# Profiling vLLM !!! warning Profiling is only intended for vLLM developers and maintainers to understand the proportion of time spent in different parts of the codebase. **vLLM end-users should never turn on profiling** as it will significantly slow down the inference. ## Profile with PyTorch Profiler We support tracing vLLM workers using the `torch.profiler` module. You can enable tracing by setting the `VLLM_TORCH_PROFILER_DIR` environment variable to the directory where you want to save the traces: `VLLM_TORCH_PROFILER_DIR=/mnt/traces/`. Additionally, you can control the profiling content by specifying the following environment variables: - `VLLM_TORCH_PROFILER_RECORD_SHAPES=1` to enable recording Tensor Shapes, off by default - `VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1` to record memory, off by default - `VLLM_TORCH_PROFILER_WITH_STACK=1` to enable recording stack information, on by default - `VLLM_TORCH_PROFILER_WITH_FLOPS=1` to enable recording FLOPs, off by default The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set. When using `vllm bench serve`, you can enable profiling by passing the `--profile` flag. Traces can be visualized using . !!! tip You can directly call bench module without installing vLLM using `python -m vllm.entrypoints.cli.main bench`. !!! tip Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly. !!! tip To stop the profiler - it flushes out all the profile trace files to the directory. This takes time, for example for about 100 requests worth of data for a llama 70b, it takes about 10 minutes to flush out on a H100. Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes. `export VLLM_RPC_TIMEOUT=1800000` ### Example commands and usage #### Offline Inference Refer to [examples/offline_inference/simple_profiling.py](../../examples/offline_inference/simple_profiling.py) for an example. #### OpenAI Server ```bash VLLM_TORCH_PROFILER_DIR=./vllm_profile \ vllm serve meta-llama/Llama-3.1-8B-Instruct ``` vllm bench command: ```bash vllm bench serve \ --backend vllm \ --model meta-llama/Llama-3.1-8B-Instruct \ --dataset-name sharegpt \ --dataset-path sharegpt.json \ --profile \ --num-prompts 2 ``` ## Profile with NVIDIA Nsight Systems Nsight systems is an advanced tool that exposes more profiling details, such as register and shared memory usage, annotated code regions and low-level CUDA APIs and events. [Install nsight-systems](https://docs.nvidia.com/nsight-systems/InstallationGuide/index.html) using your package manager. The following block is an example for Ubuntu. ```bash apt update apt install -y --no-install-recommends gnupg echo "deb http://developer.download.nvidia.com/devtools/repos/ubuntu$(source /etc/lsb-release; echo "$DISTRIB_RELEASE" | tr -d .)/$(dpkg --print-architecture) /" | tee /etc/apt/sources.list.d/nvidia-devtools.list apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub apt update apt install nsight-systems-cli ``` !!! tip When profiling with `nsys`, it is advisable to set the environment variable `VLLM_WORKER_MULTIPROC_METHOD=spawn`. The default is to use the `fork` method instead of `spawn`. More information on the topic can be found in the [Nsight Systems release notes](https://docs.nvidia.com/nsight-systems/ReleaseNotes/index.html#general-issues). The Nsight Systems profiler can be launched with `nsys profile ...`, with a few recommended flags for vLLM: `--trace-fork-before-exec=true --cuda-graph-trace=node`. ### Example commands and usage #### Offline Inference For basic usage, you can just append the profiling command before any existing script you would run for offline inference. The following is an example using the `vllm bench latency` script: ```bash nsys profile \ --trace-fork-before-exec=true \ --cuda-graph-trace=node \ vllm bench latency \ --model meta-llama/Llama-3.1-8B-Instruct \ --num-iters-warmup 5 \ --num-iters 1 \ --batch-size 16 \ --input-len 512 \ --output-len 8 ``` #### OpenAI Server To profile the server, you will want to prepend your `vllm serve` command with `nsys profile` just like for offline inference, but you will need to specify a few other arguments to enable dynamic capture similarly to the Torch Profiler: ```bash # server VLLM_TORCH_CUDA_PROFILE=1 \ nsys profile \ --trace-fork-before-exec=true \ --cuda-graph-trace=node \ --capture-range=cudaProfilerApi \ --capture-range-end repeat \ vllm serve meta-llama/Llama-3.1-8B-Instruct # client vllm bench serve \ --backend vllm \ --model meta-llama/Llama-3.1-8B-Instruct \ --dataset-name sharegpt \ --dataset-path sharegpt.json \ --profile \ --num-prompts 2 ``` With `--profile`, vLLM will capture a profile for each run of `vllm bench serve`. Once the server is killed, the profiles will all be saved. #### Analysis You can view these profiles either as summaries in the CLI, using `nsys