forked from huawei/mindspore2022
420 lines
30 KiB
C++
420 lines
30 KiB
C++
/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <pybind11/operators.h>
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#include "backend/kernel_compiler/oplib/oplib.h"
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#include "pipeline/jit/pipeline.h"
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#include "frontend/operator/composite/composite.h"
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#include "pipeline/pynative/pynative_execute.h"
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#include "utils/symbolic.h"
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#include "pybind_api/api_register.h"
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#include "pipeline/jit/parse/python_adapter.h"
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#include "utils/summary/event_writer.h"
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#include "utils/config_manager.h"
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#include "utils/mpi/mpi_config.h"
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#include "utils/ms_utils.h"
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#include "frontend/parallel/context.h"
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#include "frontend/parallel/costmodel_context.h"
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#ifdef ENABLE_GPU_COLLECTIVE
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#include "runtime/device/gpu/distribution/collective_init.h"
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#else
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#include "runtime/device/gpu/distribution/collective_fake_init.h"
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#endif
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#if ((defined ENABLE_CPU) && (!defined _WIN32))
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#include "ps/util.h"
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#endif
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#include "ps/ps_context.h"
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#include "pybind_api/gil_scoped_long_running.h"
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namespace py = pybind11;
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using EnvInstance = mindspore::EnvInstance;
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using ExecutorPy = mindspore::pipeline::ExecutorPy;
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using Pipeline = mindspore::pipeline::Pipeline;
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using PrimitivePy = mindspore::PrimitivePy;
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using MetaFuncGraph = mindspore::MetaFuncGraph;
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using EventWriter = mindspore::summary::EventWriter;
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using OpLib = mindspore::kernel::OpLib;
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using ParallelContext = mindspore::parallel::ParallelContext;
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using CostModelContext = mindspore::parallel::CostModelContext;
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using mindspore::MsCtxParam;
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using PSContext = mindspore::ps::PSContext;
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// Interface with python
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PYBIND11_MODULE(_c_expression, m) {
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// The OMP_NUM_THREADS has no effect when set in backend, so set it here in advance.
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mindspore::common::SetOMPThreadNum();
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m.doc() = "MindSpore c plugin";
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auto fns = mindspore::PybindDefineRegister::AllFuncs();
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for (auto &item : fns) {
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item.second(&m);
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}
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mindspore::ScopedLongRunning::SetHook(std::make_unique<mindspore::GilScopedLongRunningHook>());
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// Class Pipeline interface
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(void)py::class_<ExecutorPy, std::shared_ptr<ExecutorPy>>(m, "Executor_")
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.def_static("get_instance", &ExecutorPy::GetInstance, "Executor get_instance.")
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.def("__call__", &ExecutorPy::Run, py::arg("args"), py::arg("phase") = py::str(""), "Executor run function.")
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.def("del_net_res", &ExecutorPy::DelNetRes, py::arg("network_id") = py::str(""), "Delete network resource.")
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.def("get_func_graph", &ExecutorPy::GetFuncGraph, py::arg("phase") = py::str(""), "Get graph pointer.")
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.def("get_func_graph_proto", &ExecutorPy::GetFuncGraphProto, py::arg("phase") = py::str(""),
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py::arg("type") = py::str("onnx_ir"), "Get graph proto string by specifying ir type.")
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.def("compile", &ExecutorPy::Compile, py::arg("obj"), py::arg("args"), py::arg("phase") = py::str(""),
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py::arg("use_vm") = py::bool_(false), py::arg("queue_name"), "Compile obj by executor.")
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.def("updata_param_node_default_input", &ExecutorPy::UpdataParamNodeDefaultInput, py::arg("phase"),
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py::arg("params"), "Fetch the inputs of Conv or Matmul for quant export.")
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.def("get_parameter_layout", &ExecutorPy::GetParameterLayout, py::arg("phase") = py::str("train"),
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"Get Parameter Tensor Layout Dictionary.")
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.def("get_parallel_parameter_name_list", &ExecutorPy::GetParallelParameterNameList,
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py::arg("phase") = py::str("train"), "Get Parallel Parameter Name List.")
