.py:148] Compiling a graph for general shape takes 24.53 s +(VllmWorker rank=0 pid=488001) INFO 07-08 22:50:21 [backends.py:148] Compiling a graph for general shape takes 24.46 s +(VllmWorker rank=1 pid=488002) INFO 07-08 22:50:34 [monitor.py:33] torch.compile takes 30.44 s in total +(VllmWorker rank=0 pid=488001) INFO 07-08 22:50:34 [monitor.py:33] torch.compile takes 30.92 s in total +(VllmWorker rank=2 pid=488003) INFO 07-08 22:50:34 [monitor.py:33] torch.compile takes 30.71 s in total +(VllmWorker rank=3 pid=488004) INFO 07-08 22:50:34 [monitor.py:33] torch.compile takes 30.89 s in total +INFO 07-08 22:50:35 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 22:50:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 22:50:35 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:50:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:50:35 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 22:50:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 22:50:35 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 22:50:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=0 pid=488001) INFO 07-08 22:51:03 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +(VllmWorker rank=1 pid=488002) INFO 07-08 22:51:03 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +(VllmWorker rank=3 pid=488004) INFO 07-08 22:51:03 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +(VllmWorker rank=2 pid=488003) INFO 07-08 22:51:03 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +INFO 07-08 22:51:03 [core.py:159] init engine (profile, create kv cache, warmup model) took 73.01 seconds +INFO 07-08 22:51:03 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 23:02:10 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 23:02:10 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value| |Stderr| +|------------------|------:|---------------------|----:|---|-----:| +|all | |math_pass@1:1_samples| 0|± | 0| +| | |sem | 0|± | 0| +|mm\|arc_challenge\|0| 0|sem | 0|± | 0| +|mm\|arc_easy\|0 | 0|sem | 0|± | 0| +|mm\|commonsenseqa\|0| 0|sem | 0|± | 0| +|mm\|gpqa_diamond\|0 | 2|sem | 0| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples| 0|± | 0| +|mm\|math_500\|0 | 3|math_pass@1:1_samples| 0|± | 0| +|mm\|truthfulqa\|0 | 0|sem | 0|± | 0| + diff --git a/merge_llama/logs/llama_linear_1.log b/merge_llama/logs/llama_linear_1.log new file mode 100644 index 0000000000000000000000000000000000000000..81f717a99250a61cc84e337a62813666ae0ba18e --- /dev/null +++ b/merge_llama/logs/llama_linear_1.log @@ -0,0 +1,100 @@ +INFO 07-08 23:02:09 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 23:02:17 [config.py:717] This model supports multiple tasks: {'classify', 'reward', 'embed', 'generate', 'score'}. Defaulting to 'generate'. +INFO 07-08 23:02:17 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 23:02:17 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 23:02:19 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_linear_1', speculative_config=None, tokenizer='./merged2/llama_linear_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=./merged2/llama_linear_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 07-08 23:02:19 [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 07-08 23:02:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_c477e76a'), local_subscribe_addr='ipc:///tmp/b6088acf-b9f1-459f-a5fa-6c82442016e0', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:02:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_5b90ff7e'), local_subscribe_addr='ipc:///tmp/e75144c9-285e-4b53-82dc-aa7679976178', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:02:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_73bdc171'), local_subscribe_addr='ipc:///tmp/56977811-4d9b-48ab-99f8-53cbe70acdcb', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:02:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 23:02:19 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_f67b17cd'), local_subscribe_addr='ipc:///tmp/fed143fc-b7d5-4aeb-a393-4537b179ffa6', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:19 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_c0722aa4'), local_subscribe_addr='ipc:///tmp/544c2794-96a1-4657-bd3b-724724c86035', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:26 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:26 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=493893) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=2 pid=493892) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=0 pid=493890) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=1 pid=493891) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:27 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_f146de96'), local_subscribe_addr='ipc:///tmp/98063888-ebff-41e1-8c5e-50a1b3b28579', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:27 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:27 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:27 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:27 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=493893) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=2 pid=493892) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:27 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=493890) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=1 pid=493891) WARNING 07-08 23:02:27 [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. +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_1... +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_1... +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_1... +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:27 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_1... +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:41 [loader.py:458] Loading weights took 14.00 seconds +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:41 [loader.py:458] Loading weights took 14.12 seconds +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:41 [loader.py:458] Loading weights took 14.13 seconds +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:41 [loader.py:458] Loading weights took 14.09 seconds +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.270209 seconds +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.350622 seconds +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.351174 seconds +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:41 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.352205 seconds +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9c324cef5/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:48 [backends.py:430] Dynamo bytecode transform time: 6.47 s +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9c324cef5/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:48 [backends.py:430] Dynamo bytecode transform time: 6.48 s +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9c324cef5/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:48 [backends.py:430] Dynamo bytecode transform time: 6.50 s +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:48 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f9c324cef5/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:48 [backends.py:430] Dynamo bytecode transform time: 6.66 s +(VllmWorker rank=3 pid=493893) INFO 07-08 23:02:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=493891) INFO 07-08 23:02:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=493892) INFO 07-08 23:02:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=493890) INFO 07-08 23:02:51 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=493893) INFO 07-08 23:03:13 [backends.py:148] Compiling a graph for general shape takes 24.39 s +(VllmWorker rank=2 pid=493892) INFO 07-08 23:03:13 [backends.py:148] Compiling a graph for general shape takes 24.36 s +(VllmWorker rank=1 pid=493891) INFO 07-08 23:03:13 [backends.py:148] Compiling a graph for general shape takes 24.55 s +(VllmWorker rank=0 pid=493890) INFO 07-08 23:03:14 [backends.py:148] Compiling a graph for general shape takes 24.99 s +(VllmWorker rank=2 pid=493892) INFO 07-08 23:03:27 [monitor.py:33] torch.compile takes 30.86 s in total +(VllmWorker rank=1 pid=493891) INFO 07-08 23:03:27 [monitor.py:33] torch.compile takes 31.03 s in total +(VllmWorker rank=3 pid=493893) INFO 07-08 23:03:27 [monitor.py:33] torch.compile takes 30.86 s in total +(VllmWorker rank=0 pid=493890) INFO 07-08 23:03:27 [monitor.py:33] torch.compile takes 31.66 s in total +INFO 07-08 23:03:29 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 23:03:29 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 23:03:29 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:03:29 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:03:29 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:03:29 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:03:29 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 23:03:29 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=2 pid=493892) INFO 07-08 23:04:02 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 2.44 GiB +(VllmWorker rank=3 pid=493893) INFO 07-08 23:04:02 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 2.44 GiB +(VllmWorker rank=1 pid=493891) INFO 07-08 23:04:02 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 2.44 GiB +(VllmWorker rank=0 pid=493890) INFO 07-08 23:04:02 [gpu_model_runner.py:1686] Graph capturing finished in 33 secs, took 2.44 GiB +INFO 07-08 23:04:02 [core.py:159] init engine (profile, create kv cache, warmup model) took 80.54 seconds +INFO 07-08 23:04:02 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 23:15:23 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 23:15:23 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.8711|± |0.0317| +| | |sem |0.7084|± |0.0213| +|mm\|arc_challenge\|0| 0|sem |0.9502|± |0.0122| +|mm\|arc_easy\|0 | 0|sem |0.9737|± |0.0055| +|mm\|commonsenseqa\|0| 0|sem |0.8799|± |0.0194| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8699|± |0.0141| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8723|± |0.0492| +|mm\|truthfulqa\|0 | 0|sem |0.7381|± |0.0483| + diff --git a/merge_llama/logs/llama_linear_3.log b/merge_llama/logs/llama_linear_3.log new file mode 100644 index 0000000000000000000000000000000000000000..85141f30133b342b227b3f978b09427bb29a67c7 --- /dev/null +++ b/merge_llama/logs/llama_linear_3.log @@ -0,0 +1,100 @@ +INFO 07-08 23:15:22 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 23:15:31 [config.py:717] This model supports multiple tasks: {'embed', 'score', 'classify', 'reward', 'generate'}. Defaulting to 'generate'. +INFO 07-08 23:15:31 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 23:15:31 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 23:15:33 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_linear_3', speculative_config=None, tokenizer='./merged2/llama_linear_3', 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=./merged2/llama_linear_3, 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 07-08 23:15:33 [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 07-08 23:15:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_70d95901'), local_subscribe_addr='ipc:///tmp/0cdaf875-d325-4164-871a-00ecfc6f3fef', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:15:33 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_87f720e2'), local_subscribe_addr='ipc:///tmp/dd8eda34-579b-464b-a9ea-c84d1a71f7de', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:15:33 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1963e98e'), local_subscribe_addr='ipc:///tmp/b60995d3-1db8-4b3a-a3a0-f94c74816724', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:15:33 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_90b13b18'), local_subscribe_addr='ipc:///tmp/ce24b2ec-b3c5-4e04-a337-6378042c9220', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:15:33 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:33 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_99defe59'), local_subscribe_addr='ipc:///tmp/47ddc143-2247-42f5-9fac-b1cd77fdec45', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:40 