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未验证 提交 13c99434 编写于 作者: Y YuanRisheng 提交者： GitHub
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[Phi]Move log/log2/log10/log1p Kernels to Phi (#40785) 

* move activation

* fix bugs when run ce
上级 b03ef424
隐藏空白更改
内联 并排
Showing with 332 addition and 265 deletion
paddle/fluid/framework/operator.cc
浏览文件 @ 13c99434
......@@ -1122,7 +1122,15 @@ static void CheckTensorNANOrInf(const std::string& op_type,
bool OperatorWithKernel::SupportsMKLDNN(
const proto::VarType::Type data_type) const {
auto& op_kernels = OperatorWithKernel::AllOpKernels().at(type_);
auto op_kernel_iter = OperatorWithKernel::AllOpKernels().find(type_);
if (op_kernel_iter == OperatorWithKernel::AllOpKernels().end()) {
VLOG(6) << "Warning: " << type_ << " don't find its MKLDNN Kernel in Fluid "
"Registered Kernels. And We don't "
"search its kernels in phi lib, "
"SupportsMKLDNN() return false.";
return false;
}
auto& op_kernels = op_kernel_iter->second;
return std::any_of(op_kernels.begin(), op_kernels.end(),
[data_type](OpKernelMap::const_reference kern_pair) {
return platform::is_cpu_place(kern_pair.first.place_) &&
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 13c99434
......@@ -1496,6 +1496,9 @@ REGISTER_ACTIVATION_OP(hard_sigmoid, HardSigmoid, HardSigmoidFunctor,
HardSigmoidGradFunctor);
REGISTER_ACTIVATION_OP(logsigmoid, LogSigmoid, LogSigmoidFunctor,
LogSigmoidGradFunctor);
REGISTER_ACTIVATION_OP(log2, Log2, Log2Functor, Log2GradFunctor);
REGISTER_ACTIVATION_OP(log10, Log10, Log10Functor, Log10GradFunctor);
REGISTER_ACTIVATION_OP(log1p, Log1p, Log1pFunctor, Log1pGradFunctor);
/* ========================== sigmoid register =============================
*/
......@@ -1867,15 +1870,6 @@ REGISTER_OPERATOR(
ops::ActivationOpDoubleGrad<ops::LogGradGradFunctor<float>::FwdDeps()>,
ops::ActivationDoubleGradOpInplaceInferer);
REGISTER_ACTIVATION_CPU_KERNEL(log, Log, LogFunctor, LogGradFunctor);
REGISTER_OP_CPU_KERNEL(
log_grad_grad, ops::LogDoubleGradKernel<plat::CPUDeviceContext,
ops::LogGradGradFunctor<float>>,
ops::LogDoubleGradKernel<plat::CPUDeviceContext,
ops::LogGradGradFunctor<double>>,
ops::LogDoubleGradKernel<plat::CPUDeviceContext,
ops::LogGradGradFunctor<plat::float16>>);
/* ========================================================================== */
/* ========================== register checkpoint ===========================*/
......