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.def("get_strategy", &ExecutorPy::GetCNodeStrategy, py::arg("phase") = py::str("train"),
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"Get CNode Strategy Dictionary.")
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.def("get_num_parallel_ops", &ExecutorPy::GetNumOpsInfo, py::arg("phase") = py::str("train"),
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"Get the number of parallel operators.")
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.def("get_allreduce_fusion", &ExecutorPy::GetAllreduceFusion, py::arg("phase") = py::str("train"),
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"Get Allreduce Fusion Dictionary.")
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.def("fetch_info_for_quant_export", &ExecutorPy::FetchInfoForQuantExport, py::arg("phase") = py::str("train"),
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"Fetch the inputs of Conv or Matmul for quant export.")
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.def("build_data_graph", &ExecutorPy::BuildGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
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py::arg("broadcast_params") = py::dict(), "Build data graph.")
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.def("has_compiled", &ExecutorPy::HasCompiled, py::arg("phase") = py::str(""), "get if cell compiled.")
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.def("run_init_graph", &ExecutorPy::RunInitGraph, "Run init Graph.")
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.def("set_py_exe_path", &ExecutorPy::PyExePath, py::arg("phase") = py::str(""), "set python executable path.");
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(void)py::class_<EnvInstance, std::shared_ptr<EnvInstance>>(m, "EnvInstance_").def(py::init());
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(void)m.def("generate_key", &mindspore::pipeline::GenerateKey, "Generate the function graph key.");
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(void)m.def("real_run_op", &mindspore::pynative::RealRunOp, "Run op pynatively.");
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(void)m.def("reset_op_id", &mindspore::pipeline::ResetOpId, "Reset Operator Id");
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(void)m.def("init_hccl", &mindspore::pipeline::InitHccl, "Init Hccl");
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(void)m.def("finalize_hccl", &mindspore::pipeline::FinalizeHccl, "Finalize Hccl");
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(void)m.def("verify_inputs_signature", &mindspore::pipeline::VerifyInputSignature, "Verify input signature.");
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(void)m.def("init_exec_dataset", &mindspore::pipeline::InitExecDataset, py::arg("queue_name"), py::arg("size"),
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py::arg("batch_size"), py::arg("types"), py::arg("shapes"), py::arg("input_indexs"),
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py::arg("phase") = py::str("dataset"), py::arg("need_run") = py::bool_(true), "Init and exec dataset.");
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(void)m.def("_set_dataset_mode_config", &mindspore::ConfigManager::SetDatasetModeConfig, "API for set dataset mode.");
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(void)m.def("init_pipeline", &mindspore::pipeline::InitPipeline, "Init Pipeline.");
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(void)m.def("export_graph", &mindspore::pipeline::ExportGraph, "Export Graph.");
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(py::object)
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m.def("load_mindir", &mindspore::pipeline::LoadMindIR, py::arg("file_name"), py::arg("dec_key") = nullptr,
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py::arg("key_len") = py::int_(0), py::arg("dec_mode") = py::str("AES-GCM"), "Load model as Graph.");
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(void)py::class_<mindspore::MpiConfig, std::shared_ptr<mindspore::MpiConfig>>(m, "MpiConfig")
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.def_static("get_instance", &mindspore::MpiConfig::GetInstance, "Get mpi config instance.")
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.def("get_enable_mpi", &mindspore::MpiConfig::enable_mpi, "Get whether enable mpi.")
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.def("set_enable_mpi", &mindspore::MpiConfig::set_enable_mpi, "Set whether to enable mpi.");
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(void)py::class_<ParallelContext, std::shared_ptr<ParallelContext>>(m, "AutoParallelContext")
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.def_static("get_instance", &ParallelContext::GetInstance, "Get auto parallel context instance.")
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.def("get_device_num", &ParallelContext::device_num, "Get device num.")
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.def("set_device_num", &ParallelContext::set_device_num, "Set device num.")
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.def("get_device_num_is_set", &ParallelContext::device_num_is_set, "Get device num is set.")
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.def("get_global_rank", &ParallelContext::global_rank, "Get global rank.")
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.def("set_global_rank", &ParallelContext::set_global_rank, "Set global rank.")
