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@ -4,7 +4,7 @@ Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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@ -17,19 +17,19 @@ limitations under the License. */
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#include <vector>
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#include "paddle/framework/backward.h"
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#include "paddle/framework/net.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/operator.h"
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#include "paddle/framework/scope.h"
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#include "paddle/framework/tensor_py.h"
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#include "paddle/operators/net_op.h"
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#include "paddle/operators/type_alias.h"
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#include "paddle/platform/enforce.h"
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#include "paddle/platform/place.h"
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#include "paddle/pybind/tensor_bind.h"
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#include "pybind11/numpy.h"
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#include "pybind11/pybind11.h"
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#include "pybind11/stl.h"
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namespace py = pybind11;
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namespace pd = paddle::framework;
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USE_OP(add_two);
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USE_OP(onehot_cross_entropy);
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@ -41,17 +41,18 @@ USE_OP(sigmoid);
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USE_OP(softmax);
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USE_OP(rowwise_add);
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USE_OP_WITHOUT_KERNEL(recurrent_op);
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namespace paddle {
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namespace framework {
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template <typename ClassType>
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void ExposeOperator(ClassType& m) {
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void ExposeOperator(ClassType &m) {
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m.def("infer_shape", &ClassType::type::InferShape)
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.def("run", &ClassType::type::Run)
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.def("type",
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[](const typename ClassType::type& op) -> std::string {
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[](const typename ClassType::type &op) -> std::string {
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return op.type_;
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})
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.def("outputs",
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[](const typename ClassType::type& op) -> std::vector<std::string> {
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[](const typename ClassType::type &op) -> std::vector<std::string> {
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return op.outputs_;
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})
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.def("__str__", &ClassType::type::DebugString);
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@ -73,80 +74,81 @@ bool IsCompileGPU() {
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PYBIND11_PLUGIN(core) {
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py::module m("core", "C++ core of PaddlePaddle");
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py::class_<pd::Tensor>(m, "Tensor", py::buffer_protocol())
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.def_buffer([](pd::Tensor& self) -> py::buffer_info {
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return paddle::pybind::CastToPyBuffer(self);
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})
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py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
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.def_buffer(
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[](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
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.def("get_dims",
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[](const pd::Tensor& self) { return pd::vectorize(self.dims()); })
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[](const Tensor &self) { return vectorize(self.dims()); })
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.def("set_dims",
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[](pd::Tensor& self, const std::vector<int>& dim) {
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self.Resize(pd::make_ddim(dim));
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[](Tensor &self, const std::vector<int> &dim) {
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self.Resize(make_ddim(dim));
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})
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.def("alloc_float",
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[](pd::Tensor& self, paddle::platform::GPUPlace& place) {
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[](Tensor &self, paddle::platform::GPUPlace &place) {
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self.mutable_data<float>(place);
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})
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.def("alloc_float",
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[](pd::Tensor& self, paddle::platform::CPUPlace& place) {
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[](Tensor &self, paddle::platform::CPUPlace &place) {
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self.mutable_data<float>(place);
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})
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.def("alloc_int",
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[](pd::Tensor& self, paddle::platform::CPUPlace& place) {
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[](Tensor &self, paddle::platform::CPUPlace &place) {
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self.mutable_data<int>(place);
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})
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.def("alloc_int",
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[](pd::Tensor& self, paddle::platform::GPUPlace& place) {
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[](Tensor &self, paddle::platform::GPUPlace &place) {
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self.mutable_data<int>(place);
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})
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.def("set", paddle::pybind::PyCPUTensorSetFromArray<float>)
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.def("set", paddle::pybind::PyCPUTensorSetFromArray<int>)
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.def("set", PyCPUTensorSetFromArray<float>)
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.def("set", PyCPUTensorSetFromArray<int>)
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#ifndef PADDLE_ONLY_CPU
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.def("set", paddle::pybind::PyCUDATensorSetFromArray<float>)
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.def("set", paddle::pybind::PyCUDATensorSetFromArray<int>)
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.def("set", PyCUDATensorSetFromArray<float>)
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.def("set", PyCUDATensorSetFromArray<int>)
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#endif
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.def("shape",
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[](pd::Tensor& self) { return pd::vectorize(self.dims()); });
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.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
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.def("set_float_element",
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[](Tensor &self, size_t offset, float f) {
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// TODO(yuyang18): Only support GPU now.
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self.data<float>()[offset] = f;
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})
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.def("get_float_element", [](Tensor &self, size_t offset) -> float {
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// TODO(yuyang18): Only support GPU now.
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return self.data<float>()[offset];
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});
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py::class_<pd::Variable>(m, "Variable", R"DOC(Variable Class.
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py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
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All parameter, weight, gradient are variables in Paddle.