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:40 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:40 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:40 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:40 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:40 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:40 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:40 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=499920) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=3 pid=499921) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=0 pid=499918) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=1 pid=499919) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_0f5e406c'), local_subscribe_addr='ipc:///tmp/d67cf6d4-f401-4648-96ab-1bbebe197778', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:40 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:40 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:40 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:40 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:40 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:40 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=499921) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=2 pid=499920) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:40 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:40 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=499918) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=1 pid=499919) WARNING 07-08 23:15:40 [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. +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:40 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_3... +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:40 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_3... +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:40 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_3... +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:40 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_3... +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:55 [loader.py:458] Loading weights took 14.37 seconds +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:55 [loader.py:458] Loading weights took 14.44 seconds +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:55 [loader.py:458] Loading weights took 14.48 seconds +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:55 [loader.py:458] Loading weights took 14.44 seconds +(VllmWorker rank=2 pid=499920) INFO 07-08 23:15:55 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.592294 seconds +(VllmWorker rank=3 pid=499921) INFO 07-08 23:15:55 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.703862 seconds +(VllmWorker rank=1 pid=499919) INFO 07-08 23:15:55 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.710342 seconds +(VllmWorker rank=0 pid=499918) INFO 07-08 23:15:56 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.704882 seconds +(VllmWorker rank=2 pid=499920) INFO 07-08 23:16:02 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f370d68ce4/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=499920) INFO 07-08 23:16:02 [backends.py:430] Dynamo bytecode transform time: 6.40 s +(VllmWorker rank=3 pid=499921) INFO 07-08 23:16:02 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f370d68ce4/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=499921) INFO 07-08 23:16:02 [backends.py:430] Dynamo bytecode transform time: 6.41 s +(VllmWorker rank=0 pid=499918) INFO 07-08 23:16:02 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f370d68ce4/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=499918) INFO 07-08 23:16:02 [backends.py:430] Dynamo bytecode transform time: 6.47 s +(VllmWorker rank=1 pid=499919) INFO 07-08 23:16:02 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f370d68ce4/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=499919) INFO 07-08 23:16:02 [backends.py:430] Dynamo bytecode transform time: 6.51 s +(VllmWorker rank=3 pid=499921) INFO 07-08 23:16:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=499920) INFO 07-08 23:16:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=499918) INFO 07-08 23:16:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=499919) INFO 07-08 23:16:05 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=499921) INFO 07-08 23:16:27 [backends.py:148] Compiling a graph for general shape takes 24.29 s +(VllmWorker rank=2 pid=499920) INFO 07-08 23:16:27 [backends.py:148] Compiling a graph for general shape takes 24.63 s +(VllmWorker rank=0 pid=499918) INFO 07-08 23:16:28 [backends.py:148] Compiling a graph for general shape takes 24.83 s +(VllmWorker rank=1 pid=499919) INFO 07-08 23:16:28 [backends.py:148] Compiling a graph for general shape takes 25.07 s +(VllmWorker rank=0 pid=499918) INFO 07-08 23:16:41 [monitor.py:33] torch.compile takes 31.30 s in total +(VllmWorker rank=3 pid=499921) INFO 07-08 23:16:41 [monitor.py:33] torch.compile takes 30.71 s in total +(VllmWorker rank=2 pid=499920) INFO 07-08 23:16:41 [monitor.py:33] torch.compile takes 31.04 s in total +(VllmWorker rank=1 pid=499919) INFO 07-08 23:16:41 [monitor.py:33] torch.compile takes 31.58 s in total +INFO 07-08 23:16:43 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 23:16:43 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 23:16:43 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:16:43 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:16:43 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:16:43 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:16:43 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 23:16:43 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=2 pid=499920) INFO 07-08 23:17:18 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 2.44 GiB +(VllmWorker rank=3 pid=499921) INFO 07-08 23:17:18 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 2.44 GiB +(VllmWorker rank=1 pid=499919) INFO 07-08 23:17:18 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 2.44 GiB +(VllmWorker rank=0 pid=499918) INFO 07-08 23:17:18 [gpu_model_runner.py:1686] Graph capturing finished in 35 secs, took 2.44 GiB +INFO 07-08 23:17:18 [core.py:159] init engine (profile, create kv cache, warmup model) took 82.19 seconds +INFO 07-08 23:17:18 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 23:28:45 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 23:28:45 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.8765|± |0.0300| +| | |sem |0.7081|± |0.0214| +|mm\|arc_challenge\|0| 0|sem |0.9377|± |0.0135| +|mm\|arc_easy\|0 | 0|sem |0.9820|± |0.0046| +|mm\|commonsenseqa\|0| 0|sem |0.8587|± |0.0207| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8594|± |0.0146| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8936|± |0.0455| +|mm\|truthfulqa\|0 | 0|sem |0.7619|± |0.0468| + diff --git a/merge_llama/logs/llama_linear_5.log b/merge_llama/logs/llama_linear_5.log new file mode 100644 index 0000000000000000000000000000000000000000..f109b3c80d6f5be5ab31ae34bd2525c181ee9a43 --- /dev/null +++ b/merge_llama/logs/llama_linear_5.log @@ -0,0 +1,100 @@ +INFO 07-08 23:28:44 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 23:28:53 [config.py:717] This model supports multiple tasks: {'classify', 'embed', 'score', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-08 23:28:53 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 23:28:53 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 23:28:55 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_linear_5', speculative_config=None, tokenizer='./merged2/llama_linear_5', 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=./merged2/llama_linear_5, 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 07-08 23:28:55 [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 07-08 23:28:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_8163ea60'), local_subscribe_addr='ipc:///tmp/6218bc7b-5481-4a22-b4a4-d891d8bd081c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:28:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=506017) INFO 07-08 23:28:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d99d9c0a'), local_subscribe_addr='ipc:///tmp/c1f480f0-375f-4a68-971f-5b44b27b7299', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:28:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 23:28:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=506016) INFO 07-08 23:28:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_10ec160e'), local_subscribe_addr='ipc:///tmp/97c49779-d44c-4184-a5ef-c37e705321a2', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:28:55 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=506018) INFO 07-08 23:28:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d44484e4'), local_subscribe_addr='ipc:///tmp/b579de5d-5bc1-4746-88c1-910d88f19e66', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=506019) INFO 07-08 23:28:55 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_e28ddcc5'), local_subscribe_addr='ipc:///tmp/503d1ad8-1bb8-43de-91df-c286a5dd6249', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:02 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:02 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:02 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:02 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:02 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:02 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:02 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:02 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=506018) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=3 pid=506019) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=0 pid=506016) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=1 pid=506017) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:03 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_40b4c6bb'), local_subscribe_addr='ipc:///tmp/3b06376f-30ab-4059-9b60-6e0c3eb64249', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:03 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:03 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:03 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:03 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:03 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=506018) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:03 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:03 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=506016) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=3 pid=506019) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:03 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=506017) WARNING 07-08 23:29:03 [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. +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:03 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_5... +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:03 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_5... +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:03 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_5... +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:03 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_5... +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:19 [loader.py:458] Loading weights took 16.20 seconds +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:19 [loader.py:458] Loading weights took 16.26 seconds +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:19 [loader.py:458] Loading weights took 16.23 seconds +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:19 [loader.py:458] Loading weights took 16.24 seconds +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:19 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.416438 seconds +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:19 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.489719 seconds +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:19 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.499811 seconds +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:19 