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.def("get_global_rank_is_set", &ParallelContext::global_rank_is_set, "Get global rank is set.")
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.def("get_gradients_mean", &ParallelContext::gradients_mean, "Get mirror mean.")
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.def("set_gradients_mean", &ParallelContext::set_gradients_mean, "Set mirror mean.")
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.def("get_gradient_fp32_sync", &ParallelContext::gradient_fp32_sync, "Get cast before mirror.")
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.def("set_gradient_fp32_sync", &ParallelContext::set_gradient_fp32_sync, "Set cast before mirror.")
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.def("get_loss_repeated_mean", &ParallelContext::loss_repeated_mean, "Get loss repeated mean.")
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.def("set_loss_repeated_mean", &ParallelContext::set_loss_repeated_mean, "Set loss repeated mean.")
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.def("get_parallel_mode", &ParallelContext::parallel_mode, "Get parallel mode.")
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.def("set_parallel_mode", &ParallelContext::set_parallel_mode, "Set parallel mode.")
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.def("get_grad_accumulation_step", &ParallelContext::grad_accumulation_step, "Get grad accumulation step.")
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.def("set_grad_accumulation_step", &ParallelContext::set_grad_accumulation_step, "Set grad accumulation step.")
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.def("get_strategy_search_mode", &ParallelContext::strategy_search_mode, "Get strategy search mode.")
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.def("set_strategy_search_mode", &ParallelContext::set_strategy_search_mode, "Set strategy search mode.")
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.def("set_all_reduce_fusion_split_indices", &ParallelContext::SetAllReduceFusionSplitIndices,
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"Set all reduce fusion split indices.")
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.def("get_all_reduce_fusion_split_indices", &ParallelContext::GetAllReduceFusionSplitIndices,
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"Get all reduce fusion split indices.")
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.def("set_all_reduce_fusion_split_sizes", &ParallelContext::SetAllReduceFusionSplitSizes,
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"Set all reduce fusion split sizes.")
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.def("get_all_reduce_fusion_split_sizes", &ParallelContext::GetAllReduceFusionSplitSizes,
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"Get all reduce fusion split sizes.")
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.def("set_enable_all_reduce_fusion", &ParallelContext::set_enable_all_reduce_fusion,
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"Set enable/disable all reduce fusion.")
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.def("get_enable_all_reduce_fusion", &ParallelContext::enable_all_reduce_fusion,
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"Get enable/disable all reduce fusion.")
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.def("get_parameter_broadcast", &ParallelContext::parameter_broadcast, "Get parameter broadcast.")
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.def("get_parameter_broadcast_is_set", &ParallelContext::parameter_broadcast_is_set,
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"Get parameter broadcast is set.")
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.def("set_parameter_broadcast", &ParallelContext::set_parameter_broadcast, "Set parameter broadcast.")
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.def("set_strategy_ckpt_load_file", &ParallelContext::set_strategy_ckpt_load_file,
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"Set strategy checkpoint load file.")
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.def("set_strategy_ckpt_save_file", &ParallelContext::set_strategy_ckpt_save_file,
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"Set strategy checkpoint save file.")
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.def("get_strategy_ckpt_load_file", &ParallelContext::strategy_ckpt_load_file, "Get strategy checkpoint load file.")
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.def("get_strategy_ckpt_save_file", &ParallelContext::strategy_ckpt_save_file, "Get strategy checkpoint save file.")
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.def("set_group_ckpt_save_file", &ParallelContext::set_group_ckpt_save_file, "Set group checkpoint save file.")
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.def("set_pipeline_stage_split_num", &ParallelContext::set_pipeline_stage_split_num,
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"Set pipeline stage split num.")
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.def("get_pipeline_stage_split_num", &ParallelContext::pipeline_stage_split_num, "Get pipeline stage split num.")
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.def("set_full_batch", &ParallelContext::set_full_batch, "Set whether load full batch on each device.")
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.def("get_full_batch", &ParallelContext::full_batch, "Get whether load full batch on each device.")
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.def("set_dataset_strategy", &ParallelContext::set_dataset_strategy, "Set dataset sharding strategy.")
