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#include <torch/csrc/autograd/functions/tensor.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/functions/basic_ops.h>
#include <torch/csrc/autograd/functions/utils.h>
#include <torch/csrc/autograd/graph_task.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/dynamo/compiled_autograd.h>
#include <ATen/ATen.h>
#include <c10/util/irange.h>
#include <memory>
#include <stdexcept>
#include <utility>
namespace torch::autograd {
static variable_list CopyBackwards_apply_functional(
variable_list&& grads,
std::array<bool, 2> needs_input_grad,
const c10::TensorOptions& src_options) {
check_input_variables("CopyBackwards", grads, 1, -1, true);
auto grad = c10::MaybeOwned<at::Tensor>::borrowed(grads[0]);
variable_list grad_inputs(2);
if (grad->defined()) {
if (needs_input_grad[0]) {
grad_inputs[0] = at::zeros_like(*grad, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
}
if (needs_input_grad[1]) {
// Handle R->C copies without raising a warning
const auto src_type = src_options.dtype().toScalarType();
if (!c10::isComplexType(src_type) && grad->is_complex()) {
grad = c10::MaybeOwned<at::Tensor>::owned(at::real(grads[0]));
}
at::DeviceGuard device_guard(src_options.device());
grad_inputs[1] = grad->to(src_options);
}
}
return grad_inputs;
}
auto CopyBackwards::apply(variable_list&& grads) -> variable_list {
return CopyBackwards_apply_functional(
std::move(grads),
{task_should_compute_output(0), task_should_compute_output(1)},
src_options);
}
void CopyBackwards::compiled_args(CompiledNodeArgs& args) {
args.collect(src_options);
}
variable_list CopyBackwards::apply_with_saved(
const variable_list& inputs,
SwapSavedVariables& saved) {
saved.before(src_options);
auto result = apply(variable_list(inputs));
saved.after(src_options);
return result;
}
CopySlices::CopySlices(
const Variable& base_var,
at::TensorGeometry view_,
std::unique_ptr<ViewFunc> view_fn_,
std::shared_ptr<Node> fn_)
: Node(),
base(base_var),
view(std::move(view_)),
view_fn(std::move(view_fn_)),
fn(std::move(fn_)) {
// Take the next_edges of fn as our own, except for index 0 which goes
// to base instead of the view.
add_input_metadata(base_var);
const auto num_outputs = fn->num_outputs();
next_edges_.reserve(num_outputs);
add_next_edge(impl::gradient_edge(base_var));
for (const auto i : c10::irange(1, num_outputs)) {
add_next_edge(fn->next_edge(i));
}
}
// common code between apply/apply_with_saved
template <typename T>
inline variable_list CopySlices::apply_impl(
variable_list&& inputs,
const T& call_fn) {
check_input_variables("CopySlices", inputs, 1, -1, true);
auto& grad = inputs[0];
if (!grad.defined()) {
return variable_list(num_outputs());
}
// Acquire lock to here protect thread safety on fn
// see Note [Thread Safety on Autograd Node]
std::lock_guard<std::mutex> lock(mutex_);
if (!fn) {
throw std::runtime_error(ERR_BACKWARD_TWICE);
}
auto result =
grad.new_empty_strided_symint(base.sym_sizes(), base.sym_strides());
result.copy_(grad);
at::Tensor grad_slice;
if (view_fn) {
grad_slice = (*view_fn)(result);
} else {
auto offset = view.sym_storage_offset() - base.sym_storage_offset();
grad_slice =
result.as_strided_symint(view.sym_sizes(), view.sym_strides(), offset);
}
// See Note [View + Inplace update for view tensor] For more details on this
// block Since the gradient edge for the 0th input is different between `this`
// and `fn`, make sure that the one from `fn` has the same metadata in the
// current GraphTask's exec_info as the one on `this`.
const auto exec_info = get_current_graph_task_exec_info();
if (exec_info && !exec_info->empty()) {
const auto& fn_edge = fn->next_edge(0);
const auto& this_edge = this->next_edge(0);
TORCH_INTERNAL_ASSERT(fn_edge.is_valid() == this_edge.is_valid());
if (fn_edge.is_valid()) {
const auto fn_next_node = fn_edge.function.get();
auto it = exec_info->find(fn_next_node);
if (it == exec_info->end()) {
// Node is not in the exec_info already
if (task_should_compute_output(0)) {
// And we need gradient for the corresponding output
add_node_to_current_graph_task_exec_info(fn_next_node);
// There is no need to remove this after execution because we are
// guaranteed that this->next_edge(0) must be in the history of
// fn->next_edge(0) (we cannot easily assert this as it might be far
// away if there were many chained views). This means that, since
// fn->next_edge(0) was not needed (no exec_info entry for it), we
// know that nothing downstream of fn->next_edge(0) is needed either
// (otherwise the whole path from that Node to this->next_edge(0)
// would be needed as well). This means that no other Node will ever
// look at fn->next_edge(0) metadata and thus there is no need to
// clean them up.
}
} else {
TORCH_INTERNAL_ASSERT(
it->second.should_execute() == task_should_compute_output(0));
}
}
}
// Sanity check that the graph was never modified after the fact (it is
// read-only!)
TORCH_INTERNAL_ASSERT(num_outputs() == fn->num_outputs());
for (const auto i : c10::irange(1, this->num_outputs())) {
TORCH_INTERNAL_ASSERT(
fn->next_edge(i).function.get() == this->next_edge(i).function.get());
}
// TODO: We clone grad_slice because we modify it below and "fn" might save
// it for the backward of res. We might be able to avoid the clone() if
// double-backprop is disabled.
auto res = call_fn({grad_slice.clone(at::MemoryFormat::Contiguous)});
variable_list grad_inputs(num_outputs());
for (const auto i : c10::irange(res.size())) {
if (task_should_compute_output(i)) {
if (!res[i].defined()) {
// If the output is not defined, treat it as if it was a zero tensor.
// This can happen if users define a custom Function.
continue;
}
if (i == 0) {
grad_slice.copy_(res[i]);
// NOLINTNEXTLINE(clang-analyzer-cplusplus.Move)
grad_inputs[i] = std::move(result); // NOLINT(bugprone-use-after-move)
} else {
grad_inputs[i] = std::move(res[i]);
}
}
}
return grad_inputs;
}
void CopySlices::release_variables() {
// Acquire lock to here protect thread safety on fn
std::lock_guard<std::mutex> lock(mutex_);
fn = nullptr;
}
void CopySlices::compiled_args(CompiledNodeArgs& args) {
TORCH_CHECK(!view_fn, "view_fn not supported by compiled autograd")
TORCH_INTERNAL_ASSERT((bool)fn);
args.collect(base);
args.collect(view);
args.collect(fn);
fn->compiled_args(args);
}
variable_list CopySlices::apply_with_saved(
const variable_list& grads,
SwapSavedVariables& saved) {
saved.before(base);
saved.before(view);
int call_count = 0;
variable_list result = apply_impl(
variable_list(grads),
[this, &saved, &call_count](const variable_list& inputs2) {
call_count++;
return fn->apply_with_saved(inputs2, saved);
});
TORCH_INTERNAL_ASSERT(call_count == 1);
saved.after(base);
saved.after(view);
return result;
}
auto CopySlices::apply(variable_list&& inputs1) -> variable_list {
return apply_impl(std::move(inputs1), [this](variable_list&& inputs2) {
return (*fn)(std::move(inputs2));
});
}
} // namespace torch::autograd
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