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.. meta::
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:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the unified
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ROCm Docker image.
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:keywords: model, MAD, automation, dashboarding, validate
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***********************************************************
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LLM inference performance validation on AMD Instinct MI300X
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***********************************************************
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.. _vllm-benchmark-unified-docker:
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The `ROCm vLLM Docker <https://hub.docker.com/r/rocm/vllm/tags>`_ image offers
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a prebuilt, optimized environment designed for validating large language model
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(LLM) inference performance on the AMD Instinct™ MI300X accelerator. This
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ROCm vLLM Docker image integrates vLLM and PyTorch tailored specifically for the
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MI300X accelerator and includes the following components:
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* `ROCm 6.2.1 <https://github.com/ROCm/ROCm>`_
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* `vLLM 0.6.4 <https://docs.vllm.ai/en/latest>`_
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* `PyTorch 2.5.0 <https://github.com/pytorch/pytorch>`_
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* Tuning files (in CSV format)
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With this Docker image, you can quickly validate the expected inference
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performance numbers on the MI300X accelerator. This topic also provides tips on
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optimizing performance with popular AI models.
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.. _vllm-benchmark-vllm:
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.. note::
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vLLM is a toolkit and library for LLM inference and
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serving. It deploys the PagedAttention algorithm, which reduces memory
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consumption and increases throughput by leveraging dynamic key and value
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allocation in GPU memory. vLLM also incorporates many LLM acceleration
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and quantization algorithms. In addition, AMD implements high-performance
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custom kernels and modules in vLLM to enhance performance further. See
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:ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for more
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information.
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Getting started
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===============
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Use the following procedures to reproduce the benchmark results on an
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MI300X accelerator with the prebuilt vLLM Docker image.
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.. _vllm-benchmark-get-started:
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1. Disable NUMA auto-balancing.
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To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
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might hang until the periodic balancing is finalized. For more information,
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see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
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.. code-block:: shell
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# disable automatic NUMA balancing
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sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
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# check if NUMA balancing is disabled (returns 0 if disabled)
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cat /proc/sys/kernel/numa_balancing
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0
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2. Download the :ref:`ROCm vLLM Docker image <vllm-benchmark-unified-docker>`.
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Use the following command to pull the Docker image from Docker Hub.
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.. code-block:: shell
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docker pull rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
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Once setup is complete, you can choose between two options to reproduce the
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benchmark results:
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- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
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- :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
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.. _vllm-benchmark-mad:
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MAD-integrated benchmarking
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===========================
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Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
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directory and install the required packages on the host machine.
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.. code-block:: shell
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git clone https://github.com/ROCm/MAD
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cd MAD
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pip install -r requirements.txt
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Use this command to run a performance benchmark test of the Llama 3.1 8B model
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on one GPU with ``float16`` data type in the host machine.
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.. code-block:: shell
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export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
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python3 tools/run_models.py --tags pyt_vllm_llama-3.1-8b --keep-model-dir --live-output --timeout 28800
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ROCm MAD launches a Docker container with the name
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``container_ci-pyt_vllm_llama-3.1-8b``. The latency and throughput reports of the
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model are collected in the following path: ``~/MAD/reports_float16/``.
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Although the following models are preconfigured to collect latency and
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throughput performance data, you can also change the benchmarking parameters.
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Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
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Available models
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----------------
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.. hlist::
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* ``pyt_vllm_llama-3.1-8b``
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* ``pyt_vllm_llama-3.1-70b``
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* ``pyt_vllm_llama-3.1-405b``
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* ``pyt_vllm_llama-2-7b``
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* ``pyt_vllm_llama-2-70b``
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* ``pyt_vllm_mixtral-8x7b``
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* ``pyt_vllm_mixtral-8x22b``
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* ``pyt_vllm_mistral-7b``
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* ``pyt_vllm_qwen2-7b``
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* ``pyt_vllm_qwen2-72b``
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* ``pyt_vllm_jais-13b``
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* ``pyt_vllm_jais-30b``
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* ``pyt_vllm_llama-3.1-8b_fp8``
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* ``pyt_vllm_llama-3.1-70b_fp8``
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* ``pyt_vllm_llama-3.1-405b_fp8``
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* ``pyt_vllm_mixtral-8x7b_fp8``
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* ``pyt_vllm_mixtral-8x22b_fp8``
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.. _vllm-benchmark-standalone:
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Standalone benchmarking
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=======================
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You can run the vLLM benchmark tool independently by starting the
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:ref:`Docker container <vllm-benchmark-get-started>` as shown in the following
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snippet.
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.. code-block::
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docker pull rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
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docker run -it --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128G --security-opt seccomp=unconfined --security-opt apparmor=unconfined --cap-add=SYS_PTRACE -v $(pwd):/workspace --env HUGGINGFACE_HUB_CACHE=/workspace --name vllm_v0.6.4 rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
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In the Docker container, clone the ROCm MAD repository and navigate to the
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benchmark scripts directory at ``~/MAD/scripts/vllm``.
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.. code-block::
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git clone https://github.com/ROCm/MAD
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cd MAD/scripts/vllm
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Command
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-------
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To start the benchmark, use the following command with the appropriate options.
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See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
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options and their descriptions.
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.. code-block:: shell
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./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
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See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
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.. note::
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The input sequence length, output sequence length, and tensor parallel (TP) are
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already configured. You don't need to specify them with this script.
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.. note::
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If you encounter the following error, pass your access-authorized Hugging
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Face token to the gated models.
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.. code-block:: shell
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OSError: You are trying to access a gated repo.
