提交 18cf0786 编写于 作者: Y Yi Wang 提交者: GitHub

Merge pull request #3300 from dzhwinter/type_alias

"remove type alias"
......@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/framework/backward.h"
#include <list>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
......
......@@ -17,16 +17,21 @@
#include <gtest/gtest.h>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/type_alias.h"
namespace paddle {
namespace framework {
using OperatorBase = framework::OperatorBase;
using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
using OpProto = framework::OpProto;
using OpAttrChecker = framework::OpAttrChecker;
using Scope = framework::Scope;
using DeviceContext = platform::DeviceContext;
class EmptyOp : public OperatorBase {
public:
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope,
const platform::DeviceContext &dev_ctx) const override {}
void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
};
class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
......@@ -71,7 +76,7 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
}
};
class FcOp : public ops::NetOp {
class FcOp : public operators::NetOp {
public:
void Init() override {
AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
......@@ -143,6 +148,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
} // namespace paddle
namespace f = paddle::framework;
namespace ops = paddle::operators;
using EnforceNotMet = paddle::platform::EnforceNotMet;
REGISTER_OP(rowwise_add, f::EmptyOp, f::RowWiseAddOpMaker);
REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, f::EmptyOp);
......
......@@ -18,11 +18,8 @@ limitations under the License. */
#include "paddle/framework/backward.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor_py.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/type_alias.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "pybind11/numpy.h"
......@@ -45,6 +42,9 @@ USE_OP_WITHOUT_KERNEL(recurrent_op);
USE_OP(uniform_random);
namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
template <typename ClassType>
void ExposeOperator(ClassType &m) {
m.def("infer_shape", &ClassType::type::InferShape)
......@@ -150,8 +150,8 @@ All parameter, weight, gradient are variables in Paddle.
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
.def("get_net",
[](Variable &self) -> ops::NetOp * {
return self.GetMutable<ops::NetOp>();
[](Variable &self) -> operators::NetOp * {
return self.GetMutable<operators::NetOp>();
},
py::return_value_policy::reference);
......@@ -230,23 +230,24 @@ All parameter, weight, gradient are variables in Paddle.
ExposeOperator(operator_base);
py::class_<ops::NetOp, std::shared_ptr<ops::NetOp>> net(m, "Net");
py::class_<operators::NetOp, std::shared_ptr<operators::NetOp>> net(m, "Net");
net.def_static("create",
[]() -> std::shared_ptr<ops::NetOp> {
auto retv = std::make_shared<ops::NetOp>();
[]() -> std::shared_ptr<operators::NetOp> {
auto retv = std::make_shared<operators::NetOp>();
retv->type_ = "plain_net";
return retv;
})
.def("add_op", &ops::NetOp::AddOp)
.def(
"add_op",
[](ops::NetOp &self, const std::shared_ptr<ops::NetOp> &net) -> void {
self.AddOp(std::static_pointer_cast<OperatorBase>(net));
})
.def("complete_add_op", &ops::NetOp::CompleteAddOp)
.def("complete_add_op",
[](std::shared_ptr<ops::NetOp> &self) { self->CompleteAddOp(); });
.def("add_op", &operators::NetOp::AddOp)
.def("add_op",
[](operators::NetOp &self,
const std::shared_ptr<operators::NetOp> &net) -> void {
self.AddOp(std::static_pointer_cast<OperatorBase>(net));
})
.def("complete_add_op", &operators::NetOp::CompleteAddOp)
.def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
self->CompleteAddOp();
});
ExposeOperator(net);
......
......@@ -59,6 +59,7 @@ op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
cc_test(sgd_op_test SRCS sgd_op_test.cc DEPS sgd_op)
op_library(fc_op
SRCS fc_op.cc
......
