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Optimize the Executors routines. 

- Fix the key generating.
- Distinguish the executors.
This commit is contained in:
Zhang Qinghua 2021-08-27 10:33:35 +08:00
parent ba46e7cd56
commit a137fa1d0b
217 changed files with 938 additions and 940 deletions
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6
mindspore/_extends/parse/parser.py
View File

@ -30,7 +30,7 @@ from mindspore import context
from mindspore import log as logger
from mindspore import nn
from mindspore import ops
from mindspore.common.api import _MindSporeFunction
from mindspore.common.api import _MindsporeFunctionExecutor
from mindspore.common.dtype import pytype_to_dtype
from .namespace import CellNamespace, ClosureNamespace, ClassMemberNamespace
from .resources import parse_object_map, convert_object_map, trope_ns, SYMBOL_UNDEFINE, NO_IMPLEMENT
@ -177,8 +177,8 @@ def resolve_symbol(namespace, symbol):
logger.debug("resolve exception occurred, value = %r", e)
logger.debug("resolve type is invalid, namespace = %s, symbol = %s",
namespace.__str__(), symbol)
if isinstance(resolve_, _MindSporeFunction):
logger.debug("resolve class _MindSporeFunction, resolve fn instead.")
if isinstance(resolve_, _MindsporeFunctionExecutor):
logger.debug("resolve class _MindsporeFunctionExecutor, resolve fn instead.")
resolve_ = resolve_.fn
return resolve_

8
mindspore/ccsrc/debug/debugger/debugger.cc
View File

@ -342,7 +342,7 @@ void Debugger::PreExecute(const KernelGraphPtr &graph_ptr) {
// Multiple graph, and not the initial step,
// stop only when receive the first sub run graph for each step
// if we have stopped for the last kernel before, no need to stop again
if (pipeline::ExecutorPy::GetDebugTerminate()) {
if (pipeline::GraphExecutorPy::GetDebugTerminate()) {
return;
}
if (!(run_level_ == "node" && suspended_at_last_kernel_)) {
@ -449,7 +449,7 @@ void Debugger::PostExecuteGraphDebugger() {
void Debugger::PostExecute() {
// access lock for public method
std::lock_guard<std::mutex> a_lock(access_lock_);
if (pipeline::ExecutorPy::GetDebugTerminate()) {
if (pipeline::GraphExecutorPy::GetDebugTerminate()) {
return;
}
if (debugger_->DebuggerBackendEnabled()) {
@ -486,7 +486,7 @@ bool Debugger::ReadNodeDataRequired(const CNodePtr &kernel) const {
void Debugger::PostExecuteNode(const CNodePtr &kernel, bool last_kernel) {
// access lock for public method
std::lock_guard<std::mutex> a_lock(access_lock_);
if (pipeline::ExecutorPy::GetDebugTerminate()) {
if (pipeline::GraphExecutorPy::GetDebugTerminate()) {
return;
}
if (debugger_enabled_ && !is_dataset_graph_) {
@ -1074,7 +1074,7 @@ void Debugger::Exit() {
// debugger will notify main thread to exit because main thread can only exit at step boundary.
MS_LOG(INFO) << "Exit Debugger";
SetEnableHeartbeat(false);
pipeline::ExecutorPy::DebugTerminate(true);
pipeline::GraphExecutorPy::DebugTerminate(true);
}
std::list<WatchpointHit> Debugger::CheckWatchpoints(const std::string &watchnode, const CNodePtr &kernel,

2
mindspore/ccsrc/frontend/optimizer/py_pass.cc
View File

@ -201,7 +201,7 @@ void ReflectParamBackToPython(const AnfNodePtr &param, const string &param_name,
// 1. Get current cell object
auto ppm = opt::python_pass::PyPassManager::GetInstance();
auto resource = ppm->GetResource();
py::object top_cell = resource->input();
py::object top_cell = resource->source_input();
if (py::isinstance<py::none>(top_cell)) {
MS_LOG(EXCEPTION) << "Failed to get top cell from resource.";
}

2
mindspore/ccsrc/frontend/parallel/graph_util/graph_info.cc
View File

@ -70,7 +70,7 @@ bool GetLoopIndexFromCNode(const CNodePtr &cnode, size_t *loop_index) {
}
void SetOpsNumToExecutor(size_t num_ops) {
auto executor = pipeline::ExecutorPy::GetInstance();
auto executor = pipeline::GraphExecutorPy::GetInstance();
executor->SetNumOpsInfo(num_ops);
}
} // namespace parallel

2
mindspore/ccsrc/frontend/parallel/step_parallel.cc
View File

@ -2952,7 +2952,7 @@ static AnfNodePtr FindGrad(const CNodePtr &cnode, size_t curr_depth) {
void HandleRootReshapeAndSaveStrategy(const std::vector<AnfNodePtr> &all_nodes) {
// If root graph has reshape op. Find the corresponding parameter.
// Reshape's shape is the shape of the parameter.
auto executor = pipeline::ExecutorPy::GetInstance();
auto executor = pipeline::GraphExecutorPy::GetInstance();
for (auto &node : all_nodes) {
if (!node->isa<CNode>()) {
continue;

8
mindspore/ccsrc/pipeline/jit/action.cc
View File

@ -319,11 +319,11 @@ void CheckRootInputShapeAndType(const ResourcePtr &res, const FuncGraphPtr &load
bool ParseAction(const ResourcePtr &res) {
MS_EXCEPTION_IF_NULL(res);
if (!res->input()) {
if (!res->source_input()) {
MS_LOG(EXCEPTION) << "Parse error";
}
py::object input = res->input();
py::object input = res->source_input();
parse::Parser::InitParserEnvironment(input);
py::module path = py::module::import("os.path");
std::string dir = path.attr("dirname")(py::globals()["__file__"]).cast<std::string>();
@ -642,7 +642,7 @@ bool EliminateForwardCNode(const ResourcePtr &res) {
return true;
}
auto graph_executor = pipeline::ExecutorPy::GetInstance();
auto graph_executor = pipeline::GraphExecutorPy::GetInstance();
MS_EXCEPTION_IF_NULL(graph_executor);
auto phase = graph_executor->phase();
MS_LOG(DEBUG) << "The phase of current pipeline graph is: " << phase;
@ -913,7 +913,7 @@ bool ValidateAction(const ResourcePtr &res) { return ValidatePass(res); }
bool SetMindIRGraphAction(const ResourcePtr &res) {
MS_EXCEPTION_IF_NULL(res);
res->set_is_load(true);
auto cell = py::cast<CellPtr>(res->input());
auto cell = py::cast<CellPtr>(res->source_input());
if (cell == nullptr) {
MS_LOG(EXCEPTION) << "The graph loaded from mindir is null.";
}

45
mindspore/ccsrc/pipeline/jit/init.cc
View File

@ -44,7 +44,7 @@
namespace py = pybind11;
using EnvInstance = mindspore::EnvInstance;
using ExecutorPy = mindspore::pipeline::ExecutorPy;
using GraphExecutorPy = mindspore::pipeline::GraphExecutorPy;
using Pipeline = mindspore::pipeline::Pipeline;
using PrimitivePy = mindspore::PrimitivePy;
using MetaFuncGraph = mindspore::MetaFuncGraph;
@ -70,40 +70,41 @@ PYBIND11_MODULE(_c_expression, m) {
mindspore::ScopedLongRunning::SetHook(std::make_unique<mindspore::GilScopedLongRunningHook>());
// Class Pipeline interface
(void)py::class_<ExecutorPy, std::shared_ptr<ExecutorPy>>(m, "Executor_")
.def_static("get_instance", &ExecutorPy::GetInstance, "Executor get_instance.")
.def("__call__", &ExecutorPy::Run, py::arg("args"), py::arg("phase") = py::str(""), "Executor run function.")
.def("del_net_res", &ExecutorPy::DelNetRes, py::arg("network_id") = py::str(""), "Delete network resource.")
.def("get_func_graph", &ExecutorPy::GetFuncGraph, py::arg("phase") = py::str(""), "Get graph pointer.")
.def("get_func_graph_proto", &ExecutorPy::GetFuncGraphProto, py::arg("phase") = py::str(""),
(void)py::class_<GraphExecutorPy, std::shared_ptr<GraphExecutorPy>>(m, "GraphExecutor_")
.def_static("get_instance", &GraphExecutorPy::GetInstance, "Executor get_instance.")
.def("__call__", &GraphExecutorPy::Run, py::arg("args"), py::arg("phase") = py::str(""), "Executor run function.")
.def("del_net_res", &GraphExecutorPy::DelNetRes, py::arg("network_id") = py::str(""), "Delete network resource.")
.def("get_func_graph", &GraphExecutorPy::GetFuncGraph, py::arg("phase") = py::str(""), "Get graph pointer.")
.def("get_func_graph_proto", &GraphExecutorPy::GetFuncGraphProto, py::arg("phase") = py::str(""),
py::arg("type") = py::str("onnx_ir"), "Get graph proto string by specifying ir type.")
.def("compile", &ExecutorPy::Compile, py::arg("obj"), py::arg("args"), py::arg("phase") = py::str(""),
.def("compile", &GraphExecutorPy::Compile, py::arg("obj"), py::arg("args"), py::arg("phase") = py::str(""),
py::arg("use_vm") = py::bool_(false), py::arg("queue_name"), "Compile obj by executor.")
.def("updata_param_node_default_input", &ExecutorPy::UpdataParamNodeDefaultInput, py::arg("phase"),
.def("updata_param_node_default_input", &GraphExecutorPy::UpdataParamNodeDefaultInput, py::arg("phase"),
py::arg("params"), "Fetch the inputs of Conv or Matmul for quant export.")
.def("get_parameter_layout", &ExecutorPy::GetParameterLayout, py::arg("phase") = py::str("train"),
.def("get_parameter_layout", &GraphExecutorPy::GetParameterLayout, py::arg("phase") = py::str("train"),
"Get Parameter Tensor Layout Dictionary.")
.def("get_parallel_parameter_name_list", &ExecutorPy::GetParallelParameterNameList,
.def("get_parallel_parameter_name_list", &GraphExecutorPy::GetParallelParameterNameList,
py::arg("phase") = py::str("train"), "Get Parallel Parameter Name List.")
.def("get_strategy", &ExecutorPy::GetCNodeStrategy, py::arg("phase") = py::str("train"),
.def("get_strategy", &GraphExecutorPy::GetCNodeStrategy, py::arg("phase") = py::str("train"),
"Get CNode Strategy Dictionary.")
.def("get_num_parallel_ops", &ExecutorPy::GetNumOpsInfo, py::arg("phase") = py::str("train"),
.def("get_num_parallel_ops", &GraphExecutorPy::GetNumOpsInfo, py::arg("phase") = py::str("train"),
"Get the number of parallel operators.")
.def("get_allreduce_fusion", &ExecutorPy::GetAllreduceFusion, py::arg("phase") = py::str("train"),
.def("get_allreduce_fusion", &GraphExecutorPy::GetAllreduceFusion, py::arg("phase") = py::str("train"),
"Get Allreduce Fusion Dictionary.")
.def("fetch_info_for_quant_export", &ExecutorPy::FetchInfoForQuantExport, py::arg("phase") = py::str("train"),
.def("fetch_info_for_quant_export", &GraphExecutorPy::FetchInfoForQuantExport, py::arg("phase") = py::str("train"),
"Fetch the inputs of Conv or Matmul for quant export.")
.def("build_data_graph", &ExecutorPy::BuildGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
.def("build_data_graph", &GraphExecutorPy::BuildGraph, py::arg("build_params"), py::arg("phase") = py::str("train"),
py::arg("broadcast_params") = py::dict(), "Build data graph.")
.def("has_compiled", &ExecutorPy::HasCompiled, py::arg("phase") = py::str(""), "get if cell compiled.")
.def("run_init_graph", &ExecutorPy::RunInitGraph, "Run init Graph.")
.def("set_py_exe_path", &ExecutorPy::PyExePath, py::arg("py_exe_path") = py::str(""), "set python executable path.")
.def("set_kernel_build_server_dir", &ExecutorPy::KernelBuildServerDir,
py::arg("kernel_build_server_dir") = py::str(""), "set kernel build server directory path.");
.def("has_compiled", &GraphExecutorPy::HasCompiled, py::arg("phase") = py::str(""), "Get if cell compiled.")
.def("run_init_graph", &GraphExecutorPy::RunInitGraph, "Run init Graph.")
.def("set_py_exe_path", &GraphExecutorPy::PyExePath, py::arg("py_exe_path") = py::str(""),
"Set python executable path.")
.def("set_kernel_build_server_dir", &GraphExecutorPy::KernelBuildServerDir,
py::arg("kernel_build_server_dir") = py::str(""), "Set kernel build server directory path.");
(void)py::class_<EnvInstance, std::shared_ptr<EnvInstance>>(m, "EnvInstance_").def(py::init());
(void)m.def("generate_key", &mindspore::pipeline::GenerateKey, "Generate the function graph key.");
(void)m.def("generate_arguments_key", &mindspore::pipeline::GenerateArgumentsKey, "Generate unique key of argument.");
(void)m.def("real_run_op", &mindspore::pynative::RealRunOp, "Run op pynatively.");
(void)m.def("reset_op_id", &mindspore::pipeline::ResetOpId, "Reset Operator Id");
(void)m.def("init_hccl", &mindspore::pipeline::InitHccl, "Init Hccl");

