/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. Copyright (c) 2022 NVIDIA 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 #include #include // NOLINT // for call_once #include #include #include #include #include #include #include #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/custom_operator.h" #include "paddle/fluid/framework/data_layout.h" #include "paddle/fluid/framework/data_type_transform.h" #include "paddle/fluid/framework/executor.h" #include "paddle/fluid/framework/executor_cache.h" #include "paddle/fluid/framework/executor_gc_helper.h" #include "paddle/fluid/framework/feed_fetch_method.h" #include "paddle/fluid/framework/feed_fetch_type.h" #include "paddle/fluid/framework/garbage_collector.h" #include "paddle/fluid/framework/io/fs.h" #include "paddle/fluid/framework/ir/coalesce_grad_tensor_pass.h" #include "paddle/fluid/framework/ir/cost_model.h" #include "paddle/fluid/framework/ir/generate_pass.h" #include "paddle/fluid/framework/ir/pass_builder.h" #include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/lod_tensor_array.h" #include "paddle/fluid/framework/new_executor/executor_statistics.h" #include "paddle/fluid/framework/new_executor/standalone_executor.h" #include "paddle/fluid/framework/op_info.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/framework/parallel_executor.h" #include "paddle/fluid/framework/phi_utils.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_utils.h" #include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/framework/trainer.h" #include "paddle/fluid/framework/type_defs.h" #include "paddle/fluid/framework/version.h" #include "paddle/fluid/imperative/amp_auto_cast.h" #include "paddle/fluid/imperative/layer.h" #include "paddle/fluid/memory/allocation/allocator_strategy.h" #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/memory/allocation/cuda_ipc_allocator.h" #endif #include "paddle/fluid/memory/allocation/mmap_allocator.h" #include "paddle/fluid/operators/activation_op.h" #include "paddle/fluid/operators/common_infer_shape_functions.h" #include "paddle/fluid/operators/py_func_op.h" #include "paddle/fluid/platform/cpu_helper.h" #include "paddle/fluid/platform/cpu_info.h" #include "paddle/fluid/platform/device/device_wrapper.h" #include "paddle/fluid/platform/device_context.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/monitor.h" #include "paddle/fluid/platform/place.h" #include "paddle/fluid/platform/profiler.h" #include "paddle/fluid/platform/profiler/event_python.h" #include "paddle/fluid/platform/profiler/event_tracing.h" #include "paddle/fluid/platform/profiler/profiler.h" #include "paddle/fluid/pybind/cuda_streams_py.h" #include "paddle/fluid/pybind/distributed_py.h" #include "paddle/fluid/pybind/eager.h" #include "paddle/fluid/pybind/imperative.h" #include "paddle/fluid/pybind/io.h" #include "paddle/phi/core/compat/convert_utils.h" #include "paddle/phi/core/lod_utils.h" #include "paddle/utils/none.h" #ifdef PADDLE_WITH_ASCEND #include "paddle/fluid/pybind/ascend_wrapper_py.h" #endif #include "paddle/fluid/pybind/bind_cost_model.h" #include "paddle/fluid/pybind/bind_fleet_executor.h" #include "paddle/fluid/pybind/box_helper_py.h" #include "paddle/fluid/pybind/communication.h" #include "paddle/fluid/pybind/compatible.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/generator_py.h" #include "paddle/fluid/pybind/global_value_getter_setter.h" #include "paddle/fluid/pybind/gloo_context_py.h" #include "paddle/fluid/pybind/gloo_wrapper_py.h" #include "paddle/fluid/pybind/heter_wrapper_py.h" #include "paddle/fluid/pybind/inference_api.h" #include "paddle/fluid/pybind/ir.h" #include "paddle/fluid/pybind/metrics_py.h" #include "paddle/fluid/pybind/ps_gpu_wrapper_py.h" #include "paddle/fluid/pybind/pybind_boost_headers.h" #include "paddle/phi/backends/device_manager.h" #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) #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" #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" #endif #ifndef PADDLE_WITH_HIP #include "paddle/fluid/platform/device/gpu/cuda/cuda_profiler.h" #endif #include "paddle/fluid/platform/device/gpu/gpu_info.h" #endif #ifdef PADDLE_WITH_ASCEND_CL #include "paddle/fluid/platform/collective_helper.h" #include "paddle/fluid/platform/device/npu/npu_info.h" #include "paddle/fluid/platform/device/npu/npu_profiler.h" #endif #ifdef PADDLE_WITH_XPU #include "paddle/fluid/platform/device/xpu/xpu_info.h" #include "paddle/fluid/platform/device/xpu/xpu_op_list.h" #endif #include "paddle/fluid/platform/cuda_graph_with_memory_pool.h" #ifdef PADDLE_WITH_IPU #include "paddle/fluid/platform/device/ipu/ipu_backend.h" #include "paddle/fluid/platform/device/ipu/ipu_info.h" #endif #ifdef PADDLE_WITH_MLU #include "paddle/fluid/platform/device/mlu/mlu_info.h" #endif #ifdef PADDLE_WITH_CRYPTO #include "paddle/fluid/pybind/crypto.h" #endif #if defined PADDLE_WITH_PSCORE #include "paddle/fluid/pybind/fleet_py.h" #endif #ifdef PADDLE_WITH_CINN #include "paddle/fluid/framework/paddle2cinn/cinn_compiler.h" #endif #include "paddle/fluid/eager/api/utils/global_utils.h" #include "paddle/fluid/imperative/layout_autotune.h" #include "paddle/fluid/pybind/eager_utils.h" #include "paddle/phi/api/ext/op_meta_info.h" #include "paddle/phi/kernels/autotune/cache.h" #include "paddle/phi/kernels/autotune/switch_autotune.h" #include "pybind11/stl.h" DECLARE_bool(use_mkldnn); // disable auto conversion to list in Python PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray); PYBIND11_MAKE_OPAQUE(paddle::framework::FetchUnmergedList); PYBIND11_MAKE_OPAQUE(paddle::framework::FetchList); PYBIND11_MAKE_OPAQUE(paddle::framework::FetchType); namespace paddle { namespace pybind { PyTypeObject *g_place_pytype = nullptr; PyTypeObject *g_framework_scope_pytype = nullptr; PyTypeObject *g_cudaplace_pytype = nullptr; PyTypeObject *g_cpuplace_pytype = nullptr; PyTypeObject *g_xpuplace_pytype = nullptr; PyTypeObject *g_npuplace_pytype = nullptr; PyTypeObject *g_cudapinnedplace_pytype = nullptr; PyTypeObject *g_mluplace_pytype = nullptr; PyTypeObject *g_customplace_pytype = nullptr; PyTypeObject *g_framework_tensor_pytype = nullptr; PyTypeObject *g_framework_lodtensorarray_pytype = nullptr; PyTypeObject *g_custom_op_kernel_ctx_pytype = nullptr; bool IsCompiledWithCUDA() { #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP) return false; #else return true; #endif } bool IsCompiledWithNCCL() { #ifdef PADDLE_WITH_NCCL return true; #else return false; #endif } bool IsCompiledWithROCM() { #ifndef PADDLE_WITH_HIP return false; #else return true; #endif } bool IsCompiledWithAscend() { #ifndef PADDLE_WITH_ASCEND return false; #else return true; #endif } bool IsCompiledWithXPU() { #ifndef PADDLE_WITH_XPU return false; #else return true; #endif } bool IsCompiledWithNPU() { #ifndef PADDLE_WITH_ASCEND_CL return false; #else return true; #endif } bool IsCompiledWithIPU() { #ifndef PADDLE_WITH_IPU return false; #else return true; #endif } bool IsCompiledWithMKLDNN() { #ifndef PADDLE_WITH_MKLDNN return false; #else return true; #endif } bool IsCompiledWithCINN() { #ifndef PADDLE_WITH_CINN return false; #else return true; #endif } bool IsCompiledWithMLU() { #ifndef PADDLE_WITH_MLU return false; #else return true; #endif } bool IsCompiledWithHETERPS() { #ifndef PADDLE_WITH_HETERPS return false; #else return true; #endif } bool SupportsBfloat16() { #ifndef PADDLE_WITH_MKLDNN return false; #else if (platform::MayIUse(platform::cpu_isa_t::avx512_core)) return true; else return false; #endif } bool SupportsBfloat16FastPerformance() { #ifndef PADDLE_WITH_MKLDNN return false; #else if (platform::MayIUse(platform::cpu_isa_t::avx512_bf16)) return true; else return false; #endif } bool SupportsInt8() { #ifndef PADDLE_WITH_MKLDNN return false; #else return (platform::MayIUse(platform::cpu_isa_t::avx2) || platform::MayIUse(platform::cpu_isa_t::avx512f)); #endif } bool SupportsVNNI() { #ifndef PADDLE_WITH_MKLDNN return false; #else return platform::MayIUse(platform::cpu_isa_t::avx512_core_vnni); #endif } bool IsCompiledWithBrpc() { #ifndef PADDLE_WITH_DISTRIBUTE 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).GetType()); } 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(platform::errors::InvalidArgument( "Python object is not type of %s, the real type is %s", typeid(T).name(), obj->ob_type->tp_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(platform::errors::InvalidArgument( "The parameter [%s] to save 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(platform::errors::InvalidArgument( "The parameter list to save 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, platform::errors::InvalidArgument( "The name of parameter to save is None")); vec_res.emplace_back(PyObjectCast(py_name)); Py_DECREF(py_name); } } else { PADDLE_THROW(platform::errors::InvalidArgument( "The parameters to save is not 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( platform::errors::InvalidArgument("The parameter list to set 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, platform::errors::InvalidArgument( "The name of parameter to set is None")); auto para_name = PyObjectCast(py_name); Py_DECREF(py_name); auto var = scope.FindVar(para_name); if (var == nullptr) { PADDLE_ENFORCE_NOT_NULL(exe, platform::errors::InvalidArgument( "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, platform::errors::InvalidArgument( "The var_desc of parameter to set is None")); 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(phi::make_ddim(var_desc.GetShape())); tensor_temp->mutable_data( exe->GetPlace(), framework::TransToPhiDataType(var_desc.GetDataType())); } } } else { PADDLE_THROW(platform::errors::InvalidArgument( "The parameters to set is not 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_NCCL static int GetNCCLVersion() { #if NCCL_VERSION_CODE >= 2304 int ver; PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGetVersion(&ver)); return ver; #else PADDLE_THROW(platform::errors::External( "Cannot get NCCL version successfully when nccl version < 2.3.4")); #endif } #endif template static void TensorCopyFrom(framework::Tensor *dst, const framework::Tensor &src, const PlaceType &place, int64_t batch_size) { if (batch_size < 0) { framework::TensorCopy(src, place, dst); } else { auto sliced = src.Slice(0, batch_size); framework::TensorCopy(sliced, place, dst); } } #ifdef PADDLE_WITH_AVX PYBIND11_MODULE(core_avx, m) { #else PYBIND11_MODULE(core_noavx, m) { #endif BindImperative(&m); BindEager(&m); BindEagerStringTensor(&m); BindCudaStream(&m); // 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("disable_signal_handler", &DisableSignalHandler); m.def("clear_gradients", [](std::vector> param_list, bool set_to_zero) { for (auto param : param_list) { param->ClearGradient(set_to_zero); } }); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) m.def("cudnn_version", &platform::DnnVersion); m.def("gpu_memory_available", []() { size_t available = 0; size_t total = 0; paddle::platform::GpuMemoryUsage(&available, &total); return available; }); #endif #ifdef PADDLE_WITH_NCCL m.def("nccl_version", &GetNCCLVersion); #endif m.def("is_cuda_graph_capturing", &platform::IsCUDAGraphCapturing); #ifdef PADDLE_WITH_CUDA py::class_(m, "CUDAGraph") .def_static("begin_capture", [](platform::CUDAPlace place, int mode) { platform::BeginCUDAGraphCapture( place, static_cast(mode)); }) .def_static("end_capture", &platform::EndCUDAGraphCapture) .def("replay", &platform::CUDAGraph::Replay) .def("reset", &platform::CUDAGraph::Reset) .def("print_to_dot_files", &platform::CUDAGraph::PrintToDotFiles); #endif