m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
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.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'f8c6dadae1'
${ noResults }
Paddle/paddle/framework/backward_test.cc

900 lines
34 KiB
Raw Blame History

/* 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. */
#include "paddle/framework/backward.h"
#include <gtest/gtest.h>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/var_desc.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace framework {
using DeviceContext = platform::DeviceContext;
class NoneOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {}
};
template <typename Place, typename T>
class NoneKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {}
};
class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input X of Add");
AddInput("b", "Bias of Add");
AddOutput("Out", "Out of Add");
AddComment("Add Op");
}
};
class RowWiseAddGradMaker : public SingleGradOpDescMaker {
public:
using SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<OpDescBind> Apply() const override {
auto grad_op = new OpDescBind();
grad_op->SetInput(GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(GradVarName("X"), InputGrad("X"));
grad_op->SetOutput(GradVarName("b"), InputGrad("b"));
grad_op->SetType("rowwise_add_grad");
return std::unique_ptr<OpDescBind>(grad_op);
}
};
class MulOpMaker : public OpProtoAndCheckerMaker {
public:
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "A");
AddInput("Y", "B");
AddOutput("Out", "Out");
AddAttr<int>("x_num_col_dims", "").SetDefault(1).EqualGreaterThan(1);
AddAttr<int>("y_num_col_dims", "").SetDefault(1).EqualGreaterThan(1);
AddComment("Mul");
}
};
class SigmoidOpMaker : public OpProtoAndCheckerMaker {
public:
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "X");
AddOutput("Out", "Y");
AddComment("Sigmoid");
}
};
class NoGradOpMaker : public OpProtoAndCheckerMaker {
public:
NoGradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "X input");
AddOutput("Out", "Y output");
AddComment("NoGradOp, same input output. no Grad");
}
};
class FcOp : public operators::NetOp {
public:
FcOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AppendOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
auto input_b = Inputs("b");
std::string before_act = "mul_result";
if (input_b.size() != 0) {
AppendOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
{{"Out", {Output("add_result")}}}, {}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
if (out_varname != kEmptyVarName) {
this->Rename(out_varname, kEmptyVarName);
}
}
AppendOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
class FcOpMaker : public OpProtoAndCheckerMaker {
public:
FcOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x");
AddInput("W", "w");
AddInput("b", "b");
AddOutput("mul_result", "").AsIntermediate();
AddOutput("add_result", "").AsIntermediate();
AddOutput("Out", "");
AddComment("");
}
};
class ManyOutputOpMaker : public OpProtoAndCheckerMaker {
public:
ManyOutputOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("x", "x");
AddOutput("y", "y");
AddOutput("z", "z");
AddComment("");
}
};
class FillZeroOpMaker : public OpProtoAndCheckerMaker {
public:
FillZeroOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x");
AddOutput("Y", "out");
AddComment("");
}
};
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input tensors of sum operator.").AsDuplicable();
AddOutput("Out", "the output tensor of sum operator.");
AddComment("");
}
};
class MultInOutOpMaker : public OpProtoAndCheckerMaker {
public:
MultInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x");
AddInput("H", "h");
AddOutput("Y", "y");
AddOutput("Z", "z");
AddComment("");
}
};
class MinusGradOpDescMaker : public GradOpDescMakerBase {
public:
using GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<OpDescBind>> operator()() const override {
std::vector<std::unique_ptr<OpDescBind>> retv;
auto x_g = InputGrad("X");
if (!x_g.empty()) {
auto *op_desc = new OpDescBind();
op_desc->SetType("scale");
op_desc->SetInput("X", OutputGrad("Out"));
op_desc->SetOutput("Out", x_g);
op_desc->SetAttr("scale", 1.0f);
retv.emplace_back(op_desc);
}
auto y_g = InputGrad("Y");
if (!y_g.empty()) {
auto *op_desc = new OpDescBind();
op_desc->SetType("scale");