243
mindspore/ccsrc/pipeline/jit/pipeline.cc
View File

@ -99,11 +99,11 @@ const char IR_TYPE_ANF[] = "anf_ir";
const char IR_TYPE_ONNX[] = "onnx_ir";
const char IR_TYPE_MINDIR[] = "mind_ir";
ExecutorPyPtr ExecutorPy::executor_ = nullptr;
std::mutex ExecutorPy::instance_lock_;
bool ExecutorPy::debugger_terminate_ = false;
GraphExecutorPyPtr GraphExecutorPy::executor_ = nullptr;
std::mutex GraphExecutorPy::instance_lock_;
bool GraphExecutorPy::debugger_terminate_ = false;
std::unordered_map<abstract::AbstractBasePtrList, int64_t, abstract::AbstractBasePtrListHasher,
std::unordered_map<abstract::AbstractBasePtrList, uint64_t, abstract::AbstractBasePtrListHasher,
abstract::AbstractBasePtrListEqual>
g_args_cache;
@ -236,30 +236,33 @@ void CheckArgsValid(const py::tuple &args) {
}
}
py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::string, py::object> &defaults) {
MS_LOG(DEBUG) << "GenerateKey args size:" << defaults.size();
py::object GenerateArgumentsKey(const std::unordered_map<std::string, py::object> &args) {
MS_LOG(DEBUG) << "GenerateArgumentsKey args size:" << args.size();
abstract::AbstractBasePtrList args_spec;
for (const auto &arg : defaults) {
for (const auto &arg : args) {
if (py::isinstance<py::module>(arg.second)) {
MS_LOG(EXCEPTION) << "GenerateKey failed, argument input should not be py::module";
MS_LOG(EXCEPTION) << "GenerateArgumentsKey failed, argument input should not be py::module";
}
ValuePtr converted = nullptr;
if (!parse::ConvertData(arg.second, &converted)) {
MS_LOG(EXCEPTION) << "GenerateKey convert arg failed";
MS_LOG(EXCEPTION) << "GenerateArgumentsKey convert arg failed";
}
args_spec.push_back(ArgsToAbstract(converted));
}
if (g_args_cache.count(args_spec) == 0) {
static int64_t key = 0;
MS_LOG(INFO) << "Start new args and compile key:" << key;
g_args_cache[args_spec] = key++;
uint64_t key;
auto iter = g_args_cache.find(args_spec);
if (iter == g_args_cache.end()) {
static uint64_t key_counter = 0;
key = key_counter;
++key_counter;
g_args_cache[args_spec] = key;
MS_LOG(INFO) << "Generate a new compile key for new args, key: " << key;
} else {
key = iter->second;
}
constexpr size_t arg_size = 2;
auto argSpec = py::tuple(arg_size);
argSpec[0] = name;
argSpec[1] = g_args_cache[args_spec];
return argSpec;
return py::int_(key);
}
py::bool_ VerifyInputSignature(const py::list &input_signature, const py::tuple &inputs) {
@ -300,9 +303,9 @@ py::bool_ VerifyInputSignature(const py::list &input_signature, const py::tuple
return true;
}
ExecutorPy::ExecutorPy() {}
GraphExecutorPy::GraphExecutorPy() {}
ResourcePtr ExecutorPy::GetResource(const std::string &phase) {
ResourcePtr GraphExecutorPy::GetResource(const std::string &phase) {
MS_LOG(DEBUG) << "Phase size:" << info_.size();
if (info_.count(phase) == 0) {
return nullptr;
@ -310,14 +313,14 @@ ResourcePtr ExecutorPy::GetResource(const std::string &phase) {
return info_[phase]->resource;
}
FuncGraphPtr ExecutorPy::GetFuncGraph(const std::string &phase) {
FuncGraphPtr GraphExecutorPy::GetFuncGraph(const std::string &phase) {
if (info_.count(phase) == 0) {
MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
MS_LOG(EXCEPTION) << "No executor info. found for phase: " << phase;
}
return info_[phase]->func_graph;
}
FuncGraphPtr ExecutorPy::GetGradGraph(const std::string &phase) {
FuncGraphPtr GraphExecutorPy::GetGradGraph(const std::string &phase) {
if (phase.empty()) {
MS_LOG(EXCEPTION) << "The input phase is empty.";
}
@ -332,7 +335,7 @@ FuncGraphPtr ExecutorPy::GetGradGraph(const std::string &phase) {
return grad_graph;
}
void ExecutorPy::SetGradGraph(const FuncGraphPtr &grad_graph, const std::string &phase) {
void GraphExecutorPy::SetGradGraph(const FuncGraphPtr &grad_graph, const std::string &phase) {
if (phase.empty()) {
MS_LOG(EXCEPTION) << "The input phase is empty.";
}
@ -349,7 +352,7 @@ void ExecutorPy::SetGradGraph(const FuncGraphPtr &grad_graph, const std::string
execute_info->grad_graph = grad_graph;
}
compile::VmEvalFuncPtr ExecutorPy::GetVmEvalFunc(const std::string &phase) {
compile::VmEvalFuncPtr GraphExecutorPy::GetVmEvalFunc(const std::string &phase) {
ResourcePtr res = GetResource(phase);
MS_EXCEPTION_IF_NULL(res);
if (res->results().find(kOutput) != res->results().end() && res->results()[kOutput].is<compile::VmEvalFuncPtr>()) {
@ -359,14 +362,14 @@ compile::VmEvalFuncPtr ExecutorPy::GetVmEvalFunc(const std::string &phase) {
return nullptr;
}
bool ExecutorPy::HasCompiled(const std::string &phase) const {
bool GraphExecutorPy::HasCompiled(const std::string &phase) const {
if (info_.count(phase) == 0) {
return false;
}
return true;
}
py::bytes ExecutorPy::GetFuncGraphProto(const std::string &phase, const std::string &ir_type) {
py::bytes GraphExecutorPy::GetFuncGraphProto(const std::string &phase, const std::string &ir_type) {
FuncGraphPtr fg_ptr = GetFuncGraph(phase);
if (fg_ptr == nullptr) {
for (auto &item : info_) {
@ -402,40 +405,40 @@ py::bytes ExecutorPy::GetFuncGraphProto(const std::string &phase, const std::str
MS_LOG(EXCEPTION) << "Unknown ir type: " << ir_type;
}
py::dict ExecutorPy::GetParameterLayout(const std::string &phase) {
py::dict GraphExecutorPy::GetParameterLayout(const std::string &phase) {
MS_LOG(DEBUG) << "GetParameterLayout!";
std::string layout_graph = phase + kStepParallelGraph;
auto graph = GetFuncGraph(layout_graph);
return mindspore::parallel::GetParameterLayout(graph);
}
py::dict ExecutorPy::GetCNodeStrategy(const std::string &phase) {
py::dict GraphExecutorPy::GetCNodeStrategy(const std::string &phase) {
MS_LOG(DEBUG) << "GetCNodeStrategy!";
return stra_dict_[phase];
}
py::list ExecutorPy::GetParallelParameterNameList(const std::string &phase) {
py::list GraphExecutorPy::GetParallelParameterNameList(const std::string &phase) {
std::string param_graph = phase + kStepParallelGraph;
auto graph = GetFuncGraph(param_graph);
return mindspore::parallel::GetParallelParameterNameList(graph);
}
void ExecutorPy::SetCNodeStrategy(const std::string &name, const parallel::Strategys &strategy) {
void GraphExecutorPy::SetCNodeStrategy(const std::string &name, const parallel::Strategys &strategy) {
MS_LOG(DEBUG) << "SetCNodeStrategy!";
stra_dict_[phase_][py::str(name)] = strategy;
}
size_t ExecutorPy::GetNumOpsInfo(const std::string &phase) {
size_t GraphExecutorPy::GetNumOpsInfo(const std::string &phase) {
MS_LOG(DEBUG) << "GetNumOpsInfo!";
return phase_to_num_op_info_[phase];
}
void ExecutorPy::SetNumOpsInfo(size_t num_ops) {
void GraphExecutorPy::SetNumOpsInfo(size_t num_ops) {
MS_LOG(DEBUG) << "SetNumOpsInfo!";
phase_to_num_op_info_[phase_] = num_ops;
}
py::dict ExecutorPy::GetAllreduceFusion(const std::string &phase) {
py::dict GraphExecutorPy::GetAllreduceFusion(const std::string &phase) {
MS_LOG(INFO) << "GetAllreduceFusion!";
auto graph = GetFuncGraph(phase);
return mindspore::parallel::GetAllreduceFusion(graph);
@ -443,7 +446,7 @@ py::dict ExecutorPy::GetAllreduceFusion(const std::string &phase) {
// Not support multi thread, not support nested call too.
// Here using nested_called flg to avoid nested call.
void ExecutorPy::DelNetRes(const std::string &id) {
void GraphExecutorPy::DelNetRes(const std::string &id) {
static bool nested_called = false;
if (nested_called) {
return;
@ -479,17 +482,18 @@ void ExecutorPy::DelNetRes(const std::string &id) {
nested_called = false;
}
void ExecutorPy::ClearRes() {
void GraphExecutorPy::ClearRes() {
MS_LOG(INFO) << "Clean executor resource!";
executor_ = nullptr;
}
ExecutorPy::~ExecutorPy() {
GraphExecutorPy::~GraphExecutorPy() {
MS_LOG(INFO) << "Release Executor!";
ConfigManager::GetInstance().ResetConfig();
}
void ExecutorPy::GetWeightInfo(const CNodePtr &root_node, const AnfNodePtr &weight_node,
void GraphExecutorPy::GetWeightInfo(
const CNodePtr &root_node, const AnfNodePtr &weight_node,
std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> *fake_quant_table) {
MS_EXCEPTION_IF_NULL(root_node);
MS_EXCEPTION_IF_NULL(fake_quant_table);
@ -557,11 +561,11 @@ void ExecutorPy::GetWeightInfo(const CNodePtr &root_node, const AnfNodePtr &weig
(*fake_quant_table)[weight_name] = std::make_pair(quant_op->adapter(), fakequant_min_node_name);
}
std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> ExecutorPy::FetchInfoForQuantExport(
const std::string &phase_s) {
FuncGraphPtr func_graph = info_[phase_s]->resource->func_graph();
std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> GraphExecutorPy::FetchInfoForQuantExport(
const std::string &phase) {
FuncGraphPtr func_graph = info_[phase]->resource->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
MS_LOG(DEBUG) << "FetchInfoForQuantExport func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!";
MS_LOG(DEBUG) << "FetchInfoForQuantExport func graph(" << func_graph->ToString() << ") phase(" << phase << ")!";
std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> fake_quant_table;
auto filter = [](const AnfNodePtr &node) {
return !(IsPrimitiveCNode(node, prim::kPrimConv2D) || IsPrimitiveCNode(node, prim::kPrimMatMul) ||
@ -605,21 +609,21 @@ std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> ExecutorPy:
return fake_quant_table;
}
void ExecutorPy::SaveCompiledGraph(const std::string &phase_s) {
// save the graph to ExecutorPy
FuncGraphPtr func_graph = info_[phase_s]->resource->func_graph();
void GraphExecutorPy::SaveCompiledGraph(const std::string &phase) {
// save the graph to GraphExecutorPy
FuncGraphPtr func_graph = info_[phase]->resource->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(parallel::ParallelContext::GetInstance());
std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode();
MS_LOG(INFO) << "Save compiled func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!";
info_[phase_s]->func_graph = func_graph;
MS_LOG(INFO) << "Save compiled func graph(" << func_graph->ToString() << ") phase(" << phase << ")!";
info_[phase]->func_graph = func_graph;
if ((func_graph != nullptr) && func_graph->has_flag(parallel::AUTO_PARALLEL) &&
((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL))) {
MS_LOG(DEBUG) << "Save model parallel parameter layout graph!";
func_graph = info_[phase_s]->resource->results()[kStepParallelGraph].cast<FuncGraphPtr>();
func_graph = info_[phase]->resource->results()[kStepParallelGraph].cast<FuncGraphPtr>();
ExecutorInfoPtr executor_info = std::make_shared<ExecutorInfo>();
std::string layout_graph = phase_s + kStepParallelGraph;
std::string layout_graph = phase + kStepParallelGraph;
executor_info->func_graph = func_graph;
info_[layout_graph] = executor_info;
} else {
@ -628,7 +632,7 @@ void ExecutorPy::SaveCompiledGraph(const std::string &phase_s) {
MS_LOG(INFO) << "End save compiled func graph!";
}
void ExecutorPy::GetGeBackendPolicy() const {
void GraphExecutorPy::GetGeBackendPolicy() const {
auto ms_context = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(ms_context);
std::string backend = ms_context->backend_policy();
@ -637,24 +641,24 @@ void ExecutorPy::GetGeBackendPolicy() const {
}
}
bool IsPhaseExportAir(const std::string &phase_s) {
bool IsPhaseExportAir(const std::string &phase) {
auto phase_to_export = "export.air";
return phase_s.rfind(phase_to_export) != std::string::npos;
return phase.rfind(phase_to_export) != std::string::npos;
}
bool IsPhaseTrain(const std::string &phase_s) {
bool IsPhaseTrain(const std::string &phase) {
const std::string phase_to_train = "train";
return phase_s.rfind(phase_to_train) != std::string::npos;
return phase.rfind(phase_to_train) != std::string::npos;
}
bool IsPhaseLoadFromMindIR(const std::string &phase_s) {
bool IsPhaseLoadFromMindIR(const std::string &phase) {
const std::string mindir_graph = "graph_load_from_mindir";
return phase_s.rfind(mindir_graph) != std::string::npos;
return phase.rfind(mindir_graph) != std::string::npos;
}
std::vector<ActionItem> GetPipeline(const ResourcePtr &resource, const std::string &phase_s, bool use_vm) {
std::vector<ActionItem> GetPipeline(const ResourcePtr &resource, const std::string &phase, bool use_vm) {
MS_EXCEPTION_IF_NULL(resource);
bool is_air = IsPhaseExportAir(phase_s);
bool is_air = IsPhaseExportAir(phase);
std::string backend = MsContext::GetInstance()->backend_policy();
@ -682,11 +686,11 @@ std::vector<ActionItem> GetPipeline(const ResourcePtr &resource, const std::stri
resource->results()[kBackend] = backend_ptr;
// If the 'use_frontend_compile_cache' context has been set true and the cache is read successfully,
// do the backend actions only.
if (IsPhaseTrain(phase_s) && MsContext::GetInstance()->get_param<bool>(MS_CTX_LOAD_COMPILE_CACHE) &&
if (IsPhaseTrain(phase) && MsContext::GetInstance()->get_param<bool>(MS_CTX_LOAD_COMPILE_CACHE) &&
resource->func_graph() != nullptr) {
return BackendPipeline();
}
if (IsPhaseLoadFromMindIR(phase_s)) {
if (IsPhaseLoadFromMindIR(phase)) {
return MindIRPipeline();
}
return VmPipeline();
@ -694,28 +698,30 @@ std::vector<ActionItem> GetPipeline(const ResourcePtr &resource, const std::stri
return GePipeline();
}
bool ExecutorPy::CompileInner(const py::object &obj, const py::tuple &args, const py::object &phase, bool use_vm,
const std::string &queue_name) {
MS_LOG(DEBUG) << "Start ExecutorPy compile!";
if ((!py::isinstance<py::str>(phase))) {
MS_LOG(ERROR) << "Arg phase must be string.";
bool GraphExecutorPy::CompileInner(const py::object &source_obj, const py::tuple &args, const py::object &phase_obj,
bool use_vm, const std::string &queue_name) {
// Check if the phase is valid.
if ((!py::isinstance<py::str>(phase_obj))) {
MS_LOG(ERROR) << "The `phase` must be string.";
return false;
}
// check the function or net is valid
if (py::isinstance<py::none>(obj)) {
MS_LOG(ERROR) << "Find error: parse obj is None.";
// Check if the function or net is valid.
if (py::isinstance<py::none>(source_obj)) {
MS_LOG(ERROR) << "The source object to compile should not be None.";
return false;
}
// check the args of function or net is valid
// Check if the args of function or net is valid.
CheckArgsValid(args);
auto phase = py::cast<std::string>(phase_obj);
MS_LOG(INFO) << "Start compiling, phase: " << phase << ", source: {" << py::str(source_obj) << "}";
MS_LOG(DEBUG) << "args: " << py::str(const_cast<py::tuple &>(args));
#ifdef ENABLE_GE
GetGeBackendPolicy();
#endif
ExecutorInfoPtr executor_info = std::make_shared<ExecutorInfo>();
auto phase_s = py::cast<std::string>(phase);
phase_ = phase_s;
MS_LOG(INFO) << "ExecutorPy compile phase:" << phase_s << "!";
ResourcePtr resource = std::make_shared<Resource>(obj);
ResourcePtr resource = std::make_shared<Resource>(source_obj);
if (MsContext::GetInstance()->get_param<bool>(MS_CTX_LOAD_COMPILE_CACHE)) {
#ifdef ENABLE_PROFILE
@ -728,41 +734,42 @@ bool ExecutorPy::CompileInner(const py::object &obj, const py::tuple &args, cons
#endif
}
auto p_actions = GetPipeline(resource, phase_s, use_vm);
std::shared_ptr<Pipeline> pip = std::make_shared<Pipeline>(resource, FilterActions(p_actions, phase_s));
phase_ = phase;
auto actions = GetPipeline(resource, phase, use_vm);
std::shared_ptr<Pipeline> pip = std::make_shared<Pipeline>(resource, FilterActions(actions, phase));
// get the parameters items and add the value to args_spec
// Get the parameters items and add the value to args_spec.
abstract::AbstractBasePtrList args_spec;
std::size_t size = args.size();
for (std::size_t i = 0; i < size; i++) {
ValuePtr converted = nullptr;
bool succ = parse::ConvertData(args[i], &converted);
if (!succ) {
MS_LOG(EXCEPTION) << "Args convert error";
MS_LOG(EXCEPTION) << "Fail to convert the " << i << "th argument, args[" << i << "]: " << py::str(args[i]);
}
args_spec.push_back(ArgsToAbstract(converted));
}
resource->set_args_spec(args_spec);
executor_info->arg_list_size = size;
executor_info->resource = resource;
info_[phase_s] = executor_info;
pip->Run(phase_s);
info_[phase] = executor_info;
pip->Run(phase);
// save the run graph func to MsPipeLine
SaveCompiledGraph(phase_s);
// Save the compiled graph to MsPipeLine.
SaveCompiledGraph(phase);
opt::python_pass::PyPassManager::GetInstance()->ClearPipelineRes();
abstract::AnalysisContext::ClearContext();
// Reclaim all resource used by optimizer;
// Reclaim all resource used by optimizer.
ReclaimOptimizer();
resource->Clean();
MS_LOG(INFO) << "End ExecutorPy compile!";
MS_LOG(INFO) << "Finish compiling.";
return true;
}
std::vector<ActionItem> ExecutorPy::FilterActions(const std::vector<ActionItem> &actions, const std::string &phase) {
std::vector<ActionItem> GraphExecutorPy::FilterActions(const std::vector<ActionItem> &actions,
const std::string &phase) {
// filter action after validate when 'export'.
if (GetPhasePrefix(phase).rfind("export", 0) == std::string::npos) {
return actions;
@ -778,7 +785,7 @@ std::vector<ActionItem> ExecutorPy::FilterActions(const std::vector<ActionItem>
return filtered_actions;
}
void ExecutorPy::ReleaseResource(const py::object &phase) {
void GraphExecutorPy::ReleaseResource(const py::object &phase) {
ResourcePtr res = GetResource(py::cast<std::string>(phase));
if (res != nullptr) {
res->Clean();
@ -787,17 +794,11 @@ void ExecutorPy::ReleaseResource(const py::object &phase) {
ReclaimOptimizer();
}
static std::string PrintArgs(const py::tuple &args) {
py::print(args);
return "";
}
bool ExecutorPy::Compile(const py::object &obj, const py::tuple &args, const py::object &phase, bool use_vm,
bool GraphExecutorPy::Compile(const py::object &source_obj, const py::tuple &args, const py::object &phase, bool use_vm,
const std::string &queue_name) {
bool ret_value = false;
try {
MS_LOG(DEBUG) << PrintArgs(args);
ret_value = CompileInner(obj, args, phase, use_vm, queue_name);
ret_value = CompileInner(source_obj, args, phase, use_vm, queue_name);
} catch (const py::error_already_set &ex) {
if (!StaticAnalysisException::Instance().HasException()) {
// print function call stack info before release
@ -840,8 +841,8 @@ bool ExecutorPy::Compile(const py::object &obj, const py::tuple &args, const py:
return ret_value;
}
void CacheValidateFuncGraph(const std::string &phase_s, const ResourcePtr &resource) {
if (IsPhaseTrain(phase_s) && MsContext::GetInstance()->get_param<bool>(MS_CTX_SAVE_COMPILE_CACHE)) {
void CacheValidateFuncGraph(const std::string &phase, const ResourcePtr &resource) {
if (IsPhaseTrain(phase) && MsContext::GetInstance()->get_param<bool>(MS_CTX_SAVE_COMPILE_CACHE)) {
#ifdef ENABLE_PROFILE
double t1 = GetTime();
#endif
@ -853,12 +854,12 @@ void CacheValidateFuncGraph(const std::string &phase_s, const ResourcePtr &resou
}
}
void Pipeline::Run(const std::string &phase_s) {
void Pipeline::Run(const std::string &phase) {
MS_LOG(INFO) << "Pipeline run";
MS_EXCEPTION_IF_NULL(resource_);
FuncGraphPtr user_graph = nullptr;
WITH(MsProfile::GetProfile())[&user_graph, &phase_s, this]() {
WITH(MsProfile::GetProfile())[&user_graph, &phase, this]() {
size_t i = 0;
for (auto &action : actions_) {
#ifdef ENABLE_TIMELINE
@ -874,7 +875,7 @@ void Pipeline::Run(const std::string &phase_s) {
if (action.first == "task_emit") {
SetLoopCount(resource_);
} else if (action.first == "validate") {
CacheValidateFuncGraph(phase_s, resource_);
CacheValidateFuncGraph(phase, resource_);
}
if (!result) {
MS_LOG(EXCEPTION) << "Pipeline running to end, failed in step:" << action.first;
@ -983,11 +984,11 @@ void ProcessVmArgInner(const py::tuple &args, const ResourcePtr &res, VectorRef
}
}
void ExecutorPy::ProcessVmArg(const py::tuple &args, const std::string &phase, VectorRef *const arg_list) {
void GraphExecutorPy::ProcessVmArg(const py::tuple &args, const std::string &phase, VectorRef *const arg_list) {
ProcessVmArgInner(args, GetResource(phase), arg_list);
}
void ExecutorPy::TerminateDebugger() {
void GraphExecutorPy::TerminateDebugger() {
if (debugger_terminate_) {
MS_LOG(INFO) << "Terminate debugger and clear resources!";
ClearResAtexit();
@ -995,23 +996,23 @@ void ExecutorPy::TerminateDebugger() {
}
}
py::object ExecutorPy::Run(const py::tuple &args, const py::object &phase) {
py::object GraphExecutorPy::Run(const py::tuple &args, const py::object &phase_obj) {
// Mindspore debugger notify main thread to exit after one step, and will not run next step
TerminateDebugger();
std::size_t size = args.size();
if (!py::isinstance<py::str>(phase)) {
if (!py::isinstance<py::str>(phase_obj)) {
MS_LOG(EXCEPTION) << "Run failed, phase input is not a str";
}
auto phase_s = py::cast<std::string>(phase);
auto phase = py::cast<std::string>(phase_obj);
std::string backend = MsContext::GetInstance()->backend_policy();
#ifdef ENABLE_GE
if (backend == "ge") {
return ExecDFGraph(info_, args, phase_s);
return ExecDFGraph(info_, args, phase);
}
#else
auto ret_val = std::make_shared<py::object>();
if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) {
if (info_.count(phase) != 0 && info_[phase]->func_graph != nullptr) {
if (IsGraphOutputValueNodeOrParameter(info_[phase]->func_graph->output(), args, ret_val)) {
// Check the input arg must be Tensor when backend is "ms".
if (MsContext::GetInstance()->backend_policy() == kMsConvert) {
for (std::size_t i = 0; i < size; i++) {
@ -1032,24 +1033,24 @@ py::object ExecutorPy::Run(const py::tuple &args, const py::object &phase) {
return args;
}
#endif
auto iter = info_.find(phase_s);
auto iter = info_.find(phase);
if (iter == info_.end()) {
MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase_s);
MS_LOG(EXCEPTION) << "No executor info. found for phase: " << phase;
}
auto &execute_info = iter->second;
MS_EXCEPTION_IF_NULL(execute_info);
if (size > execute_info->arg_list_size) {
MS_LOG(WARNING) << "The arg num : size = " << size << ". full_arg_size = " << execute_info->arg_list_size;
}
ProcessVmArg(args, phase_s, &execute_info->arg_list);
ProcessVmArg(args, phase, &execute_info->arg_list);
// Start to run phase.
compile::VmEvalFuncPtr run = GetVmEvalFunc(phase_s);
compile::VmEvalFuncPtr run = GetVmEvalFunc(phase);
if (run == nullptr) {
MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s;
MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase;
}
// Set loopsink size for each phase.
bool vm_loop_flag = info_[phase_s]->resource->vm_loop_flag();
int64_t loop_size = info_[phase_s]->resource->loop_size();
bool vm_loop_flag = info_[phase]->resource->vm_loop_flag();
int64_t loop_size = info_[phase]->resource->loop_size();
int64_t vm_loop = 1;
if (vm_loop_flag) {
vm_loop = loop_size;
@ -1069,7 +1070,7 @@ py::object ExecutorPy::Run(const py::tuple &args, const py::object &phase) {
return ret;
}
FuncGraphPtr ExecutorPy::BuildGraph(const py::dict &init_params, const std::string &phase,
FuncGraphPtr GraphExecutorPy::BuildGraph(const py::dict &init_params, const std::string &phase,
const py::object &broadcast_params) {
#if ((defined ENABLE_GE) || (defined ENABLE_D))
return BuildDFGraph(info_, init_params, phase, broadcast_params);
@ -1078,8 +1079,8 @@ FuncGraphPtr ExecutorPy::BuildGraph(const py::dict &init_params, const std::stri
#endif
}
void ExecutorPy::UpdataParamNodeDefaultInput(const std::string &phase,
const std::unordered_map<std::string, tensor::TensorPtr> &params_value) {
void GraphExecutorPy::UpdataParamNodeDefaultInput(
const std::string &phase, const std::unordered_map<std::string, tensor::TensorPtr> &params_value) {
FuncGraphPtr func_graph = info_[phase]->resource->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
MS_LOG(DEBUG) << "UpdataParamNodeDefaultInput for func graph(" << func_graph->ToString() << ") phase(" << phase
@ -1096,13 +1097,13 @@ void ExecutorPy::UpdataParamNodeDefaultInput(const std::string &phase,
}
}
void ExecutorPy::RunInitGraph(const py::dict &init_params, const std::string &phase) const {
void GraphExecutorPy::RunInitGraph(const py::dict &init_params, const std::string &phase) const {
#ifdef ENABLE_GE
RunGEInitGraph(init_params, phase);
#endif
}
void ExecutorPy::PyExePath(const py::object &py_exe_path) {
void GraphExecutorPy::PyExePath(const py::object &py_exe_path) {
if (!py::isinstance<py::str>(py_exe_path)) {
MS_LOG(EXCEPTION) << "Failed, py_exe_path input is not a str";
}
@ -1111,7 +1112,7 @@ void ExecutorPy::PyExePath(const py::object &py_exe_path) {
ms_context->set_param<std::string>(MS_CTX_PYTHON_EXE_PATH, py_exe_path_s);
}
void ExecutorPy::KernelBuildServerDir(const py::object &kernel_build_server_dir) {
void GraphExecutorPy::KernelBuildServerDir(const py::object &kernel_build_server_dir) {
if (!py::isinstance<py::str>(kernel_build_server_dir)) {
MS_LOG(EXCEPTION) << "Failed, kernel_build_server_dir input is not a str";
}
@ -1423,7 +1424,7 @@ void ClearResAtexit() {
abstract::ClearPrimEvaluatorMap();
pipeline::GetMethodMap().clear();
pipeline::GetAttrMap().clear();
pipeline::ExecutorPy::ClearRes();
pipeline::GraphExecutorPy::ClearRes();
pipeline::ReclaimOptimizer();
pynative::PynativeExecutor::GetInstance()->ClearRes();
opt::python_pass::PyPassManager::GetInstance()->ClearRes();