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.def("get_dataset_strategy", &ParallelContext::dataset_strategy, "Get dataset sharding strategy.")
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.def("set_enable_parallel_optimizer", &ParallelContext::set_enable_parallel_optimizer,
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"Set enable/disable parallel optimizer.")
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.def("get_enable_parallel_optimizer", &ParallelContext::enable_parallel_optimizer,
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"Get enable/disable parallel optimizer.")
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.def("set_communi_parallel_mode", &ParallelContext::set_communi_parallel_mode, "Set communication parallel mode.")
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.def("get_communi_parallel_mode", &ParallelContext::communi_parallel_mode, "Get communication parallel mode.")
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.def("set_optimizer_weight_shard_size", &ParallelContext::set_optimizer_weight_shard_size,
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"Set opt shard group size when not fully use parallel optimizer.")
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.def("get_optimizer_weight_shard_size", &ParallelContext::optimizer_weight_shard_size,
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"Get opt shard group size when not fully use parallel optimizer.")
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.def("set_optimizer_weight_shard_aggregated_save", &ParallelContext::set_optimizer_weight_shard_aggregated_save,
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"Set whether to integrated save weight shard when enable parallel optimizer.")
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.def("get_optimizer_weight_shard_aggregated_save", &ParallelContext::optimizer_weight_shard_aggregated_save,
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"Get whether to integrated save weight shard when enable parallel optimizer.")
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.def("set_sharding_propagation", &ParallelContext::set_sharding_propagation,
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"Set sharding strategy propagation value.")
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.def("get_sharding_propagation", &ParallelContext::sharding_propagation, "Get sharding strategy propagation value.")
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.def("set_enable_alltoall", &ParallelContext::set_enable_all2all, "Set the enabling AllToAll value.")
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.def("get_enable_alltoall", &ParallelContext::enable_all2all, "Get the enabling AllToAll value.")
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.def("reset", &ParallelContext::Reset, "Reset auto parallel context.");
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(void)py::class_<CostModelContext, std::shared_ptr<CostModelContext>>(m, "CostModelContext")
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.def_static("get_instance", &CostModelContext::GetInstance, "Get cost_model context instance.")
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.def("set_device_memory_capacity", &CostModelContext::set_device_memory_capacity,
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"Set the capacity of device memory.")
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.def("get_device_memory_capacity", &CostModelContext::device_memory_capacity, "Get the capacity of device memory.")
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.def("set_costmodel_alpha", &CostModelContext::set_costmodel_alpha,
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"Set the parameter cost_model_alpha of the DP algorithm.")
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.def("get_costmodel_alpha", &CostModelContext::costmodel_alpha,
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"Get the parameter cost_model_alpha of the DP algorithm.")
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.def("set_costmodel_beta", &CostModelContext::set_costmodel_beta,
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"Set the parameter cost_model_beta of the DP algorithm.")
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.def("get_costmodel_beta", &CostModelContext::costmodel_beta,
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"Get the parameter cost_model_beta of the DP algorithm.")
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.def("set_costmodel_gamma", &CostModelContext::set_costmodel_gamma,
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"Set the parameter cost_model_gamma of the DP algorithm")
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.def("get_costmodel_gamma", &CostModelContext::costmodel_gamma,
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"Get the parameter cost_model_gamma of the DP algorithm.")
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.def("set_costmodel_communi_threshold", &CostModelContext::set_costmodel_communi_threshold,
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"Set the parameter cost_model_communi_threshold of the DP algorithm.")
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.def("get_costmodel_communi_threshold", &CostModelContext::costmodel_communi_threshold,
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"Get the parameter cost_model_communi_threshold of the DP algorithm.")
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.def("set_costmodel_communi_const", &CostModelContext::set_costmodel_communi_const,
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"Set the parameter cost_model_communi_const of the DP algorithm.")
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.def("get_costmodel_communi_const", &CostModelContext::costmodel_communi_const,
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"Get the parameter cost_model_communi_const of the DP algorithm.")
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.def("set_costmodel_communi_bias", &CostModelContext::set_costmodel_communi_bias,
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"Set the parameter cost_model_communi_bias of the DP algorithm.")