stats [profile-file]`, or in the GUI by installing Nsight [locally following the directions here](https://developer.nvidia.com/nsight-systems/get-started). ??? console "CLI example" ```bash nsys stats report1.nsys-rep ... ** CUDA GPU Kernel Summary (cuda_gpu_kern_sum): Time (%) Total Time (ns) Instances Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Name -------- --------------- --------- ----------- ----------- -------- --------- ----------- ---------------------------------------------------------------------------------------------------- 46.3 10,327,352,338 17,505 589,965.9 144,383.0 27,040 3,126,460 944,263.8 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_of… 14.8 3,305,114,764 5,152 641,520.7 293,408.0 287,296 2,822,716 867,124.9 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_of… 12.1 2,692,284,876 14,280 188,535.4 83,904.0 19,328 2,862,237 497,999.9 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off… 9.5 2,116,600,578 33,920 62,399.8 21,504.0 15,326 2,532,285 290,954.1 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_… 5.0 1,119,749,165 18,912 59,208.4 9,056.0 6,784 2,578,366 271,581.7 void vllm::act_and_mul_kernel, (bool)1>(T1 *, cons… 4.1 916,662,515 21,312 43,011.6 19,776.0 8,928 2,586,205 199,790.1 void cutlass::device_kernel(int)0&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kern… 1.9 418,362,605 18,912 22,121.5 3,871.0 3,328 2,523,870 175,248.2 void vllm::rotary_embedding_kernel(const long *, T1 *, T1 *, const T1 *, in… 0.7 167,083,069 18,880 8,849.7 2,240.0 1,471 2,499,996 101,436.1 void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0… ... ``` GUI example: Screenshot 2025-03-05 at 11 48 42 AM ## Continuous Profiling There is a [GitHub CI workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-profiling.yml) in the PyTorch infrastructure repository that provides continuous profiling for different models on vLLM. This automated profiling helps track performance characteristics over time and across different model configurations. ### How It Works The workflow currently runs weekly profiling sessions for selected models, generating detailed performance traces that can be analyzed using different tools to identify performance regressions or optimization opportunities. But, it can be triggered manually as well, using the Github Action tool. ### Adding New Models To extend the continuous profiling to additional models, you can modify the [profiling-tests.json](https://github.com/pytorch/pytorch-integration-testing/blob/main/vllm-profiling/cuda/profiling-tests.json) configuration file in the PyTorch integration testing repository. Simply add your model specifications to this file to include them in the automated profiling runs. ### Viewing Profiling Results The profiling traces generated by the continuous profiling workflow are publicly available on the [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm). Look for the **Profiling traces** table to access and download the traces for different models and runs. ## Profiling vLLM Python Code The Python standard library includes [cProfile](https://docs.python.org/3/library/profile.html) for profiling Python code. vLLM includes a couple of helpers that make it easy to apply it to a section of vLLM. Both the `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` functions can be used to profile a section of code. !!! note The legacy import paths `vllm.utils.cprofile` and `vllm.utils.cprofile_context` are deprecated. Please use `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` instead. ### Example usage - decorator The first helper is a Python decorator that can be used to profile a function. If a filename is specified, the profile will be saved to that file. If no filename is specified, profile data will be printed to stdout. ```python from vllm.utils.profiling import cprofile @cprofile("expensive_function.prof") def expensive_function(): # some expensive code pass ``` ### Example Usage - context manager The second helper is a context manager that can be used to profile a block of code. Similar to the decorator, the filename is optional. ```python from vllm.utils.profiling import cprofile_context def another_function(): # more expensive code pass with cprofile_context("another_function.prof"): another_function() ``` ### Analyzing Profile Results There are multiple tools available that can help analyze the profile results. One example is [snakeviz](https://jiffyclub.github.io/snakeviz/). ```bash pip install snakeviz snakeviz expensive_function.prof ``` ### Analyzing Garbage Collection Costs Leverage VLLM_GC_DEBUG environment variable to debug GC costs. - VLLM_GC_DEBUG=1: enable GC debugger with gc.collect elpased times - VLLM_GC_DEBUG='{"top_objects":5}': enable GC debugger to log top 5 collected objects for each gc.collect