......@@ -17,9 +17,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
class AddOp : public OperatorWithKernel {
class AddOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1);
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "Inputs of AddOp must all be set");
......@@ -31,9 +31,9 @@ class AddOp : public OperatorWithKernel {
}
};
class AddOpMaker : public OpProtoAndCheckerMaker {
class AddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
AddOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of add op");
AddInput("Y", "The second input of add op");
......@@ -46,14 +46,17 @@ The equation is: Out = X + Y
}
};
class AddOpGrad : public OperatorWithKernel {
class AddOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {}
void InferShape(const framework::InferShapeContext &ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker);
REGISTER_GRADIENT_OP(add_two, add_two_grad, ops::AddOpGrad);
REGISTER_OP_CPU_KERNEL(add_two, ops::AddKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::CPUPlace, float>);
......@@ -16,4 +16,6 @@
#include "paddle/framework/op_registry.h"
#include "paddle/operators/add_op.h"
REGISTER_OP_GPU_KERNEL(add_two, ops::AddKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::GPUPlace, float>);
......@@ -13,15 +13,21 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class AddKernel : public OpKernel {
class AddKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto input0 = context.Input<Tensor>(0);
auto input1 = context.Input<Tensor>(1);
auto output = context.Output<Tensor>(0);
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#include <gtest/gtest.h>
#define private public
#include <paddle/framework/op_registry.h>
#include "paddle/framework/op_registry.h"
USE_OP(add_two);
// USE_OP(add_two_grad);
TEST(AddOp, GetOpProto) {
auto& protos = paddle::framework::OpRegistry::protos();
......
......@@ -17,9 +17,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
class OnehotCrossEntropyOp : public OperatorWithKernel {
class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 2,
"Input size of OnehotCrossEntropyOp must be two");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1,
......@@ -37,9 +37,9 @@ class OnehotCrossEntropyOp : public OperatorWithKernel {
}
};
class OnehotCrossEntropyGradientOp : public OperatorWithKernel {
class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto X = ctx.Input<Tensor>("X");
......@@ -48,9 +48,10 @@ class OnehotCrossEntropyGradientOp : public OperatorWithKernel {
}
};
class OnehotCrossEntropyOpMaker : public OpProtoAndCheckerMaker {
class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
OnehotCrossEntropyOpMaker(OpProto *proto, OpAttrChecker *op_checker)
OnehotCrossEntropyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of OnehotCrossEntropyOp");
AddInput("label", "The second input of OnehotCrossEntropyOp");
......@@ -66,12 +67,14 @@ OnehotCrossEntropy Operator.
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
ops::OnehotCrossEntropyOpMaker);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(onehot_cross_entropy, onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(
onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOpKernel<ops::CPUPlace, float>);
ops::OnehotCrossEntropyGradientOpKernel<paddle::platform::CPUPlace, float>);
......@@ -14,3 +14,8 @@
#define EIGEN_USE_GPU
#include "paddle/operators/cross_entropy_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<paddle::platform::GPUPlace, float>);
......@@ -13,11 +13,13 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
T tolerable_value(T x) {
static_assert(std::is_floating_point<T>::value,
......@@ -38,9 +40,9 @@ T tolerable_value(T x) {
}
template <typename Place, typename T>
class OnehotCrossEntropyOpKernel : public OpKernel {
class OnehotCrossEntropyOpKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& ctx) const override {
void Compute(const framework::ExecutionContext& ctx) const override {
auto X = ctx.Input<Tensor>("X");
const T* Xdata = X->data<T>();
const int* label_data = ctx.Input<Tensor>(1)->data<int>();
......@@ -61,9 +63,9 @@ class OnehotCrossEntropyOpKernel : public OpKernel {
};
template <typename Place, typename T>
class OnehotCrossEntropyGradientOpKernel : public OpKernel {
class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& ctx) const override {
void Compute(const framework::ExecutionContext& ctx) const override {
auto X = ctx.Input<Tensor>("X");
auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
......
