wehub-resource-sync
533 lines
22 KiB
Plaintext
533 lines
22 KiB
Plaintext
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/phi/kernels/activation_kernel.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_device_function.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/impl/activation_grad_impl.h"
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#include "paddle/phi/kernels/impl/activation_impl.h"
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COMMON_DECLARE_bool(use_accuracy_compatible_kernel);
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namespace phi {
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template <typename T, typename Context, typename Functor>
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void ActivationGPUImpl(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out,
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const Functor& functor) {
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PADDLE_ENFORCE_NOT_NULL(out,
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errors::NotFound("Output Out should not be nullptr"));
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dev_ctx.template Alloc<T>(out);
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if (out->numel() == 0) {
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return;
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}
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std::vector<const DenseTensor*> ins = {&x};
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std::vector<DenseTensor*> outs = {out};
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funcs::ElementwiseKernel<T>(dev_ctx, ins, &outs, functor);
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}
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#define DEFINE_GPU_ACTIVATION_KERNEL(name, functor_class) \
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template <typename T, typename Context> \
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void name##Kernel( \
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const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) { \
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funcs::functor_class<T> functor; \
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ActivationGPUImpl<T, Context, funcs::functor_class<T>>( \
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dev_ctx, x, out, functor); \
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}
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#define DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(name, \
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functor_class) \
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template <typename T, typename Context> \
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void name##Kernel( \
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const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) { \
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funcs::functor_class<T> functor; \
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using U = \
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typename std::conditional_t<std::is_integral<T>::value, float, T>; \
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ActivationGPUImpl<U, Context, funcs::functor_class<T>>( \
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dev_ctx, x, out, functor); \
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}
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#define DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(name, functor_class, attr) \
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template <typename T, typename Context> \
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void name##Kernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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float attr, \
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DenseTensor* out) { \
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funcs::functor_class<T> functor; \
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auto attrs = functor.GetAttrs(); \
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*(attrs[0].second) = attr; \
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ActivationGPUImpl<T, Context, funcs::functor_class<T>>( \
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dev_ctx, x, out, functor); \
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}
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#define DEFINE_GPU_ACT_KERNEL_WITH_ONE_DOUBLE_ATTRS(name, functor_class, attr) \
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template <typename T, typename Context> \
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void name##Kernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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double attr, \
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DenseTensor* out) { \
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funcs::functor_class<T> functor; \
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auto attrs = functor.GetAttrs(); \
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*(attrs[0].second) = attr; \
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ActivationGPUImpl<T, Context, funcs::functor_class<T>>( \
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dev_ctx, x, out, functor); \
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}
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#define DEFINE_GPU_ACT_KERNEL_WITH_TWO_ATTRS( \
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name, functor_class, attr1, attr2) \
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template <typename T, typename Context> \
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void name##Kernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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float attr1, \
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float attr2, \
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DenseTensor* out) { \
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funcs::functor_class<T> functor; \
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auto attrs = functor.GetAttrs(); \
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*(attrs[0].second) = attr1; \
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*(attrs[1].second) = attr2; \
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ActivationGPUImpl<T, Context, funcs::functor_class<T>>( \
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dev_ctx, x, out, functor); \
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}
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#define DEFINE_GPU_ACT_KERNEL_WITH_TWO_DOUBLE_ATTRS( \
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name, functor_class, attr1, attr2) \
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template <typename T, typename Context> \
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void name##Kernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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double attr1, \
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double attr2, \
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DenseTensor* out) { \
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funcs::functor_class<T> functor; \
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auto attrs = functor.GetAttrs(); \
