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提交 7202f425 编写于 作者: Q qingqing01 提交者： GitHub
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Merge branch 'refactorize_framework_proto' into feature/refactorize_framework_proto

上级 030f4302 36709d05
隐藏空白更改
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Showing with 264 addition and 478 deletion
paddle/framework/grad_op_builder.cc
浏览文件 @ 7202f425
......@@ -18,59 +18,32 @@ permissions and limitations under the License. */
namespace paddle {
namespace framework {
/**
class OpRegistry;
using VarIndexMap = std::unordered_map<std::string, int>;
enum class OpArgType { IN, OUT };
static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
}
static const std::vector<int>* GetOpFormat(const OperatorBase* op,
const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
}
static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
const OpArgType& src_type, const OpArgType& dst_type,
int& idx, bool is_grad) {
const std::vector<std::string>& src_inout =
bool is_grad) {
const auto& src_inout =
src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
const std::vector<int>* src_format = GetOpFormat(src_op, src_type);
std::vector<std::string>& dst_inout =
auto& dst_inout =
dst_type == OpArgType::IN ? dst_op->inputs_ : dst_op->outputs_;
std::vector<int>* dst_format = GetOpFormat(dst_op, dst_type);
const OpProto& proto = OpRegistry::protos().at(src_op->type_);
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
std::string src_name = arg.name();
std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
(*dst_op->in_out_idxs_)[dst_name] = idx++;
int src_arg_idx = src_op->in_out_idxs_->at(src_name);
int src_begin =
src_format == nullptr ? src_arg_idx : src_format->at(src_arg_idx);
int src_end = src_format == nullptr ? src_arg_idx + 1
: src_format->at(src_arg_idx + 1);
for (int i = src_begin; i < src_end; ++i) {
std::string s =
is_grad ? src_inout[i] + kGradVarSuffix
: (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
dst_inout.emplace_back(s);
}
if (dst_format != nullptr) {
dst_format->push_back(dst_inout.size());
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
for (auto& var_name : src_inout.at(src_name)) {
std::string s = is_grad ? GradVarName(var_name)
: (arg.no_gradient() ? kEmptyVarName : var_name);
dst_inout[dst_name].emplace_back(s);
}
}
}
......@@ -80,25 +53,12 @@ OperatorBase* BuildGradOp(const OperatorBase* op) {
OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
grad_op->type_ = grad_op_type;
grad_op->attrs_ = op->attrs_;
grad_op->attrs_.erase("input_format");
grad_op->attrs_.erase("output_format");
if (GetOpFormat(op, OpArgType::IN) != nullptr) {
grad_op->attrs_["output_format"] = std::vector<int>({0});
}
if (GetOpFormat(op, OpArgType::IN) != nullptr ||
GetOpFormat(op, OpArgType::OUT) != nullptr) {
grad_op->attrs_["input_format"] = std::vector<int>({0});
}
grad_op->in_out_idxs_.reset(new VarIndexMap());
int in_idx = 0;
int out_idx = 0;
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::IN, in_idx, false); // I
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, false); // G
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, true); // OG
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::OUT, out_idx, true); // IG
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::IN, false); // I
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, false); // O
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, true); // OG
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::OUT, true); // IG
return grad_op;
}
**/
OperatorBase* BuildGradOp(const OperatorBase* op) { return nullptr; }
} // namespace framework
} // namespace paddle
paddle/framework/grad_op_builder_test.cc
浏览文件 @ 7202f425
......@@ -51,14 +51,14 @@ TEST(GradOpBuilder, AddTwo) {
"add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(static_cast<int>(grad_add_op->inputs_.size()), 4);
EXPECT_EQ(static_cast<int>(grad_add_op->outputs_.size()), 2);
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("Out@GRAD"), "out@GRAD");
EXPECT_EQ(grad_add_op->Output("X@GRAD"), "x@GRAD");
EXPECT_EQ(grad_add_op->Output("Y@GRAD"), "y@GRAD");
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);
......@@ -67,17 +67,16 @@ REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 4, 5}},
{"output_format", std::vector<int>{0, 1, 3}}};
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", {"Out2_mult"}}, {"Out2", {"out2_1", "out2_2"}}}, attrs));
"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(), 5UL + 3UL + 3UL);
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"}));
......@@ -91,7 +90,7 @@ TEST(GradOpBuilder, MutiInOut) {
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
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"),
......@@ -101,18 +100,17 @@ TEST(GradOpBuilder, MutiInOut) {
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 3, 5}},
{"output_format", std::vector<int>{0, 2, 3}}};
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"}}}, attrs));
"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(), 5UL + 3UL + 3UL);
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>({f::kEmptyVarName, f::kEmptyVarName}));
......@@ -127,7 +125,7 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
f::GradVarName("out2"));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
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>(
......
