MV2 cpp and python example

.gitignore

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 xcuserdata/
 .swiftpm/
 *.xcworkspace/
+
+# MV2
+mv2/cpp/build
+

.gitmodules

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+[submodule "mv2/cpp/executorch"]
+	path = mv2/cpp/executorch
+	url = https://github.com/pytorch/executorch.git
+	branch = release/0.6

mv2/cpp/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
+project(executorch_mv2_demo CXX)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
+# Set options for executorch build.
+option(EXECUTORCH_ENABLE_LOGGING "" ON)
+option(EXECUTORCH_BUILD_EXTENSION_DATA_LOADER "" ON)
+option(EXECUTORCH_BUILD_EXTENSION_MODULE "" ON)
+option(EXECUTORCH_BUILD_EXTENSION_TENSOR "" ON)
+option(EXECUTORCH_BUILD_KERNELS_OPTIMIZED "" ON)
+option(EXECUTORCH_BUILD_XNNPACK "" ON)
+
+# Add ExecutorTorch subdirectory
+add_subdirectory("executorch")
+
+set(DEMO_SOURCES main.cpp)
+
+# Create executable
+add_executable(executorch_mv2_demo_app ${DEMO_SOURCES})
+
+# Include directories
+target_include_directories(executorch_mv2_demo_app PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
+
+# Link libraries
+target_link_libraries(
+  executorch_mv2_demo_app
+  PRIVATE executorch
+          extension_module_static
+          extension_tensor
+          optimized_native_cpu_ops_lib
+          xnnpack_backend
+)
+
+# Set output directory
+set_target_properties(executorch_mv2_demo_app
+    PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin"
+)

mv2/cpp/README.md

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+# ExecutorTorch MobileNetV2 Demo C++ Application
+
+This is a simple C++ demo application that uses the ExecutorTorch library for MobileNetV2 model inference.
+
+## Build instructions
+
+0. Export the model. See [mv2/python/README.md](../python/README.md)
+
+1. The ExecuTorch repository is already configured as a git submodule at `~/executorch-examples/cpp/executorch/`. To initialize it:
+   ```bash
+    cd ~/executorch-examples/
+    git submodule sync
+    git submodule update --init --recursive
+   ```
+
+2. Install dev requirements for ExecuTorch
+
+    ```bash
+    cd ~/executorch-examples/mv2/cpp/executorch
+    pip install -r requirements-dev.txt
+    ```
+
+3. Build the project:
+   ```bash
+   cd ~/executorch-examples/mv2/cpp
+   chmod +x build.sh
+   ./build.sh
+   ```
+
+4. Run the demo application:
+   ```bash
+   ./build/bin/executorch_mv2_demo_app
+   ```
+
+## Dependencies
+
+- CMake 3.18 or higher
+- C++17 compatible compiler
+- ExecutorTorch library (release/0.6)
+
+## Notes
+
+- Make sure you have the correct model file (`.pte`) compatible with ExecutorTorch.
+- This demo currently initializes the input tensor with random data. In a real application, you would replace this with actual input data.

mv2/cpp/build.sh

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+#!/bin/bash
+set -e
+
+# Create build directory if it doesn't exist
+mkdir -p build
+cd build
+
+# Configure CMake
+cmake -DCMAKE_BUILD_TYPE=Release ..
+
+# Build the project
+cmake --build . -j$(nproc)
+
+echo "Build complete! Executable located at: ./bin/executorch_mv2_demo_app"

mv2/cpp/main.cpp

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+#include <executorch/extension/module/module.h>
+#include <executorch/extension/tensor/tensor.h>
+#include <iostream>
+
+using namespace ::executorch::extension;
+
+int main(int argc, char* argv[]) {
+    // Load the model.
+    Module module("../python/model_mv2_xnnpack.pte");
+
+    // Create an input tensor.
+    float input[1 * 3 * 224 * 224];
+    auto tensor = from_blob(input, {1, 3, 224, 224});
+
+    // Perform an inference.
+    const auto result = module.forward(tensor);
+
+    if (result.ok()) {
+        // Retrieve the output data.
+        const auto output = result->at(0).toTensor().const_data_ptr<float>();
+        std::cout << "Success" << std::endl;
+    }
+}

mv2/python/README.md

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+### Virtual environment setup
+Create and activate a Python virtual environment:
+```bash
+python3 -m venv .venv && source .venv/bin/activate && pip install --upgrade pip
+```
+Or alternatively, [install conda on your machine](https://conda.io/projects/conda/en/latest/user-guide/install/index.html)
+```bash
+conda create -yn executorch-examples-mv2 python=3.10.0 && conda activate executorch-examples-mv2
+```
+
+### Install dependencies
+```
+pip install -r requirements.txt
+```
+
+### Export a model
+```
+python export.py
+```
+
+### Run model via pybind
+```
+python run.py
+```

mv2/python/export.py

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+import torch
+import torchvision.models as models
+from torchvision.models.mobilenetv2 import MobileNet_V2_Weights
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
+from executorch.exir import to_edge_transform_and_lower
+
+model = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).eval()
+sample_inputs = (torch.randn(1, 3, 224, 224), )
+
+et_program = to_edge_transform_and_lower(
+    torch.export.export(model, sample_inputs),
+    partitioner=[XnnpackPartitioner()]
+).to_executorch()
+
+with open("model_mv2_xnnpack.pte", "wb") as f:
+    f.write(et_program.buffer)

mv2/python/requirements.txt

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+executorch==0.6.0

mv2/python/run.py

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+import torch
+from executorch.runtime import Runtime
+from typing import List
+
+runtime = Runtime.get()
+
+input_tensor: torch.Tensor = torch.randn(1, 3, 224, 224)
+program = runtime.load_program("model_mv2_xnnpack.pte")
+method = program.load_method("forward")
+output: List[torch.Tensor] = method.execute([input_tensor])
+print("Run succesfully via executorch")
+
+from torchvision.models.mobilenetv2 import MobileNet_V2_Weights
+import torchvision.models as models
+
+eager_reference_model = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).eval()
+eager_reference_output = eager_reference_model(input_tensor)
+
+print("Comparing against original PyTorch module")
+print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5))