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)DOC")
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.def("is_int", [](const pd::Variable& var) { return var.IsType<int>(); })
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.def("is_int", [](const Variable &var) { return var.IsType<int>(); })
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.def("set_int",
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[](pd::Variable& var, int val) -> void {
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*var.GetMutable<int>() = val;
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})
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.def("get_int",
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[](const pd::Variable& var) -> int { return var.Get<int>(); })
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[](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
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.def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
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.def("get_tensor",
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[](pd::Variable& self) -> pd::Tensor* {
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return self.GetMutable<pd::Tensor>();
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},
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[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
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py::return_value_policy::reference)
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.def("get_net",
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[](pd::Variable& self) -> pd::NetOp* {
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return self.GetMutable<pd::NetOp>();
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[](Variable &self) -> ops::NetOp * {
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return self.GetMutable<ops::NetOp>();
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},
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py::return_value_policy::reference);
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py::class_<pd::Scope>(m, "Scope", "")
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py::class_<Scope>(m, "Scope", "")
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.def("new_var",
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[](pd::Scope& self, const std::string& name) -> pd::Variable* {
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[](Scope &self, const std::string &name) -> Variable * {
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return self.NewVar(name);
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},
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py::return_value_policy::reference)
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.def("find_var", &pd::Scope::FindVar, py::return_value_policy::reference)
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.def("find_var", &Scope::FindVar, py::return_value_policy::reference)
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.def(py::init<>())
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.def("new_scope",
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[](pd::Scope& self) -> pd::Scope* { return &self.NewScope(); },
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.def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
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py::return_value_policy::reference)
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.def("drop_kids", &pd::Scope::DropKids);
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.def("drop_kids", &Scope::DropKids);
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//! @note: Be careful! PyBind will return std::string as an unicode, not
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//! Python str. If you want a str object, you should cast them in Python.
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m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
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auto& protos = pd::OpRegistry::protos();
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auto &protos = OpRegistry::protos();
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std::vector<py::bytes> ret_values;
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for (auto it = protos.begin(); it != protos.end(); ++it) {
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PADDLE_ENFORCE(it->second.IsInitialized(),
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@ -161,8 +163,8 @@ All parameter, weight, gradient are variables in Paddle.
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m.def_submodule(
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"var_names",
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"The module will return special predefined variable name in Paddle")
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.def("empty", pd::OperatorBase::EMPTY_VAR_NAME)
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.def("temp", pd::OperatorBase::TMP_VAR_NAME);
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.def("empty", OperatorBase::EMPTY_VAR_NAME)
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.def("temp", OperatorBase::TMP_VAR_NAME);
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// clang-format off
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py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
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.def_static("create",
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@ -185,43 +187,45 @@ All parameter, weight, gradient are variables in Paddle.
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py::class_<paddle::platform::CPUPlace>(m, "CPUPlace").def(py::init<>());
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py::class_<pd::OperatorBase, std::shared_ptr<pd::OperatorBase>> operator_base(
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py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
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m, "Operator");
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operator_base.def_static("create", [](py::bytes protobin) {
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pd::OpDesc desc;
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OpDesc desc;
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PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
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"Cannot parse user input to OpDesc");
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PADDLE_ENFORCE(desc.IsInitialized(),
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"User OpDesc is not initialized, reason %s",
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desc.InitializationErrorString());
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return pd::OpRegistry::CreateOp(desc);
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return OpRegistry::CreateOp(desc);
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});
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operator_base.def("backward",
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[](const pd::OperatorBase& forwardOp,
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const std::unordered_set<std::string>& no_grad_vars) {
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return pd::Backward(forwardOp, no_grad_vars);
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[](const OperatorBase &forwardOp,
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const std::unordered_set<std::string> &no_grad_vars) {
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return Backward(forwardOp, no_grad_vars);
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});
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ExposeOperator(operator_base);
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py::class_<pd::NetOp, std::shared_ptr<pd::NetOp>> net(m, "Net");
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py::class_<ops::NetOp, std::shared_ptr<ops::NetOp>> net(m, "Net");
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net.def_static("create",
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[]() -> std::shared_ptr<pd::NetOp> {
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auto retv = std::make_shared<pd::NetOp>();
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[]() -> std::shared_ptr<ops::NetOp> {
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auto retv = std::make_shared<ops::NetOp>();
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retv->type_ = "plain_net";
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return retv;
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})
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.def("add_op", &pd::NetOp::AddOp)
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.def("add_op",
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[](pd::NetOp& self, const std::shared_ptr<pd::NetOp>& net) -> void {
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self.AddOp(std::static_pointer_cast<pd::OperatorBase>(net));
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})
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.def("complete_add_op", &pd::NetOp::CompleteAddOp)
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.def("add_op", &ops::NetOp::AddOp)
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.def(
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"add_op",
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[](ops::NetOp &self, const std::shared_ptr<ops::NetOp> &net) -> void {
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self.AddOp(std::static_pointer_cast<OperatorBase>(net));
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})
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.def("complete_add_op", &ops::NetOp::CompleteAddOp)
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.def("complete_add_op",
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[](std::shared_ptr<pd::NetOp>& self) { self->CompleteAddOp(); });
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[](std::shared_ptr<ops::NetOp> &self) { self->CompleteAddOp(); });
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ExposeOperator(net);
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m.def("unique_integer", UniqueIntegerGenerator);
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@ -230,3 +234,5 @@ All parameter, weight, gradient are variables in Paddle.
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return m.ptr();
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}
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} // namespace framework
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} // namespace paddle
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qijun
8 years ago