|
|
|
|
@ -4,7 +4,7 @@ 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
|
|
|
|
|
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,
|
|
|
|
|
@ -17,19 +17,19 @@ limitations under the License. */
|
|
|
|
|
#include <vector>
|
|
|
|
|
|
|
|
|
|
#include "paddle/framework/backward.h"
|
|
|
|
|
#include "paddle/framework/net.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 "paddle/pybind/tensor_bind.h"
|
|
|
|
|
#include "pybind11/numpy.h"
|
|
|
|
|
#include "pybind11/pybind11.h"
|
|
|
|
|
#include "pybind11/stl.h"
|
|
|
|
|
|
|
|
|
|
namespace py = pybind11;
|
|
|
|
|
namespace pd = paddle::framework;
|
|
|
|
|
|
|
|
|
|
USE_OP(add_two);
|
|
|
|
|
USE_OP(onehot_cross_entropy);
|
|
|
|
|
@ -41,17 +41,18 @@ USE_OP(sigmoid);
|
|
|
|
|
USE_OP(softmax);
|
|
|
|
|
USE_OP(rowwise_add);
|
|
|
|
|
USE_OP_WITHOUT_KERNEL(recurrent_op);
|
|
|
|
|
|
|
|
|
|
namespace paddle {
|
|
|
|
|
namespace framework {
|
|
|
|
|
template <typename ClassType>
|
|
|
|
|
void ExposeOperator(ClassType& m) {
|
|
|
|
|
void ExposeOperator(ClassType &m) {
|
|
|
|
|
m.def("infer_shape", &ClassType::type::InferShape)
|
|
|
|
|
.def("run", &ClassType::type::Run)
|
|
|
|
|
.def("type",
|
|
|
|
|
[](const typename ClassType::type& op) -> std::string {
|
|
|
|
|
[](const typename ClassType::type &op) -> std::string {
|
|
|
|
|
return op.type_;
|
|
|
|
|
})
|
|
|
|
|
.def("outputs",
|
|
|
|
|
[](const typename ClassType::type& op) -> std::vector<std::string> {
|
|
|
|
|
[](const typename ClassType::type &op) -> std::vector<std::string> {
|
|
|
|
|
return op.outputs_;
|
|
|
|
|
})
|
|
|
|
|
.def("__str__", &ClassType::type::DebugString);
|
|
|
|
|
@ -73,88 +74,81 @@ bool IsCompileGPU() {
|
|
|
|
|
PYBIND11_PLUGIN(core) {
|
|
|
|
|
py::module m("core", "C++ core of PaddlePaddle");
|
|
|
|
|
|
|
|
|
|
py::class_<pd::Tensor>(m, "Tensor", py::buffer_protocol())
|
|
|
|
|
.def_buffer([](pd::Tensor& self) -> py::buffer_info {
|
|
|
|
|
return paddle::pybind::CastToPyBuffer(self);
|
|
|
|
|
})
|
|
|
|
|
py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
|
|
|
|
|
.def_buffer(
|
|
|
|
|
[](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
|
|
|
|
|
.def("get_dims",
|
|
|
|
|
[](const pd::Tensor& self) { return pd::vectorize(self.dims()); })
|
|
|
|
|
[](const Tensor &self) { return vectorize(self.dims()); })
|
|
|
|
|
.def("set_dims",
|
|
|
|
|
[](pd::Tensor& self, const std::vector<int>& dim) {
|
|
|
|
|
self.Resize(pd::make_ddim(dim));
|
|
|
|
|
[](Tensor &self, const std::vector<int> &dim) {
|
|
|
|
|
self.Resize(make_ddim(dim));
|
|
|
|
|
})
|
|
|
|
|
.def("alloc_float",
|
|
|
|
|
[](pd::Tensor& self, paddle::platform::GPUPlace& place) {
|
|
|
|
|
[](Tensor &self, paddle::platform::GPUPlace &place) {
|
|
|
|
|
self.mutable_data<float>(place);
|
|
|
|
|
})
|
|
|
|
|
.def("alloc_float",
|
|
|
|
|
[](pd::Tensor& self, paddle::platform::CPUPlace& place) {
|
|
|
|
|
[](Tensor &self, paddle::platform::CPUPlace &place) {
|
|
|
|
|
self.mutable_data<float>(place);
|
|
|
|
|
})
|
|
|
|
|
.def("alloc_int",
|
|
|
|
|
[](pd::Tensor& self, paddle::platform::CPUPlace& place) {
|
|
|
|
|
[](Tensor &self, paddle::platform::CPUPlace &place) {
|
|
|
|
|
self.mutable_data<int>(place);
|
|
|
|
|
})
|
|
|
|
|
.def("alloc_int",
|
|
|
|
|
[](pd::Tensor& self, paddle::platform::GPUPlace& place) {
|
|
|
|
|
[](Tensor &self, paddle::platform::GPUPlace &place) {
|
|
|
|
|
self.mutable_data<int>(place);
|
|
|
|
|
})
|
|
|
|
|
.def("set", paddle::pybind::PyCPUTensorSetFromArray<float>)
|
|
|
|
|
.def("set", paddle::pybind::PyCPUTensorSetFromArray<int>)
|
|
|
|
|
.def("set", PyCPUTensorSetFromArray<float>)
|
|
|
|
|
.def("set", PyCPUTensorSetFromArray<int>)
|
|
|
|
|
#ifndef PADDLE_ONLY_CPU
|
|
|
|
|
.def("set", paddle::pybind::PyCUDATensorSetFromArray<float>)
|
|
|
|
|
.def("set", paddle::pybind::PyCUDATensorSetFromArray<int>)
|
|
|
|
|
.def("set", PyCUDATensorSetFromArray<float>)
|
|
|
|
|
.def("set", PyCUDATensorSetFromArray<int>)
|
|
|
|
|
#endif
|
|
|
|
|
.def("shape", [](pd::Tensor& self) { return pd::vectorize(self.dims()); })
|
|
|
|
|
.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
|
|
|
|
|
.def("set_float_element",
|
|
|
|
|
[](pd::Tensor& self, size_t offset, float f) {
|
|
|
|
|
[](Tensor &self, size_t offset, float f) {
|
|
|
|
|
// TODO(yuyang18): Only support GPU now.
