You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
398 lines
19 KiB
398 lines
19 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#pragma once
|
|
|
|
#include <string>
|
|
#include <vector>
|
|
#include "paddle/fluid/framework/op_desc.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/operators/detail/safe_ref.h"
|
|
#include "paddle/fluid/operators/elementwise_op_function.h"
|
|
#include "paddle/fluid/operators/math/compound_functors.h"
|
|
#include "paddle/fluid/operators/math/functors.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
template <typename DeviceContext, typename T, typename BinaryFunctor,
|
|
typename UnaryFunctor>
|
|
static void RunBinaryCompoundFunctor(
|
|
const framework::ExecutionContext &ctx, const BinaryFunctor &binary_functor,
|
|
const UnaryFunctor &unary_functor, const framework::Tensor &in_x,
|
|
const framework::Tensor &in_y, std::vector<framework::Tensor *> *outputs) {
|
|
// Z = Binary(X, Unary(Y))
|
|
// intermediate_out = Unary(Y)
|
|
// out = Binary(X, Unary(Y))
|
|
// In this case, the shape of intermediate_out and out are different.
|
|
paddle::operators::math::BinaryCompoundFunctor<T, BinaryFunctor, UnaryFunctor>
|
|
compound_func(binary_functor, unary_functor);
|
|
int axis = ctx.Attr<int>("axis");
|
|
if (ctx.Attr<bool>("keep_intermediate_value")) {
|
|
FusedElemwiseAndActComputeEx<DeviceContext, T,
|
|
paddle::operators::math::BinaryCompoundFunctor<
|
|
T, BinaryFunctor, UnaryFunctor>,
|
|
true /*KeepIntermediateValue*/,
|
|
false /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
|
|
} else {
|
|
FusedElemwiseAndActComputeEx<DeviceContext, T,
|
|
paddle::operators::math::BinaryCompoundFunctor<
|
|
T, BinaryFunctor, UnaryFunctor>,
|
|
false /*KeepIntermediateValue*/,
|
|
false /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T, typename UnaryFunctor,
|
|
typename BinaryFunctor>
|
|
static void RunUnaryCompoundFunctors(
|
|
const framework::ExecutionContext &ctx, const UnaryFunctor &unary_functor,
|
|
const BinaryFunctor &binary_functor, const framework::Tensor &in_x,
|
|
const framework::Tensor &in_y, std::vector<framework::Tensor *> *outputs) {
|
|
// Z = Unary(Binary(X, Y))
|
|
// intermediate_out = Binary(X, Y)
|
|
// out = Unary(Binary(X, Y))
|
|
// In this case, the shape of intermediate_out and out are the same.
|
|
int axis = ctx.Attr<int>("axis");
|
|
|
|
paddle::operators::math::UnaryCompoundFunctor<T, UnaryFunctor, BinaryFunctor>
|
|
compound_func(unary_functor, binary_functor);
|
|
|
|
if (ctx.Attr<bool>("keep_intermediate_value")) {
|
|
FusedElemwiseAndActComputeEx<DeviceContext, T,
|
|
paddle::operators::math::UnaryCompoundFunctor<
|
|
T, UnaryFunctor, BinaryFunctor>,
|
|
true /*KeepIntermediateValue*/,
|
|
true /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
|
|
} else {
|
|
FusedElemwiseAndActComputeEx<DeviceContext, T,
|
|
paddle::operators::math::UnaryCompoundFunctor<
|
|
T, UnaryFunctor, BinaryFunctor>,
|
|
false /*KeepIntermediateValue*/,
|
|
true /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, axis, compound_func, (*outputs)[0], (*outputs)[1]);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T, typename BinaryGradFunctor,
|
|
typename UnaryFunctor, typename UnaryGradFunctor>
|
|
static void RunBinaryCompoundGradFunctors(
|
|
const framework::ExecutionContext &ctx,
|
|
const BinaryGradFunctor &binary_grad_functor,
|
|
const UnaryFunctor &unary_functor,
|
|
const UnaryGradFunctor &unary_grad_functor, const framework::Tensor *in_x,
|
|
const framework::Tensor *in_y, const framework::Tensor *in_out,