30
mindspore/ccsrc/pipeline/jit/pipeline.h
View File

@ -49,7 +49,7 @@ class Pipeline {
~Pipeline() = default;
void Run(const std::string &phase_s);
void Run(const std::string &phase);
ResourcePtr resource() { return resource_; }
@ -59,29 +59,29 @@ class Pipeline {
};
// A function pipeline.
class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
class GraphExecutorPy : public std::enable_shared_from_this<GraphExecutorPy> {
public:
static std::shared_ptr<ExecutorPy> GetInstance() {
static std::shared_ptr<GraphExecutorPy> GetInstance() {
std::lock_guard<std::mutex> i_lock(instance_lock_);
if (executor_ == nullptr) {
executor_ = std::shared_ptr<ExecutorPy>(new (std::nothrow) ExecutorPy());
executor_ = std::shared_ptr<GraphExecutorPy>(new (std::nothrow) GraphExecutorPy());
}
return executor_;
}
~ExecutorPy();
~GraphExecutorPy();
const std::string &phase() const { return phase_; }
void SaveCompiledGraph(const std::string &phase_s);
bool CompileInner(const py::object &obj, const py::tuple &args, const py::object &phase, bool use_vm,
void SaveCompiledGraph(const std::string &phase);
bool CompileInner(const py::object &source_obj, const py::tuple &args, const py::object &phase_obj, bool use_vm,
const std::string &queue_name);
bool Compile(const py::object &obj, const py::tuple &args, const py::object &phase, bool use_vm,
bool Compile(const py::object &source_obj, const py::tuple &args, const py::object &phase_obj, bool use_vm,
const std::string &queue_name);
void ProcessVmArg(const py::tuple &args, const std::string &phase, VectorRef *arg_list);
// for pynative mode when use_vm is on
py::object Run(const py::tuple &args, const py::object &phase);
py::object Run(const py::tuple &args, const py::object &phase_obj);
ResourcePtr GetResource(const std::string &phase);
FuncGraphPtr GetFuncGraph(const std::string &phase);
FuncGraphPtr GetGradGraph(const std::string &phase);
@ -105,17 +105,17 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
void SetNumOpsInfo(size_t);
py::dict GetAllreduceFusion(const std::string &phase);
void DelNetRes(const std::string &id);
void ReleaseResource(const py::object &phase);
void ReleaseResource(const py::object &phase_obj);
static void ClearRes();
static bool GetDebugTerminate() { return debugger_terminate_; }
static void DebugTerminate(bool val) { debugger_terminate_ = val; }
void TerminateDebugger();
std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> FetchInfoForQuantExport(
const std::string &phase_s);
const std::string &phase);
private:
ExecutorPy();
GraphExecutorPy();
void ConvertObjectToTensors(const py::dict &dict, std::map<std::string, tensor::TensorPtr> *tensors);
void GetWeightInfo(const CNodePtr &root_node, const AnfNodePtr &weight_node,
std::map<std::string, std::pair<PrimitivePyAdapterPtr, std::string>> *fake_quant_table);
@ -125,18 +125,18 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
static std::vector<ActionItem> FilterActions(const std::vector<ActionItem> &actions, const std::string &phase);
std::map<std::string, ExecutorInfoPtr> info_;
static std::shared_ptr<ExecutorPy> executor_;
static std::shared_ptr<GraphExecutorPy> executor_;
static std::mutex instance_lock_;
static bool debugger_terminate_;
std::map<std::string, py::dict> stra_dict_;
std::string phase_ = "";
std::map<std::string, size_t> phase_to_num_op_info_;
};
using ExecutorPyPtr = std::shared_ptr<ExecutorPy>;
using GraphExecutorPyPtr = std::shared_ptr<GraphExecutorPy>;
void CheckArgsValid(const py::tuple &args);
// Generate a key for mapping function graph
py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::string, py::object> &defaults);
py::object GenerateArgumentsKey(const std::unordered_map<std::string, py::object> &args);
py::bool_ VerifyInputSignature(const py::list &input_signature, const py::tuple &inputs);
bool InitDistribute(const std::map<std::string, std::string> &options);

2
mindspore/ccsrc/pipeline/jit/pipeline_ge.cc
View File

@ -442,7 +442,7 @@ std::shared_ptr<py::object> DoExecGraph(const FuncGraphPtr &graph, const std::ve
void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr> &info, const py::tuple &args, const std::string &phase,
std::vector<tensor::TensorPtr> *inputs) {
// check the arg and use the ExecutorPy args
// check the arg and use the GraphExecutorPy args
std::size_t size = args.size();
if (info.count(phase) == 0) {

4
mindspore/ccsrc/pipeline/jit/resource.cc
View File

@ -246,7 +246,7 @@ BuiltInTypeMap &GetAttrMap() {
Resource::Resource(const py::object &obj)
: engine_(std::make_shared<abstract::AnalysisEngine>(abstract::GetPrimEvaluatorConstructors(), manager_)),
input_(obj),
source_input_(obj),
is_cleaned_(false) {}
Resource::~Resource() {
@ -313,7 +313,7 @@ Any Resource::GetAttrPtr(const TypeId &type, const std::string &name) {
void Resource::Clean() {
// AbstractTensor->elements() will be saved in AbstractBasePtrList
args_spec_.clear();
input_ = py::none();
source_input_ = py::none();
// Context with AbstractBasePtrList may be saved in GraphEvaluator
// some Evaluator like ResolveEvaluator may save Python object in cache,
// it should be cleaned before Python Interpreter destructed.

7
mindspore/ccsrc/pipeline/jit/resource.h
View File

@ -67,7 +67,7 @@ class Resource : public ResourceBase {
static Any GetAttrPtr(const TypeId &type, const std::string &name);
const py::object &input() const { return input_; }
const py::object &source_input() const { return source_input_; }
FuncGraphPtr func_graph() const { return func_graph_; }
void set_func_graph(const FuncGraphPtr &func_graph) { func_graph_ = func_graph; }
@ -84,7 +84,7 @@ class Resource : public ResourceBase {
bool vm_loop_flag() { return vm_loop_flag_; }
int64_t loop_size() { return loop_size_; }
// Reclaim resource and clear the cache.
// ExecutorPy::Compile() can be called multiple times, so cache
// GraphExecutorPy::Compile() can be called multiple times, so cache
// should be cleared.
void Clean();
@ -92,7 +92,8 @@ class Resource : public ResourceBase {
abstract::AnalysisEnginePtr engine_;
FuncGraphPtr func_graph_;
abstract::AbstractBasePtrList args_spec_;
py::object input_;
// The source obj to compile, usually a `Cell` or `ms_function` decorated function.
py::object source_input_;
bool is_cleaned_;
// The func_graph_ is loaded from mindir
bool is_load_{false};

2
mindspore/ccsrc/pipeline/pynative/pynative_execute.cc
View File

@ -2942,7 +2942,7 @@ void GradExecutor::GradMsFunction(const py::object &out, const py::args &args) {
// Get ms_function func graph and grad graph.
const auto &phase = graph_phase();
MS_LOG(DEBUG) << "ms_function func graph phase: " << phase;
auto executor = pipeline::ExecutorPy::GetInstance();
auto executor = pipeline::GraphExecutorPy::GetInstance();
FuncGraphPtr ms_func_graph = executor->GetFuncGraph(phase);
MS_EXCEPTION_IF_NULL(ms_func_graph);
FuncGraphPtr grad_graph = executor->GetGradGraph(phase);