m.def("wait_device", [](const platform::Place &place) { platform::DeviceContextPool::Instance().Get(place)->Wait(); }); m.def("from_dlpack", [](py::capsule *dltensor) { DLManagedTensor *dmt = reinterpret_cast( PyCapsule_GetPointer(dltensor->ptr(), "dltensor")); PADDLE_ENFORCE_NOT_NULL( dmt, platform::errors::InvalidArgument( "from_dlpack received an invalid capsule. " "Note that a DLPack tensor can be consumed only once.")); PyCapsule_SetName(dltensor->ptr(), "used_dltensor"); DLTensor dl = dmt->dl_tensor; framework::Tensor tensor; if (dl.device.device_type == kDLCPU) { paddle::framework::TensorFromDLPack(dl, &tensor); } #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) if (dl.device.device_type == kDLGPU) { paddle::framework::TensorFromDLPack(dl, &tensor); } #endif return tensor; }); 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_op_version_info", [](framework::ProgramDesc &desc) { framework::compatible::pb::OpVersionMap pb_vmap{desc.OpVersionMap()}; framework::compatible::SaveOpVersions( framework::compatible::OpVersionRegistrar::GetInstance() .GetVersionMap(), &pb_vmap); }); m.def("set_printoptions", [](const py::kwargs &kwargs) { auto &print_opt = framework::PrintOptions::Instance(); if (kwargs.contains("precision")) { print_opt.precision = kwargs["precision"].cast(); } if (kwargs.contains("threshold")) { print_opt.threshold = kwargs["threshold"].cast(); } if (kwargs.contains("edgeitems")) { print_opt.edgeitems = kwargs["edgeitems"].cast(); } if (kwargs.contains("linewidth")) { print_opt.linewidth = kwargs["linewidth"].cast(); } if (kwargs.contains("sci_mode")) { print_opt.sci_mode = kwargs["sci_mode"].cast(); } VLOG(4) << "Set printoptions: precision=" << print_opt.precision << ", threshold=" << print_opt.threshold << ", edgeitems=" << print_opt.edgeitems << ", linewidth=" << print_opt.linewidth << ", sci_mode=" << print_opt.sci_mode; }); m.def("broadcast_shape", [](const std::vector &x_dim, const std::vector &y_dim) { return phi::vectorize(operators::details::BroadcastTwoDims( phi::make_ddim(x_dim), phi::make_ddim(y_dim), -1)); }); 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", [](const std::string &lib) { std::unordered_map> all_kernels_info; if (lib == "fluid" || lib == "all") { auto &all_kernels = paddle::framework::OperatorWithKernel::AllOpKernels(); 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.emplace_back( paddle::framework::KernelTypeToString(kernel_type)); } all_kernels_info.emplace(op_type, kernel_types); } } if (lib == "phi" || lib == "all") { auto phi_kernels = phi::KernelFactory::Instance().kernels(); for (auto &kernel_pair : phi_kernels) { auto op_type = phi::TransToFluidOpName(kernel_pair.first); std::vector kernel_types; for (auto &info_pair : kernel_pair.second) { framework::OpKernelType kernel_type = framework::TransPhiKernelKeyToOpKernelType(info_pair.first); auto kernel_type_str = framework::KernelTypeToString(kernel_type); if (all_kernels_info.count(op_type)) { if (std::find(all_kernels_info[op_type].begin(), all_kernels_info[op_type].end(), kernel_type_str) == all_kernels_info[op_type].end()) { all_kernels_info[op_type].emplace_back(kernel_type_str); } } else { kernel_types.emplace_back(kernel_type_str); } } if (!kernel_types.empty()) { all_kernels_info.emplace(op_type, kernel_types); } } } return all_kernels_info; }, py::arg("lib") = "all", R"DOC( Return the registered kernels in paddle. Args: lib[string]: the libarary, could be 'phi', 'fluid' and 'all'. )DOC"); // NOTE(Aganlengzi): KernelFactory static instance is initialized BEFORE // plugins are loaded for custom kernels, but de-initialized AFTER they are // unloaded. We need manually clear symbols(may contain plugins' symbols) // stored in this static instance to avoid illegal memory access. m.def("clear_kernel_factory", []() { phi::KernelFactory::Instance().kernels().clear(); }); m.def("clear_device_manager", []() { #ifdef PADDLE_WITH_CUSTOM_DEVICE phi::DeviceManager::Clear(); #endif }); // 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); m.def("_promote_types_if_complex_exists", &paddle::framework::PromoteTypesIfComplexExists); py::class_ custom_op_kernel_ctx( m, "CustomOpKernelContext", R"DOC()DOC"); g_custom_op_kernel_ctx_pytype = reinterpret_cast(custom_op_kernel_ctx.ptr()); custom_op_kernel_ctx.def(py::init<>()) .def("add_inputs", [](paddle::CustomOpKernelContext &self, const py::handle &input) { PyObject *obj = input.ptr(); if (PyList_Check(obj) || PyTuple_Check(obj)) { self.EmplaceBackInputs( std::move(CastPyArg2VectorOfTensor(obj, 1))); } else { self.EmplaceBackInput(std::move(CastPyArg2Tensor(obj, 1))); } }) .def("add_outputs", [](paddle::CustomOpKernelContext &self, py::handle &outputs) { PyObject *obj = outputs.ptr(); if (PyList_Check(obj) || PyTuple_Check(obj)) { self.EmplaceBackOutputs( std::move(CastPyArg2VectorOfTensor(obj, 1))); } else { self.EmplaceBackOutput(std::move(CastPyArg2Tensor(obj, 1))); } }) .def("add_attr", [](paddle::CustomOpKernelContext &self, bool attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, int attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, float attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, int64_t attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, const std::string &attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, const std::vector &attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, const std::vector &attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, const std::vector &attr) { self.EmplaceBackAttr(attr); }) .def("add_attr", [](paddle::CustomOpKernelContext &self, const std::vector &attr) { self.EmplaceBackAttr(attr); }); py::class_ framework_tensor(m, "Tensor", py::buffer_protocol()); g_framework_tensor_pytype = reinterpret_cast(framework_tensor.ptr()); framework_tensor .def("__array__", [](framework::Tensor &self) { return TensorToPyArray(self); }) .def("_ptr", [](const framework::Tensor &self) { return reinterpret_cast(self.data()); }) .def("_slice", &framework::Tensor::Slice) .def("_numel", &framework::Tensor::numel) .def("_is_initialized", [](const framework::Tensor &self) { return self.IsInitialized(); }) .def("_get_dims", [](const framework::Tensor &self) { return vectorize(self.dims()); }) .def("_set_dims", [](framework::Tensor &self, const std::vector &dim) { self.Resize(phi::make_ddim(dim)); }) .def("_set_layout", [](framework::Tensor &self, const std::string &layout) { self.set_layout(StringToDataLayout(layout)); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::CustomPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::CUDAPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::XPUPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::CPUPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::NPUPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::MLUPlace &place) { self.mutable_data(place); }) .def("_alloc_double", [](framework::Tensor &self, paddle::platform::CPUPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](framework::Tensor &self, paddle::platform::CPUPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](framework::Tensor &self, paddle::platform::CustomPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](framework::Tensor &self, paddle::platform::XPUPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](framework::Tensor &self, paddle::platform::CUDAPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](framework::Tensor &self, paddle::platform::MLUPlace &place) { self.mutable_data(place); }) .def("_alloc_int", [](framework::Tensor &self, paddle::platform::CUDAPinnedPlace &place) { self.mutable_data(place); }) .def("_alloc_float", [](framework::Tensor &self, paddle::platform::CUDAPinnedPlace &place) { self.mutable_data(place); }) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::CPUPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::CustomPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::XPUPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::CUDAPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::CUDAPinnedPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::MLUPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_clear", &framework::Tensor::clear) .def("_mutable_data", [](framework::Tensor &self, paddle::platform::NPUPlace &place, paddle::framework::proto::VarType::Type type) { return reinterpret_cast( self.mutable_data(place, framework::TransToPhiDataType(type))); }) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .def("_copy_from", &TensorCopyFrom, py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1) .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) .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) .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 Tensor on place with given numpy array. Args: lod (numpy.ndarray): The data to set. place (CPUPlace|CUDAPlace|XPUPlace|IPUPlace|CUDAPinnedPlace|NPUPlace|MLUPlace): The place where the Tensor 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.Tensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) )DOC") .def("shape", [](framework::Tensor &self) { return vectorize(self.dims()); }, R"DOC( Return the shape of Tensor. Returns: list[int]: The shape of Tensor. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.Tensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) print(t.shape()) # [5, 30] )DOC") .def("_to_dlpack", [](framework::Tensor &self) { DLPackTensor dlpack_tensor(self, 1); DLManagedTensor *dmt = dlpack_tensor.ToDLManagedTensor(); 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", [](framework::Tensor &self) { return self.place(); }) .def("_dtype", [](framework::Tensor &self) { return framework::TransToProtoVarType(self.type()); }) .def("_layout", [](framework::Tensor &self) { return DataLayoutToString(self.layout()); }) .def("_share_data_with", &framework::Tensor::ShareDataWith) .def("__getitem__", PySliceTensor, py::return_value_policy::reference) .def("__str__", [](const framework::Tensor &self) { std::stringstream ostr; ostr << self; return ostr.str(); }) /* ------ End of original Tensor ------ */ .def( "__init__", [](framework::Tensor &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, platform::errors::InvalidArgument( "The provided recursive_sequence_lengths info is " "invalid, " "the LoD converted by recursive_sequence_lengths is %s", new_lod)); new (&instance) framework::Tensor(new_offset_lod); }) .def("__init__", [](framework::Tensor &instance) { new (&instance) framework::Tensor(); }) // 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", [](framework::Tensor &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, platform::errors::InvalidArgument( "The provided LoD is invalid, the LoD is %s", new_lod)); self.set_lod(new_lod); }, py::arg("lod"), R"DOC( Set LoD of the Tensor. 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.Tensor() 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", [](framework::Tensor &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, platform::errors::InvalidArgument( "The provided recursive_sequence_lengths info is " "invalid, " "the LoD converted by recursive_sequence_lengths is " "%s", new_lod)); self.set_lod(new_offset_lod); }, py::arg("recursive_sequence_lengths"), R"DOC( Set LoD of the Tensor 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.Tensor() t.set(np.ndarray([5, 30]), fluid.CPUPlace()) t.set_recursive_sequence_lengths([[2, 3]]) print(t.recursive_sequence_lengths()) # [[2, 3]] print(t.lod()) # [[0, 2, 5]] )DOC") .def("lod", [](framework::Tensor &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 Tensor. Returns: list[list[int]]: The lod of the Tensor. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.Tensor() 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", [](framework::Tensor &self) -> std::vector> { // output the length-based lod info LoD lod = phi::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 Tensor. Returns: list[list[int]]: The recursive sequence lengths. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.Tensor() 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", [](framework::Tensor &self) -> bool { // Check that the lod info is valid and match the outermost // dimension of the Tensor data return CheckLoD(self.lod(), vectorize(self.dims()).front()); }, R"DOC( Check whether the LoD of the Tensor is valid. Returns: bool: Whether the LoD is valid. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np t = fluid.Tensor() 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("_as_type", [](const framework::Tensor &self, paddle::framework::proto::VarType::Type type) { framework::Tensor dst; if (self.IsInitialized() && self.numel() > 0) { TransDataType(self, type, &dst); } return dst; }) .def("_copy", [](const framework::Tensor &self, const platform::Place &place) { // follow fetch_op's inplementation framework::Tensor 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; #ifdef _WIN32 }); #else }) #ifdef PADDLE_WITH_CUDA .def("_share_buffer_with", [](framework::Tensor &self, const framework::Tensor src, py::tuple t) { auto *cuda_ipc_allocation = dynamic_cast( src.Holder().get()); PADDLE_ENFORCE_NOT_NULL( cuda_ipc_allocation, platform::errors::PreconditionNotMet( "Tensor is not Cuda IPC shared tensor. " "Now only Tensor shared by cuda ipc could use this " "api.")); size_t size = t[0].cast(); auto dtype = static_cast(t[1].cast()); auto dims = phi::make_ddim(t[2].cast>()); auto lod_info = t[3].cast(); auto device_id = t[4].cast(); auto shared_reader_holder = std::make_shared( cuda_ipc_allocation->ptr(), cuda_ipc_allocation->base_ptr(), size, platform::CUDAPlace(device_id)); self.ResetHolderWithType(shared_reader_holder, dtype); self.Resize(dims); self.set_lod(lod_info); VLOG(6) << "Reconstructed tensor with buffer shared!"; }, R"DOC( Deserialize GPU Tensor for existed shared Cuda IPC tensor. Params: tensor: Shared Cuda IPC tensor. tuple: contrains data size, data type, tensor dims, lod information, device index. )DOC") .def("_share_cuda", [](framework::Tensor self) { if (!self.IsInitialized() || self.numel() == 0) throw std::runtime_error( "Tensor not initialized or numel is 0. could not pass " "to shared memory. "); auto *holder = dynamic_cast( self.Holder().get()); PADDLE_ENFORCE_EQ( platform::is_gpu_place(holder->place()), true, platform::errors::InvalidArgument( "Tensor is not on GPU. share_cuda only support GPU " "Tensor, share_filename is for CPU tensor.")); void *base_ptr = holder->base_ptr(); ptrdiff_t offset_bytes = reinterpret_cast(holder->ptr()) - reinterpret_cast(base_ptr); cudaIpcMemHandle_t handle; PADDLE_ENFORCE_GPU_SUCCESS(cudaIpcGetMemHandle(&handle, base_ptr)); auto _handle = py::bytes(reinterpret_cast(&handle), (py::ssize_t)CUDA_IPC_HANDLE_SIZE); // TODO(ZHUI): use cuda event, to avoid sync. const auto &device_id = paddle::platform::GetCurrentDeviceId(); auto stream = paddle::platform::stream::get_current_stream(device_id); stream->Synchronize(); int type_idx = static_cast(self.type()); size_t data_size = self.numel() * framework::SizeOfType( framework::TransToProtoVarType(self.type())); return py::make_tuple(_handle, (py::size_t)offset_bytes, data_size, type_idx, vectorize(self.dims()), self.lod(), device_id); }, R"DOC( Serialize GPU Tensor by cudaIpcMemHandle. Returns: tuple: contrains handle, data size, data type, tensor dims, lod information, device index. Examples: .. code-block:: python import paddle tensor = paddle.ones([3,3]) metainfo = tensor.value().get_tensor()._share_cuda() )DOC") .def("_new_shared_cuda", [](py::tuple t) { if (t.size() != 7) throw std::runtime_error( "Invalid Tensor meta info for shared cuda tensor!"); // 1. Create a new C++ instance framework::Tensor tensor; // 2. Rebuild Allocation from handle const std::string &handle = t[0].cast(); ptrdiff_t offset_bytes = (ptrdiff_t)t[1].cast(); auto device_id = t[6].cast(); auto base_ptr = memory::allocation::GetIpcBasePtr(handle); size_t size = t[2].cast(); void *dev = base_ptr.get(); dev = reinterpret_cast(dev) + offset_bytes; auto shared_reader_holder = std::make_shared( dev, size, device_id, std::move(base_ptr)); // 3. Rebuild Tensor tensor.ResetHolderWithType( shared_reader_holder, static_cast(t[3].cast())); tensor.Resize(phi::make_ddim(t[4].cast>())); tensor.set_lod(t[5].cast()); return tensor; }, R"DOC( Deserialize GPU lod tensor from cudaIpcMemHandle. Params: tuple: contrains handle, data size, data type, tensor dims, lod information, device index. Examples: .. code-block:: python import paddle tensor = paddle.ones([3,3]) metainfo = tensor.value().get_tensor()._share_cuda() tensor_from_shared = paddle.to_tensor(paddle.fluid.core.LoDTensor._new_shared_cuda(metainfo)) )DOC") #endif .def("_share_filename", [](framework::Tensor &self) { if (!self.IsInitialized() || self.numel() == 0) throw std::runtime_error( "Tensor not initialized or numel is 0. could not pass to " "shared memory. "); auto holder = self.Holder(); PADDLE_ENFORCE_EQ( platform::is_cpu_place(holder->place()) || platform::is_cuda_pinned_place(holder->place()), true, platform::errors::InvalidArgument( "Tensor is not on CPU. share_filename only " "support CPU Tensor.")); auto *mmap_allocation = dynamic_cast< memory::allocation::RefcountedMemoryMapAllocation *>( holder.get()); // If the tensor is not shared, allocate memory map allocation. if (mmap_allocation == nullptr) { void *data_ptr = self.data(); size_t data_size = self.numel() * framework::SizeOfType( framework::TransToProtoVarType(self.type())); int flags = memory::allocation::MAPPED_SHAREDMEM | memory::allocation::MAPPED_EXCLUSIVE; std::string handle = memory::allocation::GetIPCName(); auto shared_holder = memory::allocation::AllocateRefcountedMemoryMapAllocation( handle, flags, data_size); // copy data & reset holder if (platform::is_cuda_pinned_place(holder->place())) { #ifdef PADDLE_WITH_CUDA memory::Copy(platform::CPUPlace(), shared_holder->ptr(), platform::CUDAPinnedPlace(), data_ptr, data_size); #endif } else { memory::Copy(platform::CPUPlace(), shared_holder->ptr(), platform::CPUPlace(), data_ptr, data_size); } self.ResetHolder(shared_holder); mmap_allocation = shared_holder.get(); } int type_idx = static_cast(self.type()); return py::make_tuple(mmap_allocation->ipc_name(), mmap_allocation->size(), type_idx, vectorize(self.dims()), self.lod()); }, R"DOC( Serialize CPU lod tensor in shared memory to tuple. If the tensor is not in shared memory, we will copy it first. Returns: tuple: contrains ipc name, data size, data type, tensor dims and lod imformation. Examples: .. code-block:: python import paddle tensor = paddle.ones([3,3]) metainfo = tensor.value().get_tensor()._share_filename() )DOC") .def("_new_shared_filename", [](py::tuple t) { // __setstate__ if (t.size() != 5) throw std::runtime_error("Invalid Tensor meta info state!"); framework::Tensor tensor; // 2. Rebuild Allocation const std::string &ipc_name = t[0].cast(); size_t size = t[1].cast(); int flags = memory::allocation::MAPPED_SHAREDMEM | memory::allocation::MAPPED_NOCREATE; auto shared_holder = memory::allocation::AllocateRefcountedMemoryMapAllocation( ipc_name, flags, size); // 3. Rebuild Tensor tensor.ResetHolderWithType( shared_holder, static_cast(t[2].cast())); tensor.Resize(phi::make_ddim(t[3].cast>())); tensor.set_lod(t[4].cast()); return tensor; }, R"DOC( Deserialize CPU lod tensor from shared memory. Params: tuple: contrains ipc file name, data size, data type, tensor dims and lod information. Examples: .. code-block:: python import paddle tensor = paddle.ones([3,3]) metainfo = tensor.value().get_tensor()._share_filename() tensor_from_shared = paddle.to_tensor(paddle.fluid.core.LoDTensor._new_shared_filename(metainfo)) )DOC") .def("_shared_incref", [](framework::Tensor &self) { auto *mmap_allocation = dynamic_cast< memory::allocation::RefcountedMemoryMapAllocation *>( self.Holder().get()); if (mmap_allocation) { mmap_allocation->incref(); } }, R"DOC( Increase reference count of share_filename tensor. )DOC") .def("_shared_decref", [](framework::Tensor &self) { auto *mmap_allocation = dynamic_cast< memory::allocation::RefcountedMemoryMapAllocation *>( self.Holder().get()); if (mmap_allocation) { mmap_allocation->decref(); } }, R"DOC( Decrease reference count of share_filename tensor. )DOC") .def(py::pickle( [](const framework::Tensor &t) { // __getstate__ auto holder = t.Holder(); PADDLE_ENFORCE_EQ(platform::is_cpu_place(holder->place()), true, platform::errors::PreconditionNotMet( "Tensor is not on CPU." "Now only Tensor on CPU can be serialized.")); auto *mmap_writer_allocation = dynamic_cast( holder.get()); PADDLE_ENFORCE_NOT_NULL( mmap_writer_allocation, platform::errors::PreconditionNotMet( "Tensor is not in shared memory." "Now only Tensor on shared memory can be serialized.")); int type_idx = static_cast(t.type()); return py::make_tuple(mmap_writer_allocation->ipc_name(), mmap_writer_allocation->size(), type_idx, vectorize(t.dims()), t.lod()); }, [](py::tuple t) { // __setstate__ if (t.size() != 5) throw std::runtime_error("Invalid Tensor state!"); // 1. Create a new C++ instance framework::Tensor tensor; // 2. Rebuild Allocation const std::string &ipc_name = t[0].cast(); size_t size = t[1].cast(); auto shared_reader_holder = memory::allocation::RebuildMemoryMapReaderAllocation(ipc_name, size); // 3. Maintain global fd set VLOG(3) << "Tensor ipc name: " << ipc_name; memory::allocation::MemoryMapFdSet::Instance().Insert(ipc_name); // 4. Rebuild Tensor tensor.ResetHolderWithType( shared_reader_holder, static_cast(t[2].cast())); tensor.Resize(phi::make_ddim(t[3].cast>())); tensor.set_lod(t[4].cast()); return tensor; })); #endif py::class_(m, "SelectedRows") .def("__init__", [](phi::SelectedRows &instance) { new (&instance) phi::SelectedRows(); }) .def("__init__", [](phi::SelectedRows &instance, const std::vector rows, const int64_t &height) { new (&instance) phi::SelectedRows(rows, height); }) .def("get_tensor", [](phi::SelectedRows &self) { return self.mutable_value(); }, py::return_value_policy::reference) .def("numel", [](phi::SelectedRows &self) -> int64_t { return self.value().numel(); }) .def("set_height", &phi::SelectedRows::set_height) .def("height", &phi::SelectedRows::height) .def("set_rows", [](phi::SelectedRows &self, std::vector rows) { #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP) self.set_rows(rows); #else Vector new_rows(rows); self.set_rows(new_rows); #endif }) .def("sync_index", [](phi::SelectedRows &instance) { instance.SyncIndex(); }) .def("rows", [](phi::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_bytes", [](Variable &self) { return py::bytes(*self.GetMutable()); }) .def("set_string_list", [](Variable &self, Strings str_list) { *self.GetMutable() = str_list; }) .def("set_vocab", [](Variable &self, Vocab vocab) { *self.GetMutable() = vocab; }) .def("get_string_tensor", [](Variable &self) { return self.GetMutable(); }, py::return_value_policy::reference) .def("get_map_tensor", [](Variable &self) { 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) -> phi::SelectedRows * { return self.GetMutable(); }, py::return_value_policy::reference) .def("get_lod_tensor_array", [](Variable &self) { return self.GetMutable(); }, py::return_value_policy::reference) .def("get_fetch_list", [](Variable &self) { return self.GetMutable(); }, py::return_value_policy::reference) #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) .