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.496381 seconds +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f8b4e5ecce/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:26 [backends.py:430] Dynamo bytecode transform time: 6.59 s +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f8b4e5ecce/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:26 [backends.py:430] Dynamo bytecode transform time: 6.59 s +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f8b4e5ecce/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:26 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/f8b4e5ecce/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:26 [backends.py:430] Dynamo bytecode transform time: 6.59 s +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:26 [backends.py:430] Dynamo bytecode transform time: 6.59 s +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:29 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=506019) INFO 07-08 23:29:51 [backends.py:148] Compiling a graph for general shape takes 24.18 s +(VllmWorker rank=2 pid=506018) INFO 07-08 23:29:51 [backends.py:148] Compiling a graph for general shape takes 24.30 s +(VllmWorker rank=0 pid=506016) INFO 07-08 23:29:51 [backends.py:148] Compiling a graph for general shape takes 24.38 s +(VllmWorker rank=1 pid=506017) INFO 07-08 23:29:51 [backends.py:148] Compiling a graph for general shape takes 24.39 s +(VllmWorker rank=3 pid=506019) INFO 07-08 23:30:05 [monitor.py:33] torch.compile takes 30.77 s in total +(VllmWorker rank=2 pid=506018) INFO 07-08 23:30:05 [monitor.py:33] torch.compile takes 30.89 s in total +(VllmWorker rank=1 pid=506017) INFO 07-08 23:30:05 [monitor.py:33] torch.compile takes 30.98 s in total +(VllmWorker rank=0 pid=506016) INFO 07-08 23:30:05 [monitor.py:33] torch.compile takes 30.97 s in total +INFO 07-08 23:30:06 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 23:30:06 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 23:30:06 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:30:06 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:30:06 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:30:06 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:30:06 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 23:30:06 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=1 pid=506017) INFO 07-08 23:30:37 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 2.44 GiB +(VllmWorker rank=2 pid=506018) INFO 07-08 23:30:37 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 2.44 GiB +(VllmWorker rank=3 pid=506019) INFO 07-08 23:30:37 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 2.44 GiB +(VllmWorker rank=0 pid=506016) INFO 07-08 23:30:37 [gpu_model_runner.py:1686] Graph capturing finished in 31 secs, took 2.44 GiB +INFO 07-08 23:30:37 [core.py:159] init engine (profile, create kv cache, warmup model) took 77.57 seconds +INFO 07-08 23:30:37 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 23:41:56 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 23:41:56 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.7230|± |0.0410| +| | |sem |0.7022|± |0.0220| +|mm\|arc_challenge\|0| 0|sem |0.9346|± |0.0138| +|mm\|arc_easy\|0 | 0|sem |0.9749|± |0.0054| +|mm\|commonsenseqa\|0| 0|sem |0.8516|± |0.0212| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.6801|± |0.0196| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.7660|± |0.0624| +|mm\|truthfulqa\|0 | 0|sem |0.7500|± |0.0475| + diff --git a/merge_llama/logs/llama_linear_7.log b/merge_llama/logs/llama_linear_7.log new file mode 100644 index 0000000000000000000000000000000000000000..92ea28cbcc80060794aec1c61d26c3e8dda7887a --- /dev/null +++ b/merge_llama/logs/llama_linear_7.log @@ -0,0 +1,100 @@ +INFO 07-08 23:41:55 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 23:42:03 [config.py:717] This model supports multiple tasks: {'score', 'generate', 'classify', 'reward', 'embed'}. Defaulting to 'generate'. +INFO 07-08 23:42:04 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 23:42:04 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 23:42:05 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_linear_7', speculative_config=None, tokenizer='./merged2/llama_linear_7', 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=./merged2/llama_linear_7, 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 07-08 23:42:05 [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 07-08 23:42:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_59a1f688'), local_subscribe_addr='ipc:///tmp/8c64f480-062e-422d-abb8-6970f276d987', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:42:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_0e61d2b4'), local_subscribe_addr='ipc:///tmp/16c5bd5c-da7c-4bee-ae16-224880eb754f', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:42:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_77b4c107'), local_subscribe_addr='ipc:///tmp/1f46e015-7baa-46dd-b084-843beda7543d', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:42:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 23:42:05 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_89f5a5d4'), local_subscribe_addr='ipc:///tmp/6923faf8-6d57-4996-929c-db9e9ca5de89', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:05 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_505503ac'), local_subscribe_addr='ipc:///tmp/21b5800f-4658-433c-bcaf-8250629c769f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:12 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:12 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=512040) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=3 pid=512041) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=1 pid=512035) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=0 pid=512034) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:13 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_a8d6a061'), local_subscribe_addr='ipc:///tmp/7932f9f6-dd0b-4c13-8a64-c5f02f25fbd0', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:13 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:13 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:13 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:13 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=512040) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=3 pid=512041) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:13 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=512034) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=1 pid=512035) WARNING 07-08 23:42:13 [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. +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:13 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_7... +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:13 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_7... +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:13 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_7... +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:13 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_7... +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:28 [loader.py:458] Loading weights took 14.99 seconds +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:28 [loader.py:458] Loading weights took 15.15 seconds +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:28 [loader.py:458] Loading weights took 15.10 seconds +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:28 [loader.py:458] Loading weights took 15.10 seconds +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:28 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.208618 seconds +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:29 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.358590 seconds +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:29 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.361096 seconds +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:29 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.364693 seconds +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:35 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6fb082dd12/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:35 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6fb082dd12/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:35 [backends.py:430] Dynamo bytecode transform time: 6.36 s +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:35 [backends.py:430] Dynamo bytecode transform time: 6.36 s +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:35 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6fb082dd12/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:35 [backends.py:430] Dynamo bytecode transform time: 6.43 s +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:35 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6fb082dd12/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:35 [backends.py:430] Dynamo bytecode transform time: 6.44 s +(VllmWorker rank=3 pid=512041) INFO 07-08 23:42:38 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=512040) INFO 07-08 23:42:38 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=512035) INFO 07-08 23:42:39 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=512034) INFO 07-08 23:42:39 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=512041) INFO 07-08 23:43:01 [backends.py:148] Compiling a graph for general shape takes 25.05 s +(VllmWorker rank=2 pid=512040) INFO 07-08 23:43:01 [backends.py:148] Compiling a graph for general shape takes 25.22 s +(VllmWorker rank=0 pid=512034) INFO 07-08 23:43:01 [backends.py:148] Compiling a graph for general shape takes 25.45 s +(VllmWorker rank=1 pid=512035) INFO 07-08 23:43:02 [backends.py:148] Compiling a graph for general shape takes 25.51 s +(VllmWorker rank=3 pid=512041) INFO 07-08 23:43:15 [monitor.py:33] torch.compile takes 31.41 s in total +(VllmWorker rank=1 pid=512035) INFO 07-08 23:43:15 [monitor.py:33] torch.compile takes 31.94 s in total +(VllmWorker rank=0 pid=512034) INFO 07-08 23:43:15 [monitor.py:33] torch.compile takes 31.89 s in total +(VllmWorker rank=2 pid=512040) INFO 07-08 23:43:15 [monitor.py:33] torch.compile takes 31.58 s in total +INFO 07-08 23:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 23:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 23:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:43:16 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 23:43:16 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=512041) INFO 07-08 23:43:56 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=2 pid=512040) INFO 07-08 23:43:56 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=1 pid=512035) INFO 07-08 23:43:56 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=0 pid=512034) INFO 07-08 23:43:56 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +INFO 07-08 23:43:57 [core.py:159] init engine (profile, create kv cache, warmup model) took 87.82 seconds +INFO 07-08 23:43:57 [core_client.py:439] Core engine process 0 ready. +INFO 07-08 23:55:30 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-08 23:55:30 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.8688|± |0.0264| +| | |sem |0.6724|± |0.0243| +|mm\|arc_challenge\|0| 0|sem |0.9065|± |0.0163| +|mm\|arc_easy\|0 | 0|sem |0.9653|± |0.0063| +|mm\|commonsenseqa\|0| 0|sem |0.7880|± |0.0243| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8014|± |0.0167| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.9362|± |0.0360| +|mm\|truthfulqa\|0 | 0|sem |0.7024|± |0.0502| + diff --git a/merge_llama/logs/llama_linear_9.log b/merge_llama/logs/llama_linear_9.log new file mode 100644 index 0000000000000000000000000000000000000000..42f2b06b396fbefee2efb76f500f3972750adb1a --- /dev/null +++ b/merge_llama/logs/llama_linear_9.log @@ -0,0 +1,100 @@ +INFO 07-08 23:55:29 [__init__.py:239] Automatically detected platform cuda. +INFO 07-08 23:55:38 [config.py:717] This model supports multiple tasks: {'classify', 'score', 'embed', 'generate', 'reward'}. Defaulting to 'generate'. +INFO 07-08 23:55:38 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-08 