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425 lines
20 KiB
425 lines
20 KiB
/* Copyright (c) 2018 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 <string>
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#include <vector>
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#include "paddle/fluid/framework/op_desc.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/detail/safe_ref.h"
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#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
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#include "paddle/fluid/operators/math/compound_functors.h"
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#include "paddle/fluid/operators/math/functors.h"
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namespace paddle {
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namespace operators {
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/**
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* Whether the compound function is Unary(Binary(X, Y)).
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* For Unary(Binary(X, Y)), the intermediate_out's shape is the same the final
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* out.
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*/
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bool IsUnaryCompound(const std::vector<std::string> &functor_list);
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/**
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* For the in-place unary functor, the inputs of op_desc only have Out and
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* Out@Grad.
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*/
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bool HasInPlaceUnary(const std::vector<std::string> &functor_list);
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/**
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* Whether the Input(X) could be absent.
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*/
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bool InputXCanBeAbsent(const std::vector<std::string> &functor_list);
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template <typename DeviceContext, typename T, typename BinaryFunctor,
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typename UnaryFunctor>
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static void RunBinaryCompoundFunctor(
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const framework::ExecutionContext &ctx, const BinaryFunctor &binary_functor,
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const UnaryFunctor &unary_functor, const framework::Tensor &in_x,
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const framework::Tensor &in_y, std::vector<framework::Tensor *> *outputs) {
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// Z = Binary(X, Unary(Y))
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// intermediate_out = Unary(Y)
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// out = Binary(X, Unary(Y))
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// In this case, the shape of intermediate_out and out are different.
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paddle::operators::math::BinaryCompoundFunctor<T, BinaryFunctor, UnaryFunctor>
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compound_func(binary_functor, unary_functor);
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int axis = ctx.Attr<int>("axis");
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if (ctx.Attr<bool>("save_intermediate_out")) {
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FusedElemwiseAndActComputeEx<DeviceContext, T,
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paddle::operators::math::BinaryCompoundFunctor<
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T, BinaryFunctor, UnaryFunctor>,
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true /*KeepIntermediateValue*/,
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false /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
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} else {
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FusedElemwiseAndActComputeEx<DeviceContext, T,
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paddle::operators::math::BinaryCompoundFunctor<
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T, BinaryFunctor, UnaryFunctor>,
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false /*KeepIntermediateValue*/,
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false /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
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}
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}
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template <typename DeviceContext, typename T, typename UnaryFunctor,
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typename BinaryFunctor>
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static void RunUnaryCompoundFunctors(
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const framework::ExecutionContext &ctx, const UnaryFunctor &unary_functor,
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const BinaryFunctor &binary_functor, const framework::Tensor &in_x,
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const framework::Tensor &in_y, std::vector<framework::Tensor *> *outputs) {
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// Z = Unary(Binary(X, Y))
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// intermediate_out = Binary(X, Y)
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// out = Unary(Binary(X, Y))
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// In this case, the shape of intermediate_out and out are the same.