paddle/framework/op_registry_test.cc
浏览文件 @ 7202f425
......@@ -131,14 +131,6 @@ TEST(OpRegistry, DefaultValue) {
ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
}
static void SetInputFormat(paddle::framework::OpDesc* desc) {
auto attr = desc->add_attrs();
attr->set_name("input_format");
attr->set_type(paddle::framework::INTS);
attr->mutable_ints()->Add(0);
attr->mutable_ints()->Add(1);
}
TEST(OpRegistry, CustomChecker) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("my_test_op");
......@@ -149,7 +141,6 @@ TEST(OpRegistry, CustomChecker) {
auto output = op_desc.add_outputs();
output->set_parameter("output");
*output->mutable_arguments()->Add() = "oo";
SetInputFormat(&op_desc);
// attr 'test_attr' is not set
bool caught = false;
......@@ -189,7 +180,6 @@ TEST(OpRegistry, CustomChecker) {
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_i(4);
SetInputFormat(&op_desc);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::platform::CPUDeviceContext dev_ctx;
paddle::framework::Scope scope;
......
paddle/framework/operator_test.cc
浏览文件 @ 7202f425
......@@ -185,11 +185,11 @@ TEST(OpKernel, all) {
op_desc.set_type("op_with_kernel");
auto* ipt = op_desc.mutable_inputs()->Add();
*ipt->mutable_arguments()->Add() = "IN1";
ipt->set_parameter("input");
ipt->set_parameter("x");
auto* output = op_desc.mutable_outputs()->Add();
*output->mutable_arguments()->Add() = "OUT1";
output->set_parameter("output");
output->set_parameter("y");
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
......@@ -234,21 +234,6 @@ TEST(OpKernel, multi_inputs) {
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(3.14);
auto attr0 = op_desc.mutable_attrs()->Add();
attr0->set_name("input_format");
attr0->set_type(paddle::framework::AttrType::INTS);
auto input_format = attr0->mutable_ints();
input_format->Add(0); // x0
input_format->Add(3); // k
input_format->Add(4); // end
auto attr1 = op_desc.mutable_attrs()->Add();
attr1->set_name("output_format");
attr1->set_type(paddle::framework::AttrType::INTS);
auto output_format = attr1->mutable_ints();
output_format->Add(0); // y0
output_format->Add(2); // y1
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
scope.NewVar("x0")->GetMutable<Tensor>();
......
paddle/operators/recurrent_op_test.cc
浏览文件 @ 7202f425
......@@ -22,382 +22,233 @@
#include "paddle/framework/tensor.h"
#include "paddle/operators/net_op.h"
TEST(rnn, bad) { ASSERT_TRUE(false); }
namespace paddle {
namespace operators {
// namespace paddle {
// namespace operators {
//
using namespace paddle::framework;
// using framework::make_ddim;
// using framework::DDim;
//
// class RecurrentOpTest : public ::testing::Test {
// protected:
// virtual void SetUp() override {
// CreateGlobalVariables();
// CreateStepNet();
// CreateRNNOp();
// }
//
// virtual void TearDown() override {}
//
// void CreateGlobalVariables() {
// // create input, and init content
// LOG(INFO) << "create global variable x";
// for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
// Variable* x = scope_.NewVar(inlink);
// DDim dims = make_ddim(std::vector<int>{
// 10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
// x->GetMutable<Tensor>()->mutable_data<float>(dims,
// platform::CPUPlace());
// }
// // create output alias just for test
// for (auto inlink : std::vector<std::string>{"h@alias"}) {
// Variable* x = scope_.NewVar(inlink);
// DDim dims =
// make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
// x->GetMutable<Tensor>()->mutable_data<float>(dims,
// platform::CPUPlace());
// }
//
// LOG(INFO) << "create global variable w";
// Variable* w = scope_.NewVar("rnn/w");
// w->GetMutable<Tensor>()->mutable_data<float>(
// make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
//
// for (auto boot : std::vector<std::string>{"h_boot"}) {
// LOG(INFO) << "create global variable " << boot;
// Variable* h_boot = scope_.NewVar(boot);
// h_boot->GetMutable<Tensor>()->mutable_data<float>(
// make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
// platform::CPUPlace());
// }
//
// LOG(INFO) << "create variable step_scopes";
// scope_.NewVar("step_scopes");
