mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
Summary: We are beginning to use this class in a wider reaching set of use-cases. This PR refactors it so that we always access schema properties through methods. This will make adding extra information like alias information easier (i.e. we can a version of `type()` that returns the type with alias information and another version that returns a type without that information). Pull Request resolved: https://github.com/pytorch/pytorch/pull/12967 Differential Revision: D10502674 Pulled By: zdevito fbshipit-source-id: a88783ed8f20ab3be6460c12da95f9f940891c44
156 lines
4.5 KiB
C++
156 lines
4.5 KiB
C++
#include <torch/script.h>
|
|
|
|
#include "op.h"
|
|
|
|
#include <memory>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#include <iostream>
|
|
|
|
namespace helpers {
|
|
template <typename Predicate>
|
|
void check_all_parameters(
|
|
const torch::jit::script::Module& module,
|
|
Predicate predicate) {
|
|
for (const auto& parameter : module.get_parameters()) {
|
|
AT_ASSERT(predicate(*parameter->slot()));
|
|
}
|
|
for (const auto& child : module.get_modules()) {
|
|
check_all_parameters(*child->module, predicate);
|
|
}
|
|
}
|
|
} // namespace helpers
|
|
|
|
void get_operator_from_registry_and_execute() {
|
|
auto& ops = torch::jit::getAllOperatorsFor(
|
|
torch::jit::Symbol::fromQualString("custom::op"));
|
|
AT_ASSERT(ops.size() == 1);
|
|
|
|
auto& op = ops.front();
|
|
AT_ASSERT(op->schema().name() == "custom::op");
|
|
|
|
torch::jit::Stack stack;
|
|
torch::jit::push(stack, torch::ones(5), 2.0, 3);
|
|
op->getOperation()(stack);
|
|
std::vector<at::Tensor> output;
|
|
torch::jit::pop(stack, output);
|
|
|
|
const auto manual = custom_op(torch::ones(5), 2.0, 3);
|
|
|
|
AT_ASSERT(output.size() == 3);
|
|
for (size_t i = 0; i < output.size(); ++i) {
|
|
AT_ASSERT(output[i].allclose(torch::ones(5) * 2));
|
|
AT_ASSERT(output[i].allclose(manual[i]));
|
|
}
|
|
}
|
|
|
|
void load_serialized_module_with_custom_op_and_execute(
|
|
const std::string& path_to_exported_script_module) {
|
|
std::shared_ptr<torch::jit::script::Module> module =
|
|
torch::jit::load(path_to_exported_script_module);
|
|
AT_ASSERT(module != nullptr);
|
|
|
|
std::vector<torch::jit::IValue> inputs;
|
|
inputs.push_back(torch::ones(5));
|
|
auto output = module->forward(inputs).toTensor();
|
|
|
|
AT_ASSERT(output.allclose(torch::ones(5) + 1));
|
|
}
|
|
|
|
void test_argument_checking_for_serialized_modules(
|
|
const std::string& path_to_exported_script_module) {
|
|
std::shared_ptr<torch::jit::script::Module> module =
|
|
torch::jit::load(path_to_exported_script_module);
|
|
AT_ASSERT(module != nullptr);
|
|
|
|
try {
|
|
module->forward({torch::jit::IValue(1), torch::jit::IValue(2)});
|
|
AT_ASSERT(false);
|
|
} catch (const c10::Error& error) {
|
|
AT_ASSERT(
|
|
std::string(error.what_without_backtrace())
|
|
.find("Expected at most 1 argument(s) for operator 'forward', "
|
|
"but received 2 argument(s)") == 0);
|
|
}
|
|
|
|
try {
|
|
module->forward({torch::jit::IValue(5)});
|
|
AT_ASSERT(false);
|
|
} catch (const c10::Error& error) {
|
|
AT_ASSERT(
|
|
std::string(error.what_without_backtrace())
|
|
.find("Expected value of type Dynamic for argument 'input' in "
|
|
"position 0, but instead got value of type int") == 0);
|
|
}
|
|
|
|
try {
|
|
module->forward({});
|
|
AT_ASSERT(false);
|
|
} catch (const c10::Error& error) {
|
|
AT_ASSERT(
|
|
std::string(error.what_without_backtrace())
|
|
.find("forward() is missing value for argument 'input'") == 0);
|
|
}
|
|
}
|
|
|
|
void test_move_to_device(const std::string& path_to_exported_script_module) {
|
|
std::shared_ptr<torch::jit::script::Module> module =
|
|
torch::jit::load(path_to_exported_script_module);
|
|
AT_ASSERT(module != nullptr);
|
|
|
|
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
|
|
return tensor.device().is_cpu();
|
|
});
|
|
|
|
module->to(at::kCUDA);
|
|
|
|
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
|
|
return tensor.device().is_cuda();
|
|
});
|
|
|
|
module->to(at::kCPU);
|
|
|
|
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
|
|
return tensor.device().is_cpu();
|
|
});
|
|
}
|
|
|
|
void test_move_to_dtype(const std::string& path_to_exported_script_module) {
|
|
std::shared_ptr<torch::jit::script::Module> module =
|
|
torch::jit::load(path_to_exported_script_module);
|
|
AT_ASSERT(module != nullptr);
|
|
|
|
module->to(at::kInt);
|
|
|
|
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
|
|
return tensor.dtype() == at::kInt;
|
|
});
|
|
|
|
module->to(at::kDouble);
|
|
|
|
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
|
|
return tensor.dtype() == at::kDouble;
|
|
});
|
|
}
|
|
|
|
int main(int argc, const char* argv[]) {
|
|
if (argc != 2) {
|
|
std::cerr << "usage: test_custom_ops <path-to-exported-script-module>\n";
|
|
return -1;
|
|
}
|
|
const std::string path_to_exported_script_module = argv[1];
|
|
|
|
get_operator_from_registry_and_execute();
|
|
load_serialized_module_with_custom_op_and_execute(
|
|
path_to_exported_script_module);
|
|
test_argument_checking_for_serialized_modules(path_to_exported_script_module);
|
|
test_move_to_dtype(path_to_exported_script_module);
|
|
|
|
if (at::globalContext().getNumGPUs() > 0) {
|
|
test_move_to_device(path_to_exported_script_module);
|
|
}
|
|
|
|
std::cout << "ok\n";
|
|
}
|