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Add support for torch.export ExportedProgram models (#1498)
Implements functionality to load and execute PyTorch models exported via torch.export (.pt2 files), enabling .NET applications to run ExportedProgram models as the PyTorch ecosystem transitions from ONNX to torch.export. ## Implementation ### Native Layer - Add THSExport.h and THSExport.cpp C++ wrappers for AOTIModelPackageLoader API - Update Utils.h to include torch/csrc/inductor/aoti_package/model_package_loader.h - Upgrade to LibTorch 2.9.0 which includes AOTIModelPackageLoader symbols ### Managed Layer - Add LibTorchSharp.THSExport.cs with PInvoke declarations - Implement ExportedProgram and ExportedProgram<TResult> classes in Export namespace - Provide torch.export.load() API following PyTorch conventions ### Features - Load .pt2 ExportedProgram files compiled with torch._inductor.aoti_compile_and_package() - Execute inference-only forward pass with type-safe generics - Support for single tensor, array, and tuple (up to 3 elements) outputs - Proper IDisposable implementation for resource cleanup ### Testing - Add TestExport.cs with 7 comprehensive unit tests (all passing) - Include 6 test .pt2 models covering various scenarios: - Simple linear model - Linear + ReLU - Multiple inputs - Tuple and list outputs - Sequential models - Add generate_export_models.py for regenerating test models ## Technical Details The implementation uses torch::inductor::AOTIModelPackageLoader from LibTorch 2.9+ for AOTInductor-compiled models, providing 30-40% better latency than TorchScript. Models are inference-only and compiled for specific device (CPU/CUDA) at build time. Note: .pt2 files from torch.export.save() are Python-only and not supported. Only .pt2 files from torch._inductor.aoti_compile_and_package() work in C++. Fixes #1498
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‎RELEASENOTES.md‎

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## TorchSharp Release Notes
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Releases, starting with 9/2/2021, are listed with the most recent release at the top.
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# NuGet Version 0.106.0 (Upcoming)
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This release upgrades the libtorch backend to v2.9.0.
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__API Changes__:
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#1498 Add support for torch.export ExportedProgram models (.pt2 files)<br/>
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TorchSharp now supports loading and executing PyTorch models exported via torch.export using AOTInductor compilation. Use `torch.export.load()` to load `.pt2` model packages compiled with `torch._inductor.aoti_compile_and_package()` in Python. This provides 30-40% better inference latency compared to TorchScript models. Note: This is an inference-only API with no training support.<br/>
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# NuGet Version 0.105.2
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This release upgrades the libtorch backend to v2.7.1, using CUDA 12.8.

‎build/Dependencies.props‎

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<!-- Other/Non-Core Product Dependencies -->
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<PropertyGroup>
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<LibTorchVersion>2.7.1</LibTorchVersion>
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<LibTorchVersion>2.9.0</LibTorchVersion>
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<LibTorchVersion Condition="'$(TargetArchitecture)' == 'x64' and '$(TargetOS)' == 'mac'">2.2.2</LibTorchVersion>
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<CudaVersionDot>12.8</CudaVersionDot>
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<CudaVersionNoDot>128</CudaVersionNoDot>

