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138 lines
4.3 KiB
138 lines
4.3 KiB
/* Copyright (c) 2020 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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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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HOSTDEVICE static T CalcSoftplus(T x, float threshold) {
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if (threshold > 0 && x > threshold) {
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return x;
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} else if (threshold > 0 && x < -threshold) {
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return exp(x);
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} else {
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return log1p(exp(x));
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}
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}
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// expf instead of exp should be used for float type, complement
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// and register float kernel separatelly
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HOSTDEVICE static float CalcSoftplusFP32(float x, float threshold) {
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if (threshold > 0 && x > threshold) {
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return x;
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} else if (threshold > 0 && x < -threshold) {
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return expf(x);
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} else {
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return log1pf(expf(x));
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}
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}
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template <typename DeviceContext, typename T>
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class MishCPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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const float threshold = ctx.Attr<float>("threshold");
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const T* x_data = x->data<T>();
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T* out_data = out->mutable_data<T>(ctx.GetPlace());
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int numel = x->numel();
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for (int i = 0; i < numel; i++) {
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T x_d = x_data[i];
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T sp = CalcSoftplus<T>(x_d, threshold);
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out_data[i] = x_d * std::tanh(sp);
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}
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}
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};
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template <typename DeviceContext>
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class MishFP32CPUKernel : public framework::OpKernel<float> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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const float threshold = ctx.Attr<float>("threshold");
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const float* x_data = x->data<float>();
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float* out_data = out->mutable_data<float>(ctx.GetPlace());
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int numel = x->numel();
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for (int i = 0; i < numel; i++) {
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float x_d = x_data[i];
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float sp = CalcSoftplusFP32(x_d, threshold);
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out_data[i] = x_d * std::tanh(sp);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class MishGradCPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<Tensor>("X");
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto threshold = ctx.Attr<float>("threshold");
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const T* x_data = x->data<T>();
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const T* dout_data = dout->data<T>();
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T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
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int numel = x->numel();
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for (int i = 0; i < numel; i++) {
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T x_d = x_data[i];
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T sp = CalcSoftplus<T>(x_d, threshold);
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T tsp = std::tanh(sp);
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T grad_sp = -std::expm1(-sp);
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T grad_tsp = (static_cast<T>(1) - tsp * tsp) * grad_sp;
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dx_data[i] = dout_data[i] * (x_d * grad_tsp + tsp);
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}
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}
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};
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template <typename DeviceContext>
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class MishGradFP32CPUKernel : public framework::OpKernel<float> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<Tensor>("X");
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto threshold = ctx.Attr<float>("threshold");
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const float* x_data = x->data<float>();
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const float* dout_data = dout->data<float>();
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float* dx_data = dx->mutable_data<float>(ctx.GetPlace());
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int numel = x->numel();
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for (int i = 0; i < numel; i++) {
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float x_d = x_data[i];
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float sp = CalcSoftplusFP32(x_d, threshold);
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float tsp = std::tanh(sp);
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float grad_sp = -std::expm1f(-sp);
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float grad_tsp = (static_cast<float>(1) - tsp * tsp) * grad_sp;
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dx_data[i] = dout_data[i] * (x_d * grad_tsp + tsp);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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