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@dwithchenna dwithchenna released this 02 Jul 00:21
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v1.5.0
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Release Note: Version 1.5.0

  • EoU Improvement

    • Application concurrency: improves the resource distribution across applications
    • Model Compilation time: 2x – 8x faster
    • Installation Size: 80% smaller

  • New getting started tutorial with fine-tuned ResNet BF16 model using Python / C++ for deployment on NPU

  • New object detection tutorial with Yolov8m model with BF16 / XINT8 quantization using AMD-Quark

  • Multi-model demo has been removed

  • Support for New LLMs released

    • Qwen/Qwen2.5-1.5B-Instruct
    • Qwen/Qwen2.5-3B-Instruct
    • Qwen/Qwen2.5-7B-Instruct

  • Bug fixes

  • Breaking Changes

    • The %RYZEN_AI_INSTALLATION_PATH%\deployment folder has been reorganized and flattened. Deployment DLLs are no longer organized in subfolders. If you use application build scripts that pull DLLs from the deployment folder, you need to update them based on the new paths. Refer to the :ref:Application Packaging Requirements <app-packaging> section for further details.

    • The 1x4.xclbin (PHX/HPT) and AMD_AIE2P_Nx4_Overlay.xclbin (STX/KRK) NPU binaries are no longer supported and should not be used. You should use the 4x4.xclbin (PHX/HPT) and AMD_AIE2P_4x4_Overlay.xclbin (STX/KRK) NPU binaries instead.

    • The XLNX_ENABLE_CACHE, XLNX_VART_FIRMWARE, and XLNX_TARGET_NAME environment variables are no longer supported and should not be relied upon.

    • Support for VitisAI EP cache encryption is no longer available. To encrypt the compiled models, use the ONNX Runtime :ref:EP Context Cache <ort-ep-context-cache> feature instead.

    • For INT8 models, the VitisAI EP does not save the compiled model to disk by default. To save the compiled model, use the ONNX Runtime :ref:EP Context Cache <ort-ep-context-cache> feature or set the :option:enable_cache_file_io_in_mem provider option to 0.

    • Generation of the vitisai_ep_report.json file is no longer automatic and should be manually enabled. See the :ref:Operator Assignment Report <op-assignment-report> section for details.

    • Changes to the OGA flow for LLMs:

      • OGA Version is updated to v0.7.0 (Ryzen AI 1.5) from v0.6.0 (Ryzen AI 1.4).
      • The hybrid_llm and npu_llm folders are consolidated into a new folder named LLM, which contains the model_benchmark.exe and run_model.py scripts, along with the necessary C++ headers and .lib files to support both the Hybrid LLM and NPU LLM workflows in C++ and Python.
      • For NPU LLM models, the vaip_llm.json file is no longer required. As a result, the vaip_llm.json path is removed from the genai_config.json for all NPU models. Ensure that you re-download the NPU models from Hugging Face <https://huggingface.co/collections/amd/ryzenai-15-llm-npu-models-6859846d7c13f81298990db0>_ when using the Ryzen AI 1.5 installer.
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