op_desc->SetInput("X", OutputGrad("Out"));
op_desc->SetOutput("Out", y_g);
op_desc->SetAttr("scale", -1.0f);
retv.emplace_back(op_desc);
}
return retv;
}
};
class MinusOpMaker : public OpProtoAndCheckerMaker {
public:
MinusOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddInput("Y", "");
AddOutput("Out", "");
AddComment("minus for unittest");
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
namespace ops = paddle::operators;
using EnforceNotMet = paddle::platform::EnforceNotMet;
// rowwise_add
REGISTER_OPERATOR(rowwise_add, f::NoneOp, f::RowWiseAddOpMaker,
f::RowWiseAddGradMaker);
REGISTER_OP_CPU_KERNEL(rowwise_add,
f::NoneKernel<paddle::platform::CPUPlace, float>);
REGISTER_OPERATOR(rowwise_add_grad, f::NoneOp);
REGISTER_OP_CPU_KERNEL(rowwise_add_grad,
f::NoneKernel<paddle::platform::CPUPlace, float>);
// mul
REGISTER_OP(mul, f::NoneOp, f::MulOpMaker, mul_grad, f::NoneOp);
REGISTER_OP_CPU_KERNEL(mul, f::NoneKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
f::NoneKernel<paddle::platform::CPUPlace, float>);
// sigmoid
REGISTER_OP(sigmoid, f::NoneOp, f::SigmoidOpMaker, sigmoid_grad, f::NoneOp);
REGISTER_OP_CPU_KERNEL(sigmoid,
f::NoneKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NoneOp, f::NoGradOpMaker);
// fill_zeros_like
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NoneOp, f::FillZeroOpMaker);
REGISTER_OP_CPU_KERNEL(fill_zeros_like,
f::NoneKernel<paddle::platform::CPUPlace, float>);
// sum
REGISTER_OP(sum, f::NoneOp, f::SumOpMaker, sum_grad, f::NoneOp);
REGISTER_OP_CPU_KERNEL(sum, f::NoneKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(sum_grad,
f::NoneKernel<paddle::platform::CPUPlace, float>);
// fc
REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
// many_output_op
REGISTER_OP(many_output_op, f::NoneOp, f::ManyOutputOpMaker,
many_output_op_grad, f::NoneOp);
// mult_in_out
REGISTER_OP(mult_in_out, f::NoneOp, f::MultInOutOpMaker, mult_in_out_grad,
f::NoneOp);
REGISTER_OP_CPU_KERNEL(mult_in_out,
f::NoneKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mult_in_out_grad,
f::NoneKernel<paddle::platform::CPUPlace, float>);
// minus
REGISTER_OPERATOR(minus, f::NoneOp, f::MinusOpMaker, f::MinusGradOpDescMaker);
REGISTER_OP_CPU_KERNEL(minus, f::NoneKernel<paddle::platform::CPUPlace, float>);
// scale
REGISTER_OPERATOR(scale, f::NoneOp);
REGISTER_OP_CPU_KERNEL(scale, f::NoneKernel<paddle::platform::CPUPlace, float>);
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"x"});
ASSERT_EQ(gop->Output(f::GradVarName("X")), f::kEmptyVarName);
auto no_input_gop = f::Backward(*fwd, {"x", "b"});
ASSERT_NE(no_input_gop, nullptr);
ASSERT_TRUE(no_input_gop->IsNetOp());
ASSERT_EQ(0UL, static_cast<ops::NetOp *>(no_input_gop.get())->ops_.size());
}
TEST(Backward, net_fc_backward_normal) {
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_res"}},
{"add_result", {"add_re"}},
{"Out", {"out"}}},
{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<ops::NetOp *>(gop.get());
ASSERT_NO_THROW(net->DebugString());
ASSERT_EQ(3UL, net->ops_.size());
f::OperatorBase &d_sigmoid = *net->ops_[0];
ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
f::OperatorBase &d_add = *net->ops_[1];
ASSERT_EQ("rowwise_add_grad", d_add.Type());
f::OperatorBase &d_mul = *net->ops_[2];
ASSERT_EQ("mul_grad", d_mul.Type());
}
TEST(Backward, net_fc_backward_not_have_b) {
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {}}},
{{"mul_result", {"mul_res"}},
{"add_result", {"add_res"}},
{"Out", {"tmp"}}},
{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<ops::NetOp *>(gop.get());
ASSERT_NO_THROW(net->DebugString());
ASSERT_EQ(2UL, net->ops_.size());
f::OperatorBase &d_sigmoid = *net->ops_[0];
ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
f::OperatorBase &d_mul = *net->ops_[1];
ASSERT_EQ("mul_grad", d_mul.Type());
}
TEST(Backward, net_input_of_network_not_need_grad) {
ops::NetOp net;
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x"}}, {"W", {"W1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_tmp_0"}},
{"add_result", {"add_tmp_0"}},
{"Out", {"hidden0"}}},
{}));
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_tmp_1"}},
{"add_result", {"add_tmp_1"}},
{"Out", {"hidden1"}}},
{}));
net.CompleteAddOp();
auto bwd = Backward(net, {"x"}); // x@GRAD is not need.