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, platform::errors::InvalidArgument( "The variable is not type of ReaderHolder.")); return self.GetMutable(); }, py::return_value_policy::reference) .def("get_scope", [](Variable &self) -> Scope * { auto scope_vec = self.GetMutable>(); PADDLE_ENFORCE_GT( scope_vec->size(), 0, platform::errors::InvalidArgument( "The size of scope_vec should be greater than 0")); return scope_vec->front(); }, py::return_value_policy::reference) .def("set_scope", [](Variable &self, Scope &scope) { auto scope_vec = self.GetMutable>(); scope_vec->emplace_back(&scope); }); BindReader(&m); py::class_ _Scope(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"); g_framework_scope_pytype = reinterpret_cast(_Scope.ptr()); _Scope .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("size", &Scope::Size) .def("erase", &Scope::EraseVars, py::arg("names"), 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 names to be erase. Returns: 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, platform::errors::Fatal( "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->GetDefaultAttrsMap(); } } 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; auto pruned_origin_block_id_map = Prune(*prog_with_targets.Proto(), feeded_var_names, &pruned_desc); return std::make_tuple(ProgramDesc(pruned_desc), pruned_origin_block_id_map); }); m.def("prune_backward", [](const framework::ProgramDesc &program) { return PruneBackward(program); }, R"DOC( Prune the backward part of a program, mostly called in program.clone(for_test=True). Args: program (ProgramDesc): The original program. Returns: tuple(ProgramDesc, map): The first part is the pruned program desc, and the second part is a map which contains the id pair of pruned block and corresponding origin block. )DOC"); m.def("get_serialize_comile_key", [](int64_t compilation_key) { #ifdef PADDLE_WITH_CINN auto compiler = framework::paddle2cinn::CinnCompiler::GetInstance(); auto s = compiler->SerializeKey(compilation_key); VLOG(4) << s; return s; #else PADDLE_THROW( platform::errors::PermissionDenied( "Cannot get compilation key in non-CINN version, " "Please recompile or reinstall Paddle with CINN support.")); #endif }); 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* { auto* context = new paddle::platform::CPUDeviceContext(); context->SetAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(place) .get()); context->SetHostAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(paddle::platform::CPUPlace()) .get()); context->SetZeroAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetZeroAllocator(place) .get()); return context; }) .def_static("create", [](paddle::platform::XPUPlace& place) -> paddle::platform::DeviceContext* { #ifndef PADDLE_WITH_XPU PADDLE_THROW( platform::errors::PermissionDenied( "Cannot use XPUPlace in CPU/GPU version, " "Please recompile or reinstall Paddle with XPU support.")); #else auto* context = new paddle::platform::XPUDeviceContext(place); context->SetAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(place) .get()); context->SetHostAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(paddle::platform::CPUPlace()) .get()); context->SetZeroAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetZeroAllocator(place) .get()); return context; #endif }) .def_static("create", [](paddle::platform::MLUPlace& place) -> paddle::platform::DeviceContext* { #ifndef PADDLE_WITH_MLU PADDLE_THROW( platform::errors::PermissionDenied( "Cannot use MLUPlace in CPU/GPU version, " "Please recompile or reinstall Paddle with MLU support.")); #else return new paddle::platform::MLUDeviceContext(place); #endif }) .def_static("create", [](paddle::platform::NPUPlace& place) -> paddle::platform::DeviceContext* { #ifndef PADDLE_WITH_ASCEND_CL PADDLE_THROW( platform::errors::PermissionDenied( "Cannot use NPUPlace in CPU/GPU/XPU version, " "Please recompile or reinstall Paddle with NPU support.")); #else return new paddle::platform::NPUDeviceContext(place); #endif }) .def_static("create", [](paddle::platform::CustomPlace& place) -> paddle::platform::DeviceContext* { #ifndef PADDLE_WITH_CUSTOM_DEVICE PADDLE_THROW( platform::errors::PermissionDenied( "Cannot use CustomPlace in CPU/GPU/XPU version, " "Please recompile or reinstall Paddle with " "CustomDevice support.")); #else return new paddle::platform::CustomDeviceContext(place); #endif }) .def_static("create", [](paddle::platform::CUDAPlace& place) -> paddle::platform::DeviceContext* { #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP) PADDLE_THROW( platform::errors::PermissionDenied( "Cannot use CUDAPlace in CPU only version, " "Please recompile or reinstall Paddle with CUDA support.")); #else auto* context = new paddle::platform::CUDADeviceContext(place); context->SetAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(place, context->stream()) .get()); context->SetHostAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(paddle::platform::CPUPlace()) .get()); context->SetZeroAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetZeroAllocator(place) .get()); context->SetPinnedAllocator( paddle::memory::allocation::AllocatorFacade::Instance() .GetAllocator(paddle::platform::CUDAPinnedPlace()) .get()); context->PartialInitWithAllocator(); return context; #endif }) .def_static("create", [](paddle::platform::CUDAPinnedPlace& place) -> paddle::platform::DeviceContext* { #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP) PADDLE_THROW( platform::errors::PermissionDenied( "Cannot use CUDAPinnedPlace in CPU only version, " "Please recompile or reinstall Paddle with CUDA support.")); #else return new paddle::platform::CUDAPinnedDeviceContext(place); #endif });; // clang-format on #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) py::class_(m, "Communicator").def(py::init<>()); #endif m.def("get_all_device_type", []() { std::vector device_types; #ifdef PADDLE_WITH_CUSTOM_DEVICE device_types = phi::DeviceManager::GetAllDeviceTypes(); #else LOG(WARNING) << string::Sprintf( "Cannot use get_all_device_type because you have installed" "CPU/GPU version PaddlePaddle.\n" "If you want to use get_all_device_type, please try to install" "CustomDevice version " "PaddlePaddle by: pip install paddlepaddle-core\n"); #endif return device_types; }); m.def("get_all_custom_device_type", []() { std::vector device_types; #ifdef PADDLE_WITH_CUSTOM_DEVICE device_types = phi::DeviceManager::GetAllCustomDeviceTypes(); #else LOG(WARNING) << string::Sprintf( "Cannot use get_all_custom_device_type because you have installed" "CPU/GPU version PaddlePaddle.\n" "If you want to use get_all_custom_device_type, please try to " "install CustomDevice version " "PaddlePaddle by: pip install paddlepaddle-core\n"); #endif return device_types; }); m.def("get_available_device", [] { std::vector devices; #ifdef PADDLE_WITH_CUSTOM_DEVICE devices = phi::DeviceManager::GetAllDeviceList(); #else LOG(WARNING) << string::Sprintf( "Cannot use get_available_device because you have installed" "CPU/GPU version PaddlePaddle.\n" "If you want to use get_available_device, please try to install" "CustomDevice version " "PaddlePaddle by: pip install paddlepaddle-core\n"); #endif return devices; }); m.def("get_available_custom_device", [] { std::vector devices; #ifdef PADDLE_WITH_CUSTOM_DEVICE devices = phi::DeviceManager::GetAllCustomDeviceList(); #else LOG(WARNING) << string::Sprintf( "Cannot use get_available_custom_device because you have " "installed" "CPU/GPU version PaddlePaddle.\n" "If you want to use get_available_custom_device, please try to " "install" "CustomDevice version " "PaddlePaddle by: pip install paddlepaddle-core\n"); #endif return devices; }); py::class_ customplace(m, "CustomPlace", R"DOC( CustomPlace is a descriptor of a device. It represents a custom device on which a tensor will be allocated and a model will run. Examples: .. code-block:: python import paddle fake_cpu_place = paddle.CustomPlace("FakeCPU", 0) )DOC"); g_customplace_pytype = reinterpret_cast(customplace.ptr()); customplace .def("__init__", [](platform::CustomPlace &self, const std::string &device_type, int dev_id) { #ifdef PADDLE_WITH_CUSTOM_DEVICE if (UNLIKELY(dev_id < 0)) { LOG(ERROR) << string::Sprintf( "Invalid CustomPlace(%s, %d), device id must be 0 " "or " "positive integer", device_type, dev_id); std::exit(-1); } if (LIKELY(phi::DeviceManager::HasDeviceType(device_type) && phi::DeviceManager::IsCustom(device_type))) { int dev_count = static_cast( phi::DeviceManager::GetDeviceCount(device_type)); if (UNLIKELY(dev_id >= dev_count)) { if (dev_count == 0) { LOG(ERROR) << "Cannot use " << device_type << " because there is no " << device_type << " detected on your " "machine."; std::exit(-1); } else { LOG(ERROR) << string::Sprintf( "Invalid CustomPlace(%s, %d), dev_id must " "inside " "[0, %d), because %s " "number on your machine is %d", device_type, dev_id, dev_count, device_type, dev_count); std::exit(-1); } } new (&self) platform::CustomPlace(device_type, dev_id); } else { LOG(ERROR) << string::Sprintf( "Invalid CustomPlace(%s, %d), the device type is " "not registered " "as a custom device.", device_type, dev_id); std::exit(-1); } #else LOG(ERROR) << string::Sprintf( "Cannot use CustomDevice because you have installed CPU/GPU" "version PaddlePaddle.\n" "If you want to use CustomDevice, please try to install" "CustomDevice version " "PaddlePaddle by: pip install paddlepaddle-core\n" "If you only have CPU, please change " "CustomPlace(%s, %d) to be CPUPlace().\n", device_type, dev_id); std::exit(-1); #endif }) .def("_type", &PlaceIndex) .def("get_device_id", [](const platform::CustomPlace &self) { return self.GetDeviceId(); }) .def("get_device_type", [](const platform::CustomPlace &self) { return self.GetDeviceType(); }) .def("__repr__", string::to_string) .def("__str__", string::to_string); py::class_ cudaplace(m, "CUDAPlace", R"DOC( 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 place = paddle.CUDAPlace(0) )DOC"); g_cudaplace_pytype = reinterpret_cast(cudaplace.ptr()); cudaplace .def("__init__", [](platform::CUDAPlace &self, int dev_id) { #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) 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::GetGPUDeviceCount())) { if (platform::GetGPUDeviceCount() == 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::GetGPUDeviceCount(), platform::GetGPUDeviceCount()); 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 }) #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) .def("get_device_id", [](const platform::CUDAPlace &self) { return self.GetDeviceId(); }) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_get_device_id", [](platform::CUDAPlace &self) -> int { return self.GetDeviceId(); }) #endif .def("__repr__", string::to_string) .def("__str__", string::to_string); py::class_ xpuplace(m, "XPUPlace", R"DOC( **Note**: Examples: .. code-block:: python import paddle.fluid as fluid xpu_place = fluid.XPUPlace(0) )DOC"); g_xpuplace_pytype = reinterpret_cast(xpuplace.ptr()); xpuplace .def("__init__", [](platform::XPUPlace &self, int dev_id) { #ifdef PADDLE_WITH_XPU if (UNLIKELY(dev_id < 0)) { LOG(ERROR) << string::Sprintf( "Invalid XPUPlace(%d), device id must be 0 or " "positive integer", dev_id); std::exit(-1); } if (UNLIKELY(dev_id >= platform::GetXPUDeviceCount())) { if (platform::GetXPUDeviceCount() == 0) { LOG(ERROR) << "Cannot use XPU because there is no XPU " "detected on your " "machine."; std::exit(-1); } else { LOG(ERROR) << string::Sprintf( "Invalid XPUPlace(%d), must inside [0, %d), because XPU " "number on your machine is %d", dev_id, platform::GetXPUDeviceCount(), platform::GetXPUDeviceCount()); std::exit(-1); } } new (&self) platform::XPUPlace(dev_id); #else LOG(ERROR) << string::Sprintf( "Cannot use XPU because you have installed CPU/GPU version " "PaddlePaddle.