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*(attrs[0].second) = attr1; \
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*(attrs[1].second) = attr2; \
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ActivationGPUImpl<T, Context, funcs::functor_class<T>>( \
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dev_ctx, x, out, functor); \
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}
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DEFINE_GPU_ACTIVATION_KERNEL(Cos, CudaCosFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Tan, CudaTanFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Acos, CudaAcosFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Sin, CudaSinFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Asin, CudaAsinFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Atan, CudaAtanFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Sinh, CudaSinhFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Cosh, CudaCoshFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Asinh, CudaAsinhFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Acosh, CudaAcoshFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Atanh, CudaAtanhFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Relu, CudaReluFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Tanh, CudaTanhFunctor)
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// TanhShrink: custom kernel to support FLAGS_use_accuracy_compatible_kernel
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template <typename T, typename Context>
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void TanhShrinkKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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funcs::CudaTanhShrinkFunctor<T> functor;
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functor.compatible = FLAGS_use_accuracy_compatible_kernel;
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ActivationGPUImpl<T, Context, funcs::CudaTanhShrinkFunctor<T>>(
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dev_ctx, x, out, functor);
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}
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DEFINE_GPU_ACTIVATION_KERNEL(Silu, CudaSiluFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Reciprocal, CudaReciprocalFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Square, CudaSquareFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Sqrt, CudaSqrtFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Rsqrt, CudaRsqrtFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Softsign, CudaSoftsignFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Sigmoid, CudaSigmoidFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(LogSigmoid, CudaLogSigmoidFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Floor, CudaFloorFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Ceil, CudaCeilFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL(Rint, CudaRintFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log, CudaLogFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log2, CudaLog2Functor)
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DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log10, CudaLog10Functor)
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DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log1p, CudaLog1pFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Exp, CudaExpFunctor)
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DEFINE_GPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Expm1, CudaExpm1Functor)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_DOUBLE_ATTRS(LeakyRelu,
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CudaLeakyReluFunctor,
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alpha)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_DOUBLE_ATTRS(LogitCUDA, CudaLogitFunctor, eps)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(HardShrink,
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CudaHardShrinkFunctor,
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threshold)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(SoftShrink, CudaSoftShrinkFunctor, lambda)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(Elu, CudaELUFunctor, alpha)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(Mish, CudaMishFunctor, threshold)
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DEFINE_GPU_ACT_KERNEL_WITH_ONE_ATTRS(Celu, CudaCELUFunctor, alpha)
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DEFINE_GPU_ACT_KERNEL_WITH_TWO_ATTRS(HardTanh,
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CudaHardTanhFunctor,
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t_min,
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t_max)
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DEFINE_GPU_ACT_KERNEL_WITH_TWO_ATTRS(Stanh, CudaSTanhFunctor, scale_a, scale_b)
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DEFINE_GPU_ACT_KERNEL_WITH_TWO_DOUBLE_ATTRS(Softplus,
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CudaSoftplusFunctor,
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beta,
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threshold)
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DEFINE_GPU_ACT_KERNEL_WITH_TWO_ATTRS(HardSigmoid,
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CudaHardSigmoidFunctor,
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slope,
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offset)
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DEFINE_GPU_ACT_KERNEL_WITH_TWO_ATTRS(Selu, CudaSeluFunctor, scale, alpha)
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DEFINE_GPU_ACT_KERNEL_WITH_TWO_ATTRS(ThresholdedRelu,
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CudaThresholdedReluFunctor,
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threshold,
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value)
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template <typename T, typename Context>
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void HardSwishKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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funcs::CudaHardSwishFunctor<T> functor;
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float threshold = 6;
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float scale = 6;
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float offset = 3;
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auto attrs = functor.GetAttrs();
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*(attrs[0].second) = threshold;
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*(attrs[1].second) = scale;
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*(attrs[2].second) = offset;
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ActivationGPUImpl<T, Context, funcs::CudaHardSwishFunctor<T>>(
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dev_ctx, x, out, functor);
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}
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template <typename T, typename Context>
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void SwishKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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funcs::CudaSwishFunctor<T> functor;
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auto attrs = functor.GetAttrs();