......@@ -12,11 +12,16 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "type_alias.h"
#include "paddle/operators/net_op.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using OpRegistry = framework::OpRegistry;
class FullyConnectedOp : public NetOp {
public:
void Init() override {
......@@ -39,9 +44,10 @@ class FullyConnectedOp : public NetOp {
}
};
class FullyConnectedOpMaker : public OpProtoAndCheckerMaker {
class FullyConnectedOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FullyConnectedOpMaker(OpProto *proto, OpAttrChecker *op_checker)
FullyConnectedOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input of fc operator");
AddInput("W", "the weight of fc operator");
......@@ -66,4 +72,5 @@ USE_OP(rowwise_add);
USE_OP(sigmoid);
USE_OP(softmax);
namespace ops = paddle::operators;
REGISTER_OP(fc, ops::FullyConnectedOp, ops::FullyConnectedOpMaker);
......@@ -50,8 +50,8 @@ The output will have the same size with input.
} // namespace operators
} // namespace paddle
REGISTER_OP(fill_zeros_like, paddle::operators::FillZerosLikeOp,
paddle::operators::FillZerosLikeOpMaker);
namespace ops = paddle::operators;
REGISTER_OP(fill_zeros_like, ops::FillZerosLikeOp, ops::FillZerosLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_zeros_like,
paddle::operators::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
......@@ -16,6 +16,7 @@
#include "paddle/framework/op_registry.h"
#include "paddle/operators/fill_zeros_like_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
fill_zeros_like,
paddle::operators::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
......@@ -13,7 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
......
......@@ -17,9 +17,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
class MeanOp : public OperatorWithKernel {
class MeanOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 1, "Input size of AddOp must be one");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of AddOp must be one");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "input should be set");
......@@ -28,9 +28,9 @@ class MeanOp : public OperatorWithKernel {
}
};
class MeanOpMaker : public OpProtoAndCheckerMaker {
class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").IgnoreGradient();
......@@ -38,9 +38,9 @@ class MeanOpMaker : public OpProtoAndCheckerMaker {
}
};
class MeanGradOp : public OperatorWithKernel {
class MeanGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>("X" + framework::kGradVarSuffix)
->Resize(ctx.Input<Tensor>("X")->dims());
}
......@@ -49,7 +49,10 @@ class MeanGradOp : public OperatorWithKernel {
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(mean, mean_grad, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mean_grad,
ops::MeanGradKernel<paddle::platform::CPUPlace, float>);
......@@ -16,5 +16,8 @@
#include "paddle/operators/mean_op.h"
REGISTER_OP_GPU_KERNEL(mean, ops::MeanKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mean_grad,
ops::MeanGradKernel<paddle::platform::GPUPlace, float>);
......@@ -13,15 +13,24 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class MeanKernel : public OpKernel {
class MeanKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
......@@ -36,9 +45,9 @@ class MeanKernel : public OpKernel {
};
template <typename Place, typename T>
class MeanGradKernel : public OpKernel {
class MeanGradKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto OG = context.Input<Tensor>("Out" + framework::kGradVarSuffix);
PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
"Mean Gradient should be scalar");
......
......@@ -17,9 +17,9 @@
namespace paddle {
namespace operators {
class MulOp : public OperatorWithKernel {
class MulOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
auto dim0 = ctx.Input<Tensor>(0)->dims();
auto dim1 = ctx.Input<Tensor>(1)->dims();
......@@ -37,9 +37,9 @@ class MulOp : public OperatorWithKernel {
}
};
class MulOpMaker : public OpProtoAndCheckerMaker {
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of mul op");
AddInput("Y", "The second input of mul op");
......@@ -52,9 +52,9 @@ The equation is: Out = X * Y
}
};
class MulOpGrad : public OperatorWithKernel {
class MulOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {}
void InferShape(const framework::InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "MulGrad";
return "";
......@@ -64,7 +64,8 @@ class MulOpGrad : public OperatorWithKernel {
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker);
REGISTER_GRADIENT_OP(mul, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
......@@ -15,4 +15,6 @@
#define EIGEN_USE_GPU
#include "paddle/operators/mul_op.h"
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
......@@ -13,16 +13,21 @@
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class MulKernel : public OpKernel {
class MulKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
{Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
......@@ -40,5 +45,6 @@ class MulKernel : public OpKernel {
Z.device(place) = X.contract(Y, dim_pair);
}
};
} // namespace operators
} // namespace paddle
......@@ -15,7 +15,6 @@
*/
#include "paddle/operators/net_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
......