125
mindspore/common/api.py
View File

@ -25,7 +25,7 @@ from mindspore import context
from mindspore import log as logger
from mindspore._extends.remote import kernel_build_server
from .tensor import Tensor as MsTensor
from .._c_expression import generate_key, Executor_, Tensor, MetaTensor, PynativeExecutor_
from .._c_expression import generate_arguments_key, GraphExecutor_, Tensor, MetaTensor, PynativeExecutor_
from .._c_expression import verify_inputs_signature, init_exec_dataset, _set_dataset_mode_config, init_pipeline
from ..parallel._ps_context import _is_role_pserver
from ..parallel._utils import _get_device_num, _get_global_rank, _need_to_full, _check_full_batch, _to_full_tensor, \
@ -92,7 +92,7 @@ def _wrap_func(fn):
def _exec_init_graph(obj, init_phase):
"""Execute the parameter initializer graph."""
inst_executor = Executor_.get_instance()
inst_executor = GraphExecutor_.get_instance()
param_dict = OrderedDict()
for name, param in obj.parameters_dict().items():
if not param.is_init:
@ -104,11 +104,11 @@ def _exec_init_graph(obj, init_phase):
inst_executor.run_init_graph(param_dict, init_phase)
class _MindSporeFunction:
class _MindsporeFunctionExecutor:
"""
Represents a function compiled by mind expression.
Represents a function compiled by graph compiler.
_MindSporeFunction will compile the original function for every combination
_MindsporeFunctionExecutor will compile the original function for every combination
of argument types and shapes it is given (as well as their values, optionally).
Args:
@ -127,22 +127,23 @@ class _MindSporeFunction:
self.obj = None
if hasattr(obj, fn.__name__):
self.obj = obj
self._executor = Executor_.get_instance()
self._graph_executor = GraphExecutor_.get_instance()
def build_data_init_graph(self, graph_name):
"""Build GE data graph and init graph for the given graph name."""
if self.obj is None:
logger.warning("Make sure parameter should not be used in function")
para_dict = OrderedDict()
self._executor.build_data_graph(para_dict, graph_name)
self._graph_executor.build_data_graph(para_dict, graph_name)
return
self._executor.build_data_graph(self.obj.parameters_dict(), graph_name, self.obj.parameters_broadcast_dict())
self._graph_executor.build_data_graph(self.obj.parameters_dict(), graph_name,
self.obj.parameters_broadcast_dict())
init_phase = "init_subgraph" + graph_name[graph_name.find("."):]
_exec_init_graph(self.obj, init_phase)
def compile(self, args_list, arg_names, method_name):
"""Returns pipeline for the given args."""
# verify the signature for both function and method
# Verify the signature for both function and method
if self.input_signature is not None:
signatures = []
for sig_spec in self.input_signature:
@ -155,35 +156,32 @@ class _MindSporeFunction:
dic = dict(zip(arg_names, args_list))
generate_name = self.fn.__module__ + "." + self.fn.__name__ + "." + self.fn.__code__.co_filename + "." + \
str(self.fn.__code__.co_firstlineno)
str(self.fn.__code__.co_firstlineno) + '.' + str(id(self.fn))
self.fn.__parse_method__ = method_name
# add key with obj
identify = ""
if self.obj is None:
identify = str(id(self.fn))
else:
# Add key with obj
if self.obj is not None:
if self.obj.__module__ != self.fn.__module__:
logger.error(f'`obj` module not equal to `fn` module: {self.obj.__module__}, {self.fn.__module__}')
self.obj.__parse_method__ = method_name
generate_name = self.obj.__module__ + "." + generate_name
identify = str(self.obj.create_time) + "_" + str(id(self.obj)) + '_' + str(id(self.fn))
generate_name = generate_name + '.' + str(self.obj.create_time) + '.' + str(id(self.obj))
generate_name = generate_name + "." + identify
key = generate_key(generate_name, dic)
phase = str(key[1]) + generate_name
if key not in ms_compile_cache.keys():
key = generate_arguments_key(dic)
phase = generate_name + '.' + str(key)
if phase not in ms_compile_cache.keys():
is_compile = False
if self.obj is None:
is_compile = self._executor.compile(self.fn, args_list, phase, True, "")
is_compile = self._graph_executor.compile(self.fn, args_list, phase, True, "")
else:
is_compile = self._executor.compile(self.obj, args_list, phase, True, "")
is_compile = self._graph_executor.compile(self.obj, args_list, phase, True, "")
if not is_compile:
raise RuntimeError("Executor compile failed.")
if context.get_context("enable_ge"):
self.build_data_init_graph(phase)
ms_compile_cache[key] = phase
ms_compile_cache[phase] = phase
return phase
return ms_compile_cache[key]
return phase
@_wrap_func
def __call__(self, *args):
@ -208,12 +206,13 @@ class _MindSporeFunction:
new_inputs.append(i)
elif context.get_context("grad_for_scalar") and isinstance(i, (int, float)):
new_inputs.append(i)
output = self._executor(tuple(new_inputs), phase)
output = self._graph_executor(tuple(new_inputs), phase)
if context.get_context("mode") == context.PYNATIVE_MODE:
_pynative_exec.set_graph_phase(phase)
_pynative_exec.grad_ms_function(output, *new_inputs)
_pynative_executor.set_graph_phase(phase)
_pynative_executor.grad_ms_function(output, *new_inputs)
output = output[0]
return output
@ -283,7 +282,7 @@ def ms_function(fn=None, obj=None, input_signature=None):
process_obj = None
if args and not isinstance(args[0], MsTensor) and hasattr(args[0], func.__name__):
process_obj = args[0]
out = _MindSporeFunction(func, input_signature, process_obj)(*args)
out = _MindsporeFunctionExecutor(func, input_signature, process_obj)(*args)
return out
return staging_specialize
@ -420,9 +419,9 @@ class _PynativeExecutor:
return self._executor(obj, args)
class _Executor:
class _CellGraphExecutor:
"""
An executor used to compile/manage/run graph.
An executor used to compile/manage/run graph for a Cell.
Including data_graph, train_graph, eval_graph and predict graph.
@ -437,10 +436,10 @@ class _Executor:
def __init__(self):
# create needed graph by lazy mode
self.is_init = False
self._executor = Executor_.get_instance()
self._graph_executor = GraphExecutor_.get_instance()
self.compile_cache = {}
self._executor.set_py_exe_path(sys.executable)
self._executor.set_kernel_build_server_dir(os.path.split(kernel_build_server.__file__)[0] + os.sep)
self._graph_executor.set_py_exe_path(sys.executable)
self._graph_executor.set_kernel_build_server_dir(os.path.split(kernel_build_server.__file__)[0] + os.sep)
self.queue_name = ""
def init_dataset(self, queue_name, dataset_size, batch_size, dataset_types, dataset_shapes,
@ -472,7 +471,7 @@ class _Executor:
return True
def _build_data_graph(self, obj, phase):
self._executor.build_data_graph(obj.parameters_dict(), phase, obj.parameters_broadcast_dict())
self._graph_executor.build_data_graph(obj.parameters_dict(), phase, obj.parameters_broadcast_dict())
def _set_dataset_mode(self, args_list):
"""set dataset mode."""
@ -501,12 +500,9 @@ class _Executor:
args_names, args_list = _generate_pip_args(obj, *args)
dic = dict(zip(args_names, args_list))
key = generate_key(phase, dic)
obj.phase_prefix = str(key[1])
if 'export' in phase:
phase = phase + '.' + obj.phase_prefix + '.' + str(obj.create_time) + '.' + str(id(obj))
else:
phase = obj.phase_prefix + phase + '.' + str(obj.create_time) + '.' + str(id(obj))
key = generate_arguments_key(dic)
obj.arguments_key = str(key)
phase = phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
if phase in self.compile_cache.keys():
logger.debug("%r graph has existed.", phase)
@ -524,11 +520,11 @@ class _Executor:
enable_debug_runtime = context.get_context("enable_debug_runtime")
enable_ge = context.get_context("enable_ge")
use_vm = not enable_ge or (enable_debug_runtime and context.get_context("mode") == context.PYNATIVE_MODE)
result = self._executor.compile(obj, args_list, phase, use_vm, self.queue_name)
result = self._graph_executor.compile(obj, args_list, phase, use_vm, self.queue_name)
self.compile_cache[phase] = phase
if not result:
raise RuntimeError("Executor compile failed.")
graph = self._executor.get_func_graph(phase)
graph = self._graph_executor.get_func_graph(phase)
if graph is None:
raise RuntimeError("Compile graph failed for phase {}.".format(phase))
@ -541,7 +537,6 @@ class _Executor:
# the following GE init process is not needed when use vm or ms backend
if enable_ge:
self._build_data_graph(obj, phase)
if "export" not in phase:
init_phase = "init_subgraph." + str(obj.create_time) + "." + str(id(obj))
_exec_init_graph(obj, init_phase)
@ -559,8 +554,8 @@ class _Executor:
self._updata_param_node_default_input(phase, replace)
return
obj.parameter_layout_dict = self._executor.get_parameter_layout(phase)
obj.parallel_parameter_name_list = self._executor.get_parallel_parameter_name_list(phase)
obj.parameter_layout_dict = self._graph_executor.get_parameter_layout(phase)
obj.parallel_parameter_name_list = self._graph_executor.get_parallel_parameter_name_list(phase)
replace = obj.init_parameters_data(auto_parallel_mode=True)
if _get_pipeline_stages() > 1 and (not hasattr(obj, "is_first_iteration") or not obj.is_first_iteration):
obj.remove_redundant_parameters()
@ -575,19 +570,19 @@ class _Executor:
def _updata_param_node_default_input(self, phase, replace):
new_param = {x.name: replace[x] for x in replace if id(x) != id(replace[x])}
return self._executor.updata_param_node_default_input(phase, new_param)
return self._graph_executor.updata_param_node_default_input(phase, new_param)
def _get_shard_strategy(self, obj):
real_phase = obj.phase_prefix + obj.phase + '.' + str(obj.create_time) + '.' + str(id(obj))
return self._executor.get_strategy(real_phase)
real_phase = obj.phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
return self._graph_executor.get_strategy(real_phase)
def _get_num_parallel_ops(self, obj):
real_phase = obj.phase_prefix + obj.phase + '.' + str(obj.create_time) + '.' + str(id(obj))
return self._executor.get_num_parallel_ops(real_phase)
real_phase = obj.phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
return self._graph_executor.get_num_parallel_ops(real_phase)
def _get_allreduce_fusion(self, obj):
real_phase = obj.phase_prefix + obj.phase + '.' + str(obj.create_time) + '.' + str(id(obj))
return self._executor.get_allreduce_fusion(real_phase)
real_phase = obj.phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
return self._graph_executor.get_allreduce_fusion(real_phase)
def has_compiled(self, phase='predict'):
"""
@ -599,7 +594,7 @@ class _Executor:
Returns:
bool, specifies whether the specific graph has been compiled.
"""
return self._executor.has_compiled(phase)
return self._graph_executor.has_compiled(phase)
def __call__(self, obj, *args, phase='predict'):
if context.get_context("precompile_only") or _is_role_pserver():
@ -615,7 +610,7 @@ class _Executor:
raise RuntimeError('Process method parameter is failure')
args_list = tuple(arguments_dict.values())
obj.__parse_method__ = parse_method
return self._executor(args_list, phase)
return self._graph_executor(args_list, phase)
def run(self, obj, *args, phase='predict'):
"""
@ -628,23 +623,23 @@ class _Executor:
Tensor/Tuple, return execute result.
"""
if phase == 'save':
return self._executor((), phase + '.' + str(obj.create_time) + '.' + str(id(obj)))
return self._graph_executor((), phase + '.' + str(obj.create_time) + '.' + str(id(obj)))
phase_real = obj.phase_prefix + phase + '.' + str(obj.create_time) + '.' + str(id(obj))
phase_real = phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
if self.has_compiled(phase_real):
return self._exec_pip(obj, *args, phase=phase_real)
raise KeyError('{} graph is not exist.'.format(phase_real))
def del_net_res(self, net_id):
self._executor.del_net_res(net_id)
self._graph_executor.del_net_res(net_id)
def _get_func_graph_proto(self, obj, exec_id, ir_type="onnx_ir", use_prefix=False):
"""Get graph proto from pipeline."""
if use_prefix:
exec_id = obj.phase_prefix + exec_id
if self._executor.has_compiled(exec_id) is False:
exec_id = exec_id + '.' + obj.arguments_key
if self._graph_executor.has_compiled(exec_id) is False:
return None
return self._executor.get_func_graph_proto(exec_id, ir_type)
return self._graph_executor.get_func_graph_proto(exec_id, ir_type)
def export(self, file_name, graph_id):
"""
@ -659,12 +654,12 @@ class _Executor:
def fetch_info_for_quant_export(self, exec_id):
"""Get graph proto from pipeline."""
if self._executor.has_compiled(exec_id) is False:
if self._graph_executor.has_compiled(exec_id) is False:
return None
return self._executor.fetch_info_for_quant_export(exec_id)
return self._graph_executor.fetch_info_for_quant_export(exec_id)
_executor = _Executor()
_pynative_exec = _PynativeExecutor()
_cell_graph_executor = _CellGraphExecutor()
_pynative_executor = _PynativeExecutor()
__all__ = ['ms_function']

2
mindspore/compression/export/quant_export.py
View File

@ -22,7 +22,7 @@ from ... import nn, ops
from ..._checkparam import Validator
from ...common import Tensor
from ...common import dtype as mstype
from ...common.api import _executor
from ...common.api import _cell_graph_executor as _executor
from ...common.parameter import Parameter
from ...nn import Cell
from ...nn.layer import quant

35
mindspore/nn/cell.py
View File

@ -30,7 +30,7 @@ from .. import context
from .._c_expression import init_pipeline, Cell_, FuncGraph
from .._checkparam import Validator
from ..common import dtype as mstype
from ..common.api import _executor, _pynative_exec
from ..common.api import _cell_graph_executor, _pynative_executor
from ..common.parameter import Parameter, ParameterTuple
from ..common.tensor import Tensor
from ..ops.operations import HookBackward, Cast
@ -98,7 +98,7 @@ class Cell(Cell_):
self._parallel_parameter_name_list = ()
self._parallel_parameter_merge_net_dict = {}
self._create_time = int(time.time() * 1e9)
self.phase_prefix = ""
self.arguments_key = ""
self.parameter_broadcast_done = False
init_pipeline()
@ -264,8 +264,8 @@ class Cell(Cell_):
def get_func_graph_proto(self):
"""Return graph binary proto."""
return _executor._get_func_graph_proto(self, self.phase + "." + str(self.create_time) + '.' + str(id(self)),
"anf_ir", True)
exec_id = self.phase + "." + str(self.create_time) + '.' + str(id(self))
return _cell_graph_executor._get_func_graph_proto(self, exec_id, "anf_ir", True)
def __getattr__(self, name):
if '_params' in self.__dict__:
@ -295,9 +295,9 @@ class Cell(Cell_):
def __del__(self):
if context.get_context is not None and context.get_context("mode") == context.PYNATIVE_MODE:
_pynative_exec.del_cell(str(id(self)))
_pynative_executor.del_cell(str(id(self)))
if hasattr(self, "_create_time"):
_executor.del_net_res(str(self._create_time))
_cell_graph_executor.del_net_res(str(self._create_time))
def __delattr__(self, name):
if name in self._params:
@ -338,7 +338,7 @@ class Cell(Cell_):
def do_parameter_broadcast(self):
if context.get_auto_parallel_context("parallel_mode") == ParallelMode.DATA_PARALLEL:
if not self.parameter_broadcast_done:
_pynative_exec.parameter_broadcast(self, self.phase, self._auto_parallel_mode)
_pynative_executor.parameter_broadcast(self, self.phase, self._auto_parallel_mode)
self.parameter_broadcast_done = True
def run_construct(self, cast_inputs, kwargs):
@ -381,6 +381,8 @@ class Cell(Cell_):
bound_args = inspect.signature(self.construct).bind(*inputs, **kwargs)
inputs = bound_args.args
kwargs = bound_args.kwargs
# Run in Graph mode.
if context.get_context("mode") == context.GRAPH_MODE:
self._check_construct_args(*inputs, **kwargs)
if self.enable_hook:
@ -388,13 +390,14 @@ class Cell(Cell_):
out = self.compile_and_run(*inputs)
return out
# Run in PyNative mode.
self.do_parameter_broadcast()
for item in inputs:
if isinstance(item, numpy.ndarray):
raise TypeError("The cell inputs should not be numpy arrays.")
if self.requires_grad is True:
_pynative_exec.set_grad_flag(True)
_pynative_exec.new_graph(self, *inputs, **kwargs)
_pynative_executor.set_grad_flag(True)
_pynative_executor.new_graph(self, *inputs, **kwargs)
cast_inputs = list()
if hasattr(self, "_mindspore_flags"):
if self._mindspore_flags.get('fp16'):
@ -406,7 +409,7 @@ class Cell(Cell_):
output = self.run_construct(cast_inputs, kwargs)
if isinstance(output, Parameter):
output = output.data
_pynative_exec.end_graph(self, output, *inputs, **kwargs)
_pynative_executor.end_graph(self, output, *inputs, **kwargs)
return output
def _add_attr(self, name, value):
@ -551,7 +554,7 @@ class Cell(Cell_):
"""
Replace parameters with sliced tensors by parallel strategies.
Please refer to the usage in source code of `mindspore.common._Executor.compile`.
Please refer to the usage in source code of `mindspore.common._CellGraphExecutor.compile`.
Args:
params (dict): The parameters dictionary used for initializing the data graph.
@ -635,7 +638,7 @@ class Cell(Cell_):
Args:
inputs (tuple): Inputs of the Cell object.
"""
_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode)
_cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode)
def compile_and_run(self, *inputs):
"""
@ -659,19 +662,19 @@ class Cell(Cell_):
if self._auto_parallel_mode:
if new_inputs and isinstance(new_inputs[0], Tensor) and inputs[0].virtual_flag:
# get parallel inputs in sink mode, parallel inputs set in _executor.compile
# get parallel inputs in sink mode, parallel inputs set in _cell_graph_executor.compile
parallel_inputs_run = self._parallel_inputs_run
else:
parallel_inputs_run = new_inputs
return _executor(self, *parallel_inputs_run, phase=self.phase)
return _executor(self, *new_inputs, phase=self.phase)
return _cell_graph_executor(self, *parallel_inputs_run, phase=self.phase)
return _cell_graph_executor(self, *new_inputs, phase=self.phase)
def auto_parallel_compile_and_run(self):
return self._auto_parallel_compile_and_run
def exec_checkpoint_graph(self):
"""Executes saving checkpoint graph operation."""
_executor(self, phase='save')
_cell_graph_executor(self, phase='save')
def insert_param_to_cell(self, param_name, param, check_name=True):
"""

