Qualcomm AI Engine Direct - docs fix (pytorch#14881)

### Summary
update README on python dependencies and recommended QNN version

cc @cccclai @winskuo-quic @shewu-quic @haowhsu-quic @cbilgin

Author: DannyYuyang-quic

docs/source/backends-qualcomm.md

@@ -74,10 +74,9 @@ This example is verified with SM8550 and SM8450.
  - A compiler to compile AOT parts, e.g., the GCC compiler comes with Ubuntu LTS.
  - [Android NDK](https://developer.android.com/ndk). This example is verified with NDK 26c.
  - [Qualcomm AI Engine Direct SDK](https://developer.qualcomm.com/software/qualcomm-ai-engine-direct-sdk)
-   - Click the "Get Software" button to download a version of QNN SDK.
-   - However, at the moment of updating this tutorial, the above website doesn't provide QNN SDK newer than 2.22.6.
-   - The below is public links to download various QNN versions. Hope they can be publicly discoverable soon.
-   - [QNN 2.37.0](https://softwarecenter.qualcomm.com/api/download/software/sdks/Qualcomm_AI_Runtime_Community/All/2.37.0.250724/v2.37.0.250724.zip)
+   - Click the "Get Software" button to download the latest version of the QNN SDK.
+   - Although newer versions are available, we have verified and recommend using QNN 2.37.0 for stability.
+   - You can download it directly from the following link: [QNN 2.37.0](https://softwarecenter.qualcomm.com/api/download/software/sdks/Qualcomm_AI_Runtime_Community/All/2.37.0.250724/v2.37.0.250724.zip)
 
 The directory with installed Qualcomm AI Engine Direct SDK looks like:
 ```
@@ -136,86 +135,6 @@ cd $EXECUTORCH_ROOT
 ./backends/qualcomm/scripts/build.sh --release
 ```
 
-### AOT (Ahead-of-time) components:
-
-Python APIs on x64 are required to compile models to Qualcomm AI Engine Direct binary.
-
-```bash
-cd $EXECUTORCH_ROOT
-mkdir build-x86
-cd build-x86
-# Note that the below command might change.
-# Please refer to the above build.sh for latest workable commands.
-cmake .. \
-  -DCMAKE_INSTALL_PREFIX=$PWD \
-  -DEXECUTORCH_BUILD_QNN=ON \
-  -DQNN_SDK_ROOT=${QNN_SDK_ROOT} \
-  -DEXECUTORCH_BUILD_DEVTOOLS=ON \
-  -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
-  -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \
-  -DEXECUTORCH_ENABLE_EVENT_TRACER=ON \
-  -DPYTHON_EXECUTABLE=python3
-
-# nproc is used to detect the number of available CPU.
-# If it is not applicable, please feel free to use the number you want.
-cmake --build $PWD --target "PyQnnManagerAdaptor" "PyQnnWrapperAdaptor" -j$(nproc)
-
-# install Python APIs to correct import path
-# The filename might vary depending on your Python and host version.
-cp -f backends/qualcomm/PyQnnManagerAdaptor.cpython-310-x86_64-linux-gnu.so $EXECUTORCH_ROOT/backends/qualcomm/python
-cp -f backends/qualcomm/PyQnnWrapperAdaptor.cpython-310-x86_64-linux-gnu.so $EXECUTORCH_ROOT/backends/qualcomm/python
-
-# Workaround for .fbs files in exir/_serialize
-cp $EXECUTORCH_ROOT/schema/program.fbs $EXECUTORCH_ROOT/exir/_serialize/program.fbs
-cp $EXECUTORCH_ROOT/schema/scalar_type.fbs $EXECUTORCH_ROOT/exir/_serialize/scalar_type.fbs
-```
-
-### Runtime:
-
-An example `qnn_executor_runner` executable would be used to run the compiled `pte` model.
-
-Commands to build `qnn_executor_runner` for Android:
-
-```bash
-cd $EXECUTORCH_ROOT
-mkdir build-android
-cd build-android
-# build executorch & qnn_executorch_backend
-cmake .. \
-    -DCMAKE_INSTALL_PREFIX=$PWD \
-    -DEXECUTORCH_BUILD_QNN=ON \
-    -DQNN_SDK_ROOT=$QNN_SDK_ROOT \
-    -DEXECUTORCH_BUILD_DEVTOOLS=ON \
-    -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
-    -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \
-    -DEXECUTORCH_ENABLE_EVENT_TRACER=ON \
-    -DPYTHON_EXECUTABLE=python3 \
-    -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_ROOT/build/cmake/android.toolchain.cmake \
-    -DANDROID_ABI='arm64-v8a' \
-    -DANDROID_PLATFORM=android-30
-
-# nproc is used to detect the number of available CPU.
-# If it is not applicable, please feel free to use the number you want.
-cmake --build $PWD --target install -j$(nproc)
-
-cmake ../examples/qualcomm \
-    -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_ROOT/build/cmake/android.toolchain.cmake \
-    -DANDROID_ABI='arm64-v8a' \
-    -DANDROID_PLATFORM=android-30 \
-    -DCMAKE_PREFIX_PATH="$PWD/lib/cmake/ExecuTorch;$PWD/third-party/gflags;" \
-    -DCMAKE_FIND_ROOT_PATH_MODE_PACKAGE=BOTH \
-    -DPYTHON_EXECUTABLE=python3 \
-    -Bexamples/qualcomm
-
-cmake --build examples/qualcomm -j$(nproc)
-
-# qnn_executor_runner can be found under examples/qualcomm
-# The full path is $EXECUTORCH_ROOT/build-android/examples/qualcomm/executor_runner/qnn_executor_runner
-ls examples/qualcomm
-```
-
-**Note:** If you want to build for release, add `-DCMAKE_BUILD_TYPE=Release` to the `cmake` command options.
-
 
