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Optimize ERF with MKL math function #5

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Optimize ERF with MKL math function #5

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f0c7264
mkl_func test with erf&log op, build success~
pengxin99 Jan 25, 2019
9311777
fix lint and build issues
TaoLv Jan 25, 2019
a79f7db
Try to add support to sparse array
juliusshufan Feb 22, 2019
015fd0a
fix build
TaoLv Mar 3, 2019
495ce36
Merge branch 'master' of https://github.com/apache/incubator-mxnet in…
TaoLv Apr 17, 2019
672be6a
add functions
TaoLv Apr 18, 2019
c69a25c
Fix review comments
juliusshufan Mar 9, 2019
2c5c20c
remove unecessary code
juliusshufan Mar 9, 2019
b1b6355
Update test case
juliusshufan Mar 10, 2019
f96c34a
minor fix
juliusshufan Mar 11, 2019
06c51e9
move the position of MKL_Compute
juliusshufan Apr 18, 2019
acd7b56
Merge pull request #6 from juliusshufan/erf
TaoLv Apr 25, 2019
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Fix review comments
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@juliusshufan
juliusshufan committed Apr 18, 2019
commit c69a25c7ec5694a7002a3e44bc2e6821156246d4
42 changes: 21 additions & 21 deletions src/operator/tensor/elemwise_unary_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -591,27 +591,27 @@ struct ReshapeLikeParam : public dmlc::Parameter<ReshapeLikeParam> {
* * With this macro means mxnet compile with MKL to accelerate math function with mkl.
* * Will Register FCompute with UnaryOp::MKL_Compute() to compelet the math function.
*/
#define MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(__name$, __xpu$, __kernel$, __mkl_kernel$) \
MXNET_MKL_OPERATOR_REGISTER_UNARY(__name$) \
MXNET_ADD_SPARSE_OP_ALIAS(__name$) \
.set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<1, 1, false, true, true>) \
.set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::MKL_Compute<__kernel$, __mkl_kernel$>) \
.set_attr<FComputeEx>("FComputeEx<" #__xpu$ ">", UnaryOp::MKL_ComputeEx<__kernel$, __mkl_kernel$>)

/*! \bried MKL Unary compute.
* * With this macro means mxnet compile with MKL to accelerate math function with mkl.
* * Will Register FCompute with UnaryOp::MKL_Compute() to compelet the math function.
*/
#define MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_RSP(__name$, __xpu$, __kernel$, __mkl_kernel$) \
MXNET_MKL_OPERATOR_REGISTER_UNARY(__name$) \
MXNET_ADD_SPARSE_OP_ALIAS(__name$) \
.set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<1, 1, false, true, false>)\
.set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::MKL_Compute<__kernel$, __mkl_kernel$>) \
.set_attr<FComputeEx>("FComputeEx<" #__xpu$ ">", UnaryOp::MKL_ComputeEx<__kernel$, __mkl_kerbel$>)

#define MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(__name$, __xpu$, __kernel$, __mkl_kernel$)\
MXNET_MKL_OPERATOR_REGISTER_UNARY(__name$) \
.set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::MKL_Compute<__kernel$, __mkl_kernel$>)
#define MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(__name$, __xpu$, __kernel$, __mkl_kernel$) \
MXNET_OPERATOR_REGISTER_UNARY(__name$) \
MXNET_ADD_SPARSE_OP_ALIAS(__name$) \
.set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<1, 1, false, true, true>) \
.set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::MKL_Compute<__kernel$, __mkl_kernel$>) \
.set_attr<FComputeEx>("FComputeEx<" #__xpu$ ">", UnaryOp::MKL_ComputeEx<__kernel$, __mkl_kernel$>)

/*! \bried MKL Unary compute.
* * With this macro means mxnet compile with MKL to accelerate math function with mkl.
* * Will Register FCompute with UnaryOp::MKL_Compute() to compelet the math function.
*/
#define MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_RSP(__name$, __xpu$, __kernel$, __mkl_kernel$) \
MXNET_OPERATOR_REGISTER_UNARY(__name$) \
MXNET_ADD_SPARSE_OP_ALIAS(__name$) \
.set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<1, 1, false, true, false>) \
.set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::MKL_Compute<__kernel$, __mkl_kernel$>) \
.set_attr<FComputeEx>("FComputeEx<" #__xpu$ ">", UnaryOp::MKL_ComputeEx<__kernel$, __mkl_kerbel$>)

#define MXNET_MKL_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(__name$, __xpu$, __kernel$, __mkl_kernel$) \
MXNET_OPERATOR_REGISTER_UNARY(__name$) \
.set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::MKL_Compute<__kernel$, __mkl_kernel$>)
#endif

/*! \brief Unary compute, with FComputeEx for csr and rsp available */
Expand Down
35 changes: 9 additions & 26 deletions src/operator/tensor/elemwise_unary_op_basic.cc
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Expand Up @@ -929,30 +929,23 @@ The storage type of ``cbrt`` output depends upon the input storage type:

MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_cbrt,
unary_bwd<mshadow_op::cube_root_grad>);

// erf
#if MSHADOW_USE_MKL == 1
MXNET_MKL_OPERATOR_REGISTER_UNARY(erf)
MXNET_OPERATOR_REGISTER_UNARY(erf)
.describe(R"code(Returns element-wise gauss error function of the input.

Example::

erf([0, -1., 10.]) = [0., -0.8427, 1.]

)code" ADD_FILELINE)
#if MSHADOW_USE_MKL == 1
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::MKL_Compute<mshadow_op::erf, mkl_func::erf>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_erf"});
#else
MXNET_OPERATOR_REGISTER_UNARY(erf)
.describe(R"code(Returns element-wise gauss error function of the input.

Example::

erf([0, -1., 10.]) = [0., -0.8427, 1.]

)code" ADD_FILELINE)
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::erf>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_erf"});
#endif // MSHADOW_USE_MKL == 1
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_erf"});


MXNET_OPERATOR_REGISTER_BINARY(_backward_erf)
.set_attr<FCompute>("FCompute<cpu>",
Expand Down Expand Up @@ -1030,18 +1023,6 @@ The storage type of ``exp`` output is always dense
#endif

// log
#if MSHADOW_USE_MKL == 1
MXNET_MKL_OPERATOR_REGISTER_UNARY(log)
.describe(R"code(Returns element-wise Natural logarithmic value of the input.

The natural logarithm is logarithm in base *e*, so that ``log(exp(x)) = x``

The storage type of ``log`` output is always dense

)code" ADD_FILELINE)
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::MKL_Compute<mshadow_op::log, mkl_func::log>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});
#else
MXNET_OPERATOR_REGISTER_UNARY(log)
MXNET_ADD_SPARSE_OP_ALIAS(log)
.describe(R"code(Returns element-wise Natural logarithmic value of the input.
Expand All @@ -1051,10 +1032,12 @@ The natural logarithm is logarithm in base *e*, so that ``log(exp(x)) = x``
The storage type of ``log`` output is always dense

)code" ADD_FILELINE)
#if MSHADOW_USE_MKL == 1
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::MKL_Compute<mshadow_op::log, mkl_func::log>)
#else
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::Compute<cpu, mshadow_op::log>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});
#endif // MSHADOW_USE_MKL == 1

.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});

// log10
MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(log10, cpu, mshadow_op::log10)
Expand Down