paddle/fluid/operators/activation_op.h
浏览文件 @ 13c99434
......@@ -281,6 +281,11 @@ USE_PHI_DOUBLE_GRAD_FUNCTOR(Sigmoid)
USE_PHI_TRIPLE_GRAD_FUNCTOR(Sigmoid)
USE_PHI_FUNCTOR(LogSigmoid)
USE_PHI_FUNCTOR(HardSigmoid)
USE_PHI_FUNCTOR(Log)
USE_PHI_DOUBLE_GRAD_FUNCTOR(Log)
USE_PHI_FUNCTOR(Log2)
USE_PHI_FUNCTOR(Log10)
USE_PHI_FUNCTOR(Log1p)
template <typename T>
using ELUGradNegativeAlphaFunctor = phi::funcs::ELUGradNegativeAlphaFunctor<T>;
......@@ -448,88 +453,6 @@ struct ReciprocalGradFunctor : public BaseActivationFunctor<T> {
}
};
// log(x) = natural logarithm of x
template <typename T>
struct LogFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log();
}
};
template <typename T>
struct LogGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) / x);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// log2(x) = logarithm to the base 2 of the elements of x
template <typename T>
struct Log2Functor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log() / static_cast<T>(log(2));
}
};
// the gradient of log2(x) is 1/(x*ln(2))
template <typename T>
struct Log2GradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(1) / (x * static_cast<T>(log(2)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// log10(x) = logarithm to the base 10 of the elements of x
template <typename T>
struct Log10Functor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log() / static_cast<T>(log(10));
}
};
// the gradient of log10(x) is 1/(x*ln(10))
template <typename T>
struct Log10GradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(1) / (x * static_cast<T>(log(10)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// log1p(x) = natural logarithm of x+1
template <typename T>
struct Log1pFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = (static_cast<T>(1) + x).log();
}
};
template <typename T>
struct Log1pGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) / (x + static_cast<T>(1)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// square(x) = x^2
template <typename T>
struct SquareFunctor : public BaseActivationFunctor<T> {
......@@ -1197,37 +1120,6 @@ class SquareDoubleGradKernel
}
};
template <typename DeviceContext, typename Functor>
class LogDoubleGradKernel
: public SquareDoubleGradKernel<DeviceContext, Functor> {};
template <typename DeviceContext, typename Functor>
class ELUDoubleGradKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& ctx) const override {
const framework::Tensor *X, *ddX, *dOut;
X = ddX = dOut = nullptr;
framework::Tensor *dX, *ddOut;
dX = ddOut = nullptr;
ExtractDoubleGradTensorWithInputDOut(ctx, &X, &ddX, &dX, &dOut, &ddOut);
if (dX) dX->mutable_data<T>(X->dims(), ctx.GetPlace());
if (ddOut) ddOut->mutable_data<T>(ctx.GetPlace());
auto& place = ctx.template device_context<DeviceContext>();
Functor functor;
auto attrs = functor.GetAttrs();
for (auto& attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(place, X, ddX, ddOut, dOut, dX);
}
};
template <typename DeviceContext, typename Functor>
class CELUDoubleGradKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
......@@ -1522,36 +1414,6 @@ class LogitGradKernel : public framework::OpKernel<T> {
}
};
template <typename T>
struct LogGradGradFunctor : public BaseActivationFunctor<T> {
template <typename Device>
void operator()(const Device& dev, const framework::Tensor* X,
const framework::Tensor* ddX, framework::Tensor* ddOut,
const framework::Tensor* dOut, framework::Tensor* dX) const {
auto* d = dev.eigen_device();
auto ddx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "LogGradGrad"));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "LogGradGrad"));
// ddout = ddx / x; dx = -(dout / x) * (ddx / x)
// calculate dx first, so ddout can inplace ddx
if (dX) {
auto dout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Output", "DOut", "LogGradGrad"));
auto dx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Output", "DX", "LogGradGrad"));
dx.device(*d) = dout * static_cast<T>(-1) * ddx / (x * x);
}
if (ddOut) {
auto ddout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DDOut", "LogGradGrad"));
ddout.device(*d) = ddx * static_cast<T>(1) / x;
}
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
} // namespace operators
} // namespace paddle
......@@ -1560,9 +1422,6 @@ struct LogGradGradFunctor : public BaseActivationFunctor<T> {
__macro(floor, Floor, FloorFunctor, ZeroGradFunctor); \
__macro(round, Round, RoundFunctor, ZeroGradFunctor); \
__macro(reciprocal, Reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log1p, Log1p, Log1pFunctor, Log1pGradFunctor); \
__macro(log2, Log2, Log2Functor, Log2GradFunctor); \
__macro(log10, Log10, Log10Functor, Log10GradFunctor); \
__macro(soft_relu, SoftRelu, SoftReluFunctor, SoftReluGradFunctor); \
__macro(stanh, STanh, STanhFunctor, STanhGradFunctor); \
__macro(softplus, Softplus, SoftplusFunctor, SoftplusGradFunctor); \
......
