src/operator/tensor/elemwise_binary_broadcast_op_extended.cc

/*!
 *  Copyright (c) 2016 by Contributors
 * \file elemwise_binary_scalar_op.cc
 * \brief CPU Implementation of unary function.
 */
#include "./elemwise_unary_op.h"
#include "./elemwise_binary_op.h"
#include "./elemwise_binary_broadcast_op.h"

namespace mxnet {
namespace op {
MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_power)
.describe(R"code(Returns result of first array elements raised to powers from second array, element-wise with broadcasting.

Example::

   x = [[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]]

   y = [[ 0.],
        [ 1.]]

   broadcast_power(x, y) = [[ 2.,  2.,  2.],
                            [ 4.,  4.,  4.]]

)code" ADD_FILELINE)
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});

NNVM_REGISTER_OP(_backward_broadcast_power)
.set_num_inputs(3)
.set_num_outputs(2)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<nnvm::FInplaceOption>("FInplaceOption",
  [](const NodeAttrs& attrs){
    return std::vector<std::pair<int, int> >{{0, 1}};
  })
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::power_grad,
                                                              mshadow_op::power_rgrad>);

MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_maximum)
.describe(R"code(Returns element-wise maximum of the input arrays with broadcasting.

This function compares two input arrays and returns a new array having the element-wise maxima.

Example::

   x = [[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]]

   y = [[ 0.],
        [ 1.]]

   broadcast_maximum(x, y) = [[ 1.,  1.,  1.],
                              [ 1.,  1.,  1.]]

)code" ADD_FILELINE)
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::maximum>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_maximum"});

NNVM_REGISTER_OP(_backward_broadcast_maximum)
.set_num_inputs(3)
.set_num_outputs(2)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<nnvm::FInplaceOption>("FInplaceOption",
  [](const NodeAttrs& attrs){
    return std::vector<std::pair<int, int> >{{0, 1}};
  })
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::ge,
                                                              mshadow_op::lt>);

MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_minimum)
.describe(R"code(Returns element-wise minimum of the input arrays with broadcasting.

This function compares two input arrays and returns a new array having the element-wise minima.

Example::

   x = [[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]]

   y = [[ 0.],
        [ 1.]]

   broadcast_maximum(x, y) = [[ 0.,  0.,  0.],
                              [ 1.,  1.,  1.]]

)code" ADD_FILELINE)
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::minimum>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_minimum"});

NNVM_REGISTER_OP(_backward_broadcast_minimum)
.set_num_inputs(3)
.set_num_outputs(2)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<nnvm::FInplaceOption>("FInplaceOption",
  [](const NodeAttrs& attrs){
    return std::vector<std::pair<int, int> >{{0, 1}};
  })
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::le,
                                                              mshadow_op::gt>);

MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_hypot)
.describe(R"code( Returns the hypotenuse of a right angled triangle, given its "legs"
with broadcasting.

It is equivalent to doing :math:`sqrt(x_1^2 + x_2^2)`.

Example::

   x = [[ 3.,  3.,  3.]]

   y = [[ 4.],
        [ 4.]]

   broadcast_hypot(x, y) = [[ 5.,  5.,  5.],
                            [ 5.,  5.,  5.]]

   z = [[ 0.],
        [ 4.]]

   broadcast_hypot(x, z) = [[ 3.,  3.,  3.],
                            [ 5.,  5.,  5.]]

)code" ADD_FILELINE)
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::hypot>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_broadcast_hypot" });

NNVM_REGISTER_OP(_backward_broadcast_hypot)
.set_num_inputs(3)
.set_num_outputs(2)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<nnvm::FInplaceOption>("FInplaceOption",
[](const NodeAttrs& attrs) {
  return std::vector<std::pair<int, int> > {{0, 1}};
})
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastBackwardUseIn<cpu, mshadow_op::hypot_grad_left,
                    mshadow_op::hypot_grad_right>);

}  // namespace op
}  // namespace mxnet