//
// LOG(INFO) << "create variable h";
// scope_.NewVar("h");
// }
//
// void CreateRNNOp() {
// framework::OpDesc op_desc;
//
// op_desc.set_type("recurrent_op");
// // inlinks 0
// op_desc.add_inputs("x");
// op_desc.add_inputs("x0");
// op_desc.add_inputs("x1");
// // boot_memories 3
// op_desc.add_inputs("h_boot");
// // step net 5
// op_desc.add_inputs("step_net");
// // outlinks 6
// op_desc.add_outputs("h");
// // step scopes 7
// op_desc.add_outputs("step_scopes");
//
// auto _input_format = std::vector<int>{
// 0, // in_link
// 3, // memories
// 4 // step_net
// };
// auto input_format = op_desc.add_attrs();
// input_format->set_name("input_format");
// input_format->set_type(paddle::framework::AttrType::INTS);
// for (auto i : _input_format) {
// input_format->add_ints(i);
// }
//
// auto output_format = op_desc.add_attrs();
// output_format->set_name("output_format");
// output_format->set_type(paddle::framework::AttrType::INTS);
// for (auto i : std::vector<int>{0, 1, 2}) {
// output_format->add_ints(i);
// }
//
// auto inlink_alias = op_desc.add_attrs();
// inlink_alias->set_name("inlink_alias");
// inlink_alias->set_type(paddle::framework::AttrType::STRINGS);
//
// auto outlink_alias = op_desc.add_attrs();
// outlink_alias->set_name("outlink_alias");
// outlink_alias->set_type(paddle::framework::AttrType::STRINGS);
//
// auto pre_memories = op_desc.add_attrs();
// pre_memories->set_name("pre_memories");
// pre_memories->set_type(paddle::framework::AttrType::STRINGS);
//
// auto memories = op_desc.add_attrs();
// memories->set_name("memories");
// memories->set_type(paddle::framework::AttrType::STRINGS);
//
// // create inlink_alias
// for (const auto& item :
// std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
// inlink_alias->add_strings(item);
// }
// // pre memories
// for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
// pre_memories->add_strings(item);
// }
// // memories
// for (const auto& item : std::vector<std::string>{"rnn/h"}) {
// memories->add_strings(item);
// }
// // output alias
// for (const auto& item : std::vector<std::string>{"h@alias"}) {
// outlink_alias->add_strings(item);
// }
//
// rnn_op_ = OpRegistry::CreateOp(op_desc);
//
// LOG(INFO) << "rnn_op finish init";
// }
//
// void CreateStepNet() {
// LOG(INFO) << "create variable step_net";
// Variable* var = scope_.NewVar("step_net");
// auto net = var->GetMutable<NetOp>();
// net->AddOp(
// OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
//
// net->AddOp(
// OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
// net->CompleteAddOp();
// }
//
// // father scope
// Scope scope_;
// std::shared_ptr<OperatorBase> rnn_op_;
//};
//
// TEST_F(RecurrentOpTest, Run) {
// platform::CPUDeviceContext ctx;
// rnn_op_->InferShape(scope_);
// rnn_op_->Run(scope_, ctx);
//}
//
// class RecurrentGradientAlgorithmTest : public ::testing::Test {
// protected:
// virtual void SetUp() override {
// CreateGlobalVariables();
// CreateStepScopes();
// CreateStepNet();
// CreateRNNGradientAlgorithm();
//
// // segment inputs
// SegmentInputs();
// // link forward memories
// LinkeMemories();
// }
//
// virtual void TearDown() override {}
//
// void CreateGlobalVariables() {
// // inputs: x
// LOG(INFO) << "create global variable x";
// Variable* x = scope_.NewVar("x");
// DDim dims =
// make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
// x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
// // inputs: h_boot
// LOG(INFO) << "create global variable h_boot";
// Variable* h_boot = scope_.NewVar("h_boot");
// h_boot->GetMutable<Tensor>()->mutable_data<float>(
// make_ddim({20 /*batch size*/, 30 /*input dim*/}),
// platform::CPUPlace());
// // inputs: w
// LOG(INFO) << "create global variable w";
// Variable* w = scope_.NewVar("rnn/w");
// w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
// platform::CPUPlace());
// // inputs: h_grad
// LOG(INFO) << "create variable h_grad";
// Variable* dh = scope_.NewVar("h_grad");
// dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
// platform::CPUPlace());
// // inputs: step_scopes
