/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. 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. */ #include #include #include #include #include #include // NOLINT // for call_once #include #include #include #include #include #include "paddle/fluid/framework/executor.h" #include "paddle/fluid/framework/feed_fetch_method.h" #include "paddle/fluid/framework/framework.pb.h" #include "paddle/fluid/framework/garbage_collector.h" #include "paddle/fluid/framework/ir/coalesce_grad_tensor_pass.h" #include "paddle/fluid/framework/ir/pass_builder.h" #include "paddle/fluid/framework/load_op_lib.h" #include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/lod_tensor_array.h" #include "paddle/fluid/framework/op_compatible_info.h" #include "paddle/fluid/framework/op_info.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/parallel_executor.h" #include "paddle/fluid/framework/prune.h" #include "paddle/fluid/framework/reader.h" #include "paddle/fluid/framework/save_load_util.h" #include "paddle/fluid/framework/scope_pool.h" #include "paddle/fluid/framework/selected_rows.h" #include "paddle/fluid/framework/trainer.h" #include "paddle/fluid/framework/type_defs.h" #include "paddle/fluid/framework/version.h" #include "paddle/fluid/imperative/layer.h" #include "paddle/fluid/memory/allocation/allocator_strategy.h" #include "paddle/fluid/operators/activation_op.h" #include "paddle/fluid/operators/py_func_op.h" #include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h" #include "paddle/fluid/platform/cpu_helper.h" #include "paddle/fluid/platform/cpu_info.h" #include "paddle/fluid/platform/dynload/dynamic_loader.h" #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/init.h" #include "paddle/fluid/platform/place.h" #include "paddle/fluid/platform/profiler.h" #include "paddle/fluid/pybind/box_helper_py.h" #include "paddle/fluid/pybind/const_value.h" #include "paddle/fluid/pybind/data_set_py.h" #include "paddle/fluid/pybind/exception.h" #include "paddle/fluid/pybind/fleet_wrapper_py.h" #include "paddle/fluid/pybind/global_value_getter_setter.h" #include "paddle/fluid/pybind/gloo_wrapper_py.h" #include "paddle/fluid/pybind/imperative.h" #include "paddle/fluid/pybind/inference_api.h" #include "paddle/fluid/pybind/ir.h" #include "paddle/fluid/pybind/pybind_boost_headers.h" #ifndef _WIN32 #include "paddle/fluid/pybind/nccl_wrapper_py.h" #endif #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/pybind/protobuf.h" #include "paddle/fluid/pybind/pybind.h" // NOLINT #include "paddle/fluid/pybind/reader_py.h" #include "paddle/fluid/pybind/tensor_py.h" #include "paddle/fluid/string/to_string.h" #ifdef PADDLE_WITH_CUDA #ifndef _WIN32 #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" #endif #include "paddle/fluid/platform/cuda_profiler.h" #include "paddle/fluid/platform/gpu_info.h" #endif #ifdef PADDLE_WITH_DISTRIBUTE #include "paddle/fluid/pybind/communicator_py.h" #endif #include "pybind11/stl.h" DEFINE_bool(reader_queue_speed_test_mode, false, "If set true, the queue.pop will only get data from queue but not " "remove the data from queue for speed testing"); DECLARE_bool(use_mkldnn); #ifdef PADDLE_WITH_NGRAPH DECLARE_bool(use_ngraph); #endif // disable auto conversion to list in Python PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray); namespace paddle { namespace pybind { bool IsCompiledWithCUDA() { #ifndef PADDLE_WITH_CUDA return false; #else return true; #endif } bool IsCompiledWithMKLDNN() { #ifndef PADDLE_WITH_MKLDNN return false; #else return true; #endif } bool IsCompiledWithNGRAPH() { #ifndef PADDLE_WITH_NGRAPH return false; #else return true; #endif } bool IsCompiledWithBrpc() { #ifndef PADDLE_WITH_DISTRIBUTE return false; #endif #ifdef PADDLE_WITH_GRPC return false; #endif return true; } bool IsCompiledWithDIST() { #ifdef PADDLE_WITH_DISTRIBUTE return true; #else return false; #endif } template static inline bool IsSamePlace(const PlaceType1 &p1, const PlaceType2 &p2) { return paddle::platform::Place(p1) == paddle::platform::Place(p2); } template static inline int PlaceIndex(const PlaceType &p) { return static_cast(paddle::platform::Place(p).which()); } static PyObject *GetPythonAttribute(PyObject *obj, const char *attr_name) { // NOTE(zjl): PyObject_GetAttrString would return nullptr when attr_name // is not inside obj, but it would also set the error flag of Python. // If the error flag is set in C++, C++ code would not raise Exception, // but Python would raise Exception once C++ call ends. // To avoid unexpected Exception raised in Python, we check whether // attribute exists before calling PyObject_GetAttrString. // // Caution: PyObject_GetAttrString would increase reference count of PyObject. // Developer should call Py_DECREF manually after the attribute is not used. if (PyObject_HasAttrString(obj, attr_name)) { return PyObject_GetAttrString(obj, attr_name); } else { return nullptr; } } template static T PyObjectCast(PyObject *obj) { try { return py::cast(py::handle(obj)); } catch (py::cast_error &) { PADDLE_THROW("Python object is not type of %s", typeid(T).name()); } } using PyNameVarBaseMap = std::unordered_map; static std::vector> GetVarBaseList( const PyNameVarBaseMap &state_dict) { std::vector> vec_res; vec_res.reserve(state_dict.size()); for (auto ¶ : state_dict) { PyObject *py_obj = para.second.ptr(); if (!py_obj || py_obj == Py_None) { PADDLE_THROW("Save parameter [%s] is None", para.first); } vec_res.emplace_back( PyObjectCast>(py_obj)); } return vec_res; } static std::vector inline GetNameList( const py::handle &py_handle) { std::vector vec_res; PyObject *py_obj = py_handle.ptr(); // get underlying PyObject // Python None is not nullptr in C++! if (!py_obj || py_obj == Py_None) { PADDLE_THROW("Save parameter list is None"); } if (PyList_Check(py_obj)) { size_t len = PyList_GET_SIZE(py_obj); vec_res.reserve(len); const char *kNameField = "name"; for (size_t i = 0; i < len; ++i) { PyObject *py_name = PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kNameField); PADDLE_ENFORCE_NOT_NULL(py_name); vec_res.emplace_back(PyObjectCast(py_name)); Py_DECREF(py_name); } } else { PADDLE_THROW("Set parameter should be a list"); } return vec_res; } static void inline CreateVariableIfNotExit( const py::handle &py_handle, const framework::Scope &scope, const framework::Executor *exe = nullptr) { std::vector vec_res; PyObject *py_obj = py_handle.ptr(); // get underlying PyObject // Python None is not nullptr in C++! if (!py_obj || py_obj == Py_None) { PADDLE_THROW("Save parameter list is None"); } if (PyList_Check(py_obj)) { size_t len = PyList_GET_SIZE(py_obj); vec_res.reserve(len); const char *kNameField = "name"; const char *kVarDescField = "desc"; for (size_t i = 0; i < len; ++i) { PyObject *py_name = PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kNameField); PADDLE_ENFORCE_NOT_NULL(py_name); auto para_name = PyObjectCast(py_name); Py_DECREF(py_name); auto var = scope.FindVar(para_name); if (var == nullptr) { PADDLE_ENFORCE_NE(exe, nullptr, "Parameter not Initialized, " "Please set argument [executor] not None " "or run startup program first"); PyObject *py_var_desc = PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kVarDescField); PADDLE_ENFORCE_NOT_NULL(py_var_desc); auto var_desc = PyObjectCast(py_var_desc); Py_DECREF(py_var_desc); var = const_cast(&scope)->Var(para_name); auto *tensor_temp = var->GetMutable(); tensor_temp->Resize(framework::make_ddim(var_desc.GetShape())); tensor_temp->mutable_data(exe->GetPlace(), var_desc.GetDataType()); } } } else { PADDLE_THROW("Set parameter should be a list"); } return; } static void AssertStaticGraphAndDygraphGradMakerNoDiff() { std::set ops; for (auto &pair : framework::OpInfoMap::Instance().map()) { bool has_static_grad_maker = (pair.second.grad_op_maker_ != nullptr); bool has_dygraph_grad_maker = (pair.second.dygraph_grad_op_maker_ != nullptr); if (has_static_grad_maker ^ has_dygraph_grad_maker) { bool