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WARNING: CPU IP/backtrace sampling not supported, disabling.
Try the 'nsys status --environment' command to learn more.
WARNING: CPU context switch tracing not supported, disabling.
Try the 'nsys status --environment' command to learn more.
INFO 08-13 19:21:37 [__init__.py:235] Automatically detected platform cuda.
CUDA_VISIBLE_DEVICES = 3
--- vLLM V1 基准测试（含 NVTX 标记）---
模型: Qwen/Qwen2-1.5B
批量大小: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
场景: ['prefill1_decode512']
------------------------------------------------------------
加载分词器/模型中...
INFO 08-13 19:21:46 [config.py:1604] Using max model len 4096
INFO 08-13 19:21:47 [config.py:2434] Chunked prefill is enabled with max_num_batched_tokens=8192.
INFO 08-13 19:21:52 [__init__.py:235] Automatically detected platform cuda.
INFO 08-13 19:21:54 [core.py:572] Waiting for init message from front-end.
INFO 08-13 19:21:54 [core.py:71] Initializing a V1 LLM engine (v0.10.0) with config: model='Qwen/Qwen2-1.5B', speculative_config=None, tokenizer='Qwen/Qwen2-1.5B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=Qwen/Qwen2-1.5B, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"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],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"max_capture_size":512,"local_cache_dir":null}
INFO 08-13 19:21:56 [parallel_state.py:1102] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0
WARNING 08-13 19:21:56 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.
INFO 08-13 19:21:56 [gpu_model_runner.py:1843] Starting to load model Qwen/Qwen2-1.5B...
INFO 08-13 19:21:56 [gpu_model_runner.py:1875] Loading model from scratch...
INFO 08-13 19:21:56 [cuda.py:290] Using Flash Attention backend on V1 engine.
INFO 08-13 19:21:57 [weight_utils.py:296] Using model weights format ['*.safetensors']
INFO 08-13 19:21:57 [weight_utils.py:349] No model.safetensors.index.json found in remote.
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 1.81it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 1.81it/s]
INFO 08-13 19:21:58 [default_loader.py:262] Loading weights took 0.63 seconds
INFO 08-13 19:21:58 [gpu_model_runner.py:1892] Model loading took 2.9105 GiB and 1.878581 seconds
INFO 08-13 19:22:04 [backends.py:530] Using cache directory: /home/cy/.cache/vllm/torch_compile_cache/40b61c71e9/rank_0_0/backbone for vLLM's torch.compile
INFO 08-13 19:22:04 [backends.py:541] Dynamo bytecode transform time: 5.72 s
INFO 08-13 19:22:09 [backends.py:161] Directly load the compiled graph(s) for dynamic shape from the cache, took 4.036 s
INFO 08-13 19:22:10 [monitor.py:34] torch.compile takes 5.72 s in total
INFO 08-13 19:22:11 [gpu_worker.py:255] Available KV cache memory: 12.81 GiB
INFO 08-13 19:22:11 [kv_cache_utils.py:833] GPU KV cache size: 479,536 tokens
INFO 08-13 19:22:11 [kv_cache_utils.py:837] Maximum concurrency for 4,096 tokens per request: 117.07x
Capturing CUDA graph shapes: 0%| | 0/67 [00:00<?, ?it/s]
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Capturing CUDA graph shapes: 78%|███████▊ | 52/67 [00:01<00:00, 33.79it/s]
Capturing CUDA graph shapes: 84%|████████▎ | 56/67 [00:01<00:00, 33.45it/s]
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Capturing CUDA graph shapes: 96%|█████████▌| 64/67 [00:01<00:00, 32.97it/s]
Capturing CUDA graph shapes: 100%|██████████| 67/67 [00:01<00:00, 33.62it/s]
INFO 08-13 19:22:13 [gpu_model_runner.py:2485] Graph capturing finished in 2 secs, took 0.49 GiB
INFO 08-13 19:22:13 [core.py:193] init engine (profile, create kv cache, warmup model) took 14.62 seconds
模型加载完成。
===== 场景：prefill1_decode512 | prefill=1, decode=512 =====
--- 批量大小 bs=1 ---
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Processed prompts: 100%|██████████| 1/1 [00:03<00:00, 3.31s/it, est. speed input: 0.30 toks/s, output: 154.57 toks/s]
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执行时间: 3.3194 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 512
吞吐(生成tokens/秒): 154.24
TTFT (V1 metrics): 0.0190 s
解码吞吐 (V1 metrics): 155.07 tok/s
--- 批量大小 bs=2 ---
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Processed prompts: 50%|█████ | 1/2 [00:03<00:03, 3.64s/it, est. speed input: 0.27 toks/s, output: 140.74 toks/s]
