Merge branch 'feature/grad_reg_mechanism_cont2' of https://github.com/reyoung/Paddle into dev_backward_for_op_desc_dev
fengjiayi
7 years ago
commit
a598ef5388
@ -1,97 +0,0 @@
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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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WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either
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express or implied. See the License for the specific language governing
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permissions and limitations under the License. */
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#include "paddle/framework/grad_op_builder.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace framework {
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enum class OpArgType { IN, OUT };
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static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
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bool is_grad, VariableNameMap* vars) {
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const auto& src_inout =
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src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
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auto& dst_inout = *vars;
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auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
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const auto& src_arg_list =
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src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
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for (const auto& arg : src_arg_list) {
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if (arg.not_in_gradient() && !is_grad) continue;
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const std::string src_name = arg.name();
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std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
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dst_inout[dst_name].reserve(src_inout.at(src_name).size());
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for (auto& var_name : src_inout.at(src_name)) {
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std::string s = is_grad ? GradVarName(var_name) : var_name;
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dst_inout[dst_name].emplace_back(s);
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}
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}
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}
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OperatorBase* BuildGradOp(const OperatorBase* op) {
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auto& info = OpInfoMap::Instance().Get(op->Type());
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PADDLE_ENFORCE(info.HasGradientOp());
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VariableNameMap inputs;
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VariableNameMap outputs;
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TransOpArg(op, OpArgType::IN, false, &inputs); // I
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TransOpArg(op, OpArgType::OUT, false, &inputs); // O
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TransOpArg(op, OpArgType::OUT, true, &inputs); // OG
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TransOpArg(op, OpArgType::IN, true, &outputs); // IG
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auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_);
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return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs());
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}
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static void TransOpDescArg(const OpDescBind* src_op, const OpArgType& src_type,
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bool is_grad, OpDescBind* dst_op,
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const OpArgType& dst_type) {
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PADDLE_ENFORCE(dst_op != nullptr,
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"Protobuf desc of gradient op must be initialized first.");
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const auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
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const auto& src_arg_list =
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src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
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for (const auto& arg : src_arg_list) {
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if (arg.not_in_gradient() && !is_grad) continue;
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const std::string src_name = arg.name();
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std::vector<std::string> vars = src_type == OpArgType::IN
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? src_op->Input(src_name)
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: src_op->Output(src_name);
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if (is_grad) {
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for (std::string& var : vars) {
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var = GradVarName(var);
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}
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}
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std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
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dst_type == OpArgType::IN ? dst_op->SetInput(dst_name, vars)
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: dst_op->SetOutput(dst_name, vars);
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}
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}
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void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op) {
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auto& info = OpInfoMap::Instance().Get(forw_op->Type());
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PADDLE_ENFORCE(info.HasGradientOp());
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grad_op->SetType(info.grad_op_type_);
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TransOpDescArg(forw_op, OpArgType::IN, false, grad_op, OpArgType::IN);
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TransOpDescArg(forw_op, OpArgType::OUT, false, grad_op, OpArgType::IN);
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TransOpDescArg(forw_op, OpArgType::OUT, true, grad_op, OpArgType::IN);
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TransOpDescArg(forw_op, OpArgType::IN, true, grad_op, OpArgType::OUT);
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grad_op->SetAttrMap(forw_op->GetAttrMap());
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}
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} // namespace framework
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} // namespace paddle
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@ -1,28 +0,0 @@
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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/op_desc.h"
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#include "paddle/framework/operator.h"
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namespace paddle {
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namespace framework {
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OperatorBase* BuildGradOp(const OperatorBase* op);
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void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op);
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} // namespace framework
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} // namespace paddle
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@ -1,186 +0,0 @@
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#include "paddle/framework/grad_op_builder.h"
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#include <gtest/gtest.h>
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/operator.h"
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USE_OP(sum);
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namespace paddle {
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namespace framework {
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class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
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public:
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MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("In1", "a single input");
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AddInput("In2_mult", "a multiple input").AsDuplicable();
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AddInput("In3", "another single input");
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AddOutput("Out1", "a single output");
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AddOutput("Out2_mult", "a multiple output").AsDuplicable();
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AddComment("test op with multiple inputs and outputs");
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}
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};
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class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
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public:
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IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("In1", "a single input");
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AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient();
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AddInput("In3_mult", "another multiple input").AsDuplicable();
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AddOutput("Out1_mult", "a multiple output").AsDuplicable();
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AddOutput("Out2", "a single output").NotInGradient();
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AddComment("op with inputs and outputs ignored in gradient calculating");
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}
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};
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} // namespace framework
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} // namespace paddle
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namespace f = paddle::framework;
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REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
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REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
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TEST(GradOpBuilder, MutiInOut) {
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std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
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"mult_io", {{"In1", {"in1"}},
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{"In2_mult", {"in2_1", "in2_2", "in2_3"}},
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{"In3", {"in3"}}},
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{{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
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std::shared_ptr<f::OperatorBase> grad_test_op =
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f::OpRegistry::CreateGradOp(*test_op);
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ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
