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Specify NDK version 26 #5855

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2 changes: 1 addition & 1 deletion backends/mediatek/scripts/README.md
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Expand Up @@ -16,7 +16,7 @@ Before you begin, ensure you have the following prerequisites installed and conf

### 2. Android NDK

- **Download Android NDK**: Acquire the Android NDK from the [Android developer site](https://developer.android.com/ndk/downloads).
- **Download Android NDK**: Acquire the Android NDK version 26.3.11579264 from the [Android developer site](https://developer.android.com/ndk/downloads).
- **Set NDK Path**: Ensure that the `$ANDROID_NDK` environment variable is set to the path where the NDK is located.
```bash
export ANDROID_NDK=<path_to_android_ndk>
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6 changes: 6 additions & 0 deletions examples/demo-apps/android/LlamaDemo/README.md
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Expand Up @@ -34,6 +34,12 @@ First it’s important to note that currently ExecuTorch provides support across
| QNN (Qualcomm AI Accelerators) | [link](https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md) |
| MediaTek (MediaTek AI Accelerators) | [link](https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md) |

**WARNING** NDK r27 will cause issues like:
```
java.lang.UnsatisfiedLinkError: dlopen failed: cannot locate symbol "_ZTVNSt6__ndk114basic_ifstreamIcNS_11char_traitsIcEEEE" referenced by "/data/app/~~F5IwquaXUZPdLpSEYA-JGA==/com.example.executorchllamademo-FSyx80gEhsQCsxz7hvS2Ew==/lib/arm64/libexecutorch.so"...
```
Please use NDK version 26.3.11579264.

## How to Use the App

This section will provide the main steps to use the app, along with a code snippet of the ExecuTorch API.
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6 changes: 3 additions & 3 deletions examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md
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Expand Up @@ -6,13 +6,13 @@ More specifically, it covers:
2. Building and linking libraries that are required to inference on-device for Android platform using Qualcomm AI accelerators.
3. Building the Android demo app itself.

Verified on Linux CentOS, QNN SDK [v2.26](https://softwarecenter.qualcomm.com/api/download/software/qualcomm_neural_processing_sdk/v2.26.0.240828.zip), python 3.10, Android SDK r27 and r26b.
Verified on Linux CentOS, QNN SDK [v2.26](https://softwarecenter.qualcomm.com/api/download/software/qualcomm_neural_processing_sdk/v2.26.0.240828.zip), python 3.10, Android SDK r26c.

Phone verified: OnePlus 12, Samsung 24+, Samsung 23

## Prerequisites
* Download and unzip QNN SDK [v2.26](https://softwarecenter.qualcomm.com/api/download/software/qualcomm_neural_processing_sdk/v2.26.0.240828.zip)
* Download and unzip Android SDK [r27](https://developer.android.com/ndk/downloads)
* Download and unzip Android SDK [r26](https://developer.android.com/ndk/downloads)
* Android phone with Snapdragon8 Gen3 (SM8650) or Gen2 (SM8550). Gen 1 and lower SoC might be supported but not fully validated.
* Desired Llama model weights in .PTH format. You can download them on HuggingFace ([Example](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)).

Expand Down Expand Up @@ -40,7 +40,7 @@ Install dependencies
## Setup QNN
```
# Set these variables correctly for your environment
export ANDROID_NDK_ROOT=$HOME/android-ndk-r27 # Download android SDK and unzip to home directory
export ANDROID_NDK_ROOT=$HOME/android-ndk-r26 # Download android SDK and unzip to home directory
export QNN_SDK_ROOT=$HOME/Your-SDK-Root #Folder contains lib
export EXECUTORCH_ROOT=$HOME/repos/executorch
export LD_LIBRARY_PATH=$QNN_SDK_ROOT/lib/x86_64-linux-clang/:$LD_LIBRARY_PATH
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