Processed prompts: 100%|██████████| 2/2 [00:03<00:00, 3.64s/it, est. speed input: 0.55 toks/s, output: 280.95 toks/s]
Processed prompts: 100%|██████████| 2/2 [00:03<00:00, 1.82s/it, est. speed input: 0.55 toks/s, output: 280.95 toks/s]
执行时间: 3.6484 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 1024
吞吐(生成tokens/秒): 280.67
TTFT (V1 metrics): 0.0120 s
解码吞吐 (V1 metrics): 140.73 tok/s
--- 批量大小 bs=4 ---
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Processed prompts: 25%|██▌ | 1/4 [00:03<00:10, 3.62s/it, est. speed input: 0.28 toks/s, output: 141.26 toks/s]
Processed prompts: 100%|██████████| 4/4 [00:03<00:00, 3.62s/it, est. speed input: 1.10 toks/s, output: 563.84 toks/s]
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执行时间: 3.6361 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 2048
吞吐(生成tokens/秒): 563.24
TTFT (V1 metrics): 0.0140 s
解码吞吐 (V1 metrics): 141.25 tok/s
--- 批量大小 bs=8 ---
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Processed prompts: 12%|█▎ | 1/8 [00:03<00:25, 3.71s/it, est. speed input: 0.27 toks/s, output: 137.92 toks/s]
Processed prompts: 100%|██████████| 8/8 [00:03<00:00, 3.71s/it, est. speed input: 2.15 toks/s, output: 1101.10 toks/s]
Processed prompts: 100%|██████████| 8/8 [00:03<00:00, 2.15it/s, est. speed input: 2.15 toks/s, output: 1101.10 toks/s]
执行时间: 3.7267 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 4096
吞吐(生成tokens/秒): 1099.08
TTFT (V1 metrics): 0.0149 s
解码吞吐 (V1 metrics): 137.87 tok/s
--- 批量大小 bs=16 ---
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Adding requests: 100%|██████████| 16/16 [00:00<00:00, 1911.61it/s]
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Processed prompts: 6%|▋ | 1/16 [00:03<00:57, 3.81s/it, est. speed input: 0.26 toks/s, output: 134.53 toks/s]
Processed prompts: 100%|██████████| 16/16 [00:03<00:00, 3.81s/it, est. speed input: 4.19 toks/s, output: 2147.16 toks/s]
Processed prompts: 100%|██████████| 16/16 [00:03<00:00, 4.19it/s, est. speed input: 4.19 toks/s, output: 2147.16 toks/s]
执行时间: 3.8260 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 8192
吞吐(生成tokens/秒): 2141.13
TTFT (V1 metrics): 0.0136 s
解码吞吐 (V1 metrics): 134.33 tok/s
--- 批量大小 bs=32 ---
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Processed prompts: 3%|▎ | 1/32 [00:03<02:02, 3.96s/it, est. speed input: 0.25 toks/s, output: 129.29 toks/s]
Processed prompts: 100%|██████████| 32/32 [00:03<00:00, 3.96s/it, est. speed input: 8.04 toks/s, output: 4118.30 toks/s]
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执行时间: 3.9972 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 16384
吞吐(生成tokens/秒): 4098.85
TTFT (V1 metrics): 0.0164 s
解码吞吐 (V1 metrics): 128.92 tok/s
--- 批量大小 bs=64 ---
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Processed prompts: 100%|██████████| 64/64 [00:04<00:00, 4.22s/it, est. speed input: 15.07 toks/s, output: 7714.52 toks/s]
Processed prompts: 100%|██████████| 64/64 [00:04<00:00, 15.07it/s, est. speed input: 15.07 toks/s, output: 7714.52 toks/s]
执行时间: 4.2731 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 32768
吞吐(生成tokens/秒): 7668.51
TTFT (V1 metrics): 0.0198 s
解码吞吐 (V1 metrics): 120.83 tok/s
--- 批量大小 bs=128 ---
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Adding requests: 100%|██████████| 128/128 [00:00<00:00, 2211.91it/s]
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Processed prompts: 1%| | 1/128 [00:04<09:58, 4.71s/it, est. speed input: 0.21 toks/s, output: 108.61 toks/s]
Processed prompts: 100%|██████████| 128/128 [00:04<00:00, 4.71s/it, est. speed input: 26.77 toks/s, output: 13706.03 toks/s]
Processed prompts: 100%|██████████| 128/128 [00:04<00:00, 26.77it/s, est. speed input: 26.77 toks/s, output: 13706.03 toks/s]
执行时间: 4.8421 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 65536
吞吐(生成tokens/秒): 13534.75
TTFT (V1 metrics): 0.0316 s
解码吞吐 (V1 metrics): 107.35 tok/s
--- 批量大小 bs=256 ---
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Adding requests: 100%|██████████| 256/256 [00:00<00:00, 3033.50it/s]
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Processed prompts: 0%| | 1/256 [00:07<30:48, 7.25s/it, est. speed input: 0.14 toks/s, output: 70.63 toks/s]