paddle/fluid/operators/activation_op.kps
浏览文件 @ 13c99434
......@@ -131,27 +131,6 @@ struct CudaExpm1GradFunctor : public BaseActivationFunctor<T> {
}
};
template <typename T>
struct CudaLogFunctor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
// log(x) = log(x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log(x));
}
};
template <typename T>
struct CudaLogGradFunctor : public BaseActivationFunctor<T> {
// dx = dout / x
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / x;
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaSquareFunctor : public BaseActivationFunctor<T> {
// square(x) = x * x
......@@ -220,78 +199,6 @@ struct CudaRsqrtGradFunctor : public BaseActivationFunctor<T> {
}
};
template <typename T>
struct CudaLog1pFunctor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
MPType one = static_cast<MPType>(1.0f);
// log1p(x) = log(1 + x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log(one + x));
}
};
template <typename T>
struct CudaLog1pGradFunctor : public BaseActivationFunctor<T> {
T one = static_cast<T>(1.0f);
// dx = dout / (1 + x)
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / (one + x);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaLog2Functor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
// log2(x) = log2(x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log2(x));
}
};
template <typename T>
struct CudaLog2GradFunctor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
T log_two = static_cast<T>(log(static_cast<MPType>(2.0f)));
// dx = dout / (x * log(2))
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / (x * log_two);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaLog10Functor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
// log10(x) = log10(x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log10(x));
}
};
template <typename T>
struct CudaLog10GradFunctor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
T log_ten = static_cast<T>(log(static_cast<MPType>(10.0f)));
// dx = dout / (x * log(10))
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / (x * log_ten);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaSoftReluFunctor : public BaseActivationFunctor<T> {
using MPType = typename details::MPTypeTrait<T>::Type;
......@@ -773,6 +680,10 @@ USE_PHI_FUNCTOR(CudaELU)
USE_PHI_FUNCTOR(CudaSigmoid)
USE_PHI_FUNCTOR(CudaLogSigmoid)
USE_PHI_FUNCTOR(CudaHardSigmoid)
USE_PHI_FUNCTOR(CudaLog)
USE_PHI_FUNCTOR(CudaLog2)
USE_PHI_FUNCTOR(CudaLog10)
USE_PHI_FUNCTOR(CudaLog1p)
template <typename T>
using CudaELUGradNegativeAlphaFunctor =
......@@ -975,18 +886,6 @@ REGISTER_OP_CUDA_KERNEL(
ops::CudaExpm1GradFunctor<plat::float16>>);
/* ========================================================================== */
/* ========================== Log register ==================================*/
REGISTER_ACTIVATION_CUDA_KERNEL(log, Log, CudaLogFunctor, CudaLogGradFunctor);
REGISTER_OP_CUDA_KERNEL(
log_grad_grad, ops::LogDoubleGradKernel<plat::CUDADeviceContext,
ops::LogGradGradFunctor<float>>,
ops::LogDoubleGradKernel<plat::CUDADeviceContext,
ops::LogGradGradFunctor<double>>,
ops::LogDoubleGradKernel<plat::CUDADeviceContext,
ops::LogGradGradFunctor<plat::float16>>);
/* ========================================================================== */
#define FOR_EACH_ACTIVATION_CUDA_OP(__macro) \
__macro(softshrink, SoftShrink, CudaSoftShrinkFunctor, \
CudaSoftShrinkGradFunctor); \
......@@ -995,9 +894,6 @@ REGISTER_OP_CUDA_KERNEL(
__macro(round, Round, CudaRoundFunctor, CudaZeroGradFunctor); \
__macro(reciprocal, Reciprocal, CudaReciprocalFunctor, \
CudaReciprocalGradFunctor); \
__macro(log1p, Log1p, CudaLog1pFunctor, CudaLog1pGradFunctor); \
__macro(log2, Log2, CudaLog2Functor, CudaLog2GradFunctor); \
__macro(log10, Log10, CudaLog10Functor, CudaLog10GradFunctor); \
__macro(soft_relu, SoftRelu, CudaSoftReluFunctor, CudaSoftReluGradFunctor); \
__macro(stanh, STanh, CudaSTanhFunctor, CudaSTanhGradFunctor); \
__macro(softplus, Softplus, CudaSoftplusFunctor, CudaSoftplusGradFunctor); \
......