23:55:38 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-08 23:55:40 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_linear_9', speculative_config=None, tokenizer='./merged2/llama_linear_9', 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=./merged2/llama_linear_9, 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 07-08 23:55:40 [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 07-08 23:55:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_9d6429a1'), local_subscribe_addr='ipc:///tmp/070ba2a8-bb63-456f-a426-f2ee20ab5a9c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:55:40 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_cd00e2df'), local_subscribe_addr='ipc:///tmp/3cd04d80-7be2-4871-9f12-e6c6f69edd3a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:55:40 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8d1c1579'), local_subscribe_addr='ipc:///tmp/140eb04e-beed-4f92-91dc-bb292bf2e035', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-08 23:55:40 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-08 23:55:40 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b49b2eef'), local_subscribe_addr='ipc:///tmp/897fbdba-19ed-49dd-9c65-014420376c3c', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:40 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8816473d'), local_subscribe_addr='ipc:///tmp/eed30710-9883-46da-93d6-0158a43af1de', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:42 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:42 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:42 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:42 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:42 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:42 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:42 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:42 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=518083) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=2 pid=518082) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=1 pid=518081) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=0 pid=518080) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:43 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_6dfa17bf'), local_subscribe_addr='ipc:///tmp/9c0983eb-2fb5-4da5-9fa5-dbc959b32a0e', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:43 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:43 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:43 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:43 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:43 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=518083) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:43 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=518082) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:43 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:43 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=518080) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=1 pid=518081) WARNING 07-08 23:55:43 [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. +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:43 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_9... +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:43 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_9... +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:43 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_9... +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:43 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_linear_9... +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:47 [loader.py:458] Loading weights took 4.08 seconds +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:47 [loader.py:458] Loading weights took 4.11 seconds +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:47 [loader.py:458] Loading weights took 4.14 seconds +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:47 [loader.py:458] Loading weights took 4.12 seconds +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:48 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 4.293635 seconds +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:48 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 4.364151 seconds +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:48 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 4.377319 seconds +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:48 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 4.368003 seconds +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:54 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6c580e8a9e/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:54 [backends.py:430] Dynamo bytecode transform time: 6.34 s +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:54 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6c580e8a9e/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:54 [backends.py:430] Dynamo bytecode transform time: 6.37 s +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:54 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6c580e8a9e/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:54 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6c580e8a9e/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:54 [backends.py:430] Dynamo bytecode transform time: 6.53 s +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:54 [backends.py:430] Dynamo bytecode transform time: 6.53 s +(VllmWorker rank=3 pid=518083) INFO 07-08 23:55:57 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=518082) INFO 07-08 23:55:58 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=518081) INFO 07-08 23:55:58 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=518080) INFO 07-08 23:55:58 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=518083) INFO 07-08 23:56:19 [backends.py:148] Compiling a graph for general shape takes 24.17 s +(VllmWorker rank=2 pid=518082) INFO 07-08 23:56:19 [backends.py:148] Compiling a graph for general shape takes 24.12 s +(VllmWorker rank=1 pid=518081) INFO 07-08 23:56:20 [backends.py:148] Compiling a graph for general shape takes 24.80 s +(VllmWorker rank=0 pid=518080) INFO 07-08 23:56:20 [backends.py:148] Compiling a graph for general shape takes 25.28 s +(VllmWorker rank=3 pid=518083) INFO 07-08 23:56:33 [monitor.py:33] torch.compile takes 30.52 s in total +(VllmWorker rank=1 pid=518081) INFO 07-08 23:56:33 [monitor.py:33] torch.compile takes 31.33 s in total +(VllmWorker rank=0 pid=518080) INFO 07-08 23:56:33 [monitor.py:33] torch.compile takes 31.80 s in total +(VllmWorker rank=2 pid=518082) INFO 07-08 23:56:33 [monitor.py:33] torch.compile takes 30.49 s in total +INFO 07-08 23:56:35 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-08 23:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-08 23:56:35 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:56:35 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-08 23:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-08 23:56:35 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-08 23:56:35 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=518083) INFO 07-08 23:57:19 [gpu_model_runner.py:1686] Graph capturing finished in 44 secs, took 2.44 GiB +(VllmWorker rank=2 pid=518082) INFO 07-08 23:57:19 [gpu_model_runner.py:1686] Graph capturing finished in 45 secs, took 2.44 GiB +(VllmWorker rank=0 pid=518080) INFO 07-08 23:57:20 [gpu_model_runner.py:1686] Graph capturing finished in 45 secs, took 2.44 GiB +(VllmWorker rank=1 pid=518081) INFO 07-08 23:57:20 [gpu_model_runner.py:1686] Graph capturing finished in 45 secs, took 2.44 GiB +INFO 07-08 23:57:20 [core.py:159] init engine (profile, create kv cache, warmup model) took 91.86 seconds +INFO 07-08 23:57:20 [core_client.py:439] Core engine process 0 ready. +INFO 07-09 00:09:02 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-09 00:09:02 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.6292|± |0.0461| +| | |sem |0.6029|± |0.0285| +|mm\|arc_challenge\|0| 0|sem |0.8006|± |0.0223| +|mm\|arc_easy\|0 | 0|sem |0.8587|± |0.0121| +|mm\|commonsenseqa\|0| 0|sem |0.7244|± |0.0266| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.6626|± |0.0198| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.5957|± |0.0724| +|mm\|truthfulqa\|0 | 0|sem |0.6310|± |0.0530| + diff --git a/merge_llama/logs/llama_ties_1.log b/merge_llama/logs/llama_ties_1.log new file mode 100644 index 0000000000000000000000000000000000000000..47ec0df2c29122bd900db2a76c660aa748583c9a --- /dev/null +++ b/merge_llama/logs/llama_ties_1.log @@ -0,0 +1,100 @@ +INFO 07-09 00:09:01 [__init__.py:239] Automatically detected platform cuda. +INFO 07-09 00:09:10 [config.py:717] This model supports multiple tasks: {'embed', 'classify', 'generate', 'score', 'reward'}. Defaulting to 'generate'. +INFO 07-09 00:09:10 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-09 00:09:10 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-09 00:09:12 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_ties_1', speculative_config=None, tokenizer='./merged2/llama_ties_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=./merged2/llama_ties_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 07-09 00:09:12 [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 07-09 00:09:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_22cb6533'), local_subscribe_addr='ipc:///tmp/0db87f1a-6e1d-48dd-a6dc-4e7470240703', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:09:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_66da5f57'), local_subscribe_addr='ipc:///tmp/8265f4e9-35f0-4aaf-8f81-8d20353b028e', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:09:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-09 00:09:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_66590f81'), local_subscribe_addr='ipc:///tmp/f0159fc1-5b98-4da5-9f99-e33ac7e89d65', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:09:12 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_abceaf84'), local_subscribe_addr='ipc:///tmp/306a387d-b16a-4c50-b8ec-7fad48ad5e54', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:12 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_759eb040'), local_subscribe_addr='ipc:///tmp/0b015122-0ea1-4060-bf39-6ba74032049d', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:20 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:20 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:20 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:20 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:20 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:20 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:20 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:20 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=524541) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=2 pid=524540) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=1 pid=524539) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=0 pid=524538) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:20 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_0c823e14'), local_subscribe_addr='ipc:///tmp/f6302089-1949-4416-84d2-db87ba3c1718', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:20 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:20 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:20 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:20 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:20 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:20 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=524541) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=2 pid=524540) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:20 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=524539) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:20 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=524538) WARNING 07-09 00:09:20 [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. +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:20 