Replace buck with cmake in docs

docs/source/tutorial-xnnpack-delegate-lowering.md

@@ -152,7 +152,7 @@ cmake \
     -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
     -DEXECUTORCH_BUILD_XNNPACK=ON \
     -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
-    -DEXECUTORCH_ENABLE_LOGGING=1 \
+    -DEXECUTORCH_ENABLE_LOGGING=ON \
     -DPYTHON_EXECUTABLE=python \
     -Bcmake-out .
 ```
@@ -169,15 +169,5 @@ Now you should be able to find the executable built at `./cmake-out/backends/xnn
 ./cmake-out/backends/xnnpack/xnn_executor_runner --model_path=./mv2_xnnpack_q8.pte
 ```
 
-
-## Running the XNNPACK Model with Buck
-Alternatively, you can use `buck2` to run the `.pte` file with XNNPACK delegate instructions in it on your host platform. You can follow the instructions here to install [buck2](getting-started-setup.md#Build-&-Run). You can now run it with the prebuilt `xnn_executor_runner` provided in the examples. This will run the model on some sample inputs.
-
-```bash
-buck2 run examples/xnnpack:xnn_executor_runner -- --model_path ./mv2_xnnpack_fp32.pte
-# or to run the quantized variant
-buck2 run examples/xnnpack:xnn_executor_runner -- --model_path ./mv2_xnnpack_q8.pte
-```
-
 ## Building and Linking with the XNNPACK Backend
 You can build the XNNPACK backend [BUCK target](https://github.com/pytorch/executorch/blob/main/backends/xnnpack/targets.bzl#L54) and [CMake target](https://github.com/pytorch/executorch/blob/main/backends/xnnpack/CMakeLists.txt#L83), and link it with your application binary such as an Android or iOS application. For more information on this you may take a look at this [resource](demo-apps-android.md) next.

examples/xnnpack/README.md

@@ -88,12 +88,37 @@ You can find more valid quantized example models by running:
 python3 -m examples.xnnpack.quantization.example --help
 ```
 
-A quantized model can be run via `executor_runner`:
+## Running the XNNPACK Model with CMake
+After exporting the XNNPACK Delegated model, we can now try running it with example inputs using CMake. We can build and use the xnn_executor_runner, which is a sample wrapper for the ExecuTorch Runtime and XNNPACK Backend. We first begin by configuring the CMake build like such:
 ```bash
-buck2 run examples/portable/executor_runner:executor_runner -- --model_path ./mv2_quantized.pte
+# cd to the root of executorch repo
+cd executorch
+
+# Get a clean cmake-out directory
+rm- -rf cmake-out
+mkdir cmake-out
+
+# Configure cmake
+cmake \
+    -DCMAKE_INSTALL_PREFIX=cmake-out \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
+    -DEXECUTORCH_BUILD_XNNPACK=ON \
+    -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
+    -DEXECUTORCH_ENABLE_LOGGING=ON \
+    -DPYTHON_EXECUTABLE=python \
+    -Bcmake-out .
 ```
-Please note that running a quantized model will require the presence of various quantized/dequantize operators in the [quantized kernel lib](../../kernels/quantized).
+Then you can build the runtime componenets with
 
+```bash
+cmake --build cmake-out -j9 --target install --config Release
+```
+
+Now you should be able to find the executable built at `./cmake-out/backends/xnnpack/xnn_executor_runner` you can run the executable with the model you generated as such
+```bash
+./cmake-out/backends/xnnpack/xnn_executor_runner --model_path=./mv2_quantized.pte
+```
 
 ## Delegating a Quantized Model