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[numpy] unify impl of mixed type binary op between linux and windows #18523

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Jun 11, 2020
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[numpy] unify impl of mixed type binary op between linux and windows #18523

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4 changes: 4 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -121,6 +121,10 @@ cmake_install.cmake
# Mac OS X
.DS_Store

# Windows
windows_package.7z
windows_package

#Notebook Automated Test
!tests/nightly/test_tutorial_config.txt
!tests/nightly/TestNotebook
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10 changes: 0 additions & 10 deletions src/operator/mshadow_op.h
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Expand Up @@ -150,7 +150,6 @@ struct true_divide : public mxnet_op::tunable {
return static_cast<float>(a) / static_cast<float>(b);
}

#ifndef _WIN32
template<typename DType,
typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
Expand All @@ -168,7 +167,6 @@ struct true_divide : public mxnet_op::tunable {
MSHADOW_XINLINE static double Map(DType a, double b) {
return static_cast<double>(a) / b;
}
#endif
};

struct rtrue_divide : public mxnet_op::tunable {
Expand All @@ -184,7 +182,6 @@ struct rtrue_divide : public mxnet_op::tunable {
return static_cast<float>(b) / static_cast<float>(a);
}

#ifndef _WIN32
template<typename DType,
typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
MSHADOW_XINLINE static mshadow::half::half_t Map(DType a, mshadow::half::half_t b) {
Expand All @@ -202,14 +199,12 @@ struct rtrue_divide : public mxnet_op::tunable {
MSHADOW_XINLINE static double Map(DType a, double b) {
return b / static_cast<double>(a);
}
#endif
};

MXNET_BINARY_MATH_OP_NC(left, a);

MXNET_BINARY_MATH_OP_NC(right, b);

#ifndef _WIN32
struct mixed_plus {
template<typename DType,
typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
Expand Down Expand Up @@ -347,8 +342,6 @@ struct mixed_rpower {
return static_cast<double>(math::pow(b, a));
}
};
#endif


#pragma GCC diagnostic push
#if __GNUC__ >= 7
Expand Down Expand Up @@ -584,7 +577,6 @@ MXNET_BINARY_MATH_OP(rpower, math::pow(b, a));
MXNET_BINARY_MATH_OP(rpower_grad, math::id(a) * math::log(b));

MXNET_BINARY_MATH_OP(arctan2, math::atan2(a, b));

MXNET_BINARY_MATH_OP(arctan2_grad, math::id(b) / (math::id(a * a + b * b)));

MXNET_BINARY_MATH_OP(arctan2_rgrad, -math::id(a) / (math::id(a * a + b * b)));
Expand Down Expand Up @@ -819,7 +811,6 @@ struct mod : public mxnet_op::tunable {
}
};

#ifndef _WIN32
struct mixed_mod {
template<typename DType,
typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
Expand Down Expand Up @@ -865,7 +856,6 @@ struct mixed_rmod {
return mod::Map(b, static_cast<double>(a));
}
};
#endif

struct fmod : public mxnet_op::tunable {
template<typename DType>
Expand Down
2 changes: 0 additions & 2 deletions src/operator/mxnet_op.h
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Expand Up @@ -867,7 +867,6 @@ struct op_with_req {
KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], rhs[i]));
}

#ifndef _WIN32
/*! \brief inputs are two tensors with a half_t output tensor */
template<typename DType,
typename std::enable_if<std::is_integral<DType>::value, int>::type = 0>
Expand Down Expand Up @@ -921,7 +920,6 @@ struct op_with_req {
MSHADOW_XINLINE static void Map(index_t i, double *out, const DType *lhs, const double value) {
KERNEL_ASSIGN(out[i], req, OP::Map(lhs[i], value));
}
#endif

/*! \brief inputs are two tensors with a float output tensor */
template<typename DType,
Expand Down
53 changes: 0 additions & 53 deletions src/operator/numpy/np_elemwise_broadcast_op.cc
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Expand Up @@ -60,7 +60,6 @@ bool NumpyBinaryMixedPrecisionType(const nnvm::NodeAttrs& attrs,
return true;
}

#ifndef _WIN32
#define MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(name) \
NNVM_REGISTER_OP(name) \
.set_num_inputs(2) \
Expand All @@ -81,40 +80,12 @@ bool NumpyBinaryMixedPrecisionType(const nnvm::NodeAttrs& attrs,
}) \
.add_argument("lhs", "NDArray-or-Symbol", "First input to the function") \
.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
#else
#define MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(name) \
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>{"lhs", "rhs"}; \
}) \
.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape) \
.set_attr<nnvm::FInferType>("FInferType", NumpyBinaryMixedPrecisionType) \
.set_attr<nnvm::FInplaceOption>("FInplaceOption", \
[](const NodeAttrs& attrs){ \
return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}}; \
}) \
.set_attr<FResourceRequest>("FResourceRequest", \
[](const NodeAttrs& attrs) { \
return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; \
}) \
.add_argument("lhs", "NDArray-or-Symbol", "First input to the function") \
.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function")
#endif

MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_add)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
op::mshadow_op::mixed_plus>)
#else
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::plus>)
#endif
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_add"});

NNVM_REGISTER_OP(_backward_npi_broadcast_add)
Expand All @@ -133,16 +104,10 @@ NNVM_REGISTER_OP(_backward_npi_broadcast_add)
mshadow_op::posone>);

MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_subtract)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastCompute<cpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
op::mshadow_op::mixed_rminus>)
#else
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastCompute<cpu, op::mshadow_op::minus>)
#endif
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_sub"});

NNVM_REGISTER_OP(_backward_npi_broadcast_sub)
Expand All @@ -161,16 +126,10 @@ NNVM_REGISTER_OP(_backward_npi_broadcast_sub)
mshadow_op::negone>);

MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_multiply)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
op::mshadow_op::mixed_mul>)
#else
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::mul>)
#endif
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_mul"});

NNVM_REGISTER_OP(_backward_npi_broadcast_mul)
Expand All @@ -189,16 +148,10 @@ NNVM_REGISTER_OP(_backward_npi_broadcast_mul)
mshadow_op::left>);

MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_mod)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastCompute<cpu, op::mshadow_op::mod, op::mshadow_op::mixed_mod,
op::mshadow_op::mixed_rmod>)
#else
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastCompute<cpu, op::mshadow_op::mod>)
#endif
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_mod"});

NNVM_REGISTER_OP(_backward_npi_broadcast_mod)
Expand All @@ -217,16 +170,10 @@ NNVM_REGISTER_OP(_backward_npi_broadcast_mod)
mshadow_op::mod_rgrad>);

MXNET_OPERATOR_REGISTER_NP_BINARY_MIXED_PRECISION(_npi_power)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::power, op::mshadow_op::mixed_power,
op::mshadow_op::mixed_rpower>)
#else
.set_attr<FCompute>(
"FCompute<cpu>",
NumpyBinaryBroadcastComputeWithBool<cpu, op::mshadow_op::power>)
#endif
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_broadcast_power"});

NNVM_REGISTER_OP(_backward_npi_broadcast_power)
Expand Down
30 changes: 0 additions & 30 deletions src/operator/numpy/np_elemwise_broadcast_op.cu
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Expand Up @@ -29,80 +29,50 @@ namespace mxnet {
namespace op {

NNVM_REGISTER_OP(_npi_add)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::plus, op::mshadow_op::mixed_plus,
op::mshadow_op::mixed_plus>);
#else
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::plus>);
#endif

NNVM_REGISTER_OP(_backward_npi_broadcast_add)
.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::posone,
mshadow_op::posone>);

NNVM_REGISTER_OP(_npi_subtract)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastCompute<gpu, op::mshadow_op::minus, op::mshadow_op::mixed_minus,
op::mshadow_op::mixed_rminus>);
#else
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastCompute<gpu, op::mshadow_op::minus>);
#endif

NNVM_REGISTER_OP(_backward_npi_broadcast_sub)
.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::posone,
mshadow_op::negone>);

NNVM_REGISTER_OP(_npi_multiply)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::mul, op::mshadow_op::mixed_mul,
op::mshadow_op::mixed_mul>);
#else
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::mul>);
#endif

NNVM_REGISTER_OP(_backward_npi_broadcast_mul)
.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::right,
mshadow_op::left>);

NNVM_REGISTER_OP(_npi_mod)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastCompute<gpu, op::mshadow_op::mod, op::mshadow_op::mixed_mod,
op::mshadow_op::mixed_rmod>);
#else
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastCompute<gpu, op::mshadow_op::mod>);
#endif

NNVM_REGISTER_OP(_backward_npi_broadcast_mod)
.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::mod_grad,
mshadow_op::mod_rgrad>);

NNVM_REGISTER_OP(_npi_power)
#ifndef _WIN32
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::power, op::mshadow_op::mixed_power,
op::mshadow_op::mixed_rpower>);
#else
.set_attr<FCompute>(
"FCompute<gpu>",
NumpyBinaryBroadcastComputeWithBool<gpu, op::mshadow_op::power>);
#endif

NNVM_REGISTER_OP(_backward_npi_broadcast_power)
.set_attr<FCompute>("FCompute<gpu>", NumpyBinaryBackwardUseIn<gpu, mshadow_op::power_grad,
Expand Down
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