|
|
const framework::Tensor *in_intermediate_out,
|
|
const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
|
|
framework::Tensor *y_grad) {
|
|
// Z = Binary(X, Unary(Y))
|
|
int axis = ctx.Attr<int>("axis");
|
|
|
|
using BinaryCompoundDxFunctor =
|
|
paddle::operators::math::BinaryCompoundGradDxFunctor<T, BinaryGradFunctor,
|
|
UnaryFunctor>;
|
|
using BinaryCompoundDyFunctor =
|
|
paddle::operators::math::BinaryCompoundGradDyFunctor<
|
|
T, BinaryGradFunctor, UnaryFunctor, UnaryGradFunctor>;
|
|
|
|
if (in_intermediate_out) {
|
|
FusedElemwiseAndActGradComputeEx<
|
|
DeviceContext, T, BinaryCompoundDxFunctor, BinaryCompoundDyFunctor,
|
|
true /*UseIntermediateOut*/,
|
|
false /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
|
|
y_grad, BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
|
|
BinaryCompoundDyFunctor(binary_grad_functor, unary_functor,
|
|
unary_grad_functor));
|
|
} else {
|
|
FusedElemwiseAndActGradComputeEx<
|
|
DeviceContext, T, BinaryCompoundDxFunctor, BinaryCompoundDyFunctor,
|
|
false /*UseIntermediateOut*/,
|
|
false /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
|
|
y_grad, BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
|
|
BinaryCompoundDyFunctor(binary_grad_functor, unary_functor,
|
|
unary_grad_functor));
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T, typename UnaryGradFunctor,
|
|
typename BinaryFunctor, typename BinaryGradFunctor,
|
|
bool Recomputation = true>
|
|
static void RunUnaryCompoundGradFunctors(
|
|
const framework::ExecutionContext &ctx,
|
|
const UnaryGradFunctor &unary_grad_functor,
|
|
const BinaryFunctor &binary_functor,
|
|
const BinaryGradFunctor &binary_grad_functor, const framework::Tensor *in_x,
|
|
const framework::Tensor *in_y, const framework::Tensor *in_out,
|
|
const framework::Tensor *in_intermediate_out,
|
|
const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
|
|
framework::Tensor *y_grad) {
|
|
// Z = Unary(Binary(X, Y))
|
|
int axis = ctx.Attr<int>("axis");
|
|
|
|
using UnaryCompoundDxFunctor =
|
|
paddle::operators::math::UnaryCompoundGradDxFunctor<
|
|
T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, Recomputation>;
|
|
using UnaryCompoundDyFunctor =
|
|
paddle::operators::math::UnaryCompoundGradDyFunctor<
|
|
T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, Recomputation>;
|
|
|
|
if (in_intermediate_out) {
|
|
FusedElemwiseAndActGradComputeEx<
|
|
DeviceContext, T, UnaryCompoundDxFunctor, UnaryCompoundDyFunctor,
|
|
true /*UseIntermediateOut*/, true /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
|
|
y_grad, UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
|
|
binary_grad_functor),
|
|
UnaryCompoundDyFunctor(unary_grad_functor, binary_functor,
|
|
binary_grad_functor));
|
|
} else {
|
|
FusedElemwiseAndActGradComputeEx<DeviceContext, T, UnaryCompoundDxFunctor,
|
|
UnaryCompoundDyFunctor,
|
|
false /*UseIntermediateOut*/,
|
|
true /*SameShapeOfIntermediateOutAndOut*/>(
|
|
ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
|
|
y_grad, UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
|
|
binary_grad_functor),
|
|
UnaryCompoundDyFunctor(unary_grad_functor, binary_functor,
|
|
binary_grad_functor));
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
static void RunFunctors(const framework::ExecutionContext &ctx,
|
|
const framework::Tensor &in_x,
|
|
const framework::Tensor &in_y,
|
|
std::vector<framework::Tensor *> *outputs) {
|
|
auto &functors = ctx.Attr<std::vector<std::string>>("functor_list");
|
|
|
|
// TODO(zcd): The following code can be refined.