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EXPECT_EQ(grad_test_op->Input("In1"), "in1");
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EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
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std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
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EXPECT_EQ(grad_test_op->Input("In3"), "in3");
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EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
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EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
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std::vector<std::string>({"out2_1", "out2_2"}));
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EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")),
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f::GradVarName("out1"));
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EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")),
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std::vector<std::string>(
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{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
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ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
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EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
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EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
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std::vector<std::string>({f::GradVarName("in2_1"),
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f::GradVarName("in2_2"),
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f::GradVarName("in2_3")}));
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EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3"));
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}
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TEST(GradOpBuilder, IOIgnoredInGradient) {
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std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
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"io_ignored", {{"In1", {"in1"}},
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{"In2_mult", {"in2_1", "in2_2"}},
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{"In3_mult", {"in3_1", "in3_2"}}},
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{{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
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std::shared_ptr<f::OperatorBase> grad_test_op =
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f::OpRegistry::CreateGradOp(*test_op);
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// 'In2' and 'Out2' are ignored in gradient calculating
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ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
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EXPECT_EQ(grad_test_op->Input("In1"), "in1");
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EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
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std::vector<std::string>({"in3_1", "in3_2"}));
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EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
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std::vector<std::string>({"out1_1", "out1_2"}));
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EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
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std::vector<std::string>(
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{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
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EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
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f::GradVarName("out2"));
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ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
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EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
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EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
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std::vector<std::string>(
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{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
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EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")),
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std::vector<std::string>(
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{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
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}
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TEST(GradOpDescBuilder, MutiInOut) {
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f::OpDescBind *forw_op = new f::OpDescBind();
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forw_op->SetType("mult_io");
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forw_op->SetInput("In1", {"in1"});
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forw_op->SetInput("In2_mult", {"in2_1", "in2_2", "in2_3"});
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forw_op->SetInput("In3", {"in3"});
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forw_op->SetOutput("Out1", {"out1"});
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forw_op->SetOutput("Out2_mult", {"out2_1", "out2_2"});
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f::OpDescBind *grad_op = new f::OpDescBind();
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f::CompleteGradOpDesc(forw_op, grad_op);
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EXPECT_EQ(grad_op->Type(), "mult_io_grad");
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ASSERT_EQ(grad_op->InputNames().size(), 3UL + 2UL + 2UL);
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EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
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EXPECT_EQ(grad_op->Input("In2_mult"),
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std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
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EXPECT_EQ(grad_op->Input("In3"), std::vector<std::string>({"in3"}));
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EXPECT_EQ(grad_op->Input("Out1"), std::vector<std::string>({"out1"}));
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EXPECT_EQ(grad_op->Input("Out2_mult"),
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std::vector<std::string>({"out2_1", "out2_2"}));
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EXPECT_EQ(grad_op->Input(f::GradVarName("Out1")),
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std::vector<std::string>({f::GradVarName("out1")}));
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EXPECT_EQ(grad_op->Input(f::GradVarName("Out2_mult")),
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std::vector<std::string>(
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{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
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ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
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EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
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std::vector<std::string>({f::GradVarName("in1")}));
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EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
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std::vector<std::string>({f::GradVarName("in2_1"),
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f::GradVarName("in2_2"),
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f::GradVarName("in2_3")}));
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EXPECT_EQ(grad_op->Output(f::GradVarName("In3")),
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std::vector<std::string>({f::GradVarName("in3")}));
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delete forw_op;
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delete grad_op;
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}
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TEST(GradOpDescBuilder, IOIgnoredInGradient) {
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f::OpDescBind *forw_op = new f::OpDescBind();
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forw_op->SetType("io_ignored");
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forw_op->SetInput("In1", {"in1"});
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forw_op->SetInput("In2_mult", {"in2_1", "in2_2"});
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forw_op->SetInput("In3_mult", {"in3_1", "in3_2"});
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forw_op->SetOutput("Out1_mult", {"out1_1", "out1_2"});
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forw_op->SetOutput("Out2", {"out2"});
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f::OpDescBind *grad_op = new f::OpDescBind();
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f::CompleteGradOpDesc(forw_op, grad_op);
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EXPECT_EQ(grad_op->Type(), "io_ignored_grad");
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// 'In2' and 'Out2' are ignored in gradient calculating
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ASSERT_EQ(grad_op->InputNames().size(), 2UL + 1UL + 2UL);
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EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
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EXPECT_EQ(grad_op->Input("In3_mult"),
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std::vector<std::string>({"in3_1", "in3_2"}));
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EXPECT_EQ(grad_op->Input("Out1_mult"),
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std::vector<std::string>({"out1_1", "out1_2"}));
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EXPECT_EQ(grad_op->Input(f::GradVarName("Out1_mult")),
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std::vector<std::string>(
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{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
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EXPECT_EQ(grad_op->Input(f::GradVarName("Out2")),
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std::vector<std::string>({f::GradVarName("out2")}));
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ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
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EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
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std::vector<std::string>({f::GradVarName("in1")}));
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EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
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std::vector<std::string>(
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{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
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EXPECT_EQ(grad_op->Output(f::GradVarName("In3_mult")),
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std::vector<std::string>(
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{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
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delete forw_op;
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delete grad_op;
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}
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