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_1... +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:20 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_1... +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:20 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_1... +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:20 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_1... +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:36 [loader.py:458] Loading weights took 14.94 seconds +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:36 [loader.py:458] Loading weights took 15.10 seconds +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:36 [loader.py:458] Loading weights took 15.09 seconds +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:36 [loader.py:458] Loading weights took 15.05 seconds +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:36 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.347854 seconds +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:36 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.348878 seconds +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:36 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.347142 seconds +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:36 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.239616 seconds +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/067e9a182b/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:43 [backends.py:430] Dynamo bytecode transform time: 6.51 s +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/067e9a182b/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:43 [backends.py:430] Dynamo bytecode transform time: 6.52 s +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/067e9a182b/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:43 [backends.py:430] Dynamo bytecode transform time: 6.55 s +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/067e9a182b/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:43 [backends.py:430] Dynamo bytecode transform time: 6.58 s +(VllmWorker rank=2 pid=524540) INFO 07-09 00:09:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=524541) INFO 07-09 00:09:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=524539) INFO 07-09 00:09:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=524538) INFO 07-09 00:09:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=524541) INFO 07-09 00:10:08 [backends.py:148] Compiling a graph for general shape takes 24.96 s +(VllmWorker rank=1 pid=524539) INFO 07-09 00:10:08 [backends.py:148] Compiling a graph for general shape takes 25.03 s +(VllmWorker rank=2 pid=524540) INFO 07-09 00:10:08 [backends.py:148] Compiling a graph for general shape takes 25.16 s +(VllmWorker rank=0 pid=524538) INFO 07-09 00:10:09 [backends.py:148] Compiling a graph for general shape takes 25.30 s +(VllmWorker rank=2 pid=524540) INFO 07-09 00:10:22 [monitor.py:33] torch.compile takes 31.69 s in total +(VllmWorker rank=1 pid=524539) INFO 07-09 00:10:22 [monitor.py:33] torch.compile takes 31.61 s in total +(VllmWorker rank=0 pid=524538) INFO 07-09 00:10:22 [monitor.py:33] torch.compile takes 31.86 s in total +(VllmWorker rank=3 pid=524541) INFO 07-09 00:10:22 [monitor.py:33] torch.compile takes 31.47 s in total +INFO 07-09 00:10:23 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-09 00:10:23 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-09 00:10:23 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:10:23 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:10:23 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:10:23 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:10:23 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-09 00:10:23 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=1 pid=524539) INFO 07-09 00:10:53 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=3 pid=524541) INFO 07-09 00:10:53 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=2 pid=524540) INFO 07-09 00:10:53 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=0 pid=524538) INFO 07-09 00:10:53 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +INFO 07-09 00:10:53 [core.py:159] init engine (profile, create kv cache, warmup model) took 77.23 seconds +INFO 07-09 00:10:54 [core_client.py:439] Core engine process 0 ready. +INFO 07-09 00:22:08 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-09 00:22:08 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.7509|± |0.0421| +| | |sem |0.6841|± |0.0235| +|mm\|arc_challenge\|0| 0|sem |0.9190|± |0.0153| +|mm\|arc_easy\|0 | 0|sem |0.9677|± |0.0061| +|mm\|commonsenseqa\|0| 0|sem |0.8198|± |0.0229| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.7996|± |0.0168| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.7021|± |0.0674| +|mm\|truthfulqa\|0 | 0|sem |0.7143|± |0.0496| + diff --git a/merge_llama/logs/llama_ties_3.log b/merge_llama/logs/llama_ties_3.log new file mode 100644 index 0000000000000000000000000000000000000000..7278e528ccc4ddb8197f14be4633ab4b6d5383d4 --- /dev/null +++ b/merge_llama/logs/llama_ties_3.log @@ -0,0 +1,100 @@ +INFO 07-09 00:22:07 [__init__.py:239] Automatically detected platform cuda. +INFO 07-09 00:22:16 [config.py:717] This model supports multiple tasks: {'score', 'generate', 'classify', 'reward', 'embed'}. Defaulting to 'generate'. +INFO 07-09 00:22:16 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-09 00:22:16 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-09 00:22:17 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_ties_3', speculative_config=None, tokenizer='./merged2/llama_ties_3', 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=./merged2/llama_ties_3, 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 07-09 00:22:17 [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 07-09 00:22:17 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_f6d94697'), local_subscribe_addr='ipc:///tmp/72bda55a-f51b-459c-ae78-0e361ddef361', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:22:18 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-09 00:22:18 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_caf45539'), local_subscribe_addr='ipc:///tmp/657ca8fa-d3f7-494b-98be-1a2735014e8c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:22:18 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_76ca5672'), local_subscribe_addr='ipc:///tmp/01b24fe7-b147-4b55-9574-02bf4d12067a', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:22:18 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a60240fa'), local_subscribe_addr='ipc:///tmp/720e0b50-c6ff-4f27-b746-fd8c1e70160f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:18 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_aab53d73'), local_subscribe_addr='ipc:///tmp/1be93d98-c236-48d0-8e63-26e85724e38f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:21 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:21 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:21 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:21 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:21 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:21 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:21 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:21 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=530449) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=2 pid=530448) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=0 pid=530446) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=1 pid=530447) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:21 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_61afd87c'), local_subscribe_addr='ipc:///tmp/f360117e-903d-4fdd-b0ba-45158dd46d49', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:21 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:21 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:21 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:21 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:21 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:21 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=530449) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:21 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=530448) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=0 pid=530446) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:21 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=530447) WARNING 07-09 00:22:21 [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. +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:21 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_3... +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:21 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_3... +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:21 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_3... +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:21 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_3... +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:36 [loader.py:458] Loading weights took 14.75 seconds +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:36 [loader.py:458] Loading weights took 14.87 seconds +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:36 [loader.py:458] Loading weights took 14.85 seconds +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:36 [loader.py:458] Loading weights took 14.87 seconds +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:36 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 14.996291 seconds +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.088520 seconds +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.091452 seconds +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:37 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 15.093427 seconds +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c501ef649a/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:43 [backends.py:430] Dynamo bytecode transform time: 6.41 s +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c501ef649a/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:43 [backends.py:430] Dynamo bytecode transform time: 6.42 s +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c501ef649a/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:43 [backends.py:430] Dynamo bytecode transform time: 6.43 s +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:43 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/c501ef649a/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:43 [backends.py:430] Dynamo bytecode transform time: 6.45 s +(VllmWorker rank=1 pid=530447) INFO 07-09 00:22:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=530448) INFO 07-09 00:22:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=530449) INFO 07-09 00:22:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=530446) INFO 07-09 00:22:46 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=530447) INFO 07-09 00:23:08 [backends.py:148] Compiling a graph for general shape takes 24.10 s +(VllmWorker rank=0 pid=530446) INFO 07-09 00:23:08 [backends.py:148] Compiling a graph for general shape takes 24.25 s +(VllmWorker rank=2 pid=530448) INFO 07-09 00:23:08 [backends.py:148] Compiling a graph for general shape takes 24.28 s +(VllmWorker rank=3 pid=530449) INFO 07-09 00:23:08 [backends.py:148] Compiling a graph for general shape takes 24.27 s +(VllmWorker rank=0 pid=530446) INFO 07-09 00:23:21 [monitor.py:33] torch.compile takes 30.68 s in total +(VllmWorker rank=2 pid=530448) INFO 07-09 00:23:21 [monitor.py:33] torch.compile takes 30.70 s in total +(VllmWorker rank=3 pid=530449) INFO 07-09 00:23:21 [monitor.py:33] torch.compile takes 30.72 s in total +(VllmWorker rank=1 pid=530447) INFO 07-09 00:23:21 [monitor.py:33] torch.compile takes 30.51 s in total +INFO 07-09 00:23:22 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-09 00:23:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-09 00:23:22 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:23:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:23:22 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:23:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:23:22 