......@@ -14,13 +14,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/operators/type_alias.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace operators {
......
......@@ -2,31 +2,27 @@
#include <gtest/gtest.h>
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
using Scope = framework::Scope;
using DeviceContext = platform::DeviceContext;
static int infer_shape_cnt = 0;
static int run_cnt = 0;
class TestOp : public OperatorBase {
class TestOp : public framework::OperatorBase {
public:
void InferShape(const framework::Scope& scope) const override {
++infer_shape_cnt;
}
void Run(const framework::Scope& scope,
const paddle::platform::DeviceContext& dev_ctx) const override {
void InferShape(const Scope& scope) const override { ++infer_shape_cnt; }
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
++run_cnt;
}
};
class EmptyOp : public OperatorBase {
class EmptyOp : public framework::OperatorBase {
public:
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {}
};
template <typename T>
......@@ -72,7 +68,7 @@ TEST(OpKernel, all) {
net->Run(scope, dev_ctx);
ASSERT_EQ(2, infer_shape_cnt);
ASSERT_EQ(2, run_cnt);
ASSERT_THROW(net->AddOp(op2), paddle::platform::EnforceNotMet);
ASSERT_THROW(net->AddOp(op2), platform::EnforceNotMet);
}
TEST(NetOp, insert_op) {
......
......@@ -14,17 +14,19 @@
#include "paddle/operators/recurrent_op.h"
#include <glog/logging.h>
#include <cstring>
#include <sstream>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace operators {
using Scope = framework::Scope;
using Variable = framework::Variable;
using Tensor = framework::Tensor;
void RecurrentAlgorithm::InferShape(const Scope& scope) const {
seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
->GetMutable<Tensor>()
......@@ -135,10 +137,11 @@ void RecurrentOp::Init() {
alg_.Init(std::move(arg));
}
class RecurrentAlgorithmProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
class RecurrentAlgorithmProtoAndCheckerMaker
: public framework::OpProtoAndCheckerMaker {
public:
RecurrentAlgorithmProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
RecurrentAlgorithmProtoAndCheckerMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
const auto& name = RecurrentOp::kArgName;
// inputs and outputs stored in proto
......
......@@ -27,6 +27,10 @@ namespace operators {
using framework::make_ddim;
using framework::DDim;
using framework::Tensor;
using framework::Variable;
using framework::Scope;
using framework::OpRegistry;
class RecurrentOpTest : public ::testing::Test {
protected:
......@@ -164,7 +168,7 @@ class RecurrentOpTest : public ::testing::Test {
// father scope
Scope scope_;
std::shared_ptr<OperatorBase> rnn_op_;
std::shared_ptr<framework::OperatorBase> rnn_op_;
};
TEST_F(RecurrentOpTest, Run) {
......
......@@ -18,7 +18,9 @@ namespace paddle {
namespace operators {
namespace rnn {
namespace fmw = paddle::framework;
namespace f = paddle::framework;
using Tensor = framework::Tensor;
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
......@@ -30,10 +32,10 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
fmw::DDim dims = input->dims();
f::DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
fmw::DDim step_dims = slice_ddim(dims, 1, dims.size());
f::DDim step_dims = slice_ddim(dims, 1, dims.size());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
......@@ -58,11 +60,10 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal);
PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
outlinks[i].internal);
fmw::DDim step_dims =
step_scope_var->template GetMutable<Tensor>()->dims();
f::DDim step_dims = step_scope_var->template GetMutable<Tensor>()->dims();
std::vector<int> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(fmw::make_ddim(dims_vec));
output->Resize(f::make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
......@@ -104,7 +105,7 @@ void LinkMemories(const std::vector<Scope*>& scopes,
}
void InitArgument(const ArgumentName& name, Argument* arg,
const OperatorBase& op) {
const framework::OperatorBase& op) {
arg->step_net = op.Input(name.step_net);
arg->step_scopes = op.Output(name.step_scopes);
......
......@@ -17,12 +17,13 @@
#include <string>
#include "paddle/framework/operator.h"
#include "paddle/operators/type_alias.h"
namespace paddle {
namespace operators {
namespace rnn {
using Scope = framework::Scope;
/**
* Memory of a RNN (same as the role of `Momory` in PaddlePaddle).
