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update clang format rule

This commit is contained in:
zhoufeng 2020-04-21 21:21:19 +08:00
parent 31a12009dd
commit c2b3360d69
278 changed files with 4447 additions and 4441 deletions
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2
.clang-format
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@ -94,7 +94,7 @@ PenaltyBreakString: 1000
PenaltyBreakTemplateDeclaration: 10 PenaltyBreakTemplateDeclaration: 10
PenaltyExcessCharacter: 1000000 PenaltyExcessCharacter: 1000000
PenaltyReturnTypeOnItsOwnLine: 200 PenaltyReturnTypeOnItsOwnLine: 200
PointerAlignment: Left PointerAlignment: Right
RawStringFormats: RawStringFormats:
- Language: Cpp - Language: Cpp
Delimiters: Delimiters:

6
mindspore/ccsrc/operator/composite/composite.cc
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@ -1199,9 +1199,9 @@ FuncGraphPtr TensorSlice::GenerateFuncGraph(const AbstractBasePtrList& args_spec
return ret_graph; return ret_graph;
} }
REGISTER_PYBIND_DEFINE( REGISTER_PYBIND_DEFINE(TupleAdd_, ([](const py::module *m) {
TupleAdd_, ([](const py::module* m) { (void)py::class_<TupleAdd, MetaFuncGraph, std::shared_ptr<TupleAdd>>(*m, "TupleAdd_")
(void)py::class_<TupleAdd, MetaFuncGraph, std::shared_ptr<TupleAdd>>(*m, "TupleAdd_").def(py::init<std::string&>()); .def(py::init<std::string &>());
})); }));
REGISTER_PYBIND_DEFINE(TupleSlice_, ([](const py::module *m) { REGISTER_PYBIND_DEFINE(TupleSlice_, ([](const py::module *m) {

14
mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h
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@ -161,8 +161,8 @@ class CostGraph {
// Applying Triangle Elimination in DP algorithm. return the left_node // Applying Triangle Elimination in DP algorithm. return the left_node
OperatorInfoPtr EliminationTriangle(const OperatorInfoPtr &elimi_op, const EdgePtr &edge_left_right); OperatorInfoPtr EliminationTriangle(const OperatorInfoPtr &elimi_op, const EdgePtr &edge_left_right);
void CreateTriangleEliminationCostList(const OperatorInfoPtr &, const CostPtrList &, const CostPtrList &, void CreateTriangleEliminationCostList(const OperatorInfoPtr &, const CostPtrList &, const CostPtrList &,
const StrategyPtr&, const StrategyPtr&, const StrategyPtr&, const CostPtrList&, const StrategyPtr &, const StrategyPtr &, const StrategyPtr &,
const CostPtrList&, const CostPtrList&, CostPtrList*); const CostPtrList &, const CostPtrList &, const CostPtrList &, CostPtrList *);
// Given the relevant costlist, create the TriangleElimination cost // Given the relevant costlist, create the TriangleElimination cost
void CreateTriangleEliminationSubCostList(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr &, const CostPtrList &, void CreateTriangleEliminationSubCostList(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr &, const CostPtrList &,
const CostPtrList &, const CostPtr &, const CostPtrList &, CostPtrList *); const CostPtrList &, const CostPtr &, const CostPtrList &, CostPtrList *);
@ -170,11 +170,11 @@ class CostGraph {
// Applying the Star Elimination in DP algorithm. Return the successive edges of this merged_op // Applying the Star Elimination in DP algorithm. Return the successive edges of this merged_op
// NOTE: this elimination MUST be performed only when the above 5 operation cannot be applied. // NOTE: this elimination MUST be performed only when the above 5 operation cannot be applied.
std::vector<EdgePtr> EliminationStar(const OperatorInfoPtr &op); std::vector<EdgePtr> EliminationStar(const OperatorInfoPtr &op);
void CreateStarEliminationCostList(std::vector<EdgePtr>&, const StrategyPtr&, const CostPtrList&, const CostPtrList&, void CreateStarEliminationCostList(std::vector<EdgePtr> &, const StrategyPtr &, const CostPtrList &,
const StrategyPtr&, const CostPtrList&, CostPtrList*); const CostPtrList &, const StrategyPtr &, const CostPtrList &, CostPtrList *);
void CreateStarEliminationSubCostList(const StrategyPtr&, const CostPtrList&, const CostPtrList&, const StrategyPtr&, void CreateStarEliminationSubCostList(const StrategyPtr &, const CostPtrList &, const CostPtrList &,
const CostPtrList&, std::vector<StrategyPtr>, CostPtrList&, CostPtrList&, const StrategyPtr &, const CostPtrList &, std::vector<StrategyPtr>,
CostPtrList*); CostPtrList &, CostPtrList &, CostPtrList *);
// When the input of a operator is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then // When the input of a operator is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then
// the memory cost can be resused. // the memory cost can be resused.
Status CalculateOpsMemoryCost(); Status CalculateOpsMemoryCost();

