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[Pico2] Add CMSIS-NN INT8 support and latency instrumentation #18612
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
|
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| """ | ||
| Export the TinyMLP MNIST model with INT8 quantization for CMSIS-NN acceleration. | ||
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| Uses the CortexMQuantizer to produce INT8 quantized ops that map to CMSIS-NN | ||
| kernels on Cortex-M33 (RP2350/Pico2). The model I/O stays float — quantize and | ||
| dequantize nodes are inserted inside the graph. | ||
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| Usage: | ||
| python export_mlp_mnist_cmsis.py | ||
| python export_mlp_mnist_cmsis.py --output my_model.pte | ||
| python export_mlp_mnist_cmsis.py --num-calibration 200 | ||
| """ | ||
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| import argparse | ||
| import logging | ||
| import os | ||
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| import torch | ||
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| from executorch.backends.cortex_m.passes.cortex_m_pass_manager import CortexMPassManager | ||
| from executorch.backends.cortex_m.quantizer.quantizer import CortexMQuantizer | ||
| from executorch.exir import EdgeCompileConfig, ExecutorchBackendConfig, to_edge | ||
| from executorch.extension.export_util.utils import save_pte_program | ||
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| from export_mlp_mnist import create_balanced_model, IMAGE_SIZE, test_comprehensive | ||
| from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e | ||
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| FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s" | ||
| logging.basicConfig(level=logging.INFO, format=FORMAT) | ||
| logger = logging.getLogger(__name__) | ||
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| def get_calibration_data(num_samples: int = 100): | ||
| """ | ||
| Generate calibration data for quantization. | ||
| Mixes structured digit-like patterns and random noise so the observer | ||
| sees a representative activation range. | ||
| """ | ||
| calibration_data = [] | ||
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| # Structured patterns that look like the digits the model will see | ||
| for _ in range(num_samples // 2): | ||
| x = torch.zeros(1, IMAGE_SIZE, IMAGE_SIZE) | ||
| # Random vertical / horizontal strokes | ||
| col = torch.randint(5, 23, (1,)).item() | ||
| row = torch.randint(5, 23, (1,)).item() | ||
| x[0, 2:26, col - 1 : col + 2] = 1.0 # vertical stroke | ||
| x[0, row - 1 : row + 2, 5:23] = 1.0 # horizontal stroke | ||
| calibration_data.append(x) | ||
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| # Random pixel patterns | ||
| for _ in range(num_samples - num_samples // 2): | ||
| x = (torch.rand(1, IMAGE_SIZE, IMAGE_SIZE) > 0.7).float() | ||
| calibration_data.append(x) | ||
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| return calibration_data | ||
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| def quantize_model(model, calibration_data): | ||
| quantizer = CortexMQuantizer() | ||
| example_input = calibration_data[0] | ||
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| exported = torch.export.export(model, (example_input,)) | ||
| graph_module = exported.module() | ||
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| prepared = prepare_pt2e(graph_module, quantizer) | ||
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| logger.info(f"Calibrating with {len(calibration_data)} samples...") | ||
| with torch.no_grad(): | ||
| for i, data in enumerate(calibration_data): | ||
| prepared(data) | ||
| if (i + 1) % 25 == 0: | ||
| logger.info(f" Calibrated {i + 1}/{len(calibration_data)} samples") | ||
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| quantized = convert_pt2e(prepared) | ||
| return quantized, example_input | ||
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| def export_to_pte(quantized_model, example_input, output_path: str): | ||
| exported_program = torch.export.export(quantized_model, (example_input,)) | ||
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| edge_config = EdgeCompileConfig( | ||
| _check_ir_validity=False, | ||
| preserve_ops=[torch.ops.aten.linear.default], | ||
| ) | ||
| edge_program = to_edge(exported_program, compile_config=edge_config) | ||
| logger.info("Edge program created") | ||
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| logger.info("Applying Cortex-M optimization passes...") | ||
| pass_manager = CortexMPassManager(edge_program.exported_program()) | ||
| transformed_ep = pass_manager.transform() | ||
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| edge_program = to_edge(transformed_ep, compile_config=edge_config) | ||
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| logger.info("Converting to ExecuTorch format...") | ||
| exec_program = edge_program.to_executorch( | ||
| config=ExecutorchBackendConfig(extract_delegate_segments=False) | ||
| ) | ||
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| save_pte_program(exec_program, output_path) | ||
| file_size = os.path.getsize(output_path) | ||
| logger.info(f"Model saved to {output_path} ({file_size / 1024:.1f} KB)") | ||
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| def main(): | ||
| parser = argparse.ArgumentParser( | ||
| description="Export TinyMLP MNIST for Cortex-M with CMSIS-NN (INT8)" | ||
| ) | ||
| parser.add_argument( | ||
| "--output", | ||
| type=str, | ||
| default="balanced_tiny_mlp_mnist_cmsis.pte", | ||
| help="Output .pte file path", | ||
| ) | ||
| parser.add_argument( | ||
| "--num-calibration", | ||
| type=int, | ||
| default=100, | ||
| help="Number of calibration samples for quantization", | ||
| ) | ||
| parser.add_argument("--debug", action="store_true", help="Enable debug logging") | ||
| args = parser.parse_args() | ||
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| if args.debug: | ||
| logging.getLogger().setLevel(logging.DEBUG) | ||
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| logger.info("Creating balanced MLP MNIST model...") | ||
| model = create_balanced_model() | ||
| model.eval() | ||
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| logger.info("Testing FP32 model before quantization:") | ||
| test_comprehensive(model) | ||
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| calibration_data = get_calibration_data(args.num_calibration) | ||
| quantized_model, example_input = quantize_model(model, calibration_data) | ||
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| logger.info("Testing quantized model:") | ||
| with torch.no_grad(): | ||
| test_input = torch.zeros(1, IMAGE_SIZE, IMAGE_SIZE) | ||
| test_input[0, 2:26, 13:16] = 1.0 # digit-1-like pattern | ||
| output = quantized_model(test_input) | ||
| pred = output.argmax(dim=1).item() | ||
| logger.info(f" Digit-1 pattern -> predicted: {pred}") | ||
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| export_to_pte(quantized_model, example_input, args.output) | ||
| logger.info("Export complete!") | ||
| logger.info(f"Input shape: (1, {IMAGE_SIZE}, {IMAGE_SIZE})") | ||
| logger.info("Input format: Float [0.0, 1.0] (same as FP32 variant)") | ||
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| if __name__ == "__main__": | ||
| main() | ||
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The script enables ExecuTorch selective build via -DEXECUTORCH_SELECT_OPS_MODEL, but the Pico2 firmware CMake is never told to link the selective kernel library (USE_SELECTIVE_BUILD option added in CMakeLists.txt). As a result, the firmware still links the full portable_ops_lib under --whole-archive, negating the binary-size benefits of selective build. Consider propagating this automatically (e.g., add -DUSE_SELECTIVE_BUILD=ON when SELECT_OPS_FLAGS is set) so selective build works end-to-end.