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#include <c10/util/irange.h>
#include <torch/script.h>
#include <torch/cuda.h>
#include "op.h"
#include <memory>
#include <string>
#include <vector>
#include <iostream>
namespace helpers {
template <typename Predicate>
void check_all_parameters(
const torch::jit::Module& module,
Predicate predicate) {
for (at::Tensor parameter : module.parameters()) {
AT_ASSERT(predicate(parameter));
}
}
template<class Result, class... Args>
Result get_operator_from_registry_and_execute(const char* op_name, Args&&... args) {
auto& ops = torch::jit::getAllOperatorsFor(
torch::jit::Symbol::fromQualString(op_name));
TORCH_INTERNAL_ASSERT(ops.size() == 1);
auto& op = ops.front();
TORCH_INTERNAL_ASSERT(op->schema().name() == op_name);
torch::jit::Stack stack;
torch::jit::push(stack, std::forward<Args>(args)...);
op->getOperation()(stack);
TORCH_INTERNAL_ASSERT(1 == stack.size());
return torch::jit::pop(stack).to<Result>();
}
} // namespace helpers
void get_operator_from_registry_and_execute() {
std::vector<torch::Tensor> output =
helpers::get_operator_from_registry_and_execute<std::vector<torch::Tensor>>("custom::op", torch::ones(5), 2.0, 3);
const auto manual = custom_op(torch::ones(5), 2.0, 3);
TORCH_INTERNAL_ASSERT(output.size() == 3);
for (const auto i : c10::irange(output.size())) {
TORCH_INTERNAL_ASSERT(output[i].allclose(torch::ones(5) * 2));
TORCH_INTERNAL_ASSERT(output[i].allclose(manual[i]));
}
}
void get_autograd_operator_from_registry_and_execute() {
torch::Tensor x = torch::randn({5,5}, torch::requires_grad());
torch::Tensor y = torch::randn({5,5}, torch::requires_grad());
torch::Tensor z = torch::randn({5,5}, torch::requires_grad());
torch::Tensor output =
helpers::get_operator_from_registry_and_execute<torch::Tensor>("custom::op_with_autograd", x, 2, y, c10::optional<torch::Tensor>());
TORCH_INTERNAL_ASSERT(output.allclose(x + 2*y + x*y));
auto go = torch::ones({}, torch::requires_grad());
output.sum().backward(go, false, true);
TORCH_INTERNAL_ASSERT(torch::allclose(x.grad(), y + torch::ones({5,5})));
TORCH_INTERNAL_ASSERT(torch::allclose(y.grad(), x + torch::ones({5,5})*2));
// Test with optional argument.
at::zero_(x.mutable_grad());
at::zero_(y.mutable_grad());
output = helpers::get_operator_from_registry_and_execute<torch::Tensor>(
"custom::op_with_autograd", x, 2, y, z);
TORCH_INTERNAL_ASSERT(output.allclose(x + 2*y + x*y + z));
go = torch::ones({}, torch::requires_grad());
output.sum().backward(go, false, true);
TORCH_INTERNAL_ASSERT(torch::allclose(x.grad(), y + torch::ones({5,5})));
TORCH_INTERNAL_ASSERT(torch::allclose(y.grad(), x + torch::ones({5,5})*2));
TORCH_INTERNAL_ASSERT(torch::allclose(z.grad(), torch::ones({5,5})));
}
void get_autograd_operator_from_registry_and_execute_in_nograd_mode() {
at::AutoDispatchBelowAutograd guard;
torch::Tensor x = torch::randn({5,5}, torch::requires_grad());
torch::Tensor y = torch::randn({5,5}, torch::requires_grad());
torch::Tensor output =
helpers::get_operator_from_registry_and_execute<torch::Tensor>("custom::op_with_autograd", x, 2, y, c10::optional<torch::Tensor>());
TORCH_INTERNAL_ASSERT(output.allclose(x + 2*y + x*y));
}
void load_serialized_module_with_custom_op_and_execute(
const std::string& path_to_exported_script_module) {
torch::jit::Module module =
torch::jit::load(path_to_exported_script_module);
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) {
torch::jit::Module module =
torch::jit::load(path_to_exported_script_module);
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 2 argument(s) for operator 'forward', "
"but received 3 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("forward() Expected a value of type 'Tensor' "
"for argument 'input' but instead found 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) {
torch::jit::Module module =
torch::jit::load(path_to_exported_script_module);
helpers::check_all_parameters(module, [](const torch::Tensor& tensor) {
return tensor.device().is_cpu();
});
module.to(torch::kCUDA);
helpers::check_all_parameters(module, [](const torch::Tensor& tensor) {
return tensor.device().is_cuda();
});
module.to(torch::kCPU);
helpers::check_all_parameters(module, [](const torch::Tensor& tensor) {
return tensor.device().is_cpu();
});
}
void test_move_to_dtype(const std::string& path_to_exported_script_module) {
torch::jit::Module module =
torch::jit::load(path_to_exported_script_module);
module.to(torch::kFloat16);
helpers::check_all_parameters(module, [](const torch::Tensor& tensor) {
return tensor.dtype() == torch::kFloat16;
});
module.to(torch::kDouble);
helpers::check_all_parameters(module, [](const torch::Tensor& tensor) {
return tensor.dtype() == torch::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();
get_autograd_operator_from_registry_and_execute();
get_autograd_operator_from_registry_and_execute_in_nograd_mode();
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 (torch::cuda::device_count() > 0) {
test_move_to_device(path_to_exported_script_module);
}
std::cout << "ok\n";
}
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