mirror of
https://github.com/zebrajr/pytorch.git
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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/21674 ghimport-source-id: b8e27f0ce9b8b362daf73556ee67457fb5355062 Reviewed By: eellison Differential Revision: D15777726 Pulled By: zdevito fbshipit-source-id: 718ac676c9a1bcf99b856862fd29631d825645da
157 lines
4.5 KiB
C++
157 lines
4.5 KiB
C++
#include <torch/script.h>
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#include <torch/cuda.h>
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#include "op.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include <iostream>
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namespace helpers {
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template <typename Predicate>
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void check_all_parameters(
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const torch::jit::script::Module& module,
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Predicate predicate) {
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for (const auto& parameter : module.get_parameters()) {
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AT_ASSERT(predicate(parameter.value().toTensor()));
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}
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for (const auto& child : module.get_modules()) {
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check_all_parameters(*child, predicate);
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}
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}
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} // namespace helpers
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void get_operator_from_registry_and_execute() {
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auto& ops = torch::jit::getAllOperatorsFor(
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torch::jit::Symbol::fromQualString("custom::op"));
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AT_ASSERT(ops.size() == 1);
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auto& op = ops.front();
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AT_ASSERT(op->schema().name() == "custom::op");
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torch::jit::Stack stack;
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torch::jit::push(stack, torch::ones(5), 2.0, 3);
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op->getOperation()(stack);
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std::vector<torch::Tensor> output;
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torch::jit::pop(stack, output);
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const auto manual = custom_op(torch::ones(5), 2.0, 3);
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AT_ASSERT(output.size() == 3);
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for (size_t i = 0; i < output.size(); ++i) {
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AT_ASSERT(output[i].allclose(torch::ones(5) * 2));
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AT_ASSERT(output[i].allclose(manual[i]));
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}
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}
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void load_serialized_module_with_custom_op_and_execute(
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const std::string& path_to_exported_script_module) {
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std::shared_ptr<torch::jit::script::Module> module =
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torch::jit::load(path_to_exported_script_module);
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AT_ASSERT(module != nullptr);
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std::vector<torch::jit::IValue> inputs;
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inputs.push_back(torch::ones(5));
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auto output = module->forward(inputs).toTensor();
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AT_ASSERT(output.allclose(torch::ones(5) + 1));
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}
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void test_argument_checking_for_serialized_modules(
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const std::string& path_to_exported_script_module) {
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std::shared_ptr<torch::jit::script::Module> module =
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torch::jit::load(path_to_exported_script_module);
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AT_ASSERT(module != nullptr);
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try {
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module->forward({torch::jit::IValue(1), torch::jit::IValue(2)});
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AT_ASSERT(false);
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} catch (const c10::Error& error) {
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AT_ASSERT(
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std::string(error.what_without_backtrace())
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.find("Expected at most 2 argument(s) for operator 'forward', "
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"but received 3 argument(s)") == 0);
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}
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try {
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module->forward({torch::jit::IValue(5)});
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AT_ASSERT(false);
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} catch (const c10::Error& error) {
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AT_ASSERT(
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std::string(error.what_without_backtrace())
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.find("forward() Expected a value of type 'Tensor' "
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"for argument 'input' but instead found type 'int'") == 0);
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}
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try {
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module->forward({});
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AT_ASSERT(false);
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} catch (const c10::Error& error) {
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AT_ASSERT(
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std::string(error.what_without_backtrace())
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.find("forward() is missing value for argument 'input'") == 0);
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}
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}
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void test_move_to_device(const std::string& path_to_exported_script_module) {
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std::shared_ptr<torch::jit::script::Module> module =
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torch::jit::load(path_to_exported_script_module);
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AT_ASSERT(module != nullptr);
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helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
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return tensor.device().is_cpu();
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});
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module->to(torch::kCUDA);
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helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
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return tensor.device().is_cuda();
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});
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module->to(torch::kCPU);
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helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
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return tensor.device().is_cpu();
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});
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}
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void test_move_to_dtype(const std::string& path_to_exported_script_module) {
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std::shared_ptr<torch::jit::script::Module> module =
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torch::jit::load(path_to_exported_script_module);
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AT_ASSERT(module != nullptr);
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module->to(torch::kInt);
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helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
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return tensor.dtype() == torch::kInt;
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});
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module->to(torch::kDouble);
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helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
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return tensor.dtype() == torch::kDouble;
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});
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}
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int main(int argc, const char* argv[]) {
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if (argc != 2) {
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std::cerr << "usage: test_custom_ops <path-to-exported-script-module>\n";
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return -1;
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}
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const std::string path_to_exported_script_module = argv[1];
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get_operator_from_registry_and_execute();
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load_serialized_module_with_custom_op_and_execute(
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path_to_exported_script_module);
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test_argument_checking_for_serialized_modules(path_to_exported_script_module);
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test_move_to_dtype(path_to_exported_script_module);
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if (torch::cuda::device_count() > 0) {
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test_move_to_device(path_to_exported_script_module);
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}
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std::cout << "ok\n";
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}
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