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Fix Prefast Warnings (#12717)

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fix prefast warnings
This commit is contained in:
Vincent Wang 2022-08-25 17:09:37 +08:00 committed by GitHub
parent 5be3e87c71
commit 5104c7dbd3
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GPG key ID: 4AEE18F83AFDEB23
3 changed files with 25 additions and 34 deletions
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49
onnxruntime/core/providers/cuda/math/variadic_elementwise_ops.cc
View file

@ -235,46 +235,37 @@ using MinOp = VariadicElementwiseOp<variadic_elementwise_ops::Min, uint32_t, uin
using MaxOp = VariadicElementwiseOp<variadic_elementwise_ops::Max, uint32_t, uint64_t, int32_t, int64_t, MLFloat16,
float, double, BFloat16>;
const DeleteOnUnloadPtr<std::vector<MLDataType>> k_uzilhfd_datatypes = new std::vector<MLDataType>(
BuildKernelDefConstraints<uint32_t, uint64_t, int32_t, int64_t, MLFloat16, float, double, BFloat16>());
const DeleteOnUnloadPtr<std::vector<MLDataType>> k_hfd_datatypes =
new std::vector<MLDataType>(BuildKernelDefConstraints<MLFloat16, float, double, BFloat16>());
} // namespace
// kernel registration
#define REGISTER_KERNEL(name, impl_class, version, datatypes) \
ONNX_OPERATOR_KERNEL_EX( \
name, \
kOnnxDomain, \
version, \
kCudaExecutionProvider, \
(*KernelDefBuilder::Create()).TypeConstraint("T", datatypes), \
impl_class)
#define REGISTER_KERNEL(name, impl_class, version, datatypes) \
ONNX_OPERATOR_KERNEL_EX(name, kOnnxDomain, version, kCudaExecutionProvider, \
(*KernelDefBuilder::Create()).TypeConstraint("T", BuildKernelDefConstraints<datatypes>()), \
impl_class)
#define REGISTER_VERSIONED_KERNEL(name, impl_class, start_version, end_version, datatypes) \
ONNX_OPERATOR_VERSIONED_KERNEL_EX( \
name, \
kOnnxDomain, \
start_version, end_version, \
kCudaExecutionProvider, \
(*KernelDefBuilder::Create()).TypeConstraint("T", datatypes), \
impl_class)
name, kOnnxDomain, start_version, end_version, kCudaExecutionProvider, \
(*KernelDefBuilder::Create()).TypeConstraint("T", BuildKernelDefConstraints<datatypes>()), impl_class)
REGISTER_KERNEL(Sum, SumOp, 13, *k_hfd_datatypes)
REGISTER_VERSIONED_KERNEL(Sum, SumOp, 8, 12, *k_hfd_datatypes)
REGISTER_VERSIONED_KERNEL(Sum, SumOp, 6, 7, *k_hfd_datatypes)
#define UZILHFD_TYPES uint32_t, uint64_t, int32_t, int64_t, MLFloat16, float, double, BFloat16
#define HFD_TYPES MLFloat16, float, double, BFloat16
REGISTER_KERNEL(Min, MinOp, 13, *k_uzilhfd_datatypes)
REGISTER_VERSIONED_KERNEL(Min, MinOp, 12, 12, *k_uzilhfd_datatypes)
REGISTER_VERSIONED_KERNEL(Min, MinOp, 6, 11, *k_hfd_datatypes)
REGISTER_KERNEL(Sum, SumOp, 13, HFD_TYPES)
REGISTER_VERSIONED_KERNEL(Sum, SumOp, 8, 12, HFD_TYPES)
REGISTER_VERSIONED_KERNEL(Sum, SumOp, 6, 7, HFD_TYPES)
REGISTER_KERNEL(Max, MaxOp, 13, *k_uzilhfd_datatypes)
REGISTER_VERSIONED_KERNEL(Max, MaxOp, 12, 12, *k_uzilhfd_datatypes)
REGISTER_VERSIONED_KERNEL(Max, MaxOp, 6, 11, *k_hfd_datatypes)
REGISTER_KERNEL(Min, MinOp, 13, UZILHFD_TYPES)
REGISTER_VERSIONED_KERNEL(Min, MinOp, 12, 12, UZILHFD_TYPES)
REGISTER_VERSIONED_KERNEL(Min, MinOp, 6, 11, HFD_TYPES)
REGISTER_KERNEL(Max, MaxOp, 13, UZILHFD_TYPES)
REGISTER_VERSIONED_KERNEL(Max, MaxOp, 12, 12, UZILHFD_TYPES)
REGISTER_VERSIONED_KERNEL(Max, MaxOp, 6, 11, HFD_TYPES)
#undef HFD_TYPES
#undef UZILHFD_TYPES
#undef REGISTER_VERSIONED_KERNEL
#undef REGISTER_KERNEL