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*(attrs[0].second) = 1.0;
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ActivationGPUImpl<T, Context, funcs::CudaSwishFunctor<T>>(
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dev_ctx, x, out, functor);
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}
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template <typename T, typename Context>
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void Relu6Kernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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funcs::CudaRelu6Functor<T> functor;
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auto attrs = functor.GetAttrs();
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*(attrs[0].second) = 6.0;
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ActivationGPUImpl<T, Context, funcs::CudaRelu6Functor<T>>(
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dev_ctx, x, out, functor);
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}
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template <typename T, typename Context>
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void RoundKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const int decimals,
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DenseTensor* out) {
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funcs::CudaRoundFunctor<T> functor;
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auto attrs = functor.GetAttrs();
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*(attrs[0].second) = decimals;
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ActivationGPUImpl<T, Context, funcs::CudaRoundFunctor<T>>(
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dev_ctx, x, out, functor);
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}
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template <typename T, typename Context>
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void PowKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& factor,
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DenseTensor* out) {
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if constexpr (std::is_integral<T>::value) {
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PADDLE_ENFORCE_GE(
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factor.to<double>(),
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0,
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common::errors::InvalidArgument(
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"Integers to negative integer powers are not allowed."));
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} else {
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if (factor.to<double>() == 0.5) {
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funcs::CudaSqrtFunctor<T> functor;
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ActivationGPUImpl<T, Context, funcs::CudaSqrtFunctor<T>>(
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dev_ctx, x, out, functor);
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return;
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}
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if (factor.to<double>() == -0.5) {
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funcs::CudaRsqrtFunctor<T> functor;
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ActivationGPUImpl<T, Context, funcs::CudaRsqrtFunctor<T>>(
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dev_ctx, x, out, functor);
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return;
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}
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if (factor.to<double>() == -1) {
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funcs::CudaReciprocalFunctor<T> functor;
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ActivationGPUImpl<T, Context, funcs::CudaReciprocalFunctor<T>>(
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dev_ctx, x, out, functor);
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return;
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}
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if (factor.to<double>() == -2) {
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funcs::CudaRsquareFunctor<T> functor;
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ActivationGPUImpl<T, Context, funcs::CudaRsquareFunctor<T>>(
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dev_ctx, x, out, functor);
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return;
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}
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}
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if (factor.to<double>() == 0) {
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std::vector<int64_t> vec_dims = vectorize(out->dims());
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Full<T, Context>(dev_ctx, IntArray(vec_dims), static_cast<T>(1), out);
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return;
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}
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if (factor.to<double>() == 1) {
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Copy<Context>(dev_ctx, x, dev_ctx.GetPlace(), false, out);
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return;
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}
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if (factor.to<double>() == 2) {
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funcs::CudaSquareFunctor<T> functor;
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ActivationGPUImpl<T, Context, funcs::CudaSquareFunctor<T>>(
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dev_ctx, x, out, functor);
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return;
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}
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if (factor.to<double>() == 3) {
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funcs::CudaCubeFunctor<T> functor;
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ActivationGPUImpl<T, Context, funcs::CudaCubeFunctor<T>>(
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dev_ctx, x, out, functor);
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return;
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}
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funcs::CudaPowFunctor<T> functor;
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functor.SetFactor(factor.to<double>());
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ActivationGPUImpl<T, Context, funcs::CudaPowFunctor<T>>(
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dev_ctx, x, out, functor);
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}
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} // namespace phi
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#ifdef PADDLE_WITH_HIP
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PD_REGISTER_KERNEL(
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relu, GPU, ALL_LAYOUT, phi::ReluKernel, float, double, phi::float16) {}
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#else
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PD_REGISTER_KERNEL(relu,
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GPU,
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ALL_LAYOUT,