// LOG(INFO) << "create variable step_scopes";
// scope_.NewVar("step_scopes");
// // inputs: step_net
// LOG(INFO) << "create variable step_net";
// scope_.NewVar("step_net");
// // outputs: w_grad
// LOG(INFO) << "create global variable w_grad";
// scope_.NewVar("rnn/w_grad");
// // outputs: x_grad
// LOG(INFO) << "create global variable x_grad";
// scope_.NewVar("x_grad");
// // outputs: h_boot_grad
// LOG(INFO) << "create global variable h_boot_grad";
// scope_.NewVar("h_boot_grad");
// }
//
// void CreateStepScopes() {
// auto step_scopes =
// scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
// for (int i = 0; i < 10; ++i) {
// auto& scope = scope_.NewScope();
// auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
// pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
// auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
// tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
//
// // for unit test of ConcatOutputs
// auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
// xg->mutable_data<float>({20, 30}, platform::CPUPlace());
//
// step_scopes->emplace_back(&scope);
// }
//
// // last time step
// auto g =
// (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
// g->mutable_data<float>({20, 30}, platform::CPUPlace());
// }
//
// void CreateRNNGradientAlgorithm() {
// std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
// arg->step_net = "step_net";
// arg->step_scopes = "step_scopes";
// rnn::Link inlink;
// inlink.external = "h_grad";
// inlink.internal = "rnn/h_grad";
// arg->inlinks = std::vector<rnn::Link>{inlink};
//
// rnn::Link outlink;
// outlink.external = "x_grad";
// outlink.internal = "rnn/x_grad";
// arg->outlinks = std::vector<rnn::Link>{outlink};
//
// rnn::MemoryAttr mem_attr;
// mem_attr.pre_var = "rnn/h_pre_grad";
// mem_attr.var = "rnn/h_grad";
// mem_attr.boot_var = "h_boot_grad";
// arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
//
// rnn_grad_algo_.Init(std::move(arg));
// }
//
// void CreateStepNet() {
// LOG(INFO) << "create variable step_net";
// Variable* var = scope_.NewVar("step_net");
// auto net = var->GetMutable<NetOp>();
// net->AddOp(OpRegistry::CreateOp("mul", {"rnn/h_pre", "rnn/w",
// "rnn/s_grad"},
// {"rnn/h_pre_grad", "rnn/w_grad"}, {}));
//
// net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
// {"rnn/x_grad", "rnn/s_grad"}, {}));
// net->CompleteAddOp();
// }
//
// void SegmentInputs() {
// LOG(INFO) << "segment inputs";
// std::vector<std::string> inlinks = {"x"};
// std::vector<std::string> inlinks_alias = {"rnn/x"};
//
// rnn::Link inlink;
// inlink.external = "x";
// inlink.internal = "rnn/x";
// auto step_scopes =
// scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
// rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
// true /*infer_shape_mode*/);
// }
//
// void LinkeMemories() {
// LOG(INFO) << "link memories";
// rnn::MemoryAttr mem_attr;
// mem_attr.pre_var = "rnn/h_pre";
// mem_attr.var = "rnn/h";
// mem_attr.boot_var = "boot_h";
// std::vector<rnn::MemoryAttr> memories;
// memories.push_back(mem_attr);
// auto step_scopes =
// scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
// for (int i = 1; i < 10; ++i) {
// rnn::LinkMemories(*step_scopes, memories, i, -1,
// true /*infer_shape_mode*/);
// }
// }
//
// Scope scope_;
// RecurrentGradientAlgorithm rnn_grad_algo_;
//};
//
//// TEST_F(RecurrentGradientAlgorithmTest, Run) {
//// platform::CPUDeviceContext ctx;
//// rnn_grad_algo_.Run(scope_, ctx);
//// }
//
//} // namespace operators
//} // namespace paddle
//
// TEST(RecurrentOp, LinkMemories) {
// using namespace paddle::framework;
// using namespace paddle::platform;
// using namespace paddle::operators;
//
// // create and init step scopes
// size_t len = 10;
// std::vector<Scope*> step_scopes;
// for (size_t i = 0; i < len; ++i) {
// auto scope = new Scope();
// scope->NewVar("pre_h");
// auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
// float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
// for (size_t j = 0; j < 15 * 20; ++j) {
// data[j] = rand() * (1. / (double)RAND_MAX);
// }
