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#include <torch/csrc/autograd/functions/basic_ops.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/functions/utils.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/dynamo/compiled_autograd.h>
#include <ATen/ATen.h>
#include <memory>
#include <utility>
namespace torch::autograd {
auto Error::apply(variable_list&& inputs) -> variable_list {
throw std::runtime_error(msg);
}
void Error::compiled_args(CompiledNodeArgs& args) {
// throw the error durring collect, the graph won't get compiled
apply(variable_list());
}
variable_list Error::apply_with_saved(
const variable_list& inputs,
SwapSavedVariables& saved) {
TORCH_INTERNAL_ASSERT(false, "unreachable");
}
auto DelayedError::apply(variable_list&& inputs) -> variable_list {
tensor_list outputs;
outputs.reserve(inputs.size());
for (auto& var : inputs) {
// FIXME: share version counters
outputs.emplace_back(var.defined() ? var.tensor_data() : at::Tensor());
}
return wrap_outputs(inputs, std::move(outputs), [&](edge_list&& next_edges) {
return std::make_shared<Error>(msg, std::move(next_edges));
});
}
auto UndefinedGrad::apply(variable_list&& inputs) -> variable_list {
tensor_list outputs;
outputs.reserve(inputs.size());
for (auto& var : inputs) {
outputs.emplace_back(
var.defined() ? var.clone().tensor_data() : at::Tensor());
}
return wrap_outputs(inputs, std::move(outputs), [&](edge_list&& next_edges) {
return std::make_shared<UndefinedGradBackward>(std::move(next_edges));
});
}
auto UndefinedGradBackward::apply(variable_list&& output_grads)
-> variable_list {
tensor_list input_grads;
output_grads.reserve(input_grads.size());
for (auto& grad : output_grads) {
(void)grad; // Suppress unused variable warning
input_grads.emplace_back();
}
return input_grads;
}
auto Identity::apply(variable_list&& grads) -> variable_list {
return std::move(grads);
}
void GraphRoot::compiled_args(CompiledNodeArgs& args) {
args.collect(outputs);
}
variable_list GraphRoot::apply_with_saved(
const variable_list& inputs,
SwapSavedVariables& saved) {
saved.before(outputs);
variable_list result(outputs);
saved.after(outputs);
return result;
}
} // namespace torch::autograd
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