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int axis = ctx.Attr<int>("axis");
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paddle::operators::math::UnaryCompoundFunctor<T, UnaryFunctor, BinaryFunctor>
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compound_func(unary_functor, binary_functor);
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if (ctx.Attr<bool>("save_intermediate_out")) {
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FusedElemwiseAndActComputeEx<DeviceContext, T,
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paddle::operators::math::UnaryCompoundFunctor<
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T, UnaryFunctor, BinaryFunctor>,
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true /*KeepIntermediateValue*/,
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true /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
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} else {
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FusedElemwiseAndActComputeEx<DeviceContext, T,
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paddle::operators::math::UnaryCompoundFunctor<
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T, UnaryFunctor, BinaryFunctor>,
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false /*KeepIntermediateValue*/,
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true /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
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}
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}
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template <typename DeviceContext, typename T, typename BinaryGradFunctor,
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typename UnaryFunctor, typename UnaryGradFunctor, bool InPlace>
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static void RunBinaryCompoundGradFunctors(
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const framework::ExecutionContext &ctx,
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const BinaryGradFunctor &binary_grad_functor,
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const UnaryFunctor &unary_functor,
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const UnaryGradFunctor &unary_grad_functor, const framework::Tensor *in_x,
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const framework::Tensor *in_y, const framework::Tensor *in_out,
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const framework::Tensor *in_intermediate_out,
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const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
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framework::Tensor *y_grad, framework::Tensor *d_intermediate_out) {
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// Z = Binary(X, Unary(Y))
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int axis = ctx.Attr<int>("axis");
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using BinaryCompoundDxFunctor =
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paddle::operators::math::BinaryCompoundGradDxFunctor<T, BinaryGradFunctor,
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UnaryFunctor>;
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using BinaryCompoundDyFunctor =
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paddle::operators::math::BinaryCompoundGradDyFunctor<
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T, BinaryGradFunctor, UnaryFunctor, UnaryGradFunctor, InPlace>;
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using BinaryCompoundDIntermedaiteOutFunctor =
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paddle::operators::math::BinaryCompoundGradDIntermedaiteOutFunctor<
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T, BinaryGradFunctor, UnaryFunctor>;
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if (in_intermediate_out) {
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FusedElemwiseAndActGradComputeEx<
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DeviceContext, T, BinaryCompoundDxFunctor, BinaryCompoundDyFunctor,
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BinaryCompoundDIntermedaiteOutFunctor, true /*UseIntermediateOut*/,
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false /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
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y_grad, d_intermediate_out,
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BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
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BinaryCompoundDyFunctor(binary_grad_functor, unary_functor,
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unary_grad_functor),
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BinaryCompoundDIntermedaiteOutFunctor(binary_grad_functor,
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unary_functor));
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} else {
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FusedElemwiseAndActGradComputeEx<
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DeviceContext, T, BinaryCompoundDxFunctor, BinaryCompoundDyFunctor,
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BinaryCompoundDIntermedaiteOutFunctor, false /*UseIntermediateOut*/,
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false /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
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y_grad, d_intermediate_out,
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BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
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BinaryCompoundDyFunctor(binary_grad_functor, unary_functor,
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unary_grad_functor),
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BinaryCompoundDIntermedaiteOutFunctor(binary_grad_functor,
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unary_functor));
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}
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}
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template <typename DeviceContext, typename T, typename UnaryGradFunctor,
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typename BinaryFunctor, typename BinaryGradFunctor, bool InPlace>
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static void RunUnaryCompoundGradFunctors(
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const framework::ExecutionContext &ctx,
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const UnaryGradFunctor &unary_grad_functor,
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const BinaryFunctor &binary_functor,
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const BinaryGradFunctor &binary_grad_functor, const framework::Tensor *in_x,
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const framework::Tensor *in_y, const framework::Tensor *in_out,
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const framework::Tensor *in_intermediate_out,
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const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
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framework::Tensor *y_grad, framework::Tensor *d_intermediate_out) {
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// Z = Unary(Binary(X, Y))
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int axis = ctx.Attr<int>("axis");
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using UnaryCompoundDxFunctor =
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paddle::operators::math::UnaryCompoundGradDxFunctor<
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T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, InPlace>;
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using UnaryCompoundDyFunctor =
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paddle::operators::math::UnaryCompoundGradDyFunctor<
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T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, InPlace>;
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using UnaryCompoundDIntermediateFunctor =
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paddle::operators::math::UnaryCompoundGradDIntermediateFunctor<
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T, UnaryGradFunctor, BinaryFunctor, InPlace>;
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if (in_intermediate_out) {
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FusedElemwiseAndActGradComputeEx<
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DeviceContext, T, UnaryCompoundDxFunctor, UnaryCompoundDyFunctor,
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UnaryCompoundDIntermediateFunctor, true /*UseIntermediateOut*/,
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true /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
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y_grad, d_intermediate_out,
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UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
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binary_grad_functor),
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UnaryCompoundDyFunctor(unary_grad_functor, binary_functor,
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binary_grad_functor),
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UnaryCompoundDIntermediateFunctor(unary_grad_functor, binary_functor));
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} else {
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FusedElemwiseAndActGradComputeEx<
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DeviceContext, T, UnaryCompoundDxFunctor, UnaryCompoundDyFunctor,
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UnaryCompoundDIntermediateFunctor, false /*UseIntermediateOut*/,
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true /*SameShapeOfIntermediateOutAndOut*/>(
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ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
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y_grad, d_intermediate_out,
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UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
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binary_grad_functor),
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UnaryCompoundDyFunctor(unary_grad_functor, binary_functor,
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binary_grad_functor),
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UnaryCompoundDIntermediateFunctor(unary_grad_functor, binary_functor));
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}
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}
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template <typename DeviceContext, typename T>
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static void RunFunctors(const framework::ExecutionContext &ctx,
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const framework::Tensor &in_x,
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const framework::Tensor &in_y,
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std::vector<framework::Tensor *> *outputs) {
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auto &functors = ctx.Attr<std::vector<std::string>>("functor_list");
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// TODO(zcd): The following code can be refined.