20
mindspore/ops/composite/base.py
View File

@ -23,7 +23,7 @@ from mindspore import context
from ..._c_expression import EnvInstance_, GradOperation_, HyperMap_, Map_, MultitypeFuncGraph_, Tail_, \
TupleAdd_, TupleSlice_, UnpackCall_, ZipOperation_, ListAppend_, TupleGetItemTensor_
from ...common import dtype as mstype
from ...common.api import ms_function, _pynative_exec, _wrap_func
from ...common.api import ms_function, _pynative_executor, _wrap_func
from ..primitive import Primitive
from ..operations import _grad_ops
from .. import operations as P
@ -341,14 +341,14 @@ class GradOperation(GradOperation_):
new_kwargs = kwargs.copy()
new_kwargs.pop('sens')
if isinstance(fn, FunctionType):
if not _pynative_exec.check_run(fn, *args, **new_kwargs):
_pynative_exec.set_grad_flag(True)
_pynative_exec.new_graph(fn, *args, **new_kwargs)
if not _pynative_executor.check_run(fn, *args, **new_kwargs):
_pynative_executor.set_grad_flag(True)
_pynative_executor.new_graph(fn, *args, **new_kwargs)
output = fn(*args, **new_kwargs)
_pynative_exec.end_graph(fn, output, *args, **new_kwargs)
_pynative_executor.end_graph(fn, output, *args, **new_kwargs)
else:
# Check if fn have run already
if not _pynative_exec.check_run(fn, *args, **new_kwargs):
if not _pynative_executor.check_run(fn, *args, **new_kwargs):
fn.set_grad()
fn(*args, **new_kwargs)
@ -368,12 +368,12 @@ class GradOperation(GradOperation_):
else:
@_wrap_func
def after_grad(*args, **kwargs):
if _pynative_exec.check_graph(fn, *args, **kwargs):
if _pynative_executor.check_graph(fn, *args, **kwargs):
print("Another grad step is running")
self._pynative_forward_run(args, kwargs, fn)
_pynative_exec.grad(grad_, fn, weights, *args, **kwargs)
out = _pynative_exec(fn, *args, **kwargs)
_pynative_exec.clear_grad(fn, *args, **kwargs)
_pynative_executor.grad(grad_, fn, weights, *args, **kwargs)
out = _pynative_executor(fn, *args, **kwargs)
_pynative_executor.clear_grad(fn, *args, **kwargs)
return out
self.grad_fn = after_grad
self.fn = fn

4
mindspore/train/_utils.py
View File

@ -22,7 +22,7 @@ from mindspore.common.tensor import Tensor
from mindspore.common.dtype import dtype_to_nptype, pytype_to_dtype
from mindspore.common import dtype as mstype
from mindspore import log as logger
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.train.mind_ir_pb2 import ModelProto as mindir_model
from mindspore.train.checkpoint_pb2 import Checkpoint
from mindspore.train.node_strategy_pb2 import ParallelStrategyMap as ckpt_strategy
@ -64,7 +64,7 @@ def _exec_datagraph(exec_dataset, dataset_size, phase='dataset', create_data_inf
send_epoch_end = bool(dataset_size == -1)
exec_dataset = exec_dataset.device_que(send_epoch_end=send_epoch_end, create_data_info_queue=create_data_info_queue)
_executor.init_dataset(exec_dataset.queue_name,
_cell_graph_executor.init_dataset(exec_dataset.queue_name,
dataset_size,
batch_size,
dataset_types,

4
mindspore/train/model.py
View File

@ -37,7 +37,7 @@ from ..context import ParallelMode
from ..parallel._cost_model_context import _set_multi_subgraphs
from .dataset_helper import DatasetHelper, connect_network_with_dataset
from . import amp
from ..common.api import _pynative_exec
from ..common.api import _pynative_executor
def _transfer_tensor_to_tuple(inputs):
@ -53,7 +53,7 @@ def _transfer_tensor_to_tuple(inputs):
class _StepSync(Callback):
@staticmethod
def step_end(run_context):
_pynative_exec.sync()
_pynative_executor.sync()
class Model:

2
mindspore/train/serialization.py
View File

@ -37,7 +37,7 @@ from mindspore.train.mind_ir_pb2 import GraphProto as graph_proto
from mindspore.common.tensor import Tensor
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor as _executor
from mindspore.common import dtype as mstype
from mindspore._checkparam import check_input_data, Validator
from mindspore.compression.export import quant_export

4
model_zoo/research/3d/DeepLM/lm_solver/solver.py
View File

@ -20,7 +20,7 @@ from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.composite import GradOperation
from mindspore.nn import Cell
from mindspore.common.api import _pynative_exec
from mindspore.common.api import _pynative_executor
from . import jacobian as jb
from . import listvec as lv
@ -231,7 +231,7 @@ class LMSolver:
return mem
def timing(self):
_pynative_exec.sync()
_pynative_executor.sync()
return time()
def initialize_variables(self):

4
tests/mindspore_test_framework/utils/block_util.py
View File

@ -21,7 +21,7 @@ import numpy as np
from mindspore import ParameterTuple
from mindspore import nn, context
from mindspore.common.api import _executor, ms_function
from mindspore.common.api import _cell_graph_executor, ms_function
from mindspore.common.tensor import Tensor
from mindspore.ops import functional as F
from mindspore.ops import operations as P
@ -45,7 +45,7 @@ def set_block_param_with_rand(net, rand_func=None):
def compile_block(net, *inputs, rand_func=None, training=True):
set_block_training(net, training)
set_block_param_with_rand(net, rand_func)
return _executor.compile(net, *inputs)
return _cell_graph_executor.compile(net, *inputs)
def run_block(net, *inputs, rand_func=None, training=True):

10
tests/ops_common.py
View File

@ -20,7 +20,7 @@ import mindspore.ops.composite as C
import mindspore.ops.functional as F
import mindspore.ops.operations as P
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
@ -252,18 +252,18 @@ def get_loss_fun(construct_net, num_input, output_index):
def build_construct_graph(net, *inputs, execute=True):
net.set_train()
_executor.compile(net, *inputs)
_cell_graph_executor.compile(net, *inputs)
if execute:
_executor(net, inputs)
_cell_graph_executor(net, inputs)
def build_backward_graph(net, output_shapes, inputs, execute=True):
inputs = append_sens_to_inputs(output_shapes, inputs)
net = gen_backward_net(net, len(inputs) - 1)
net.set_train()
_executor.compile(net, inputs)
_cell_graph_executor.compile(net, inputs)
if execute:
_executor(net, inputs)
_cell_graph_executor(net, inputs)
def convert(shp, dtype=np.float32, scale=6):

6
tests/perf_test/test_lenet.py
View File

@ -22,7 +22,7 @@ import mindspore.nn as nn
import mindspore.ops.composite as C
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
context.set_context(mode=context.GRAPH_MODE)
@ -59,7 +59,7 @@ def test_compile():
height,
weight) * 3, np.float32))
_executor.compile(net, inp)
_cell_graph_executor.compile(net, inp)
def test_compile_grad():
@ -72,4 +72,4 @@ def test_compile_grad():
sens = Tensor(np.ones([batch_size, num_class]).astype(np.float32))
grad_op = LeNetGrad(net)
_executor.compile(grad_op, inp, sens)
_cell_graph_executor.compile(grad_op, inp, sens)

4
tests/perf_test/test_resnet_infer.py
View File

@ -18,11 +18,11 @@
import numpy as np
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from .resnet_example import resnet50
def test_compile():
net = resnet50()
inp = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32))
_executor.compile(net, inp)
_cell_graph_executor.compile(net, inp)

4
tests/perf_test/test_resnet_train.py
View File

@ -19,7 +19,7 @@ import numpy as np
import mindspore.context as context
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from .resnet_example import resnet50
from ..train_step_wrap import train_step_with_loss_warp
@ -31,4 +31,4 @@ def test_train_step():
net.set_train()
inp = Tensor(np.ones([1, 3, 224, 224], np.float32))
label = Tensor(np.zeros([1, 10], np.float32))
_executor.compile(net, inp, label)
_cell_graph_executor.compile(net, inp, label)

4
tests/st/data_transfer/test_tdt_data_transfer.py
View File

@ -17,7 +17,7 @@ import numpy as np
import pytest
from mindspore import context, nn, Tensor
from mindspore import log as logger
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
import mindspore.dataset as de
@ -121,7 +121,7 @@ def op_network_with_step_num(dataset, step_num):
net = SingleOpNetwork(dataset_shapes)
net_with_dataset = NetWithTDT(net, dataset_types, dataset_shapes, queue_name)
# when device type is Davinci, net should has get_next operation before call init_dataset
_executor.init_dataset(dataset.queue_name, 1, batch_size, dataset_types, dataset_shapes, (), "")
_cell_graph_executor.init_dataset(dataset.queue_name, 1, batch_size, dataset_types, dataset_shapes, (), "")
dataset_send_tdt(dataset)
return op_network_with_epoch(net_with_dataset, step_num)

4
tests/st/gnn/test_gat_model.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.nn as nn
import mindspore.context as context
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from gat import GAT
context.set_context(mode=context.GRAPH_MODE)
@ -44,4 +44,4 @@ def test_GAT():
ftr_drop=0.6,
activation=activation,
residual=residual)
_executor.compile(net, input_data, biases)
_cell_graph_executor.compile(net, input_data, biases)

10
tests/st/gnn/test_gnn_aggregator.py
View File

@ -20,7 +20,7 @@ import mindspore.context as context
import mindspore.nn as nn
import mindspore.ops.composite as C
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
context.set_context(mode=context.GRAPH_MODE)
@ -45,7 +45,7 @@ def test_MeanAggregator():
"""Compile MeanAggregator forward graph"""
aggregator = MeanAggregator(32, 64, activation="relu", dropout_ratio=0.5)
input_data = Tensor(np.array(np.random.rand(32, 3, 32), dtype=np.float32))
_executor.compile(aggregator, input_data)
_cell_graph_executor.compile(aggregator, input_data)
def test_MeanAggregator_grad():
@ -54,7 +54,7 @@ def test_MeanAggregator_grad():
input_data = Tensor(np.array(np.random.rand(32, 3, 32), dtype=np.float32))
sens = Tensor(np.ones([32, 64]).astype(np.float32))
grad_op = MeanAggregatorGrad(aggregator)
_executor.compile(grad_op, input_data, sens)
_cell_graph_executor.compile(grad_op, input_data, sens)
def test_AttentionHead():
@ -66,11 +66,11 @@ def test_AttentionHead():
residual=False)
input_data = Tensor(np.array(np.random.rand(1, 2708, 1433), dtype=np.float32))
biases = Tensor(np.array(np.random.rand(1, 2708, 2708), dtype=np.float32))
_executor.compile(head, input_data, biases)
_cell_graph_executor.compile(head, input_data, biases)
def test_AttentionAggregator():
input_data = Tensor(np.array(np.random.rand(1, 2708, 1433), dtype=np.float32))
biases = Tensor(np.array(np.random.rand(1, 2708, 2708), dtype=np.float32))
net = AttentionAggregator(1433, 8, 8)
_executor.compile(net, input_data, biases)
_cell_graph_executor.compile(net, input_data, biases)

4
tests/st/ops/ascend/test_tdt_data_ms.py
View File

@ -19,7 +19,7 @@ import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
import mindspore.nn as nn
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.tensor import Tensor
from mindspore.dataset.vision import Inter
from mindspore.ops import operations as P
@ -92,7 +92,7 @@ if __name__ == '__main__':
net = dataiter()
net.set_train()
_executor.init_dataset(ds1.queue_name, 39, batch_size,
_cell_graph_executor.init_dataset(ds1.queue_name, 39, batch_size,
dataset_types, dataset_shapes, (), 'dataset')
ds1.send()

4
tests/ut/cpp/python_input/gtest_input/pipeline/parse/parse_class.py
View File

@ -22,7 +22,7 @@ import logging
import numpy as np
import mindspore.nn as nn
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
@ -82,4 +82,4 @@ def test_get_object_graph():
Y = Tensor(np.ones([2, 2, 2]).astype(np.float32))
network = SimpleNet(ResNet(X), Y, True)
print(network.parameters_dict())
return _executor.compile(network, X, Y)
return _cell_graph_executor.compile(network, X, Y)

4
tests/ut/cpp/python_input/gtest_input/pipeline/parse/parser_integrate.py
View File

@ -19,7 +19,7 @@ import numpy as np
import mindspore._c_expression as me
import mindspore.nn as nn
from mindspore.common import dtype
from mindspore.common.api import ms_function, _executor
from mindspore.common.api import ms_function, _cell_graph_executor
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore.ops import functional as F
@ -153,7 +153,7 @@ class TestNet(nn.Cell):
def test_compile_conv2d():
net = Net()
inputs = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32))
_executor.compile(net, inputs)
_cell_graph_executor.compile(net, inputs)
def test_none(x, y):

4
tests/ut/cpp/utils/callback_test.cc
View File

@ -38,7 +38,7 @@ class TestCallback : public UT::Common {
* # ut and python static info not share
TEST_F(TestCallback, test_get_anf_tensor_shape) {
py::object obj = python_adapter::CallPyFn("gtest_input.pipeline.parse.parse_class", "test_get_object_graph");
FuncGraphPtr func_graph = pipeline::ExecutorPy::GetInstance()->GetFuncGraphPy(obj);
FuncGraphPtr func_graph = pipeline::GraphExecutorPy::GetInstance()->GetFuncGraphPy(obj);
transform::DfGraphManager::GetInstance().SetAnfGraph(func_graph);
std::shared_ptr<std::vector<int64_t>> param_shape_ptr = std::make_shared<std::vector<int64_t>>();
bool get_shape = callbacks::GetParameterShape(func_graph, "weight", param_shape_ptr);
@ -47,7 +47,7 @@ TEST_F(TestCallback, test_get_anf_tensor_shape) {
TEST_F(TestCallback, test_checkpoint_save_op) {
py::object obj = python_adapter::CallPyFn("gtest_input.pipeline.parse.parse_class", "test_get_object_graph");
FuncGraphPtr func_graph = pipeline::ExecutorPy::GetInstance()->GetFuncGraphPy(obj);
FuncGraphPtr func_graph = pipeline::GraphExecutorPy::GetInstance()->GetFuncGraphPy(obj);
transform::DfGraphManager::GetInstance().SetAnfGraph(func_graph);
#define DTYPE float

14
tests/ut/python/communication/test_comm.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.communication._comm_helper import Backend
from mindspore.communication.management import HCCL_WORLD_COMM_GROUP, NCCL_WORLD_COMM_GROUP, GlobalComm, init
from mindspore.nn import Dense
@ -154,7 +154,7 @@ def run_allreduce(op):
momentum=0.9)
network = WithLossCell(network, loss_fn)
network = TrainOneStepCell(network, optimizer)
_executor.compile(network, input_tensor, label_tensor)
_cell_graph_executor.compile(network, input_tensor, label_tensor)
def test_allreduce():
@ -178,7 +178,7 @@ def test_allgather():
momentum=0.9)
network = WithLossCell(network, loss_fn)
network = TrainOneStepCell(network, optimizer)
_executor.compile(network, input_tensor, label_tensor)
_cell_graph_executor.compile(network, input_tensor, label_tensor)
def test_allswap():
"""run_allswap"""
@ -192,7 +192,7 @@ def test_allswap():
momentum=0.9)
network = WithLossCell(network, loss_fn)
network = TrainOneStepCell(network, optimizer)
_executor.compile(network, input_tensor, label_tensor)
_cell_graph_executor.compile(network, input_tensor, label_tensor)
def run_reducescatter(op):
@ -207,7 +207,7 @@ def run_reducescatter(op):
momentum=0.9)
network = WithLossCell(network, loss_fn)
network = TrainOneStepCell(network, optimizer)
_executor.compile(network, input_tensor, label_tensor)
_cell_graph_executor.compile(network, input_tensor, label_tensor)
def test_reducescatter():
@ -228,7 +228,7 @@ def test_broadcast():
momentum=0.9)
network = WithLossCell(network, loss_fn)
network = TrainOneStepCell(network, optimizer)
_executor.compile(network, input_tensor_1, label_tensor)
_cell_graph_executor.compile(network, input_tensor_1, label_tensor)
def test_alltoall():
@ -243,4 +243,4 @@ def test_alltoall():
momentum=0.9)
network = WithLossCell(network, loss_fn)
network = TrainOneStepCell(network, optimizer)
_executor.compile(network, input_tensor, label_tensor)
_cell_graph_executor.compile(network, input_tensor, label_tensor)

4
tests/ut/python/communication/test_data_parallel_dense.py
View File

@ -21,7 +21,7 @@ import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import Momentum
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.ops import operations as P
@ -70,5 +70,5 @@ def test_data_parallel_dense():
net = WithLossCell(net, loss_fn)
net = TrainOneStepCell(net, optimizer)
_executor.compile(net, inp, label)
_cell_graph_executor.compile(net, inp, label)
context.reset_auto_parallel_context()

4
tests/ut/python/exec/test_eval.py
View File

@ -19,7 +19,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from ..ut_filter import non_graph_engine
@ -63,7 +63,7 @@ def test_compile_train_eval():
train_input_data = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
context.set_context(mode=context.GRAPH_MODE)
ms_executor = _executor
ms_executor = _cell_graph_executor
ms_executor.init_dataset("train", 1, 1, [ms.float32], [[1, 3, 32, 32]], (), 'dataset')

