forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
input_buffer.h
48 lines (37 loc) · 1.56 KB
/
input_buffer.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
#pragma once
// The InputBuffer class accumulates a list of Variables for use by a
// function. It implements logic to avoid modifying the passed
// values in-place (adding an input twice will accumulate the result).
// This behaviour is needed and used only in backward graphs.
#include <vector>
#include <utility>
#include <memory>
#include <ATen/ATen.h>
#include <torch/csrc/autograd/variable.h>
#include <c10/util/Optional.h>
#include <c10/core/Stream.h>
namespace torch { namespace autograd {
struct InputBuffer {
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
explicit InputBuffer(size_t size)
: buffer(size) {}
InputBuffer(const InputBuffer& other) = delete;
InputBuffer(InputBuffer&& other) = default;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
explicit InputBuffer(variable_list&& inputs): buffer(std::move(inputs)) {};
InputBuffer& operator=(InputBuffer&& other) = default;
// Accumulates the variable at a specified index.
// The optional CUDA streams determine which stream the accumulation
// is run on and how the addition is synchronized.
void add(size_t pos,
Variable&& var,
const c10::optional<c10::Stream>& opt_producer_stream,
const c10::optional<c10::Stream>& opt_consumer_stream);
at::Device device() const;
Variable operator[](size_t pos) { return buffer[pos]; }
// Returns the inputs as a list of variables. Destroys given InputBuffer.
static std::vector<Variable> variables(InputBuffer&& g);
private:
std::vector<Variable> buffer;
};
}} // namespace torch::autograd