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提交 8b30fada 编写于 作者: C chengduoZH
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refine elementwise sub,div,min,max

上级 c43995ed
隐藏空白更改
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Showing with 34 addition and 277 deletion
paddle/fluid/operators/elementwise_div_op.h
浏览文件 @ 8b30fada
...@@ -41,77 +41,14 @@ class ElementwiseDivKernel : public framework::OpKernel<T> { ...@@ -41,77 +41,14 @@ class ElementwiseDivKernel : public framework::OpKernel<T> {
}; };
template <typename T> template <typename T>
struct ElementwiseDivGradFunctor { struct DivGradDX {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout / y; }
typename dY, typename dZ>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto z_e = framework::EigenVector<T>::Flatten(*z);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = dz_e / y_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = -1.0 * dz_e * z_e / y_e;
}
}
};
template <typename T>
struct ElementwiseDivBroadCastGradFunctor {
template <typename Device, typename X, typename Y, typename Z, typename dX,
typename dY, typename dZ, typename Pre, typename N>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
.broadcast(Eigen::DSizes<int, 2>(pre, 1))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = dz_e / y_e_bcast;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
.reshape(Eigen::DSizes<int, 2>(pre, n))
.sum(Eigen::array<int, 1>{{0}});
}
}
}; };
template <typename T> template <typename T>
struct ElementwiseDivBroadCast2GradFunctor { struct DivGradDY {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
typename dY, typename dZ, typename Pre, typename N, typename Post> return -dout * x / (y * y);
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
Post post) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
.broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = dz_e / y_e_bcast;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
.reshape(Eigen::DSizes<int, 3>(pre, n, post))
.sum(Eigen::array<int, 2>{{0, 2}});
}
} }
}; };
...@@ -128,10 +65,8 @@ class ElementwiseDivGradKernel : public framework::OpKernel<T> { ...@@ -128,10 +65,8 @@ class ElementwiseDivGradKernel : public framework::OpKernel<T> {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
int axis = ctx.Attr<int>("axis"); int axis = ctx.Attr<int>("axis");
ElementwiseGradCompute<DeviceContext, T, ElementwiseDivGradFunctor<T>, ElemwiseGradCompute<DeviceContext, T, DivGradDX<T>, DivGradDY<T>>(
ElementwiseDivBroadCastGradFunctor<T>, ctx, *x, *y, *out, *dout, axis, dx, dy, DivGradDX<T>(), DivGradDY<T>());
ElementwiseDivBroadCast2GradFunctor<T>>(
ctx, x, y, out, dout, axis, dx, dy);
} }
}; };
......
paddle/fluid/operators/elementwise_max_op.h
浏览文件 @ 8b30fada
...@@ -41,76 +41,16 @@ class ElementwiseMaxKernel : public framework::OpKernel<T> { ...@@ -41,76 +41,16 @@ class ElementwiseMaxKernel : public framework::OpKernel<T> {
}; };
template <typename T> template <typename T>
struct ElementwiseMaxGradFunctor { struct MaxGradDx {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
typename dY, typename dZ> return dout * (x > y);
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = (x_e > y_e).template cast<T>() * dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (x_e <= y_e).template cast<T>() * dz_e;
}
} }
}; };
template <typename T> template <typename T>
struct ElementwiseMaxBroadCastGradFunctor { struct MaxGradDy {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
typename dY, typename dZ, typename Pre, typename N> return dout * (x <= y);
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
.broadcast(Eigen::DSizes<int, 2>(pre, 1))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = (x_e > y_e_bcast).template cast<T>() * dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = ((x_e <= y_e_bcast).template cast<T>() * dz_e)
.reshape(Eigen::DSizes<int, 2>(pre, n))
