docs: add ONNXRuntime QNN Execution Provider zh docs

Signed-off-by: ZIFENG278 <[email protected]>

Author: ZIFENG278

docs/common/ai/_qnn_onnxrt_execution_provider.mdx

@@ -0,0 +1,303 @@
+ONNX Runtime 的 **[QNN Execution Provider](https://onnxruntime.ai/docs/execution-providers/QNN-ExecutionProvider.html)** 可在高通 SoC 平台上启用 **NPU 硬件加速推理 ONNX 格式模型**。
+它使用 **[Qualcomm® AI Runtime (QAIRT SDK)](./qairt-sdk#qairt)**，将 **ONNX 模型** 构建为一个 **QNN 计算图**，并通过 **加速器后端库** 来执行该计算图。
+ONNX Runtime 的 **QNN Execution Provider** 可用于搭载 **高通平台 SoC** 的 **Linux**, **Android**, **Windows** 设备。
+
+## 支持设备
+
+- [**瑞莎 Dragon Q6A**](/dragon/q6a/) (Linux)
+
+- [**瑞莎 Fogwise AIRbox Q900**](/fogwise/airbox-q900) (Linux)
+
+## 安装方法
+
+:::tip
+安装方式有两种，可选择 **pip 安装** 与 **源码编译安装**
+
+无论选择哪种方式都需要按照 [**QAIRT SDK 安装**](./qairt-install) 下载 QAIRT SDK
+:::
+
+### 创建 python 虚拟环境
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+sudo apt install python3-venv
+python3 -m venv .venv
+source .venv/bin/activate
+pip3 install --upgrade pip
+```
+
+</NewCodeBlock>
+
+### pip 安装
+
+radxa 已经提供预编译 Linux 版本的 `onnxruntime-qnn whl` 文件
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+pip3 install https://github.com/ZIFENG278/onnxruntime/releases/download/v1.23.2/onnxruntime_qnn-1.23.2-cp312-cp312-linux_aarch64.whl
+```
+
+</NewCodeBlock>
+
+### 源码编译
+
+#### 克隆 onnxruntime 仓库
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+git clone --depth 1 -b v1.23.2 https://github.com/microsoft/onnxruntime.git
+```
+
+</NewCodeBlock>
+
+#### 修改 CMakeLists.txt
+
+因为 onnxruntime 不直接支持 Linux 系统，若编译为支持 Linux 系统的 onnxruntime-qnn whl 包，需要手动更改 `cmake/CMakeLists.txt` 的 840 行。
+
+将 L840 `set(QNN_ARCH_ABI aarch64-android)` 修改为 `set(QNN_ARCH_ABI aarch64-oe-linux-gcc11.2)`
+
+:::tip
+若编译为 **Android** 或 **Windows** 则不需要修改。
+:::
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+cd onnxruntime
+vim cmake/CMakeLists.txt
+```
+
+</NewCodeBlock>
+```bash
+diff --git a/cmake/CMakeLists.txt b/cmake/CMakeLists.txt
+index 0b37ade..f4621e5 100644
+--- a/cmake/CMakeLists.txt
++++ b/cmake/CMakeLists.txt
+@@ -837,7 +837,7 @@ if (onnxruntime_USE_QNN OR onnxruntime_USE_QNN_INTERFACE)
+           if (${GEN_PLATFORM} STREQUAL "x86_64")
+             set(QNN_ARCH_ABI x86_64-linux-clang)
+           else()
+-            set(QNN_ARCH_ABI aarch64-android)
++            set(QNN_ARCH_ABI aarch64-oe-linux-gcc11.2)
+           endif()
+         endif()
+       endif()
+```
+
+#### 编译项目
+
+:::tip
+请根据实际 QAIRT SDK 路径修改 `QNN_SDK_PATH` 路径
+:::
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+pip3 install -r requirements.txt
+./build.sh --use_qnn --qnn_home [QNN_SDK_PATH] --build_shared_lib --build_wheel --config Release --parallel --skip_tests --build_dir build/Linux
+```
+
+</NewCodeBlock>
+
+项目编译完成后，目标 whl 包生成在 `build/Linux/Release/dist` 下
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+pip3 install ./build/Linux/Release/dist/onnxruntime_qnn-1.23.2-cp312-cp312-linux_aarch64.whl
+```
+
+</NewCodeBlock>
+
+## 验证 QNN Execution Provider
+
+:::tip
+验证 QNN Execution Provider 前，请先按照 [**板端启用 NPU**](./fastrpc_setup) 与 [**NPU 快速验证**](./quick-example) 验证 NPU 可用性，确保 NPU 功能正常后再测试 QNN Execution Provider。
+:::
+
+### 导入环境变量
+
+<Tabs>
+    <TabItem value="QCS6490">
+
+    <NewCodeBlock tip="Device" type="device">
+    ```bash
+    export PRODUCT_SOC=6490 DSP_ARCH=68
+    ```
+    </NewCodeBlock>
+
+
+    </TabItem>
+
+    <TabItem value="QCS9075">
+
+    <NewCodeBlock tip="Device" type="device">
+    ```bash
+    export PRODUCT_SOC=9075 DSP_ARCH=73
+    ```
+    </NewCodeBlock>
+
+</TabItem>
+
+</Tabs>
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+cd qairt/2.37.1.250807
+source bin/envsetup.sh
+export ADSP_LIBRARY_PATH=$QNN_SDK_ROOT/lib/hexagon-v${DSP_ARCH}/unsigned
+```
+
+</NewCodeBlock>
+
+### 下载 INT8 量化的 ONNX 模型
+
+在 [**Qualcomm AI Hub**](https://aihub.qualcomm.com/iot/models/resnet50?chipsets=qualcomm-qcs6490) 上下载一个 ONNX Runtime 格式的 w8a8 量化模型
+
+<div style={{ textAlign: "center" }}>
+  <img
+    src="/img/dragon/q6a/qaihub_onnxruntime_model.webp"
+    style={{ width: "85%" }}
+  />
+</div>
+
+### 测试 QNN Execution Provider
+
+以下 Python 代码使用 QNN EP 创建 ONNX Runtime 会话，并使用 NPU 推理 w8a8 量化 ONNX 模型。代码参考 [Running a quantized model on Windows ARM64](https://onnxruntime.ai/docs/execution-providers/QNN-ExecutionProvider.html#running-a-quantized-model-on-windows-arm64-onnxruntime-qnn-version--1180)
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+vim run_qdq_model.py
+```
+
+</NewCodeBlock>
+
+:::tip
+请根据实际 QAIRT SDK 路径修改 `backend_path` 路径
+
+请根据下载的 onnx 模型路径修改 `InferenceSession` 中的模型路径参数
+:::
+
+```python
+# run_qdq_model.py
+
+import onnxruntime
+import numpy as np
+
+options = onnxruntime.SessionOptions()
+
+# (Optional) Enable configuration that raises an exception if the model can't be
+# run entirely on the QNN HTP backend.
+options.add_session_config_entry("session.disable_cpu_ep_fallback", "1")
+
+# Create an ONNX Runtime session.
+# TODO: Provide the path to your ONNX model
+session = onnxruntime.InferenceSession("job_jpy6ye005_optimized_onnx/model.onnx",
+                                       sess_options=options,
+                                       providers=["QNNExecutionProvider"],
+                                       provider_options=[{"backend_path": "libQnnHtp.so"}]) # Provide path to Htp dll in QNN SDK
+
+# Run the model with your input.
+# TODO: Use numpy to load your actual input from a file or generate random input.
+input0 = np.ones((1,3,224,224), dtype=np.uint8)
+result = session.run(None, {"image_tensor": input0})
+
+# Print output.
+print(result)
+```
+
+<NewCodeBlock tip="Device" type="device">
+
+```bash
+python3 run_qdq_model.py
+```
+
+</NewCodeBlock>
+
+```bash
+(.venv) rock@radxa-dragon-q6a:~/ssd/qualcomm/onnxruntime/build/Linux/Release$ python3 run_qdq_model.py
+2025-12-22 06:31:37.527811909 [W:onnxruntime:Default, device_discovery.cc:164 DiscoverDevicesForPlatform] GPU device discovery failed: device_discovery.cc:89 ReadFileContents Failed to open file: "/sys/class/drm/card0/device/vendor"
+/prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:38:dummy call to rpcmem_init, rpcmem APIs will be used from libxdsprpc
+
+====== DDR bandwidth summary ======
+spill_bytes=0
+fill_bytes=0
+write_total_bytes=65536
+read_total_bytes=25976832
+
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+/prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:42:dummy call to rpcmem_deinit, rpcmem APIs will be used from libxdsprpc
+```
+
+## 详细文档
+
+关于 QNNExecutionProvider 的详细使用方法请参考
+
+- [**QNN Execution Provider**](https://onnxruntime.ai/docs/execution-providers/QNN-ExecutionProvider.html#qnn-execution-provider)

