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提交 d64bedf6 编写于 作者: Y Yu Yang
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上级 c23af80a
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Showing with 25 addition and 354 deletion
paddle/framework/backward.cc
浏览文件 @ d64bedf6
......@@ -154,6 +154,9 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
net->InsertOp(pos.first + 1, std::move(pos.second));
}
} else {
OpDescBind fwd_desc;
fwd_desc.SetInput(forwardOp.Inputs());
std::unique_ptr<OperatorBase> grad_op(OpRegistry::CreateGradOp(forwardOp));
ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
......
paddle/framework/backward_test.cc
浏览文件 @ d64bedf6
......@@ -159,16 +159,16 @@ REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
f::NOP);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(1UL, gop->Inputs().size());
ASSERT_EQ("rowwise_add_grad", gop->Type());
ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
}
// TEST(Backward, simple_op_grad) {
// auto fwd = f::OpRegistry::CreateOp(
// "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
// ASSERT_NE(fwd, nullptr);
// auto gop = f::OpRegistry::CreateGradOp(*fwd);
// ASSERT_EQ(1UL, gop->Inputs().size());
// ASSERT_EQ("rowwise_add_grad", gop->Type());
// ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
// ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
//}
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp(
......@@ -286,17 +286,6 @@ TEST(Backward, net_shared_weight) {
ASSERT_EQ("add", bwd_net->ops_[2]->Type());
}
TEST(Backward, op_register_grad_not_for_network) {
auto fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_out"}},
{"add_result", {"add_out"}},
{"Out", {"out1"}}},
{{"temporary_index", std::vector<int>{0, 1}}});
ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet);
}
TEST(Backward, op_all_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
......
paddle/framework/grad_op_builder.cc 已删除 100644 → 0
浏览文件 @ c23af80a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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,
WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either
express or implied. See the License for the specific language governing
permissions and limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
enum class OpArgType { IN, OUT };
static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
bool is_grad, VariableNameMap* vars) {
const auto& src_inout =
src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
auto& dst_inout = *vars;
auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
if (arg.not_in_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_inout[dst_name].reserve(src_inout.at(src_name).size());
for (auto& var_name : src_inout.at(src_name)) {
std::string s = is_grad ? GradVarName(var_name) : var_name;
dst_inout[dst_name].emplace_back(s);
}
}
}
OperatorBase* BuildGradOp(const OperatorBase* op) {
auto& info = OpInfoMap::Instance().Get(op->Type());
PADDLE_ENFORCE(info.HasGradientOp());
VariableNameMap inputs;
VariableNameMap outputs;
TransOpArg(op, OpArgType::IN, false, &inputs); // I
TransOpArg(op, OpArgType::OUT, false, &inputs); // O
TransOpArg(op, OpArgType::OUT, true, &inputs); // OG
TransOpArg(op, OpArgType::IN, true, &outputs); // IG
auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_);
return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs());
}
static void TransOpDescArg(const OpDescBind* src_op, const OpArgType& src_type,
bool is_grad, OpDescBind* dst_op,
const OpArgType& dst_type) {
PADDLE_ENFORCE(dst_op != nullptr,
"Protobuf desc of gradient op must be initialized first.");
const auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
if (arg.not_in_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::vector<std::string> vars = src_type == OpArgType::IN
? src_op->Input(src_name)
: src_op->Output(src_name);
if (is_grad) {
for (std::string& var : vars) {
var = GradVarName(var);
}
}
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_type == OpArgType::IN ? dst_op->SetInput(dst_name, vars)
: dst_op->SetOutput(dst_name, vars);
}
}
void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op) {
auto& info = OpInfoMap::Instance().Get(forw_op->Type());
PADDLE_ENFORCE(info.HasGradientOp());
grad_op->SetType(info.grad_op_type_);
TransOpDescArg(forw_op, OpArgType::IN, false, grad_op, OpArgType::IN);
TransOpDescArg(forw_op, OpArgType::OUT, false, grad_op, OpArgType::IN);
TransOpDescArg(forw_op, OpArgType::OUT, true, grad_op, OpArgType::IN);
TransOpDescArg(forw_op, OpArgType::IN, true, grad_op, OpArgType::OUT);
grad_op->SetAttrMap(forw_op->GetAttrMap());
}
} // namespace framework
} // namespace paddle
paddle/framework/grad_op_builder.h 已删除 100644 → 0
浏览文件 @ c23af80a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "paddle/framework/op_desc.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
OperatorBase* BuildGradOp(const OperatorBase* op);
void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op);
} // namespace framework
} // namespace paddle
paddle/framework/grad_op_builder_test.cc 已删除 100644 → 0
浏览文件 @ c23af80a
#include "paddle/framework/grad_op_builder.h"
#include <gtest/gtest.h>
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(add);
namespace paddle {
namespace framework {
class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
public:
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").AsDuplicable();
AddInput("In3", "another single input");
AddOutput("Out1", "a single output");
AddOutput("Out2_mult", "a multiple output").AsDuplicable();
