Add example for Espressif ESP32 executorch runner with no optimizations

CMakePresets.json

@@ -294,6 +294,14 @@
         "EXECUTORCH_BUILD_PRESET_FILE": "${sourceDir}/tools/cmake/preset/arm_ethosu_linux.cmake",
         "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/examples/arm/ethos-u-setup/aarch64-linux-musl-toolchain.cmake"
       }
+    },
+    {
+      "name": "esp-baremetal",
+      "displayName": "Build ExecuTorch for ESP baremetal",
+      "inherits": ["common"],
+      "cacheVariables": {
+        "EXECUTORCH_BUILD_PRESET_FILE": "${sourceDir}/tools/cmake/preset/esp_baremetal.cmake"
+      }
     }
   ],
   "buildPresets": [

examples/espressif/README.md

@@ -0,0 +1,276 @@
+# ExecuTorch Executor Runner for Espressif ESP32/ESP32-S3
+
+This example demonstrates how to run an ExecuTorch model on Espressif ESP32 and
+ESP32-S3 microcontrollers. It is based on the
+[Arm Cortex-M executor runner](../arm/executor_runner/) and adapted for the
+ESP-IDF build system and ESP32 memory architecture.
+
+## Supported Targets
+
+| Chip     | CPU           | Internal SRAM | PSRAM (optional) |
+|----------|---------------|---------------|------------------|
+| ESP32    | Xtensa LX6 (dual-core, 240MHz) | ~520KB | 4-8MB |
+| ESP32-S3 | Xtensa LX7 (dual-core, 240MHz) | ~512KB | 2-32MB (Octal) |
+
+## Prerequisites
+
+1. **ESP-IDF v5.1+**: Install the ESP-IDF toolchain following the
+   [official guide](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/get-started/).
+
+2. **ExecuTorch**: Clone and set up ExecuTorch:
+   ```bash
+   git clone https://github.com/pytorch/executorch.git
+   cd executorch
+   pip install -e .
+   ```
+
+3. **Cross-compiled ExecuTorch libraries**: Build ExecuTorch for the ESP32
+   target. See the [Cross-Compilation](#cross-compiling-executorch) section.
+
+4. **A .pte model file**: Export a PyTorch model to the ExecuTorch `.pte`
+   format. For small models suitable for ESP32, consider:
+   - A simple add/multiply model
+   - MobileNet V2 (quantized, with PSRAM)
+   - Custom small models
+
+## Project Structure
+
+```
+examples/espressif/
+├── README.md                    # This file
+├── build.sh                     # Build helper script
+├── executor_runner/
+│   ├── CMakeLists.txt           # Component/standalone CMake build
+│   ├── esp_executor_runner.cpp  # Main executor runner
+│   ├── esp_memory_allocator.h   # Custom memory allocator
+│   ├── esp_memory_allocator.cpp
+│   ├── esp_perf_monitor.h       # Performance monitoring
+│   ├── esp_perf_monitor.cpp
+│   └── pte_to_header.py         # Convert .pte to C header
+└── project/
+    ├── CMakeLists.txt           # ESP-IDF project file
+    ├── sdkconfig.defaults       # Default ESP-IDF configuration
+    ├── sdkconfig.defaults.esp32s3  # ESP32-S3 specific config
+    ├── partitions.csv  # Adjusted partition table to increase app size
+    └── main/
+        ├── CMakeLists.txt       # Main component
+        └── main.cpp             # Entry point
+```
+
+## Quick Start
+
+The following example has been tested only on an ESP32-S3 dev board with 8 MB of Octal PSRAM. You may need to adjust the `sdkconfig` file for your specific board.
+
+### 1. Export a simple model
+
+```python
+import torch
+from executorch.exir import to_edge
+
+class SimpleModel(torch.nn.Module):
+    def forward(self, x):
+        return x + x
+
+model = SimpleModel()
+example_input = (torch.randn(1, 8),)
+
+# Export to ExecuTorch
+exported = torch.export.export(model, example_input)
+edge = to_edge(exported)
+et_program = edge.to_executorch()
+
+with open("simple_add.pte", "wb") as f:
+    f.write(et_program.buffer)
+```
+
+### 2. Convert the model to a C header
+
+```bash
+python3 examples/espressif/executor_runner/pte_to_header.py \
+    --pte simple_add.pte \
+    --outdir examples/espressif/project/build/model/
+```
+
+### 3. Build with ESP-IDF
+
+```bash
+# Source ESP-IDF environment
+. $IDF_PATH/export.sh
+
+# Using the build script:
+./examples/espressif/build.sh --target esp32s3 --pte simple_add.pte
+
+# Or manually:
+cd examples/espressif/project
+idf.py set-target esp32s3
+idf.py build
+```
+
+### 4. Flash and Monitor
+
+```bash
+cd examples/espressif/project
+idf.py -p /dev/ttyUSB0 flash monitor
+```
+
+You should see output like:
+```
+Starting executorch runner !