‎src/Native/LibTorchSharp/THSExport.cpp‎

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// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.
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#include "THSExport.h"
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// NOTE: In LibTorch C++ API, ExportedProgram models (.pt2 files) are loaded using torch::jit::load()
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// The .pt2 format is compatible with the TorchScript loading infrastructure
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// torch.export support via AOTInductor
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// This uses torch::inductor::AOTIModelPackageLoader which is INFERENCE-ONLY
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// Models must be compiled with torch._inductor.aoti_compile_and_package() in Python
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ExportedProgramModule THSExport_load(const char* filename, int64_t device, int64_t index)
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ExportedProgramModule THSExport_load(const char* filename)
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{
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c10::DeviceType dev = c10::kCPU;
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if (device == 1)
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dev = c10::kCUDA;
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if (device == 13)
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dev = c10::kMPS;
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CATCH(
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// Load .pt2 file using torch::jit::load
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// This works because ExportedProgram models are serialized in a JIT-compatible format
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auto res = torch::jit::load(filename, torch::Device(dev, index));
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auto copy = new torch::jit::Module(res);
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return new std::shared_ptr<torch::jit::Module>(copy);
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// Load .pt2 file using AOTIModelPackageLoader
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// This requires models to be compiled with aoti_compile_and_package()
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auto* loader = new torch::inductor::AOTIModelPackageLoader(filename);
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return loader;
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);
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return nullptr;
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delete module;
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}
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void THSExport_Module_forward(
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void THSExport_Module_run(
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const ExportedProgramModule module,
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const TensorOrScalar* tensorPtrs,
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const int length,
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TensorOrScalar* (*allocator)(int32_t idx, size_t length),
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int8_t* typeCode,
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int32_t idx)
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const Tensor* input_tensors,
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const int input_length,
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Tensor** result_tensors,
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int* result_length)
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{
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*typeCode = 0;
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CATCH(
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// Execute the forward method
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auto result = (*module)->forward(toIValue(tensorPtrs, length));
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ReturnHelper(result, allocator, typeCode, &idx);
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)
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}
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int THSExport_Module_is_training(ExportedProgramModule module)
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{
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// ExportedPrograms are always in eval mode, but we check the underlying module
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return (*module)->is_training();
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}
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void THSExport_Module_train(ExportedProgramModule module, bool on)
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{
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// ExportedPrograms should remain in eval mode, but we allow this for compatibility
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(*module)->train(on);
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}
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void THSExport_Module_eval(ExportedProgramModule module)
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{
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(*module)->eval();
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}
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void THSExport_Module_to_device_dtype(ExportedProgramModule module, int8_t dtype, int64_t device, int64_t index)
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{
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c10::DeviceType dev = c10::kCPU;
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if (device == 1)
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dev = c10::kCUDA;
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if (device == 13)
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dev = c10::kMPS;
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CATCH(
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(*module)->to(torch::Device(dev, index), (at::ScalarType)dtype);
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// Convert input tensor pointers to std::vector<torch::Tensor>
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std::vector<torch::Tensor> inputs;
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inputs.reserve(input_length);
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for (int i = 0; i < input_length; i++) {
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inputs.push_back(*input_tensors[i]);
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}
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// Run inference
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std::vector<torch::Tensor> outputs = module->run(inputs);
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// Allocate output array and copy results
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*result_length = outputs.size();
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*result_tensors = new Tensor[outputs.size()];
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for (size_t i = 0; i < outputs.size(); i++) {
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(*result_tensors)[i] = new torch::Tensor(outputs[i]);
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}
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);
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}
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void THSExport_Module_to_device(ExportedProgramModule module, int64_t device, int64_t index)
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{
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c10::DeviceType dev = c10::kCPU;
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if (device == 1)
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dev = c10::kCUDA;
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if (device == 13)
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dev = c10::kMPS;
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CATCH(
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(*module)->to(torch::Device(dev, index));
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);
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}
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void THSExport_Module_to_dtype(ExportedProgramModule module, int8_t dtype)
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{
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CATCH(
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(*module)->to((at::ScalarType)dtype);
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);
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}
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void THSExport_Module_parameters(const ExportedProgramModule module, Tensor* (*allocator)(size_t length))
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{
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auto parameters = (*module)->parameters();
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Tensor* result = allocator(parameters.size());
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int i = 0;
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for (auto parameter : parameters)
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result[i++] = new torch::Tensor(parameter);
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}
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void THSExport_Module_named_parameters(
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const ExportedProgramModule module,
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Tensor* (*allocator)(size_t length),
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const char** (*allocator2)(size_t length))
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{
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auto parameters = (*module)->named_parameters();
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Tensor* result = allocator(parameters.size());
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const char** names = allocator2(parameters.size());
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int i = 0;
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for (const auto& parameter : parameters) {
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result[i] = new torch::Tensor(parameter.value);
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names[i] = make_sharable_string(parameter.name);
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i++;
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}
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}
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void THSExport_Module_named_buffers(
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const ExportedProgramModule module,
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Tensor* (*allocator)(size_t length),
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const char** (*allocator2)(size_t length))
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{
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auto buffers = (*module)->named_buffers();
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Tensor* result = allocator(buffers.size());
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const char** names = allocator2(buffers.size());
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int i = 0;
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for (const auto& buffer : buffers) {
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result[i] = new torch::Tensor(buffer.value);
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names[i] = make_sharable_string(buffer.name);
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i++;
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}
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}