Processed prompts: 91%|█████████ | 232/256 [00:07<00:00, 44.78it/s, est. speed input: 31.57 toks/s, output: 16162.98 toks/s]
Processed prompts: 100%|██████████| 256/256 [00:07<00:00, 44.78it/s, est. speed input: 34.82 toks/s, output: 17826.08 toks/s]
Processed prompts: 100%|██████████| 256/256 [00:07<00:00, 34.81it/s, est. speed input: 34.82 toks/s, output: 17826.08 toks/s]
执行时间: 7.4408 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 131072
吞吐(生成tokens/秒): 17615.41
TTFT (V1 metrics): 0.0433 s
解码吞吐 (V1 metrics): 69.70 tok/s
--- 批量大小 bs=512 ---
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Adding requests: 28%|██▊ | 142/512 [00:00<00:00, 419.62it/s]
Adding requests: 77%|███████▋ | 394/512 [00:00<00:00, 1042.72it/s]
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Processed prompts: 30%|███ | 156/512 [00:12<00:17, 20.80it/s, est. speed input: 12.91 toks/s, output: 6610.35 toks/s]
Processed prompts: 63%|██████▎ | 321/512 [00:12<00:03, 56.88it/s, est. speed input: 26.35 toks/s, output: 13489.37 toks/s]
Processed prompts: 100%|██████████| 512/512 [00:12<00:00, 56.88it/s, est. speed input: 41.94 toks/s, output: 21474.03 toks/s]
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执行时间: 12.6794 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 262144
吞吐(生成tokens/秒): 20674.72
TTFT (V1 metrics): 0.1809 s
解码吞吐 (V1 metrics): 42.38 tok/s
--- 批量大小 bs=1024 ---
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Processed prompts: 14%|█▍ | 141/1024 [00:24<00:59, 14.82it/s, est. speed input: 5.75 toks/s, output: 2946.22 toks/s]
Processed prompts: 41%|████ | 416/1024 [00:24<00:10, 59.55it/s, est. speed input: 16.91 toks/s, output: 8657.05 toks/s]
Processed prompts: 93%|█████████▎| 951/1024 [00:24<00:00, 178.49it/s, est. speed input: 38.43 toks/s, output: 19674.52 toks/s]
Processed prompts: 100%|██████████| 1024/1024 [00:25<00:00, 178.49it/s, est. speed input: 40.84 toks/s, output: 20911.92 toks/s]
Processed prompts: 100%|██████████| 1024/1024 [00:25<00:00, 40.84it/s, est. speed input: 40.84 toks/s, output: 20911.92 toks/s]
[rank0]:[W813 19:23:32.135663883 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
执行时间: 25.2865 s
实际平均输入 tokens: 1.00（目标 1）
生成总 tokens: 524288
吞吐(生成tokens/秒): 20733.89
TTFT (V1 metrics): 0.1191 s
解码吞吐 (V1 metrics): 20.77 tok/s
完成。提示：在 Nsight Systems 中可通过 NVTX 区间快速定位各场景/批量的调用。
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[3/8] Executing 'nvtx_sum' stats report
Time (%) Total Time (ns) Instances Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Style Range
-------- --------------- --------- ---------------- ---------------- -------------- -------------- ----------- ------- --------------------------------------
31.5 35,202,819,763 1 35,202,819,763.0 35,202,819,763.0 35,202,819,763 35,202,819,763 0.0 PushPop :LLM_init
22.6 25,285,906,551 1 25,285,906,551.0 25,285,906,551.0 25,285,906,551 25,285,906,551 0.0 PushPop :generate [prefill1_decode512] bs=1024
11.3 12,679,311,252 1 12,679,311,252.0 12,679,311,252.0 12,679,311,252 12,679,311,252 0.0 PushPop :generate [prefill1_decode512] bs=512
6.7 7,440,608,085 1 7,440,608,085.0 7,440,608,085.0 7,440,608,085 7,440,608,085 0.0 PushPop :generate [prefill1_decode512] bs=256
4.3 4,841,914,697 1 4,841,914,697.0 4,841,914,697.0 4,841,914,697 4,841,914,697 0.0 PushPop :generate [prefill1_decode512] bs=128
3.8 4,272,889,441 1 4,272,889,441.0 4,272,889,441.0 4,272,889,441 4,272,889,441 0.0 PushPop :generate [prefill1_decode512] bs=64
3.6 3,997,075,015 1 3,997,075,015.0 3,997,075,015.0 3,997,075,015 3,997,075,015 0.0 PushPop :generate [prefill1_decode512] bs=32
3.4 3,825,710,172 1 3,825,710,172.0 3,825,710,172.0 3,825,710,172 3,825,710,172 0.0 PushPop :generate [prefill1_decode512] bs=16
3.3 3,726,603,655 1 3,726,603,655.0 3,726,603,655.0 3,726,603,655 3,726,603,655 0.0 PushPop :generate [prefill1_decode512] bs=8
3.3 3,648,294,896 1 3,648,294,896.0 3,648,294,896.0 3,648,294,896 3,648,294,896 0.0 PushPop :generate [prefill1_decode512] bs=2
3.2 3,635,960,724 1 3,635,960,724.0 3,635,960,724.0 3,635,960,724 3,635,960,724 0.0 PushPop :generate [prefill1_decode512] bs=4
