ZiyueHuang commented on a change in pull request #9540: refactor regression ops
to nnvm interface
URL: https://github.com/apache/incubator-mxnet/pull/9540#discussion_r164027751
##########
File path: src/operator/regression_output.cc
##########
@@ -18,86 +18,96 @@
*/
/*!
- * Copyright (c) 2015 by Contributors
- * \file regression_output.cc
- * \brief regression output operator
+ * \file regression_ouput.cc
+ * \brief Regression output operator.
*/
+
#include "./regression_output-inl.h"
-#include "./mshadow_op.h"
+
+#define MXNET_OPERATOR_REGISTER_REGRESSION_FWD(__name$, __kernel$, __bwdop$)
\
+ NNVM_REGISTER_OP(__name$)
\
+ .set_num_inputs(2)
\
+ .set_num_outputs(1)
\
+ .set_attr<nnvm::FListInputNames>("FListInputNames",
\
+ [](const NodeAttrs& attrs) {
\
+ return std::vector<std::string>{"data", "label"};
\
+ })
\
+ .set_attr<nnvm::FInferShape>("FInferShape", RegressionOpShape)
\
+ .set_attr<nnvm::FGradient>("FGradient", RegressionOpGrad{__bwdop$})
\
+ .set_attr<nnvm::FInplaceOption>("FInplaceOption",
\
+ [](const NodeAttrs& attrs){
\
+ return std::vector<std::pair<int, int> >{{0, 0}};
\
+ })
\
+ .set_attr<FCompute>("FCompute<cpu>", RegressionForward<cpu, __kernel$>)
\
+ .add_argument("data", "NDArray-or-Symbol", "Input data to the function.")
\
+ .add_argument("label", "NDArray-or-Symbol", "Input label to the function.")
\
+ .add_arguments(RegressionOutputParam::__FIELDS__())
+
+#define MXNET_OPERATOR_REGISTER_REGRESSION_BWD(__name$, __kernel$) \
+ NNVM_REGISTER_OP(__name$) \
+ .set_num_inputs(2) \
+ .set_num_outputs(2) \
+ .set_attr_parser(ParamParser<RegressionOutputParam>) \
+ .set_attr<nnvm::TIsBackward>("TIsBackward", true) \
+ .set_attr<nnvm::FInplaceOption>("FInplaceOption", \
+ [](const NodeAttrs& attrs){ \
+ return std::vector<std::pair<int, int> >{{1, 0}}; \
+ }) \
+ .set_attr<FCompute>("FCompute<cpu>", RegressionBackward<cpu, __kernel$>)
namespace mxnet {
namespace op {
-template<>
-Operator *CreateRegressionOutputOp<cpu>(reg_enum::RegressionOutputType type,
- RegressionOutputParam param) {
- switch (type) {
- case reg_enum::kLinear:
- return new RegressionOutputOp<cpu, op::mshadow_op::identity,
op::mshadow_op::minus>(param);
- case reg_enum::kLogistic:
- return new RegressionOutputOp<cpu, mshadow_op::sigmoid,
op::mshadow_op::minus>(param);
- case reg_enum::kMAE:
- return new RegressionOutputOp<cpu, op::mshadow_op::identity,
mshadow_op::minus_sign>(param);
- default:
- LOG(FATAL) << "unknown activation type " << type;
- }
- return nullptr;
-}
-
-// DO_BIND_DISPATCH comes from operator_common.h
-template<reg_enum::RegressionOutputType type>
-Operator *RegressionOutputProp<type>::CreateOperator(Context ctx) const {
- DO_BIND_DISPATCH(CreateRegressionOutputOp, type, param_);
-}
DMLC_REGISTER_PARAMETER(RegressionOutputParam);
-MXNET_REGISTER_OP_PROPERTY(LinearRegressionOutput,
RegressionOutputProp<reg_enum::kLinear>)
+MXNET_OPERATOR_REGISTER_REGRESSION_FWD(LinearRegressionOutput,
+ mshadow_op::identity, "_backward_linear_reg_out")
.describe(R"code(Computes and optimizes for squared loss during backward
propagation.
Just outputs ``data`` during forward propagation.
If :math:`\hat{y}_i` is the predicted value of the i-th sample, and
:math:`y_i` is the corresponding target value,
then the squared loss estimated over :math:`n` samples is defined as
-:math:`\text{SquaredLoss}(y, \hat{y} ) = \frac{1}{n} \sum_{i=0}^{n-1} \left(
y_i - \hat{y}_i \right)^2`
+:math:`\text{SquaredLoss}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n}
\sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_2`
.. note::
Use the LinearRegressionOutput as the final output layer of a net.
-By default, gradients of this loss function are scaled by factor `1/n`, where
n is the number of training examples.
-The parameter `grad_scale` can be used to change this scale to `grad_scale/n`.
+By default, gradients of this loss function are scaled by factor `1/m`, where
m is the number of features of a training example.
+The parameter `grad_scale` can be used to change this scale to `grad_scale/m`.
+
+)code" ADD_FILELINE);
-)code" ADD_FILELINE)
-.add_argument("data", "NDArray-or-Symbol", "Input data to the function.")
-.add_argument("label", "NDArray-or-Symbol", "Input label to the function.")
-.add_arguments(RegressionOutputParam::__FIELDS__());
+MXNET_OPERATOR_REGISTER_REGRESSION_BWD(_backward_linear_reg_out,
mshadow_op::minus);
-MXNET_REGISTER_OP_PROPERTY(MAERegressionOutput,
RegressionOutputProp<reg_enum::kMAE>)
+MXNET_OPERATOR_REGISTER_REGRESSION_FWD(MAERegressionOutput,
+ mshadow_op::identity, "_backward_mae_reg_out")
.describe(R"code(Computes mean absolute error of the input.
MAE is a risk metric corresponding to the expected value of the absolute error.
If :math:`\hat{y}_i` is the predicted value of the i-th sample, and
:math:`y_i` is the corresponding target value,
then the mean absolute error (MAE) estimated over :math:`n` samples is defined
as
-:math:`\text{MAE}(y, \hat{y} ) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i -
\hat{y}_i \right|`
+:math:`\text{MAE}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n}
\sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_1`
.. note::
Use the MAERegressionOutput as the final output layer of a net.
-By default, gradients of this loss function are scaled by factor `1/n`, where
n is the number of training examples.
-The parameter `grad_scale` can be used to change this scale to `grad_scale/n`.
+By default, gradients of this loss function are scaled by factor `1/m`, where
m is the number of features of a training example.
Review comment:
Updated
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