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auto funcs_str = functors[0] + "," + functors[1];
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if (funcs_str == "elementwise_add,scale") {
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// Z = Binary(X, Unary(Y))
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T scale = static_cast<T>(ctx.Attr<float>("scale"));
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RunBinaryCompoundFunctor<DeviceContext, T,
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paddle::operators::math::AddFunctor<T>,
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paddle::operators::math::ScaleFunctor<T>>(
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ctx, paddle::operators::math::AddFunctor<T>(),
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paddle::operators::math::ScaleFunctor<T>(scale), in_x, in_y, outputs);
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} else if (funcs_str == "scale,elementwise_add") {
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// Z = Unary(Binary(X, Y))
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T scale = static_cast<T>(ctx.Attr<float>("scale"));
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RunUnaryCompoundFunctors<DeviceContext, T,
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paddle::operators::math::ScaleFunctor<T>,
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paddle::operators::math::AddFunctor<T>>(
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ctx, paddle::operators::math::ScaleFunctor<T>(scale),
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paddle::operators::math::AddFunctor<T>(), in_x, in_y, outputs);
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} else if (funcs_str == "elementwise_add,relu") {
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// Z = Binary(X, Unary(Y))
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RunBinaryCompoundFunctor<DeviceContext, T,
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paddle::operators::math::AddFunctor<T>,
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paddle::operators::math::ReluFunctor<T>>(
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ctx, paddle::operators::math::AddFunctor<T>(),
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paddle::operators::math::ReluFunctor<T>(), in_x, in_y, outputs);
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} else if (funcs_str == "relu,elementwise_add") {
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// Z = Unary(Binary(X, Y))
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RunUnaryCompoundFunctors<DeviceContext, T,
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paddle::operators::math::ReluFunctor<T>,
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paddle::operators::math::AddFunctor<T>>(
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ctx, paddle::operators::math::ReluFunctor<T>(),
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paddle::operators::math::AddFunctor<T>(), in_x, in_y, outputs);
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} else if (funcs_str == "elementwise_mul,scale") {
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// Z = Binary(X, Unary(Y))
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T scale = static_cast<T>(ctx.Attr<float>("scale"));
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RunBinaryCompoundFunctor<DeviceContext, T,
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paddle::operators::math::MulFunctor<T>,
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paddle::operators::math::ScaleFunctor<T>>(
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ctx, paddle::operators::math::MulFunctor<T>(),
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paddle::operators::math::ScaleFunctor<T>(scale), in_x, in_y, outputs);
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} else {
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PADDLE_THROW("%s has not been implemented.", funcs_str);
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}
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}
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template <typename DeviceContext, typename T, bool InPlace>
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static void RunGradFunctors(
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const framework::ExecutionContext &ctx, const framework::Tensor *in_x,
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const framework::Tensor *in_y, const framework::Tensor *in_out,
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const framework::Tensor *in_intermediate_out,
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const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
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framework::Tensor *y_grad, framework::Tensor *d_intermediate_out) {
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auto &functors = ctx.Attr<std::vector<std::string>>("functor_list");
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auto funcs_str = functors[0] + "," + functors[1];
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if (funcs_str == "elementwise_add_grad,scale_grad") {
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// The backward of Z = Binary(X, Unary(Y))
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T scale = static_cast<T>(ctx.Attr<float>("scale"));
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RunBinaryCompoundGradFunctors<