.sum(Eigen::array<int, 1>{{0}});
}
}
};
template <typename T>
struct ElementwiseMaxBroadCast2GradFunctor {
template <typename Device, typename X, typename Y, typename Z, typename dX,
typename dY, typename dZ, typename Pre, typename N, typename Post>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
Post post) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
.broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = (x_e > y_e_bcast).template cast<T>() * dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = ((x_e <= y_e_bcast).template cast<T>() * dz_e)
.reshape(Eigen::DSizes<int, 3>(pre, n, post))
.sum(Eigen::array<int, 2>{{0, 2}});
}
} }
}; };
...@@ -127,12 +67,9 @@ class ElementwiseMaxGradKernel : public framework::OpKernel<T> { ...@@ -127,12 +67,9 @@ class ElementwiseMaxGradKernel : public framework::OpKernel<T> {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
int axis = ctx.Attr<int>("axis"); int axis = ctx.Attr<int>("axis");
ElementwiseGradCompute<DeviceContext, T, ElementwiseMaxGradFunctor<T>, ElemwiseGradCompute<DeviceContext, T, MaxGradDx<T>, MaxGradDy<T>>(
ElementwiseMaxBroadCastGradFunctor<T>, ctx, *x, *y, *out, *dout, axis, dx, dy, MaxGradDx<T>(), MaxGradDy<T>());
ElementwiseMaxBroadCast2GradFunctor<T>>(
ctx, x, y, out, dout, axis, dx, dy);
} }
}; };
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
paddle/fluid/operators/elementwise_min_op.h
浏览文件 @ 8b30fada
...@@ -41,76 +41,16 @@ class ElementwiseMinKernel : public framework::OpKernel<T> { ...@@ -41,76 +41,16 @@ class ElementwiseMinKernel : public framework::OpKernel<T> {
}; };
template <typename T> template <typename T>
struct ElementwiseMinGradFunctor { struct MinGradDx {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
typename dY, typename dZ> return dout * (x < y);
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = (x_e < y_e).template cast<T>() * dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (x_e >= y_e).template cast<T>() * dz_e;
}
} }
}; };
template <typename T> template <typename T>
struct ElementwiseMinBroadCastGradFunctor { struct MinGradDy {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
typename dY, typename dZ, typename Pre, typename N> return dout * (x >= y);
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
.broadcast(Eigen::DSizes<int, 2>(pre, 1))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = (x_e < y_e_bcast).template cast<T>() * dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = ((x_e >= y_e_bcast).template cast<T>() * dz_e)
.reshape(Eigen::DSizes<int, 2>(pre, n))
.sum(Eigen::array<int, 1>{{0}});
}
}
};
template <typename T>
struct ElementwiseMinBroadCast2GradFunctor {
template <typename Device, typename X, typename Y, typename Z, typename dX,
typename dY, typename dZ, typename Pre, typename N, typename Post>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
Post post) {
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
.broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = (x_e < y_e_bcast).template cast<T>() * dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = ((x_e >= y_e_bcast).template cast<T>() * dz_e)
.reshape(Eigen::DSizes<int, 3>(pre, n, post))
.sum(Eigen::array<int, 2>{{0, 2}});
}
} }
}; };
...@@ -127,12 +67,9 @@ class ElementwiseMinGradKernel : public framework::OpKernel<T> { ...@@ -127,12 +67,9 @@ class ElementwiseMinGradKernel : public framework::OpKernel<T> {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
int axis = ctx.Attr<int>("axis"); int axis = ctx.Attr<int>("axis");
ElementwiseGradCompute<DeviceContext, T, ElementwiseMinGradFunctor<T>, ElemwiseGradCompute<DeviceContext, T, MinGradDx<T>, MinGradDy<T>>(
ElementwiseMinBroadCastGradFunctor<T>, ctx, *x, *y, *out, *dout, axis, dx, dy, MinGradDx<T>(), MinGradDy<T>());
ElementwiseMinBroadCast2GradFunctor<T>>(
ctx, x, y, out, dout, axis, dx, dy);
} }
}; };
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