+I [executorch:esp_executor_runner.cpp:237 et_pal_init()] ESP32 ExecuTorch runner initialized. Free heap: 6097812 bytes.
+I [executorch:esp_executor_runner.cpp:242 et_pal_init()] PSRAM available. Free PSRAM: 5764716 bytes.
+I [executorch:esp_executor_runner.cpp:1047 executor_runner_main()] PTE @ 0x3c05f9f0 [----ET12]
+I [executorch:esp_executor_runner.cpp:568 runner_init()] PTE Model data loaded. Size: 952 bytes.
+I [executorch:esp_executor_runner.cpp:583 runner_init()] Model buffer loaded, has 1 methods
+I [executorch:esp_executor_runner.cpp:593 runner_init()] Running method forward
+I [executorch:esp_executor_runner.cpp:604 runner_init()] Setup Method allocator pool. Size: 2097152 bytes.
+I [executorch:esp_executor_runner.cpp:620 runner_init()] Setting up planned buffer 0, size 64.
+I [executorch:esp_executor_runner.cpp:716 runner_init()] Method 'forward' loaded.
+I [executorch:esp_executor_runner.cpp:718 runner_init()] Preparing inputs...
+I [executorch:esp_executor_runner.cpp:780 runner_init()] Input prepared.
+I [executorch:esp_executor_runner.cpp:979 run_model()] Starting running 1 inferences...
+I [executorch:esp_perf_monitor.cpp:41 StopMeasurements()] Profiler report:
+I [executorch:esp_perf_monitor.cpp:42 StopMeasurements()] Number of inferences: 1
+I [executorch:esp_perf_monitor.cpp:43 StopMeasurements()] Total CPU cycles: 49545 (49545.00 per inference)
+I [executorch:esp_perf_monitor.cpp:48 StopMeasurements()] Total wall time: 205 us (205.00 us per inference)
+I [executorch:esp_perf_monitor.cpp:53 StopMeasurements()] Average inference time: 0.205 ms
+I [executorch:esp_perf_monitor.cpp:59 StopMeasurements()] Free heap: 6097576 bytes
+I [executorch:esp_perf_monitor.cpp:63 StopMeasurements()] Min free heap ever: 6097576 bytes
+I [executorch:esp_executor_runner.cpp:999 run_model()] 1 inferences finished
+I [executorch:esp_executor_runner.cpp:867 print_outputs()] 1 outputs: 
+Output[0][0]: (float) 2.000000
+Output[0][1]: (float) 2.000000
+Output[0][2]: (float) 2.000000
+Output[0][3]: (float) 2.000000
+Output[0][4]: (float) 2.000000
+Output[0][5]: (float) 2.000000
+Output[0][6]: (float) 2.000000
+Output[0][7]: (float) 2.000000
+
+```
+
+## Cross-Compiling ExecuTorch
+
+ExecuTorch needs to be cross-compiled for the ESP32 target (Xtensa architecture).
+
+### Using the ESP-IDF toolchain
+
+```bash
+# Set up the cross-compilation toolchain
+export IDF_TARGET=esp32s3  # or esp32
+
+# Configure ExecuTorch build for ESP32
+#Make sure to adjust the list of ops for your model or alter to use one of the selective build methods
+cmake --preset esp-baremetal -B cmake-out-esp \
+    -DCMAKE_TOOLCHAIN_FILE=$IDF_PATH/tools/cmake/toolchain-${IDF_TARGET}.cmake \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DEXECUTORCH_BUILD_DEVTOOLS=ON \
+    -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=OFF \
+    -DEXECUTORCH_SELECT_OPS_LIST="aten::add.out," \
+    .