3.0 3,319,210,677 1 3,319,210,677.0 3,319,210,677.0 3,319,210,677 3,319,210,677 0.0 PushPop :generate [prefill1_decode512] bs=1
0.0 90,630 2 45,315.0 45,315.0 41,468 49,162 5,440.5 PushPop CCCL:cub::DeviceSegmentedRadixSort
[4/8] Executing 'osrt_sum' stats report
Time (%) Total Time (ns) Num Calls Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Name
-------- ----------------- --------- --------------- ---------------- --------- -------------- ---------------- ----------------------
29.7 1,284,023,267,442 49,709 25,830,800.6 28,820.0 1,000 96,534,138,823 969,651,617.8 pthread_cond_timedwait
24.2 1,045,773,118,476 73,135 14,299,215.4 10,062,843.0 1,010 81,708,518,315 463,992,514.8 epoll_wait
23.9 1,031,963,983,489 549 1,879,715,816.9 15,827.0 1,644 96,535,575,076 13,212,126,179.1 pthread_cond_wait
8.3 357,289,068,553 57 6,268,229,272.9 10,000,073,611.0 10,419 10,000,146,360 4,730,020,985.8 sem_timedwait
8.2 355,562,427,280 39,343 9,037,501.6 1,512.0 1,000 12,219,368,064 127,255,975.8 poll
3.0 131,715,516,186 11,467 11,486,484.4 7,170,376.0 28,456 585,263,318 14,022,810.8 sem_wait
2.6 112,179,081,690 41,286 2,717,121.6 2,213.0 1,000 94,667,308,194 468,888,772.5 read
0.0 793,423,700 330 2,404,314.2 1,354,549.5 1,900 18,406,079 2,639,127.9 pthread_rwlock_wrlock
0.0 494,035,258 199,029 2,482.2 1,380.0 1,000 72,123,224 161,811.1 munmap
0.0 298,099,876 8,608 34,630.6 10,089.5 1,002 29,694,619 390,579.6 ioctl
0.0 220,903,685 369 598,655.0 2,510.0 1,159 22,536,588 3,264,985.3 fopen
0.0 121,576,605 24 5,065,691.9 5,064,718.5 5,053,737 5,087,479 7,589.0 nanosleep
0.0 110,429,690 30,645 3,603.5 2,536.0 1,000 19,587,041 111,890.3 open64
0.0 88,325,670 79 1,118,046.5 3,103.0 1,011 81,521,546 9,166,201.0 waitpid
0.0 76,471,088 18,154 4,212.4 3,660.0 1,000 1,659,563 15,417.3 mmap64
0.0 74,586,704 96 776,944.8 3,874.0 1,020 19,635,272 3,727,062.1 open
0.0 72,521,171 8,897 8,151.2 4,707.0 1,022 2,826,985 34,660.7 recv
0.0 71,841,066 8,895 8,076.6 5,429.0 1,571 84,986 7,239.5 send
0.0 69,801,955 41,067 1,699.7 1,627.0 1,000 32,060 794.7 pthread_cond_signal
0.0 67,085,379 39 1,720,137.9 470,979.0 3,544 10,373,042 3,340,509.2 pthread_join
0.0 56,617,564 10 5,661,756.4 18,705.5 8,315 56,388,994 17,823,747.2 connect
0.0 51,207,160 14,809 3,457.8 2,380.0 1,013 139,725 5,733.7 write
0.0 40,211,592 4,773 8,424.8 6,319.0 1,000 661,710 13,119.1 pthread_mutex_lock
0.0 16,225,859 10,123 1,602.9 1,387.0 1,000 17,416 730.6 epoll_ctl
0.0 9,852,733 147 67,025.4 68,737.0 55,805 95,256 5,155.2 sleep
0.0 7,858,705 22 357,213.9 474,706.5 8,796 678,261 278,233.7 pthread_rwlock_rdlock
0.0 7,721,440 131 58,942.3 56,260.0 21,296 195,560 26,034.2 pthread_create
0.0 7,224,126 929 7,776.2 3,096.0 1,000 86,864 11,142.9 fgets
0.0 1,723,609 344 5,010.5 4,755.0 1,827 40,649 2,516.1 fopen64
0.0 1,708,972 62 27,564.1 2,983.5 1,002 230,421 59,773.0 futex
0.0 1,347,355 1,069 1,260.4 1,023.0 1,000 12,904 880.3 fclose
0.0 1,149,466 196 5,864.6 3,579.5 1,105 168,420 13,582.2 mmap
0.0 878,967 1 878,967.0 878,967.0 878,967 878,967 0.0 fork
0.0 364,215 65 5,603.3 5,028.0 1,909 15,104 3,123.9 pipe2
0.0 247,833 41 6,044.7 4,941.0 1,709 17,457 4,172.6 socket
0.0 188,362 19 9,913.8 3,097.0 1,045 62,742 16,639.4 bind
0.0 128,433 34 3,777.4 3,261.0 1,187 14,840 2,461.4 pthread_cond_broadcast
0.0 76,747 7 10,963.9 9,959.0 3,576 31,262 9,493.1 fread
0.0 65,399 41 1,595.1 1,200.0 1,012 5,988 1,063.6 fcntl
0.0 49,079 5 9,815.8 9,542.0 4,750 17,158 4,761.0 accept4
0.0 42,725 25 1,709.0 1,806.0 1,011 2,296 397.2 sigaction
0.0 40,441 20 2,022.1 2,166.5 1,063 3,618 818.8 dup2
0.0 39,878 15 2,658.5 2,065.0 1,267 7,040 1,459.6 stat
0.0 31,245 12 2,603.8 1,918.0 1,006 5,220 1,771.4 fflush
0.0 27,179 5 5,435.8 5,277.0 1,662 9,374 3,035.3 fwrite
0.0 21,540 4 5,385.0 5,545.5 4,572 5,877 575.6 lstat
0.0 17,255 4 4,313.8 4,516.5 2,856 5,366 1,051.1 flock
0.0 16,827 9 1,869.7 1,599.0 1,008 3,313 844.3 pread
0.0 15,569 10 1,556.9 1,444.0 1,184 2,260 325.7 listen
0.0 13,074 3 4,358.0 4,294.0 4,285 4,495 118.7 fputs_unlocked
0.0 12,439 5 2,487.8 2,713.0 1,831 3,023 566.6 mprotect
0.0 7,489 4 1,872.3 1,856.5 1,636 2,140 206.8 flockfile
0.0 6,919 1 6,919.0 6,919.0 6,919 6,919 0.0 kill