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-09 00:23:22 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=530449) INFO 07-09 00:23:50 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +(VllmWorker rank=2 pid=530448) INFO 07-09 00:23:50 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +(VllmWorker rank=0 pid=530446) INFO 07-09 00:23:50 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +(VllmWorker rank=1 pid=530447) INFO 07-09 00:23:50 [gpu_model_runner.py:1686] Graph capturing finished in 27 secs, took 2.44 GiB +INFO 07-09 00:23:50 [core.py:159] init engine (profile, create kv cache, warmup model) took 73.30 seconds +INFO 07-09 00:23:50 [core_client.py:439] Core engine process 0 ready. +INFO 07-09 00:33:36 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-09 00:33:36 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.8667|± |0.0330| +| | |sem |0.6943|± |0.0225| +|mm\|arc_challenge\|0| 0|sem |0.9470|± |0.0125| +|mm\|arc_easy\|0 | 0|sem |0.9677|± |0.0061| +|mm\|commonsenseqa\|0| 0|sem |0.8304|± |0.0223| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8822|± |0.0135| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.8511|± |0.0525| +|mm\|truthfulqa\|0 | 0|sem |0.7262|± |0.0489| + diff --git a/merge_llama/logs/llama_ties_5.log b/merge_llama/logs/llama_ties_5.log new file mode 100644 index 0000000000000000000000000000000000000000..9b5d97e80f415aedd1649cb4c100d9bbd8e155f7 --- /dev/null +++ b/merge_llama/logs/llama_ties_5.log @@ -0,0 +1,100 @@ +INFO 07-09 00:33:35 [__init__.py:239] Automatically detected platform cuda. +INFO 07-09 00:33:44 [config.py:717] This model supports multiple tasks: {'embed', 'generate', 'score', 'classify', 'reward'}. Defaulting to 'generate'. +INFO 07-09 00:33:44 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-09 00:33:44 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-09 00:33:46 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_ties_5', speculative_config=None, tokenizer='./merged2/llama_ties_5', 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=./merged2/llama_ties_5, 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 07-09 00:33:46 [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 07-09 00:33:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_36a32a91'), local_subscribe_addr='ipc:///tmp/611c2833-c3c2-4124-811d-59e70c5749af', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:33:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_775809a8'), local_subscribe_addr='ipc:///tmp/c5c76a32-bbbb-4c38-a1b8-e25de3fb27a1', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:33:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-09 00:33:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=537036) INFO 07-09 00:33:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_2eb5f542'), local_subscribe_addr='ipc:///tmp/20dcdcf4-fe50-4e40-9530-4bd114269339', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:33:46 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=537037) INFO 07-09 00:33:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a8806c63'), local_subscribe_addr='ipc:///tmp/bce69914-38fa-4442-9929-573313b661ad', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=537038) INFO 07-09 00:33:46 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_8d83c7ae'), local_subscribe_addr='ipc:///tmp/5f012cf6-56a6-4966-87ba-49df9bd6b9db', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=537036) INFO 07-09 00:33:58 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=537036) INFO 07-09 00:33:58 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:58 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:58 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=537037) INFO 07-09 00:33:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=537037) INFO 07-09 00:33:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=537038) INFO 07-09 00:33:59 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=537038) INFO 07-09 00:33:59 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=1 pid=537036) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=0 pid=537035) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=3 pid=537038) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=2 pid=537037) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:59 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_dbf7fd6d'), local_subscribe_addr='ipc:///tmp/f57e3049-2cda-4a2c-909a-c0b86dc47103', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:59 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=537035) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=2 pid=537037) INFO 07-09 00:33:59 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=1 pid=537036) INFO 07-09 00:33:59 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=1 pid=537036) INFO 07-09 00:33:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=537038) INFO 07-09 00:33:59 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=537036) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=2 pid=537037) INFO 07-09 00:33:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=537037) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=3 pid=537038) INFO 07-09 00:33:59 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=537038) WARNING 07-09 00:33:59 [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. +(VllmWorker rank=0 pid=537035) INFO 07-09 00:33:59 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_5... +(VllmWorker rank=2 pid=537037) INFO 07-09 00:33:59 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_5... +(VllmWorker rank=3 pid=537038) INFO 07-09 00:33:59 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_5... +(VllmWorker rank=1 pid=537036) INFO 07-09 00:33:59 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_5... +(VllmWorker rank=0 pid=537035) INFO 07-09 00:34:15 [loader.py:458] Loading weights took 15.93 seconds +(VllmWorker rank=1 pid=537036) INFO 07-09 00:34:15 [loader.py:458] Loading weights took 16.04 seconds +(VllmWorker rank=3 pid=537038) INFO 07-09 00:34:15 [loader.py:458] Loading weights took 16.03 seconds +(VllmWorker rank=2 pid=537037) INFO 07-09 00:34:15 [loader.py:458] Loading weights took 16.01 seconds +(VllmWorker rank=1 pid=537036) INFO 07-09 00:34:16 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.287811 seconds +(VllmWorker rank=3 pid=537038) INFO 07-09 00:34:16 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.289166 seconds +(VllmWorker rank=0 pid=537035) INFO 07-09 00:34:16 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.209153 seconds +(VllmWorker rank=2 pid=537037) INFO 07-09 00:34:16 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 16.294447 seconds +(VllmWorker rank=3 pid=537038) INFO 07-09 00:34:22 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6462abaa9e/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=537038) INFO 07-09 00:34:22 [backends.py:430] Dynamo bytecode transform time: 6.35 s +(VllmWorker rank=1 pid=537036) INFO 07-09 00:34:22 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6462abaa9e/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=537036) INFO 07-09 00:34:22 [backends.py:430] Dynamo bytecode transform time: 6.39 s +(VllmWorker rank=2 pid=537037) INFO 07-09 00:34:22 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6462abaa9e/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=537037) INFO 07-09 00:34:22 [backends.py:430] Dynamo bytecode transform time: 6.39 s +(VllmWorker rank=0 pid=537035) INFO 07-09 00:34:22 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/6462abaa9e/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=537035) INFO 07-09 00:34:22 [backends.py:430] Dynamo bytecode transform time: 6.45 s +(VllmWorker rank=3 pid=537038) INFO 07-09 00:34:25 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=537036) INFO 07-09 00:34:26 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=537037) INFO 07-09 00:34:26 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=537035) INFO 07-09 00:34:26 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=3 pid=537038) INFO 07-09 00:34:47 [backends.py:148] Compiling a graph for general shape takes 24.29 s +(VllmWorker rank=1 pid=537036) INFO 07-09 00:34:47 [backends.py:148] Compiling a graph for general shape takes 24.35 s +(VllmWorker rank=2 pid=537037) INFO 07-09 00:34:47 [backends.py:148] Compiling a graph for general shape takes 24.39 s +(VllmWorker rank=0 pid=537035) INFO 07-09 00:34:48 [backends.py:148] Compiling a graph for general shape takes 24.70 s +(VllmWorker rank=1 pid=537036) INFO 07-09 00:35:01 [monitor.py:33] torch.compile takes 30.74 s in total +(VllmWorker rank=3 pid=537038) INFO 07-09 00:35:01 [monitor.py:33] torch.compile takes 30.64 s in total +(VllmWorker rank=2 pid=537037) INFO 07-09 00:35:01 [monitor.py:33] torch.compile takes 30.78 s in total +(VllmWorker rank=0 pid=537035) INFO 07-09 00:35:01 [monitor.py:33] torch.compile takes 31.15 s in total +INFO 07-09 00:35:02 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-09 00:35:02 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-09 00:35:02 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:35:02 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:35:02 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:35:02 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:35:02 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-09 00:35:02 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=2 pid=537037) INFO 07-09 00:35:42 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=1 pid=537036) INFO 07-09 00:35:42 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=3 pid=537038) INFO 07-09 00:35:42 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +(VllmWorker rank=0 pid=537035) INFO 07-09 00:35:42 [gpu_model_runner.py:1686] Graph capturing finished in 40 secs, took 2.44 GiB +INFO 07-09 00:35:42 [core.py:159] init engine (profile, create kv cache, warmup model) took 86.64 seconds +INFO 07-09 00:35:43 [core_client.py:439] Core engine process 0 ready. +INFO 07-09 00:45:40 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-09 00:45:40 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.9411|± |0.0068| +| | |sem |0.6766|± |0.0238| +|mm\|arc_challenge\|0| 0|sem |0.9346|± |0.0138| +|mm\|arc_easy\|0 | 0|sem |0.9641|± |0.0064| +|mm\|commonsenseqa\|0| 0|sem |0.8057|± |0.0236| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.8822|± |0.0135| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|1.0000|± |0.0000| +|mm\|truthfulqa\|0 | 0|sem |0.6786|± |0.0513| + diff --git a/merge_llama/logs/llama_ties_7.log b/merge_llama/logs/llama_ties_7.log new file mode 100644 index 0000000000000000000000000000000000000000..09384d43f1356bfc4df7dcc1105ef3ddf0c0f272 --- /dev/null +++ b/merge_llama/logs/llama_ties_7.log @@ -0,0 +1,100 @@ +INFO 07-09 00:45:39 [__init__.py:239] Automatically detected platform cuda. +INFO 07-09 00:45:47 [config.py:717] This model supports multiple tasks: {'classify', 'generate', 'score', 'reward', 'embed'}. Defaulting to 'generate'. +INFO 07-09 00:45:47 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-09 00:45:47 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-09 00:45:49 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_ties_7', speculative_config=None, tokenizer='./merged2/llama_ties_7', 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=./merged2/llama_ties_7, 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 07-09 00:45:49 [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 07-09 00:45:49 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_a3cf4d2f'), local_subscribe_addr='ipc:///tmp/72e71256-af18-4947-8269-b7b77083388d', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:45:49 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=543606) INFO 07-09 00:45:49 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_ea367d82'), local_subscribe_addr='ipc:///tmp/deec4ca9-a303-416c-bcc7-6f9047f2ac26', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:45:49 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:49 