has_kernel = (framework::OperatorWithKernel::AllOpKernels().count(pair.first) > 0); if (has_kernel) { ops.insert(pair.first); } else { VLOG(5) << pair.first << " has no kernels, skip"; } } } PADDLE_ENFORCE_EQ(ops.empty(), true, platform::errors::Unimplemented( "OperatorWithKernel [%s] have only static graph grad " "maker or have only dygraph grad maker, which is not " "allowed", string::join_strings(ops, ','))); } #ifdef PADDLE_WITH_AVX PYBIND11_MODULE(core_avx, m) { #else PYBIND11_MODULE(core_noavx, m) { #endif // Not used, just make sure cpu_info.cc is linked. paddle::platform::CpuTotalPhysicalMemory(); paddle::memory::allocation::UseAllocatorStrategyGFlag(); AssertStaticGraphAndDygraphGradMakerNoDiff(); m.doc() = "C++ core of PaddlePaddle"; // using framework in this function. Since it is inside a function, it will // not cause namespace pollution. using namespace paddle::framework; // NOLINT BindException(&m); m.def("set_num_threads", &platform::SetNumThreads); m.def("from_dlpack", [](py::capsule *dltensor) { DLManagedTensor *dmt = reinterpret_cast( PyCapsule_GetPointer(dltensor->ptr(), "dltensor")); PyCapsule_SetName(dltensor->ptr(), "used_dltensor"); DLTensor dl = dmt->dl_tensor; Tensor tensor; if (dl.ctx.device_type == kDLCPU) { paddle::framework::TensorFromDLPack(dl, &tensor); } #ifdef PADDLE_WITH_CUDA if (dl.ctx.device_type == kDLGPU) { paddle::framework::TensorFromDLPack(dl, &tensor); } #endif return tensor; }); m.def("_save_static_dict", [](const std::string &str_file_name, const py::handle &vec_var_list, const Scope &scope) { std::vector vec_name_list = GetNameList(vec_var_list); SaveStaticNameListToDisk(str_file_name, vec_name_list, scope); }); m.def("_load_static_dict", [](const std::string &str_file_name, const py::handle &vec_var_list, const Scope &scope, const Executor *executor) { std::vector vec_name_list = GetNameList(vec_var_list); CreateVariableIfNotExit(vec_var_list, scope, executor); LoadStaticNameListFromDisk(str_file_name, vec_name_list, scope); }); m.def("_create_loaded_parameter", [](const py::handle &vec_var_list, const Scope &scope, const Executor *executor) { CreateVariableIfNotExit(vec_var_list, scope, executor); }); m.def("_save_dygraph_dict", [](const std::string &str_file_name, const PyNameVarBaseMap &state_dict) { auto vec_var_base_list = GetVarBaseList(state_dict); SaveDygraphVarBaseListToDisk(str_file_name, vec_var_base_list); }); m.def("_load_dygraph_dict", [](const std::string &str_file_name) { auto load_tensor = LoadDygraphVarBaseListFromDisk(str_file_name); std::unordered_map> map_output; for (size_t i = 0; i < load_tensor.size(); ++i) { map_output.emplace(load_tensor[i]->Name(), load_tensor[i]); } return map_output; }); m.def("save_op_compatible_info", [](framework::ProgramDesc &desc) { framework::OpCompatibleMap op_compatible_map; op_compatible_map.InitOpCompatibleMap(); return op_compatible_map.ConvertToProto(desc.OpCompatibleMap()); }); m.def( "_append_python_callable_object_and_return_id", [](py::object py_obj) -> size_t { return paddle::operators::AppendPythonCallableObjectAndReturnId(py_obj); }); m.def("_get_use_default_grad_op_desc_maker_ops", [] { return OpInfoMap::Instance().GetUseDefaultGradOpDescMakerOps(); }); m.def("_get_all_register_op_kernels", [] { auto &all_kernels = paddle::framework::OperatorWithKernel::AllOpKernels(); std::unordered_map> all_kernels_info; for (auto &kernel_pair : all_kernels) { auto op_type = kernel_pair.first; std::vector kernel_types; for (auto &info_pair : kernel_pair.second) { paddle::framework::OpKernelType kernel_type = info_pair.first; kernel_types.push_back( paddle::framework::KernelTypeToString(kernel_type)); } all_kernels_info.emplace(op_type, kernel_types); } return all_kernels_info; }); // NOTE(zjl): ctest would load environment variables at the beginning even // though we have not `import paddle.fluid as fluid`. So we add this API // to enable eager deletion mode in unittest. m.def("_set_eager_deletion_mode", &paddle::framework::SetEagerDeletionMode); m.def("_set_fuse_parameter_group_size", &paddle::framework::ir::SetFuseParameterGroupsSize); m.def("_set_fuse_parameter_memory_size", &paddle::framework::ir::SetFuseParameterMemorySize); m.add_object("_cleanup", py::capsule([]() { ScopePool::Instance().Clear(); })); m.def("_set_paddle_lib_path", &paddle::platform::dynload::SetPaddleLibPath); BindImperative(&m); py::class_(m, "Tensor", py::buffer_protocol()) .def("__array__", [](Tensor &self) { return TensorToPyArray(self); }) .def("_is_initialized", [](const Tensor &self) { return self.IsInitialized(); }) .def("_get_dims", [](const Tensor &self) { return vectorize(self.dims()); }) .def("_set_dims", [](Tensor &self, const std::vector &dim) { self.Resize(make_ddim(dim)); }) .def("_set_layout", [](Tensor &self, const std::string &layout) { self.set_layout(StringToDataLayout(layout)); }) .def("_alloc_float", [](Tensor &self, paddle::platform::CUDAPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](Tensor &self, paddle::platform::CPUPlace &place) { self.mutable_data(place); }) .def("_alloc_double", [](Tensor &self, paddle::platform::CPUPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](Tensor &self, paddle::platform::CPUPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](Tensor &self, paddle::platform::CUDAPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) { self.mutable_data(place); }) .def("_mutable_data", [](Tensor &self, paddle::platform::CPUPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast(self.mutable_data(place, type)); }) .def("_mutable_data", [](Tensor &self, paddle::platform::CUDAPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast(self.mutable_data(place, type)); }) .def("_mutable_data", [](Tensor &self, paddle::platform::CUDAPinnedPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast(self.mutable_data(place, type)); }) .def("_clear", &Tensor::clear) .def("set", SetTensorFromPyArray, py::arg("array"), py::arg("place"), py::arg("zero_copy") = false) .def("set", SetTensorFromPyArray, py::arg("array"), py::arg("place"), py::arg("zero_copy") = false) .def("set", SetTensorFromPyArray, py::arg("array"), py::arg("place"), py::arg("zero_copy") = false, R"DOC( Set the data of LoDTensor on place with given numpy array. Args: lod (numpy.ndarray): The data to set. place (CPUPlace|CUDAPlace|CUDAPinnedPlace): The place where the LoDTensor is to be set. zero_copy (bool, optional): Whether to share memory with the input numpy array. This parameter only works with CPUPlace. Default: False. Returns: None. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) )DOC") .def("shape", [](Tensor &self) { return vectorize(self.dims()); }, R"DOC( Return the shape of LoDTensor. Returns: list[int]: The shape of LoDTensor. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) print(t.shape()) # [5, 30] )DOC") .def("_to_dlpack", [](Tensor &self) { DLPackTensor dlpack_tensor(self, 1); DLManagedTensor *dmt = dlpack_tensor.ToCudfCompatibleDLManagedTensor(); auto capsule = py::capsule( static_cast(dmt), "dltensor", [](PyObject *ptr) { if (ptr) { auto dltensor = new DLManagedTensor; try { dltensor = reinterpret_cast( PyCapsule_GetPointer(ptr, "used_dltensor")); return; } catch (...) { dltensor = reinterpret_cast( PyCapsule_GetPointer(ptr, "dltensor")); } dltensor->deleter(dltensor); } }); return capsule; }) .def("_set_float_element", TensorSetElement) .def("_get_float_element", TensorGetElement) .def("_set_double_element", TensorSetElement) .def("_get_double_element", TensorGetElement) .def("_place", [](Tensor &self) { return self.place(); }) .def("_dtype", [](Tensor &self) { return self.type(); }) .def("_share_data_with", &Tensor::ShareDataWith) .def("__getitem__", PySliceTensor, py::return_value_policy::reference) .def("__str__", [](const Tensor &self) { std::stringstream ostr; ostr << self; return ostr.str(); }); // TODO(cql): add reference: en_user_guide_lod_tensor py::class_(m, "LoDTensor", R"DOC( LoDTensor is a Tensor with optional LoD (Level of Details) information, it can be used for variable-length sequences, see :ref:`user_guide_lod_tensor` for details. LoDTensor can be converted to numpy array using :code:`numpy.array(lod_tensor)`. You can skip the following explanation if you don't need to know details of LoDTensor. The following two examples show how to use LODtensor to represent variable-length sequences. Example 1: Suppose x is a LoDTensor representing a variable-length sequence. It contains two logical subsequences, the length of first logical sequence is 2 (e.g., number of samples is 2), the length of second logical sequence is 3, and the total length is 5. The data of the first logical sequence is [1, 2], [3, 4], and the data of the second logical sequence is [5, 6], [7, 8], [9, 10]. The data dimension of each sample is 2. So, the final shape of the LoDTensor is [5, 2], of which 5 is the total length and 2 is the dimension of each sample. Logically, we can represent the variable-length sequence in two ways: one is in the form of recursive sequence lengths, that is, x.recursive_sequence_lengths=[[2, 3]]; the other is in the form of offsets, that is, x.lod=[[0, 2, 2+3]]. These two representations are equivalent, and you can set and retrieve recursive_sequence_lengths or LoD through the corresponding interfaces of LoDTensor introduced later. Actually, in order to access sequence faster, Paddle uses offset to store different lengths of sequences. Therefore, the operations on recursive_sequence_lengths will be converted to the operations on LoD eventually. .. code-block:: python y.data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]] y.shape = [2+2+3, 2] y.recursive_sequence_lengths = [[2, 1], [2, 2, 3]] y.lod = [[0, 2, 3], [0, 2, 4, 7]] Example 2: LoD may have more than one level (for example, a paragraph may have more than one sentence and a sentence may have more than one word). Suppose y is a LoDTensor and its lod_level is 2. From level = 0, there are two logical sequences, the length of which is 2 and 1, respectively, indicating that the first logical sequence contains two sub-sequences and the second logical sequence contains one sub-sequence. From level = 1, the lengths of two sub-sequences contained by the first logical sequence is 2 and 2, and the length of sub-sequence contained by the second logical sequence is 3. Therefore, the LoDTensor is represented in the form of recursive sequence lengths as y.recursive_sequence_lengths=[[2,1], [2,2,3]]; and equally, in the form of offset, it is represented as y.lod=[[0,2,3], [0,2,4,7]]. .. code-block:: python y.data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]] y.shape = [2+2+3, 2] y.recursive_sequence_lengths = [[2, 1], [2, 2, 3]] y.lod = [[0, 2, 3], [0, 2, 4, 7]] Examples: .. code-block:: python import paddle.fluid as fluid t = fluid.LoDTensor() )DOC") .def("__array__", [](Tensor &self) { return TensorToPyArray(self); }) .def("__init__", [](LoDTensor &instance, const std::vector> &recursive_sequence_lengths) { LoD new_lod; new_lod.reserve(recursive_sequence_lengths.size()); std::copy(recursive_sequence_lengths.begin(), recursive_sequence_lengths.end(), std::back_inserter(new_lod)); LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod); PADDLE_ENFORCE_EQ( CheckLoD(new_offset_lod, -1), true, "the provided recursive_sequence_lengths info is invalid"); new (&instance) LoDTensor(new_offset_lod); }) .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); }) // We implement offset based LOD in C++ while we use length based with // Python API. So we changed set_lod to set_recursive_sequence_lengths // to // avoid misuse. // The discussion is here: // https://github.com/PaddlePaddle/Paddle/issues/10855 .def("set_lod", [](LoDTensor &self, const std::vector> &lod) { // the input lod is offset-based level-of-detail info LoD new_lod; new_lod.reserve(lod.size()); std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); PADDLE_ENFORCE_EQ( CheckLoD(new_lod, vectorize(self.dims()).front()), true, "the provided lod info is invalid"); self.set_lod(new_lod); }, py::arg("lod"), R"DOC( Set LoD of the LoDTensor. Args: lod (list[list[int]]): The lod to set. Returns: None. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) t.set_lod([[0, 2, 5]]) print(t.lod()) # [[0, 2, 5]] )DOC") .def("set_recursive_sequence_lengths", [](LoDTensor &self, const std::vector> &recursive_sequence_lengths) { // the input recursive_sequence_lengths is length-based // level-of-detail info LoD new_lod; new_lod.reserve(recursive_sequence_lengths.size()); std::copy(recursive_sequence_lengths.begin(), recursive_sequence_lengths.end(), std::back_inserter(new_lod)); LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod); PADDLE_ENFORCE_EQ( CheckLoD(new_offset_lod, vectorize(self.dims()).front()), true, "the provided recursive_sequence_lengths info is invalid"); self.set_lod(new_offset_lod); }, py::arg("recursive_sequence_lengths"), R"DOC( Set LoD of the LoDTensor according to recursive sequence lengths. For example, if recursive_sequence_lengths=[[2, 3]], which means there are two sequences with length 2 and 3 respectively, the corresponding lod would be [[0, 2, 2+3]], i.e., [[0, 2, 5]]. Args: recursive_sequence_lengths (list[list[int]]): The recursive sequence lengths. Returns: None. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) t.set_recursive_sequence_lengths([[2, 3]]) print(t.recursive_sequence_length()) # [[2, 3]] print(t.lod()) # [[0, 2, 5]] )DOC") .def("lod", [](LoDTensor &self) -> std::vector> { // output the offset-based lod info LoD lod = self.lod(); std::vector> new_lod; new_lod.reserve(lod.size()); std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); return new_lod; }, R"DOC( Return the LoD of the LoDTensor. Returns: list[list[int]]: The lod of the LoDTensor. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) t.set_lod([[0, 2, 5]]) print(t.lod()) # [[0, 2, 5]] )DOC") // Set above comments of set_lod. .def("recursive_sequence_lengths", [](LoDTensor &self) -> std::vector> { // output the length-based lod info LoD lod = ConvertToLengthBasedLoD(self.lod()); std::vector> new_lod; new_lod.reserve(lod.size()); std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); return new_lod; }, R"DOC( Return the recursive sequence lengths corresponding to of the LodD of the LoDTensor. Returns: list[list[int]]: The recursive sequence lengths. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) t.set_recursive_sequence_lengths([[2, 3]]) print(t.recursive_sequence_lengths()) # [[2, 3]] )DOC") .def("has_valid_recursive_sequence_lengths", [](LoDTensor &self) -> bool { // Check that the lod info is valid and match the outermost // dimension of the LoDTensor data return CheckLoD(self.lod(), vectorize(self.dims()).front()); }, R"DOC( Check whether the LoD of the LoDTensor is valid. Returns: bool: Whether the LoD is valid. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) t.set_recursive_sequence_lengths([[2, 3]]) print(t.has_valid_recursive_sequence_lengths()) # True )DOC") .def("__getitem__", PySliceTensor, py::return_value_policy::reference, R"DOC( Slice the original Tensor, and remove the LoD information. Returns: out (Tensor): new Tensor(NOT LoDTensor). )DOC") .def("__str__", [](const LoDTensor &self) { std::stringstream ostr; ostr << self; return ostr.str(); }) .def("_copy", [](const LoDTensor &self, const platform::Place &place) { // follow fetch_op's inplementation LoDTensor dst; if (self.IsInitialized() && self.numel() > 0) { TensorCopySync(self, place, &dst); } else { // Not copy, if the src tensor is empty. dst.clear(); dst.Resize({0}); } dst.set_lod(self.lod()); return dst; }); py::class_(m, "SelectedRows") .def("__init__", [](SelectedRows &instance) { new (&instance) SelectedRows(); }) .def("__init__", [](SelectedRows &instance, const std::vector rows, const int64_t &height) { new (&instance) SelectedRows(rows, height); }) .def("get_tensor", [](SelectedRows &self) { return self.mutable_value(); }, py::return_value_policy::reference) .def("numel", [](SelectedRows &self) -> int64_t { return self.value().numel(); }) .def("set_height", &SelectedRows::set_height) .def("height", &SelectedRows::height) .def("set_rows", [](SelectedRows &self, std::vector rows) { #ifndef PADDLE_WITH_CUDA self.set_rows(rows); #else Vector new_rows(rows); self.set_rows(new_rows); #endif }) .def("sync_index", [](SelectedRows &instance) { instance.SyncIndex(); }) .def("rows", [](SelectedRows &self) { auto rows = self.rows(); std::vector new_rows; new_rows.reserve(rows.size()); std::copy(rows.begin(), rows.end(), std::back_inserter(new_rows)); return new_rows; }); py::class_(m, "Variable", R"DOC(Variable Class. All parameter, weight, gradient are variables in Paddle. )DOC") .def(py::init<>()) .def("is_int", [](const Variable &var) { return var.IsType(); }) .def("set_int", [](Variable &var, int val) -> void { *var.GetMutable() = val; }) .def("get_int", [](const Variable &var) -> int { return var.Get(); }) .def("is_float", [](const Variable &var) { return var.IsType(); }) .def("set_float", [](Variable &var, float val) -> void { *var.GetMutable() = val; }) .def("get_float", [](const Variable &var) -> float { return var.Get(); }) .def("get_tensor", [](Variable &self) -> LoDTensor * { return self.GetMutable(); }, py::return_value_policy::reference) .def("get_lod_rank_table", [](Variable &self) { return self.GetMutable(); }, py::return_value_policy::reference) .def("get_selected_rows", [](Variable &self) -> SelectedRows * { return self.GetMutable(); }, py::return_value_policy::reference) .def("get_lod_tensor_array", [](Variable &self) { return self.GetMutable(); }, py::return_value_policy::reference) #if (defined(PADDLE_WITH_CUDA) && !defined(_WIN32)) .def("get_communicator", [](Variable &self) -> platform::Communicator * { return self.GetMutable(); }, py::return_value_policy::reference) #endif .def("get_reader", [](Variable &self) -> framework::ReaderHolder * { PADDLE_ENFORCE_EQ(self.IsType(), true); return self.GetMutable(); }, py::return_value_policy::reference); BindReader(&m); using LoDTensorBlockingQueue = ::paddle::operators::reader::LoDTensorBlockingQueue; using LoDTensorBlockingQueueHolder = ::paddle::operators::reader::LoDTensorBlockingQueueHolder; py::class_>( m, "LoDTensorBlockingQueue", "") .def("push", [](LoDTensorBlockingQueue &self, const std::vector &lod_tensor_vec) { pybind11::gil_scoped_release release; return self.Push(lod_tensor_vec); }) .def("size", &LoDTensorBlockingQueue::Size) .def("capacity", &LoDTensorBlockingQueue::Cap) .def("close", &LoDTensorBlockingQueue::Close) .def("kill", &LoDTensorBlockingQueue::Kill) .def("is_closed", &LoDTensorBlockingQueue::IsClosed); m.def("init_lod_tensor_blocking_queue", [](Variable &var, size_t capacity) -> std::shared_ptr { VLOG(1) << "init_lod_tensor_blocking_queue"; auto *holder = var.GetMutable(); holder->InitOnce(capacity, FLAGS_reader_queue_speed_test_mode); return holder->GetQueue(); }, py::return_value_policy::copy); py::class_(m, "_Scope", R"DOC( Scope is an association of a name to Variable. All variables belong to Scope. Variables in a parent scope can be retrieved from local scope. You need to specify a scope to run a Net, i.e., `exe.Run(&scope)`. One net can run in different scopes and update different variable in the scope. You can create var in a scope and get it from the scope. Examples: .. code-block:: python import paddle.fluid as fluid # create tensor from a scope and set value to it. param = scope.var('Param').get_tensor() param_array = np.full((height, row_numel), 5.0).astype("float32") param.set(param_array, place) )DOC") .def("_remove_from_pool", [](Scope &self) { ScopePool::Instance().Remove(&self); }) .def("var", [](Scope &self, const std::string &name) -> Variable * { return self.Var(name); }, py::arg("name"), R"DOC( Find or create variable named :code:`name` in the current scope. If the variable named :code:`name` does not exist in the current scope, the variable would be created. Otherwise, return the existing variable. Args: name (str): the variable name. Returns: out (core.Variable): the found or created variable. )DOC", py::return_value_policy::reference) .def("find_var", &Scope::FindVar, py::arg("name"), R"DOC( Find variable named :code:`name` in the current scope or its parent scope. Return None if not found. Args: name (str): the variable name. Returns: out (core.Variable|None): the found variable or None. )DOC", py::return_value_policy::reference) .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); }, R"DOC( Create a new sub-scope of the current scope. Returns: out (core._Scope): the created sub-scope. )DOC", py::return_value_policy::reference) .def("drop_kids", &Scope::DropKids, R"DOC( Delete all sub-scopes of the current scope. )DOC") .def("_kids", &Scope::kids); m.def("Scope", []() -> Scope * { auto *s = new Scope(); ScopePool::Instance().Insert(std::unique_ptr(s)); return s; }, R"DOC( Create a new scope. Returns: out (core._Scope): the created scope. )DOC", py::return_value_policy::reference); //! @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 { std::vector ret_values; for (auto &iter : OpInfoMap::Instance().map()) { auto &info = iter.second; if (info.HasOpProtoAndChecker()) { std::string str; PADDLE_ENFORCE_EQ( info.Proto().SerializeToString(&str), true, "Serialize OpProto Error. This could be a bug of Paddle."); ret_values.emplace_back(str); } } return ret_values; }); m.def("get_op_attrs_default_value", [](py::bytes byte_name) -> paddle::framework::AttributeMap { std::string op_type = byte_name; paddle::framework::AttributeMap res; auto info = OpInfoMap::Instance().GetNullable(op_type); if (info != nullptr) { if (info->HasOpProtoAndChecker()) { auto op_checker = info->Checker(); res = op_checker->GetAttrsDefaultValuesMap(); } } return res; }); m.def( "get_grad_op_desc", [](const OpDesc &op_desc, const std::unordered_set &no_grad_set, const std::vector &grad_sub_block) { std::unordered_map grad_to_var; std::vector> grad_op_descs = framework::OpInfoMap::Instance() .Get(op_desc.Type()) .GradOpMaker()(op_desc, no_grad_set, &grad_to_var, grad_sub_block); std::vector grad_op_desc_ptrs(grad_op_descs.size()); std::transform(grad_op_descs.begin(), grad_op_descs.end(), grad_op_desc_ptrs.begin(), [](std::unique_ptr &p) { return p.release(); }); return std::make_pair(grad_op_desc_ptrs, grad_to_var); }); m.def("has_grad_op_maker", [](const std::string op_type) { return framework::OpInfoMap::Instance().Get(op_type).HasGradOpMaker(); }); m.def("has_non_empty_grad_op_maker", [](const std::string op_type) { return framework::OpInfoMap::Instance() .Get(op_type) .HasNonEmptyGradOpMaker(); }); m.def("has_infer_inplace", [](const std::string op_type) { return framework::OpInfoMap::Instance().Get(op_type).HasInferInplace(); }); m.def("infer_no_need_buffer_slots", [](const std::string op_type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, const framework::AttributeMap &attrs) { auto infer_func = framework::OpInfoMap::Instance() .Get(op_type) .NoNeedBufferVarsInferer(); if (infer_func) { return infer_func(inputs, outputs, attrs); } else { std::unordered_set empty = {}; return empty; } }); m.def("prune", [](const ProgramDesc &origin, const std::set &feeded_var_names, const std::vector> &targets) { ProgramDesc prog_with_targets(origin); for (const auto &t : targets) { prog_with_targets.MutableBlock(t[0])->Op(t[1])->SetIsTarget(true); } proto::ProgramDesc pruned_desc; Prune(*prog_with_targets.Proto(), feeded_var_names, &pruned_desc); return new ProgramDesc(pruned_desc); }); m.def("prune_backward", [](const framework::ProgramDesc &program) { return PruneBackward(program); }); m.def("empty_var_name", []() { return std::string(framework::kEmptyVarName); }); m.def("grad_var_suffix", []() { return