0.0 5,460 2 2,730.0 2,730.0 2,008 3,452 1,021.1 openat64
0.0 5,297 3 1,765.7 1,842.0 1,157 2,298 574.3 fstat
0.0 3,627 1 3,627.0 3,627.0 3,627 3,627 0.0 fputs
[5/8] Executing 'cuda_api_sum' stats report
Time (%) Total Time (ns) Num Calls Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Name
-------- --------------- --------- ----------- ----------- -------- ----------- ----------- ------------------------------------------
65.2 20,318,348,211 12,196 1,665,984.6 3,806.0 1,713 143,905,820 4,877,424.6 cudaStreamSynchronize
19.6 6,114,479,110 979,090 6,245.1 4,893.0 826 61,317,707 103,504.7 cudaLaunchKernel
5.9 1,844,232,020 151,960 12,136.3 9,991.0 7,437 6,397,280 55,443.5 cudaGraphLaunch_v10000
4.4 1,366,270,456 61,593 22,182.2 8,605.0 2,898 97,438,741 427,651.1 cudaMemcpyAsync
2.1 657,913,070 123,014 5,348.3 4,791.0 646 11,218,430 74,172.4 cuLaunchKernel
0.7 225,191,175 1,943 115,898.7 75,223.0 40,921 1,507,774 191,247.4 cudaGraphInstantiateWithFlags_v11040
0.6 190,028,321 2,135 89,006.2 32,930.0 5,778 121,430,749 2,627,383.4 cudaDeviceSynchronize
0.4 131,733,072 24,728 5,327.3 5,346.0 183 7,263,692 48,804.8 cudaMemsetAsync
0.4 117,166,497 154,261 759.5 737.0 297 9,955 164.6 cudaStreamIsCapturing_v10000
0.2 54,817,946 222 246,927.7 125,544.5 64,964 2,389,846 359,442.8 cudaFree
0.1 41,574,143 348 119,465.9 111,957.5 6,496 1,314,648 70,124.0 cudaMalloc
0.1 25,470,407 10 2,547,040.7 2,568,202.0 60,182 4,674,895 1,473,736.1 cuLibraryLoadData
0.0 14,126,639 13,502 1,046.3 512.0 267 4,070,645 36,594.4 cuKernelGetFunction
0.0 11,511,739 169 68,116.8 73,800.0 26,538 398,968 40,288.3 cuModuleLoadData
0.0 9,477,345 18,895 501.6 477.0 305 7,151 120.4 cudaStreamGetCaptureInfo_v2_v11030
0.0 8,547,159 1,943 4,398.9 4,349.0 3,274 12,306 644.1 cudaStreamBeginCapture_v10000
0.0 7,583,507 1,943 3,903.0 3,886.0 2,371 10,115 530.0 cudaGraphDestroy_v10000
0.0 2,953,354 128 23,073.1 2,127.0 1,339 976,651 118,496.6 cudaStreamCreateWithPriority
0.0 2,583,759 1,943 1,329.8 1,322.0 973 2,362 129.3 cudaStreamEndCapture_v10000
0.0 1,910,887 26 73,495.7 12,773.5 3,625 1,207,162 232,915.8 cudaHostAlloc
0.0 1,625,828 1,943 836.8 771.0 625 3,016 254.6 cudaGraphGetNodes_v10000
0.0 943,862 310 3,044.7 2,639.0 879 11,991 1,944.4 cudaEventQuery
0.0 731,374 311 2,351.7 2,439.0 991 7,657 1,133.3 cudaEventRecord
0.0 219,541 8 27,442.6 26,305.5 8,804 64,233 18,995.2 cudaMemGetInfo
0.0 140,500 810 173.5 143.0 85 1,704 110.6 cuGetProcAddress_v2
0.0 21,914 21 1,043.5 438.0 339 4,729 1,202.9 cudaEventCreateWithFlags
0.0 16,258 16 1,016.1 849.5 502 2,663 551.3 cuLibraryGetKernel
0.0 8,991 14 642.2 586.0 346 1,420 261.0 cudaThreadExchangeStreamCaptureMode_v10010
0.0 4,849 3 1,616.3 1,664.0 1,386 1,799 210.6 cuInit
0.0 3,460 4 865.0 749.0 110 1,852 882.7 cuModuleGetLoadingMode
0.0 3,416 1 3,416.0 3,416.0 3,416 3,416 0.0 cudaStreamWaitEvent
0.0 1,901 1 1,901.0 1,901.0 1,901 1,901 0.0 cudaEventDestroy
0.0 1,166 2 583.0 583.0 248 918 473.8 cudaGetDriverEntryPoint_v11030
[6/8] Executing 'cuda_gpu_kern_sum' stats report
Time (%) Total Time (ns) Instances Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Name
-------- --------------- --------- ----------- ----------- --------- --------- ----------- ----------------------------------------------------------------------------------------------------
33.5 9,507,682,829 84,588 112,399.9 58,880.0 5,728 569,477 137,306.6 void flash::flash_fwd_splitkv_kernel<Flash_fwd_kernel_traits<(int)128, (int)64, (int)128, (int)4, (…
27.2 7,733,720,607 29,164 265,180.4 333,123.0 33,344 763,622 112,810.5 ampere_bf16_s1688gemm_bf16_64x128_sliced1x2_ldg8_f2f_tn
7.4 2,089,759,636 1,164 1,795,326.1 1,390,859.0 40,065 4,518,698 1,024,759.1 ampere_bf16_s1688gemm_bf16_128x128_ldg8_f2f_stages_32x1_tn
3.3 942,739,585 5,754 163,840.7 13,376.0 1,951 1,008,234 293,922.8 void at::native::unrolled_elementwise_kernel<at::native::direct_copy_kernel_cuda(at::TensorIterator…
3.3 942,069,183 76,664 12,288.3 8,032.0 6,240 73,248 8,482.6 void flash::flash_fwd_splitkv_kernel<Flash_fwd_kernel_traits<(int)128, (int)64, (int)128, (int)4, (…
3.3 926,915,977 5,958 155,575.0 73,120.5 7,649 549,540 194,687.6 void cutlass::Kernel2<cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_32x6_tn_align8>(T1::Param…