paddle/fluid/operators/elementwise_mul_op.h
浏览文件 @ 8b30fada
...@@ -40,14 +40,15 @@ class ElementwiseMulKernel : public framework::OpKernel<T> { ...@@ -40,14 +40,15 @@ class ElementwiseMulKernel : public framework::OpKernel<T> {
}; };
template <typename T> template <typename T>
struct IdentityGrad_DX { struct MulGradDX {
HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout * y; } HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout * y; }
}; };
template <typename T> template <typename T>
struct IdentityGrad_DY { struct MulGradDY {
HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout * x; } HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout * x; }
}; };
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
class ElementwiseMulGradKernel : public framework::OpKernel<T> { class ElementwiseMulGradKernel : public framework::OpKernel<T> {
public: public:
...@@ -61,10 +62,8 @@ class ElementwiseMulGradKernel : public framework::OpKernel<T> { ...@@ -61,10 +62,8 @@ class ElementwiseMulGradKernel : public framework::OpKernel<T> {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
int axis = ctx.Attr<int>("axis"); int axis = ctx.Attr<int>("axis");
ElemwiseGradCompute<DeviceContext, T, IdentityGrad_DX<T>, ElemwiseGradCompute<DeviceContext, T, MulGradDX<T>, MulGradDY<T>>(
IdentityGrad_DY<T>>(ctx, *x, *y, *out, *dout, axis, dx, ctx, *x, *y, *out, *dout, axis, dx, dy, MulGradDX<T>(), MulGradDY<T>());
dy, IdentityGrad_DX<T>(),
IdentityGrad_DY<T>());
} }
}; };
} // namespace operators } // namespace operators
......
paddle/fluid/operators/elementwise_sub_op.h
浏览文件 @ 8b30fada
...@@ -40,61 +40,13 @@ class ElementwiseSubKernel : public framework::OpKernel<T> { ...@@ -40,61 +40,13 @@ class ElementwiseSubKernel : public framework::OpKernel<T> {
}; };
template <typename T> template <typename T>
struct ElementwiseSubGradFunctor { struct SubGradDX {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return dout; }
typename dY, typename dZ>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (-1.0) * dz_e;
}
}
}; };
template <typename T> template <typename T>
struct ElementwiseSubBroadCastGradFunctor { struct SubGradDY {
template <typename Device, typename X, typename Y, typename Z, typename dX, HOSTDEVICE T operator()(T x, T y, T out, T dout) const { return -dout; }
typename dY, typename dZ, typename Pre, typename N>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (-1.0) *
dz_e.reshape(Eigen::DSizes<int, 2>(pre, n))
.sum(Eigen::array<int, 1>{{0}});
}
}
};
template <typename T>
struct ElementwiseSubBroadCast2GradFunctor {
template <typename Device, typename X, typename Y, typename Z, typename dX,
typename dY, typename dZ, typename Pre, typename N, typename Post>
void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
Post post) {
auto dz_e = framework::EigenVector<T>::Flatten(*dz);
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(d) = dz_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(d) = (-1.0) *
dz_e.reshape(Eigen::DSizes<int, 3>(pre, n, post))
.sum(Eigen::array<int, 2>{{0, 2}});
}
}
}; };
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
...@@ -110,12 +62,9 @@ class ElementwiseSubGradKernel : public framework::OpKernel<T> { ...@@ -110,12 +62,9 @@ class ElementwiseSubGradKernel : public framework::OpKernel<T> {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
int axis = ctx.Attr<int>("axis"); int axis = ctx.Attr<int>("axis");
ElementwiseGradCompute<DeviceContext, T, ElementwiseSubGradFunctor<T>, ElemwiseGradCompute<DeviceContext, T, SubGradDX<T>, SubGradDY<T>>(
ElementwiseSubBroadCastGradFunctor<T>, ctx, *x, *y, *out, *dout, axis, dx, dy, SubGradDX<T>(), SubGradDY<T>());
ElementwiseSubBroadCast2GradFunctor<T>>(
ctx, x, y, out, dout, axis, dx, dy);
} }
}; };
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
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