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phi::ReluKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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#endif
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#define PD_REGISTER_ACTIVATION_KERNEL(name, func) \
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PD_REGISTER_KERNEL(name, \
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GPU, \
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ALL_LAYOUT, \
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phi::func, \
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float, \
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double, \
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phi::float16, \
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phi::bfloat16) {}
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#define PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(name, func) \
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PD_REGISTER_KERNEL(name, \
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GPU, \
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ALL_LAYOUT, \
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phi::func, \
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float, \
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double, \
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phi::float16, \
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phi::bfloat16, \
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phi::complex64, \
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phi::complex128) {}
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sin, SinKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(cos, CosKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(tan, TanKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(acos, AcosKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(asin, AsinKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(atan, AtanKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sinh, SinhKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(cosh, CoshKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(asinh, AsinhKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(acosh, AcoshKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(atanh, AtanhKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(tanh, TanhKernel)
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PD_REGISTER_ACTIVATION_KERNEL(hardtanh, HardTanhKernel)
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PD_REGISTER_ACTIVATION_KERNEL(thresholded_relu, ThresholdedReluKernel)
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PD_REGISTER_ACTIVATION_KERNEL(relu6, Relu6Kernel)
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PD_REGISTER_ACTIVATION_KERNEL(leaky_relu, LeakyReluKernel)
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PD_REGISTER_ACTIVATION_KERNEL(mish, MishKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(stanh, StanhKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(reciprocal, ReciprocalKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sqrt, SqrtKernel)
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PD_REGISTER_ACTIVATION_KERNEL(rsqrt, RsqrtKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(softplus, SoftplusKernel)
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PD_REGISTER_KERNEL(exp,
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GPU,
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ALL_LAYOUT,
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phi::ExpKernel,
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float,
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double,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(expm1,
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GPU,
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ALL_LAYOUT,
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phi::Expm1Kernel,
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float,
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double,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(square,
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GPU,
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ALL_LAYOUT,
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phi::SquareKernel,
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float,
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double,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_ACTIVATION_KERNEL(hard_shrink, HardShrinkKernel)
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PD_REGISTER_ACTIVATION_KERNEL(softshrink, SoftShrinkKernel)
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PD_REGISTER_ACTIVATION_KERNEL(tanh_shrink, TanhShrinkKernel)
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PD_REGISTER_ACTIVATION_KERNEL(elu, EluKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(silu, SiluKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(softsign, SoftsignKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sigmoid, SigmoidKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(logsigmoid, LogSigmoidKernel)
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PD_REGISTER_ACTIVATION_KERNEL(hardsigmoid, HardSigmoidKernel)
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PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(hardswish, HardSwishKernel)
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PD_REGISTER_ACTIVATION_KERNEL(swish, SwishKernel)
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PD_REGISTER_ACTIVATION_KERNEL(celu, CeluKernel)
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PD_REGISTER_ACTIVATION_KERNEL(selu, SeluKernel)
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PD_REGISTER_ACTIVATION_KERNEL(logit, LogitCUDAKernel)
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PD_REGISTER_KERNEL(rint,
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GPU,
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|
ALL_LAYOUT,
|
|
phi::RintKernel,
|
|
int,
|
|
int64_t,
|
|
float,
|
|
double,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|
|
PD_REGISTER_KERNEL(round,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::RoundKernel,
|
|
int,
|
|
int64_t,
|
|
float,
|
|
double,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
PD_REGISTER_KERNEL(log,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::LogKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
PD_REGISTER_KERNEL(log2,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::Log2Kernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
PD_REGISTER_KERNEL(log10,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::Log10Kernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
PD_REGISTER_KERNEL(log1p,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::Log1pKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
PD_REGISTER_KERNEL(pow,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::PowKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
PD_REGISTER_KERNEL(ceil,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::CeilKernel,
|
|
float,
|
|
double,
|
|
uint8_t,
|
|
int8_t,
|
|
int16_t,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|
|
PD_REGISTER_KERNEL(floor,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::FloorKernel,
|
|
float,
|
|
double,
|
|
uint8_t,
|
|
int8_t,
|
|
int16_t,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|