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_65989810'), local_subscribe_addr='ipc:///tmp/5fd19ca0-a9b1-4376-99ac-bb6eeba7103c', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:45:49 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=543607) INFO 07-09 00:45:49 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_5f38be70'), local_subscribe_addr='ipc:///tmp/e89dad31-1af2-4f0f-a1f1-5a27472377ca', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:45:49 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=3 pid=543608) INFO 07-09 00:45:49 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_97b5bd5a'), local_subscribe_addr='ipc:///tmp/13a792b8-9200-4c09-b7cd-aa12eb18ebb7', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=543606) INFO 07-09 00:45:52 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:52 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=543606) INFO 07-09 00:45:52 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:52 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=543608) INFO 07-09 00:45:52 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=543607) INFO 07-09 00:45:52 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=543608) INFO 07-09 00:45:52 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=543607) INFO 07-09 00:45:52 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=543607) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=3 pid=543608) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=1 pid=543606) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=0 pid=543605) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:52 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_7e24baf1'), local_subscribe_addr='ipc:///tmp/0afd1442-b4f3-418e-b183-eb7559030c2c', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=543608) INFO 07-09 00:45:52 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=1 pid=543606) INFO 07-09 00:45:52 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=543607) INFO 07-09 00:45:52 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:52 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=3 pid=543608) INFO 07-09 00:45:52 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=543607) INFO 07-09 00:45:52 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=543608) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=2 pid=543607) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:52 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=543606) INFO 07-09 00:45:52 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=543605) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=1 pid=543606) WARNING 07-09 00:45:52 [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. +(VllmWorker rank=2 pid=543607) INFO 07-09 00:45:52 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_7... +(VllmWorker rank=3 pid=543608) INFO 07-09 00:45:52 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_7... +(VllmWorker rank=1 pid=543606) INFO 07-09 00:45:52 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_7... +(VllmWorker rank=0 pid=543605) INFO 07-09 00:45:52 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_7... +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:06 [loader.py:458] Loading weights took 13.68 seconds +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:06 [loader.py:458] Loading weights took 13.74 seconds +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:06 [loader.py:458] Loading weights took 13.68 seconds +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:06 [loader.py:458] Loading weights took 13.70 seconds +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:06 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 13.896493 seconds +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:06 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 13.965019 seconds +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:06 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 13.965931 seconds +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:06 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 13.959341 seconds +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1243c53fbf/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:13 [backends.py:430] Dynamo bytecode transform time: 6.50 s +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1243c53fbf/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:13 [backends.py:430] Dynamo bytecode transform time: 6.50 s +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1243c53fbf/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:13 [backends.py:430] Dynamo bytecode transform time: 6.54 s +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:13 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/1243c53fbf/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:13 [backends.py:430] Dynamo bytecode transform time: 6.62 s +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:16 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:17 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:38 [backends.py:148] Compiling a graph for general shape takes 24.39 s +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:38 [backends.py:148] Compiling a graph for general shape takes 24.77 s +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:39 [backends.py:148] Compiling a graph for general shape takes 24.88 s +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:39 [backends.py:148] Compiling a graph for general shape takes 25.05 s +(VllmWorker rank=2 pid=543607) INFO 07-09 00:46:52 [monitor.py:33] torch.compile takes 31.38 s in total +(VllmWorker rank=1 pid=543606) INFO 07-09 00:46:52 [monitor.py:33] torch.compile takes 31.59 s in total +(VllmWorker rank=0 pid=543605) INFO 07-09 00:46:52 [monitor.py:33] torch.compile takes 31.01 s in total +(VllmWorker rank=3 pid=543608) INFO 07-09 00:46:52 [monitor.py:33] torch.compile takes 31.28 s in total +INFO 07-09 00:46:53 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-09 00:46:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-09 00:46:53 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:46:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:46:53 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:46:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:46:53 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-09 00:46:53 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=543608) INFO 07-09 00:47:23 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=1 pid=543606) INFO 07-09 00:47:23 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=2 pid=543607) INFO 07-09 00:47:23 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=0 pid=543605) INFO 07-09 00:47:23 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +INFO 07-09 00:47:23 [core.py:159] init engine (profile, create kv cache, warmup model) took 76.72 seconds +INFO 07-09 00:47:24 [core_client.py:439] Core engine process 0 ready. +INFO 07-09 00:58:41 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-09 00:58:41 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.5137|± |0.0473| +| | |sem |0.3423|± |0.0317| +|mm\|arc_challenge\|0| 0|sem |0.4548|± |0.0278| +|mm\|arc_easy\|0 | 0|sem |0.4982|± |0.0173| +|mm\|commonsenseqa\|0| 0|sem |0.4134|± |0.0293| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.5167|± |0.0210| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.5106|± |0.0737| +|mm\|truthfulqa\|0 | 0|sem |0.3452|± |0.0522| + diff --git a/merge_llama/logs/llama_ties_9.log b/merge_llama/logs/llama_ties_9.log new file mode 100644 index 0000000000000000000000000000000000000000..c0988818450967b3cdd2f93eada2dc94a0b94a26 --- /dev/null +++ b/merge_llama/logs/llama_ties_9.log @@ -0,0 +1,100 @@ +INFO 07-09 00:58:40 [__init__.py:239] Automatically detected platform cuda. +INFO 07-09 00:58:49 [config.py:717] This model supports multiple tasks: {'score', 'generate', 'reward', 'embed', 'classify'}. Defaulting to 'generate'. +INFO 07-09 00:58:49 [config.py:1770] Defaulting to use mp for distributed inference +INFO 07-09 00:58:49 [config.py:2003] Chunked prefill is enabled with max_num_batched_tokens=16384. +INFO 07-09 00:58:50 [core.py:58] Initializing a V1 LLM engine (v0.8.5.post1) with config: model='./merged2/llama_ties_9', speculative_config=None, tokenizer='./merged2/llama_ties_9', 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=./merged2/llama_ties_9, 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 07-09 00:58:50 [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 07-09 00:58:50 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 10485760, 10, 'psm_ff185e1e'), local_subscribe_addr='ipc:///tmp/11203ebb-bc3a-42fb-955b-82a28a9ed260', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:58:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-09 00:58:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=1 pid=549669) INFO 07-09 00:58:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_aa588064'), local_subscribe_addr='ipc:///tmp/630e5164-739b-4042-9439-e10ea086226f', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_f7b24a90'), local_subscribe_addr='ipc:///tmp/83153c54-81dd-44fe-b96e-7c78e4cdb5b8', remote_subscribe_addr=None, remote_addr_ipv6=False) +WARNING 07-09 00:58:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +WARNING 07-09 00:58:51 [utils.py:2522] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in +(VllmWorker rank=2 pid=549670) INFO 07-09 00:58:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d6e4736e'), local_subscribe_addr='ipc:///tmp/4619752f-b97d-4c16-9b5b-460535d6fdaf', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=549671) INFO 07-09 00:58:51 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_999aa29e'), local_subscribe_addr='ipc:///tmp/b3b77c9d-6a6b-48d4-8159-037fb77d3bdc', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=1 pid=549669) INFO 07-09 00:58:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=1 pid=549669) INFO 07-09 00:58:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=549670) INFO 07-09 00:58:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=3 pid=549671) INFO 07-09 00:58:53 [utils.py:1055] Found nccl from library libnccl.so.2 +(VllmWorker rank=2 pid=549670) INFO 07-09 00:58:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=3 pid=549671) INFO 07-09 00:58:53 [pynccl.py:69] vLLM is using nccl==2.21.5 +(VllmWorker rank=2 pid=549670) WARNING 07-09 00:58:53 [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. +(VllmWorker rank=3 pid=549671) WARNING 07-09 00:58:53 [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. +(VllmWorker rank=0 pid=549668) WARNING 07-09 00:58:53 [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. +(VllmWorker rank=1 pid=549669) WARNING 07-09 00:58:53 [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. +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:53 [shm_broadcast.py:266] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_cd06b9c8'), local_subscribe_addr='ipc:///tmp/c00bc0aa-6e41-418a-9145-628414f1e3ca', remote_subscribe_addr=None, remote_addr_ipv6=False) +(VllmWorker rank=3 pid=549671) INFO 07-09 00:58:53 [parallel_state.py:1004] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3 +(VllmWorker rank=2 pid=549670) INFO 07-09 00:58:53 [parallel_state.py:1004] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2 +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:53 [parallel_state.py:1004] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0 +(VllmWorker rank=1 pid=549669) INFO 07-09 00:58:53 [parallel_state.py:1004] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1 +(VllmWorker rank=2 pid=549670) INFO 07-09 00:58:53 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=3 pid=549671) INFO 07-09 00:58:53 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=2 pid=549670) WARNING 07-09 00:58:53 [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. +(VllmWorker rank=3 pid=549671) WARNING 07-09 00:58:53 [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. +(VllmWorker rank=1 pid=549669) INFO 07-09 00:58:53 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=1 