16
mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc
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@ -195,7 +195,8 @@ double ActivationCost::GetBackwardComputationCost(const std::vector<TensorInfo>&
} }
// Return the per device communication cost in the forward phase. // Return the per device communication cost in the forward phase.
double SoftmaxCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const { double SoftmaxCost::GetForwardCommCost(const std::vector<TensorInfo> &, const std::vector<TensorInfo> &,
int32_t) const {
// In the forward phase, the communication cost = 0 // In the forward phase, the communication cost = 0
return 0.0; return 0.0;
} }
@ -262,7 +263,8 @@ double TmpIdentityCost::GetForwardComputationCost(const std::vector<mindspore::p
// Return the per device computation cost in the backward phase. The cost is calculated according to the bytes // Return the per device computation cost in the backward phase. The cost is calculated according to the bytes
// this operator uses // this operator uses
double TmpIdentityCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo> &, double TmpIdentityCost::GetBackwardComputationCost(const std::vector<mindspore::parallel::TensorInfo> &,
const std::vector<mindspore::parallel::TensorInfo>&, int32_t) const { const std::vector<mindspore::parallel::TensorInfo> &,
int32_t) const {
return 0.0; return 0.0;
} }
@ -386,7 +388,8 @@ double OneHotCost::GetForwardCommCost(const std::vector<TensorInfo>&, const std:
} }
// return the per device communication cost in the backward phase. // return the per device communication cost in the backward phase.
double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const { double OneHotCost::GetBackwardCommCost(const std::vector<TensorInfo> &, const std::vector<TensorInfo> &,
int32_t) const {
// onehot does not need communication in the backward phase // onehot does not need communication in the backward phase
return 0.0; return 0.0;
} }
@ -509,8 +512,8 @@ double ArithmeticCost::GetForwardComputationCost(const std::vector<TensorInfo>&
return result; return result;
} }
double ArithmeticCost::GetBackwardComputationCost(const std::vector<TensorInfo>& inputs, const std::vector<TensorInfo>&, double ArithmeticCost::GetBackwardComputationCost(const std::vector<TensorInfo> &inputs,
int32_t stage_id) const { const std::vector<TensorInfo> &, int32_t stage_id) const {
double result = 0.0; double result = 0.0;
CheckGlobalDeviceManager(); CheckGlobalDeviceManager();
MS_EXCEPTION_IF_NULL(g_device_manager); MS_EXCEPTION_IF_NULL(g_device_manager);
@ -689,7 +692,8 @@ double DropOutCost::GetForwardComputationCost(const std::vector<TensorInfo>& inp
} }
// return the per device communication cost in the forward phase. // return the per device communication cost in the forward phase.
double GatherV2Cost::GetForwardCommCost(const std::vector<TensorInfo>&, const std::vector<TensorInfo>&, int32_t) const { double GatherV2Cost::GetForwardCommCost(const std::vector<TensorInfo> &, const std::vector<TensorInfo> &,
int32_t) const {
// GatherV2Cost does not need communication in the forward phase // GatherV2Cost does not need communication in the forward phase
return 0.0; return 0.0;
} }

4
mindspore/ccsrc/transform/op_adapter_base.h
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@ -56,7 +56,9 @@ class CustomOperator : public Operator {
void CustomOutputRegister(const string &name) { Operator::OutputRegister(name); } void CustomOutputRegister(const string &name) { Operator::OutputRegister(name); }
void CustomInferFuncRegister(const std::function<graphStatus(Operator&)>& func) { Operator::InferFuncRegister(func); } void CustomInferFuncRegister(const std::function<graphStatus(Operator &)> &func) {
Operator::InferFuncRegister(func);
}
}; };
} // namespace ge } // namespace ge