✨ Simplify install script and remove pybind flags

Author: jathu

.github/workflows/trunk.yml

@@ -305,7 +305,7 @@ jobs:
         # Install requirements
         ${CONDA_RUN} sh install_requirements.sh
         ${CONDA_RUN} sh backends/apple/coreml/scripts/install_requirements.sh
-        ${CONDA_RUN} python install_executorch.py --pybind coreml
+        ${CONDA_RUN} python install_executorch.py
         ${CONDA_RUN} sh examples/models/llama/install_requirements.sh
 
         # Test ANE llama

backends/apple/mps/setup.md

@@ -97,7 +97,7 @@ I 00:00:00.122615 executorch:mps_executor_runner.mm:501] Model verified successf
 ### [Optional] Run the generated model directly using pybind
 1. Make sure `pybind` MPS support was installed:
 ```bash
-./install_executorch.sh --pybind mps
+CMAKE_ARGS="-DEXECUTORCH_BUILD_MPS=ON" ./install_executorch.sh
 ```
 2. Run the `mps_example` script to trace the model and run it directly from python:
 ```bash

docs/source/backends-mps.md

@@ -97,7 +97,7 @@ I 00:00:00.122615 executorch:mps_executor_runner.mm:501] Model verified successf
 ### [Optional] Run the generated model directly using pybind
 1. Make sure `pybind` MPS support was installed:
 ```bash
-./install_executorch.sh --pybind mps
+CMAKE_ARGS="-DEXECUTORCH_BUILD_MPS=ON" ./install_executorch.sh
 ```
 2. Run the `mps_example` script to trace the model and run it directly from python:
 ```bash

docs/source/using-executorch-building-from-source.md

@@ -64,25 +64,15 @@ Or alternatively, [install conda on your machine](https://conda.io/projects/cond
    ./install_executorch.sh
    ```
 
-   Use the [`--pybind` flag](https://github.com/pytorch/executorch/blob/main/install_executorch.sh#L26-L29) to install with pybindings and dependencies for other backends.
-   ```bash
-   ./install_executorch.sh --pybind <coreml | mps | xnnpack>
-
-   # Example: pybindings with CoreML *only*
-   ./install_executorch.sh --pybind coreml
-
-   # Example: pybinds with CoreML *and* XNNPACK
-   ./install_executorch.sh --pybind coreml xnnpack
-   ```
+   Not all backends are built into the pip wheel by default. You can link these missing/experimental backends by turning on the corresponding cmake flag. For example, to include the MPS backend:
 
-   By default, `./install_executorch.sh` command installs pybindings for XNNPACK. To disable any pybindings altogether:
-   ```bash
-   ./install_executorch.sh --pybind off
-   ```
+  ```bash
+  CMAKE_ARGS="-DEXECUTORCH_BUILD_MPS=ON" ./install_executorch.sh
+  ```
 
    For development mode, run the command with `--editable`, which allows us to modify Python source code and see changes reflected immediately.
    ```bash
-   ./install_executorch.sh --editable [--pybind xnnpack]
+   ./install_executorch.sh --editable
 
    # Or you can directly do the following if dependencies are already installed
    # either via a previous invocation of `./install_executorch.sh` or by explicitly installing requirements via `./install_requirements.sh` first.

examples/demo-apps/react-native/rnllama/README.md

@@ -26,7 +26,7 @@ A React Native mobile application for running LLaMA language models using ExecuT
 
 3. Pull submodules: `git submodule sync && git submodule update --init`
 
-4. Install dependencies: `./install_executorch.sh --pybind xnnpack && ./examples/models/llama/install_requirements.sh`
+4. Install dependencies: `./install_executorch.sh && ./examples/models/llama/install_requirements.sh`
 
 5. Follow the instructions in the [README](https://github.com/pytorch/executorch/blob/main/examples/models/llama/README.md#option-a-download-and-export-llama32-1b3b-model) to export a model as `.pte`
 

examples/models/llama/README.md

@@ -148,7 +148,7 @@ Llama 3 8B performance was measured on the Samsung Galaxy S22, S24, and OnePlus
 ## Step 1: Setup
 > :warning: **double check your python environment**: make sure `conda activate <VENV>` is run before all the bash and python scripts.
 
-1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. For installation run `./install_executorch.sh --pybind xnnpack`
+1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. For installation run `./install_executorch.sh`
 2. Run `examples/models/llama/install_requirements.sh` to install a few dependencies.
 
 
@@ -528,7 +528,7 @@ This example tries to reuse the Python code, with minimal modifications to make
 git clean -xfd
 pip uninstall executorch
 ./install_executorch.sh --clean
-./install_executorch.sh --pybind xnnpack
+./install_executorch.sh
 ```
 - If you encounter `pthread` related issues during link time, add `pthread` in `target_link_libraries` in `CMakeLists.txt`
 - On Mac, if there is linking error in Step 4 with error message like

examples/models/phi-3-mini/README.md

@@ -3,7 +3,7 @@ This example demonstrates how to run a [Phi-3-mini](https://huggingface.co/micro
 
 # Instructions
 ## Step 1: Setup
-1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. For installation run `./install_executorch.sh --pybind xnnpack`
+1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. For installation run `./install_executorch.sh`
 2. Currently, we support transformers v4.44.2. Install transformers with the following command:
 ```
 pip uninstall -y transformers ; pip install transformers==4.44.2

extension/pybindings/README.md

@@ -2,28 +2,18 @@
 This Python module, named `portable_lib`, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
 
 ```bash
-CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
-```
-
-Or when installing the rest of dependencies:
+./install_executorch.sh
 
-```bash
-install_executorch.sh --pybind
+# ...or use pip directly
+pip install . --no-build-isolation
 ```
 
 # Link Backends
 
-You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
-
-```bash
-CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
-  pip install . --no-build-isolation
-```
-
-Similarly, when installing the rest of dependencies:
+Not all backends are built into the pip wheel by default. You can link these missing/experimental backends by turning on the corresponding cmake flag. For example, to include the MPS backend:
 
 ```bash
-install_executorch.sh --pybind xnnpack coreml mps
+CMAKE_ARGS="-DEXECUTORCH_BUILD_MPS=ON" ./install_executorch.sh
 ```
 
 ## Functions