pid=549669) WARNING 07-09 00:58:54 [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. +(VllmWorker rank=2 pid=549670) INFO 07-09 00:58:54 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_9... +(VllmWorker rank=3 pid=549671) INFO 07-09 00:58:54 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_9... +(VllmWorker rank=1 pid=549669) INFO 07-09 00:58:54 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_9... +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:54 [cuda.py:221] Using Flash Attention backend on V1 engine. +(VllmWorker rank=0 pid=549668) WARNING 07-09 00:58:54 [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. +(VllmWorker rank=0 pid=549668) INFO 07-09 00:58:54 [gpu_model_runner.py:1329] Starting to load model ./merged2/llama_ties_9... +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:03 [loader.py:458] Loading weights took 9.59 seconds +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:03 [loader.py:458] Loading weights took 9.71 seconds +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:03 [loader.py:458] Loading weights took 9.76 seconds +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:03 [loader.py:458] Loading weights took 9.71 seconds +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:04 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 9.822053 seconds +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:04 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 9.988516 seconds +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:04 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 9.984207 seconds +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:04 [gpu_model_runner.py:1347] Model loading took 3.7711 GiB and 9.964846 seconds +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:10 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a4cc4bdb58/rank_3_0 for vLLM's torch.compile +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:10 [backends.py:430] Dynamo bytecode transform time: 6.39 s +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:10 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a4cc4bdb58/rank_2_0 for vLLM's torch.compile +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:10 [backends.py:430] Dynamo bytecode transform time: 6.48 s +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:11 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a4cc4bdb58/rank_1_0 for vLLM's torch.compile +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:11 [backends.py:430] Dynamo bytecode transform time: 6.54 s +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:11 [backends.py:420] Using cache directory: /home/jiangli/.cache/vllm/torch_compile_cache/a4cc4bdb58/rank_0_0 for vLLM's torch.compile +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:11 [backends.py:430] Dynamo bytecode transform time: 6.61 s +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:14 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:14 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:14 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:14 [backends.py:136] Cache the graph of shape None for later use +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:36 [backends.py:148] Compiling a graph for general shape takes 24.35 s +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:36 [backends.py:148] Compiling a graph for general shape takes 24.72 s +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:36 [backends.py:148] Compiling a graph for general shape takes 24.70 s +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:36 [backends.py:148] Compiling a graph for general shape takes 25.05 s +(VllmWorker rank=0 pid=549668) INFO 07-09 00:59:49 [monitor.py:33] torch.compile takes 30.97 s in total +(VllmWorker rank=1 pid=549669) INFO 07-09 00:59:49 [monitor.py:33] torch.compile takes 31.59 s in total +(VllmWorker rank=3 pid=549671) INFO 07-09 00:59:49 [monitor.py:33] torch.compile takes 31.11 s in total +(VllmWorker rank=2 pid=549670) INFO 07-09 00:59:49 [monitor.py:33] torch.compile takes 31.17 s in total +INFO 07-09 00:59:51 [kv_cache_utils.py:634] GPU KV cache size: 2,028,880 tokens +INFO 07-09 00:59:51 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.66x +INFO 07-09 00:59:51 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:59:51 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:59:51 [kv_cache_utils.py:634] GPU KV cache size: 2,028,624 tokens +INFO 07-09 00:59:51 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 990.54x +INFO 07-09 00:59:51 [kv_cache_utils.py:634] GPU KV cache size: 2,029,904 tokens +INFO 07-09 00:59:51 [kv_cache_utils.py:637] Maximum concurrency for 2,048 tokens per request: 991.16x +(VllmWorker rank=3 pid=549671) INFO 07-09 01:00:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=2 pid=549670) INFO 07-09 01:00:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=1 pid=549669) INFO 07-09 01:00:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +(VllmWorker rank=0 pid=549668) INFO 07-09 01:00:20 [gpu_model_runner.py:1686] Graph capturing finished in 30 secs, took 2.44 GiB +INFO 07-09 01:00:20 [core.py:159] init engine (profile, create kv cache, warmup model) took 76.33 seconds +INFO 07-09 01:00:21 [core_client.py:439] Core engine process 0 ready. +INFO 07-09 01:11:41 [importing.py:53] Triton module has been replaced with a placeholder. +INFO 07-09 01:11:41 [__init__.py:239] Automatically detected platform cuda. +| Task |Version| Metric |Value | |Stderr| +|------------------|------:|---------------------|-----:|---|-----:| +|all | |math_pass@1:1_samples|0.5278|± |0.0473| +| | |sem |0.4440|± |0.0321| +|mm\|arc_challenge\|0| 0|sem |0.5732|± |0.0276| +|mm\|arc_easy\|0 | 0|sem |0.6323|± |0.0167| +|mm\|commonsenseqa\|0| 0|sem |0.5618|± |0.0295| +|mm\|gpqa_diamond\|0 | 2|sem |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|0.5237|± |0.0210| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|0.5319|± |0.0736| +|mm\|truthfulqa\|0 | 0|sem |0.4524|± |0.0546| + diff --git a/merge_llama/logs/show_results.log b/merge_llama/logs/show_results.log new file mode 100644 index 0000000000000000000000000000000000000000..2b215b70ddc6dbea85c48e21e6d023dc15668359 --- /dev/null +++ b/merge_llama/logs/show_results.log @@ -0,0 +1,108 @@ +| Task |Version| Metric | Model |Value | |Stderr| +|------------------|------:|---------------------|-----------------------------|-----:|---|-----:| +|mm\|arc_challenge\|0| 0|sem |._merged2_llama_dare_linear_1|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_3|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_5|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_7|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_9|0.0000|± |0.0000| +| | | |._merged2_llama_linear_1 |0.9502|± |0.0122| +| | | |._merged2_llama_linear_3 |0.9377|± |0.0135| +| | | |._merged2_llama_linear_5 |0.9346|± |0.0138| +| | | |._merged2_llama_linear_7 |0.9065|± |0.0163| +| | | |._merged2_llama_linear_9 |0.8006|± |0.0223| +| | | |._merged2_llama_ties_1 |0.9190|± |0.0153| +| | | |._merged2_llama_ties_3 |0.9470|± |0.0125| +| | | |._merged2_llama_ties_5 |0.9346|± |0.0138| +| | | |._merged2_llama_ties_7 |0.4548|± |0.0278| +| | | |._merged2_llama_ties_9 |0.5732|± |0.0276| +|mm\|arc_easy\|0 | 0|sem |._merged2_llama_dare_linear_1|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_3|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_5|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_7|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_9|0.0000|± |0.0000| +| | | |._merged2_llama_linear_1 |0.9737|± |0.0055| +| | | |._merged2_llama_linear_3 |0.9820|± |0.0046| +| | | |._merged2_llama_linear_5 |0.9749|± |0.0054| +| | | |._merged2_llama_linear_7 |0.9653|± |0.0063| +| | | |._merged2_llama_linear_9 |0.8587|± |0.0121| +| | | |._merged2_llama_ties_1 |0.9677|± |0.0061| +| | | |._merged2_llama_ties_3 |0.9677|± |0.0061| +| | | |._merged2_llama_ties_5 |0.9641|± |0.0064| +| | | |._merged2_llama_ties_7 |0.4982|± |0.0173| +| | | |._merged2_llama_ties_9 |0.6323|± |0.0167| +|mm\|commonsenseqa\|0| 0|sem |._merged2_llama_dare_linear_1|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_3|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_5|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_7|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_9|0.0000|± |0.0000| +| | | |._merged2_llama_linear_1 |0.8799|± |0.0194| +| | | |._merged2_llama_linear_3 |0.8587|± |0.0207| +| | | |._merged2_llama_linear_5 |0.8516|± |0.0212| +| | | |._merged2_llama_linear_7 |0.7880|± |0.0243| +| | | |._merged2_llama_linear_9 |0.7244|± |0.0266| +| | | |._merged2_llama_ties_1 |0.8198|± |0.0229| +| | | |._merged2_llama_ties_3 |0.8304|± |0.0223| +| | | |._merged2_llama_ties_5 |0.8057|± |0.0236| +| | | |._merged2_llama_ties_7 |0.4134|± |0.0293| +| | | |._merged2_llama_ties_9 |0.5618|± |0.0295| +|mm\|gpqa_diamond\|0 | 2|sem |._merged2_llama_dare_linear_1|0.0000| | | +| | | |._merged2_llama_dare_linear_3|0.0000| | | +| | | |._merged2_llama_dare_linear_5|0.0000| | | +| | | |._merged2_llama_dare_linear_7|0.0000| | | +| | | |._merged2_llama_dare_linear_9|0.0000| | | +| | | |._merged2_llama_linear_1 |0.0000| | | +| | | |._merged2_llama_linear_3 |0.0000| | | +| | | |._merged2_llama_linear_5 |0.0000| | | +| | | |._merged2_llama_linear_7 |0.0000| | | +| | | |._merged2_llama_linear_9 |0.0000| | | +| | | |._merged2_llama_ties_1 |0.0000| | | +| | | |._merged2_llama_ties_3 |0.0000| | | +| | | |._merged2_llama_ties_5 |0.0000| | | +| | | |._merged2_llama_ties_7 |0.0000| | | +| | | |._merged2_llama_ties_9 |0.0000| | | +|mm\|gsm8k\|0 | 0|math_pass@1:1_samples|._merged2_llama_dare_linear_1|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_3|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_5|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_7|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_9|0.0000|± |0.0000| +| | | |._merged2_llama_linear_1 |0.8699|± |0.0141| +| | | |._merged2_llama_linear_3 |0.8594|± |0.0146| +| | | |._merged2_llama_linear_5 |0.6801|± |0.0196| +| | | |._merged2_llama_linear_7 |0.8014|± |0.0167| +| | | |._merged2_llama_linear_9 |0.6626|± |0.0198| +| | | |._merged2_llama_ties_1 |0.7996|± |0.0168| +| | | |._merged2_llama_ties_3 |0.8822|± |0.0135| +| | | |._merged2_llama_ties_5 |0.8822|± |0.0135| +| | | |._merged2_llama_ties_7 |0.5167|± |0.0210| +| | | |._merged2_llama_ties_9 |0.5237|± |0.0210| +|mm\|math_500\|0 | 3|math_pass@1:1_samples|._merged2_llama_dare_linear_1|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_3|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_5|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_7|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_9|0.0000|± |0.0000| +| | | |._merged2_llama_linear_1 |0.8723|± |0.0492| +| | | |._merged2_llama_linear_3 |0.8936|± |0.0455| +| | | |._merged2_llama_linear_5 |0.7660|± |0.0624| +| | | |._merged2_llama_linear_7 |0.9362|± |0.0360| +| | | |._merged2_llama_linear_9 |0.5957|± |0.0724| +| | | |._merged2_llama_ties_1 |0.7021|± |0.0674| +| | | |._merged2_llama_ties_3 |0.8511|± |0.0525| +| | | |._merged2_llama_ties_5 |1.0000|± |0.0000| +| | | |._merged2_llama_ties_7 |0.5106|± |0.0737| +| | | |._merged2_llama_ties_9 |0.5319|± |0.0736| +|mm\|truthfulqa\|0 | 0|sem |._merged2_llama_dare_linear_1|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_3|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_5|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_7|0.0000|± |0.0000| +| | | |._merged2_llama_dare_linear_9|0.0000|± |0.0000| +| | | |._merged2_llama_linear_1 |0.7381|± |0.0483| +| | | |._merged2_llama_linear_3 |0.7619|± |0.0468| +| | | |._merged2_llama_linear_5 |0.7500|± |0.0475| +| | | |._merged2_llama_linear_7 |0.7024|± |0.0502| +| | | |._merged2_llama_linear_9 |0.6310|± |0.0530| +| | | |._merged2_llama_ties_1 |0.7143|± |0.0496| +| | | |._merged2_llama_ties_3 |0.7262|± |0.0489| +| | | |._merged2_llama_ties_5 |0.6786|± |0.0513| +| | | |._merged2_llama_ties_7 |0.3452|± |0.0522| +| | | |._merged2_llama_ties_9 |0.4524|± |0.0546| + diff --git 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