|
|
auto funcs_str = functors[0] + "," + functors[1];
|
|
if (funcs_str == "elementwise_add,scale") {
|
|
// Z = Binary(X, Unary(Y))
|
|
T scale = static_cast<T>(ctx.Attr<float>("scale"));
|
|
RunBinaryCompoundFunctor<DeviceContext, T,
|
|
paddle::operators::math::AddFunctor<T>,
|
|
paddle::operators::math::ScaleFunctor<T>>(
|
|
ctx, paddle::operators::math::AddFunctor<T>(),
|
|
paddle::operators::math::ScaleFunctor<T>(scale), in_x, in_y, outputs);
|
|
} else if (funcs_str == "scale,elementwise_add") {
|
|
// Z = Unary(Binary(X, Y))
|
|
T scale = static_cast<T>(ctx.Attr<float>("scale"));
|
|
RunUnaryCompoundFunctors<DeviceContext, T,
|
|
paddle::operators::math::ScaleFunctor<T>,
|
|
paddle::operators::math::AddFunctor<T>>(
|
|
ctx, paddle::operators::math::ScaleFunctor<T>(scale),
|
|
paddle::operators::math::AddFunctor<T>(), in_x, in_y, outputs);
|
|
} else if (funcs_str == "elementwise_add,relu") {
|
|
// Z = Binary(X, Unary(Y))
|
|
RunBinaryCompoundFunctor<DeviceContext, T,
|
|
paddle::operators::math::AddFunctor<T>,
|
|
paddle::operators::math::ReluFunctor<T>>(
|
|
ctx, paddle::operators::math::AddFunctor<T>(),
|
|
paddle::operators::math::ReluFunctor<T>(), in_x, in_y, outputs);
|
|
} else if (funcs_str == "relu,elementwise_add") {
|
|
// Z = Unary(Binary(X, Y))
|
|
RunUnaryCompoundFunctors<DeviceContext, T,
|
|
paddle::operators::math::ReluFunctor<T>,
|
|
paddle::operators::math::AddFunctor<T>>(
|
|
ctx, paddle::operators::math::ReluFunctor<T>(),
|
|
paddle::operators::math::AddFunctor<T>(), in_x, in_y, outputs);
|
|
} else if (funcs_str == "elementwise_mul,scale") {
|
|
// Z = Binary(X, Unary(Y))
|
|
T scale = static_cast<T>(ctx.Attr<float>("scale"));
|
|
RunBinaryCompoundFunctor<DeviceContext, T,
|
|
paddle::operators::math::MulFunctor<T>,
|
|
paddle::operators::math::ScaleFunctor<T>>(
|
|
ctx, paddle::operators::math::MulFunctor<T>(),
|
|
paddle::operators::math::ScaleFunctor<T>(scale), in_x, in_y, outputs);
|
|
} else {
|
|
PADDLE_THROW("%s has not been implemented.", funcs_str);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T, bool ReComputation>
|
|
static void RunGradFunctors(const framework::ExecutionContext &ctx,
|
|
const framework::Tensor *in_x,
|
|
const framework::Tensor *in_y,
|
|
const framework::Tensor *in_out,
|
|
const framework::Tensor *in_intermediate_out,
|
|
const framework::Tensor *in_out_grad,
|
|
framework::Tensor *x_grad,
|
|
framework::Tensor *y_grad) {
|
|
auto &functors = ctx.Attr<std::vector<std::string>>("functor_list");
|
|
auto funcs_str = functors[0] + "," + functors[1];
|
|
|
|
// TODO(zcd): The following code can be refined. for example, use registrition
|
|
if (funcs_str == "elementwise_add_grad,scale_grad") {
|
|
// The backward of Z = Binary(X, Unary(Y))
|
|
T scale = static_cast<T>(ctx.Attr<float>("scale"));
|
|
RunBinaryCompoundGradFunctors<DeviceContext, T,
|
|
paddle::operators::math::AddGradFunctor<T>,
|
|
paddle::operators::math::ScaleFunctor<T>,
|
|
paddle::operators::math::ScaleGradFunctor<T>>(
|
|
ctx, paddle::operators::math::AddGradFunctor<T>(),