2.8 781,701,830 1,991 392,617.7 496,547.0 10,528 506,724 194,713.8 void cutlass::Kernel2<cutlass_80_wmma_tensorop_bf16_s161616gemm_bf16_16x16_128x2_tn_align8>(T1::Par…
2.5 718,309,736 5,756 124,793.2 9,920.0 5,151 716,420 213,447.3 void at::native::reduce_kernel<(int)512, (int)1, at::native::ReduceOp<float, at::native::ArgMaxOps<…
2.4 679,170,209 292,768 2,319.8 1,889.0 1,631 6,304 962.1 void at::native::elementwise_kernel<(int)128, (int)4, void at::native::gpu_kernel_impl_nocast<at::n…
2.1 605,586,081 14,252 42,491.3 42,529.0 26,240 62,817 1,642.0 ampere_bf16_s1688gemm_bf16_128x64_sliced1x2_ldg8_relu_f2f_tn
1.8 516,743,427 13,776 37,510.4 37,472.0 36,608 42,560 332.8 ampere_bf16_s1688gemm_bf16_64x64_sliced1x4_ldg8_f2f_tn
1.3 366,625,152 16,268 22,536.6 23,936.0 1,055 462,659 19,521.4 triton_poi_fused_mul_silu_1
1.2 345,171,402 112 3,081,887.5 3,078,316.5 3,036,028 3,128,477 29,283.9 void flash::flash_fwd_splitkv_kernel<Flash_fwd_kernel_traits<(int)128, (int)64, (int)128, (int)4, (…
0.9 260,532,523 513 507,860.7 507,843.0 506,403 509,475 427.2 void cutlass::Kernel2<cutlass_80_wmma_tensorop_bf16_s161616gemm_bf16_16x16_128x1_tn_align8>(T1::Par…
0.9 255,622,286 604 423,215.7 487,970.0 7,008 488,866 160,519.3 std::enable_if<!T7, void>::type internal::gemvx::kernel<int, int, __nv_bfloat16, __nv_bfloat16, __n…
0.9 242,493,430 161,056 1,505.6 1,280.0 1,023 3,488 458.1 void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0…
0.7 206,425,050 146,384 1,410.2 1,344.0 1,183 2,208 221.6 void at::native::elementwise_kernel<(int)128, (int)2, void at::native::gpu_kernel_impl_nocast<at::n…
0.7 203,499,133 184 1,105,973.5 579,541.0 369,795 2,808,909 981,858.0 ampere_bf16_s16816gemm_bf16_128x64_ldg8_f2f_tn
0.7 187,786,954 1,120 167,666.9 158,929.0 40,416 1,415,463 206,084.0 ampere_bf16_s1688gemm_bf16_128x64_sliced1x2_ldg8_f2f_tn
0.6 180,432,004 16,268 11,091.2 11,936.0 1,505 111,617 4,905.8 triton_red_fused__to_copy_add_mean_mul_pow_rsqrt_2
0.5 135,678,243 43,792 3,098.2 3,104.0 2,943 3,616 73.3 void flash::flash_fwd_splitkv_combine_kernel<Flash_fwd_kernel_traits<(int)128, (int)64, (int)128, (…
0.3 98,055,325 32,872 2,982.9 2,945.0 2,847 3,233 98.3 void flash::flash_fwd_splitkv_combine_kernel<Flash_fwd_kernel_traits<(int)128, (int)64, (int)128, (…
0.3 83,656,872 16,268 5,142.4 5,472.0 1,536 79,136 3,233.8 triton_red_fused__to_copy_add_mean_mul_pow_rsqrt_0
0.2 52,052,585 15,687 3,318.2 3,457.0 1,344 22,048 905.0 triton_poi_fused_cat_3
0.1 39,252,777 8 4,906,597.1 4,863,769.0 4,802,745 5,085,370 117,043.6 void at_cuda_detail::cub::DeviceSegmentedRadixSortKernel<at_cuda_detail::cub::DeviceRadixSortPolicy…
0.1 35,101,287 15,687 2,237.6 2,336.0 863 16,672 687.2 triton_poi_fused_view_5
0.1 26,987,031 17,256 1,563.9 1,408.0 1,023 2,784 465.1 void at::native::unrolled_elementwise_kernel<at::native::direct_copy_kernel_cuda(at::TensorIterator…
0.1 22,634,043 784 28,870.0 12,543.5 11,616 62,720 20,595.1 ampere_bf16_s16816gemm_bf16_64x64_ldg8_f2f_stages_64x5_tn
0.1 22,584,985 15,687 1,439.7 1,440.0 1,215 6,720 194.4 triton_poi_fused_cat_4
0.1 20,606,081 5,888 3,499.7 3,136.0 2,687 7,488 970.6 void at::native::index_elementwise_kernel<(int)128, (int)4, void at::native::gpu_index_kernel<void …
0.1 20,451,721 4 5,112,930.3 5,112,153.5 4,937,754 5,289,660 201,308.2 void at_cuda_detail::cub::DeviceSegmentedRadixSortKernel<at_cuda_detail::cub::DeviceRadixSortPolicy…
0.1 15,066,944 5,752 2,619.4 2,368.0 1,952 3,969 554.4 void at::native::index_elementwise_kernel<(int)128, (int)4, void at::native::gpu_index_kernel<void …
0.1 14,293,381 28 510,477.9 512,002.5 468,706 513,474 8,208.5 void at::native::vectorized_elementwise_kernel<(int)4, at::native::FillFunctor<signed char>, std::a…
0.0 9,733,874 4 2,433,468.5 2,435,244.5 2,367,692 2,495,693 60,628.3 void at::native::<unnamed>::cunn_SoftMaxForward<(int)4, float, float, float, at::native::<unnamed>:…
0.0 9,136,049 28 326,287.5 326,210.0 324,514 329,538 982.3 ampere_bf16_s1688gemm_bf16_128x128_ldg8_relu_f2f_stages_32x1_tn
0.0 8,425,678 224 37,614.6 37,568.5 36,608 38,880 409.5 void cutlass::Kernel2<cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x128_32x6_tn_align8>(T1::Para…