6
orttraining/orttraining/training_ops/cuda/nn/conv_grad.cc
View file

@ -140,7 +140,7 @@ struct AlgoSearch<T_BwdDataPerf> {
static constexpr int num_algos = CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
ORT_ENFORCE(sizeof(algos) / sizeof(algos[0]) == num_algos, "Missing cuDNN convolution backward data algorithms.");
int perf_count;
std::unique_ptr<T_BwdDataPerf[]> candidates(new T_BwdDataPerf[num_algos]);
std::unique_ptr<T_BwdDataPerf[]> candidates = std::make_unique<T_BwdDataPerf[]>(num_algos);
if (args.params.algo_mode == OrtCudnnConvAlgoSearchHeuristic) {
CUDNN_RETURN_IF_ERROR(cudnnGetConvolutionBackwardDataAlgorithm_v7(args.handle, args.w_desc, args.y_tensor,
args.conv_desc, args.x_tensor, num_algos,
@ -180,7 +180,7 @@ struct AlgoSearch<T_BwdFilterPerf> {
// NOTE: - 1 because ALGO_WINOGRAD is not implemented.
static constexpr int num_algos = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT - 1;
ORT_ENFORCE(sizeof(algos) / sizeof(algos[0]) == num_algos, "Missing cuDNN convolution backward filter algorithms.");
std::unique_ptr<T_BwdFilterPerf[]> candidates(new T_BwdFilterPerf[num_algos]);
std::unique_ptr<T_BwdFilterPerf[]> candidates = std::make_unique<T_BwdFilterPerf[]>(num_algos);
int perf_count;
if (args.params.algo_mode == OrtCudnnConvAlgoSearchHeuristic) {
CUDNN_RETURN_IF_ERROR(cudnnGetConvolutionBackwardFilterAlgorithm_v7(args.handle, args.x_tensor, args.y_tensor,
@ -222,7 +222,7 @@ class AlgoIterator {
Status TryAll(const CUDAExecutionProvider* provider, std::function<Status(const T_Perf& perf)> f) {
auto& cache = AlgoSearch<T_Perf>::Cache();
if (T_Perf algo_perf; cache.Find(args_.params, &algo_perf) && f(algo_perf) == Status::OK()) {
return Status::OK();
}

4
orttraining/orttraining/training_ops/rocm/nn/conv_grad.cc
View file

@ -140,7 +140,7 @@ struct AlgoSearch<T_BwdDataAlgo> {
static constexpr int num_algos = MIOPEN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
ORT_ENFORCE(sizeof(algos) / sizeof(algos[0]) == num_algos, "Missing MIOpen convolution backward data algorithms.");
int perf_count;
std::unique_ptr<T_BwdDataPerf[]> candidates(new T_BwdDataPerf[num_algos]);
std::unique_ptr<T_BwdDataPerf[]> candidates = std::make_unique<T_BwdDataPerf[]>(num_algos);
size_t max_workspace_size = provider->GetMiopenConvUseMaxWorkspace() ? GetMaxWorkspaceSize(args, algos, num_algos)
: AlgoSearchWorkspaceSize;
// Use GetTransientScratchBuffer() so the workspace can be freed instead of cached.
@ -169,7 +169,7 @@ struct AlgoSearch<T_BwdFilterAlgo> {
static constexpr int num_algos = MIOPEN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
ORT_ENFORCE(sizeof(algos) / sizeof(algos[0]) == num_algos, "Missing MIOpen convolution backward filter algorithms.");
std::unique_ptr<T_BwdFilterPerf[]> candidates(new T_BwdFilterPerf[num_algos]);
std::unique_ptr<T_BwdFilterPerf[]> candidates = std::make_unique<T_BwdFilterPerf[]>(num_algos);
int perf_count;
size_t max_workspace_size = provider->GetMiopenConvUseMaxWorkspace() ? GetMaxWorkspaceSize(args, algos, num_algos)
: AlgoSearchWorkspaceSize;