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#include <torch/csrc/autograd/autograd.h>
#include <torch/csrc/autograd/variable.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
#include <ATen/ops/ones_like.h>
#endif
#include <torch/csrc/autograd/edge.h>
#include <torch/csrc/autograd/engine.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/functions/basic_ops.h>
#include <c10/util/irange.h>
namespace torch {
namespace autograd {
// NB: This code duplicates existing logic at torch/autograd/__init__.py and
// torch._C._EngineBase.run_backward in torch/csrc/autograd/python_engine.cpp
// This is a purely C++ API for Autograd without any dependencies on python
// it can be exposed in PyTorch C++ API and TorchScript. We will need to
// maintain the logic equality of this file and the python file together if one
// changes.
// TODO: Make the Python API above to just call this C++ API.
variable_list _make_grads(
const variable_list& outputs,
const variable_list& grad_outputs) {
size_t num_tensors = outputs.size();
size_t num_gradients = grad_outputs.size();
variable_list new_grads;
new_grads.reserve(num_tensors);
if (grad_outputs.empty()) {
for (const Variable& output : outputs) {
if (output.requires_grad()) {
TORCH_CHECK(
output.numel() == 1,
"grad can be implicitly created only for scalar outputs");
new_grads.emplace_back(
at::ones_like(output, LEGACY_CONTIGUOUS_MEMORY_FORMAT));
}
}
} else {
TORCH_CHECK(
num_tensors == num_gradients,
"got ",
num_tensors,
" tensors and ",
num_gradients,
" gradients");
for (const auto i : c10::irange(outputs.size())) {
const Variable& output = outputs[i];
const Variable& grad_output = grad_outputs[i];
if (!grad_output.defined()) {
if (output.requires_grad()) {
TORCH_CHECK(
output.numel() == 1,
"grad can be implicitly created only for scalar outputs");
new_grads.emplace_back(
at::ones_like(output, LEGACY_CONTIGUOUS_MEMORY_FORMAT));
}
} else {
TORCH_CHECK(
grad_output.is_complex() == output.is_complex(),
"For complex Tensors, both grad_output and output are required ",
"to have the same dtype. Mismatch in dtype: grad_output[",
grad_output,
"] has a dtype of ",
grad_output.scalar_type(),
" and output[",
output,
"] has a dtype of ",
output.scalar_type(),
".");
// grad output is defined, just append to the new_grads
new_grads.emplace_back(grad_output);
}
}
}
return new_grads;
}
variable_list run_backward(
const variable_list& outputs,
const variable_list& grad_outputs,
bool keep_graph,
bool create_graph,
const variable_list& inputs,
bool allow_unused,
bool accumulate_grad) {
size_t num_tensors = outputs.size();
edge_list roots;
roots.reserve(num_tensors);
for (const auto i : c10::irange(num_tensors)) {
const Variable& output = outputs[i];
auto gradient_edge = impl::gradient_edge(output);
TORCH_CHECK(
gradient_edge.function,
"element ",
i,
" of tensors does not require grad and does not have a grad_fn");
roots.push_back(std::move(gradient_edge));
}
edge_list output_edges;
if (!inputs.empty()) {
size_t num_inputs = inputs.size();
output_edges.reserve(num_inputs);
for (const auto i : c10::irange(num_inputs)) {
const Variable& input = inputs[i];
const auto output_nr = input.output_nr();
auto grad_fn = input.grad_fn();
if (!grad_fn) {
grad_fn = impl::try_get_grad_accumulator(input);
}
if (accumulate_grad) {
input.retain_grad();
}
TORCH_CHECK(
input.requires_grad(),
"One of the differentiated Tensors does not require grad");
if (!grad_fn) {
// See NOTE [ Autograd Unreachable Input ] for details
output_edges.emplace_back(std::make_shared<Identity>(), 0);
} else {
output_edges.emplace_back(grad_fn, output_nr);
}
}
}
variable_list grad_inputs = Engine::get_default_engine().execute(
roots,
grad_outputs,
keep_graph,
create_graph,
accumulate_grad,
output_edges);
// check if grad_inputs contains None or not base on the allow_unused flag
if (!inputs.empty() && !allow_unused) {
size_t num_inputs = inputs.size();
for (const auto i : c10::irange(num_inputs)) {
TORCH_CHECK(
grad_inputs[i].defined(),
"One of the "
"differentiated Tensors appears to not have been used "
"in the graph. Set allow_unused=True if this is the "
"desired behavior.");
}
}
return grad_inputs;
}
void backward(
const variable_list& tensors,
const variable_list& grad_tensors,
c10::optional<bool> retain_graph,
bool create_graph,
const variable_list& inputs) {
variable_list gradients = _make_grads(tensors, grad_tensors);
if (!retain_graph) {
retain_graph = create_graph;
}
run_backward(
tensors,
gradients,
retain_graph.value(),
create_graph,
inputs,
/*allow_unused=*/true,
/*accumulate_grad=*/true);
}
variable_list grad(
const variable_list& outputs,
const variable_list& inputs,
const variable_list& grad_outputs,
c10::optional<bool> retain_graph,
bool create_graph,
bool allow_unused) {
variable_list gradients = _make_grads(outputs, grad_outputs);
if (!retain_graph) {
retain_graph = create_graph;
}
return run_backward(
outputs,
gradients,
retain_graph.value(),
create_graph,
inputs,
allow_unused,
/*accumulate_grad=*/false);
}
namespace forward_ad {
uint64_t enter_dual_level() {
return ForwardADLevel::get_next_idx();
}
void exit_dual_level(uint64_t level) {
ForwardADLevel::release_idx(level);
}
} // namespace forward_ad
} // namespace autograd
} // namespace torch
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