*
......@@ -86,7 +87,7 @@ void LinkMemories(const std::vector<Scope*>& step_scopes,
const int offset, bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg,
const OperatorBase& op);
const framework::OperatorBase& op);
} // namespace rnn
} // namespace operators
......
......@@ -13,12 +13,13 @@
limitations under the License. */
#include "paddle/operators/rowwise_add_op.h"
namespace paddle {
namespace operators {
class RowWiseAddOp : public OperatorWithKernel {
class RowWiseAddOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2UL,
"Two inputs is needed by rowwise add");
auto dim0 = ctx.Input<Tensor>(0)->dims();
......@@ -32,9 +33,10 @@ class RowWiseAddOp : public OperatorWithKernel {
}
};
class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
class RowWiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
RowWiseAddOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The left input of row-wise add op, must be matrix");
AddInput("b", "The right input of row-wise add op, must be vector");
......@@ -50,6 +52,7 @@ for i in xrange(X.shape[0]):
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(rowwise_add, ops::RowWiseAddOp, ops::RowWiseAddOpMaker);
REGISTER_OP_CPU_KERNEL(rowwise_add,
ops::RowWiseAddKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
rowwise_add, ops::RowWiseAddKernel<paddle::platform::CPUPlace, float>);
......@@ -15,5 +15,6 @@
#define EIGEN_USE_GPU
#include "paddle/operators/rowwise_add_op.h"
REGISTER_OP_GPU_KERNEL(rowwise_add,
ops::RowWiseAddKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
rowwise_add, ops::RowWiseAddKernel<paddle::platform::GPUPlace, float>);
......@@ -13,15 +13,24 @@
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class RowWiseAddKernel : public OpKernel {
class RowWiseAddKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto out = context.Output<Tensor>(0);
out->mutable_data<T>(context.GetPlace());
......
......@@ -17,9 +17,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
class SGDOp : public OperatorWithKernel {
class SGDOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 2, "Input size of SGDOp must be two");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of SGDOp must be one");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "inputs[0] mast be set");
......@@ -31,9 +31,9 @@ class SGDOp : public OperatorWithKernel {
}
};
class SGDOpMaker : public OpProtoAndCheckerMaker {
class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SGDOpMaker(OpProto *proto, OpAttrChecker *op_checker)
SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("param", "input parameter");
AddInput("grad", "input gradient");
......@@ -51,5 +51,7 @@ param_out = param - learning_rate * grad;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sgd, ops::SGDOp, ops::SGDOpMaker);
REGISTER_OP_CPU_KERNEL(sgd, ops::SGDOpKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(sgd,
ops::SGDOpKernel<paddle::platform::CPUPlace, float>);
......@@ -15,4 +15,6 @@
#define EIGEN_USE_GPU
#include "paddle/operators/sgd_op.h"
REGISTER_OP_GPU_KERNEL(sgd, ops::SGDOpKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(sgd,
ops::SGDOpKernel<paddle::platform::GPUPlace, float>);
......@@ -13,15 +13,21 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class SGDOpKernel : public OpKernel {
class SGDOpKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& ctx) const override {
void Compute(const framework::ExecutionContext& ctx) const override {
auto param = ctx.Input<Tensor>("param");
auto grad = ctx.Input<Tensor>("grad");
auto param_out = ctx.Output<Tensor>(0);
......
