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363 lines
14 KiB
363 lines
14 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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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/activation_op.h"
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#include <string>
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#include "paddle/fluid/platform/xpu_header.h"
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namespace paddle {
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namespace operators {
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using paddle::framework::Tensor;
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template <typename Functor>
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class XPUActivationKernel
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: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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Functor functor;
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auto attrs = functor.GetAttrs();
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for (auto &attr : attrs) {
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*attr.second = context.Attr<float>(attr.first);
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}
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functor(context);
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}
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};
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template <typename Functor>
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class XPUActivationGradKernel
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: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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Functor functor;
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auto attrs = functor.GetAttrs();
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for (auto &attr : attrs) {
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*attr.second = context.Attr<float>(attr.first);
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}
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functor(context);
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}
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};
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template <typename DeviceContext, typename T>
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void xpu_activation_forward(
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const framework::ExecutionContext &ctx,
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std::function<int(xpu::Context *, const T *, T *, int)> func) {
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const auto *x = ctx.Input<Tensor>("X");
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auto *y = ctx.Output<Tensor>("Out");
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const T *x_data = x->data<T>();
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T *y_data = y->mutable_data<T>(ctx.GetPlace());
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auto xpu_context = ctx.device_context<DeviceContext>().x_context();
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int r = func(xpu_context, x_data, y_data, x->numel());
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External("XPU activation op return wrong value[%d %s].",
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r, XPUAPIErrorMsg[r]));
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}
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template <typename DeviceContext, typename T>
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void xpu_activation_backward(const framework::ExecutionContext &ctx,
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std::function<int(xpu::Context *, const T *,
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const T *, const T *, T *, int)>
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func) {
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/* TODO: relu tanh sigmoid are inplace */
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const auto *x = ctx.Input<Tensor>("X");
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auto *y = ctx.Input<Tensor>("Out");
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auto *dOut = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto *dX = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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const T *x_data = nullptr;
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const T *y_data = nullptr;
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const T *y_grad = nullptr;
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if (x != nullptr) x_data = x->data<T>();
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if (y != nullptr) y_data = y->data<T>();
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if (dOut != nullptr) y_grad = dOut->data<T>();
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T *x_grad = dX->mutable_data<T>(ctx.GetPlace());
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auto xpu_context = ctx.device_context<DeviceContext>().x_context();
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int r = func(xpu_context, x_data, y_data, y_grad, x_grad, dX->numel());
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"XPU activation grad op return wrong value[%d %s].", r,
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XPUAPIErrorMsg[r]));
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}
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template <typename T>
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struct XPUReluFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
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xpu::relu<T>);
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}
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};
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template <typename T>
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struct XPUSigmoidFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::sigmoid<T>);
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}
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};
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template <typename T>
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struct XPUTanhFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
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xpu::tanh<T>);
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}
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};
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template <typename T>
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struct XPUGeluFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
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xpu::gelu<T>);
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}
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};
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template <typename T>
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struct XPULogFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
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xpu::log<T>);
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}
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};
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template <typename T>
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struct XPUSquareFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::square<T>);
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}
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};
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template <typename T>
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struct XPUSqrtFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
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xpu::sqrt<T>);
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}
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};
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template <typename T>
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struct XPUAbsFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
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xpu::abs<T>);
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}
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};
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template <typename T>
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struct XPUPowFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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const auto *x = ctx.Input<Tensor>("X");
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auto *y = ctx.Output<Tensor>("Out");
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auto pow_factor = ctx.Attr<float>("factor");
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const T *x_data = x->data<T>();
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T *y_data = y->mutable_data<T>(ctx.GetPlace());
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T *factor_data = nullptr;
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auto xpu_context =
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ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
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PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void **>(&factor_data),
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x->numel() * sizeof(T)),
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XPU_SUCCESS, platform::errors::ResourceExhausted(
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"XPU has no enough memory"));
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int r = xpu::constant<T>(xpu_context, factor_data, x->numel(), pow_factor);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External("XPU constant op return"
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" wrong value[%d %s] in pow op.",
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r, XPUAPIErrorMsg[r]));
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r = xpu::pow(xpu_context, x_data, factor_data, y_data, x->numel());
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External("XPU pow op return"
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" wrong value[%d %s].",
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r, XPUAPIErrorMsg[r]));
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if (xpu_context->xpu_stream != nullptr) {
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xpu_wait(xpu_context->xpu_stream);
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}