paddle/phi/kernels/activation_grad_kernel.h
浏览文件 @ 13c99434
......@@ -135,6 +135,14 @@ void SigmoidTripleGradKernel(const Context& dev_ctx,
DenseTensor* d_dout,
DenseTensor* d_ddx);
template <typename T, typename Context>
void LogDoubleGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& dout,
const DenseTensor& ddx,
DenseTensor* dx,
DenseTensor* ddout);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Cos);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Tan);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Acos);
......@@ -149,6 +157,10 @@ DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Atanh);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(TanhShrink);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Silu);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(LogSigmoid);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log2);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log10);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log1p);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Relu);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Tanh);
......
paddle/phi/kernels/activation_kernel.h
浏览文件 @ 13c99434
......@@ -56,6 +56,10 @@ DECLARE_ACTIVATION_KERNEL(TanhShrink)
DECLARE_ACTIVATION_KERNEL(Silu)
DECLARE_ACTIVATION_KERNEL(Sigmoid)
DECLARE_ACTIVATION_KERNEL(LogSigmoid)
DECLARE_ACTIVATION_KERNEL(Log)
DECLARE_ACTIVATION_KERNEL(Log2)
DECLARE_ACTIVATION_KERNEL(Log10)
DECLARE_ACTIVATION_KERNEL(Log1p)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(LeakyRelu, alpha)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(ThresholdedRelu, threshold)
......
paddle/phi/kernels/cpu/activation_grad_kernel.cc
浏览文件 @ 13c99434
......@@ -121,6 +121,10 @@ DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(Atanh, AtanhGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(TanhShrink, TanhShrinkGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(Silu, SiluGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(LogSigmoid, LogSigmoidGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(Log, LogGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(Log2, Log2GradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(Log10, Log10GradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPX(Log1p, Log1pGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPOUT(Relu, ReluGradFunctor);
DEFINE_CPU_ACTIVATION_GRAD_KERNEL_DEPOUT(Tanh, TanhGradFunctor);
......@@ -233,3 +237,8 @@ PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_double_grad, SigmoidDoubleGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_triple_grad, SigmoidTripleGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(hard_sigmoid_grad, HardSigmoidGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(logsigmoid_grad, LogSigmoidGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log_grad, LogGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log2_grad, Log2GradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log10_grad, Log10GradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log1p_grad, Log1pGradKernel)
PD_REGISTER_ACTIVATION_DOUBLE_GRAD_KERNEL(log_double_grad, LogDoubleGradKernel)
paddle/phi/kernels/cpu/activation_kernel.cc
浏览文件 @ 13c99434
......@@ -74,6 +74,10 @@ DEFINE_CPU_ACTIVATION_KERNEL(TanhShrink, TanhShrinkFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Silu, SiluFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Sigmoid, SigmoidFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(LogSigmoid, LogSigmoidFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Log, LogFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Log2, Log2Functor)
DEFINE_CPU_ACTIVATION_KERNEL(Log10, Log10Functor)
DEFINE_CPU_ACTIVATION_KERNEL(Log1p, Log1pFunctor)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(LeakyRelu, LeakyReluFunctor, alpha)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(ThresholdedRelu,
......@@ -118,3 +122,7 @@ PD_REGISTER_ACTIVATION_KERNEL(silu, SiluKernel)