2
tests/ut/python/ir/test_tensor.py
View File

@ -286,7 +286,7 @@ def test_return_tensor():
net = Net(0)
input_data = ms.Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
input_data.set_dtype(ms.float32)
exe = me._executor
exe = me._cell_graph_executor
exe.compile(net, input_data)
tensor_ = exe(net, input_data)

8
tests/ut/python/ir/test_tensor_py.py
View File

@ -17,7 +17,7 @@ import numpy as np
import mindspore as ms
import mindspore.common.initializer as init
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import Cell
from mindspore.ops import operations as P
from ..ut_filter import non_graph_engine
@ -133,7 +133,7 @@ def test_tensor_method_sub():
x = ms.Tensor(np.ones([5, 3], np.float32))
y = ms.Tensor(np.ones([8, 5, 3], np.float32))
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)
def test_tensor_method_mul():
@ -152,7 +152,7 @@ def test_tensor_method_mul():
x = ms.Tensor(np.ones([5, 3], np.float32))
y = ms.Tensor(np.ones([8, 5, 3], np.float32))
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)
def test_tensor_method_div():
@ -171,4 +171,4 @@ def test_tensor_method_div():
x = ms.Tensor(np.ones([5, 3], np.float32))
y = ms.Tensor(np.ones([8, 5, 3], np.float32))
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)

8
tests/ut/python/model/res18_example.py
View File

@ -19,7 +19,7 @@ import numpy as np
import mindspore.nn as nn # pylint: disable=C0414
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops.operations import Add
from ...train_step_wrap import train_step_with_loss_warp
@ -244,14 +244,14 @@ def resnet9():
def test_compile():
net = resnet18()
input_data = Tensor(np.ones([1, 3, 224, 224]))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
def test_train_step():
net = train_step_with_loss_warp(resnet9())
input_data = Tensor(np.ones([1, 3, 224, 224]))
label = Tensor(np.zeros([1, 10]))
_executor.compile(net, input_data, label)
_cell_graph_executor.compile(net, input_data, label)
def test_train_step_training():
@ -259,4 +259,4 @@ def test_train_step_training():
input_data = Tensor(np.ones([1, 3, 224, 224]))
label = Tensor(np.zeros([1, 10]))
net.set_train()
_executor.compile(net, input_data, label)
_cell_graph_executor.compile(net, input_data, label)

8
tests/ut/python/model/test_lenet.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import operations as P
from ....train_step_wrap import train_step_with_loss_warp, train_step_with_sens
@ -53,14 +53,14 @@ def test_lenet5_train_step():
predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
label = Tensor(np.zeros([1, 10]).astype(np.float32))
net = train_step_with_loss_warp(LeNet5())
_executor.compile(net, predict, label)
_cell_graph_executor.compile(net, predict, label)
def test_lenet5_train_sens():
predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
sens = Tensor(np.ones([1, 10]).astype(np.float32))
net = train_step_with_sens(LeNet5(), sens)
_executor.compile(net, predict)
_cell_graph_executor.compile(net, predict)
def test_lenet5_train_step_training():
@ -68,4 +68,4 @@ def test_lenet5_train_step_training():
label = Tensor(np.zeros([1, 10]).astype(np.float32))
net = train_step_with_loss_warp(LeNet5())
net.set_train()
_executor.compile(net, predict, label)
_cell_graph_executor.compile(net, predict, label)

4
tests/ut/python/model/test_lenet_core_after_exception.py
View File

@ -17,7 +17,7 @@ import numpy as np
import pytest
import mindspore.nn as nn
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from ....train_step_wrap import train_step_with_loss_warp
@ -54,5 +54,5 @@ def test_lenet5_exception():
label = Tensor(in2)
net = train_step_with_loss_warp(LeNet5())
with pytest.raises(RuntimeError) as info:
_executor.compile(net, predict, label)
_cell_graph_executor.compile(net, predict, label)
assert "x_shape[C_in] / group must equal to w_shape[C_in] = " in str(info.value)

6
tests/ut/python/model/test_mix_precision.py
View File

@ -19,7 +19,7 @@ import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.common import ParameterTuple
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.parameter import Parameter
from mindspore.nn import Momentum
from mindspore.nn import TrainOneStepCell, WithLossCell
@ -111,7 +111,7 @@ def test_data_parallel_with_cast():
net = WithLossCell(net, loss_fn)
net = TrainOneStepCell(net, optimizer)
_executor.compile(net, predict, label)
_cell_graph_executor.compile(net, predict, label)
context.reset_auto_parallel_context()
@ -128,7 +128,7 @@ def test_nn_prelu():
x = Tensor(np.ones([1, 16, 10, 10]).astype(np.float32) * 0.01)
net = NetForPReLU().set_train()
net.add_flags_recursive(fp16=True)
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
class NetForCast(nn.Cell):

4
tests/ut/python/nn/optim/test_ada_grad.py
View File

@ -19,7 +19,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Adagrad
from mindspore.ops import operations as P
@ -55,4 +55,4 @@ def test_ada_grad():
optimizer = Adagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)

26
tests/ut/python/nn/optim/test_adafactor.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim.adafactor import AdaFactor
from mindspore.ops import operations as P
@ -82,7 +82,7 @@ def test_adafactor_compile1():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_compile2():
@ -97,7 +97,7 @@ def test_adafactor_compile2():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_compile3():
@ -113,7 +113,7 @@ def test_adafactor_compile3():
warmup_init=False, compression=False)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_compile4():
@ -133,7 +133,7 @@ def test_adafactor_compile4():
warmup_init=warmup_init, compression=compression)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_compile5():
@ -153,7 +153,7 @@ def test_adafactor_compile5():
warmup_init=warmup_init, compression=compression)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_compile6():
@ -173,7 +173,7 @@ def test_adafactor_compile6():
warmup_init=warmup_init, compression=compression)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_group1():
@ -192,7 +192,7 @@ def test_adafactor_group1():
optimizer = AdaFactor(group_params, learning_rate=poly_decay_lr, relative_step=False)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_group2():
@ -210,7 +210,7 @@ def test_adafactor_group2():
{'params': [all_params[1]]}]
optimizer = AdaFactor(group_params, learning_rate=schedule_lr, relative_step=False)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_group3():
@ -227,7 +227,7 @@ def test_adafactor_group3():
optimizer = AdaFactor(group_params, learning_rate=None)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_group4():
@ -244,7 +244,7 @@ def test_adafactor_group4():
{'params': [all_params[1]]}]
optimizer = AdaFactor(group_params, learning_rate=None)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_group5():
@ -261,7 +261,7 @@ def test_adafactor_group5():
{'params': [all_params[1]]}]
optimizer = AdaFactor(group_params, learning_rate=None, beta1=0.1)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adafactor_group6():
@ -278,4 +278,4 @@ def test_adafactor_group6():
{'params': [all_params[1]]}]
optimizer = AdaFactor(group_params, learning_rate=None, beta1=0.2)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)

18
tests/ut/python/nn/optim/test_adam.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Adam, AdamWeightDecay
from mindspore.ops import operations as P
@ -86,7 +86,7 @@ def test_adamw_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adam_compile():
@ -101,7 +101,7 @@ def test_adam_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_sparse_adam_compile():
@ -114,7 +114,7 @@ def test_sparse_adam_compile():
optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
optimizer.target = 'CPU'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)
def test_sparse_adam():
@ -126,7 +126,7 @@ def test_sparse_adam():
optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)
def test_adam_group1():
@ -146,7 +146,7 @@ def test_adam_group1():
optimizer = nn.Adam(group_params, learning_rate=0.1)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adam_group2():
@ -164,7 +164,7 @@ def test_adam_group2():
{'params': [all_params[1]]}]
optimizer = nn.Adam(group_params, learning_rate=schedule_lr)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adamweightdecay_group():
@ -182,7 +182,7 @@ def test_adamweightdecay_group():
{'params': [all_params[1]]}]
optimizer = nn.AdamWeightDecay(group_params, learning_rate=schedule_lr)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_adamoffload_group():
@ -200,7 +200,7 @@ def test_adamoffload_group():
{'params': [all_params[1]]}]
optimizer = nn.AdamOffload(group_params, learning_rate=schedule_lr)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_AdamWeightDecay_beta1():

8
tests/ut/python/nn/optim/test_ftrl.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import FTRL
from mindspore.ops import operations as P
@ -66,7 +66,7 @@ def test_ftrl():
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_spares_ftrl_compile():
@ -79,7 +79,7 @@ def test_spares_ftrl_compile():
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'CPU'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)
def test_spares_ftrl():
@ -92,4 +92,4 @@ def test_spares_ftrl():
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'Ascend'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)

8
tests/ut/python/nn/optim/test_lamb.py
View File

@ -17,7 +17,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Lamb
from mindspore.ops import operations as P
@ -83,7 +83,7 @@ def test_lamb_compile_dynamic_lr():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_lamb_compile():
@ -98,7 +98,7 @@ def test_lamb_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_lamb_group():
@ -116,4 +116,4 @@ def test_lamb_group():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)

6
tests/ut/python/nn/optim/test_lars.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import LARS, Momentum
from mindspore.ops import operations as P
@ -61,7 +61,7 @@ def test_lars_multi_step_lr():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_lars_float_lr():
@ -78,4 +78,4 @@ def test_lars_float_lr():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)

8
tests/ut/python/nn/optim/test_lazyadam.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import LazyAdam
from mindspore.ops import operations as P
@ -70,7 +70,7 @@ def test_lazy_adam_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_spares_lazy_adam_compile():
@ -83,7 +83,7 @@ def test_spares_lazy_adam_compile():
optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'CPU'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)
def test_spares_lazy_adam():
@ -96,7 +96,7 @@ def test_spares_lazy_adam():
optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'Ascend'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)
def test_lazy_adam_error():

4
tests/ut/python/nn/optim/test_momentum.py
View File

@ -17,7 +17,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
@ -50,4 +50,4 @@ def test_momentum_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)

8
tests/ut/python/nn/optim/test_proximal_ada_grad.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import ProximalAdagrad
from mindspore.ops import operations as P
@ -66,7 +66,7 @@ def test_proximal_ada_grad():
optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_spares_proximal_ada_grad_compile():
@ -79,7 +79,7 @@ def test_spares_proximal_ada_grad_compile():
optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
optimizer.target = 'CPU'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)
def test_spares_proximal_ada_grad():
@ -91,4 +91,4 @@ def test_spares_proximal_ada_grad():
optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
_cell_graph_executor.compile(train_network, indices, label)

4
tests/ut/python/nn/optim/test_rmsprop.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import RMSProp
from mindspore.ops import operations as P
@ -51,7 +51,7 @@ def test_rmsprop_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_rmsprop_e():

4
tests/ut/python/nn/probability/dpn/test_vae.py
View File

@ -18,7 +18,7 @@ import numpy as np
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn.probability.dpn import VAE
@ -54,4 +54,4 @@ def test_vae():
decoder = Decoder()
net = VAE(encoder, decoder, hidden_size=3, latent_size=2)
input_data = Tensor(np.random.rand(32, 6), dtype=mstype.float32)
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)

8
tests/ut/python/nn/test_activation.py
View File

@ -17,7 +17,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from ..ut_filter import non_graph_engine
@ -74,7 +74,7 @@ class Net1(nn.Cell):
def test_compile_relu():
net = Net1()
input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
class Net_gelu(nn.Cell):
@ -89,7 +89,7 @@ class Net_gelu(nn.Cell):
def test_compile_gelu():
net = Net_gelu()
input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
class NetLeakyReLU(nn.Cell):
@ -104,4 +104,4 @@ class NetLeakyReLU(nn.Cell):
def test_compile_leaky_relu():
net = NetLeakyReLU(alpha=0.1)
input_data = Tensor(np.array([[1.6, 0, 0.6], [6, 0, -6]], dtype=np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)

6
tests/ut/python/nn/test_batchnorm.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
def test_bn_pars_valid1():
@ -55,7 +55,7 @@ class Net(nn.Cell):
def test_compile():
net = Net()
input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
class GroupNet(nn.Cell):
@ -70,4 +70,4 @@ class GroupNet(nn.Cell):
def test_compile_groupnorm():
net = nn.GroupNorm(16, 64)
input_data = Tensor(np.random.rand(1, 64, 256, 256).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)

4
tests/ut/python/nn/test_cell.py
View File

@ -19,7 +19,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
class ModA(nn.Cell):
@ -299,4 +299,4 @@ def test_cell_names():
ta = Tensor(np.ones([2, 3]))
mn = ModelName(ta)
with pytest.raises(ValueError):
_executor.compile(mn)
_cell_graph_executor.compile(mn)

12
tests/ut/python/nn/test_cell_wrapper.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, WithLossCell, ParameterUpdate
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
@ -50,7 +50,7 @@ def test_parameter_update_int32_and_tensor():
train_network.set_train()
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
# test tensor
param_lr = train_network.parameters_dict()['learning_rate']
@ -58,14 +58,14 @@ def test_parameter_update_int32_and_tensor():
update_network.phase = 'update_param'
input_lr = Tensor(np.array([0.2, 0.02, 0.002]), mstype.float32)
_executor.compile(update_network, input_lr)
_cell_graph_executor.compile(update_network, input_lr)
# test int32
param_step = train_network.parameters_dict()['global_step']
update_global_step = ParameterUpdate(param_step)
input_step = Tensor(np.array([1000]), mstype.int32)
_executor.compile(update_global_step, input_step)
_cell_graph_executor.compile(update_global_step, input_step)
def test_parameter_update_float32():
@ -81,7 +81,7 @@ def test_parameter_update_float32():
train_network.set_train()
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
# construct and compile update graph
param_lr = train_network.parameters_dict()['learning_rate']
@ -89,7 +89,7 @@ def test_parameter_update_float32():
update_network.phase = 'update_param'
input_lr = Tensor(0.0001, mstype.float32)
_executor.compile(update_network, input_lr)
_cell_graph_executor.compile(update_network, input_lr)
def test_parameter_update_error():

8
tests/ut/python/nn/test_central_crop.py
View File

@ -21,7 +21,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
class CentralCropNet(nn.Cell):
@ -37,14 +37,14 @@ def test_compile_3d_central_crop():
central_fraction = 0.2
net = CentralCropNet(central_fraction)
image = Tensor(np.random.random((3, 16, 16)), mstype.float32)
_executor.compile(net, image)
_cell_graph_executor.compile(net, image)
def test_compile_4d_central_crop():
central_fraction = 0.5
net = CentralCropNet(central_fraction)
image = Tensor(np.random.random((8, 3, 16, 16)), mstype.float32)
_executor.compile(net, image)
_cell_graph_executor.compile(net, image)
def test_central_fraction_bool():
@ -71,4 +71,4 @@ def test_central_crop_invalid_5d_input():
net = CentralCropNet(central_fraction=0.5)
with pytest.raises(ValueError):
_executor.compile(net, invalid_image)
_cell_graph_executor.compile(net, invalid_image)

18
tests/ut/python/nn/test_dense.py
View File

@ -20,7 +20,7 @@ import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from ..ut_filter import non_graph_engine
@ -115,12 +115,12 @@ def test_compile():
bias = Tensor(np.random.randint(0, 255, [8]).astype(np.float32))
net = Net(64, 8, weight=weight, bias=bias)
input_data = Tensor(np.random.randint(0, 255, [128, 64]).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
# training
net_train = Net(64, 8, weight=weight, bias=bias)
net_train.set_train()
_executor.compile(net_train, input_data)
_cell_graph_executor.compile(net_train, input_data)
def test_compile_2():
@ -129,12 +129,12 @@ def test_compile_2():
weight = Tensor(np.random.randint(0, 255, [8, 64]).astype(np.float32))
net = Net(64, 8, weight=weight, has_bias=False)
input_data = Tensor(np.random.randint(0, 255, [128, 64]).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
# training
net_train = Net(64, 8, weight=weight, has_bias=False)
net_train.set_train()
_executor.compile(net_train, input_data)
_cell_graph_executor.compile(net_train, input_data)
def test_compile_3():
@ -144,12 +144,12 @@ def test_compile_3():
context.set_context(mode=context.GRAPH_MODE)
net = Net(128, 10)
input_data = Tensor(np.random.randint(0, 255, [128, 128]).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
# training
net_train = Net(128, 10)
net_train.set_train()
_executor.compile(net_train, input_data)
_cell_graph_executor.compile(net_train, input_data)
def test_compile_4():
@ -159,9 +159,9 @@ def test_compile_4():
context.set_context(mode=context.GRAPH_MODE)
net = Net(128, 10, has_bias=False)
input_data = Tensor(np.random.randint(0, 255, [128, 128]).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
# training
net_train = Net(128, 10, has_bias=False)
net_train.set_train()
_executor.compile(net_train, input_data)
_cell_graph_executor.compile(net_train, input_data)

4
tests/ut/python/nn/test_flatten.py
View File

@ -19,7 +19,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
class Net(nn.Cell):
@ -34,4 +34,4 @@ class Net(nn.Cell):
def test_compile():
net = Net()
input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype(np.float32))
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)

