torch/csrc/cudnn/cuDNN.cwrap

/**
 * WARNING!  THIS FILE IS AUTOMATICALLY GENERATED.
 * Please edit cuDNN.cwrap instead.
 */

#include <Python.h>
#include <cudnn.h>
#include "Types.h"
#include "Handles.h"
#include "BatchNorm.h"
#include "Conv.h"
#include "GridSampler.h"
#include "AffineGridGenerator.h"
#include "torch/csrc/cuda/THCP.h"
#include "../PtrWrapper.h"


using namespace torch::cudnn;
using namespace torch;

extern THCState* state;

[[
  name: cudnn_convolution_forward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* weight
    - THTensor* output
    - Convolution* info
    - bool benchmark
]]

[[
  name: cudnn_convolution_add_bias
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* bias
    - THTensor* output
    - Convolution* info
]]

[[
  name: cudnn_convolution_full_forward
  return: Convolution*
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* weight
    - type: THTensor*
      name: bias
      nullable: True
    - THTensor* output
    - std::vector<int> pad
    - std::vector<int> stride
    - std::vector<int> dilation
    - int groups
    - bool benchmark
]]

[[
  name: cudnn_convolution_transpose_full_forward
  return: Convolution*
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* weight
    - type: THTensor*
      name: bias
      nullable: True
    - THTensor* output
    - std::vector<int> pad
    - std::vector<int> stride
    - std::vector<int> dilation
    - int groups
    - bool benchmark
]]

[[
  name: cudnn_convolution_backward_data
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* grad_output
    - THTensor* grad_input
    - THTensor* weight
    - Convolution* info
    - bool benchmark
]]

[[
  name: cudnn_convolution_backward_filter
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* grad_output
    - THTensor* input
    - THTensor* grad_weight
    - Convolution* info
    - bool benchmark
]]

[[
  name: cudnn_convolution_backward_bias
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* grad_output
    - THTensor* grad_bias
    - Convolution* info
]]

[[
  name: cudnn_batch_norm_forward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* output
    - THTensor* weight
    - THTensor* bias
    - THTensor* running_mean
    - THTensor* running_var
    - THTensor* save_mean
    - THTensor* save_var
    - bool training
    - double exponential_average_factor
    - double epsilon
]]

[[
  name: cudnn_batch_norm_backward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* grad_output
    - THTensor* grad_input
    - THTensor* grad_weight
    - THTensor* grad_bias
    - THTensor* weight
    - THTensor* running_mean
    - THTensor* running_var
    - THTensor* save_mean
    - THTensor* save_var
    - bool training
    - double epsilon
]]

[[
  name: cudnn_grid_sampler_forward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* grid
    - THTensor* output
]]

[[
  name: cudnn_grid_sampler_backward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* input
    - THTensor* grad_input
    - THTensor* grid
    - THTensor* grad_grid
    - THTensor* grad_output
]]

[[
  name: cudnn_affine_grid_generator_forward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* theta
    - THTensor* grid
    - int N
    - int C
    - int H
    - int W
]]

[[
  name: cudnn_affine_grid_generator_backward
  return: void
  arguments:
    - cudnnHandle_t handle
    - cudnnDataType_t dataType
    - THTensor* grad_theta
    - THTensor* grad_grid
    - int N
    - int C
    - int H
    - int W
]]

[[
  name: cudnn_version
  only_register: True
]]
static PyObject * THCUDNN_cudnn_version(PyObject *self, PyObject *args)
{
  return PyLong_FromLong(CUDNN_VERSION);
}