\n" "If you want to use XPU, please try to install XPU version " "PaddlePaddle by: pip install paddlepaddle-xpu\n" "If you only have CPU, please change XPUPlace(%d) to be " "CPUPlace().\n", dev_id); std::exit(-1); #endif }) #ifdef PADDLE_WITH_XPU .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("get_device_id", [](const platform::XPUPlace &self) { return self.GetDeviceId(); }) #endif .def("__repr__", string::to_string) .def("__str__", string::to_string); #ifdef PADDLE_WITH_XPU py::enum_(m, "XPUVersion", py::arithmetic()) .value("XPU1", phi::backends::xpu::XPUVersion::XPU1) .value("XPU2", phi::backends::xpu::XPUVersion::XPU2) .export_values(); m.def("get_xpu_device_count", platform::GetXPUDeviceCount); m.def("get_xpu_device_version", [](int device_id) { return platform::get_xpu_version(device_id); }); #ifdef PADDLE_WITH_XPU_KP m.def("get_xpu_device_op_support_types", [](const std::string &op_name, phi::backends::xpu::XPUVersion version) { return platform::get_xpu_kp_op_support_type(op_name, version); }); #else m.def("get_xpu_device_op_support_types", [](const std::string &op_name, phi::backends::xpu::XPUVersion version) { return platform::get_xpu_op_support_type(op_name, version); }); #endif m.def("get_xpu_device_op_list", [](phi::backends::xpu::XPUVersion version) { return platform::get_xpu_op_list(version); }); m.def("is_float16_supported", [](const platform::XPUPlace &place) -> bool { // XPUs with Compute Capability > xpu2 support float16 and bfloat16 return platform::get_xpu_version(place.device) > phi::backends::xpu::XPUVersion::XPU1; }); m.def("is_bfloat16_supported", [](const platform::XPUPlace &place) -> bool { // XPUs with Compute Capability > xpu2 support float16 and bfloat16 return platform::get_xpu_version(place.device) > phi::backends::xpu::XPUVersion::XPU1; }); #endif py::class_ cpuplace(m, "CPUPlace", R"DOC( CPUPlace is a descriptor of a device. It represents a CPU device on which a tensor will be allocated and a model will run. Examples: .. code-block:: python import paddle cpu_place = paddle.CPUPlace() )DOC"); g_cpuplace_pytype = reinterpret_cast(cpuplace.ptr()); cpuplace.def(py::init<>()) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("__repr__", string::to_string) .def("__str__", string::to_string); py::class_ cudapinnedplace( 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 place = paddle.CUDAPinnedPlace() )DOC"); g_cudapinnedplace_pytype = reinterpret_cast(cudapinnedplace.ptr()); cudapinnedplace .def("__init__", [](platform::CUDAPinnedPlace &self) { #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP) PADDLE_THROW(platform::errors::PermissionDenied( "Cannot use CUDAPinnedPlace in CPU only version, " "Please recompile or reinstall Paddle with CUDA support.")); #endif new (&self) platform::CUDAPinnedPlace(); }) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("__repr__", string::to_string) .def("__str__", string::to_string); // NPUPlace py::class_ npuplace(m, "NPUPlace", R"DOC( NPUPlace is a descriptor of a device. It represents a NPU device on which a tensor will be allocated and a model will run. Examples: .. code-block:: python import paddle npu_place = paddle.NPUPlace(0) )DOC"); g_npuplace_pytype = reinterpret_cast(npuplace.ptr()); npuplace .def("__init__", [](platform::NPUPlace &self, int dev_id) { #ifdef PADDLE_WITH_ASCEND_CL if (UNLIKELY(dev_id < 0)) { LOG(ERROR) << string::Sprintf( "Invalid NPUPlace(%d), device id must be 0 or " "positive integer", dev_id); std::exit(-1); } if (UNLIKELY(dev_id >= platform::GetNPUDeviceCount())) { if (platform::GetNPUDeviceCount() == 0) { LOG(ERROR) << "Cannot use NPU because there is no NPU " "detected on your " "machine."; std::exit(-1); } else { LOG(ERROR) << string::Sprintf( "Invalid NPUPlace(%d), must inside [0, %d), because NPU " "number on your machine is %d", dev_id, platform::GetNPUDeviceCount(), platform::GetNPUDeviceCount()); std::exit(-1); } } new (&self) platform::NPUPlace(dev_id); #else LOG(ERROR) << string::Sprintf( "Cannot use NPU because you have installed CPU/GPU version " "PaddlePaddle.\n" "If you want to use NPU, please try to install NPU version " "PaddlePaddle by: pip install paddlepaddle-npu\n" "If you only have CPU, please change NPUPlace(%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("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("get_device_id", [](const platform::NPUPlace &self) { return self.GetDeviceId(); }) .def("__str__", string::to_string); // IPUPlace py::class_(m, "IPUPlace", R"DOC( IPUPlace is a descriptor of a device. It represents a IPU device on which a tensor will be allocated and a model will run. Examples: .. code-block:: python import paddle # required: ipu ipu_place = paddle.IPUPlace() )DOC") .def("__init__", [](platform::IPUPlace &self) { #ifdef PADDLE_WITH_IPU if (platform::GetIPUDeviceCount() == 0) { LOG(ERROR) << "Cannot use IPU because there is no IPU " "detected on your " "machine."; std::exit(-1); } // use ipu(0) to comile, while run with the number user configure // in sharding and pipline. new (&self) platform::IPUPlace(0); #else LOG(ERROR) << string::Sprintf( "Cannot use IPU because you didn't install IPU version " "PaddlePaddle.\n" "If you want to use IPU, please try to install IPU version " "PaddlePaddle by: pip install paddlepaddle*\n" "If you only have CPU, please change IPUPlace to be " "CPUPlace().\n"); std::exit(-1); #endif }) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) #ifdef PADDLE_WITH_IPU .def("get_device_id", [](const platform::IPUPlace &self) { return self.GetDeviceId(); }) #endif .def("__str__", string::to_string); // MLUPlace py::class_ mluplace(m, "MLUPlace", R"DOC( MLUPlace is a descriptor of a device. It represents a MLU device on which a tensor will be allocated and a model will run. Examples: .. code-block:: python import paddle # required: mlu mlu_place = paddle.MLUPlace(0) )DOC"); g_mluplace_pytype = reinterpret_cast(mluplace.ptr()); mluplace .def("__init__", [](platform::MLUPlace &self, int dev_id) { #ifdef PADDLE_WITH_MLU if (UNLIKELY(dev_id < 0)) { LOG(ERROR) << string::Sprintf( "Invalid MLUPlace(%d), device id must be 0 or " "positive integer", dev_id); std::exit(-1); } if (UNLIKELY(dev_id >= platform::GetMLUDeviceCount())) { if (platform::GetMLUDeviceCount() == 0) { LOG(ERROR) << "Cannot use MLU because there is no MLU " "detected on your " "machine."; std::exit(-1); } else { LOG(ERROR) << string::Sprintf( "Invalid MLUPlace(%d), must inside [0, %d), because MLU " "number on your machine is %d", dev_id, platform::GetMLUDeviceCount(), platform::GetMLUDeviceCount()); std::exit(-1); } } new (&self) platform::MLUPlace(dev_id); #else LOG(ERROR) << string::Sprintf( "Cannot use MLU because you have installed CPU/GPU/... " "version " "PaddlePaddle.\n" "If you want to use MLU, please try to install MLU version " "PaddlePaddle by: pip install paddlepaddle-mlu\n" "If you only have CPU, please change MLUPlace(%d) to be " "CPUPlace().\n", dev_id); std::exit(-1); #endif }) .def("_type", &PlaceIndex) #ifdef PADDLE_WITH_MLU .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("get_device_id", [](const platform::MLUPlace &self) { return self.GetDeviceId(); }) #endif .def("__str__", string::to_string); py::class_ platformplace(m, "Place"); g_place_pytype = reinterpret_cast(platformplace.ptr()); platformplace.def(py::init<>()) .def("_type", &PlaceIndex) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .def("_equals", &IsSamePlace) .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_xpu_place", [](platform::Place &self) { return platform::is_xpu_place(self); }) .def("is_npu_place", [](platform::Place &self) { return platform::is_npu_place(self); }) .def("is_ipu_place", [](platform::Place &self) { return platform::is_ipu_place(self); }) .def("is_cuda_pinned_place", [](platform::Place &self) { return platform::is_cuda_pinned_place(self); }) .def("is_mlu_place", [](platform::Place &self) { return platform::is_mlu_place(self); }) .def( "is_custom_place", [](platform::Place &self) { return platform::is_custom_place(self); }) .def("gpu_device_id", [](platform::Place &self) { return self.device; }) .def("xpu_device_id", [](platform::Place &self) { return self.device; }) .def("npu_device_id", [](platform::Place &self) { return self.device; }) .def("ipu_device_id", [](platform::Place &self) { return self.device; }) .def("mlu_device_id", [](platform::Place &self) { return self.device; }) .def("custom_device_id", [](platform::Place &self) { return 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::XPUPlace &xpu_place) { self = xpu_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; }) .def("set_place", [](platform::Place &self, const platform::NPUPlace &npu_place) { self = npu_place; }) .def("set_place", [](platform::Place &self, const platform::IPUPlace &ipu_place) { self = ipu_place; }) .def("set_place", [](platform::Place &self, const platform::MLUPlace &mlu_place) { self = mlu_place; }) .def("set_place", [](platform::Place &self, const platform::CustomPlace &plug_place) { self = plug_place; }) .def("__repr__", string::to_string) .def("__str__", string::to_string); py::class_(m, "Operator") .def_static("create", [](py::bytes protobin) { proto::OpDesc desc; PADDLE_ENFORCE_EQ(desc.ParsePartialFromString(protobin), true, platform::errors::InvalidArgument( "Cannot parse user input to OpDesc")); PADDLE_ENFORCE_EQ(desc.IsInitialized(), true, platform::errors::InvalidArgument( "The provided OpDesc is not " "initialized, the reason is: %s", desc.InitializationErrorString())); return OpRegistry::CreateOp(desc); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CPUPlace &place) { pybind11::gil_scoped_release release; self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::XPUPlace &place) { pybind11::gil_scoped_release release; self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::NPUPlace &place) { pybind11::gil_scoped_release release; self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CUDAPlace &place) { pybind11::gil_scoped_release release; self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CUDAPinnedPlace &place) { pybind11::gil_scoped_release release; self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::MLUPlace &place) { pybind11::gil_scoped_release release; self.Run(scope, place); }) .def("run", [](OperatorBase &self, const Scope &scope, const platform::CustomPlace &place) { pybind11::gil_scoped_release release; 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) .def("ResetDataset", &TrainerBase::ResetDataset); m.def("_get_eager_deletion_vars", &framework::GetEagerDeletionCleanVars); 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); }); py::class_(m, "CostInfo") .def(py::init<>()) .def("total_time", [](interpreter::CostInfo &self) { return self.total_time; }) .def("device_memory_bytes", [](interpreter::CostInfo &self) { return self.device_memory_bytes; }); py::class_(m, "StandaloneExecutor") .def(py::init()) .def("run", [](StandaloneExecutor &self, const std::unordered_map &input_dict, std::vector fetch_names) { std::vector feed_tensors; std::vector feed_names; for (auto &item : input_dict) { framework::LoDTensor t; SetTensorFromPyArray( &t, item.second, platform::CPUPlace(), false); feed_names.push_back(item.first); feed_tensors.push_back(t); } paddle::framework::FetchList ret; { pybind11::gil_scoped_release release; ret = self.Run(feed_names, feed_tensors, fetch_names); } return py::cast(std::move(ret)); }) .def("run", [](StandaloneExecutor &self, const std::unordered_map &input_dict, std::vector fetch_names) { std::vector feed_tensors; std::vector feed_names; for (auto &item : input_dict) { feed_names.push_back(item.first); feed_tensors.push_back(item.second); } paddle::framework::FetchList ret; { pybind11::gil_scoped_release release; ret = self.Run(feed_names, feed_tensors, fetch_names); } return py::cast(std::move(ret)); }) .def("run", [](StandaloneExecutor &self, std::vector feed_names, std::vector fetch_names) { paddle::framework::FetchList ret; { pybind11::gil_scoped_release release; ret = self.Run(feed_names, fetch_names); } return py::cast(std::move(ret)); }) .def("dry_run", [](StandaloneExecutor &self, const std::unordered_map &input_dict) { std::vector feed_tensors; std::vector feed_names; for (auto &item : input_dict) { framework::LoDTensor t; SetTensorFromPyArray( &t, item.second, platform::CPUPlace(), false); feed_names.push_back(item.first); feed_tensors.push_back(t); } framework::interpreter::CostInfo cost_info; { pybind11::gil_scoped_release release; cost_info = self.DryRun(feed_names, feed_tensors); } return cost_info; }); m.def("init_gflags", framework::InitGflags); m.def("init_glog", framework::InitGLOG); m.def("load_op_meta_info_and_register_op", [](const std::string dso_name) { egr::Controller::Instance().MergeOpMetaInfoMap( framework::LoadOpMetaInfoAndRegisterOp(dso_name)); }); m.def("init_devices", []() { framework::InitDevices(); }); m.def("init_default_kernel_signatures", []() { framework::InitDefaultKernelSignatureMap(); }); m.def("is_compiled_with_cuda", IsCompiledWithCUDA); m.def("is_compiled_with_ascend", IsCompiledWithAscend); m.def("is_compiled_with_rocm", IsCompiledWithROCM); m.def("is_compiled_with_npu", IsCompiledWithNPU); m.def("is_compiled_with_ipu", IsCompiledWithIPU); m.def("is_compiled_with_xpu", IsCompiledWithXPU); m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN); m.def("is_compiled_with_nccl", IsCompiledWithNCCL); m.def("is_compiled_with_cinn", IsCompiledWithCINN); m.def("is_compiled_with_mlu", IsCompiledWithMLU); m.def("_is_compiled_with_heterps", IsCompiledWithHETERPS); m.def("supports_bfloat16", SupportsBfloat16); m.def("supports_bfloat16_fast_performance", SupportsBfloat16FastPerformance); m.def("supports_int8", SupportsInt8); m.def("supports_vnni", SupportsVNNI); m.def("op_supported_infos", imperative::OpSupportedInfos); m.def("is_compiled_with_brpc", IsCompiledWithBrpc); m.def("is_compiled_with_dist", IsCompiledWithDIST); m.def("_cuda_synchronize", [](const platform::CUDAPlace &place) { platform::DeviceContextPool::Instance().Get(place)->Wait(); }); m.def("get_float_stats", []() { std::vector> float_stats; paddle::platform::StatRegistry::Instance().publish(float_stats); std::unordered_map stats_map; for (const auto &stat : float_stats) { stats_map[stat.key] = stat.value; } return stats_map; }); m.def("get_int_stats", []() { std::vector> int_stats; paddle::platform::StatRegistry::Instance().publish(int_stats); std::unordered_map stats_map; for (const auto &stat : int_stats) { stats_map[stat.key] = stat.value; } return stats_map; }); m.def("device_memory_stat_current_value", memory::DeviceMemoryStatCurrentValue); m.def("device_memory_stat_peak_value", memory::DeviceMemoryStatPeakValue); m.def("run_cmd", [](const std::string &cmd, int time_out = -1, int sleep_inter = -1) -> const std::string { return paddle::framework::shell_get_command_output(cmd, time_out, sleep_inter); }, py::arg("cmd"), py::arg("time_out") = -1, py::arg("sleep_inter") = -1); m.def("shell_execute_cmd", [](const std::string &cmd, int time_out = 0, int sleep_inter = 0, bool redirect_stderr = false) -> std::vector { return paddle::framework::shell_execute_cmd( cmd, time_out, sleep_inter, redirect_stderr); }, py::arg("cmd"), py::arg("time_out") = 0, py::arg("sleep_inter") = 0, py::arg("redirect_stderr") = false); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) m.def("is_float16_supported", [](const platform::CUDAPlace &place) -> bool { // Only GPUs with Compute Capability >= 53 support float16 return platform::GetGPUComputeCapability(place.device) >= 53; }); m.def("is_bfloat16_supported", [](const platform::CUDAPlace &place) -> bool { // Only GPUs with Compute Capability >= 80 support bfloat16 return platform::GetGPUComputeCapability(place.device) >= 80; }); #endif m.def("set_feed_variable", static_cast(&framework::SetFeedVariable)); m.def("set_feed_variable", static_cast(&framework::SetFeedVariable)); m.def("get_fetch_variable", [](const Scope &scope, const std::string &var_name, size_t index) -> py::object { auto &var = framework::GetFetchVariable(scope, var_name, index); if (data_is_lod_tensor(var)) { return py::cast(BOOST_GET(LoDTensor, var)); } else { return py::cast(BOOST_GET(LoDTensorArray, var)); } }); m.def("get_variable_tensor", framework::GetVariableTensor); m.def("_is_program_version_supported", IsProgramVersionSupported); BindProgramDesc(&m); BindBlockDesc(&m); BindVarDsec(&m); BindOpDesc(&m); BindCostModel(&m); BindConstValue(&m); BindGlobalValueGetterSetter(&m); BindProcessMeshDesc(&m); BindFleetExecutor(&m); BindTCPStore(&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_ pylodtensorarray(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"); g_framework_lodtensorarray_pytype = reinterpret_cast(pylodtensorarray.ptr()); pylodtensorarray .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(), platform::errors::InvalidArgument( "The index to set is larger than the size " "of LoDTensorArray.")); 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); py::class_(m, "FetchList", R"DOC( FetchList is a vector of boost::variant. )DOC") .def("_move_to_list", [](FetchList &self) -> py::list { py::list res(self.size()); for (size_t i = 0; i < self.size(); ++i) { if (data_is_lod_tensor(self[i])) { auto &data = BOOST_GET(LoDTensor, self[i]); res[i] = py::cast(std::move(data)); } else { auto &data = BOOST_GET(LoDTensorArray, self[i]); py::list tmp(data.size()); for (size_t j = 0; j < data.size(); ++j) { tmp[j] = py::cast(std::move(data[j])); } res[i] = std::move(tmp); } } self.clear(); return res; }, py::return_value_policy::take_ownership) .def("append", [](FetchList &self, const LoDTensor &t) { self.emplace_back(); auto &lod_tensor = BOOST_GET(LoDTensor, self.back()); lod_tensor.ShareDataWith(t); lod_tensor.set_lod(t.lod()); }, py::arg("var")) .def("append", [](FetchList &self, const LoDTensorArray &t) { self.emplace_back(); auto &lod_tensor_array = BOOST_GET(LoDTensorArray, self.back()); for (size_t i = 0; i < t.size(); ++i) { lod_tensor_array[i].ShareDataWith(t[i]); lod_tensor_array[i].set_lod(t[i].lod()); } }, py::arg("var")); py::class_(m, "FetchUnmergedList", R"DOC( FetchUnmergedList is 2-D array of FetchType(boost::variant(LoDTensor, LoDTensorArray)). )DOC") .def("_move_to_list", [](FetchUnmergedList &self) -> py::list { py::list res(self.size()); for (size_t i = 0; i < self.size(); ++i) { py::list tmp(self[i].size()); for (size_t j = 0; j < self[i].size(); ++j) { if (data_is_lod_tensor(self[i][j])) { auto &var = BOOST_GET(LoDTensor, self[i][j]); tmp[j] = py::cast(std::move(var)); } else { auto &var = BOOST_GET(LoDTensorArray, self[i][j]); py::list tmp_array(var.size()); for (size_t k = 0; k < var.size(); ++k) { tmp_array[k] = std::move(var[k]); } tmp[j] = std::move(tmp_array); } } res[i] = std::move(tmp); self[i].clear(); } self.clear(); return res; }, py::return_value_policy::take_ownership); m.def("op_support_gpu", OpSupportGPU); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) m.def("get_cuda_device_count", platform::GetGPUDeviceCount); m.def("get_cuda_current_device_id", &platform::GetCurrentDeviceId); m.def("cuda_empty_cache", [] { for (int dev_id : platform::GetSelectedDevices()) { auto *dev_ctx = platform::DeviceContextPool::Instance().GetByPlace( platform::CUDAPlace(dev_id)); dev_ctx->cudnn_workspace_handle().ResetWorkspace(); } platform::EmptyCache(); }); m.def("get_device_properties", [](int id) -> const gpuDeviceProp & { return platform::GetDeviceProperties(id); }, py::return_value_policy::copy); py::class_(m, "_gpuDeviceProperties") .def_property_readonly( "name", [](const gpuDeviceProp &prop) { return prop.name; }) .def_property_readonly( "major", [](const gpuDeviceProp &prop) { return prop.major; }) .def_property_readonly( "minor", [](const gpuDeviceProp &prop) { return prop.minor; }) .def_property_readonly( "total_memory", [](const gpuDeviceProp &prop) { return prop.totalGlobalMem; }) .def_property_readonly( "multi_processor_count", [](const gpuDeviceProp &prop) { return prop.multiProcessorCount; }) .def_property_readonly( "is_multi_gpu_board", [](const gpuDeviceProp &prop) { return prop.isMultiGpuBoard; }) .def_property_readonly( "is_integrated", [](const gpuDeviceProp &prop) { return prop.integrated; }) .def("__repr__", [](const gpuDeviceProp &prop) { std::stringstream ostr; ostr << "_gpuDeviceProperties(name='" << prop.name << "', major=" << prop.major << ", minor=" << prop.minor << ", total_memory=" << prop.totalGlobalMem / (1024 * 1024) << "MB, multi_processor_count=" << prop.multiProcessorCount << ")"; return ostr.str(); }); #if !defined(PADDLE_WITH_HIP) && !defined(_WIN32) m.def("nvprof_init", platform::CudaProfilerInit); m.def("nvprof_start", platform::CudaProfilerStart); m.def("nvprof_stop", platform::CudaProfilerStop); m.def("nvprof_nvtx_push", platform::CudaNvtxRangePush); m.def("nvprof_nvtx_pop", platform::CudaNvtxRangePop); m.def("nvprof_enable_record_event", platform::NvprofEnableRecordEvent); m.def("nvprof_disable_record_event", platform::NvprofDisableRecordEvent); #endif #endif #ifdef PADDLE_WITH_ASCEND_CL m.def("get_npu_device_count", platform::GetNPUDeviceCount); m.def("npu_finalize", []() { platform::HCCLCommContext::Instance().ReleaseHCCLComms(); auto &pool = platform::DeviceContextPool::Instance(); auto devices = platform::GetSelectedNPUDevices(); for (size_t i = 0; i < devices.size(); ++i) { platform::NPUDeviceGuard guard(devices[i]); pool.Get(platform::NPUPlace(devices[i]))->Wait(); } platform::AclInstance::Instance().Finalize(); }); py::class_(m, "NPUProfConfigWrapper"); m.def("npu_prof_init", platform::NPUProfilerInit); m.def("npu_prof_start", [](platform::NPUProfConfigWrapper c) { platform::NPUProfilerStart(c.ptr()); }); m.def("npu_prof_stop", [](platform::NPUProfConfigWrapper c) { platform::NPUProfilerStop(c.ptr()); }); m.def("npu_prof_finalize", platform::NPUProfilerFinalize); m.def("npu_prof_create_config", []() { return platform::NPUProfConfigWrapper(platform::NPUProfilerCreateConfig()); }); m.def("npu_prof_destropy_config", [](platform::NPUProfConfigWrapper c) { platform::NPUProfilerDestroyConfig(c.ptr()); }); #endif #ifdef PADDLE_WITH_IPU m.def("get_ipu_device_count", platform::GetIPUDeviceCount); #endif #ifdef PADDLE_WITH_MLU m.def("get_mlu_device_count", platform::GetMLUDeviceCount); #endif