diff --git a/mindspore/ccsrc/debug/common.cc b/mindspore/ccsrc/debug/common.cc index d64cfd3256..817b7ba26d 100644 --- a/mindspore/ccsrc/debug/common.cc +++ b/mindspore/ccsrc/debug/common.cc @@ -296,8 +296,8 @@ bool Common::FileExists(const std::string &filepath) { struct GlogLogDirRegister { GlogLogDirRegister() { - const char *logtostderr = ::getenv("GLOG_logtostderr"); - const char *log_dir = ::getenv("GLOG_log_dir"); + const char *logtostderr = std::getenv("GLOG_logtostderr"); + const char *log_dir = std::getenv("GLOG_log_dir"); if (logtostderr != nullptr && log_dir != nullptr) { std::string logtostderr_str = std::string(logtostderr); std::string log_dir_str = std::string(log_dir); diff --git a/mindspore/ccsrc/debug/data_dump/dump_json_parser.h b/mindspore/ccsrc/debug/data_dump/dump_json_parser.h index 6e187c8c33..4ccd389f34 100644 --- a/mindspore/ccsrc/debug/data_dump/dump_json_parser.h +++ b/mindspore/ccsrc/debug/data_dump/dump_json_parser.h @@ -64,7 +64,7 @@ class DumpJsonParser { void UpdateNeedDumpKernels(NotNull kernel_graph); void ClearGraph() { graphs_.clear(); } - void SaveGraph(session::KernelGraph *graph) { graphs_.emplace_back(graph); } + void SaveGraph(session::KernelGraph *graph) { (void)graphs_.emplace_back(graph); } std::vector &graphs() { return graphs_; } private: diff --git a/mindspore/ccsrc/debug/debug_services.cc b/mindspore/ccsrc/debug/debug_services.cc index 4b0c6adcb9..02f4a03a02 100644 --- a/mindspore/ccsrc/debug/debug_services.cc +++ b/mindspore/ccsrc/debug/debug_services.cc @@ -405,8 +405,9 @@ void DebugServices::ReadTensorFromNpy(const std::string &file_name, std::string MS_LOG(ERROR) << "Failed to read file (In ReadTensorFromNpy) " << file_path; return; } - uint16_t header_len = *reinterpret_cast(buffer->data() + 8); - std::string header(buffer->data() + 9, header_len); + constexpr int header_len_offset = 8; + uint16_t header_len = *reinterpret_cast(buffer->data() + header_len_offset); + std::string header(buffer->data() + header_len_offset + 1, header_len); std::size_t type_i = header.find("descr") + 10; *tensor_type = header.substr(type_i, 2); std::size_t shape_i_open = header.find("("); diff --git a/mindspore/ccsrc/debug/debugger/debugger_utils.cc b/mindspore/ccsrc/debug/debugger/debugger_utils.cc index a219f1de64..4a5f462bd4 100644 --- a/mindspore/ccsrc/debug/debugger/debugger_utils.cc +++ b/mindspore/ccsrc/debug/debugger/debugger_utils.cc @@ -33,7 +33,6 @@ using KernelGraph = mindspore::session::KernelGraph; using AnfAlgo = mindspore::session::AnfRuntimeAlgorithm; namespace mindspore { - static const size_t PARAMETER_OUTPUT_INDEX = 0; std::vector CheckRealOutput(const std::string &node_name, const size_t &output_size) { @@ -158,5 +157,4 @@ void ReadDataAndDump(const CNodePtr &cnode, const KernelLaunchInfo *launch_info_ bool last_kernel = !AnfAlgo::IsInplaceNode(cnode, "skip"); debugger->PostExecuteNode(cnode, last_kernel); } - } // namespace mindspore diff --git a/mindspore/ccsrc/frontend/operator/cc_implementations.cc b/mindspore/ccsrc/frontend/operator/cc_implementations.cc index ca5a5596c1..43e45cb8cd 100644 --- a/mindspore/ccsrc/frontend/operator/cc_implementations.cc +++ b/mindspore/ccsrc/frontend/operator/cc_implementations.cc @@ -65,7 +65,7 @@ bool IsMulOverflow(const T &x, const T &y, const T &max, const T &min) { } template -bool IsDivOverflow(const T &x, const T &y, const T &max, const T &min) { +bool IsDivOverflow(const T &x, const T &y, const T &min) { return (x == min && static_cast(y) == -1); } @@ -89,7 +89,7 @@ bool IsSignedIntOverflow(T x, T y, OpType opType) { } if (opType == OpType::DIV || opType == OpType::MOD) { - return IsDivOverflow(x, y, max, min); + return IsDivOverflow(x, y, min); } MS_LOG(EXCEPTION) << "Unsupported operation type."; diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/bool_scalar_eliminate.cc b/mindspore/ccsrc/frontend/optimizer/irpass/bool_scalar_eliminate.cc index 58bb15778a..f83bd6db15 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/bool_scalar_eliminate.cc +++ b/mindspore/ccsrc/frontend/optimizer/irpass/bool_scalar_eliminate.cc @@ -49,7 +49,7 @@ AnfNodePtr BoolScalarEliminate::operator()(const OptimizerPtr &optimizer, const AnfNodeIndexSet node_idx_set = iter->second; for (auto &item : node_idx_set) { - manager->Replace(item.first, vnode); + (void)manager->Replace(item.first, vnode); } return nullptr; } diff --git a/mindspore/ccsrc/pipeline/jit/action.cc b/mindspore/ccsrc/pipeline/jit/action.cc index 1035e187ef..69ef859080 100644 --- a/mindspore/ccsrc/pipeline/jit/action.cc +++ b/mindspore/ccsrc/pipeline/jit/action.cc @@ -809,27 +809,27 @@ static std::vector CommonPipeline() { std::vector actions; // Parse the python ast to ANF graph - actions.emplace_back(std::make_pair("parse", ParseAction)); + (void)actions.emplace_back(std::make_pair("parse", ParseAction)); // Resolve the python func - actions.emplace_back(std::make_pair("symbol_resolve", SymbolResolveAction)); + (void)actions.emplace_back(std::make_pair("symbol_resolve", SymbolResolveAction)); auto multi_graphs = parallel::CostModelContext::GetInstance()->is_multi_subgraphs(); if (!multi_graphs) { - actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs)); + (void)actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs)); } - actions.emplace_back(std::make_pair("inference_opt_prepare", InferenceOptPrepareAction)); + (void)actions.emplace_back(std::make_pair("inference_opt_prepare", InferenceOptPrepareAction)); // Evaluate type and shape, and specialize - actions.emplace_back(std::make_pair("abstract_specialize", AbstractSpecializeAction)); + (void)actions.emplace_back(std::make_pair("abstract_specialize", AbstractSpecializeAction)); // Auto-monad for side-effects handling. - actions.emplace_back(std::make_pair("auto_monad", AutoMonadAction)); + (void)actions.emplace_back(std::make_pair("auto_monad", AutoMonadAction)); // Do data structure simplifications and inline - actions.emplace_back(std::make_pair("inline", OptInlineAction)); + (void)actions.emplace_back(std::make_pair("inline", OptInlineAction)); // Add pre-ad, post-inline python pass stub - actions.emplace_back(std::make_pair("py_pre_ad", PreAdActionPyStub)); + (void)actions.emplace_back(std::make_pair("py_pre_ad", PreAdActionPyStub)); // Do PipelineSplit - actions.emplace_back(std::make_pair("pipeline_split", PipelineSplitAction)); + (void)actions.emplace_back(std::make_pair("pipeline_split", PipelineSplitAction)); return actions; } @@ -837,13 +837,13 @@ static std::vector CommonPipeline() { std::vector GePipeline() { auto actions = CommonPipeline(); // optimize - actions.emplace_back(std::make_pair("optimize", GeOptimizeAction)); + (void)actions.emplace_back(std::make_pair("optimize", GeOptimizeAction)); // Add opt-stage python pass stub - actions.emplace_back(std::make_pair("py_opt", OptActionGePyStub)); - actions.emplace_back(std::make_pair("remove_value_node_duplications", RemoveValueNodeDuplicationsAction)); - actions.emplace_back(std::make_pair("auto_monad_reorder", OrderEnforceAction)); - actions.emplace_back(std::make_pair("remove_monad_from_random_op", RemoveRandomOpMonadAction)); - actions.emplace_back(std::make_pair("validate", ValidateAction)); + (void)actions.emplace_back(std::make_pair("py_opt", OptActionGePyStub)); + (void)actions.emplace_back(std::make_pair("remove_value_node_duplications", RemoveValueNodeDuplicationsAction)); + (void)actions.emplace_back(std::make_pair("auto_monad_reorder", OrderEnforceAction)); + (void)actions.emplace_back(std::make_pair("remove_monad_from_random_op", RemoveRandomOpMonadAction)); + (void)actions.emplace_back(std::make_pair("validate", ValidateAction)); return actions; } @@ -851,31 +851,31 @@ std::vector VmPipeline() { auto actions = CommonPipeline(); // optimize - actions.emplace_back(std::make_pair("optimize", VmOptimizeAction)); + (void)actions.emplace_back(std::make_pair("optimize", VmOptimizeAction)); // Add opt-stage python pass stub - actions.emplace_back(std::make_pair("py_opt", OptActionVmPyStub)); + (void)actions.emplace_back(std::make_pair("py_opt", OptActionVmPyStub)); - actions.emplace_back(std::make_pair("auto_monad_reorder", OrderEnforceAction)); + (void)actions.emplace_back(std::make_pair("auto_monad_reorder", OrderEnforceAction)); - actions.emplace_back(std::make_pair("remove_monad_from_random_op", RemoveRandomOpMonadAction)); + (void)actions.emplace_back(std::make_pair("remove_monad_from_random_op", RemoveRandomOpMonadAction)); - actions.emplace_back(std::make_pair("validate", ValidateAction)); + (void)actions.emplace_back(std::make_pair("validate", ValidateAction)); #if ((defined ENABLE_CPU) && (!defined _WIN32)) if (ps::PSContext::instance()->is_worker()) { std::string server_mode = ps::PSContext::instance()->server_mode(); if (server_mode == ps::kServerModeFL || server_mode == ps::kServerModeHybrid) { - actions.emplace_back(std::make_pair("worker", StartFLWorkerAction)); + (void)actions.emplace_back(std::make_pair("worker", StartFLWorkerAction)); } else { - actions.emplace_back(std::make_pair("worker", StartPSWorkerAction)); + (void)actions.emplace_back(std::make_pair("worker", StartPSWorkerAction)); } } #endif // compile the ANF graph - actions.emplace_back(std::make_pair("task_emit", TaskEmitAction)); + (void)actions.emplace_back(std::make_pair("task_emit", TaskEmitAction)); // to execute the graph - actions.emplace_back(std::make_pair("execute", ExecuteAction)); + (void)actions.emplace_back(std::make_pair("execute", ExecuteAction)); return actions; } @@ -883,34 +883,34 @@ std::vector VmPipeline() { std::vector BackendPipeline() { std::vector actions; // compile the ANF graph - actions.emplace_back(std::make_pair("task_emit", TaskEmitAction)); + (void)actions.emplace_back(std::make_pair("task_emit", TaskEmitAction)); // to execute the graph - actions.emplace_back(std::make_pair("execute", ExecuteAction)); + (void)actions.emplace_back(std::make_pair("execute", ExecuteAction)); return actions; } #if ((defined ENABLE_CPU) && (!defined _WIN32)) std::vector ServerPipeline() { auto actions = CommonPipeline(); - actions.emplace_back(std::make_pair("optimize", VmOptimizeAction)); - actions.emplace_back(std::make_pair("validate", ValidateAction)); - actions.emplace_back(std::make_pair("server", StartServerAction)); + (void)actions.emplace_back(std::make_pair("optimize", VmOptimizeAction)); + (void)actions.emplace_back(std::make_pair("validate", ValidateAction)); + (void)actions.emplace_back(std::make_pair("server", StartServerAction)); return actions; } std::vector PServerPipeline() { auto actions = CommonPipeline(); - actions.emplace_back(std::make_pair("optimize", VmOptimizeAction)); - actions.emplace_back(std::make_pair("auto_monad_reorder", OrderEnforceAction)); - actions.emplace_back(std::make_pair("remove_monad_from_random_op", RemoveRandomOpMonadAction)); - actions.emplace_back(std::make_pair("validate", ValidateAction)); - actions.emplace_back(std::make_pair("pserver", StartPSServerAction)); + (void)actions.emplace_back(std::make_pair("optimize", VmOptimizeAction)); + (void)actions.emplace_back(std::make_pair("auto_monad_reorder", OrderEnforceAction)); + (void)actions.emplace_back(std::make_pair("remove_monad_from_random_op", RemoveRandomOpMonadAction)); + (void)actions.emplace_back(std::make_pair("validate", ValidateAction)); + (void)actions.emplace_back(std::make_pair("pserver", StartPSServerAction)); return actions; } std::vector PSchedulerPipeline() { std::vector actions; - actions.emplace_back(std::make_pair("scheduler", StartPSSchedulerAction)); + (void)actions.emplace_back(std::make_pair("scheduler", StartPSSchedulerAction)); return actions; } #endif diff --git a/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc b/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc index d0dc12aede..8f083a7ec5 100644 --- a/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc +++ b/mindspore/ccsrc/pipeline/jit/parse/parse_dynamic.cc @@ -25,7 +25,7 @@ #include "mindspore/core/ir/cell.h" namespace mindspore::parse { -static std::unordered_set cell_input_args_; +static std::unordered_set cell_input_args_ = {}; static const std::set ignore_judge_dynamic_cell = { "Cell mindspore.nn.layer.basic.Dense", "Cell mindspore.nn.probability.distribution.normal.Normal", "Cell src.transformer.create_attn_mask.CreateAttentionMaskFromInputMask", "Cell