|
|
|
|
|
self.data<float>()[offset] = f;
|
|
|
|
|
})
|
|
|
|
|
.def("get_float_element", [](pd::Tensor& self, size_t offset) -> float {
|
|
|
|
|
.def("get_float_element", [](Tensor &self, size_t offset) -> float {
|
|
|
|
|
// TODO(yuyang18): Only support GPU now.
|
|
|
|
|
return self.data<float>()[offset];
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
py::class_<pd::Variable>(m, "Variable", R"DOC(Variable Class.
|
|
|
|
|
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
|
|
|
|
|
|
|
|
|
|
All parameter, weight, gradient are variables in Paddle.
|
|
|
|
|
)DOC")
|
|
|
|
|
.def("is_int", [](const pd::Variable& var) { return var.IsType<int>(); })
|
|
|
|
|
.def("is_int", [](const Variable &var) { return var.IsType<int>(); })
|
|
|
|
|
.def("set_int",
|
|
|
|
|
[](pd::Variable& var, int val) -> void {
|
|
|
|
|
*var.GetMutable<int>() = val;
|
|
|
|
|
})
|
|
|
|
|
.def("get_int",
|
|
|
|
|
[](const pd::Variable& var) -> int { return var.Get<int>(); })
|
|
|
|
|
[](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
|
|
|
|
|
.def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
|
|
|
|
|
.def("get_tensor",
|
|
|
|
|
[](pd::Variable& self) -> pd::Tensor* {
|
|
|
|
|
return self.GetMutable<pd::Tensor>();
|
|
|
|
|
},
|
|
|
|
|
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
|
|
|
|
|
py::return_value_policy::reference)
|
|
|
|
|
.def("get_net",
|
|
|
|
|
[](pd::Variable& self) -> pd::NetOp* {
|
|
|
|
|
return self.GetMutable<pd::NetOp>();
|
|
|
|
|
[](Variable &self) -> ops::NetOp * {
|
|
|
|
|
return self.GetMutable<ops::NetOp>();
|
|
|
|
|
},
|
|
|
|
|
py::return_value_policy::reference);
|
|
|
|
|
|
|
|
|
|
py::class_<pd::Scope>(m, "Scope", "")
|
|
|
|
|
py::class_<Scope>(m, "Scope", "")
|
|
|
|
|
.def("new_var",
|
|
|
|
|
[](pd::Scope& self, const std::string& name) -> pd::Variable* {
|
|
|
|
|
[](Scope &self, const std::string &name) -> Variable * {
|
|
|
|
|
return self.NewVar(name);
|
|
|
|
|
},
|
|
|
|
|
py::return_value_policy::reference)
|
|
|
|
|
.def("find_var", &pd::Scope::FindVar, py::return_value_policy::reference)
|
|
|
|
|
.def("find_var", &Scope::FindVar, py::return_value_policy::reference)
|
|
|
|
|
.def(py::init<>())
|
|
|
|
|
.def("new_scope",
|
|
|
|
|
[](pd::Scope& self) -> pd::Scope* { return &self.NewScope(); },
|
|
|
|
|
.def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
|
|
|
|
|
py::return_value_policy::reference)
|
|
|
|
|
.def("drop_kids", &pd::Scope::DropKids);
|
|
|
|
|
.def("drop_kids", &Scope::DropKids);
|
|
|
|
|
|
|
|
|
|
//! @note: Be careful! PyBind will return std::string as an unicode, not
|
|
|
|
|
//! Python str. If you want a str object, you should cast them in Python.