|
|
paddle::operators::math::ScaleFunctor<T>(scale),
|
|
paddle::operators::math::ScaleGradFunctor<T>(scale), in_x, in_y, in_out,
|
|
in_intermediate_out, in_out_grad, x_grad, y_grad);
|
|
} else if (funcs_str == "scale_grad,elementwise_add_grad") {
|
|
// The backward of Z = Unary(Binary(X, Y))
|
|
T scale = static_cast<T>(ctx.Attr<float>("scale"));
|
|
RunUnaryCompoundGradFunctors<DeviceContext, T,
|
|
paddle::operators::math::ScaleGradFunctor<T>,
|
|
paddle::operators::math::AddFunctor<T>,
|
|
paddle::operators::math::AddGradFunctor<T>,
|
|
ReComputation /*Recomputation*/>(
|
|
ctx, paddle::operators::math::ScaleGradFunctor<T>(scale),
|
|
paddle::operators::math::AddFunctor<T>(),
|
|
paddle::operators::math::AddGradFunctor<T>(), in_x, in_y, in_out,
|
|
in_intermediate_out, in_out_grad, x_grad, y_grad);
|
|
} else if (funcs_str == "elementwise_add_grad,relu_grad") {
|
|
RunBinaryCompoundGradFunctors<DeviceContext, T,
|
|
paddle::operators::math::AddGradFunctor<T>,
|
|
paddle::operators::math::ReluFunctor<T>,
|
|
paddle::operators::math::ReluGradFunctor<T>>(
|
|
ctx, paddle::operators::math::AddGradFunctor<T>(),
|
|
paddle::operators::math::ReluFunctor<T>(),
|
|
paddle::operators::math::ReluGradFunctor<T>(), in_x, in_y, in_out,
|
|
in_intermediate_out, in_out_grad, x_grad, y_grad);
|
|
} else if (funcs_str == "relu_grad,elementwise_add_grad") {
|
|
RunUnaryCompoundGradFunctors<DeviceContext, T,
|
|
paddle::operators::math::ReluGradFunctor<T>,
|
|
paddle::operators::math::AddFunctor<T>,
|
|
paddle::operators::math::AddGradFunctor<T>,
|
|
ReComputation /*Recomputation*/>(
|
|
ctx, paddle::operators::math::ReluGradFunctor<T>(),
|
|
paddle::operators::math::AddFunctor<T>(),
|
|
paddle::operators::math::AddGradFunctor<T>(), in_x, in_y, in_out,
|
|
in_intermediate_out, in_out_grad, x_grad, y_grad);
|
|
} else if (funcs_str == "elementwise_mul_grad,scale_grad") {
|
|
// The backward of Z = Binary(X, Unary(Y))
|
|
T scale = static_cast<T>(ctx.Attr<float>("scale"));
|
|
RunBinaryCompoundGradFunctors<DeviceContext, T,
|
|
paddle::operators::math::MulGradFunctor<T>,
|
|
paddle::operators::math::ScaleFunctor<T>,
|
|
paddle::operators::math::ScaleGradFunctor<T>>(
|
|
ctx, paddle::operators::math::MulGradFunctor<T>(),
|
|
paddle::operators::math::ScaleFunctor<T>(scale),
|
|
paddle::operators::math::ScaleGradFunctor<T>(scale), in_x, in_y, in_out,
|
|
in_intermediate_out, in_out_grad, x_grad, y_grad);
|
|
} else {
|
|
PADDLE_THROW("%s has not been implemented.", funcs_str);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FusedElemwiseActivationKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext &ctx) const override {
|
|
auto &in_x = detail::Ref(ctx.Input<framework::Tensor>("X"),
|
|
"Cannot get input tensor %s, variable name = %s",
|
|
"X", ctx.op().Input("X"));
|
|
auto &in_y = detail::Ref(ctx.Input<framework::Tensor>("Y"),
|
|
"Cannot get input tensor %s, variable name = %s",
|
|
"Y", ctx.op().Input("Y"));
|
|
PADDLE_ENFORCE(ctx.HasOutput("Out"), "The output(Out) should not be empty");