+
+cmake --build cmake-out-esp -j$(nproc)
+cmake --build cmake-out-esp --target install
+```
+
+## Memory Considerations
+
+### ESP32 (no PSRAM)
+- Total available SRAM: ~520KB (shared between code and data)
+- Recommended method allocator pool: 128-256KB
+- Recommended scratch pool: 64-128KB
+- **Only very small models will fit!**
+
+### ESP32 / ESP32-S3 with PSRAM
+- Internal SRAM: ~512KB (used for code and fast data)
+- PSRAM: 2-32MB (used for model data and large buffers)
+- Recommended method allocator pool: 1-4MB
+- Recommended scratch pool: 256KB-1MB
+
+### Configuring Memory Pools
+
+Memory pool sizes auto-adjust based on PSRAM availability. Override with:
+
+```cmake
+# In your project CMakeLists.txt or via idf.py menuconfig
+set(ET_ESP_METHOD_ALLOCATOR_POOL_SIZE "1048576")    # 1MB
+set(ET_ESP_SCRATCH_TEMP_ALLOCATOR_POOL_SIZE "524288") # 512KB
+```
+
+Or as compile definitions:
+```bash
+idf.py build -DET_ESP_METHOD_ALLOCATOR_POOL_SIZE=1048576
+```
+
+## Loading Models
+
+### Compiled-in (default)
+The model `.pte` file is converted to a C array and compiled into the firmware.
+This is the simplest approach but increases firmware size.
+
+### Filesystem (SPIFFS/LittleFS)
+For larger models, load from the filesystem at runtime:
+
+1. Add `-DFILESYSTEM_LOAD=ON` to your build
+2. Create a SPIFFS partition with your model:
+   ```bash
+   # Add to partitions.csv:
+   # storage, data, spiffs, , 0x200000
+
+   # Create and flash SPIFFS image:
+   $IDF_PATH/components/spiffs/spiffsgen.py 0x200000 model_dir spiffs.bin
+   esptool.py write_flash 0x210000 spiffs.bin
+   ```
+
+## Configuration Options
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `ET_NUM_INFERENCES` | 1 | Number of inference runs |
+| `ET_LOG_DUMP_INPUT` | OFF | Log input tensor values |
+| `ET_LOG_DUMP_OUTPUT` | ON | Log output tensor values |
+| `ET_BUNDLE_IO` | OFF | Enable BundleIO test support |
+| `ET_EVENT_TRACER_ENABLED` | OFF | Enable ETDump profiling |
+| `FILESYSTEM_LOAD` | OFF | Load model from filesystem |
+| `ET_ESP_METHOD_ALLOCATOR_POOL_SIZE` | Auto | Method allocator size |
+| `ET_ESP_SCRATCH_TEMP_ALLOCATOR_POOL_SIZE` | Auto | Scratch allocator size |
+
+## Differences from the Arm Example
+
+| Feature | Arm (Cortex-M) | ESP32/ESP32-S3 |
+|---------|----------------|----------------|
+| Build system | Bare-metal CMake + Arm toolchain | ESP-IDF (FreeRTOS-based) |
+| NPU | Ethos-U55/U65/U85 | None (CPU only) |
+| Memory | ITCM/DTCM/SRAM/DDR via linker script | IRAM/DRAM/PSRAM via ESP-IDF |
+| Performance monitor | ARM PMU + Ethos-U PMU | CPU cycle counter + esp_timer |
+| Semihosting | FVP simulator filesystem access | SPIFFS/LittleFS/SD filesystem |
+| Entry point | `main()` bare-metal | `app_main()` via FreeRTOS |
+| Timing | ARM_PMU_Get_CCNTR() | esp_cpu_get_cycle_count() |
+
+## Troubleshooting
+
+### Model too large for flash
+- Use filesystem loading (`FILESYSTEM_LOAD=ON`) with SPIFFS or SD card
+- Quantize the model to reduce size
+- Use a simpler/smaller model architecture
+
+### Out of memory during inference
+- Enable PSRAM if your board has it (`CONFIG_SPIRAM=y`)
+- Increase memory pool sizes
+- Use a smaller model
+- Check `log_mem_status()` output for memory usage details
+
+### Build errors with ExecuTorch libraries
+- Ensure ExecuTorch was cross-compiled with the same ESP-IDF toolchain
+- Check that `ET_BUILD_DIR_PATH` points to the correct build directory
+- Verify the target architecture matches (Xtensa LX6 for ESP32, LX7 for ESP32-S3)
+
+### Watchdog timer resets
+- Long inference times may trigger the task watchdog
+- Disable with `CONFIG_ESP_TASK_WDT_EN=n` in sdkconfig
+- Or increase the timeout: `CONFIG_ESP_TASK_WDT_TIMEOUT_S=30`
+
+## License
+
+This project is licensed under the BSD-style license found in the
+[LICENSE](../../../LICENSE) file in the root directory of the ExecuTorch
+source tree.