PD_REGISTER_ACTIVATION_KERNEL(sigmoid, SigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(logsigmoid, LogSigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(hard_sigmoid, HardSigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(log, LogKernel)
PD_REGISTER_ACTIVATION_KERNEL(log2, Log2Kernel)
PD_REGISTER_ACTIVATION_KERNEL(log10, Log10Kernel)
PD_REGISTER_ACTIVATION_KERNEL(log1p, Log1pKernel)
paddle/phi/kernels/funcs/activation_functor.h
浏览文件 @ 13c99434
......@@ -1223,6 +1223,133 @@ struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
}
};
// log(x) = natural logarithm of x
template <typename T>
struct LogFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log();
}
};
template <typename T>
struct LogGradFunctor : public BaseActivationFunctor<T> {
template <typename Device,
typename X,
typename Out,
typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) / x);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// log2(x) = logarithm to the base 2 of the elements of x
template <typename T>
struct Log2Functor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log() / static_cast<T>(log(2));
}
};
// the gradient of log2(x) is 1/(x*ln(2))
template <typename T>
struct Log2GradFunctor : public BaseActivationFunctor<T> {
template <typename Device,
typename X,
typename Out,
typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(1) / (x * static_cast<T>(log(2)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// log10(x) = logarithm to the base 10 of the elements of x
template <typename T>
struct Log10Functor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.log() / static_cast<T>(log(10));
}
};
// the gradient of log10(x) is 1/(x*ln(10))
template <typename T>
struct Log10GradFunctor : public BaseActivationFunctor<T> {
template <typename Device,
typename X,
typename Out,
typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(1) / (x * static_cast<T>(log(10)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
// log1p(x) = natural logarithm of x+1
template <typename T>
struct Log1pFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = (static_cast<T>(1) + x).log();
}
};
template <typename T>
struct Log1pGradFunctor : public BaseActivationFunctor<T> {
template <typename Device,
typename X,
typename Out,
typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) / (x + static_cast<T>(1)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct LogGradGradFunctor : public BaseActivationFunctor<T> {
template <typename Device>
void operator()(const Device& dev,
const DenseTensor* X,
const DenseTensor* ddX,
DenseTensor* ddOut,
const DenseTensor* dOut,
DenseTensor* dX) const {
auto* d = dev.eigen_device();
auto ddx = EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "LogGradGrad"));
auto x = EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "LogGradGrad"));
// ddout = ddx / x; dx = -(dout / x) * (ddx / x)
// calculate dx first, so ddout can inplace ddx
if (dX) {
auto dout = EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Output", "DOut", "LogGradGrad"));
auto dx = EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Output", "DX", "LogGradGrad"));
dx.device(*d) = dout * static_cast<T>(-1) * ddx / (x * x);
}
if (ddOut) {
auto ddout = EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DDOut", "LogGradGrad"));
ddout.device(*d) = ddx * static_cast<T>(1) / x;
}
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
#if defined(__NVCC__) || defined(__HIPCC__) || defined(__xpu__)
template <typename T>
struct CudaReluFunctor : public BaseActivationFunctor<T> {
......@@ -1970,6 +2097,99 @@ struct CudaHardSigmoidGradFunctor : public BaseActivationFunctor<T> {
}
};
template <typename T>
struct CudaLogFunctor : public BaseActivationFunctor<T> {
using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
// log(x) = log(x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log(x));
}
};
template <typename T>
