ai-content-maker/.venv/Lib/site-packages/torch/include/ATen/FunctionalStorageImpl.h

127 lines
4.5 KiB
C++

#pragma once
#include <ATen/Tensor.h>
namespace at::functionalization {
// See Note [Functionalization Pass In Core]
// ViewMeta is a class used by the functionalization pass to navigate between
// a base tensor and a view tensor.
// For example, if I call `b = a.view1(...)`
// the functionalization pass will generate and store a ViewMeta on b that looks
// like:
//
// ViewMeta(
// [<captures>](const Tensor& base, int64_t mutated_view_idx) {
// return base.view1(...);
// },
// [<captures>](const at::Tensor& base, const at::Tensor& mutated_view,
// int64_t mutated_view_idx) -> at::Tensor {
// return at::functionalization::impl::view1_inverse(base, mutated_view,
// ...);
// }
//
// The forward_fn lambda describes how to replay view1 on a tensor.
//
// The reverse_fn lambda describes how, given a tensor that is already a view,
// how to get the corresponding base tensor. See Note [Functionalization Pass:
// View Inverses] for details.
struct ViewMeta {
ViewMeta(
std::function<Tensor(const Tensor&, int64_t)> forward,
std::function<Tensor(const Tensor&, const Tensor&, int64_t)> reverse,
bool is_multi_output = false,
int64_t out_idx = 0)
: forward_fn(std::move(forward)),
reverse_fn(std::move(reverse)),
out_index(out_idx),
is_multi_output(is_multi_output) {}
std::function<Tensor(const Tensor&, int64_t)> forward_fn;
std::function<Tensor(const Tensor&, const Tensor&, int64_t)> reverse_fn;
// See Note [out_idx in ViewMeta]
int64_t out_index;
// Tells us if this is a multi-output view
bool is_multi_output;
// Returns a copy of the current ViewMeta, if out_idx matches the current
// out_index. Otherwise, returns a new ViewMeta with the same forward/reverse
// functions, but a new out index.
ViewMeta to_out_idx(int64_t out_idx);
};
// FunctionalStorageImpl is a subclass of StorageImpl used by the
// functionalization pass. It has no underlying data (similar to meta storage).
// It also knows how to reflect mutations to tensors in the absence of a valid
// data pointer.
//
// A storage represents the state shared by (potentially multiple) views of the
// same tensor. For example, in the following code:
//
// b = a.view1(...)
// c = b.view2(...)
// b.add_(1)
// --> storage.add_update(b, {view1_meta})
//
// The call to add_(1) will result in a call to alias.add_update(b,
// {view1_meta}), queueing up the mutation from b onto the alias. Later, suppose
// c is used in an expression (e.g. you try to print c, or pass it to an
// operator). Doing so will involve "syncing" c. First we apply any pending
// updates to the alias, and then we regenerate c by replaying its views off of
// the updated alias. E.g:
//
// print(str(c))
// --> c.sync_()
// --> alias.apply_updates() // after this, the alias will be updated to
// reflect the mutation to b
struct TORCH_API FunctionalStorageImpl : public c10::StorageImpl {
public:
struct Update {
// NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
const at::Tensor new_val;
// NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
const std::vector<ViewMeta> view_metas;
};
explicit FunctionalStorageImpl(const Tensor& value);
void add_update(
const Tensor& updated_val,
const std::vector<ViewMeta>& view_metas);
bool apply_updates();
const Tensor& base() {
return base_;
}
size_t generation() const {
return generation_;
}
void freeze() {
frozen_ = true;
}
~FunctionalStorageImpl() override = default;
private:
// NB: base_ should always point to a tensor BELOW the current
// functionalization layer. This is mainly to avoid reference cycles. e.g.
// given `b = a.view(...)` Both a.storage_ and b.storage_ are a
// FunctionStorageImpl containing an Walualias, with contains a Tensor
// `base_`. In this case (where a and b are FunctionalTensorWrapper's), base_
// should point not to a, but to a's unwrapped value, a.value_` See Note
// [Functionalization: Walualias Removal] for a diagram that shows this
// visually.
at::Tensor base_;
std::vector<Update> updates_;
// generation_ gets incremented every time a mutation is queued onto the
// alias. It is used to determine if a given tensor is "up to date", or if it
// needs to be regenerated from the alias.
size_t generation_ = 0;
// If frozen, no more mutations are allowed on this storage. Once frozen, a
// storage cannot be unfrozen.
bool frozen_ = false;
};
} // namespace at::functionalization