py::enum_(m, "TracerOption", py::arithmetic()) .value("kDefault", platform::TracerOption::kDefault) .value("kOpDetail", platform::TracerOption::kOpDetail) .value("kAllOpDetail", platform::TracerOption::kAllOpDetail) .export_values(); 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("set_tracer_option", platform::SetTracerOption); 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("register_pass", [](const std::string &pass_type, py::object callable) { PADDLE_ENFORCE_EQ( framework::ir::PassRegistry::Instance().Has(pass_type), false, platform::errors::AlreadyExists("Pass '%s' is registered more than " "once. Please use another name.", pass_type)); callable.inc_ref(); framework::ir::PassRegistry::Instance().Insert(pass_type, [pass_type, callable]() { py::gil_scoped_acquire guard; std::unique_ptr pass( new framework::ir::GeneratePass(py::cast(callable()))); return pass; }); }); 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); py::class_(m, "_ProfilerResult") .def(py::init<>()) .def("get_data", &paddle::platform::ProfilerResult::GetData, py::return_value_policy::automatic_reference) .def("save", &paddle::platform::ProfilerResult::Save) .def("get_extra_info", &paddle::platform::ProfilerResult::GetExtraInfo); py::class_(m, "DevicePythonNode") .def(py::init<>()) .def_readwrite("name", &paddle::platform::DevicePythonNode::name) .def_readwrite("type", &paddle::platform::DevicePythonNode::type) .def_readwrite("start_ns", &paddle::platform::DevicePythonNode::start_ns) .def_readwrite("end_ns", &paddle::platform::DevicePythonNode::end_ns) .def_readwrite("device_id", &paddle::platform::DevicePythonNode::device_id) .def_readwrite("context_id", &paddle::platform::DevicePythonNode::context_id) .def_readwrite("stream_id", &paddle::platform::DevicePythonNode::stream_id); py::class_(m, "HostPythonNode") .def(py::init<>()) .def_readwrite("name", &paddle::platform::HostPythonNode::name) .def_readwrite("type", &paddle::platform::HostPythonNode::type) .def_readwrite("start_ns", &paddle::platform::HostPythonNode::start_ns) .def_readwrite("end_ns", &paddle::platform::HostPythonNode::end_ns) .def_readwrite("process_id", &paddle::platform::HostPythonNode::process_id) .def_readwrite("thread_id", &paddle::platform::HostPythonNode::thread_id) .def_readwrite("children_node", &paddle::platform::HostPythonNode::children_node_ptrs) .def_readwrite("runtime_node", &paddle::platform::HostPythonNode::runtime_node_ptrs) .def_readwrite("device_node", &paddle::platform::HostPythonNode::device_node_ptrs); py::class_(m, "_Profiler") .def("create", &paddle::platform::Profiler::Create, py::return_value_policy::take_ownership) .def("is_cupti_supported", &paddle::platform::Profiler::IsCuptiSupported) .def("is_cnpapi_supported", &paddle::platform::Profiler::IsCnpapiSupported) .def("prepare", [](paddle::platform::Profiler *profiler) { platform::EnableHostEventRecorder(); profiler->Prepare(); }) .def("start", &paddle::platform::Profiler::Start) .def("stop", [](paddle::platform::Profiler *profiler) { platform::DisableHostEventRecorder(); auto result = profiler->Stop(); framework::StaticGraphExecutorPerfStatistics( result->GetNodeTrees()); return result; }, py::return_value_policy::automatic_reference); py::class_(m, "ProfilerOptions") .def(py::init<>()) .def_readwrite("trace_switch", &paddle::platform::ProfilerOptions::trace_switch); py::class_(m, "_RecordEvent") .def(py::init([](std::string name, platform::TracerEventType type) { return std::make_unique( name, type, 1, paddle::platform::EventRole::kOrdinary); })) .def("end", [](platform::RecordEvent *event) { event->End(); }); py::enum_(m, "TracerEventType") .value("Operator", paddle::platform::TracerEventType::Operator) .value("Dataloader", paddle::platform::TracerEventType::Dataloader) .value("ProfileStep", paddle::platform::TracerEventType::ProfileStep) .value("CudaRuntime", paddle::platform::TracerEventType::CudaRuntime) .value("Kernel", paddle::platform::TracerEventType::Kernel) .value("Memcpy", paddle::platform::TracerEventType::Memcpy) .value("Memset", paddle::platform::TracerEventType::Memset) .value("UserDefined", paddle::platform::TracerEventType::UserDefined) .value("OperatorInner", paddle::platform::TracerEventType::OperatorInner) .value("Forward", paddle::platform::TracerEventType::Forward) .value("Backward", paddle::platform::TracerEventType::Backward) .value("Optimization", paddle::platform::TracerEventType::Optimization) .value("Communication", paddle::platform::TracerEventType::Communication) .value("PythonOp", paddle::platform::TracerEventType::PythonOp) .value("PythonUserDefined", paddle::platform::TracerEventType::PythonUserDefined); m.def("load_profiler_result", &paddle::platform::LoadProfilerResult); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) m.def("set_cublas_switch", platform::SetAllowTF32Cublas); m.def("get_cublas_switch", platform::AllowTF32Cublas); m.def("set_cudnn_switch", platform::SetAllowTF32Cudnn); m.def("get_cudnn_switch", platform::AllowTF32Cudnn); #endif // PADDLE_WITH_CUDA m.def("clear_executor_cache", []() { framework::ExecutorInfoCache::Instance().Finalize(); }); 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, bool val) { self.Set(name, new bool(val)); }) .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::vector set) { self.Set(name, new std::vector(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, 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. Returns: ExecutionStrategy: An ExecutionStrategy object. Examples: .. code-block:: python import paddle import paddle.static as static import paddle.nn.functional as F paddle.enable_static() x = static.data(name='x', shape=[None, 13], dtype='float32') y = static.data(name='y', shape=[None, 1], dtype='float32') y_predict = static.nn.fc(input=x, size=1, act=None) cost = F.square_error_cost(input=y_predict, label=y) avg_loss = paddle.mean(cost) sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_loss) exec_strategy = static.ExecutionStrategy() exec_strategy.num_threads = 4 train_exe = static.ParallelExecutor(use_cuda=False, loss_name=avg_loss.name, exec_strategy=exec_strategy) )DOC"); py::enum_(m, "DeviceType", py::arithmetic()) .value("CPU", paddle::platform::DeviceType::CPU) .value("CUDA", paddle::platform::DeviceType::CUDA) .value("XPU", paddle::platform::DeviceType::XPU); 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. Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() exec_strategy = static.ExecutionStrategy() exec_strategy.num_threads = 4 )DOC") .def_property( "_use_device", [](const ExecutionStrategy &self) { return self.use_device_; }, [](ExecutionStrategy &self, paddle::platform::DeviceType use_device) { self.use_device_ = use_device; }) // NOTE(liuyuhui): Doesn't add doc for 'use_device', because // use_device isn‘t exposed to users. .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 100. .. note:: 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`. Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() exec_strategy = static.ExecutionStrategy() exec_strategy.num_iteration_per_drop_scope = 10 )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。Default: 1. Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() exec_strategy = static.ExecutionStrategy() exec_strategy.num_iteration_per_run = 10 )DOC") .def_property( "use_thread_barrier", [](const ExecutionStrategy &self) { return self.thread_barrier_; }, [](ExecutionStrategy &self, bool use_thread_barrier) { self.thread_barrier_ = use_thread_barrier; }, R"DOC(This config that the this is distributed training with parameter server )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. Returns: BuildStrategy: An BuildStrategy object. Examples: .. code-block:: python import os import paddle import paddle.static as static paddle.enable_static() os.environ['CPU_NUM'] = str(2) places = static.cpu_places() data = static.data(name="x", shape=[None, 1], dtype="float32") hidden = static.nn.fc(input=data, size=10) loss = paddle.mean(hidden) paddle.optimizer.SGD(learning_rate=0.01).minimize(loss) build_strategy = static.BuildStrategy() build_strategy.enable_inplace = True build_strategy.memory_optimize = True build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce program = static.CompiledProgram(static.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) .value("_NoReduce", BuildStrategy::ReduceStrategy::kNoReduce); 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("_clear_finalized", &BuildStrategy::ClearFinalized) .def_property( "reduce_strategy", [](const BuildStrategy &self) { return self.reduce_; }, [](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce )DOC") .def_property( "gradient_scale_strategy", [](const BuildStrategy &self) { return self.gradient_scale_; }, [](BuildStrategy &self, BuildStrategy::GradientScaleStrategy strategy) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); self.gradient_scale_ = strategy; }, R"DOC((paddle.static.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 numpy import os import paddle import paddle.static as static paddle.enable_static() use_cuda = True place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() exe = static.Executor(place) # NOTE: If you use CPU to run the program, you need # to specify the CPU_NUM, otherwise, paddle 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 = static.cpu_places() else: places = static.cuda_places() data = static.data(name='X', shape=[None, 1], dtype='float32') hidden = static.nn.fc(input=data, size=10) loss = paddle.mean(hidden) paddle.optimizer.SGD(learning_rate=0.01).minimize(loss) exe.run(static.default_startup_program()) build_strategy = static.BuildStrategy() build_strategy.gradient_scale_strategy = \ static.BuildStrategy.GradientScaleStrategy.Customized compiled_prog = static.CompiledProgram( static.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_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.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_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.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_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.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(platform::errors::Unavailable( "Distribution mode is not supported on Windows platform.")); #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( "bkcl_comm_num", [](const BuildStrategy &self) { return self.bkcl_comm_num_; }, [](BuildStrategy &self, int bkcl_comm_num) { self.bkcl_comm_num_ = bkcl_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_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.fuse_elewise_add_act_ops = True )DOC") .def_property( "fuse_gemm_epilogue", [](const BuildStrategy &self) { return self.fuse_gemm_epilogue_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); self.fuse_gemm_epilogue_ = b; }, R"DOC((bool, optional): fuse_gemm_epilogue indicate whether to fuse matmul_op, elemenewist_add_op and activation_op, it may make the execution faster. Default is False. Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.fuse_gemm_epilogue = True )DOC") .def_property( "fuse_bn_act_ops", [](const BuildStrategy &self) { return self.fuse_bn_act_ops_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.fuse_bn_act_ops = True )DOC") .def_property( "fuse_bn_add_act_ops", [](const BuildStrategy &self) { return self.fuse_bn_add_act_ops_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); self.fuse_bn_add_act_ops_ = b; }, R"DOC((bool, optional): fuse_bn_add_act_ops indicate whether to fuse batch_norm, elementwise_add and activation_op, it may make the execution faster. Default is True Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.fuse_bn_add_act_ops = True )DOC") .def_property( "enable_auto_fusion", [](const BuildStrategy &self) { return self.enable_auto_fusion_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); self.enable_auto_fusion_ = b; }, R"DOC((bool, optional): Whether to enable fusing subgraph to a fusion_group. Now we only support fusing subgraph that composed of elementwise-like operators, such as elementwise_add/mul without broadcast and activations. Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.enable_auto_fusion = True )DOC") .def_property( "fuse_relu_depthwise_conv", [](const BuildStrategy &self) { return self.fuse_relu_depthwise_conv_; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.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_ == paddle::none; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, " "cannot be configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.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_ == paddle::none; }, [](BuildStrategy &self, bool b) { PADDLE_ENFORCE_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, " "cannot be configured again.")); 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_NE(self.IsFinalized(), true, platform::errors::PreconditionNotMet( "BuildStrategy has been finlaized, cannot be " "configured again.")); 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 import paddle.static as static paddle.enable_static() build_strategy = static.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_ = paddle::none; } else if (PyBool_Check(py_obj)) { self.memory_optimize_ = (py_obj == Py_True); } else { PADDLE_THROW(platform::errors::InvalidArgument( "BuildStrategy.memory_optimize must be set to 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. Examples: .. code-block:: python import paddle import paddle.static as static paddle.enable_static() build_strategy = static.BuildStrategy() build_strategy.memory_optimize = True )DOC") .def_property( "is_distribution", [](const BuildStrategy &self) { return self.is_distribution_; }, [](BuildStrategy &self, bool b) { #ifdef WIN32 if (b) { PADDLE_THROW(platform::errors::Unavailable( "Distribution mode is not supported on Windows platform.")); } #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( "enable_addto", [](const BuildStrategy &self) { return self.enable_addto_; }, [](BuildStrategy &self, bool b) { self.enable_addto_ = b; }) .def_property( "fuse_all_reduce_ops", [](const BuildStrategy &self) { return self.fuse_all_reduce_ops_ == true || self.fuse_all_reduce_ops_ == paddle::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_property( "fix_op_run_order", [](const BuildStrategy &self) { return self.fix_op_run_order_; }, [](BuildStrategy &self, bool fix_op_run_order) { self.fix_op_run_order_ = fix_op_run_order; }) .def_property("allow_cuda_graph_capture", [](const BuildStrategy &self) { return self.allow_cuda_graph_capture_; }, [](BuildStrategy &self, bool allow_cuda_graph_capture) { self.allow_cuda_graph_capture_ = allow_cuda_graph_capture; }) .def("_copy", [](const BuildStrategy &self) { auto new_bs = self; new_bs.ClearFinalized(); return new_bs; }) .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"); m.def("_set_cached_executor_build_strategy", [](int64_t program_id, const BuildStrategy &build_strategy) { auto &cached_exe_info = framework::ExecutorInfoCache::Instance(); cached_exe_info.SetBuildStrategy(program_id, build_strategy); }); 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, bool return_merged) -> py::object { paddle::framework::FetchResultType ret; { pybind11::gil_scoped_release release; ret = self.Run(fetch_tensors, return_merged); } if (return_merged) { return py::cast( std::move(BOOST_GET(paddle::framework::FetchList, ret))); } else { return py::cast(std::move( BOOST_GET(paddle::framework::FetchUnmergedList, ret))); } }) .def("device_count", &ParallelExecutor::DeviceCount); #ifdef PADDLE_WITH_IPU py::class_>( m, "IpuBackend") // manage IpuBackend in C++ .def("get_instance", []() { return std::unique_ptr( platform::ipu::IpuBackend::GetInstance()); }, py::return_value_policy::reference) .def("weights_to_host", &platform::ipu::IpuBackend::WeightsToHost) .def("detach", &platform::ipu::IpuBackend::Detach) .def("reset", &platform::ipu::IpuBackend::Reset) .def("set_scope", &platform::ipu::IpuBackend::SetScope) .def("set_ipu_strategy", &platform::ipu::IpuBackend::SetIpuStrategy) .def("save_model_proto", &platform::ipu::IpuBackend::SaveModelProto); py::class_(m, "IpuStrategy") .def(py::init()) .def("set_options", [](platform::ipu::IpuStrategy &self, const py::dict &opt) { for (auto element : opt) { auto option_name = element.first.cast(); VLOG(10) << "Set option: " << option_name; if (option_name == "compilation_progress_logger") { self.SetCompilationProgressLogger( element.second.cast()); } else if (py::isinstance(element.second)) { self.AddBoolOption(option_name, element.second.cast()); } else if (py::isinstance(element.second)) { self.AddDoubleOption(option_name, element.second.cast()); } else if (py::isinstance(element.second)) { self.AddUint64Option(option_name, element.second.cast()); } else if (py::isinstance(element.second)) { self.AddStringOption(option_name, element.second.cast()); } else if (py::isinstance(element.second) || py::isinstance(element.second)) { for (auto option : element.second.cast()) { std::string option_val; if (py::isinstance(option)) { option_val = option.cast(); } else if (py::isinstance(option)) { option_val = std::to_string(option.cast()); } else { PADDLE_THROW(platform::errors::Unimplemented( "Failed to convert type: %s when set IpuStrategy " "option: %s", option.get_type(), option_name)); } self.InsertStringOption(option_name, option_val); } } else if (py::isinstance(element.second)) { if (option_name.rfind("location_", 0) == 0) { for (auto option : element.second.cast()) { self.SetTensorLocation( option_name, option.first.cast(), option.second.cast()); } } else if (option_name == "replicated_collectives_settings") { for (auto option : element.second.cast()) { self.SetReplicatedCollectivesSettings( option.first.cast(), option.second.cast()); } } else if (option_name == "accumulate_outer_fragment") { for (auto option : element.second.cast()) { std::vector values; for (auto value : option.second.cast()) { values.push_back(value.cast()); } self.SetAccumulateOuterFragmentSettings( option.first.cast(), values); } } else if (option_name == "custom_op") { std::string paddle_op; std::string popart_op; std::string domain; int version = -1; for (auto option : element.second.cast()) { std::string option_key = option.first.cast(); if (option_key == "paddle_op") { paddle_op = option.second.cast(); } else if (option_key == "popart_op") { popart_op = option.second.cast(); } else if (option_key == "domain") { domain = option.second.cast(); } else if (option_key == "version") { version = option.second.cast(); } else { PADDLE_THROW(platform::errors::InvalidArgument( "Invalid argument, key must be one of paddle_op, " "popart_op, domain or version, but revecived %s", option_key)); } } self.AddCustomOp(paddle_op, popart_op, domain, version); } else { for (auto option : element.second.cast()) { std::string option_key = option.first.cast(); std::string option_val; if (py::isinstance(option.second)) { option_val = option.second.cast(); } else if (py::isinstance(option.second)) { option_val = std::to_string(option.second.cast()); } else { PADDLE_THROW(platform::errors::Unimplemented( "Failed to convert value type: %s when set " "IpuStrategy option: %s", option.second.get_type(), option_key)); } self.InsertStringPairOption(option_name, option_key, option_val); } } } else { PADDLE_THROW(platform::errors::InvalidArgument( "Invalid IpuStrategy option value type: %s, please check " "input value for option: %s", element.second.get_type(), option_name)); } } }) .def("get_option", [](platform::ipu::IpuStrategy &self, const std::string &name) { py::dict res; auto option_type = self.GetOptionType(name); res["name"] = name; res["type"] = option_type; if (option_type == "vector") { auto value = self.GetVectorOption(name); res["value"] = value; } else if (option_type == "map") { auto value = self.GetMapOption(name); res["value"] = value; } else { auto value_s = self.GetOption(name); res["value_s"] = value_s; if (option_type == "bool") { res["value"] = static_cast(std::stoi(value_s)); } else if (option_type == "uint64") { res["value"] = std::stoul(value_s); } else if (option_type == "double") { res["value"] = std::stod(value_s); } else if (option_type == "string") { res["value"] = value_s; } } return res; }) .def("get_all_option_names", &platform::ipu::IpuStrategy::GetAllOptionNames) .def("enable_pattern", &platform::ipu::IpuStrategy::EnablePattern) .def("disable_pattern", &platform::ipu::IpuStrategy::DisablePattern) .def("is_pattern_enabled", &platform::ipu::IpuStrategy::IsPatternEnabled); #endif m.def("enable_autotune", [] { return phi::autotune::AutoTuneStatus::Instance().EnableAutoTune(); }); m.def("disable_autotune", [] { return phi::autotune::AutoTuneStatus::Instance().DisableAutoTune(); }); m.def("set_autotune_range", [](int64_t start, int64_t stop) { return phi::autotune::AutoTuneStatus::Instance().SetAutoTuneRange(start, stop); }); m.def("update_autotune_status", [] { return phi::autotune::AutoTuneStatus::Instance().Update(); }); m.def("autotune_status", [] { py::dict res; phi::autotune::AutoTuneCache::Instance().UpdateStatus(); res["step_id"] = phi::autotune::AutoTuneStatus::Instance().StepID(); res["cache_size"] = phi::autotune::AutoTuneCache::Instance().Size(); res["cache_hit_rate"] = phi::autotune::AutoTuneCache::Instance().CacheHitRate(); return res; }); m.def("enable_layout_autotune", [] { return paddle::imperative::LayoutAutoTune::Instance() .EnableLayoutAutoTune(); }); m.def("disable_layout_autotune", [] { return paddle::imperative::LayoutAutoTune::Instance() .DisableLayoutAutoTune(); }); m.def("use_layout_autotune", [] { return paddle::imperative::LayoutAutoTune::Instance().UseLayoutAutoTune(); }); BindFleetWrapper(&m); BindIO(&m); #if defined(PADDLE_WITH_PSLIB) && !defined(PADDLE_WITH_HETERPS) BindHeterWrapper(&m); BindMetrics(&m); #endif #ifdef PADDLE_WITH_HETERPS BindPSGPUWrapper(&m); #ifdef PADDLE_WITH_PSLIB BindAfsWrapper(&m); #endif #endif BindGlooWrapper(&m); BindBoxHelper(&m); #ifdef PADDLE_WITH_BOX_PS BindBoxWrapper(&m); #endif #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) BindNCCLWrapper(&m); #endif #ifdef PADDLE_WITH_GLOO BindGlooContext(&m); #endif BindGraph(&m); BindNode(&m); BindPass(&m); BindInferenceApi(&m); BindCompatible(&m); BindDataset(&m); BindGenerator(&m); #ifndef PADDLE_ON_INFERENCE BindDistributed(&m); #endif #ifdef PADDLE_WITH_ASCEND BindAscendWrapper(&m); BindAscendGraph(&m); BindAscendDevice(&m); #endif #ifdef PADDLE_WITH_CRYPTO BindCrypto(&m); #endif #if defined PADDLE_WITH_PSCORE BindDistFleetWrapper(&m); BindPSHost(&m); BindCommunicatorContext(&m); BindDistCommunicator(&m); BindHeterClient(&m); BindGraphPyFeatureNode(&m); BindGraphNode(&m); BindGraphPyService(&m); BindGraphPyServer(&m); BindGraphPyClient(&m); BindIndexNode(&m); BindTreeIndex(&m); BindIndexWrapper(&m); BindIndexSampler(&m); #ifdef PADDLE_WITH_HETERPS BindNodeQueryResult(&m); BindNeighborSampleQuery(&m); BindNeighborSampleResult(&m); BindGraphGpuWrapper(&m); #endif #endif } } // namespace pybind } // namespace paddle