docs/dragon/q6a/app-dev/npu-dev/llama3.2-1b-qairt-v68.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 10
+sidebar_position: 11
 ---
 
 # Llama3.2-1B 大模型

docs/dragon/q6a/app-dev/npu-dev/qai-appbuilder-demo/README.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 9
+sidebar_position: 99
 ---
 
 # Demos 示例

docs/dragon/q6a/app-dev/npu-dev/qnn_onnxrt_execution_provider.md

@@ -0,0 +1,9 @@
+---
+sidebar_position: 10
+---
+
+# QNN Execution Provider
+
+import QNNONNXRTEXECUTIONPROVIDER from '../../../../common/ai/\_qnn_onnxrt_execution_provider.mdx';
+
+<QNNONNXRTEXECUTIONPROVIDER />

docs/fogwise/airbox-q900/ai-dev/qai-appbuilder-demo/README.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 9
+sidebar_position: 99
 ---
 
 # Demos 示例

docs/fogwise/airbox-q900/ai-dev/qnn_onnxrt_execution_provider.md

@@ -0,0 +1,9 @@
+---
+sidebar_position: 10
+---
+
+# QNN Execution Provider
+
+import QNNONNXRTEXECUTIONPROVIDER from '../../../common/ai/\_qnn_onnxrt_execution_provider.mdx';
+
+<QNNONNXRTEXECUTIONPROVIDER />