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xpu_free(factor_data);
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}
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};
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template <typename T>
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struct XPUHardSwishFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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float threshold = ctx.Attr<float>("threshold");
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float scale = ctx.Attr<float>("scale");
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float offset = ctx.Attr<float>("offset");
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PADDLE_ENFORCE_EQ(threshold, 6.0f,
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platform::errors::External(
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"Not support threshold [%f] in XPU", threshold));
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PADDLE_ENFORCE_EQ(scale, 6.0f, platform::errors::External(
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"Not support scale [%f] in XPU", scale));
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PADDLE_ENFORCE_EQ(
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offset, 3.0f,
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platform::errors::External("Not support offset [%f] in XPU", offset));
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xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::hard_swish<T>);
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}
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};
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template <typename T>
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struct XPUReluGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::relu_grad<T>);
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}
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};
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template <typename T>
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struct XPUTanhGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::tanh_grad<T>);
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}
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};
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template <typename T>
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struct XPUSigmoidGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::sigmoid_grad<T>);
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}
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};
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template <typename T>
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struct XPUGeluGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::gelu_grad<T>);
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}
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};
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template <typename T>
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struct XPUSqrtGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::sqrt_grad<T>);
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}
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};
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template <typename T>
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struct XPUSquareGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::square_grad<T>);
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}
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};
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template <typename T>
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struct XPUHardSwishGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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float threshold = ctx.Attr<float>("threshold");
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float scale = ctx.Attr<float>("scale");
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float offset = ctx.Attr<float>("offset");
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PADDLE_ENFORCE_EQ(threshold, 6.0f,
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platform::errors::External(
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"Not support threshold [%f] in XPU", threshold));
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PADDLE_ENFORCE_EQ(scale, 6.0f, platform::errors::External(
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"Not support scale [%f] in XPU", scale));
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PADDLE_ENFORCE_EQ(
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offset, 3.0f,
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platform::errors::External("Not support offset [%f] in XPU", offset));
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xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
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ctx, xpu::hard_swish_grad<T>);
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}
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};
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template <typename T>
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struct XPULeakyReluFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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const auto *x = ctx.Input<Tensor>("X");
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auto *y = ctx.Output<Tensor>("Out");
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float alpha = ctx.Attr<float>("alpha");
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const T *x_data = x->data<T>();
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T *y_data = y->mutable_data<T>(ctx.GetPlace());
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auto xpu_context =
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ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
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int r = xpu::leaky_relu(xpu_context, x_data, y_data, x->numel(), alpha);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External("XPU leaky_relu return wrong value[%d %s].",
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r, XPUAPIErrorMsg[r]));
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}
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};
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template <typename T>
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struct XPULeakyReluGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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const auto *x = ctx.Input<Tensor>("X");
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auto *dOut = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto *dX = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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float alpha = ctx.Attr<float>("alpha");
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const T *x_data = nullptr;
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const T *y_grad = nullptr;
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if (x != nullptr) x_data = x->data<T>();
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if (dOut != nullptr) y_grad = dOut->data<T>();
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T *x_grad = dX->mutable_data<T>(ctx.GetPlace());
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auto xpu_context =
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ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
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// The signs of x and y are the same,
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// y == nullptr here,
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// so we give 2 x to the api
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int r = xpu::leaky_relu_grad(
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xpu_context, reinterpret_cast<const float *>(x_data),
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reinterpret_cast<const float *>(x_data),
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reinterpret_cast<const float *>(y_grad),
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reinterpret_cast<float *>(x_grad), dX->numel(), alpha);
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"XPU leaky_relu_grad return wrong value[%d %s].", r,
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XPUAPIErrorMsg[r]));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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#define REGISTER_ACTIVATION_XPU_KERNEL(act_type, functor, grad_functor) \
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REGISTER_OP_XPU_KERNEL(act_type, \
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ops::XPUActivationKernel<ops::functor<float>>); \
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REGISTER_OP_XPU_KERNEL( \
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act_type##_grad, \
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ops::XPUActivationGradKernel<ops::grad_functor<float>>);
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REGISTER_ACTIVATION_XPU_KERNEL(relu, XPUReluFunctor, XPUReluGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(tanh, XPUTanhFunctor, XPUTanhGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(sigmoid, XPUSigmoidFunctor,
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XPUSigmoidGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(gelu, XPUGeluFunctor, XPUGeluGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(sqrt, XPUSqrtFunctor, XPUSqrtGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(square, XPUSquareFunctor, XPUSquareGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(hard_swish, XPUHardSwishFunctor,
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XPUHardSwishGradFunctor)
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REGISTER_ACTIVATION_XPU_KERNEL(leaky_relu, XPULeakyReluFunctor,
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XPULeakyReluGradFunctor)
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REGISTER_OP_XPU_KERNEL(log,
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ops::XPUActivationKernel<ops::XPULogFunctor<float>>);
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REGISTER_OP_XPU_KERNEL(pow,
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ops::XPUActivationKernel<ops::XPUPowFunctor<float>>);
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REGISTER_OP_XPU_KERNEL(abs,
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ops::XPUActivationKernel<ops::XPUAbsFunctor<float>>);
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#endif // PADDLE_WITH_XPU
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