Android NDK use r27b (pytorch#6092)

Summary:
Replace all r25, r26, r26c with r27b

Pull Request resolved: pytorch#6092

Reviewed By: cccclai

Differential Revision: D64429391

Pulled By: kirklandsign

fbshipit-source-id: 5334e126c24590e56c7dab57f3cc104aefa9d940

Author: kirklandsign

.ci/docker/build.sh

@@ -41,7 +41,7 @@ case "${IMAGE_NAME}" in
     LINTRUNNER=""
     CLANG_VERSION=12
     # From https://developer.android.com/ndk/downloads
-    ANDROID_NDK_VERSION=r26c
+    ANDROID_NDK_VERSION=r27b
     ;;
   *)
     echo "Invalid image name ${IMAGE_NAME}"

.github/workflows/android.yml

@@ -53,7 +53,7 @@ jobs:
     # NB: Use metal install for KVM support to run the emulator faster
     runs-on: linux.24xl.spr-metal
     env:
-      ANDROID_NDK_VERSION: r26c
+      ANDROID_NDK_VERSION: r27b
       API_LEVEL: 34
     steps:
       - name: Setup SSH (Click me for login details)

backends/vulkan/README.md

@@ -150,7 +150,7 @@ when building with CMake.
 
 First, make sure that you have the Android NDK installed; any NDK version past
 NDK r19c should work. Note that the examples in this doc have been validated with
-NDK r25. The Android SDK should also be installed so that you have access to `adb`.
+NDK r27b. The Android SDK should also be installed so that you have access to `adb`.
 
 The instructions in this page assumes that the following environment variables
 are set.

examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md

@@ -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 r26c.
+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 r27b.
 
 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 [r26](https://developer.android.com/ndk/downloads)
+* Download and unzip Android SDK [r27b](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)).
 
@@ -40,7 +40,7 @@ Install dependencies
 ## Setup QNN
 ```
 # Set these variables correctly for your environment
-export ANDROID_NDK_ROOT=$HOME/android-ndk-r26 # Download android SDK and unzip to home directory
+export ANDROID_NDK_ROOT=$HOME/android-ndk-r27b # 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

examples/models/llama2/README.md

@@ -356,7 +356,7 @@ To build for CoreML backend and validate on Mac, replace `-DEXECUTORCH_BUILD_XNN
 
 **1. Build llama runner binary for Android**
 
-*Pre-requisite*: Android NDK (tested with r26c) which can be downloaded from [here](https://developer.android.com/ndk/downloads). Note that the mac binary can be unpackaged and you can locate NDK folder from it.
+*Pre-requisite*: Android NDK (tested with r27b) which can be downloaded from [here](https://developer.android.com/ndk/downloads). Note that the mac binary can be unpackaged and you can locate NDK folder from it.
 
 **1.1 Set Android NDK**
 ```