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.def("get_costmodel_communi_bias", &CostModelContext::costmodel_communi_bias,
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"Get the parameter cost_model_communi_bias of the DP algorithm.")
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.def("set_multi_subgraphs", &CostModelContext::set_multi_subgraphs, "Set the parameter is_multi_subgraphs.")
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.def("get_multi_subgraphs", &CostModelContext::is_multi_subgraphs, "Get the parameter is_multi_subgraphs.")
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.def("set_run_phase", &CostModelContext::set_run_phase, "Set the flag run_phase.")
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.def("get_run_phase", &CostModelContext::run_phase, "Get the flag run_phase.")
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.def("set_costmodel_allreduce_fusion_algorithm", &CostModelContext::set_costmodel_allreduce_fusion_algorithm,
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"Set the parameter gradient AllReduce fusion algorithm.")
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.def("get_costmodel_allreduce_fusion_algorithm", &CostModelContext::costmodel_allreduce_fusion_algorithm,
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"Get the parameter gradient AllReduce fusion algorithm.")
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.def("set_costmodel_allreduce_fusion_times", &CostModelContext::set_costmodel_allreduce_fusion_times,
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"Set the parameter gradient AllReduce times.")
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.def("get_costmodel_allreduce_fusion_times", &CostModelContext::costmodel_allreduce_fusion_times,
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"Get the parameter gradient AllReduce times.")
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.def("set_costmodel_allreduce_fusion_tail_percent", &CostModelContext::set_costmodel_allreduce_fusion_tail_percent,
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"Set the parameter gradient AllReduce fusion tail percent.")
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.def("get_costmodel_allreduce_fusion_tail_percent", &CostModelContext::costmodel_allreduce_fusion_tail_percent,
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"Get the parameter gradient AllReduce fusion tail percent.")
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.def("set_costmodel_allreduce_fusion_tail_time", &CostModelContext::set_costmodel_allreduce_fusion_tail_time,
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"Set the parameter gradient AllReduce fusion tail time.")
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.def("get_costmodel_allreduce_fusion_tail_time", &CostModelContext::costmodel_allreduce_fusion_tail_time,
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"Get the parameter gradient AllReduce fusion tail time.")
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.def("set_costmodel_allreduce_fusion_allreduce_inherent_time",
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&CostModelContext::set_costmodel_allreduce_fusion_allreduce_inherent_time,
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"Set the parameter gradient AllReduce fusion allreduce inherent time.")
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.def("get_costmodel_allreduce_fusion_allreduce_inherent_time",
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&CostModelContext::costmodel_allreduce_fusion_allreduce_inherent_time,
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"Get the parameter gradient AllReduce fusion allreduce inherent time.")
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.def("set_costmodel_allreduce_fusion_allreduce_bandwidth",
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&CostModelContext::set_costmodel_allreduce_fusion_allreduce_bandwidth,
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"Set the parameter gradient AllReduce fusion allreduce bandwidth.")
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.def("get_costmodel_allreduce_fusion_allreduce_bandwidth",
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&CostModelContext::costmodel_allreduce_fusion_allreduce_bandwidth,
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"Get the parameter gradient AllReduce fusion allreduce bandwidth.")
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.def("set_costmodel_allreduce_fusion_computation_time_parameter",
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&CostModelContext::set_costmodel_allreduce_fusion_computation_time_parameter,
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"Set the parameter gradient AllReduce fusion computation time parameter.")
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.def("get_costmodel_allreduce_fusion_computation_time_parameter",
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&CostModelContext::costmodel_allreduce_fusion_computation_time_parameter,
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"Get the parameter gradient AllReduce fusion computation time parameter.")
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.def("set_tensor_slice_align_enable", &CostModelContext::set_tensor_slice_alignment_enable,
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"Set the parameter tensor_slice_align_enable in strategy generation.")
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.def("get_tensor_slice_align_enable", &CostModelContext::tensor_slice_alignment_enable,
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"Get the parameter tensor_slice_align_enable in strategy generation.")
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.def("set_tensor_slice_align_size", &CostModelContext::set_tensor_slice_alignment_size,
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"Set the parameter tensor_slice_size in strategy generation.")