|
|
auto output = ctx.Output<framework::Tensor>("Out");
|
|
|
|
std::vector<framework::Tensor *> outputs;
|
|
outputs.emplace_back(output);
|
|
|
|
if (ctx.Attr<bool>("keep_intermediate_value")) {
|
|
PADDLE_ENFORCE(ctx.HasOutput("IntermediateOut"),
|
|
"The keep_intermediate_value is enable, so the "
|
|
"IntermediateOut should not be empty.");
|
|
auto intermediate_out = ctx.Output<framework::Tensor>("IntermediateOut");
|
|
outputs.emplace_back(intermediate_out);
|
|
} else {
|
|
outputs.emplace_back(nullptr);
|
|
}
|
|
|
|
RunFunctors<DeviceContext, T>(ctx, in_x, in_y, &outputs);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FusedElemwiseActivationGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext &ctx) const override {
|
|
auto x = ctx.Input<framework::Tensor>("X");
|
|
auto y = ctx.Input<framework::Tensor>("Y");
|
|
|
|
auto in_out = ctx.Input<framework::Tensor>("Out");
|
|
auto in_out_grad =
|
|
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
|
|
|
|
framework::Tensor *x_grad =
|
|
ctx.Output<framework::Tensor>(framework::GradVarName("X"));
|
|
framework::Tensor *y_grad =
|
|
ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
|
|
|
|
PADDLE_ENFORCE(y != nullptr, "Input(Y) should not be nullptr.");
|
|
|
|
if (ctx.Attr<bool>("recomputation")) {
|
|
PADDLE_ENFORCE(
|
|
x != nullptr,
|
|
"The recomputation is opened, so Input(X) should not be absent.");
|
|
} else {
|
|
PADDLE_ENFORCE(in_out != nullptr,
|
|
"The recomputation is disabled, so the Input('Out') "
|
|
"should not be empty.");
|
|
}
|
|
|
|
framework::Tensor *in_x;
|
|
auto functor_list = ctx.Attr<std::vector<std::string>>("functor_list");
|
|
|
|
// If functor_list contains elementwise_add, the backward doesn't use
|
|
// in_x, and in_outs.
|
|
if (x == nullptr) {
|
|
PADDLE_ENFORCE(functor_list[0] == "elementwise_add_grad" ||
|
|
functor_list[1] == "elementwise_add_grad",
|
|
"Only when the compoundfunctor contains "
|
|
"elementwise_add_grad, the 'X' could be absent.");
|
|
in_x = const_cast<framework::Tensor *>(in_out_grad);
|
|
in_out = const_cast<framework::Tensor *>(in_out_grad);
|
|
} else {
|
|
in_x = const_cast<framework::Tensor *>(x);
|
|
}
|
|
|
|
framework::Tensor *in_intermediate_out;
|
|
if (ctx.Attr<bool>("keep_intermediate_value")) {
|
|
in_intermediate_out = const_cast<framework::Tensor *>(
|
|
ctx.Input<framework::Tensor>("IntermediateOut"));
|
|
PADDLE_ENFORCE(in_intermediate_out != nullptr,
|
|
"The option of 'keep_intermediate_value' is opened, "
|
|
"so the number of 'Out' should be two.");
|
|
} else {
|
|
in_intermediate_out = nullptr;
|
|
}
|
|
|
|
if (ctx.Attr<bool>("recomputation")) {
|
|
RunGradFunctors<DeviceContext, T, true /*Recomputation*/>(
|
|
ctx, in_x, y, in_out, in_intermediate_out, in_out_grad, x_grad,
|
|
y_grad);
|
|
} else {
|
|
RunGradFunctors<DeviceContext, T, false /*Recomputation*/>(
|
|
ctx, in_x, y, in_out, in_intermediate_out, in_out_grad, x_grad,
|
|
y_grad);
|
|
}
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|