struct CudaLogGradFunctor : public BaseActivationFunctor<T> {
// dx = dout / x
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / x;
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaLog1pFunctor : public BaseActivationFunctor<T> {
using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
MPType one = static_cast<MPType>(1.0f);
// log1p(x) = log(1 + x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log(one + x));
}
};
template <typename T>
struct CudaLog1pGradFunctor : public BaseActivationFunctor<T> {
T one = static_cast<T>(1.0f);
// dx = dout / (1 + x)
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / (one + x);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaLog2Functor : public BaseActivationFunctor<T> {
using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
// log2(x) = log2(x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log2(x));
}
};
template <typename T>
struct CudaLog2GradFunctor : public BaseActivationFunctor<T> {
using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
T log_two = static_cast<T>(log(static_cast<MPType>(2.0f)));
// dx = dout / (x * log(2))
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / (x * log_two);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
template <typename T>
struct CudaLog10Functor : public BaseActivationFunctor<T> {
using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
// log10(x) = log10(x)
__device__ __forceinline__ T operator()(const T arg_x) const {
MPType x = static_cast<MPType>(arg_x);
return static_cast<T>(log10(x));
}
};
template <typename T>
struct CudaLog10GradFunctor : public BaseActivationFunctor<T> {
using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
T log_ten = static_cast<T>(log(static_cast<MPType>(10.0f)));
// dx = dout / (x * log(10))
__device__ __forceinline__ T operator()(const T dout, const T x) const {
return dout / (x * log_ten);
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
};
#endif
} // namespace funcs
......
paddle/phi/kernels/gpu/activation_grad_kernel.cu
浏览文件 @ 13c99434
......@@ -177,6 +177,10 @@ DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(Atanh, CudaAtanhGradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(TanhShrink, CudaTanhShrinkGradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(Silu, CudaSiluGradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(LogSigmoid, CudaLogSigmoidGradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(Log, CudaLogGradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(Log2, CudaLog2GradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(Log10, CudaLog10GradFunctor);
DEFINE_GPU_ACTIVATION_GRAD_KERNEL_DEPX(Log1p, CudaLog1pGradFunctor);
DEFINE_GPU_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(LeakyRelu,
CudaLeakyReluGradFunctor,
......@@ -300,3 +304,14 @@ PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_double_grad, SigmoidDoubleGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_triple_grad, SigmoidTripleGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(hard_sigmoid_grad, HardSigmoidGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(logsigmoid_grad, LogSigmoidGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log_grad, LogGradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log2_grad, Log2GradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log10_grad, Log10GradKernel)
PD_REGISTER_ACTIVATION_GRAD_KERNEL(log1p_grad, Log1pGradKernel)
PD_REGISTER_KERNEL(log_double_grad,
GPU,
ALL_LAYOUT,
phi::LogDoubleGradKernel,
float,
double,
phi::dtype::float16) {}
paddle/phi/kernels/gpu/activation_kernel.cu
浏览文件 @ 13c99434
......@@ -19,7 +19,7 @@ limitations under the License. */
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/impl/activation_grad_impl.h"
#include "paddle/phi/kernels/impl/activation_impl.h"
#include "paddle/fluid/platform/device/gpu/gpu_device_function.h"
......@@ -93,6 +93,10 @@ DEFINE_GPU_ACTIVATION_KERNEL(TanhShrink, CudaTanhShrinkFunctor)