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# pass your HF_TOKEN
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export HF_TOKEN=$your_personal_hf_token
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.. _vllm-benchmark-standalone-options:
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Options
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-------
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.. list-table::
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:header-rows: 1
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:align: center
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* - Name
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- Options
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- Description
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* - ``$test_option``
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- latency
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- Measure decoding token latency
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* -
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- throughput
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- Measure token generation throughput
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* -
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- all
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- Measure both throughput and latency
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* - ``$model_repo``
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- ``meta-llama/Meta-Llama-3.1-8B-Instruct``
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- Llama 3.1 8B
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* - (``float16``)
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- ``meta-llama/Meta-Llama-3.1-70B-Instruct``
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- Llama 3.1 70B
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* -
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- ``meta-llama/Meta-Llama-3.1-405B-Instruct``
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- Llama 3.1 405B
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* -
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- ``meta-llama/Llama-2-7b-chat-hf``
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- Llama 2 7B
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* -
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- ``meta-llama/Llama-2-70b-chat-hf``
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- Llama 2 70B
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* -
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- ``mistralai/Mixtral-8x7B-Instruct-v0.1``
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- Mixtral 8x7B
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* -
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- ``mistralai/Mixtral-8x22B-Instruct-v0.1``
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- Mixtral 8x22B
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* -
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- ``mistralai/Mistral-7B-Instruct-v0.3``
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- Mixtral 7B
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* -
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- ``Qwen/Qwen2-7B-Instruct``
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- Qwen2 7B
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* -
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- ``Qwen/Qwen2-72B-Instruct``
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- Qwen2 72B
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* -
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- ``core42/jais-13b-chat``
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- JAIS 13B
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* -
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- ``core42/jais-30b-chat-v3``
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- JAIS 30B
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* - ``$model_repo``
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- ``amd/Meta-Llama-3.1-8B-Instruct-FP8-KV``
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- Llama 3.1 8B
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* - (``float8``)
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- ``amd/Meta-Llama-3.1-70B-Instruct-FP8-KV``
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- Llama 3.1 70B
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* -
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- ``amd/Meta-Llama-3.1-405B-Instruct-FP8-KV``
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- Llama 3.1 405B
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* -
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- ``amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV``
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- Mixtral 8x7B
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* -
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- ``amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV``
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- Mixtral 8x22B
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* - ``$num_gpu``
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- 1 or 8
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- Number of GPUs
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* - ``$datatype``
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- ``float16`` or ``float8``
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- Data type
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.. _vllm-benchmark-run-benchmark:
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Running the benchmark on the MI300X accelerator
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-----------------------------------------------
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Here are some examples of running the benchmark with various options.
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See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
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options and their descriptions.
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Latency benchmark example
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^^^^^^^^^^^^^^^^^^^^^^^^^
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Use this command to benchmark the latency of the Llama 3.1 8B model on one GPU with the ``float16`` and ``float8`` data types.
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.. code-block::
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./vllm_benchmark_report.sh -s latency -m meta-llama/Meta-Llama-3.1-8B-Instruct -g 1 -d float16
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./vllm_benchmark_report.sh -s latency -m amd/Meta-Llama-3.1-8B-Instruct-FP8-KV -g 1 -d float8
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Find the latency reports at:
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- ``./reports_float16/summary/Meta-Llama-3.1-8B-Instruct_latency_report.csv``
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- ``./reports_float8/summary/Meta-Llama-3.1-8B-Instruct-FP8-KV_latency_report.csv``
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Throughput benchmark example
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Use this command to benchmark the throughput of the Llama 3.1 8B model on one GPU with the ``float16`` and ``float8`` data types.
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.. code-block:: shell
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./vllm_benchmark_report.sh -s throughput -m meta-llama/Meta-Llama-3.1-8B-Instruct -g 1 -d float16
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./vllm_benchmark_report.sh -s throughput -m amd/Meta-Llama-3.1-8B-Instruct-FP8-KV -g 1 -d float8
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Find the throughput reports at:
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- ``./reports_float16/summary/Meta-Llama-3.1-8B-Instruct_throughput_report.csv``
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- ``./reports_float8/summary/Meta-Llama-3.1-8B-Instruct-FP8-KV_throughput_report.csv``
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.. raw:: html
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<style>
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mjx-container[jax="CHTML"][display="true"] {
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text-align: left;
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margin: 0;
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}
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</style>
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.. note::
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Throughput is calculated as:
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- .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
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- .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
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Further reading
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===============
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- For application performance optimization strategies for HPC and AI workloads,
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including inference with vLLM, see :doc:`/how-to/tuning-guides/mi300x/workload`.
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- To learn more about the options for latency and throughput benchmark scripts,
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see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
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- For application performance optimization strategies for HPC and AI workloads,
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including inference with vLLM, see :doc:`/how-to/tuning-guides/mi300x/workload`.
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- To learn more about system settings and management practices to configure your system for
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MI300X accelerators, see :doc:`/how-to/system-optimization/mi300x`.
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- To learn how to run LLM models from Hugging Face or your own model, see
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:doc:`Using ROCm for AI </how-to/rocm-for-ai/index>`.
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- To learn how to optimize inference on LLMs, see
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:doc:`Fine-tuning LLMs and inference optimization </how-to/llm-fine-tuning-optimization/index>`.
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- For a list of other ready-made Docker images for ROCm, see the
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:doc:`Docker image support matrix <rocm-install-on-linux:reference/docker-image-support-matrix>`.
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- To compare with the previous version of the ROCm vLLM Docker image for performance validation, refer to
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`LLM inference performance validation on AMD Instinct MI300X (ROCm 6.2.0) <https://rocm.docs.amd.com/en/docs-6.2.0/how-to/performance-validation/mi300x/vllm-benchmark.html>`_.
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Peter Park