|
|
|
|
|
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
|
|
|
|
|
auto& protos = pd::OpRegistry::protos();
|
|
|
|
|
auto &protos = OpRegistry::protos();
|
|
|
|
|
std::vector<py::bytes> ret_values;
|
|
|
|
|
for (auto it = protos.begin(); it != protos.end(); ++it) {
|
|
|
|
|
PADDLE_ENFORCE(it->second.IsInitialized(),
|
|
|
|
|
@ -169,8 +163,8 @@ All parameter, weight, gradient are variables in Paddle.
|
|
|
|
|
m.def_submodule(
|
|
|
|
|
"var_names",
|
|
|
|
|
"The module will return special predefined variable name in Paddle")
|
|
|
|
|
.def("empty", pd::OperatorBase::EMPTY_VAR_NAME)
|
|
|
|
|
.def("temp", pd::OperatorBase::TMP_VAR_NAME);
|
|
|
|
|
.def("empty", OperatorBase::EMPTY_VAR_NAME)
|
|
|
|
|
.def("temp", OperatorBase::TMP_VAR_NAME);
|
|
|
|
|
// clang-format off
|
|
|
|
|
py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
|
|
|
|
|
.def_static("create",
|
|
|
|
|
@ -193,43 +187,45 @@ All parameter, weight, gradient are variables in Paddle.
|
|
|
|
|
|
|
|
|
|
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace").def(py::init<>());
|
|
|
|
|
|
|
|
|
|
py::class_<pd::OperatorBase, std::shared_ptr<pd::OperatorBase>> operator_base(
|
|
|
|
|
py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
|
|
|
|
|
m, "Operator");
|
|
|
|
|
|
|
|
|
|
operator_base.def_static("create", [](py::bytes protobin) {
|
|
|
|
|
pd::OpDesc desc;
|
|
|
|
|
OpDesc desc;
|
|
|
|
|
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
|
|
|
|
|
"Cannot parse user input to OpDesc");
|
|
|
|
|
PADDLE_ENFORCE(desc.IsInitialized(),
|
|
|
|
|
"User OpDesc is not initialized, reason %s",
|
|
|
|
|
desc.InitializationErrorString());
|
|
|
|
|
return pd::OpRegistry::CreateOp(desc);
|
|
|
|
|
return OpRegistry::CreateOp(desc);
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
operator_base.def("backward",
|
|
|
|
|
[](const pd::OperatorBase& forwardOp,
|
|
|
|
|
const std::unordered_set<std::string>& no_grad_vars) {
|
|
|
|
|
return pd::Backward(forwardOp, no_grad_vars);
|
|
|
|
|
[](const OperatorBase &forwardOp,
|
|
|
|
|
const std::unordered_set<std::string> &no_grad_vars) {
|
|
|
|
|
return Backward(forwardOp, no_grad_vars);
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
ExposeOperator(operator_base);
|
|
|
|
|
|
|
|
|
|
py::class_<pd::NetOp, std::shared_ptr<pd::NetOp>> net(m, "Net");
|
|
|
|
|
py::class_<ops::NetOp, std::shared_ptr<ops::NetOp>> net(m, "Net");
|
|
|
|
|
|
|
|
|
|
net.def_static("create",
|
|
|
|
|
[]() -> std::shared_ptr<pd::NetOp> {
|
|
|
|
|
auto retv = std::make_shared<pd::NetOp>();
|
|
|
|
|
[]() -> std::shared_ptr<ops::NetOp> {
|
|
|
|
|
auto retv = std::make_shared<ops::NetOp>();
|
|
|
|
|
retv->type_ = "plain_net";
|
|
|
|
|
return retv;
|
|
|
|
|
})
|
|
|
|
|
.def("add_op", &pd::NetOp::AddOp)
|
|
|
|
|
.def("add_op",
|
|
|
|
|
[](pd::NetOp& self, const std::shared_ptr<pd::NetOp>& net) -> void {
|
|
|
|
|
self.AddOp(std::static_pointer_cast<pd::OperatorBase>(net));
|
|
|
|
|
})
|
|
|
|
|
.def("complete_add_op", &pd::NetOp::CompleteAddOp)
|
|
|
|
|
.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<pd::NetOp>& self) { self->CompleteAddOp(); });
|
|
|
|
|
[](std::shared_ptr<ops::NetOp> &self) { self->CompleteAddOp(); });
|
|
|
|
|
|
|
|
|
|
ExposeOperator(net);
|
|
|
|
|
|
|
|
|
|
m.def("unique_integer", UniqueIntegerGenerator);
|
|
|
|
|
@ -238,3 +234,5 @@ All parameter, weight, gradient are variables in Paddle.
|
|
|
|
|
|
|
|
|
|
return m.ptr();
|
|
|
|
|
}
|
|
|
|
|
} // namespace framework
|
|
|
|
|
} // namespace paddle
|
Yu Yang
8 years ago