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.def("get_tensor_slice_align_size", &CostModelContext::tensor_slice_alignment_size,
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"Get the parameter tensor_slice_size in strategy generation.")
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.def("set_fully_use_devices", &CostModelContext::set_fully_use_device,
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"Set the parameter fully_use_devices in the DP algorithm.")
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.def("get_fully_use_devices", &CostModelContext::fully_use_device,
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"Get the parameter fully_use_devices in the DP algorithm.")
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.def("set_elementwise_op_strategy_follow", &CostModelContext::set_elementwise_stra_follow,
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"Set the parameter elementwise_op_strategy_follow in the DP algorithm.")
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.def("get_elementwise_op_strategy_follow", &CostModelContext::elementwise_stra_follow,
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"Get the parameter elementwise_op_strategy_follow in the DP algorithm.")
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.def("set_dp_algo_enable_approxi", &CostModelContext::set_dp_algo_enable_approxi,
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"Set the flag whether enabling approximation in the DP algorithm.")
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.def("get_dp_algo_enable_approxi", &CostModelContext::dp_algo_enable_approxi,
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"Get the flag whether enabling approximation in the DP algorithm.")
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.def("set_dp_algo_approxi_epsilon", &CostModelContext::set_dp_algo_approxi_epsilon,
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"Set the epsilon which is used in the approximation of DP algorithm.")
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.def("get_dp_algo_approxi_epsilon", &CostModelContext::dp_algo_approxi_epsilon,
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"Get the epsilon which is used in the approximation of DP algorithm.")
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.def("set_dp_algo_single_loop", &CostModelContext::set_dp_algo_single_loop,
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"Set the flag of generating a single suite of OperatorInfos in for-loop.")
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.def("get_dp_algo_single_loop", &CostModelContext::dp_algo_single_loop,
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"Get the flag of whether or not generating a single suite of OperatorInfos in for-loop.")
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.def("reset_cost_model", &CostModelContext::ResetCostModel, "Reset the CostModelContext.")
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.def("reset_algo_parameters", &CostModelContext::ResetAlgoParameters, "Reset the AlgoParameters.");
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(void)py::module::import("atexit").attr("register")(py::cpp_function{[&]() -> void {
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// only in case that c++ calling python interface, ClearResAtexit should be called.
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if (mindspore::parse::python_adapter::IsPythonEnv()) {
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mindspore::pipeline::ClearResAtexit();
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#ifdef ENABLE_MINDDATA
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py::module iterators = py::module::import("mindspore.dataset.engine.iterators");
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(void)iterators.attr("_cleanup")();
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#endif
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}
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}});
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(void)py::class_<EventWriter, std::shared_ptr<EventWriter>>(m, "EventWriter_")
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.def(py::init<const std::string &>())
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.def("GetFileName", &EventWriter::GetFileName, "Get the file name.")
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.def("Open", &EventWriter::Open, "Open the write file.")
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.def("Write", &EventWriter::Write, "Write the serialize event.")
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.def("EventCount", &EventWriter::GetWriteEventCount, "Write event count.")
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.def("Flush", &EventWriter::Flush, "Flush the event.")
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.def("Close", &EventWriter::Close, "Close the write.")
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.def("Shut", &EventWriter::Shut, "Final close the write.");
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(void)py::class_<OpLib, std::shared_ptr<OpLib>>(m, "Oplib")
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.def(py::init())
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.def_static("reg_op", &OpLib::RegOp, "Register op info.");
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#ifdef ENABLE_GPU_COLLECTIVE
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(void)m.def("init_gpu_collective", &mindspore::device::gpu::CollectiveInitializer::InitCollective,
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"Init gpu collective communication mode.");
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(void)m.def("finalize_gpu_collective", &mindspore::device::gpu::CollectiveInitializer::FinalizeCollective,
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"Finalize gpu collective communication mode.");
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#else
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(void)m.def("init_gpu_collective", &mindspore::device::gpu::CollectiveFakeInitializer::InitCollective,
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"Init gpu collective communication mode.");
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(void)m.def("finalize_gpu_collective", &mindspore::device::gpu::CollectiveFakeInitializer::FinalizeCollective,
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"Finalize gpu collective communication mode.");
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#endif
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(void)py::class_<PSContext, std::shared_ptr<PSContext>>(m, "PSContext")
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.def_static("get_instance", &PSContext::instance, "Get PS context instance.")