0.0 7,777,256 2 3,888,628.0 3,888,628.0 3,705,715 4,071,541 258,678.0 void at::native::_scatter_gather_elementwise_kernel<(int)128, (int)8, void at::native::_cuda_scatte…
0.0 7,758,429 8,970 864.9 864.0 767 1,280 77.7 void at::native::vectorized_elementwise_kernel<(int)2, at::native::FillFunctor<long>, std::array<ch…
0.0 7,519,688 5,754 1,306.9 1,152.0 1,023 2,048 279.6 void at::native::unrolled_elementwise_kernel<at::native::direct_copy_kernel_cuda(at::TensorIterator…
0.0 7,231,396 336 21,522.0 21,440.0 21,056 22,592 362.7 ampere_bf16_s16816gemm_bf16_128x64_ldg8_relu_f2f_stages_64x3_tn
0.0 6,130,365 476 12,878.9 12,800.5 11,744 14,432 645.4 ampere_bf16_s16816gemm_bf16_64x64_ldg8_relu_f2f_stages_64x5_tn
0.0 5,896,061 4 1,474,015.3 1,473,463.0 1,473,191 1,475,944 1,292.3 void at::native::vectorized_elementwise_kernel<(int)4, at::native::<unnamed>::masked_fill_kernel(at…
0.0 5,380,367 5,752 935.4 896.0 863 1,344 76.3 void at::native::unrolled_elementwise_kernel<at::native::CUDAFunctorOnSelf_add<int>, std::array<cha…
0.0 4,603,692 5,292 869.9 864.0 767 1,185 33.2 void at::native::unrolled_elementwise_kernel<at::native::FillFunctor<int>, std::array<char *, (unsi…
0.0 3,996,949 2 1,998,474.5 1,998,474.5 1,996,490 2,000,459 2,806.5 void at::native::vectorized_elementwise_kernel<(int)4, at::native::BinaryFunctor<float, float, floa…
0.0 3,842,156 4,143 927.4 896.0 800 1,856 93.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::FillFunctor<int>, std::array<cha…
0.0 3,593,747 56 64,174.1 64,144.5 63,105 65,728 478.5 void cutlass::Kernel2<cutlass_80_wmma_tensorop_bf16_s161616gemm_bf16_32x32_64x1_tn_align8>(T1::Para…
0.0 3,433,971 4 858,492.8 858,901.0 855,877 860,292 2,168.9 void at::native::elementwise_kernel<(int)128, (int)4, void at::native::gpu_kernel_impl_nocast<at::n…
0.0 3,191,215 2 1,595,607.5 1,595,607.5 1,560,359 1,630,856 49,848.9 void at::native::tensor_kernel_scan_innermost_dim<float, std::plus<float>>(T1 *, const T1 *, unsign…
0.0 2,871,002 1,512 1,898.8 1,760.0 1,312 2,912 445.3 void cublasLt::splitKreduce_kernel<(int)32, (int)16, int, __nv_bfloat16, __nv_bfloat16, float, (boo…
0.0 2,643,071 581 4,549.2 4,671.0 1,984 36,256 1,427.2 triton_red_fused__to_copy_add_embedding_mean_mul_pow_rsqrt_0
0.0 2,581,742 2 1,290,871.0 1,290,871.0 1,290,663 1,291,079 294.2 at::native::<unnamed>::fill_reverse_indices_kernel(long *, int, at::cuda::detail::IntDivider<unsign…
0.0 2,581,389 2 1,290,694.5 1,290,694.5 1,290,406 1,290,983 408.0 void at::native::elementwise_kernel<(int)128, (int)2, void at::native::gpu_kernel_impl_nocast<at::n…
0.0 2,421,998 112 21,625.0 21,552.0 9,408 34,465 12,020.4 void cutlass::Kernel2<cutlass_80_wmma_tensorop_bf16_s161616gemm_bf16_32x32_128x2_tn_align8>(T1::Par…
0.0 1,835,794 581 3,159.7 3,200.0 1,632 39,200 1,543.9 triton_poi_fused_cat_1
0.0 1,365,128 2 682,564.0 682,564.0 677,764 687,364 6,788.2 void at::native::<unnamed>::distribution_elementwise_grid_stride_kernel<float, (int)4, void at::nat…
0.0 1,304,994 581 2,246.1 2,368.0 863 14,272 629.4 triton_poi_fused_view_3
0.0 1,202,982 56 21,481.8 21,456.0 21,152 21,888 275.5 ampere_bf16_s16816gemm_bf16_128x64_ldg8_f2f_stages_32x6_tn
0.0 1,027,854 1,153 891.5 896.0 800 1,216 36.7 void at::native::vectorized_elementwise_kernel<(int)2, at::native::FillFunctor<int>, std::array<cha…
0.0 956,098 28 34,146.4 34,736.5 17,920 35,200 3,188.0 std::enable_if<!T7, void>::type internal::gemvx::kernel<int, int, __nv_bfloat16, float, float, floa…
0.0 847,695 581 1,459.0 1,440.0 1,216 9,408 336.4 triton_poi_fused_cat_2
0.0 611,794 673 909.1 896.0 864 1,025 28.1 void at::native::unrolled_elementwise_kernel<at::native::FillFunctor<long>, std::array<char *, (uns…
0.0 417,574 308 1,355.8 1,344.0 1,311 1,504 21.9 void vllm::merge_attn_states_kernel<__nv_bfloat16, (unsigned int)128>(T1 *, float *, const T1 *, co…
0.0 295,335 168 1,757.9 1,760.0 1,535 2,080 119.1 void cublasLt::splitKreduce_kernel<(int)32, (int)16, int, __nv_bfloat16, __nv_bfloat16, float, (boo…
0.0 155,841 1 155,841.0 155,841.0 155,841 155,841 0.0 void at::native::<unnamed>::CatArrayBatchedCopy_aligned16_contig<at::native::<unnamed>::OpaqueType<…
