qingqing01
8 years ago
committed by
GitHub
commit
fe480b9ebe
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#include "paddle/framework/op_info.h"
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namespace paddle {
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namespace framework {
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static OpInfoMap* g_op_info_map = nullptr;
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OpInfoMap& OpInfoMap::Instance() {
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if (g_op_info_map == nullptr) {
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g_op_info_map = new OpInfoMap();
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}
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return *g_op_info_map;
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}
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} // namespace framework
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} // namespace paddle
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 <functional>
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#include <map>
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#include <string>
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#include <unordered_map>
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#include "paddle/framework/attribute.h"
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namespace paddle {
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namespace framework {
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class OperatorBase;
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using VariableNameMap = std::map<std::string, std::vector<std::string>>;
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using OpCreator = std::function<OperatorBase*(
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const std::string& /*type*/, const VariableNameMap& /*inputs*/,
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const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
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struct OpInfo {
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OpCreator creator_;
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std::string grad_op_type_;
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OpProto* proto_;
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OpAttrChecker* checker_;
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bool HasOpProtoAndChecker() const {
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return proto_ != nullptr && checker_ != nullptr;
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}
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const OpProto& Proto() const {
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PADDLE_ENFORCE_NOT_NULL(proto_, "Operator Proto has not been registered");
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PADDLE_ENFORCE(proto_->IsInitialized(),
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"Operator Proto must be initialized in op info");
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return *proto_;
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}
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const OpAttrChecker& Checker() const {
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PADDLE_ENFORCE_NOT_NULL(checker_,
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"Operator Checker has not been registered");
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return *checker_;
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}
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const OpCreator& Creator() const {
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PADDLE_ENFORCE_NOT_NULL(creator_,
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"Operator Creator has not been registered");
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return creator_;
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}
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bool HasGradientOp() const { return !grad_op_type_.empty(); }
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};
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class OpInfoMap {
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public:
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static OpInfoMap& Instance();
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OpInfoMap(const OpInfoMap& o) = delete;
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OpInfoMap(OpInfoMap&& o) = delete;
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OpInfoMap& operator=(const OpInfoMap& o) = delete;
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OpInfoMap& operator=(OpInfoMap&& o) = delete;
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bool Has(const std::string& op_type) const {
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return map_.find(op_type) != map_.end();
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}
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void Insert(const std::string& type, const OpInfo& info) {
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PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
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map_.insert({type, info});
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}
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const OpInfo& Get(const std::string& type) const {
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auto it = map_.find(type);
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PADDLE_ENFORCE(it != map_.end(), "Operator %s are not found", type);
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return it->second;
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}
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template <typename Callback>
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void IterAllInfo(Callback callback) {
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for (auto& it : map_) {
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callback(it.first, it.second);
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}
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}
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private:
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OpInfoMap() = default;
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std::unordered_map<std::string, const OpInfo> map_;
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};
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} // namespace framework
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} // namespace paddle
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#include "paddle/operators/scale_op.h"
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#include "paddle/operators/net_op.h"
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namespace paddle {
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namespace operators {
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class ScaleOp : public framework::OperatorWithKernel {
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public:
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ScaleOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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auto *in = ctx.Input<framework::Tensor>("X");
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auto *out = ctx.Output<framework::Tensor>("Out");
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out->Resize(in->dims());
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}
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};
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template <typename AttrType>
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class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The input tensor of scale operator.").NotInGradient();
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AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
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AddComment(R"DOC(Scale operator
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The equation is: Out = scale*X
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)DOC");
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AddAttr<AttrType>("scale", "scale of scale operator.").SetDefault(1.0);
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}
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};
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// Identity Op's gradient is identity op, too.
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// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
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template <typename AttrType>
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class ScaleGradOp : public NetOp {
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public:
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ScaleGradOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: NetOp(type, inputs, outputs, attrs) {
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AppendOp(framework::OpRegistry::CreateOp(
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"scale", {{"X", {Input(framework::GradVarName("Out"))}}},
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{{"Out", {Output(framework::GradVarName("X"))}}},
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{{"scale", GetAttr<AttrType>("scale")}}));
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CompleteAddOp(false);
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}
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};
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// identity is a alias of scale op. This is also a example for creating a alias
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// operator.
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template <typename AttrType>
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class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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IdentityOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "input tensor of identity op");
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AddOutput("Out", "output tensor of identity op");
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AddComment("identity operator. Just a alias of scale op which scale = 1.0");
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}
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};
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template <typename AttrType>
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class IdentityOp : public NetOp {
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public:
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IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: NetOp(type, inputs, outputs, attrs) {
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AppendOp(framework::OpRegistry::CreateOp(
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"scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
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{{"scale", static_cast<AttrType>(1)}}));
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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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REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
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ops::ScaleGradOp<float>);
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REGISTER_OP_CPU_KERNEL(scale,
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ops::ScaleKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
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ops::IdentityOpMaker<float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#include "paddle/operators/scale_op.h"
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REGISTER_OP_GPU_KERNEL(
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scale, paddle::operators::ScaleKernel<paddle::platform::GPUPlace, float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T, typename AttrType = T>
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class ScaleKernel : public framework::OpKernel {
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public:
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virtual void Compute(const framework::ExecutionContext& context) const {
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auto* tensor = context.Output<framework::Tensor>("Out");
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auto* in = context.Input<framework::Tensor>("X");
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tensor->mutable_data<T>(in->place());
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auto scale = static_cast<T>(context.op_.GetAttr<AttrType>("scale"));
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auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
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auto eigen_in = framework::EigenVector<T>::Flatten(*in);
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auto& dev = context.GetEigenDevice<Place>();
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eigen_out.device(dev) = scale * eigen_in;
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}
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};
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} // namespace operators
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} // namespace paddle
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import unittest
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from op_test_util import OpTestMeta
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from gradient_checker import GradientChecker, create_op
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import numpy as np
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from paddle.v2.framework.op import Operator
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class IdentityTest(unittest.TestCase):
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__metaclass__ = OpTestMeta
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def setUp(self):
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self.type = "identity"
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self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
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self.outputs = {'Out': self.inputs['X']}
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class IdentityGradOpTest(GradientChecker):
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def test_normal(self):
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op = create_op("identity")
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inputs = {"X": np.random.random((10, 10)).astype("float32")}
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self.check_grad(op, inputs, set("X"), "Out")
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class ScaleTest(unittest.TestCase):
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__metaclass__ = OpTestMeta
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def setUp(self):
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self.type = "scale"
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self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
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self.attrs = {'scale': -2.3}
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self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']}
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class ScaleGradTest(GradientChecker):
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def test_normal(self):
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op = Operator("scale", X="X", Out="Out", scale=3.2)
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self.check_grad(op,
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{"X": np.random.random((10, 10)).astype("float32")},
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set("X"), "Out")
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if __name__ == '__main__':
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unittest.main()
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