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support mkl log when dtype is fp32 or fp64 #13150

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Dec 14, 2018
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support mkl log when dtype is fp32 or fp64 #13150

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96f7ab3
support mkl log when dtype is fp32 or fp64
XiaotaoChen Nov 1, 2018
49b89e4
remove macro
XiaotaoChen Dec 10, 2018
2ba108f
ensure data size less than or equal MKL_INT_MAX
XiaotaoChen Dec 11, 2018
e70998e
code specification
XiaotaoChen Dec 12, 2018
9206a72
fix indent
XiaotaoChen Dec 13, 2018
faf4fef
for retrigger
XiaotaoChen Dec 14, 2018
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40 changes: 40 additions & 0 deletions src/operator/tensor/elemwise_unary_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,10 @@
#include "../mxnet_op.h"
#include "../elemwise_op_common.h"
#include "../../ndarray/ndarray_function.h"
#if MSHADOW_USE_MKL == 1
#include "mkl.h"
#endif
#include<climits>
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need space before < and put this include before #include "./cast_storage-inl.h"

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all is done


namespace mxnet {
namespace op {
Expand Down Expand Up @@ -348,6 +352,42 @@ class UnaryOp : public OpBase {
LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
}
}

#if MSHADOW_USE_MKL == 1
static inline void MKLLog(MKL_INT size, const float* pIn, float* pOut) {
vsLn(size, pIn, pOut);
}

static inline void MKLLog(MKL_INT size, const double* pIn, double* pOut) {
vdLn(size, pIn, pOut);
}
#endif

template<typename xpu, typename OP>
static void LogCompute(const nnvm::NodeAttrs& attrs,
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const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
if (req[0] == kNullOp) return;
// if defined MSHADOW_USE_MKL then call mkl log when req is KWriteTo, type_flag is
// mshadow::kFloat32 or mshadow::kFloat64 and data size less than or equal MKL_INT_MAX
#if MSHADOW_USE_MKL == 1
auto type_flag = inputs[0].type_flag_;
const size_t MKL_INT_MAX = (sizeof(MKL_INT) == sizeof(int)) ? INT_MAX : LLONG_MAX;
size_t input_size = inputs[0].Size();
if (req[0] == kWriteTo && (type_flag == mshadow::kFloat32
|| type_flag == mshadow::kFloat64) && input_size <= MKL_INT_MAX) {
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fix indent.

MSHADOW_SGL_DBL_TYPE_SWITCH(type_flag, DType, {
MKLLog(input_size, inputs[0].dptr<DType>(), outputs[0].dptr<DType>());
})
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need ; here.

} else {
Compute<xpu, OP>(attrs, ctx, inputs, req, outputs);
}
#else
Compute<xpu, OP>(attrs, ctx, inputs, req, outputs);
#endif
}
};

/*! \brief Map legacy unary_bwd to backward_grad */
Expand Down
3 changes: 2 additions & 1 deletion src/operator/tensor/elemwise_unary_op_basic.cc
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Expand Up @@ -940,7 +940,7 @@ The storage type of ``exp`` output is always dense
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_mul"});

// log
MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(log, cpu, mshadow_op::log)
MXNET_OPERATOR_REGISTER_UNARY(log)
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Sorry, I do not understand this change. Can you please explain?

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the defination of MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR in elemwise_unary_op.h is here:

#define MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(__name$, __xpu$, __kernel$)        \
  MXNET_OPERATOR_REGISTER_UNARY(__name$)                                                \
  .set_attr<FCompute>("FCompute<" #__xpu$ ">", UnaryOp::Compute<__xpu$, __kernel$>)

it will set FCompute attribute to UnaryOp::Compute automatically. To call Our LogCompute, we replace it with below codes.

MXNET_OPERATOR_REGISTER_UNARY(log)
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::LogCompute<cpu, mshadow_op::log>)

MXNET_ADD_SPARSE_OP_ALIAS(log)
.describe(R"code(Returns element-wise Natural logarithmic value of the input.

Expand All @@ -949,6 +949,7 @@ 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::LogCompute<cpu, mshadow_op::log>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_log"});

// log10
Expand Down