// step_scopes.push_back(scope);
// }
//
// // create MemoryAttr
// rnn::MemoryAttr mem_attr;
// mem_attr.pre_var = "pre_h";
// mem_attr.var = "h";
// mem_attr.boot_var = "boot_h";
// std::vector<rnn::MemoryAttr> memories;
// memories.push_back(mem_attr);
//
// for (size_t i = 1; i < len; ++i) {
// rnn::LinkMemories(step_scopes, memories, i, -1, false
// /*infer_shape_mode*/);
// }
// // check
// for (size_t i = 0; i < len - 1; ++i) {
// const float* a =
// step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
// const float* b = step_scopes[i + 1]
// ->FindVar("pre_h")
// ->GetMutable<Tensor>()
// ->data<float>();
// for (size_t j = 0; j < 15 * 20; ++j) {
// ASSERT_FLOAT_EQ(a[j], b[j]);
// }
// }
//
// for (int i = len - 2; i >= 0; --i) {
// rnn::LinkMemories(step_scopes, memories, i, 1, false
// /*infer_shape_mode*/);
// }
// // check
// for (int i = len - 2; i >= 0; --i) {
// const float* a =
// step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
// const float* b =
// step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
// for (size_t j = 0; j < 15 * 20; ++j) {
// ASSERT_FLOAT_EQ(a[j], b[j]);
// }
// }
//
// for (auto s : step_scopes) {
// delete s;
// }
//}
//
// USE_OP(add_two);
// USE_OP(mul);
// USE_OP_WITHOUT_KERNEL(recurrent_op);
class RecurrentGradientAlgorithmTest : public ::testing::Test {
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepScopes();
CreateStepNet();
CreateRNNGradientAlgorithm();
// segment inputs
SegmentInputs();
// link forward memories
LinkeMemories();
}
virtual void TearDown() override {}
void CreateGlobalVariables() {
// inputs: x
LOG(INFO) << "create global variable x";
Variable* x = scope_.NewVar("x");
DDim dims =
make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
// inputs: h_boot
LOG(INFO) << "create global variable h_boot";
Variable* h_boot = scope_.NewVar("h_boot");
h_boot->GetMutable<Tensor>()->mutable_data<float>(
make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
// inputs: w
LOG(INFO) << "create global variable w";
Variable* w = scope_.NewVar("rnn/w");
w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
platform::CPUPlace());
// inputs: h_grad
LOG(INFO) << "create variable h_grad";
Variable* dh = scope_.NewVar("h_grad");
dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
platform::CPUPlace());
// inputs: step_scopes
LOG(INFO) << "create variable step_scopes";
scope_.NewVar("step_scopes");
// inputs: step_net
LOG(INFO) << "create variable step_net";
scope_.NewVar("step_net");
// outputs: w_grad
LOG(INFO) << "create global variable w_grad";
scope_.NewVar("rnn/w_grad");
// outputs: x_grad
LOG(INFO) << "create global variable x_grad";
scope_.NewVar("x_grad");
// outputs: h_boot_grad
LOG(INFO) << "create global variable h_boot_grad";
scope_.NewVar("h_boot_grad");
}
void CreateStepScopes() {
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 0; i < 10; ++i) {
auto& scope = scope_.NewScope();
auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
// for unit test of ConcatOutputs
auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
xg->mutable_data<float>({20, 30}, platform::CPUPlace());
step_scopes->emplace_back(&scope);
}
// last time step
auto g = (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
g->mutable_data<float>({20, 30}, platform::CPUPlace());
}
void CreateRNNGradientAlgorithm() {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
arg->step_net = "step_net";
arg->step_scopes = "step_scopes";
rnn::Link inlink;
inlink.external = "h_grad";
inlink.internal = "rnn/h_grad";
arg->inlinks = std::vector<rnn::Link>{inlink};
rnn::Link outlink;
outlink.external = "x_grad";
outlink.internal = "rnn/x_grad";
arg->outlinks = std::vector<rnn::Link>{outlink};
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre_grad";
mem_attr.var = "rnn/h_grad";
mem_attr.boot_var = "h_boot_grad";
arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
rnn_grad_algo_.Init(std::move(arg));
}
void CreateStepNet() {
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
// TODO(qingqing) modify backward op create for RNNOp unit test
// and the unit test will be removed to Python.