mindspore.nn.layer.math.MatMul"}; @@ -59,7 +59,7 @@ void DynamicParser::ParseInputArgs(const std::shared_ptr &ast, for (size_t i = 1; i < args.size(); i++) { std::string arg_name = py::cast(args[i].attr("arg")); MS_LOG(DEBUG) << "Input arg name: " << arg_name; - cell_input_args_.emplace(arg_name); + (void)cell_input_args_.emplace(arg_name); } } diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc index aca3518db4..4d7a1f4661 100644 --- a/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc +++ b/mindspore/ccsrc/pipeline/jit/static_analysis/auto_monad.cc @@ -569,7 +569,7 @@ class SideEffectFinder { size_t input_index = 0; // Support tuple index is negative if (top_index < 0) { - if (cnode->size() + top_index < 0) { + if (SizeToLong(cnode->size()) + top_index < 0) { MS_LOG(EXCEPTION) << "Invalid make_tuple: " << cnode->DebugString() << " index=" << top_index; } input_index = static_cast(cnode->size() + top_index); diff --git a/mindspore/ccsrc/pybind_api/ir/tensor_py.cc b/mindspore/ccsrc/pybind_api/ir/tensor_py.cc index 60498d99ab..60992640c1 100644 --- a/mindspore/ccsrc/pybind_api/ir/tensor_py.cc +++ b/mindspore/ccsrc/pybind_api/ir/tensor_py.cc @@ -420,19 +420,19 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) { return TensorPy::MakeTensor(input, type_ptr); }), py::arg("input"), py::arg("dtype") = nullptr) - .def(py::init([](py::float_ input, const TypePtr &type_ptr) { + .def(py::init([](const py::float_ input, const TypePtr &type_ptr) { return TensorPy::MakeTensor(py::array(input), type_ptr); }), py::arg("input"), py::arg("dtype") = nullptr) - .def(py::init([](py::int_ input, const TypePtr &type_ptr) { + .def(py::init([](const py::int_ input, const TypePtr &type_ptr) { return TensorPy::MakeTensor(py::array(input), type_ptr); }), py::arg("input"), py::arg("dtype") = nullptr) - .def(py::init([](py::list input, const TypePtr &type_ptr) { + .def(py::init([](const py::list &input, const TypePtr &type_ptr) { return TensorPy::MakeTensor(py::array(input), type_ptr); }), py::arg("input"), py::arg("dtype") = nullptr) - .def(py::init([](py::tuple input, const TypePtr &type_ptr) { + .def(py::init([](const py::tuple &input, const TypePtr &type_ptr) { return TensorPy::MakeTensor(py::array(input), type_ptr); }), py::arg("input"), py::arg("dtype") = nullptr) diff --git a/mindspore/ccsrc/pybind_api/random_normal/philox_generator.cc b/mindspore/ccsrc/pybind_api/random_normal/philox_generator.cc index 819c7d2a62..066d058999 100644 --- a/mindspore/ccsrc/pybind_api/random_normal/philox_generator.cc +++ b/mindspore/ccsrc/pybind_api/random_normal/philox_generator.cc @@ -43,19 +43,19 @@ void PhiloxGenerator::JumpStep(uint64_t step) { counter_[3] = static_cast(max_counter >> kShiftNum); } -std::array PhiloxGenerator::Compute(const std::array &counter_, - const std::array &key_var_) { +std::array PhiloxGenerator::Compute(const std::array &counter, + const std::array &key_var) const { std::array min_value; std::array max_value; for (size_t i = 0; i < gResultNum; i += 2) { - uint64_t temp = static_cast(keyConstant[i]) * counter_[i]; + uint64_t temp = static_cast(keyConstant[i]) * counter[i]; min_value[i] = static_cast(temp); max_value[i] = static_cast(temp >> kShiftNum); } std::array result; - result[0] = (max_value[2] ^ counter_[1] ^ key_var_[0]); + result[0] = (max_value[2] ^ counter[1] ^ key_var[0]); result[1] = min_value[2]; - result[2] = (max_value[0] ^ counter_[3] ^ key_var_[0]); + result[2] = (max_value[0] ^ counter[3] ^ key_var[0]); result[3] = min_value[0]; return result; } diff --git a/mindspore/ccsrc/pybind_api/random_normal/philox_generator.h b/mindspore/ccsrc/pybind_api/random_normal/philox_generator.h index c3862e15a6..3af1969e1e 100644 --- a/mindspore/ccsrc/pybind_api/random_normal/philox_generator.h +++ b/mindspore/ccsrc/pybind_api/random_normal/philox_generator.h @@ -47,8 +47,8 @@ class PhiloxGenerator { void JumpStep(uint64_t step); - std::array Compute(const std::array &counter_, - const std::array &key_var_); + std::array Compute(const std::array &counter, + const std::array &key_var) const; std::array operator()();