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180 lines
7.3 KiB
180 lines
7.3 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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#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(const framework::ExecutionContext &ctx,
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xpu::Activation_t type) {
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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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int r = 0;
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if (xpu::Activation_t::ACT_POW == type.type) {
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type.pow_factor = ctx.Attr<float>("factor");
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}
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auto xpu_context = ctx.device_context<DeviceContext>().x_context();
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r = xpu::activation_forward(xpu_context, type, x->numel(),
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reinterpret_cast<const float *>(x_data),
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reinterpret_cast<float *>(y_data));
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PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
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platform::errors::External(
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"XPU API return wrong value[%d], please check whether "
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"Baidu Kunlun Card is properly installed.",
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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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xpu::Activation_t type) {
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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 = xpu::activation_backward(xpu_context, type, dX->numel(),
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reinterpret_cast<const float *>(x_data),
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reinterpret_cast<const float *>(y_data),
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reinterpret_cast<const float *>(y_grad),
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reinterpret_cast<float *>(x_grad));
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PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
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platform::errors::External(
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"XPU API return wrong value[%d], please check whether "
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"Baidu Kunlun Card is properly installed.",
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r));
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}
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template <typename T, xpu::Activation_t::act_enum algorithm>
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struct XPUActivationFunc : 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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algorithm);
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}
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};
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template <typename T, xpu::Activation_t::act_enum algorithm>
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struct XPUActivationGradFunc : 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>(ctx,
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algorithm);
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}
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};
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template <typename T>
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using XPUReluFunctor = XPUActivationFunc<T, xpu::Activation_t::RELU>;
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template <typename T>
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using XPUSigmoidFunctor = XPUActivationFunc<T, xpu::Activation_t::SIGMOID>;
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template <typename T>
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using XPUTanhFunctor = XPUActivationFunc<T, xpu::Activation_t::TANH>;
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template <typename T>
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using XPUGeluFunctor = XPUActivationFunc<T, xpu::Activation_t::GELU>;
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template <typename T>
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using XPULogFunctor = XPUActivationFunc<T, xpu::Activation_t::LOG>;
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template <typename T>
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using XPUSquareFunctor = XPUActivationFunc<T, xpu::Activation_t::SQUARE>;
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template <typename T>
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using XPUSuareGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::SQUARE>;
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template <typename T>
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using XPUReluGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::RELU>;
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template <typename T>
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using XPUSigmoidGradFunctor =
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XPUActivationGradFunc<T, xpu::Activation_t::SIGMOID>;
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template <typename T>
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using XPUTanhGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::TANH>;
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template <typename T>
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using XPUGeluGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::GELU>;
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template <typename T>
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using XPUSqrtFunctor = XPUActivationFunc<T, xpu::Activation_t::SQRT>;
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template <typename T>
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using XPUSqrtGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::SQRT>;
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template <typename T>
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using XPUACTPowFunctor = XPUActivationFunc<T, xpu::Activation_t::ACT_POW>;
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template <typename T>
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using XPUABSFunctor = XPUActivationFunc<T, xpu::Activation_t::ABS>;
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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, XPUSuareGradFunctor)
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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::XPUACTPowFunctor<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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