DEFINE_GPU_ACTIVATION_KERNEL(Silu, CudaSiluFunctor)
DEFINE_GPU_ACTIVATION_KERNEL(Sigmoid, CudaSigmoidFunctor)
DEFINE_GPU_ACTIVATION_KERNEL(LogSigmoid, CudaLogSigmoidFunctor)
DEFINE_GPU_ACTIVATION_KERNEL(Log, CudaLogFunctor)
DEFINE_GPU_ACTIVATION_KERNEL(Log2, CudaLog2Functor)
DEFINE_GPU_ACTIVATION_KERNEL(Log10, CudaLog10Functor)
DEFINE_GPU_ACTIVATION_KERNEL(Log1p, CudaLog1pFunctor)
DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(LeakyRelu, CudaLeakyReluFunctor, alpha)
DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(ThresholdedRelu,
......@@ -164,3 +168,7 @@ PD_REGISTER_ACTIVATION_KERNEL(silu, SiluKernel)
PD_REGISTER_ACTIVATION_KERNEL(sigmoid, SigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(logsigmoid, LogSigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(hard_sigmoid, HardSigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(log, LogKernel)
PD_REGISTER_ACTIVATION_KERNEL(log2, Log2Kernel)
PD_REGISTER_ACTIVATION_KERNEL(log10, Log10Kernel)
PD_REGISTER_ACTIVATION_KERNEL(log1p, Log1pKernel)
paddle/phi/kernels/impl/activation_grad_impl.h
浏览文件 @ 13c99434
......@@ -275,4 +275,22 @@ void SigmoidTripleGradKernel(const Context& dev_ctx,
d_ddx);
}
template <typename T, typename Context>
void LogDoubleGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& dout,
const DenseTensor& ddx,
DenseTensor* dx,
DenseTensor* ddout) {
if (dx) {
dx->Resize(x.dims());
dev_ctx.template Alloc<T>(dx);
}
if (ddout) {
dev_ctx.template Alloc<T>(ddout);
}
funcs::LogGradGradFunctor<T> functor;
functor(dev_ctx, &x, &ddx, ddout, &dout, dx);
}
} // namespace phi
paddle/phi/ops/compat/activation_sig.cc
浏览文件 @ 13c99434
......@@ -57,6 +57,10 @@ DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(HardShrink, "hard_shrink", "threshold");
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(TanhShrink, "tanh_shrink", ); // NOLINT
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(Silu, "silu", ); // NOLINT
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(LogSigmoid, "logsigmoid", ); // NOLINT
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(Log, "log", ); // NOLINT
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(Log2, "log2", ); // NOLINT
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(Log10, "log10", ); // NOLINT
DEFINE_ACT_GRAD_DEPX_OP_ARGMAP(Log1p, "log1p", ); // NOLINT
DEFINE_ACT_GRAD_DEPOUT_OP_ARGMAP(Relu, "relu", ); // NOLINT
DEFINE_ACT_GRAD_DEPOUT_OP_ARGMAP(Tanh, "tanh", ); // NOLINT
......@@ -125,6 +129,12 @@ KernelSignature EluDoubleGradOpArgumentMapping(
"elu_double_grad", {"X", "DOut", "DDX"}, {"alpha"}, {"DX", "DDOut"});
}
KernelSignature LogDoubleGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature(
"log_double_grad", {"X", "DOut", "DDX"}, {}, {"DX", "DDOut"});
}
} // namespace phi
PD_REGISTER_BASE_KERNEL_NAME(relu_grad_grad, relu_double_grad);
......@@ -134,6 +144,7 @@ PD_REGISTER_BASE_KERNEL_NAME(softshrink, soft_shrink);
PD_REGISTER_BASE_KERNEL_NAME(softshrink_grad, soft_shrink_grad);
PD_REGISTER_BASE_KERNEL_NAME(elu_grad_grad, elu_double_grad);
PD_REGISTER_BASE_KERNEL_NAME(sigmoid_grad_grad, sigmoid_double_grad);
PD_REGISTER_BASE_KERNEL_NAME(log_grad_grad, log_double_grad);
PD_REGISTER_ARG_MAPPING_FN(cos_grad, phi::CosGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(tan_grad, phi::TanGradOpArgumentMapping);
......@@ -181,3 +192,8 @@ PD_REGISTER_ARG_MAPPING_FN(logsigmoid_grad,
phi::LogSigmoidGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(hard_sigmoid_grad,
phi::HardSigmoidGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(log_grad, phi::LogGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(log_grad_grad, phi::LogDoubleGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(log2_grad, phi::Log2GradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(log10_grad, phi::Log10GradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(log1p_grad, phi::Log1pGradOpArgumentMapping);
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