 ## Deploying and running on device
 
@@ -365,7 +284,7 @@ The model, inputs, and output location are passed to `qnn_executorch_runner` by
 
 ## Supported model list
 
-Please refer to `$EXECUTORCH_ROOT/examples/qualcomm/scripts/` and `EXECUTORCH_ROOT/examples/qualcomm/oss_scripts/` to the list of supported models.
+Please refer to `$EXECUTORCH_ROOT/examples/qualcomm/scripts/` and `$EXECUTORCH_ROOT/examples/qualcomm/oss_scripts/` to the list of supported models.
 
 ## How to Support a Custom Model in HTP Backend
 

examples/qualcomm/README.md

@@ -111,12 +111,13 @@ This section outlines the essential APIs and utilities provided to streamline th
    Creates a clean directory for storing model outputs or intermediate results. If the directory already exists, it will be deleted and recreated to ensure a consistent environment for each run.
 
 ## Additional Dependency
+This example requires the following Python packages:
+- pandas and scikit-learn: used in the mobilebert multi-class text classification example.
+- graphviz (optional): used for visualizing QNN graphs during debugging.
 
-The mobilebert multi-class text classification example requires `pandas` and `sklearn`.
 Please install them by something like
-
 ```bash
-pip install scikit-learn pandas
+pip install scikit-learn pandas graphviz
 ```
 
 ## Limitation

examples/qualcomm/oss_scripts/llama/README.md

@@ -38,6 +38,7 @@ We offer the following modes to execute the model:
 ### Step 1: Setup
 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch.
 2. Follow the [tutorial](https://pytorch.org/executorch/main/backends-qualcomm) to build Qualcomm AI Engine Direct Backend.
+3. Please install the llm eval dependency via [examples/models/llama/install_requirements.sh](https://github.com/pytorch/executorch/blob/main/examples/models/llama/install_requirements.sh)
 
 ### Step 2: Prepare Model