......@@ -13,21 +13,23 @@
limitations under the License. */
#include "paddle/operators/sigmoid_op.h"
namespace paddle {
namespace operators {
class SigmoidOp : public OperatorWithKernel {
class SigmoidOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Sigmoid Op only have one output");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};
class SigmoidOpMaker : public OpProtoAndCheckerMaker {
class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
SigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "sigmoid input");
AddOutput("Y", "sigmoid output");
......@@ -35,9 +37,9 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker {
}
};
class SigmoidOpGrad : public OperatorWithKernel {
class SigmoidOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};
......@@ -45,9 +47,11 @@ class SigmoidOpGrad : public OperatorWithKernel {
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker);
REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad);
REGISTER_OP_CPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(sigmoid_grad,
ops::SigmoidGradKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::CPUPlace, float>);
......@@ -15,6 +15,9 @@
#define EIGEN_USE_GPU
#include "paddle/operators/sigmoid_op.h"
REGISTER_OP_GPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(sigmoid_grad,
ops::SigmoidGradKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::GPUPlace, float>);
......@@ -13,16 +13,21 @@
limitations under the License. */
#pragma once
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class SigmoidKernel : public OpKernel {
class SigmoidKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
......@@ -37,9 +42,9 @@ class SigmoidKernel : public OpKernel {
};
template <typename Place, typename T>
class SigmoidGradKernel : public OpKernel {
class SigmoidGradKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto Y_t = context.Input<Tensor>("Y");
auto dY_t = context.Input<Tensor>(framework::GradVarName("Y"));
auto dX_t = context.Output<Tensor>(framework::GradVarName("X"));
......
......@@ -17,9 +17,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
class SoftmaxOp : public OperatorWithKernel {
class SoftmaxOp : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
"Only one input is need for softmax");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims().size(), 2UL,
......@@ -30,9 +30,10 @@ class SoftmaxOp : public OperatorWithKernel {
}
};
class SoftmaxOpMaker : public OpProtoAndCheckerMaker {
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(OpProto *proto, OpAttrChecker *op_checker)
SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input of softmax");
AddOutput("Y", "output of softmax");
......@@ -40,9 +41,9 @@ class SoftmaxOpMaker : public OpProtoAndCheckerMaker {
}
};
class SoftmaxOpGrad : public OperatorWithKernel {
class SoftmaxOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL,
"Input of SoftmaxOpGrad should be 3, X, Y, YG");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
......@@ -61,8 +62,11 @@ class SoftmaxOpGrad : public OperatorWithKernel {
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(softmax,
ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(softmax_grad,
ops::SoftmaxGradKernel<ops::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
softmax_grad, ops::SoftmaxGradKernel<paddle::platform::CPUPlace, float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
/* 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.
......@@ -13,9 +13,11 @@
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/softmax_op.h"
REGISTER_OP_GPU_KERNEL(softmax, ops::SoftmaxKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(softmax_grad,
ops::SoftmaxGradKernel<ops::GPUPlace, float>);
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(softmax,
ops::SoftmaxKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
softmax_grad, ops::SoftmaxGradKernel<paddle::platform::GPUPlace, float>);
......@@ -13,19 +13,21 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/ddim.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/type_alias.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class SoftmaxKernel : public OpKernel {
class SoftmaxKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
auto input = context.Input<Tensor>("X");
auto output = context.Output<Tensor>("Y");
output->mutable_data<T>(context.GetPlace());
......@@ -62,9 +64,9 @@ class SoftmaxKernel : public OpKernel {
};
template <typename Place, typename T>
class SoftmaxGradKernel : public OpKernel {
class SoftmaxGradKernel : public framework::OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
void Compute(const framework::ExecutionContext& context) const override {
std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
auto Y = context.Input<Tensor>("Y");
......
/* 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/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
using OpKernel = framework::OpKernel;
using OperatorBase = framework::OperatorBase;
using InferShapeContext = framework::InferShapeContext;
using ExecutionContext = framework::ExecutionContext;
using Variable = framework::Variable;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Tensor = framework::Tensor;
using Scope = framework::Scope;
using OperatorWithKernel = framework::OperatorWithKernel;
using OperatorBase = framework::OperatorBase;
using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
using OpProto = framework::OpProto;
using OpAttrChecker = framework::OpAttrChecker;
using CPUPlace = platform::CPUPlace;
using GPUPlace = platform::GPUPlace;
using OpRegistry = framework::OpRegistry;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
cc_library(paddle_pybind SHARED
SRCS pybind.cc
DEPS pybind python backward
fc_op
sgd_op
add_op
mean_op
cross_entropy_op
recurrent_op
fill_zeros_like_op)
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