std::string(framework::kGradVarSuffix); }); m.def_submodule( "var_names", "The module will return special predefined variable name in Paddle") .def("empty", []() { return kEmptyVarName; }) .def("temp", []() { return kTempVarName; }); // clang-format off py::class_(m, "DeviceContext") .def_static("create", [](paddle::platform::CPUPlace& place) -> paddle::platform::DeviceContext* { return new paddle::platform::CPUDeviceContext(); }) .def_static("create", [](paddle::platform::CUDAPlace& place) -> paddle::platform::DeviceContext* { #ifndef PADDLE_WITH_CUDA PADDLE_THROW("CUDAPlace is not supported in CPU device."); #else return new paddle::platform::CUDADeviceContext(place); #endif }) .def_static("create", [](paddle::platform::CUDAPinnedPlace& place) -> paddle::platform::DeviceContext* { #ifndef PADDLE_WITH_CUDA PADDLE_THROW( "CUDAPinnedPlace is not supported in CPU device."); #else return new paddle::platform::CUDAPinnedDeviceContext(place); #endif });; // clang-format on #if (defined(PADDLE_WITH_CUDA) && !defined(_WIN32)) py::class_(m, "Communicator").def(py::init<>()); #endif py::class_(m, "CUDAPlace", R"DOC( **Note**: For multi-card tasks, please use `FLAGS_selected_gpus` environment variable to set the visible GPU device. The next version will fix the problem with `CUDA_VISIBLE_DEVICES` environment variable. CUDAPlace is a descriptor of a device. It represents a GPU device allocated or to be allocated with Tensor or LoDTensor. Each CUDAPlace has a dev_id to indicate the graphics card ID represented by the current CUDAPlace, staring from 0. The memory of CUDAPlace with different dev_id is not accessible. Numbering here refers to the logical ID of the visible graphics card, not the actual ID of the graphics card. You can set visible GPU devices by setting the `CUDA_VISIBLE_DEVICES` environment variable. When the program starts, visible GPU devices will be numbered from 0. If `CUDA_VISIBLE_DEVICES` is not set, all devices are visible by default, and the logical ID is the same as the actual ID. Parameters: id (int): GPU device ID. Examples: .. code-block:: python import paddle.fluid as fluid gpu_place = fluid.CUDAPlace(0) )DOC") .def("__init__", [](platform::CUDAPlace &self, int dev_id) { #ifdef PADDLE_WITH_CUDA if (UNLIKELY(dev_id < 0)) { LOG(ERROR) << string::Sprintf( "Invalid CUDAPlace(%d), device id must be 0 or " "positive integer", dev_id); std::exit(-1); } if (UNLIKELY(dev_id >= platform::GetCUDADeviceCount())) { if (platform::GetCUDADeviceCount() == 0) { LOG(ERROR) << "Cannot use GPU because there is no GPU " "detected on your " "machine."; std::exit(-1); } else { LOG(ERROR) << string::Sprintf( "Invalid CUDAPlace(%d), must inside [0, %d), because GPU " "number on your machine is %d", dev_id, platform::GetCUDADeviceCount(), platform::GetCUDADeviceCount()); std::exit(-1); } } new (&self) platform::CUDAPlace(dev_id); #else LOG(ERROR) << string::Sprintf( "Cannot use GPU because you have installed CPU version " "PaddlePaddle.\n" "If you want to use GPU, please try to install GPU version " "PaddlePaddle by: pip install paddlepaddle-gpu\n" "If you only have CPU, please change CUDAPlace(%d) to be " "CPUPlace().\n", dev_id); std::exit(-1); #endif }) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("__str__", string::to_string); py::class_(m, "CPUPlace", R"DOC( CPUPlace is a descriptor of a device. It represents a CPU device allocated or to be allocated with Tensor or LoDTensor. Examples: .. code-block:: python import paddle.fluid as fluid cpu_place = fluid.CPUPlace()to be allocated )DOC") .def(py::init<>()) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("__str__", string::to_string); py::class_(m, "CUDAPinnedPlace", R"DOC( CUDAPinnedPlace is a descriptor of a device. It refers to the page locked memory allocated by the CUDA function `cudaHostAlloc()` in the host memory. The host operating system will not paging and exchanging the memory. It can be accessed through direct memory access technology to speed up the copy of data between the host and GPU. For more information on CUDA data transfer and `pinned memory`, please refer to `official document `_ . Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CUDAPinnedPlace() )DOC") .def("__init__", [](platform::CUDAPinnedPlace &self) { #ifndef PADDLE_WITH_CUDA PADDLE_THROW("Cannot use CUDAPinnedPlace in CPU only version"); #endif new (&self) platform::CUDAPinnedPlace(); }) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("__str__", string::to_string); py::class_(m, "Place") .def(py::init<>()) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("is_gpu_place", [](platform::Place &self) { return platform::is_gpu_place(self); }) .def("is_cpu_place", [](platform::Place &self) { return platform::is_cpu_place(self); }) .def("is_cuda_pinned_place", [](platform::Place &self) { return platform::is_cuda_pinned_place(self); }) .def("gpu_device_id", [](platform::Place &self) { return boost::get(self).device; }) .def("set_place", [](platform::Place &self, const platform::Place &other) { self = other; }) .def("set_place", [](platform::Place &self, const platform::CPUPlace &cpu_place) { self = cpu_place; }) .def("set_place", [](platform::Place &self, const platform::CUDAPlace &gpu_place) { self = gpu_place; }) .def("set_place", [](platform::Place &self, const platform::CUDAPinnedPlace &cuda_pinned_place) { self = cuda_pinned_place; }); py::class_(m, "Operator") .def_static( "create", [](py::bytes protobin) { proto::OpDesc desc; PADDLE_ENFORCE_EQ(desc.ParsePartialFromString(protobin), true, "Cannot parse user input to OpDesc"); PADDLE_ENFORCE_EQ(desc.IsInitialized(), true, "User OpDesc is not initialized, reason %s", desc.InitializationErrorString()); return OpRegistry::CreateOp(desc); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CPUPlace &place) { self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CUDAPlace &place) { self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CUDAPinnedPlace &place) { self.Run(scope, place); }) .def("type", [](const OperatorBase &op) -> std::string { return op.Type(); }) .def("outputs", [](const OperatorBase &op) -> std::map> { return op.Outputs(); }) .def("output_vars", [](const OperatorBase &op) { return op.OutputVars(true); }) .def("inputs", [](const OperatorBase &op) { return op.Inputs(); }) .def("input_vars", [](const OperatorBase &op) { return op.InputVars(); }) .def("__str__", &OperatorBase::DebugString) .def("no_intermediate_outputs", [](const OperatorBase &op) { return op.OutputVars(false); }) .def("support_gpu", &OperatorBase::SupportGPU); py::class_(m, "ExecutorPrepareContext") .def(py::init()); py::class_>( m, "TrainerBase") .def("get_worker_scope", [](TrainerBase &self, int thread_id) -> Scope * { return self.GetWorkerScope(thread_id); }, py::return_value_policy::reference) .def("finalize", &TrainerBase::Finalize); py::class_(m, "Executor") .def(py::init()) .def("close", &Executor::Close) .def("run_from_dataset", &Executor::RunFromDataset, py::call_guard()) .def("release_trainer", &Executor::ReleaseTrainer, py::call_guard()) .def("init_for_dataset", [](Executor &self, const ProgramDesc &prog, const std::string &trainer_desc, Scope *scope, Dataset *dataset) -> std::shared_ptr { pybind11::gil_scoped_release release; return self.InitForDataset(prog, trainer_desc, scope, dataset); }) .def("run_from_dataset", [](Executor &self, std::shared_ptr trainer) { pybind11::gil_scoped_release release; self.RunFromDataset(trainer); }) .def("run_prepared_ctx", [](Executor &self, ExecutorPrepareContext *ctx, Scope *scope, std::map *feed_targets, std::map *fetch_targets, bool create_local_scope = true, bool create_vars = true, const std::string &feed_holder_name = "feed", const std::string &fetch_holder_name = "fetch") { pybind11::gil_scoped_release release; self.RunPreparedContext(ctx, scope, feed_targets, fetch_targets, create_local_scope, create_vars, feed_holder_name, fetch_holder_name); }) .def("run_prepared_ctx", [](Executor &self, ExecutorPrepareContext *ctx, Scope *scope, bool create_local_scope = true, bool create_vars = true, bool keep_kids = false) { pybind11::gil_scoped_release release; self.RunPreparedContext(ctx, scope, create_local_scope, create_vars, keep_kids); }) .def("prepare", [](Executor &self, const ProgramDesc &program, int block_id, const std::vector &skip_ref_cnt_vars = std::vector(), bool force_disable_gc = false) { pybind11::gil_scoped_release release; return self.Prepare(program, block_id, skip_ref_cnt_vars, force_disable_gc); }) .def("create_variables", &Executor::CreateVariables) .def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope, int block_id, bool create_local_scope, bool create_vars, const std::vector &fetch_vars) { pybind11::gil_scoped_release release; self.Run(prog, scope, block_id, create_local_scope, create_vars, fetch_vars); }); m.def("init_gflags", framework::InitGflags); m.def("init_glog", framework::InitGLOG); m.def("load_op_library", framework::LoadOpLib); m.def("init_devices", [](bool init_p2p) { framework::InitDevices(init_p2p); }); m.def("is_compiled_with_ngraph", IsCompiledWithNGRAPH); m.def("is_compiled_with_cuda", IsCompiledWithCUDA); m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN); m.def("is_compiled_with_brpc", IsCompiledWithBrpc); m.def("is_compiled_with_dist", IsCompiledWithDIST); #ifdef PADDLE_WITH_CUDA m.def("is_float16_supported", [](const platform::CUDAPlace &place) -> bool { // Only GPUs with Compute Capability >= 53 support float16 return platform::GetCUDAComputeCapability(place.device) >= 53; }); #endif m.def("set_feed_variable", framework::SetFeedVariable); m.def("get_fetch_variable", framework::GetFetchVariable); m.def("get_variable_tensor", framework::GetVariableTensor); m.def("_is_program_version_supported", IsProgramVersionSupported); BindProgramDesc(&m); BindBlockDesc(&m); BindVarDsec(&m); BindOpDesc(&m); BindConstValue(&m); BindGlobalValueGetterSetter(&m); py::class_(m, "LodRankTable") .def("items", [](framework::LoDRankTable &table) { std::vector> res; for (auto &item : table.items()) { res.push_back({item.index, item.length}); } return res; }); py::class_(m, "LoDTensorArray", R"DOC( LoDTensorArray is array of LoDTensor, it supports operator[], len() and for-loop iteration. Examples: .. code-block:: python import paddle.fluid as fluid arr = fluid.LoDTensorArray() )DOC") .def("__init__", [](LoDTensorArray &instance) { new (&instance) LoDTensorArray(); }) .def("__getitem__", [](LoDTensorArray &self, size_t i) { return &self.at(i); }, py::return_value_policy::reference) .def("__len__", [](LoDTensorArray &self) { return self.size(); }) .def("__setitem__", [](LoDTensorArray &self, size_t i, const LoDTensor &t) { PADDLE_ENFORCE_LT(i, self.size()); self[i].ShareDataWith(t); self[i].set_lod(t.lod()); }) .def("append", [](LoDTensorArray &self, const LoDTensor &t) { self.emplace_back(); self.back().ShareDataWith(t); self.back().set_lod(t.lod()); }, py::arg("tensor"), R"DOC( Append a LoDensor to LoDTensorArray. Args: tensor (LoDTensor): The LoDTensor to be appended. Returns: None. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np arr = fluid.LoDTensorArray() t = fluid.LoDTensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) arr.append(t) )DOC") .def("_move_to_list", [](LoDTensorArray &self) -> py::list { py::list res(self.size()); for (size_t i = 0; i < self.size(); ++i) { res[i] = py::cast(std::move(self[i])); } self.clear(); return res; }, py::return_value_policy::take_ownership); m.def("op_support_gpu", OpSupportGPU); #ifdef PADDLE_WITH_CUDA m.def("get_cuda_device_count", platform::GetCUDADeviceCount); #ifndef _WIN32 m.def("nvprof_init", platform::CudaProfilerInit); m.def("nvprof_start", platform::CudaProfilerStart); m.def("nvprof_stop", platform::CudaProfilerStop); #endif #endif py::enum_(m, "ProfilerState", py::arithmetic()) .value("kDisabled", platform::ProfilerState::kDisabled) .value("kCPU", platform::ProfilerState::kCPU) .value("kCUDA", platform::ProfilerState::kCUDA) .value("kAll", platform::ProfilerState::kAll) .export_values(); py::enum_(m, "EventSortingKey", py::arithmetic()) .value("kDefault", platform::EventSortingKey::kDefault) .value("kCalls", platform::EventSortingKey::kCalls) .value("kTotal", platform::EventSortingKey::kTotal) .value("kMin", platform::EventSortingKey::kMin) .value("kMax", platform::EventSortingKey::kMax) .value("kAve", platform::EventSortingKey::kAve) .export_values(); m.def("enable_profiler", platform::EnableProfiler); m.def("disable_profiler", platform::DisableProfiler); m.def("is_profiler_enabled", platform::IsProfileEnabled); m.def("reset_profiler", platform::ResetProfiler); m.def("get_pass", [](const std::string &pass_type) { auto pass = framework::ir::PassRegistry::Instance().Get(pass_type); return std::shared_ptr(std::move(pass)); }); m.def("size_of_dtype", framework::SizeOfType); using VarQuantScale = std::unordered_map>; py::class_> pass(m, "Pass"); pass.def(py::init()) .def("has", &ir::Pass::Has) .def("set_not_owned", [](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) { self.SetNotOwned(attr_name, &attr); }) .def( "set", [](ir::Pass &self, const std::string &name, const std::string &attr) { self.Set(name, new std::string(attr)); }) .def("set", [](ir::Pass &self, const std::string &name, int val) { self.Set(name, new int(val)); }) .def("set", [](ir::Pass &self, const std::string &name, std::unordered_set set) { self.Set(name, new std::unordered_set(set)); }) .def("set", [](ir::Pass &self, const std::string &name, std::unordered_set set) { self.Set(name, new std::unordered_set(set)); }) .def("set", [](ir::Pass &self, const std::string &name, VarQuantScale scales) { self.Set(name, new VarQuantScale(scales)); }) .def("type", &ir::Pass::Type) .def("apply", [](ir::Pass &self, std::shared_ptr graph) { self.Apply(graph.get()); }); py::class_> pb( m, "PassBuilder"); pb.def(py::init()) .def("append_pass", [](ir::PassBuilder &self, const std::string &pass_type) -> std::shared_ptr { return self.AppendPass(pass_type); }) .def("all_passes", [](ir::PassBuilder &self) { return self.AllPasses(); }) .def("insert_pass", [](ir::PassBuilder &self, size_t idx, const std::string &pass_type) { return self.InsertPass(idx, pass_type); }) .def("remove_pass", [](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); }); // -- python binds for parallel executor. py::class_ pe(m, "ParallelExecutor"); py::class_ exec_strategy(pe, "ExecutionStrategy", R"DOC( ExecutionStrategy allows the user to more preciously control how to run the program in ParallelExecutor by setting the property. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[13], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_loss = fluid.layers.mean(cost) sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_loss) exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = 4 train_exe = fluid.ParallelExecutor(use_cuda=False, loss_name=avg_loss.name, exec_strategy=exec_strategy) )DOC"); exec_strategy.def(py::init()) .def_property( "num_threads", [](const ExecutionStrategy &self) { return self.num_threads_; }, [](ExecutionStrategy &self, size_t num_threads) { self.num_threads_ = num_threads; }, R"DOC(The type is INT, num_threads represents the size of thread pool that used to run the operators of the current program in ParallelExecutor. If :math:`num\_threads=1`, all the operators will execute one by one, but the order maybe difference between iterations. If it is not set, it will be set in ParallelExecutor according to the device type and device count, for GPU, :math:`num\_threads=device\_count*4`, for CPU, :math:`num\_threads=CPU\_NUM*4`, the explanation of:math:`CPU\_NUM` is in ParallelExecutor. if it is not set, ParallelExecutor will get the cpu count by calling `multiprocessing.cpu_count()`. Default 0.)DOC") .def_property( "use_cuda", [](const ExecutionStrategy &self) { return self.use_cuda_; }, [](ExecutionStrategy &self, bool use_cuda) { self.use_cuda_ = use_cuda; }) // FIXME(chengduo): Doesn't add doc for 'use_cuda', use_cuda may // make user confuse, because ParallelExecutor has a parameter named // 'use_cuda' too, in current implementation, ParallelExecutor's // 'use_cuda' will rewrite ExecutionStrategy's 'use_cuda'. .def_property( "allow_op_delay", [](const ExecutionStrategy &self) { return self.allow_op_delay_; }, [](ExecutionStrategy &self, bool allow_op_delay) { self.allow_op_delay_ = allow_op_delay; }, R"DOC(The type is BOOL, allow_op_delay represents whether to delay the communication operators to run, it may make the execution faster. Note that this option is invalid now, and it will be removed in next version. Default False.)DOC") .def_property( "num_iteration_per_drop_scope", [](const ExecutionStrategy &self) { return self.num_iteration_per_drop_scope_; }, [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) { self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope; }, R"DOC(The type is INT, num_iteration_per_drop_scope indicates how many iterations to clean up the temp variables which is generated during execution. It may make the execution faster, because the temp variable's shape maybe the same between two iterations. Default 1. NOTES: 1. If you fetch data when calling the 'run', the ParallelExecutor will clean up the temp variables at the end of the current iteration. 2. In some NLP model, it may cause the GPU memory is insufficient, in this case, you should reduce `num_iteration_per_drop_scope`. )DOC") .def_property( "num_iteration_per_run", [](const ExecutionStrategy &self) { return self.num_iteration_per_run_; }, [](ExecutionStrategy &self, size_t num_iteration_per_run) { self.num_iteration_per_run_ = num_iteration_per_run; }, R"DOC(This config that how many iteration the executor will run when user call exe.run() in python )DOC") .def_property("_dry_run", [](const ExecutionStrategy &self) { return self.dry_run_; }, [](ExecutionStrategy &self, bool dry_run) { self.dry_run_ = dry_run; }); exec_strategy.def_property( "use_experimental_executor", [](const ExecutionStrategy &self) { return self.type_ == ExecutionStrategy::kExperimental; }, [](ExecutionStrategy &self, bool experimental) { self.type_ = experimental ? ExecutionStrategy::kExperimental : ExecutionStrategy::kDefault; }); py::class_ build_strategy(pe, "BuildStrategy", R"DOC( BuildStrategy allows the user to more preciously control how to build the SSA Graph in ParallelExecutor by setting the property. Examples: .. code-block:: python import os import numpy as np import paddle.fluid as fluid os.environ["CPU_NUM"] = '2' places = fluid.cpu_places() data = fluid.layers.data(name="x", shape=[1], dtype="float32") hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) build_strategy = fluid.BuildStrategy() build_strategy.enable_inplace = True build_strategy.memory_optimize = True build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce program = fluid.compiler.CompiledProgram(fluid.default_main_program()) program = program.with_data_parallel(loss_name=loss.name, build_strategy=build_strategy, places=places) )DOC"); py::enum_(build_strategy, "ReduceStrategy") .value("Reduce", BuildStrategy::ReduceStrategy::kReduce) .value("AllReduce", BuildStrategy::ReduceStrategy::kAllReduce); py::enum_(build_strategy, "GradientScaleStrategy") .value("CoeffNumDevice", BuildStrategy::GradientScaleStrategy::kCoeffNumDevice) .value("One", BuildStrategy::GradientScaleStrategy::kOne) .value("Customized", BuildStrategy::GradientScaleStrategy::kCustomized); build_strategy.def(py::init()) .def_property( "reduce_strategy", [](const BuildStrategy &self) { return self.reduce_; }, [](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.reduce_ = strategy; }, R"DOC((fluid.BuildStrategy.ReduceStrategy, optional): there are two reduce strategies in ParallelExecutor, AllReduce and Reduce. If you want that all the parameters' optimization are done on all devices independently, you should choose AllReduce; otherwise, if you choose Reduce, all the parameters' optimization will be evenly distributed to different devices, and then broadcast the optimized parameter to other devices. Default is 'AllReduce'. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce )DOC") .def_property( "gradient_scale_strategy", [](const BuildStrategy &self) { return self.gradient_scale_; }, [](BuildStrategy &self, BuildStrategy::GradientScaleStrategy strategy) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finalized."); self.gradient_scale_ = strategy; }, R"DOC((fluid.BuildStrategy.GradientScaleStrategy, optional): there are three ways of defining :math:`loss@grad` in ParallelExecutor, that is, CoeffNumDevice, One and Customized. By default, ParallelExecutor sets the :math:`loss@grad` according to the number of devices. If you want to customize :math:`loss@grad`, you can choose Customized. Default is 'CoeffNumDevice'. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.compiler as compiler import numpy import os use_cuda = True place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) # NOTE: If you use CPU to run the program, you need # to specify the CPU_NUM, otherwise, fluid will use # all the number of the logic core as the CPU_NUM, # in that case, the batch size of the input should be # greater than CPU_NUM, if not, the process will be # failed by an exception. if not use_cuda: os.environ['CPU_NUM'] = str(2) places = fluid.cpu_places() else: places = places = fluid.cuda_places() data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) fluid.default_startup_program().random_seed=1 exe.run(fluid.default_startup_program()) build_strategy = fluid.BuildStrategy() build_strategy.gradient_scale_strategy = \ fluid.BuildStrategy.GradientScaleStrategy.Customized compiled_prog = compiler.CompiledProgram( fluid.default_main_program()).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy, places = places) dev_count = len(places) x = numpy.random.random(size=(10, 1)).astype('float32') loss_grad = numpy.ones((dev_count)).astype("float32") * 0.01 loss_grad_name = loss.name+"@GRAD" loss_data = exe.run(compiled_prog, feed={"X": x, loss_grad_name : loss_grad}, fetch_list=[loss.name, loss_grad_name]) )DOC") .def_property( "debug_graphviz_path", [](const BuildStrategy &self) { return self.debug_graphviz_path_; }, [](BuildStrategy &self, const std::string &path) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.debug_graphviz_path_ = path; }, R"DOC((str, optional): debug_graphviz_path indicates the path that writing the SSA Graph to file in the form of graphviz. It is useful for debugging. Default is empty string, that is, "" Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.debug_graphviz_path = "./graph" )DOC") .def_property( "enable_sequential_execution", [](const BuildStrategy &self) { return self.enable_sequential_execution_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.enable_sequential_execution_ = b; }, R"DOC((bool, optional): If set True, the execution order of ops would be the same as what is in the program. Default is False. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.enable_sequential_execution = True )DOC") .def_property( "remove_unnecessary_lock", [](const BuildStrategy &self) { return self.remove_unnecessary_lock_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.remove_unnecessary_lock_ = b; }, R"DOC((bool, optional): If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default is True. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.remove_unnecessary_lock = True )DOC") .def_property( "num_trainers", [](const BuildStrategy &self) { return self.num_trainers_; }, [](BuildStrategy &self, int num_trainers) { #ifdef WIN32 PADDLE_THROW("Windows has NO support to distribute mode."); #endif self.num_trainers_ = num_trainers; }) .def_property( "trainers_endpoints", [](const BuildStrategy &self) { return self.trainers_endpoints_; }, [](BuildStrategy &self, const std::vector &trainers_endpoints) { self.trainers_endpoints_ = trainers_endpoints; }) .def_property("trainer_id", [](const BuildStrategy &self) { return self.trainer_id_; }, [](BuildStrategy &self, int trainer_id) { self.trainer_id_ = trainer_id; }) .def_property( "nccl_comm_num", [](const BuildStrategy &self) { return self.nccl_comm_num_; }, [](BuildStrategy &self, int nccl_comm_num) { self.nccl_comm_num_ = nccl_comm_num; }) .def_property("use_hierarchical_allreduce", [](const BuildStrategy &self) { return self.use_hierarchical_allreduce_; }, [](BuildStrategy &self, bool use) { self.use_hierarchical_allreduce_ = use; }) .def_property("hierarchical_allreduce_inter_nranks", [](const BuildStrategy &self) { return self.hierarchical_allreduce_inter_nranks_; }, [](BuildStrategy &self, int nranks) { self.hierarchical_allreduce_inter_nranks_ = nranks; }) .def_property( "fuse_elewise_add_act_ops", [](const BuildStrategy &self) { return self.fuse_elewise_add_act_ops_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.fuse_elewise_add_act_ops_ = b; }, R"DOC((bool, optional): fuse_elewise_add_act_ops indicate whether to fuse elementwise_add_op and activation_op, it may make the execution faster. Default is False. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.fuse_elewise_add_act_ops = True )DOC") .def_property( "fuse_bn_act_ops", [](const BuildStrategy &self) { return self.fuse_bn_act_ops_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy is finlaized.")); self.fuse_bn_act_ops_ = b; }, R"DOC((bool, optional): fuse_bn_act_ops indicate whether to fuse batch_norm and activation_op, it may make the execution faster. Default is False. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.fuse_bn_act_ops = True )DOC") .def_property( "fuse_relu_depthwise_conv", [](const BuildStrategy &self) { return self.fuse_relu_depthwise_conv_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.fuse_relu_depthwise_conv_ = b; }, R"DOC((bool, optional): fuse_relu_depthwise_conv indicate whether to fuse relu and depthwise_conv2d, it will save GPU memory and may make the execution faster. This options is only available in GPU devices. Default is False. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.fuse_relu_depthwise_conv = True )DOC") .def_property("fuse_broadcast_ops", [](const BuildStrategy &self) { return self.fuse_broadcast_ops_ == true || self.fuse_broadcast_ops_ == boost::none; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.fuse_broadcast_ops_ = b; }, R"DOC((bool, optional): fuse_broadcast_op indicates whether to fuse the broadcast ops. Note that, in Reduce mode, fusing broadcast ops may make the program faster. Because fusing broadcast OP equals delaying the execution of all broadcast Ops, in this case, all nccl streams are used only for NCCLReduce operations for a period of time. Default False. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.fuse_broadcast_ops = True )DOC") .def_property("fuse_all_optimizer_ops", [](const BuildStrategy &self) { return self.fuse_all_optimizer_ops_ == true || self.fuse_all_optimizer_ops_ == boost::none; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.fuse_all_optimizer_ops_ = b; }) .def_property( "sync_batch_norm", [](const BuildStrategy &self) { return self.sync_batch_norm_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_EQ(!self.IsFinalized(), true, "BuildStrategy is finlaized."); self.sync_batch_norm_ = b; }, R"DOC((bool, optional): sync_batch_norm indicates whether to use synchronous batch normalization which synchronizes the mean and variance through multi-devices in training phase. Current implementation doesn't support FP16 training and CPU. And only synchronous on one machine, not all machines. Default is False. Examples: .. code-block:: python import paddle.fluid as fluid build_strategy = fluid.BuildStrategy() build_strategy.sync_batch_norm = True )DOC") .def_property( "memory_optimize", [](const BuildStrategy &self) -> py::object { if (self.memory_optimize_) { return py::cast(self.memory_optimize_.get()); } else { return py::cast(nullptr); } }, [](BuildStrategy &self, const py::handle &value) { auto *py_obj = value.ptr(); if (py_obj == nullptr || py_obj == Py_None) { self.memory_optimize_ = boost::none; } else if (PyBool_Check(py_obj)) { self.memory_optimize_ = (py_obj == Py_True); } else { PADDLE_THROW( "BuildStrategy.memory_optimize must be None, False or " "True"); } }, R"DOC((bool, optional): memory opitimize aims to save total memory consumption, set to True to enable it. Default None. None means framework would choose to use or not use this strategy automatically. Currently, None means that it is enabled when GC is disabled, and disabled when GC is enabled. True means enabling and False means disabling. Default is None.)DOC") .def_property( "is_distribution", [](const BuildStrategy &self) { return self.is_distribution_; }, [](BuildStrategy &self, bool b) { #ifdef WIN32 if (b) { PADDLE_THROW("Windows has NO support to distribute mode."); } #else self.is_distribution_ = b; #endif }) .def_property("async_mode", [](const BuildStrategy &self) { return self.async_mode_; }, [](BuildStrategy &self, bool b) { self.async_mode_ = b; }) .def_property( "enable_inplace", [](const BuildStrategy &self) { return self.enable_inplace_; }, [](BuildStrategy &self, bool b) { self.enable_inplace_ = b; }) .def_property( "fuse_all_reduce_ops", [](const BuildStrategy &self) { return self.fuse_all_reduce_ops_ == true || self.fuse_all_reduce_ops_ == boost::none; }, [](BuildStrategy &self, bool b) { self.fuse_all_reduce_ops_ = b; }) .def_property("enable_backward_optimizer_op_deps", [](const BuildStrategy &self) { return self.enable_backward_optimizer_op_deps_; }, [](BuildStrategy &self, bool b) { self.enable_backward_optimizer_op_deps_ = b; }) .def_property( "cache_runtime_context", [](const BuildStrategy &self) { return self.cache_runtime_context_; }, [](BuildStrategy &self, bool b) { self.cache_runtime_context_ = b; }) .def_property( "mkldnn_enabled_op_types", [](const BuildStrategy &self) { return self.mkldnn_enabled_op_types_; }, [](BuildStrategy &self, const std::unordered_set &mkldnn_enabled_op_types) { self.mkldnn_enabled_op_types_ = mkldnn_enabled_op_types; }) .def("_finalize_strategy_and_create_passes", [](BuildStrategy &self) -> std::shared_ptr { return self.CreatePassesFromStrategy(true); }, R"DOC(Allow user to customized passes. Normally model-specific optimization passes should be defined in this way. BuildStrategy cannot be updated after being finalized.)DOC"); pe.def(py::init &, const std::vector &, const std::string &, Scope *, std::vector &, const ExecutionStrategy &, const BuildStrategy &, ir::Graph *>()) // NOTE: even we return a vec* to Python use reference policy. // We still cannot get local_scope from this vector, since the element // of vec will be freed by Python GC. We can only return Scope* // one by one and mark them as reference. .def("local_scopes", [](ParallelExecutor &self) -> std::vector * { return &self.GetLocalScopes(); }, py::return_value_policy::reference) .def("drop_local_exe_scopes", &ParallelExecutor::DropLocalExeScopes) .def("_need_create_local_exe_scopes", &ParallelExecutor::NeedCreateLocalExeScope) .def("feed_tensors_into_local_scopes", &ParallelExecutor::FeedTensorsIntoLocalScopes) .def("feed_and_split_tensor_into_local_scopes", &ParallelExecutor::FeedAndSplitTensorIntoLocalScopes) .def("run", [](ParallelExecutor &self, const std::vector &fetch_tensors) { pybind11::gil_scoped_release release; return self.Run(fetch_tensors); }) .def("device_count", &ParallelExecutor::DeviceCount); BindFleetWrapper(&m); BindGlooWrapper(&m); BindBoxHelper(&m); #ifndef _WIN32 BindNCCLWrapper(&m); #endif BindGraph(&m); BindNode(&m); BindInferenceApi(&m); BindDataset(&m); #ifdef PADDLE_WITH_DISTRIBUTE BindCommunicator(&m); #endif } } // namespace pybind } // namespace paddle