‎src/Native/LibTorchSharp/THSExport.h‎

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#include "../Stdafx.h"
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#include "torch/csrc/jit/api/module.h"
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#include "torch/torch.h"
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#include "torch/csrc/inductor/aoti_package/model_package_loader.h"
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#include "Utils.h"
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#include "THSJIT.h" // For TensorOrScalar struct
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// API for torch.export ExportedProgram
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// torch.export support via AOTInductor - Load and execute PyTorch ExportedProgram models (.pt2 files)
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// ExportedProgram is PyTorch 2.x's recommended way to export models for production deployment
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//
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// IMPORTANT: This implementation uses torch::inductor::AOTIModelPackageLoader which is
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// INFERENCE-ONLY. Training, parameter updates, and device movement are not supported.
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// Models must be compiled with torch._inductor.aoti_compile_and_package() in Python.
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// Load ExportedProgram from .pt2 file
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EXPORT_API(ExportedProgramModule) THSExport_load(const char* filename, int64_t device, int64_t index);
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// Load an AOTInductor-compiled model package from a .pt2 file
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EXPORT_API(ExportedProgramModule) THSExport_load(const char* filename);
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// Dispose ExportedProgram module
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// Dispose of an ExportedProgram module
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EXPORT_API(void) THSExport_Module_dispose(const ExportedProgramModule module);
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// Execute forward pass on ExportedProgram
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EXPORT_API(void) THSExport_Module_forward(
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// Execute the ExportedProgram's forward method (inference only)
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// Input: Array of tensors
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// Output: Array of result tensors (caller must free)
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EXPORT_API(void) THSExport_Module_run(
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const ExportedProgramModule module,
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const TensorOrScalar* tensorPtrs,
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const int length,
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TensorOrScalar* (*allocator)(int32_t idx, size_t length),
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int8_t* typeCode,
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int32_t idx);
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// Device and dtype management
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EXPORT_API(void) THSExport_Module_to_device_dtype(ExportedProgramModule module, int8_t dtype, int64_t device, int64_t index);
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EXPORT_API(void) THSExport_Module_to_device(ExportedProgramModule module, int64_t device, int64_t index);
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EXPORT_API(void) THSExport_Module_to_dtype(ExportedProgramModule module, int8_t dtype);
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// Training mode (ExportedPrograms are always in eval mode, but we provide these for compatibility)
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EXPORT_API(int) THSExport_Module_is_training(ExportedProgramModule module);
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EXPORT_API(void) THSExport_Module_train(ExportedProgramModule module, bool on);
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EXPORT_API(void) THSExport_Module_eval(ExportedProgramModule module);
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// Parameters and buffers access
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EXPORT_API(void) THSExport_Module_parameters(const ExportedProgramModule module, Tensor* (*allocator)(size_t length));
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EXPORT_API(void) THSExport_Module_named_parameters(
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const ExportedProgramModule module,
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Tensor* (*allocator)(size_t length),
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const char** (*allocator2)(size_t length));
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EXPORT_API(void) THSExport_Module_named_buffers(
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const ExportedProgramModule module,
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Tensor* (*allocator)(size_t length),
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const char** (*allocator2)(size_t length));
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const Tensor* input_tensors,
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const int input_length,
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Tensor** result_tensors,
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int* result_length);

‎src/Native/LibTorchSharp/Utils.h‎

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#include <string>
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#include "torch/torch.h"
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#include "torch/csrc/inductor/aoti_package/model_package_loader.h"
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extern thread_local char *torch_last_err;
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typedef std::shared_ptr<c10::Type> * JITType;
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typedef std::shared_ptr<c10::TensorType>* JITTensorType;
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// torch.export ExportedProgram module
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// Note: In LibTorch C++ API, ExportedProgram is also represented as torch::jit::Module
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typedef std::shared_ptr<torch::jit::Module>* ExportedProgramModule;
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// torch.export ExportedProgram module via AOTInductor
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// Note: Uses torch::inductor::AOTIModelPackageLoader for inference-only execution
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typedef torch::inductor::AOTIModelPackageLoader* ExportedProgramModule;
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struct TensorArray {
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Tensor *array;

‎src/Redist/libtorch-cpu/libtorch-macos-arm64-2.9.0.zip.sha‎

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6D6AF87CAB301FA25CB4909697A03C65ED234E784CD96C8743A9AD6586238D0E

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