AddComment("test op with multiple inputs and outputs");
}
};
class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
public:
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient();
AddInput("In3_mult", "another multiple input").AsDuplicable();
AddOutput("Out1_mult", "a multiple output").AsDuplicable();
AddOutput("Out2", "a single output").NotInGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
"add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
EXPECT_EQ(grad_add_op->Input("X"), "x");
EXPECT_EQ(grad_add_op->Input("Y"), "y");
EXPECT_EQ(grad_add_op->Input("Out"), "out");
EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"mult_io", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2", "in2_3"}},
{"In3", {"in3"}}},
{{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
EXPECT_EQ(grad_test_op->Input("In3"), "in3");
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")),
f::GradVarName("out1"));
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")),
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
f::GradVarName("in2_2"),
f::GradVarName("in2_3")}));
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3"));
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"io_ignored", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2"}},
{"In3_mult", {"in3_1", "in3_2"}}},
{{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
std::vector<std::string>(
{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
f::GradVarName("out2"));
ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>(
{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")),
std::vector<std::string>(
{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
}
TEST(GradOpDescBuilder, MutiInOut) {
f::OpDescBind *forw_op = new f::OpDescBind();
forw_op->SetType("mult_io");
forw_op->SetInput("In1", {"in1"});
forw_op->SetInput("In2_mult", {"in2_1", "in2_2", "in2_3"});
forw_op->SetInput("In3", {"in3"});
forw_op->SetOutput("Out1", {"out1"});
forw_op->SetOutput("Out2_mult", {"out2_1", "out2_2"});
f::OpDescBind *grad_op = new f::OpDescBind();
f::CompleteGradOpDesc(forw_op, grad_op);
EXPECT_EQ(grad_op->Type(), "mult_io_grad");
ASSERT_EQ(grad_op->InputNames().size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
EXPECT_EQ(grad_op->Input("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
EXPECT_EQ(grad_op->Input("In3"), std::vector<std::string>({"in3"}));
EXPECT_EQ(grad_op->Input("Out1"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op->Input("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out1")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out2_mult")),
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
std::vector<std::string>({f::GradVarName("in1")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
f::GradVarName("in2_2"),
f::GradVarName("in2_3")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In3")),
std::vector<std::string>({f::GradVarName("in3")}));
delete forw_op;
delete grad_op;
}
TEST(GradOpDescBuilder, IOIgnoredInGradient) {
f::OpDescBind *forw_op = new f::OpDescBind();
forw_op->SetType("io_ignored");
forw_op->SetInput("In1", {"in1"});
forw_op->SetInput("In2_mult", {"in2_1", "in2_2"});
forw_op->SetInput("In3_mult", {"in3_1", "in3_2"});
forw_op->SetOutput("Out1_mult", {"out1_1", "out1_2"});
forw_op->SetOutput("Out2", {"out2"});
f::OpDescBind *grad_op = new f::OpDescBind();
f::CompleteGradOpDesc(forw_op, grad_op);
EXPECT_EQ(grad_op->Type(), "io_ignored_grad");
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_op->InputNames().size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
EXPECT_EQ(grad_op->Input("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_op->Input("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out1_mult")),
std::vector<std::string>(
{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out2")),
std::vector<std::string>({f::GradVarName("out2")}));
ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
std::vector<std::string>({f::GradVarName("in1")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
std::vector<std::string>(
{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In3_mult")),
std::vector<std::string>(
{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
delete forw_op;
delete grad_op;
}
\ No newline at end of file
paddle/framework/op_desc.h
浏览文件 @ d64bedf6
......@@ -74,6 +74,18 @@ class OpDescBind {
return MapKeys(outputs_);
}
void SetInput(
const std::unordered_map<std::string, std::vector<std::string>> &input) {
this->inputs_ = input;
this->need_update_ = true;
}
void SetOutput(
const std::unordered_map<std::string, std::vector<std::string>> &output) {
this->outputs_ = output;
this->need_update_ = true;
}
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
......
paddle/framework/op_registry.cc
浏览文件 @ d64bedf6
......@@ -52,10 +52,5 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
return CreateOp(op_desc.type(), inputs, outputs, attrs);
}
std::unique_ptr<OperatorBase> OpRegistry::CreateGradOp(const OperatorBase& op) {
PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops");
return std::unique_ptr<OperatorBase>(BuildGradOp(&op));
}
} // namespace framework
} // namespace paddle
paddle/framework/op_registry.h
浏览文件 @ d64bedf6
......@@ -79,8 +79,6 @@ class OpRegistry {
AttributeMap attrs);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
static std::unique_ptr<OperatorBase> CreateGradOp(const OperatorBase& op);
};
template <typename OpType, typename ProtoMakerType, typename GradOpType>
......
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