8
tests/ut/python/nn/test_image_gradients.py
View File

@ -20,7 +20,7 @@ import mindspore.common.dtype as mstype
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.api import ms_function
context.set_context(device_target="Ascend")
@ -40,7 +40,7 @@ def test_compile():
# input shape 1 x 1 x 2 x 2
image = Tensor(np.array([[[[1, 2], [3, 4]]]]), dtype=mstype.int32)
net = Net()
_executor.compile(net, image)
_cell_graph_executor.compile(net, image)
def test_compile_multi_channel():
@ -51,7 +51,7 @@ def test_compile_multi_channel():
[[[5, 10], [15, 20]], [[25, 30], [35, 40]]],
[[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]), dtype=dtype)
net = Net()
_executor.compile(net, image)
_cell_graph_executor.compile(net, image)
def test_invalid_5d_input():
@ -59,4 +59,4 @@ def test_invalid_5d_input():
image = Tensor(np.random.random([4, 1, 16, 16, 1]), dtype=dtype)
net = Net()
with pytest.raises(ValueError):
_executor.compile(net, image)
_cell_graph_executor.compile(net, image)

16
tests/ut/python/nn/test_learning_rate_schedule.py
View File

@ -17,7 +17,7 @@ import pytest
from mindspore import Tensor
from mindspore.nn import learning_rate_schedule as lr_schedules
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
import mindspore.common.dtype as mstype
@ -124,34 +124,34 @@ class TestInit:
def test_exponential_decay():
lr_schedule = lr_schedules.ExponentialDecayLR(learning_rate, decay_rate, decay_steps, True)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)
def test_enatural_exp_decay():
lr_schedule = lr_schedules.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)
def test_inverse_decay():
lr_schedule = lr_schedules.InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)
def test_cosine_decay():
lr_schedule = lr_schedules.CosineDecayLR(min_lr, max_lr, decay_steps)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)
def test_polynomial_decay():
lr_schedule = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)
def test_polynomial_decay2():
lr_schedule = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power, True)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)
def test_warmup():
lr_schedule = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
_executor.compile(lr_schedule, global_step)
_cell_graph_executor.compile(lr_schedule, global_step)

16
tests/ut/python/nn/test_msssim.py
View File

@ -21,7 +21,7 @@ import pytest
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
_MSSSIM_WEIGHTS = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333)
@ -39,7 +39,7 @@ def test_compile():
net = MSSSIMNet(power_factors=factors)
img1 = Tensor(np.random.random((8, 3, 128, 128)))
img2 = Tensor(np.random.random((8, 3, 128, 128)))
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_compile_grayscale():
@ -48,7 +48,7 @@ def test_compile_grayscale():
net = MSSSIMNet(max_val=max_val, power_factors=factors)
img1 = Tensor(np.random.randint(0, 256, (8, 3, 128, 128), np.uint8))
img2 = Tensor(np.random.randint(0, 256, (8, 3, 128, 128), np.uint8))
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_msssim_max_val_negative():
@ -102,7 +102,7 @@ def test_msssim_different_shape():
img2 = Tensor(np.random.random(shape_2))
net = MSSSIMNet(power_factors=factors)
with pytest.raises(ValueError):
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_msssim_different_dtype():
@ -113,7 +113,7 @@ def test_msssim_different_dtype():
img2 = Tensor(np.random.random((8, 3, 128, 128)), dtype=dtype_2)
net = MSSSIMNet(power_factors=factors)
with pytest.raises(TypeError):
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_msssim_invalid_5d_input():
@ -128,8 +128,8 @@ def test_msssim_invalid_5d_input():
net = MSSSIMNet(power_factors=factors)
with pytest.raises(ValueError):
_executor.compile(net, invalid_img1, img2)
_cell_graph_executor.compile(net, invalid_img1, img2)
with pytest.raises(ValueError):
_executor.compile(net, img1, invalid_img2)
_cell_graph_executor.compile(net, img1, invalid_img2)
with pytest.raises(ValueError):
_executor.compile(net, invalid_img1, invalid_img2)
_cell_graph_executor.compile(net, invalid_img1, invalid_img2)

10
tests/ut/python/nn/test_nn_embedding.py
View File

@ -18,7 +18,7 @@ import pytest
from mindspore import Tensor
from mindspore.common import dtype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import Embedding, MultiFieldEmbeddingLookup
from ..ut_filter import non_graph_engine
@ -27,21 +27,21 @@ from ..ut_filter import non_graph_engine
def test_check_embedding_1():
net = Embedding(20000, 768, False)
input_data = Tensor(np.ones([8, 128]), dtype.int32)
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
@non_graph_engine
def test_check_embedding_2():
net = Embedding(20000, 768, True)
input_data = Tensor(np.ones([8, 128]), dtype.int32)
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
@non_graph_engine
def test_check_embedding_3():
net = Embedding(20000, 768, True, "zeros")
input_data = Tensor(np.ones([8, 128]), dtype.int32)
_executor.compile(net, input_data)
_cell_graph_executor.compile(net, input_data)
def compile_multi_field_embedding(shape_id, shape_value, shape_field,
@ -50,7 +50,7 @@ def compile_multi_field_embedding(shape_id, shape_value, shape_field,
input_data = Tensor(np.ones(shape_id), type_id)
input_value = Tensor(np.ones(shape_value), type_value)
input_field = Tensor(np.ones(shape_field), type_field)
_executor.compile(net, input_data, input_value, input_field)
_cell_graph_executor.compile(net, input_data, input_value, input_field)
@non_graph_engine

4
tests/ut/python/nn/test_norm.py
View File

@ -17,7 +17,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from ..ut_filter import non_graph_engine
@ -34,4 +34,4 @@ class NormNet(nn.Cell):
def test_compile_norm():
net = NormNet()
x = Tensor(np.array([2.0, 1.0]))
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)

8
tests/ut/python/nn/test_pooling.py
View File

@ -19,7 +19,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
class AvgNet(nn.Cell):
@ -36,7 +36,7 @@ class AvgNet(nn.Cell):
def test_compile_avg():
net = AvgNet(3, 1)
x = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32))
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
class MaxNet(nn.Cell):
@ -58,7 +58,7 @@ class MaxNet(nn.Cell):
def test_compile_max():
net = MaxNet(3, stride=1, padding=0)
x = Tensor(np.random.randint(0, 255, [1, 3, 6, 6]).astype(np.float32))
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
class Avg1dNet(nn.Cell):
@ -75,4 +75,4 @@ class Avg1dNet(nn.Cell):
def test_avg1d():
net = Avg1dNet(6, 1)
input_ = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
_executor.compile(net, input_)
_cell_graph_executor.compile(net, input_)

16
tests/ut/python/nn/test_psnr.py
View File

@ -21,7 +21,7 @@ import pytest
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
class PSNRNet(nn.Cell):
@ -38,7 +38,7 @@ def test_compile_psnr():
net = PSNRNet(max_val)
img1 = Tensor(np.random.random((8, 3, 16, 16)))
img2 = Tensor(np.random.random((8, 3, 16, 16)))
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_compile_psnr_grayscale():
@ -46,7 +46,7 @@ def test_compile_psnr_grayscale():
net = PSNRNet(max_val)
img1 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
img2 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_psnr_max_val_negative():
@ -74,7 +74,7 @@ def test_psnr_different_shape():
img2 = Tensor(np.random.random(shape_2))
net = PSNRNet()
with pytest.raises(ValueError):
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_psnr_different_dtype():
@ -84,7 +84,7 @@ def test_psnr_different_dtype():
img2 = Tensor(np.random.random((8, 3, 16, 16)), dtype=dtype_2)
net = PSNRNet()
with pytest.raises(TypeError):
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_psnr_invalid_5d_input():
@ -98,8 +98,8 @@ def test_psnr_invalid_5d_input():
net = PSNRNet()
with pytest.raises(ValueError):
_executor.compile(net, invalid_img1, img2)
_cell_graph_executor.compile(net, invalid_img1, img2)
with pytest.raises(ValueError):
_executor.compile(net, img1, invalid_img2)
_cell_graph_executor.compile(net, img1, invalid_img2)
with pytest.raises(ValueError):
_executor.compile(net, invalid_img1, invalid_img2)
_cell_graph_executor.compile(net, invalid_img1, invalid_img2)

14
tests/ut/python/nn/test_ssim.py
View File

@ -21,7 +21,7 @@ import pytest
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
class SSIMNet(nn.Cell):
@ -37,7 +37,7 @@ def test_compile():
net = SSIMNet()
img1 = Tensor(np.random.random((8, 3, 16, 16)), mstype.float32)
img2 = Tensor(np.random.random((8, 3, 16, 16)), mstype.float32)
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_ssim_max_val_negative():
@ -85,7 +85,7 @@ def test_ssim_different_shape():
img2 = Tensor(np.random.random(shape_2))
net = SSIMNet()
with pytest.raises(TypeError):
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_ssim_different_dtype():
@ -95,7 +95,7 @@ def test_ssim_different_dtype():
img2 = Tensor(np.random.random((8, 3, 16, 16)), dtype=dtype_2)
net = SSIMNet()
with pytest.raises(TypeError):
_executor.compile(net, img1, img2)
_cell_graph_executor.compile(net, img1, img2)
def test_ssim_invalid_5d_input():
@ -109,8 +109,8 @@ def test_ssim_invalid_5d_input():
net = SSIMNet()
with pytest.raises(TypeError):
_executor.compile(net, invalid_img1, img2)
_cell_graph_executor.compile(net, invalid_img1, img2)
with pytest.raises(TypeError):
_executor.compile(net, img1, invalid_img2)
_cell_graph_executor.compile(net, img1, invalid_img2)
with pytest.raises(TypeError):
_executor.compile(net, invalid_img1, invalid_img2)
_cell_graph_executor.compile(net, invalid_img1, invalid_img2)

34
tests/ut/python/nn/test_transformer.py
View File

@ -19,7 +19,7 @@ from mindspore import Tensor
from mindspore.common import dtype
from mindspore.parallel.nn import MultiHeadAttention, FeedForward, TransformerEncoderLayer, TransformerEncoder, \
TransformerDecoder, TransformerDecoderLayer, Transformer, CrossEntropyLoss, AttentionMask, FixedSparseAttention
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
def test_transformer_encoder_only():
@ -34,7 +34,7 @@ def test_transformer_encoder_only():
encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
_executor.compile(model, encoder_input_value, encoder_input_mask)
_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
def test_transformer_encoder_log_softmax():
@ -51,7 +51,7 @@ def test_transformer_encoder_log_softmax():
encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
_executor.compile(model, encoder_input_value, encoder_input_mask)
_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
def test_transformer_encoder_leakyrelu():
@ -67,7 +67,7 @@ def test_transformer_encoder_leakyrelu():
encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
_executor.compile(model, encoder_input_value, encoder_input_mask)
_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
def test_transformer_encoder_logsigmoid():
@ -83,7 +83,7 @@ def test_transformer_encoder_logsigmoid():
encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
_executor.compile(model, encoder_input_value, encoder_input_mask)
_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
def test_encoder_and_decoder():
@ -102,7 +102,7 @@ def test_encoder_and_decoder():
decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
_executor.compile(model, encoder_input_value, encoder_input_mask,
_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask,
decoder_input_value,
decoder_input_mask,
memory_mask)
@ -119,7 +119,7 @@ def test_transformer_encoder():
encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
_executor.compile(model,
_cell_graph_executor.compile(model,
encoder_input_value,
encoder_input_mask)
@ -131,7 +131,7 @@ def test_transformer_encoder_layer():
encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
_executor.compile(model,
_cell_graph_executor.compile(model,
encoder_input_value,
encoder_input_mask)
@ -145,7 +145,7 @@ def test_transformer_encoder_layer_post_ture():
encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
_executor.compile(model,
_cell_graph_executor.compile(model,
encoder_input_value,
encoder_input_mask)
@ -165,7 +165,7 @@ def test_transformer_decoder():
decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
_executor.compile(model, decoder_input_value, decoder_input_mask,
_cell_graph_executor.compile(model, decoder_input_value, decoder_input_mask,
encoder_input_value,
memory_mask)
@ -185,7 +185,7 @@ def test_transformer_decoder_layer():
decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
_executor.compile(model, decoder_input_value, decoder_input_mask,
_cell_graph_executor.compile(model, decoder_input_value, decoder_input_mask,
encoder_input_value,
memory_mask)
@ -200,7 +200,7 @@ def test_multihead_attention():
to_tensor = Tensor(np.ones((2, 20, 15)), dtype.float16)
attention_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
def test_multihead_attention_wrong_batch():
@ -214,7 +214,7 @@ def test_multihead_attention_wrong_batch():
attention_mask = Tensor(np.ones((3, 20, 20)), dtype.float16)
with pytest.raises(ValueError):
_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
def test_feedforward_layer():
@ -224,7 +224,7 @@ def test_feedforward_layer():
hidden_act='relu')
tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
_executor.compile(model, tensor)
_cell_graph_executor.compile(model, tensor)
def test_cross_entroy():
@ -233,13 +233,13 @@ def test_cross_entroy():
labels_np = np.array([1]).astype(np.int32)
input_mask = Tensor(np.ones(1).astype(np.float32))
labels = Tensor(labels_np)
_executor.compile(model, logits, labels, input_mask)
_cell_graph_executor.compile(model, logits, labels, input_mask)
def test_attention_mask():
model = AttentionMask(seq_length=19)
inputs = Tensor(np.ones((2, 19)), dtype.float32)
_executor.compile(model, inputs)
_cell_graph_executor.compile(model, inputs)
def test_sparse_attention():
@ -252,4 +252,4 @@ def test_sparse_attention():
k = Tensor(np.ones((2, 1024, 512)), dtype.float16)
v = Tensor(np.ones((2, 1024, 512)), dtype.float16)
mask = Tensor(np.ones((2, 1024)), dtype.float32)
_executor.compile(model, q, k, v, mask)
_cell_graph_executor.compile(model, q, k, v, mask)

6
tests/ut/python/ops/test_ops_check.py
View File

@ -21,7 +21,7 @@ import pytest
import mindspore.context as context
from mindspore import Tensor
from mindspore import nn
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import operations as P
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
@ -63,7 +63,7 @@ def test_net_without_construct():
""" test_net_without_construct """
net = NetMissConstruct()
inp = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32))
_executor.compile(net, inp)
_cell_graph_executor.compile(net, inp)
class NetWithRaise(nn.Cell):
@ -83,7 +83,7 @@ def test_net_with_raise():
net = NetWithRaise()
inp = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32))
with pytest.raises(RuntimeError) as err:
_executor.compile(net, inp)
_cell_graph_executor.compile(net, inp)
assert "Unsupported syntax 'Raise'." in str(err.value)

10
tests/ut/python/optimizer/test_optimizer_with_parameter_groups.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.tensor import Tensor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum, SGD, RMSProp, Adam
@ -80,7 +80,7 @@ def test_group_lr():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, opt)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_group_dynamic_1():
@ -114,7 +114,7 @@ def test_group_dynamic_1():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, opt)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_group_dynamic_2():
@ -144,7 +144,7 @@ def test_group_dynamic_2():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, opt)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_group_dynamic_no_same_size():
@ -214,7 +214,7 @@ def test_weight_decay():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, opt)
_executor.compile(train_network, inputs, label)
_cell_graph_executor.compile(train_network, inputs, label)
def test_group_repeat_param():

13
tests/ut/python/optimizer/test_python_pass.py
View File

@ -23,7 +23,7 @@ from mindspore.ops import _constants as Constants
from mindspore.graph_utils.python_pass import register_pass, unregister_pass, set_renorm, gen_new_parameter,\
cancel_new_parameter, set_reopt
from mindspore.common.api import _generate_pip_args
from mindspore._c_expression import generate_key, Executor_
from mindspore._c_expression import generate_arguments_key, GraphExecutor_
from mindspore.graph_utils.graph_pattern import OneOf, Prim, Call, NoneOf, Any, NewTensor, NewParameter, Imm
context.set_context(mode=context.GRAPH_MODE)
@ -31,13 +31,10 @@ context.set_context(mode=context.GRAPH_MODE)
def get_func_graph(obj, *args, phase="validate"):
args_names, args_list = _generate_pip_args(obj, *args)
dic = dict(zip(args_names, args_list))
key = generate_key(phase, dic)
phase_prefix = str(key[1])
if phase == 'export':
phase = phase + '.' + phase_prefix + '.' + str(obj.create_time)
else:
phase = phase_prefix + phase + '.' + str(obj.create_time)
_executor = Executor_.get_instance()
key = generate_arguments_key(dic)
obj.arguments_key = str(key)
phase = phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
_executor = GraphExecutor_.get_instance()
_executor.compile(obj, args_list, phase, False, "")
return _executor.get_func_graph(phase)

4
tests/ut/python/parallel/test_adafactor.py
View File

@ -16,7 +16,7 @@ import numpy as np
import mindspore as ms
from mindspore import context, Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import Cell, TrainOneStepCell
from mindspore.nn.optim.adafactor import AdaFactor
from mindspore.ops import operations as P
@ -53,7 +53,7 @@ def compile_net(net):
train_net = TrainOneStepCell(net, optimizer)
train_net.set_auto_parallel()
train_net.set_train()
_executor.compile(train_net, _x, _b)
_cell_graph_executor.compile(train_net, _x, _b)
context.reset_auto_parallel_context()