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
auto output_vars = bwd_net->OutputVars(true);
std::unordered_set<std::string> all_outputs =
std::unordered_set<std::string>(output_vars.begin(), output_vars.end());
all_outputs.erase(f::kEmptyVarName);
for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
ASSERT_NE(all_outputs.find(f::GradVarName(out)), all_outputs.end());
}
// Not Generated X
ASSERT_EQ(all_outputs.find(f::GradVarName("X")), all_outputs.end());
ASSERT_EQ(2UL, bwd_net->ops_.size());
ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
ASSERT_EQ(3UL, first_fc_grad->ops_.size());
ASSERT_EQ(f::kEmptyVarName,
first_fc_grad->ops_[2]->Output(f::GradVarName("X")));
}
TEST(Backward, net_shared_weight) {
ops::NetOp net;
net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
{{"Out", {"out"}}}, {}));
net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
{{"Out", {"FinalOut"}}}, {}));
net.CompleteAddOp();
auto bwd = f::Backward(net, {});
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
ASSERT_EQ(3UL, bwd_net->ops_.size());
ASSERT_EQ("sum", bwd_net->ops_[2]->Type());
}
TEST(Backward, op_all_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"x", "b"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
TEST(Backward, op_all_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"out"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
TEST(Backward, op_part_of_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
{{"y", {"Y"}}, {"z", {"Z"}}}, {});
auto backward = f::Backward(*fwd, {"Z"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(net->ops_.size(), 2UL);
auto &fill_zero = *net->ops_[0];
ASSERT_EQ("fill_zeros_like", fill_zero.Type());
ASSERT_EQ(1UL, fill_zero.Inputs("X").size());
ASSERT_EQ("Z", fill_zero.Input("X"));
ASSERT_EQ(1UL, fill_zero.Outputs("Y").size());
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.Output("Y"));
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.Type());
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.Inputs().size()); // I/O/OG
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix,
d_many_out.Input(f::GradVarName("z")));
ASSERT_EQ(f::GradVarName("Y"), d_many_out.Input(f::GradVarName("y")));
ASSERT_EQ(f::GradVarName("X"), d_many_out.Output(f::GradVarName("x")));
}
TEST(Backward, op_part_of_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("mul", {{"X", {"a"}}, {"Y", {"b"}}},
{{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"a"});
auto &grad_mul = *backward;
ASSERT_EQ(grad_mul.Type(), "mul_grad");
ASSERT_EQ(grad_mul.Inputs().size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.Outputs().size(), 2UL);
ASSERT_EQ(grad_mul.Output(f::GradVarName("X")), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output(f::GradVarName("Y")), f::GradVarName("b"));
ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
ASSERT_EQ(grad_mul.Input("X"), "a");
ASSERT_EQ(grad_mul.Input("Y"), "b");
ASSERT_EQ(grad_mul.Input("Out"), "out");
}
TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
ops::NetOp net;
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x1"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_out1"}},
{"add_result", {"add_out1"}},
{"Out", {"out1"}}},
{}));
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_out2"}},
{"add_result", {"tmp_out2"}},
{"Out", {"out2"}}},
{}));
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
{{"mul_result", {"mul_out3"}},
{"add_result", {"tmp_out3"}},
{"Out", {"out3"}}},
{}));
net.CompleteAddOp();
auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
ASSERT_TRUE(backward->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(bwd_net->ops_.size(), 3UL);
auto &grad_fc = *bwd_net->ops_[0];
const char *all = paddle::operators::NetOp::kAll;
EXPECT_EQ(grad_fc.Inputs(all).size(),
2UL /* external input number */
+ 1UL /* external output number*/
+ 1UL /* number of gradient of external output*/
+ 2UL /* internal variable number*/
);
EXPECT_EQ(grad_fc.Outputs(all).size(),
2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add*/
+ 1UL /* input number of sigmod */
- 1UL /* out2 is not needed*/);
EXPECT_EQ(bwd_net->ops_[1]->Inputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->Outputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL);
}
TEST(Backward, simple_single_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("rowwise_add");
op->SetInput("X", {"x"});
op->SetInput("b", {"b"});
op->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
auto var_to_grad = AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 3UL);
f::OpDescBind *fill_op = block->AllOps()[1];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op = block->AllOps()[2];
EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op->InputNames().size(), 1UL);
ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b")}));
EXPECT_EQ(var_to_grad.size(), 3UL);
EXPECT_EQ(var_to_grad.at("b"), f::GradVarInfo(f::GradVarName("b"), 0, 2));
EXPECT_EQ(var_to_grad.at("x"), f::GradVarInfo(f::GradVarName("x"), 0, 2));
EXPECT_TRUE(block->HasVar(f::GradVarName("b")));
EXPECT_TRUE(block->HasVar(f::GradVarName("x")));
}
TEST(Backward, default_attribute) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("mul");
op->SetInput("X", {"x"});
op->SetInput("Y", {"y"});
op->SetOutput("Out", {"out"});
op->CheckAttrs();
auto target = f::VarDescBind("out");
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 3UL);
EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(op->GetAttr("y_num_col_dims")), 1);
f::OpDescBind *fill_op = block->AllOps()[1];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op = block->AllOps()[2];
ASSERT_EQ(grad_op->Type(), "mul_grad");
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("y_num_col_dims")), 1);
}
TEST(Backward, simple_mult_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"out1"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out2"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 6UL + 1);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op1 = block->AllOps()[6];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op2 = block->AllOps()[5];
EXPECT_EQ(grad_op2->Type(), "mul_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op2->Input("X"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op2->Input("Y"), std::vector<std::string>({"y2"}));
EXPECT_EQ(grad_op2->Input("Out"), std::vector<std::string>({"out2"}));
EXPECT_EQ(grad_op2->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out2")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *grad_op3 = block->AllOps()[4];
EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out3")}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out2")}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b3")}));
EXPECT_EQ(var_to_grad.size(), 7UL);
EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 6));
EXPECT_EQ(var_to_grad.at("b1"), f::GradVarInfo(f::GradVarName("b1"), 0, 6));
EXPECT_EQ(var_to_grad.at("out1"),
f::GradVarInfo(f::GradVarName("out1"), 0, 5));
EXPECT_EQ(var_to_grad.at("y2"), f::GradVarInfo(f::GradVarName("y2"), 0, 5));
EXPECT_EQ(var_to_grad.at("out2"),
f::GradVarInfo(f::GradVarName("out2"), 0, 4));
EXPECT_EQ(var_to_grad.at("b3"), f::GradVarInfo(f::GradVarName("b3"), 0, 4));
EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("b1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("out1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("y2")));
EXPECT_TRUE(block->HasVar(f::GradVarName("out2")));
EXPECT_TRUE(block->HasVar(f::GradVarName("b3")));
}
TEST(Backward, intermedia_var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"x2"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out2"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
f::OpDescBind *op4 = block->AppendOp();
op4->SetType("mul");
op4->SetInput("X", {"out1"});
op4->SetInput("Y", {"out3"});
op4->SetOutput("Out", {"out4"});
auto target = f::VarDescBind("out4");
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"out3"});
ASSERT_EQ(block->AllOps().size(), 7UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op1 = block->AllOps()[6];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op4 = block->AllOps()[5];
EXPECT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op4->Input("X"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op4->Input("Y"), std::vector<std::string>({"out3"}));
EXPECT_EQ(grad_op4->Input("Out"), std::vector<std::string>({"out4"}));
EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out4")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), std::vector<std::string>());
EXPECT_EQ(var_to_grad.size(), 4UL);
EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 6));
EXPECT_EQ(var_to_grad.at("b1"), f::GradVarInfo(f::GradVarName("b1"), 0, 6));
EXPECT_EQ(var_to_grad.at("out1"),
f::GradVarInfo(f::GradVarName("out1"), 0, 5));
EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("b1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("out1")));
}
TEST(Backward, var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("mult_in_out");
op1->SetInput("X", {"x1"});
op1->SetInput("H", {"h1"});
op1->SetOutput("Y", {"y1"});
op1->SetOutput("Z", {"z1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mult_in_out");
op2->SetInput("X", {"y1"});
op2->SetInput("H", {"z1"});
op2->SetOutput("Y", {"y2"});
op2->SetOutput("Z", {"z2"});
auto target = f::VarDescBind("z2");
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"z1"});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op2 = block->AllOps()[3];
ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 6UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op2->Input("X"), std::vector<std::string>({"y1"}));
EXPECT_EQ(grad_op2->Input("H"), std::vector<std::string>({"z1"}));
EXPECT_EQ(grad_op2->Input("Y"), std::vector<std::string>({"y2"}));
EXPECT_EQ(grad_op2->Input("Z"), std::vector<std::string>({"z2"}));
EXPECT_EQ(grad_op2->Input(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
EXPECT_EQ(grad_op2->Input(f::GradVarName("Z")),
std::vector<std::string>({f::GradVarName("z2")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("y1")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), std::vector<std::string>());
f::OpDescBind *fill_zero_op = block->AllOps()[4];
ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like");
ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL);
ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL);
EXPECT_EQ(fill_zero_op->Input("X"), std::vector<std::string>({"z1"}));
EXPECT_EQ(fill_zero_op->Output("Y"),
std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
f::OpDescBind *grad_op1 = block->AllOps()[5];
ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 6UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input("X"), std::vector<std::string>({"x1"}));
EXPECT_EQ(grad_op1->Input("H"), std::vector<std::string>({"h1"}));
EXPECT_EQ(grad_op1->Input("Y"), std::vector<std::string>({"y1"}));
EXPECT_EQ(grad_op1->Input("Z"), std::vector<std::string>({"z1"}));
EXPECT_EQ(grad_op1->Input(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y1")}));
EXPECT_EQ(grad_op1->Input(f::GradVarName("Z")),
std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("H")),
std::vector<std::string>({f::GradVarName("h1")}));
EXPECT_EQ(var_to_grad.size(), 4UL);
EXPECT_EQ(var_to_grad.at("y1"), f::GradVarInfo(f::GradVarName("y1"), 0, 3));
EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 5));
EXPECT_EQ(var_to_grad.at("h1"), f::GradVarInfo(f::GradVarName("h1"), 0, 5));
EXPECT_TRUE(block->HasVar(f::GradVarName("y1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("h1")));
}
TEST(Backward, shared_var) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"out1"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out1"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 8UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op3 = block->AllOps()[4];
ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out3")}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1") + "@RENAME@0"}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b3")}));
f::OpDescBind *grad_op4 = block->AllOps()[5];
ASSERT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op4->Input("X"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op4->Input("Y"), std::vector<std::string>({"y2"}));
EXPECT_EQ(grad_op4->Input("Out"), std::vector<std::string>({"out2"}));
EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out2")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1") + "@RENAME@1"}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *sum_op = block->AllOps()[6];
ASSERT_EQ(sum_op->Type(), "sum");
ASSERT_EQ(sum_op->InputNames().size(), 1UL);
ASSERT_EQ(sum_op->OutputNames().size(), 1UL);
EXPECT_EQ(sum_op->Input("X"),
std::vector<std::string>({f::GradVarName("out1") + "@RENAME@0",
f::GradVarName("out1") + "@RENAME@1"}));
EXPECT_EQ(sum_op->Output("Out"),
std::vector<std::string>({f::GradVarName("out1")}));
f::OpDescBind *grad_op1 = block->AllOps()[7];
ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
EXPECT_EQ(var_to_grad.size(), 6UL);
EXPECT_EQ(var_to_grad.at("b3"), f::GradVarInfo(f::GradVarName("b3"), 0, 4));
EXPECT_EQ(var_to_grad.at("y2"), f::GradVarInfo(f::GradVarName("y2"), 0, 5));
EXPECT_EQ(var_to_grad.at("out1"),
f::GradVarInfo(f::GradVarName("out1"), 0, 6));
EXPECT_EQ(var_to_grad.at("x1"), f::GradVarInfo(f::GradVarName("x1"), 0, 7));
EXPECT_EQ(var_to_grad.at("b1"), f::GradVarInfo(f::GradVarName("b1"), 0, 7));
EXPECT_TRUE(block->HasVar(f::GradVarName("b3")));
EXPECT_TRUE(block->HasVar(f::GradVarName("y2")));
EXPECT_TRUE(block->HasVar(f::GradVarName("out1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("x1")));
EXPECT_TRUE(block->HasVar(f::GradVarName("b1")));
}
TEST(Backward, half_backward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
auto *op1 = block->AppendOp();
op1->SetType("minus");
op1->SetInput("X", {"a"});
op1->SetInput("Y", {"b"});
op1->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"b"});
f::OpDescBind *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
auto ops = block->AllOps();
ASSERT_EQ(3UL, ops.size());
EXPECT_EQ(var_to_grad.size(), 2UL);
EXPECT_EQ(var_to_grad.at("a"),
f::GradVarInfo(f::GradVarName("a"), 0, forward_len + 1));
}
Reference in new issue View Git Blame Copy Permalink