FM / functions

/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/autograd/functions/

/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/autograd/functions

Name	Size	Mode	Actions
accumulate_grad.h	11466	0644	edit dl rm
basic_ops.h	2668	0644	edit dl rm
comm.h	1271	0644	edit dl rm
pybind.h	314	0644	edit dl rm
tensor.h	3926	0644	edit dl rm
utils.h	2883	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/autograd/functions/basic_ops.h (2668B)

#pragma once

#include <torch/csrc/WindowsTorchApiMacro.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/variable.h>

#include <c10/util/irange.h>

#include <memory>
#include <string>
#include <vector>

namespace torch { namespace autograd {

struct TORCH_API Error : public Node {
  Error(std::string msg, edge_list&& next_edges)
    : Node(std::move(next_edges))
    , msg(std::move(msg)) {}

Error(std::string msg)
    : msg(std::move(msg)) {}

variable_list apply(variable_list&& inputs) override;

std::string msg;
};

// We print grad_fn names in tensor printing. For functions with backward
// NYI, grad_fn=<Error> will be printed if we use Error, which is confusing. So
// special case with a new NotImplemented function here.
struct TORCH_API NotImplemented : public Error {
  NotImplemented(const std::string& forward_fn, edge_list&& next_edges)
    : Error("derivative for " + forward_fn + " is not implemented",
            std::move(next_edges)) {}

NotImplemented(const std::string& forward_fn)
    : Error("derivative for " + forward_fn + " is not implemented") {}
};

// Identity in forward, Error in backward. Used to implement @once_differentiable
struct TORCH_API DelayedError : public Node {
  DelayedError(std::string msg, int num_inputs)
    : msg(std::move(msg)) {
    // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
    for(int i = 0; i < num_inputs; i++) {
      add_input_metadata(Node::undefined_input());
    }
  }

variable_list apply(variable_list&& inputs) override;

std::string msg;
};

struct TORCH_API UndefinedGrad : public Node {
  UndefinedGrad() {
    add_input_metadata(Node::undefined_input());
  }

variable_list apply(variable_list&& inputs) override;
};

struct TORCH_API UndefinedGradBackward : public Node {
  UndefinedGradBackward(edge_list&& next_edges)
    : Node(std::move(next_edges)) {}

UndefinedGradBackward() = default;

variable_list apply(variable_list&& inputs) override;
};

struct TORCH_API GraphRoot : public Node {
  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
  GraphRoot(edge_list functions, variable_list inputs)
      : Node(std::move(functions)),
      outputs(std::move(inputs)) {
    // Ensures calls to stream() on a GraphRoot instance reflect current stream(s)
    // on devices of root grad tensors at the time the instance is constructed.
    for (const auto& t : outputs) {
      add_input_metadata(t);
    }
  }

variable_list apply(variable_list&& inputs) override {
    return outputs;
  }

variable_list outputs;
};

struct TORCH_API Identity : public Node {
  variable_list apply(variable_list&& inputs) override;
};

}}

#pragma once #include #include #include #include #include #include #include namespace torch { namespace autograd { struct TORCH_API Error : public Node { Error(std::string msg, edge_list&& next_edges) : Node(std::move(next_edges)) , msg(std::move(msg)) {} Error(std::string msg) : msg(std::move(msg)) {} variable_list apply(variable_list&& inputs) override; std::string msg; }; // We print grad_fn names in tensor printing. For functions with backward // NYI, grad_fn= will be printed if we use Error, which is confusing. So // special case with a new NotImplemented function here. struct TORCH_API NotImplemented : public Error { NotImplemented(const std::string& forward_fn, edge_list&& next_edges) : Error("derivative for " + forward_fn + " is not implemented", std::move(next_edges)) {} NotImplemented(const std::string& forward_fn) : Error("derivative for " + forward_fn + " is not implemented") {} }; // Identity in forward, Error in backward. Used to implement @once_differentiable struct TORCH_API DelayedError : public Node { DelayedError(std::string msg, int num_inputs) : msg(std::move(msg)) { // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores) for(int i = 0; i < num_inputs; i++) { add_input_metadata(Node::undefined_input()); } } variable_list apply(variable_list&& inputs) override; std::string msg; }; struct TORCH_API UndefinedGrad : public Node { UndefinedGrad() { add_input_metadata(Node::undefined_input()); } variable_list apply(variable_list&& inputs) override; }; struct TORCH_API UndefinedGradBackward : public Node { UndefinedGradBackward(edge_list&& next_edges) : Node(std::move(next_edges)) {} UndefinedGradBackward() = default; variable_list apply(variable_list&& inputs) override; }; struct TORCH_API GraphRoot : public Node { // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init) GraphRoot(edge_list functions, variable_list inputs) : Node(std::move(functions)), outputs(std::move(inputs)) { // Ensures calls to stream() on a GraphRoot instance reflect current stream(s) // on devices of root grad tensors at the time the instance is constructed. for (const auto& t : outputs) { add_input_metadata(t); } } variable_list apply(variable_list&& inputs) override { return outputs; } variable_list outputs; }; struct TORCH_API Identity : public Node { variable_list apply(variable_list&& inputs) override; }; }}