4
tests/ut/python/parallel/test_add_relu_redistribution.py
View File

@ -17,7 +17,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -60,7 +60,7 @@ class Grad(nn.Cell):
def compile_net(net, x, y):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)
def test_add_relu_stride_slice():

4
tests/ut/python/parallel/test_allreduce_fusion.py
View File

@ -18,7 +18,7 @@ import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
from mindspore.parallel import _cost_model_context as cost_model_context
@ -120,7 +120,7 @@ def train_common(net):
model = Model(net, loss, opt)
model.train(epoch_size, dataset, dataset_sink_mode=False)
allreduce_fusion_dict = _executor._get_allreduce_fusion(model._train_network)
allreduce_fusion_dict = _cell_graph_executor._get_allreduce_fusion(model._train_network)
print(allreduce_fusion_dict)
return allreduce_fusion_dict

22
tests/ut/python/parallel/test_alltoall.py
View File

@ -20,7 +20,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.parameter import Parameter
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
@ -97,7 +97,7 @@ def all_to_all_common(strategy1):
model = Model(net, loss, opt)
model.train(epoch_size, dataset, dataset_sink_mode=False)
strategys = _executor._get_shard_strategy(model._train_network)
strategys = _cell_graph_executor._get_shard_strategy(model._train_network)
return strategys
@ -137,7 +137,7 @@ def test_all_to_all_success():
return out
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_split_count_value_failed():
@ -159,7 +159,7 @@ def test_all_to_all_invalid_split_count_value_failed():
with pytest.raises(ValueError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_split_count_type_failed():
@ -181,7 +181,7 @@ def test_all_to_all_invalid_split_count_type_failed():
with pytest.raises(TypeError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_split_dim_value_failed():
@ -203,7 +203,7 @@ def test_all_to_all_invalid_split_dim_value_failed():
with pytest.raises(IndexError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_split_dim_type_failed():
@ -225,7 +225,7 @@ def test_all_to_all_invalid_split_dim_type_failed():
with pytest.raises(TypeError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_concat_dim_value_failed():
@ -247,7 +247,7 @@ def test_all_to_all_invalid_concat_dim_value_failed():
with pytest.raises(IndexError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_concat_dim_type_failed():
@ -269,7 +269,7 @@ def test_all_to_all_invalid_concat_dim_type_failed():
with pytest.raises(TypeError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_split_count_cannot_be_divisible_failed():
@ -291,7 +291,7 @@ def test_all_to_all_invalid_split_count_cannot_be_divisible_failed():
with pytest.raises(ValueError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
def test_all_to_all_invalid_group_type_failed():
@ -313,7 +313,7 @@ def test_all_to_all_invalid_group_type_failed():
with pytest.raises(TypeError):
net = Net()
_executor.compile(net, _x1)
_cell_graph_executor.compile(net, _x1)
if __name__ == '__main__':

10
tests/ut/python/parallel/test_arithmetic.py
View File

@ -17,7 +17,7 @@ import numpy as np
import mindspore as ms
import mindspore.nn as nn
from mindspore import Parameter, Tensor, context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -49,7 +49,7 @@ class GradWrap(nn.Cell):
def compile_net(net, x, y, b):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, b)
_cell_graph_executor.compile(net, x, y, b)
def test_matmul_sub():
@ -651,7 +651,7 @@ def test_assign_sub():
def compile_sub_net(net, x):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
context.set_auto_parallel_context(device_num=64, global_rank=15)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
@ -699,7 +699,7 @@ def test_assign_add():
def compile_sub_net(net, x):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
context.set_auto_parallel_context(device_num=64, global_rank=15)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
@ -747,7 +747,7 @@ def test_assign():
def compile_sub_net(net, x):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
context.set_auto_parallel_context(device_num=64, global_rank=15)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")

4
tests/ut/python/parallel/test_auto_parallel_BN_PReLU.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -74,4 +74,4 @@ def test_auto_parallel_bn_with_prelu():
net = GradWrap(NetWithLoss(Net()))
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)

4
tests/ut/python/parallel/test_auto_parallel_activation.py
View File

@ -16,7 +16,7 @@ import numpy as np
import mindspore as ms
from mindspore import context, Tensor, Parameter
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import Cell, TrainOneStepCell, Momentum
from mindspore.ops import operations as P
@ -44,7 +44,7 @@ def compile_net(net):
train_net = TrainOneStepCell(net, optimizer)
train_net.set_auto_parallel()
train_net.set_train()
_executor.compile(train_net, _x, _b)
_cell_graph_executor.compile(train_net, _x, _b)
context.reset_auto_parallel_context()

12
tests/ut/python/parallel/test_auto_parallel_arithmetic.py
View File

@ -19,7 +19,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.parallel._utils import _reset_op_id as reset_op_id
@ -54,7 +54,7 @@ class GradWrap(nn.Cell):
def compile_net(net, x, y, b, phase):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, b, phase=phase)
_cell_graph_executor.compile(net, x, y, b, phase=phase)
def test_auto_parallel_arithmetic():
@ -78,7 +78,7 @@ def test_auto_parallel_arithmetic():
y = Tensor(np.ones([32, 128]), dtype=ms.float32)
b = Tensor(np.ones([64, 128]), dtype=ms.float32)
compile_net(net, x, y, b, phase='train')
strategies = _executor._get_shard_strategy(net)
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('FloorDiv-op', k) is not None:
assert v == [[2, 4], [2, 4]]
@ -107,7 +107,7 @@ def test_auto_parallel_arithmetic_broadcast_both():
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
compile_net(net, x, y, b, phase='train')
strategies = _executor._get_shard_strategy(net)
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('FloorDiv-op', k) is not None:
assert v == [[8, 1], [1, 1]]
@ -136,7 +136,7 @@ def test_auto_parallel_arithmetic_broadcast_right():
y = Tensor(np.ones([32, 32]), dtype=ms.float32)
b = Tensor(np.ones([32]), dtype=ms.float32)
compile_net(net, x, y, b, phase='train')
strategies = _executor._get_shard_strategy(net)
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('FloorDiv-op', k) is not None:
assert v == [[4, 2], [2]]
@ -165,7 +165,7 @@ def test_auto_parallel_arithmetic_broadcast_left():
y = Tensor(np.ones([32, 32]), dtype=ms.float32)
b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
compile_net(net, x, y, b, phase="train")
strategies = _executor._get_shard_strategy(net)
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('FloorDiv-op', k) is not None:
assert v == [[4, 2], [1, 4, 2]]

6
tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py
View File

@ -19,7 +19,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.parallel._utils import _reset_op_id as reset_op_id
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -62,8 +62,8 @@ def test_auto_parallel_assign_sub_with_ref_key():
reset_op_id()
net.set_train()
_executor.compile(net, x, phase="train")
strategies = _executor._get_shard_strategy(net)
_cell_graph_executor.compile(net, x, phase="train")
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('PReLU-op', k) is not None:
assert v == [[1, 1, 1, 8], [1]]

6
tests/ut/python/parallel/test_auto_parallel_cast.py
View File

@ -19,7 +19,7 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.parallel._utils import _reset_op_id as reset_op_id
@ -82,8 +82,8 @@ def test_double_star_graph():
reset_op_id()
net.set_train()
_executor.compile(net, x, y, z, w, phase='train')
strategies = _executor._get_shard_strategy(net)
_cell_graph_executor.compile(net, x, y, z, w, phase='train')
strategies = _cell_graph_executor._get_shard_strategy(net)
expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]],
'Default/network-Net/Cast-op3': [[1, 8]],
'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]],

4
tests/ut/python/parallel/test_auto_parallel_common_parameter.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -73,4 +73,4 @@ def test_common_parameter():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, z)
_cell_graph_executor.compile(net, x, y, z)

6
tests/ut/python/parallel/test_auto_parallel_double_sources.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -80,7 +80,7 @@ def test_double_source_graph():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, z, w, a)
_cell_graph_executor.compile(net, x, y, z, w, a)
def test_double_source_complex_graph():
@ -116,4 +116,4 @@ def test_double_source_complex_graph():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, z, w, a)
_cell_graph_executor.compile(net, x, y, z, w, a)

4
tests/ut/python/parallel/test_auto_parallel_double_star.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -84,4 +84,4 @@ def test_double_star_graph():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, z, w, a, b, c)
_cell_graph_executor.compile(net, x, y, z, w, a, b, c)

8
tests/ut/python/parallel/test_auto_parallel_double_subgraphs.py
View File

@ -19,7 +19,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, Parameter, ParameterTuple
from mindspore import context, Model
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn.optim import Adam, FTRL
from mindspore.ops import composite as C
from mindspore.ops import functional as F
@ -114,8 +114,8 @@ def test_double_subgraphs():
x = Tensor(np.ones([8, 8, 8, 8]), dtype=ms.float32)
reset_op_id()
net.set_train()
_executor.compile(net, x, phase='train')
strategies = _executor._get_shard_strategy(net)
_cell_graph_executor.compile(net, x, phase='train')
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('ReduceMean-op', k) is not None:
assert v == [[8, 1, 1, 1]]
@ -165,7 +165,7 @@ def test_double_subgraphs_train():
ds_train = DatasetLenet(Tensor(batch_ids), None)
model = Model(net)
model.train(1, ds_train, dataset_sink_mode=False)
strategies = _executor._get_shard_strategy(net)
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('ReduceMean-op', k) is not None:
assert v == [[1, 1, 1, 1]]

4
tests/ut/python/parallel/test_auto_parallel_fc_nobias.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -71,4 +71,4 @@ def test_two_matmul():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, b)
_cell_graph_executor.compile(net, x, y, b)

6
tests/ut/python/parallel/test_auto_parallel_for_loop.py
View File

@ -21,7 +21,7 @@ import mindspore.nn as nn
from mindspore.ops import operations as P, functional as F
from mindspore.common.initializer import initializer
import mindspore.common.dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from tests.dataset_mock import MindData
@ -123,7 +123,7 @@ def test_auto_parallel():
net = Full(_w1, 3)
net.set_auto_parallel()
net.set_train()
_executor.compile(net, _x, phase='train')
num_ops = _executor._get_num_parallel_ops(net)
_cell_graph_executor.compile(net, _x, phase='train')
num_ops = _cell_graph_executor._get_num_parallel_ops(net)
expected_num = 16
assert num_ops == expected_num

6
tests/ut/python/parallel/test_auto_parallel_for_loop_multi_subgraph.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, Parameter, ParameterTuple
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.nn.optim import Adam, FTRL
from mindspore.ops import composite as C
from mindspore.ops import functional as F
@ -130,7 +130,7 @@ def test_double_subgraphs():
x = Tensor(np.ones([8, 8, 8, 8]), dtype=ms.float32)
reset_op_id()
net.set_train()
_executor.compile(net, x, phase='train')
num_ops = _executor._get_num_parallel_ops(net)
_cell_graph_executor.compile(net, x, phase='train')
num_ops = _cell_graph_executor._get_num_parallel_ops(net)
expected_num = 7
assert expected_num == num_ops

6
tests/ut/python/parallel/test_auto_parallel_for_loop_reshape.py
View File

@ -21,7 +21,7 @@ import mindspore.nn as nn
from mindspore.ops import operations as P, functional as F
from mindspore.common.initializer import initializer
import mindspore.common.dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from tests.dataset_mock import MindData
@ -130,7 +130,7 @@ def test_auto_parallel():
net = Full(_w1, 3)
net.set_auto_parallel()
net.set_train()
_executor.compile(net, _x, phase='train')
num_ops = _executor._get_num_parallel_ops(net)
_cell_graph_executor.compile(net, _x, phase='train')
num_ops = _cell_graph_executor._get_num_parallel_ops(net)
expected_num = 16
assert num_ops == expected_num

4
tests/ut/python/parallel/test_auto_parallel_four_matmul.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -50,7 +50,7 @@ class GradWrap(nn.Cell):
def compile_net(net, x, y, z, w, b):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, z, w, b)
_cell_graph_executor.compile(net, x, y, z, w, b)
# model_parallel test

4
tests/ut/python/parallel/test_auto_parallel_l2normalize.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.parallel._utils import _reset_op_id as reset_op_id
@ -74,4 +74,4 @@ def test_auto_parallel_l2normalize():
y = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
b = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
net.set_train()
_executor.compile(net, x, y, b, phase='train')
_cell_graph_executor.compile(net, x, y, b, phase='train')

4
tests/ut/python/parallel/test_auto_parallel_matmul_drop.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -71,4 +71,4 @@ def test_two_matmul_dropout():
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
net.set_train()
_executor.compile(net, x, y, b)
_cell_graph_executor.compile(net, x, y, b)

6
tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py
View File

@ -19,7 +19,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.parallel._utils import _reset_op_id as reset_op_id
@ -75,8 +75,8 @@ def test_matmul_prelu():
reset_op_id()
net.set_train()
_executor.compile(net, x, y, b, phase='train')
strategies = _executor._get_shard_strategy(net)
_cell_graph_executor.compile(net, x, y, b, phase='train')
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('PReLU-op', k) is not None:
assert v == [[16, 1, 1, 1], [1]]

4
tests/ut/python/parallel/test_auto_parallel_multi_graph.py
View File

@ -15,7 +15,7 @@
import numpy as np
import mindspore as ms
import mindspore.context as context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore import Tensor, Parameter
from mindspore.nn import Cell, TrainOneStepCell, Momentum
from mindspore.ops import operations as P
@ -59,7 +59,7 @@ def compile_net(net):
train_net = TrainOneStepCell(net, optimizer)
train_net.set_auto_parallel()
train_net.set_train()
_executor.compile(train_net, inputs_, label_)
_cell_graph_executor.compile(train_net, inputs_, label_)
context.reset_auto_parallel_context()

4
tests/ut/python/parallel/test_auto_parallel_onehot.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.parameter import Parameter
from mindspore.nn.optim.momentum import Momentum
from mindspore.ops import composite as C
@ -100,7 +100,7 @@ def test_auto_parallel_arithmetic():
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
b = Tensor(np.ones([64]), dtype=ms.int32)
net.set_train()
_executor.compile(net, x, y, b)
_cell_graph_executor.compile(net, x, y, b)
def test_auto_parallel_arithmetic_model():

6
tests/ut/python/parallel/test_auto_parallel_parameter_cast.py
View File

@ -20,7 +20,7 @@ import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore import context
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import operations as P
from mindspore.parallel import set_algo_parameters
from mindspore.parallel._utils import _reset_op_id as reset_op_id
@ -69,8 +69,8 @@ def test_common_parameter():
reset_op_id()
net.set_train()
_executor.compile(net, x, y, phase='train')
strategies = _executor._get_shard_strategy(net)
_cell_graph_executor.compile(net, x, y, phase='train')
strategies = _cell_graph_executor._get_shard_strategy(net)
for (k, v) in strategies.items():
if re.search('MatMul-op', k) is not None:
assert v == [[8, 1], [1, 1]]

4
tests/ut/python/parallel/test_auto_parallel_partial_strategy.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -78,4 +78,4 @@ def test_four_matmul_linear():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, z, w, b)
_cell_graph_executor.compile(net, x, y, z, w, b)

4
tests/ut/python/parallel/test_auto_parallel_reduce_method.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -50,7 +50,7 @@ class GradWrap(nn.Cell):
def compile_net(net, x, y, b):
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y, b)
_cell_graph_executor.compile(net, x, y, b)
# model_parallel test

24
tests/ut/python/parallel/test_auto_parallel_reshape.py
View File

@ -18,7 +18,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.api import _cell_graph_executor
from mindspore.common.parameter import Parameter
from mindspore.ops import composite as C
from mindspore.ops import operations as P
@ -69,7 +69,7 @@ def test_reshape_matmul():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_reshape():
class Net(nn.Cell):
@ -92,7 +92,7 @@ def test_reshape_reshape():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_auto_1():
@ -118,7 +118,7 @@ def test_reshape_auto_1():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_auto_2():
@ -147,7 +147,7 @@ def test_reshape_auto_2():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_auto_3():
@ -173,7 +173,7 @@ def test_reshape_auto_3():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_auto_4():
@ -200,7 +200,7 @@ def test_reshape_auto_4():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_auto_5():
@ -251,7 +251,7 @@ def test_reshape_auto_5():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)
def test_reshape_auto_6():
class NetWithLoss6(nn.Cell):
@ -299,7 +299,7 @@ def test_reshape_auto_6():
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)
def test_reshape_auto_7():
class Net(nn.Cell):
@ -322,7 +322,7 @@ def test_reshape_auto_7():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x)
_cell_graph_executor.compile(net, x)
def test_reshape_depend_reshape():
class Net(nn.Cell):
@ -371,9 +371,9 @@ def test_reshape_depend_reshape():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
net.set_auto_parallel()
net.set_train()
_executor.compile(net, x, y)
_cell_graph_executor.compile(net, x, y)
net_auto = GradWrap1(NetWithLoss1(Net()))
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net_auto.set_auto_parallel()
net_auto.set_train()
_executor.compile(net_auto, x, y)
_cell_graph_executor.compile(net_auto, x, y)

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