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.def("set_ps_enable", &PSContext::SetPSEnable, "Set PS mode enabled or disabled.")
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.def("is_ps_mode", &PSContext::is_ps_mode, "Get PS mode enable-disable status.")
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.def("reset", &PSContext::Reset, "Reset PS context attributes.")
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.def("is_worker", &PSContext::is_worker, "Get whether the role of this process is Worker.")
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.def("is_server", &PSContext::is_server, "Get whether the role of this process is PServer.")
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.def("is_scheduler", &PSContext::is_scheduler, "Get whether the role of this process is Scheduler.")
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.def("ps_rank_id", &PSContext::ps_rank_id, "Get Worker and PServer rank id.")
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.def("insert_hash_table_size", &PSContext::InsertHashTableSize, "Insert hash table size.")
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.def("reinsert_hash_table_size", &PSContext::ReInsertHashTableSize,
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"Insert hash table size with new parameter name.")
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.def("insert_weight_init_info", &PSContext::InsertWeightInitInfo, "Insert embedding table initialization seed.")
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.def("insert_accumu_init_info", &PSContext::InsertAccumuInitInfo, "Insert accumulation initialization value.")
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.def("clone_hash_table", &PSContext::CloneHashTable, "Clone a hash table.")
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.def("set_cache_enable", &PSContext::set_cache_enable, "Set ps mode cache enable or not.")
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.def("set_rank_id", &PSContext::set_rank_id, "Set rank id for worker on ps mode.")
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.def("set_server_mode", &PSContext::set_server_mode, "Set server mode.")
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.def("server_mode", &PSContext::server_mode, "Get server mode.")
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.def("set_ms_role", &PSContext::set_ms_role, "Set role for this process.")
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.def("ms_role", &PSContext::ms_role, "Get role for this process.")
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.def("set_worker_num", &PSContext::set_worker_num, "Set worker number.")
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.def("worker_num", &PSContext::worker_num, "Get worker number.")
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.def("set_server_num", &PSContext::set_server_num, "Set server number.")
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.def("server_num", &PSContext::server_num, "Get server number.")
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|
.def("set_scheduler_ip", &PSContext::set_scheduler_ip, "Set scheduler ip.")
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.def("scheduler_ip", &PSContext::scheduler_ip, "Get scheduler ip.")
|
|
.def("set_scheduler_port", &PSContext::set_scheduler_port, "Set scheduler port.")
|
|
.def("scheduler_port", &PSContext::scheduler_port, "Get scheduler port.")
|
|
.def("set_fl_server_port", &PSContext::set_fl_server_port, "Set federated learning server port.")
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|
.def("fl_server_port", &PSContext::fl_server_port, "Get federated learning server port.")
|
|
.def("set_fl_client_enable", &PSContext::set_fl_client_enable, "Set federated learning client.")
|
|
.def("fl_client_enable", &PSContext::fl_client_enable, "Get federated learning client.")
|
|
.def("set_start_fl_job_threshold", &PSContext::set_start_fl_job_threshold,
|
|
"Set threshold count for startFLJob round.")
|
|
.def("start_fl_job_threshold", &PSContext::start_fl_job_threshold, "Get threshold count for startFLJob round.")
|
|
.def("set_start_fl_job_time_window", &PSContext::set_start_fl_job_time_window,
|
|
"Set time window for startFLJob round.")
|
|
.def("start_fl_job_time_window", &PSContext::start_fl_job_time_window, "Get time window for startFLJob round.")
|
|
.def("set_update_model_ratio", &PSContext::set_update_model_ratio,
|
|
"Set threshold count ratio for updateModel round.")
|
|
.def("update_model_ratio", &PSContext::update_model_ratio, "Get threshold count ratio for updateModel round.")
|
|
.def("set_update_model_time_window", &PSContext::set_update_model_time_window,
|
|
"Set time window for updateModel round.")
