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Merge pull request #3124 from qingqing01/rnn_infershape

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Refine InferShape for recurrent_network_op
cblas_new
qingqing01 8 years ago committed by GitHub
parent 0478780c5c 7c49a4f31c
commit 017182c6b1
5 changed files with 131 additions and 164 deletions
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9
paddle/operators/CMakeLists.txt
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@ -60,10 +60,5 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(fc_op
SRCS fc_op.cc
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net)
op_library(recurrent_network_op
SRCS recurrent_network_op.cc
DEPS op_desc tensor net)
cc_test(recurrent_network_op_test
SRCS recurrent_network_op_test.cc
DEPS recurrent_network_op mul_op add_op)
op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net)
cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)

182
paddle/operators/recurrent_network_op.cc → paddle/operators/recurrent_op.cc
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15
paddle/operators/recurrent_network_op.h → paddle/operators/recurrent_op.h
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@ -72,19 +72,22 @@ struct ArgumentName {
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len);
const size_t seq_len,
bool infer_shape_mode);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len);
const size_t seq_len,
bool infer_shape_mode);
void LinkMemories(const std::vector<Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories,
size_t step_id,
int offset);
const size_t step_id,
const int offset,
bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg);
@ -122,7 +125,7 @@ protected:
return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
}
void InitMemories(Scope* step_scopes) const;
void InitMemories(Scope* step_scopes, bool infer_shape_mode) const;
private:
std::unique_ptr<rnn::Argument> arg_;
@ -145,7 +148,7 @@ public:
void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
void LinkBootMemoryGradients(Scope* step_scopes) const;
void LinkBootMemoryGradients(Scope* step_scopes, bool infer_shape_mode) const;
/**
* InferShape must be called before Run.

53
paddle/operators/recurrent_network_op_test.cc → paddle/operators/recurrent_op_test.cc
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@ -18,7 +18,7 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/recurrent_network_op.h"
#include "paddle/operators/recurrent_op.h"
namespace paddle {
namespace operators {
@ -55,7 +55,7 @@ protected:
w->GetMutable<Tensor>()->mutable_data<float>(
make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
for (auto boot : std::vector<std::string>{"x_boot", "h_boot"}) {
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>(
@ -79,7 +79,6 @@ protected:
op_desc.add_inputs("x0");
op_desc.add_inputs("x1");
// boot_memories 3
op_desc.add_inputs("x_boot");
op_desc.add_inputs("h_boot");
// step net 5
op_desc.add_inputs("step_net");
@ -91,7 +90,7 @@ protected:
auto _input_format = std::vector<int>{
0, // in_link
3, // memories
5 // step_net
4 // step_net
};
auto input_format = op_desc.add_attrs();
input_format->set_name("input_format");
@ -129,12 +128,11 @@ protected:
inlink_alias->add_strings(item);
}
// pre memories
for (const auto& item :
std::vector<std::string>{"rnn/x@pre", "rnn/h@pre"}) {
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/x", "rnn/h"}) {
for (const auto& item : std::vector<std::string>{"rnn/h"}) {
memories->add_strings(item);
}
// output alias
@ -151,14 +149,11 @@ protected:
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
// rnn/s is net's input or output?
net->inputs_ = {"rnn/h@pre", "rnn/w", "rnn/x"};
net->inputs_ = {"rnn/s", "rnn/h"};
net->AddOp(
OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
net->AddOp(
OpRegistry::CreateOp("add_two", {"rnn/x", "rnn/s"}, {"rnn/h"}, {}));
OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
net->CompleteAddOp();
}
@ -297,7 +292,10 @@ protected:
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);
rnn::SegmentInputs(*step_scopes,
std::vector<rnn::Link>{inlink},
10,
true /*infer_shape_mode*/);
}
void LinkeMemories() {
@ -311,7 +309,8 @@ protected:
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);
rnn::LinkMemories(
*step_scopes, memories, i, -1, true /*infer_shape_mode*/);
}
}
@ -333,14 +332,14 @@ TEST(RecurrentOp, LinkMemories) {
using namespace paddle::operators;
// create and init step scopes
int len = 10;
size_t len = 10;
std::vector<Scope*> step_scopes;
for (int i = 0; i < len; ++i) {
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 (int j = 0; j < 15 * 20; ++j) {
for (size_t j = 0; j < 15 * 20; ++j) {
data[j] = rand() * (1. / (double)RAND_MAX);
}
step_scopes.push_back(scope);
@ -354,24 +353,24 @@ TEST(RecurrentOp, LinkMemories) {
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
for (int i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1);
for (size_t i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
}
// check
for (int i = 0; i < len - 1; ++i) {
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 i = 0; i < 15 * 20; ++i) {
ASSERT_FLOAT_EQ(a[i], b[i]);
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);
rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
}
// check
for (int i = len - 2; i >= 0; --i) {
@ -379,8 +378,8 @@ TEST(RecurrentOp, LinkMemories) {
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 i = 0; i < 15 * 20; ++i) {
ASSERT_FLOAT_EQ(a[i], b[i]);
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
@ -391,9 +390,3 @@ TEST(RecurrentOp, LinkMemories) {
USE_OP(add_two);
USE_OP(mul);
// int main() {
// //! TODO(yuyang18): Temporary disable this unit-test because implementation
// //! error.
// return 0;
//}

2
paddle/pybind/CMakeLists.txt
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@ -6,4 +6,4 @@ cc_library(paddle_pybind SHARED
add_op
mean_op
cross_entropy_op
recurrent_network_op)
recurrent_op)

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