0.0 78,880 1 78,880.0 78,880.0 78,880 78,880 0.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::bfloat16_copy_kernel_cuda(at::Te…
0.0 63,740 58 1,099.0 896.0 864 11,360 1,372.6 void at::native::vectorized_elementwise_kernel<(int)4, at::native::FillFunctor<c10::BFloat16>, std:…
0.0 43,936 1 43,936.0 43,936.0 43,936 43,936 0.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::sin_kernel_cuda(at::TensorIterat…
0.0 36,570 28 1,306.1 1,312.0 1,280 1,376 19.5 void cublasLt::splitKreduce_kernel<(int)32, (int)16, int, float, __nv_bfloat16, float, (bool)0, __n…
0.0 26,816 1 26,816.0 26,816.0 26,816 26,816 0.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::cos_kernel_cuda(at::TensorIterat…
0.0 19,520 1 19,520.0 19,520.0 19,520 19,520 0.0 void at::native::elementwise_kernel<(int)128, (int)2, void at::native::gpu_kernel_impl_nocast<at::n…
0.0 11,936 11 1,085.1 864.0 864 1,568 286.4 void at::native::vectorized_elementwise_kernel<(int)4, at::native::FillFunctor<float>, std::array<c…
0.0 10,752 2 5,376.0 5,376.0 5,120 5,632 362.0 void at::native::_scatter_gather_elementwise_kernel<(int)128, (int)8, void at::native::_cuda_scatte…
0.0 9,152 2 4,576.0 4,576.0 4,480 4,672 135.8 void at::native::<unnamed>::distribution_elementwise_grid_stride_kernel<float, (int)4, void at::nat…
0.0 3,616 2 1,808.0 1,808.0 1,600 2,016 294.2 void at::native::elementwise_kernel<(int)128, (int)4, void at::native::gpu_kernel_impl_nocast<at::n…
0.0 3,424 2 1,712.0 1,712.0 1,664 1,760 67.9 void at::native::vectorized_elementwise_kernel<(int)2, at::native::CUDAFunctorOnOther_add<long>, st…
0.0 3,136 2 1,568.0 1,568.0 1,504 1,632 90.5 void at::native::vectorized_elementwise_kernel<(int)2, at::native::<unnamed>::where_kernel_impl(at:…
0.0 3,104 2 1,552.0 1,552.0 1,344 1,760 294.2 void at::native::vectorized_elementwise_kernel<(int)4, void at::native::compare_scalar_kernel<float…
0.0 2,975 2 1,487.5 1,487.5 992 1,983 700.7 void <unnamed>::elementwise_kernel_with_index<int, at::native::arange_cuda_out(const c10::Scalar &,…
0.0 2,912 2 1,456.0 1,456.0 1,344 1,568 158.4 void at::native::vectorized_elementwise_kernel<(int)4, at::native::CUDAFunctorOnOther_add<float>, s…
0.0 2,336 1 2,336.0 2,336.0 2,336 2,336 0.0 void at::native::elementwise_kernel<(int)128, (int)4, void at::native::gpu_kernel_impl<at::native::…
0.0 1,184 1 1,184.0 1,184.0 1,184 1,184 0.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::reciprocal_kernel_cuda(at::Tenso…
0.0 1,024 1 1,024.0 1,024.0 1,024 1,024 0.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::AUnaryFunctor<float, float, floa…
0.0 1,024 1 1,024.0 1,024.0 1,024 1,024 0.0 void at::native::vectorized_elementwise_kernel<(int)4, at::native::BUnaryFunctor<float, float, floa…
0.0 896 1 896.0 896.0 896 896 0.0 void at::native::vectorized_elementwise_kernel<(int)2, at::native::FillFunctor<double>, std::array<…
[7/8] Executing 'cuda_gpu_mem_time_sum' stats report
Time (%) Total Time (ns) Count Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Operation
-------- --------------- ------ -------- -------- -------- ---------- ----------- ------------------------------
93.2 540,571,743 41,277 13,096.2 352.0 287 97,068,545 513,408.1 [CUDA memcpy Host-to-Device]
3.2 18,710,334 14,564 1,284.7 896.0 864 1,362,855 22,521.7 [CUDA memcpy Device-to-Device]
2.5 14,536,294 21,760 668.0 768.0 287 7,744 311.5 [CUDA memset]
1.1 6,503,130 5,752 1,130.6 1,120.0 863 1,760 95.6 [CUDA memcpy Device-to-Host]
[8/8] Executing 'cuda_gpu_mem_size_sum' stats report
Total (MB) Count Avg (MB) Med (MB) Min (MB) Max (MB) StdDev (MB) Operation
---------- ------ -------- -------- -------- -------- ----------- ------------------------------
4,190.741 41,277 0.102 0.000 0.000 466.747 2.619 [CUDA memcpy Host-to-Device]
2,534.048 14,564 0.174 0.003 0.003 622.330 10.312 [CUDA memcpy Device-to-Device]
14.589 21,760 0.001 0.001 0.000 0.006 0.000 [CUDA memset]
4.192 5,752 0.001 0.000 0.000 0.004 0.001 [CUDA memcpy Device-to-Host]
Generated:
/data/cy/kv_cache_vs_util/sim_traverse_bs/traverse_bs_util_sim_decoding.nsys-rep
/data/cy/kv_cache_vs_util/sim_traverse_bs/traverse_bs_util_sim_decoding.sqlite
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