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DeviceContext, T, paddle::operators::math::AddGradFunctor<T>,
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paddle::operators::math::ScaleFunctor<T>,
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paddle::operators::math::ScaleGradFunctor<T>, InPlace>(
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ctx, paddle::operators::math::AddGradFunctor<T>(),
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paddle::operators::math::ScaleFunctor<T>(scale),
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paddle::operators::math::ScaleGradFunctor<T>(scale), in_x, in_y, in_out,
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in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
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} else if (funcs_str == "scale_grad,elementwise_add_grad") {
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// The backward of Z = Unary(Binary(X, Y))
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T scale = static_cast<T>(ctx.Attr<float>("scale"));
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RunUnaryCompoundGradFunctors<
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DeviceContext, T, paddle::operators::math::ScaleGradFunctor<T>,
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paddle::operators::math::AddFunctor<T>,
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paddle::operators::math::AddGradFunctor<T>, InPlace>(
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ctx, paddle::operators::math::ScaleGradFunctor<T>(scale),
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paddle::operators::math::AddFunctor<T>(),
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paddle::operators::math::AddGradFunctor<T>(), in_x, in_y, in_out,
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in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
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} else if (funcs_str == "elementwise_add_grad,relu_grad") {
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RunBinaryCompoundGradFunctors<
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DeviceContext, T, paddle::operators::math::AddGradFunctor<T>,
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paddle::operators::math::ReluFunctor<T>,
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paddle::operators::math::ReluGradFunctor<T>, InPlace>(
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ctx, paddle::operators::math::AddGradFunctor<T>(),
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paddle::operators::math::ReluFunctor<T>(),
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paddle::operators::math::ReluGradFunctor<T>(), in_x, in_y, in_out,
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in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
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} else if (funcs_str == "relu_grad,elementwise_add_grad") {
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RunUnaryCompoundGradFunctors<
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DeviceContext, T, paddle::operators::math::ReluGradFunctor<T>,
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paddle::operators::math::AddFunctor<T>,
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paddle::operators::math::AddGradFunctor<T>, InPlace>(
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ctx, paddle::operators::math::ReluGradFunctor<T>(),
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paddle::operators::math::AddFunctor<T>(),
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paddle::operators::math::AddGradFunctor<T>(), in_x, in_y, in_out,
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in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
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} else if (funcs_str == "elementwise_mul_grad,scale_grad") {
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// The backward of Z = Binary(X, Unary(Y))
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T scale = static_cast<T>(ctx.Attr<float>("scale"));
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RunBinaryCompoundGradFunctors<
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DeviceContext, T, paddle::operators::math::MulGradFunctor<T>,
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paddle::operators::math::ScaleFunctor<T>,
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paddle::operators::math::ScaleGradFunctor<T>, InPlace>(
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ctx, paddle::operators::math::MulGradFunctor<T>(),
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paddle::operators::math::ScaleFunctor<T>(scale),
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paddle::operators::math::ScaleGradFunctor<T>(scale), in_x, in_y, in_out,
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in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
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} else {
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PADDLE_THROW("%s has not been implemented.", funcs_str);
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}
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}
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template <typename DeviceContext, typename T>
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class FusedElemwiseActivationKernel : 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 &in_x = detail::Ref(ctx.Input<framework::Tensor>("X"),
