Anacondagit12/15/2023 ![]() ![]() However, AMD reserves the right to revise this information and to make changes from time to time to the content hereof without obligation of AMD to notify any person of such revisions or changes. AMD assumes no obligation to update or otherwise correct or revise this information. ![]() Any computer system has risks of security vulnerabilities that cannot be completely prevented or mitigated. The information contained herein is subject to change and may be rendered inaccurate for many reasons, including but not limited to product and roadmap changes, component and motherboard version changes, new model and/or product releases, product differences between differing manufacturers, software changes, BIOS flashes, firmware upgrades, or the like. The information presented in this document is for informational purposes only and may contain technical inaccuracies, omissions, and typographical errors. Microsoft Olive is an active branch which changes often, so the interfaces and setup may look slightly different depending on when the branch is downloaded. Links to third-party sites are provided for convenience and unless explicitly stated, AMD is not responsible for the contents of such linked sites, and no endorsement is implied. conda create -name=llama2_chat python=3.9.To use Chat App which is an interactive interface for running llama_v2 model, follow these steps: Python run_llama_v2_io_binding.py -prompt="what is the capital of California and what is California famous for?".The end result should look like this when using the following prompt: The optimized model folder structure should look like this: pip install onnxruntime_directml // make sure it’s 1.16.2 or newer.Ĭopy the optimized models here (“Olive\examples\directml\llama_v2\models” folder).Once the optimized ONNX model is generated from Step 2, or if you already have the models locally, see the below instructions for running Llama2 on AMD Graphics.ģ.1 Run Llama 2 using Python Command Line When requested, paste the URL that was sent to your e-mail address by Meta (the link is valid for 24 hours)ģ. Note: The first time this script is invoked can take some time since it will need to download the Llama 2 weights from Meta.Request access to the Llama 2 weights from Meta, Convert to ONNX, and optimize the ONNX models conda create -name=llama2_Optimize python=3.9.Open Anaconda terminal and input the following commands: ![]() Using the instructions from Microsoft Olive, download Llama model weights and generate optimized ONNX models for efficient execution on AMD GPUs Convert Llama2 model to ONNX format and optimize the models for executionĭownload the Llama2 models from Meta’s release, use Microsoft Olive to convert it to ONNX format and optimize the ONNX model for GPU hardware acceleration. Driver: AMD Software: Adrenalin Edition™ 23.11.1 or newer ( )Ģ.Platform having AMD Graphics Processing Units (GPU).If you have already optimized the ONNX model for execution and just want to run the inference, please advance to Step 3 below. AMD driver resident ML metacommands utilizes AMD Matrix Processing Cores wavemma intrinsics to accelerate DirectML based ML workloads including Stable Diffusion and Llama2.Īs we continue to further optimize Llama2, watch out for future updates and improvements via Microsoft Olive and AMD Graphics drivers.īelow are brief instructions on how to optimize the Llama2 model with Microsoft Olive, and how to run the model on any DirectML capable AMD graphics card with ONNXRuntime, accelerated via the DirectML platform API. Following up to our earlier improvements made to Stable Diffusion workloads, we are happy to share that Microsoft and AMD engineering teams worked closely to optimize Llama2 to run on AMD GPUs accelerated via the Microsoft DirectML platform API and AMD driver ML metacommands. Microsoft and AMD continue to collaborate enabling and accelerating AI workloads across AMD GPUs on Windows platforms. Prepared by Hisham Chowdhury (AMD) and Sonbol Yazdanbakhsh (AMD). ![]()
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