|
|
.def("update_model_time_window", &PSContext::update_model_time_window, "Get time window for updateModel round.")
|
|
.def("set_share_secrets_ratio", &PSContext::set_share_secrets_ratio,
|
|
"Set threshold count ratio for share secrets round.")
|
|
.def("share_secrets_ratio", &PSContext::share_secrets_ratio, "Get threshold count ratio for share secrets round.")
|
|
.def("set_cipher_time_window", &PSContext::set_cipher_time_window, "Set time window for each cipher round.")
|
|
.def("set_reconstruct_secrets_threshold", &PSContext::set_reconstruct_secrets_threshold,
|
|
"Set threshold count for reconstruct secrets round.")
|
|
.def("reconstruct_secrets_threshold", &PSContext::reconstruct_secrets_threshold,
|
|
"Get threshold count for reconstruct secrets round.")
|
|
.def("set_fl_name", &PSContext::set_fl_name, "Set federated learning name.")
|
|
.def("fl_name", &PSContext::fl_name, "Get federated learning name.")
|
|
.def("set_fl_iteration_num", &PSContext::set_fl_iteration_num, "Set federated learning iteration number.")
|
|
.def("fl_iteration_num", &PSContext::fl_iteration_num, "Get federated learning iteration number.")
|
|
.def("set_client_epoch_num", &PSContext::set_client_epoch_num, "Set federated learning client epoch number.")
|
|
.def("client_epoch_num", &PSContext::client_epoch_num, "Get federated learning client epoch number.")
|
|
.def("set_client_batch_size", &PSContext::set_client_batch_size, "Set federated learning client batch size.")
|
|
.def("client_batch_size", &PSContext::client_batch_size, "Get federated learning client batch size.")
|
|
.def("set_client_learning_rate", &PSContext::set_client_learning_rate,
|
|
"Set worker's standalone training step number before communicating with server.")
|
|
.def("client_learning_rate", &PSContext::client_learning_rate,
|
|
"Get worker's standalone training step number before communicating with server.")
|
|
.def("set_worker_step_num_per_iteration", &PSContext::set_worker_step_num_per_iteration,
|
|
"Set federated learning client learning rate.")
|
|
.def("worker_step_num_per_iteration", &PSContext::worker_step_num_per_iteration,
|
|
"Get federated learning client learning rate.")
|
|
.def("set_scheduler_manage_port", &PSContext::set_scheduler_manage_port,
|
|
"Set scheduler manage port used to scale out/in.")
|
|
.def("scheduler_manage_port", &PSContext::scheduler_manage_port, "Get scheduler manage port used to scale out/in.")
|
|
.def("set_enable_ssl", &PSContext::set_enable_ssl, "Set PS SSL mode enabled or disabled.")
|
|
.def("enable_ssl", &PSContext::enable_ssl, "Get PS SSL mode enabled or disabled.")
|
|
.def("set_config_file_path", &PSContext::set_config_file_path,
|
|
"Set configuration files required by the communication layer.")
|
|
.def("config_file_path", &PSContext::config_file_path,
|
|
"Get configuration files required by the communication layer.")
|
|
.def("set_dp_eps", &PSContext::set_dp_eps, "Set dp epsilon for federated learning secure aggregation.")
|
|
.def("set_dp_delta", &PSContext::set_dp_delta, "Set dp delta for federated learning secure aggregation.")
|
|
.def("set_dp_norm_clip", &PSContext::set_dp_norm_clip,
|
|
"Set dp norm clip for federated learning secure aggregation.")
|
|
.def("set_encrypt_type", &PSContext::set_encrypt_type,
|
|
"Set encrypt type for federated learning secure aggregation.");
|
|
|
|
(void)m.def("_encrypt", &mindspore::pipeline::PyEncrypt, "Encrypt the data.");
|
|
(void)m.def("_decrypt", &mindspore::pipeline::PyDecrypt, "Decrypt the data.");
|
|
(void)m.def("_is_cipher_file", &mindspore::pipeline::PyIsCipherFile, "Determine whether the file is encrypted");
|
|
}
|