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"Cannot get input tensor %s, variable name = %s",
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"X", ctx.op().Input("X"));
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auto &in_y = detail::Ref(ctx.Input<framework::Tensor>("Y"),
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"Cannot get input tensor %s, variable name = %s",
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"Y", ctx.op().Input("Y"));
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PADDLE_ENFORCE(ctx.HasOutput("Out"), "The output(Out) should not be empty");
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auto output = ctx.Output<framework::Tensor>("Out");
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std::vector<framework::Tensor *> outputs;
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outputs.emplace_back(output);
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if (ctx.Attr<bool>("save_intermediate_out")) {
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PADDLE_ENFORCE(ctx.HasOutput("IntermediateOut"),
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"The save_intermediate_out is enable, so the "
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"IntermediateOut should not be empty.");
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auto intermediate_out = ctx.Output<framework::Tensor>("IntermediateOut");
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outputs.emplace_back(intermediate_out);
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} else {
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outputs.emplace_back(nullptr);
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}
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RunFunctors<DeviceContext, T>(ctx, in_x, in_y, &outputs);
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}
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};
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template <typename DeviceContext, typename T>
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class FusedElemwiseActivationGradKernel : 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 in_y = ctx.Input<framework::Tensor>("Y");
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PADDLE_ENFORCE(in_y != nullptr, "Input(Y) should not be nullptr.");
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auto in_out = ctx.Input<framework::Tensor>("Out");
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PADDLE_ENFORCE(in_out != nullptr, "Input(Out) should not be nullptr.");
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auto in_out_grad =
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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PADDLE_ENFORCE(in_out_grad != nullptr,
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"Input(Out@Grad) should not be nullptr.");
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framework::Tensor *in_x =
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const_cast<framework::Tensor *>(ctx.Input<framework::Tensor>("X"));
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framework::Tensor *x_grad =
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ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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framework::Tensor *y_grad =
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ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
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framework::Tensor *d_intermediate_out = ctx.Output<framework::Tensor>(
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framework::GradVarName("IntermediateOut"));
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auto functor_list = ctx.Attr<std::vector<std::string>>("functor_list");
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// Get intermediate_out
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framework::Tensor *in_intermediate_out = nullptr;
|
|
if (ctx.Attr<bool>("save_intermediate_out")) {
|
|
// if save_intermediate_out is true, for Unary(Binary(x, y)) and
|
|
// Binary(x, Unary(y)), the Binary(x, y) and Unary(y) not need to
|
|
// recompute.
|
|
in_intermediate_out = const_cast<framework::Tensor *>(
|
|
ctx.Input<framework::Tensor>("IntermediateOut"));
|
|
PADDLE_ENFORCE(in_intermediate_out != nullptr,
|
|
"The option of 'save_intermediate_out' is opened, "
|
|
"so the number of 'Out' should be two.");
|
|
} else {
|
|
if (!InputXCanBeAbsent(functor_list)) {
|
|
PADDLE_ENFORCE(in_x != nullptr, "Input(X) should not be null.");
|
|
}
|
|
}
|
|
|
|
// Get in_x
|
|
if (ctx.HasInput("X")) {
|
|
PADDLE_ENFORCE(in_x != nullptr, "Input(X) should not be nullptr.");
|
|
} else {
|
|
// If functor_list contains elementwise_add, the backward doesn't use
|
|
// in_x, in_y and in_out.
|
|
PADDLE_ENFORCE(InputXCanBeAbsent(functor_list),
|
|
"Only when the compoundfunctor contains "
|
|
"elementwise_add_grad, the 'X' could be absent.");
|
|
in_x = const_cast<framework::Tensor *>(in_out_grad);
|
|
}
|
|
|
|
bool has_in_place = HasInPlaceUnary(functor_list);
|
|
if (has_in_place) {
|
|
RunGradFunctors<DeviceContext, T, true /*InPlace*/>(
|
|
ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad,
|
|
y_grad, d_intermediate_out);
|
|
} else {
|
|
RunGradFunctors<DeviceContext, T, false /*InPlace*/>(
|
|
ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad,
|
|
y_grad, d_intermediate_out);
|
|
}
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|