// net->AddOp(OpRegistry::CreateOp("mul", {"X", {"rnn/h_pre", "rnn/w",
// "rnn/s_grad"}}, {"Y", {"rnn/h_pre_grad", "rnn/w_grad"}}, {}));
// net->AddOp(OpRegistry::CreateOp("add_two", {"X", {"rnn/h_grad"}},
// {"Y", {"rnn/x_grad"}}, {"Out", "rnn/s_grad"}}, {}));
net->CompleteAddOp();
}
void SegmentInputs() {
LOG(INFO) << "segment inputs";
std::vector<std::string> inlinks = {"x"};
std::vector<std::string> inlinks_alias = {"rnn/x"};
rnn::Link inlink;
inlink.external = "x";
inlink.internal = "rnn/x";
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
true /*infer_shape_mode*/);
}
void LinkeMemories() {
LOG(INFO) << "link memories";
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre";
mem_attr.var = "rnn/h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 1; i < 10; ++i) {
rnn::LinkMemories(*step_scopes, memories, i, -1,
true /*infer_shape_mode*/);
}
}
Scope scope_;
RecurrentGradientAlgorithm rnn_grad_algo_;
};
// TEST_F(RecurrentGradientAlgorithmTest, Run) {
// platform::CPUDeviceContext ctx;
// rnn_grad_algo_.Run(scope_, ctx);
// }
} // namespace operators
} // namespace paddle
TEST(RecurrentOp, LinkMemories) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators;
// create and init step scopes
size_t len = 10;
std::vector<Scope*> step_scopes;
for (size_t i = 0; i < len; ++i) {
auto scope = new Scope();
scope->NewVar("pre_h");
auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
for (size_t j = 0; j < 15 * 20; ++j) {
data[j] = rand() * (1. / (double)RAND_MAX);
}
step_scopes.push_back(scope);
}
// create MemoryAttr
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "pre_h";
mem_attr.var = "h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
for (size_t i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1, false
/*infer_shape_mode*/);
}
// check
for (size_t i = 0; i < len - 1; ++i) {
const float* a =
step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
const float* b = step_scopes[i + 1]
->FindVar("pre_h")
->GetMutable<Tensor>()
->data<float>();
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (int i = len - 2; i >= 0; --i) {
rnn::LinkMemories(step_scopes, memories, i, 1, false
/*infer_shape_mode*/);
}
// check
for (int i = len - 2; i >= 0; --i) {
const float* a =
step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
const float* b =
step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (auto s : step_scopes) {
delete s;
}
}
USE_OP(add_two);
USE_OP(mul);
USE_OP_WITHOUT_KERNEL(recurrent_op);
python/paddle/v2/framework/tests/test_operator.py
浏览文件 @ 7202f425
......@@ -74,6 +74,7 @@ class TestOpDescCreationMethod(unittest.TestCase):
expected1.inputs.extend(['x', 'w', 'b'])
expected1.outputs.extend(['y'])
expected1.type = 'fc'
# the input_format can be removed after testing
attr = expected1.attrs.add()
attr.name = 'input_format'
attr.type = attribute_pb2.INTS
......@@ -86,6 +87,7 @@ class TestOpDescCreationMethod(unittest.TestCase):
expected2.inputs.extend(['x1', 'x2', 'x3', 'w1', 'w2', 'w3', 'b'])
expected2.outputs.extend(['y'])
expected2.type = 'fc'
# the input_format can be removed after testing
attr = expected2.attrs.add()
attr.name = 'input_format'
attr.type = attribute_pb2.INTS
......
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