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#include <c10/util/irange.h>
#include <torch/csrc/autograd/functions/utils.h>
#include <torch/csrc/autograd/edge.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/variable.h>
#include <sstream>
#include <vector>
namespace torch {
namespace autograd {
variable_list wrap_outputs(
const variable_list& inputs,
tensor_list&& outputs,
const function_constructor& ctr) {
variable_list result;
result.reserve(outputs.size());
if (!any_variable_requires_grad(inputs)) {
for (auto& output : outputs) {
if (output.defined()) {
result.push_back(make_variable(output, /*requires_grad=*/false));
} else {
result.emplace_back();
}
}
} else {
auto grad_fn =
ctr(GradMode::is_enabled() ? collect_next_edges(inputs) : edge_list());
for (auto& output : outputs) {
if (output.defined()) {
auto variable =
autograd::make_variable(output, /*requires_grad=*/false);
autograd::create_gradient_edge(variable, grad_fn);
result.push_back(std::move(variable));
} else {
grad_fn->add_input_metadata(Node::undefined_input());
result.emplace_back();
}
}
}
return result;
}
void check_input_variables(
const char* name,
const variable_list& inputs,
int args,
int required_args,
bool allow_undefined) {
if (required_args == -1) {
required_args = args;
}
if (inputs.size() != (size_t)args) {
std::stringstream ss;
ss << name << ": expected " << args << " arguments (got " << inputs.size();
ss << ")";
throw std::runtime_error(ss.str());
}
for (const auto i : c10::irange(required_args)) {
if (!inputs[i].defined() && !allow_undefined) {
std::stringstream ss;
ss << name << ": expected Tensor at argument " << i << " (got None)";
throw std::runtime_error(ss.str());
}
}
}
} // namespace autograd
} // namespace torch
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