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Fix some too popular warnings. (#3578)

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Some pointless and noisy warnings either fixed or disabled.
This commit is contained in:
Dmitri Smirnov 2020-04-18 17:05:05 -07:00 committed by GitHub
parent d68245853e
commit 38a18023c7
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GPG key ID: 4AEE18F83AFDEB23
7 changed files with 21 additions and 11 deletions
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6
cmake/CMakeLists.txt
View file

@ -583,6 +583,8 @@ if (WIN32)
string(APPEND CMAKE_CXX_FLAGS " /wd4127")
# class needs to have dll-interface to be used by clients
string(APPEND CMAKE_CXX_FLAGS " /wd4251")
# issued by thrust nonstandard extension used: nameless struct/union
string(APPEND CMAKE_CXX_FLAGS " /wd4201")
if (onnxruntime_ENABLE_STATIC_ANALYSIS)
string(APPEND CMAKE_CXX_FLAGS
" /analyze:stacksize 131072"
@ -795,6 +797,10 @@ if (onnxruntime_USE_CUDA)
if (NOT WIN32)
set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} --expt-relaxed-constexpr --compiler-options -fPIC")
endif()
# Options passed to cudafe
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcudafe \"--diag_suppress=bad_friend_decl\"")
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcudafe \"--diag_suppress=unsigned_compare_with_zero\"")
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcudafe \"--diag_suppress=expr_has_no_effect\"")
endif()
if (onnxruntime_USE_TENSORRT)

4
onnxruntime/core/common/threadpool.cc
View file

@ -224,7 +224,7 @@ static ParallelForBlock CalculateParallelForBlock(const ptrdiff_t n, const Eigen
// Calculate parallel efficiency as fraction of total CPU time used for
// computations:
double max_efficiency =
static_cast<double>(block_count) / (Eigen::divup<int>(block_count, num_threads) * num_threads);
static_cast<double>(block_count) / (Eigen::divup<ptrdiff_t>(block_count, num_threads) * num_threads);
// Now try to increase block size up to max_block_size as long as it
// doesn't decrease parallel efficiency.
@ -245,7 +245,7 @@ static ParallelForBlock CalculateParallelForBlock(const ptrdiff_t n, const Eigen
assert(coarser_block_count < prev_block_count);
prev_block_count = coarser_block_count;
const double coarser_efficiency =
static_cast<double>(coarser_block_count) / (Eigen::divup<int>(coarser_block_count, num_threads) * num_threads);
static_cast<double>(coarser_block_count) / (Eigen::divup<ptrdiff_t>(coarser_block_count, num_threads) * num_threads);
if (coarser_efficiency + 0.01 >= max_efficiency) {
// Taking it.
block_size = coarser_block_size;

2
onnxruntime/core/framework/data_transfer.h
View file

@ -23,6 +23,8 @@ class IDataTransfer {
class CPUDataTransfer : public IDataTransfer {
public:
CPUDataTransfer() = default;
// Dampen MSVC warning about not fully overriding CopyTensor
using IDataTransfer::CopyTensor;
bool CanCopy(const OrtDevice& src_device, const OrtDevice& dst_device) const override;
common::Status CopyTensor(const Tensor& src, Tensor& dst, int exec_queue_id) const override;
};

4
onnxruntime/core/graph/contrib_ops/contrib_defs.cc
View file

@ -40,7 +40,7 @@ void convTransposeWithDynamicPadsShapeInference(InferenceContext& ctx) {
}
// first dim is the batch axis and the next is the number of channels.
size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - size_t{2});
std::vector<int64_t> dilations;
if (getRepeatedAttribute(ctx, "dilations", dilations)) {
@ -2046,7 +2046,7 @@ Example 4:
// fill with zeros if needed to reach appropriate size
if (pads_data.size() != 2 * static_cast<size_t>(input_rank))
pads_data.resize(2 * input_rank, 0);
pads_data.resize(size_t{2} * input_rank, 0);
const auto& output_shape =
ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();

8
onnxruntime/core/graph/contrib_ops/range_schema_defs.cc
View file

@ -69,10 +69,10 @@ template <typename T>
static int64_t CalcRangeDim(const TensorProto* startShapeInitializer,
const TensorProto* limitShapeInitializer,
const TensorProto* deltaShapeInitializer) {
T start = GetFirstElement<T>(startShapeInitializer);
T limit = GetFirstElement<T>(limitShapeInitializer);
T delta = GetFirstElement<T>(deltaShapeInitializer);
if (delta == T{0}) {
auto start = static_cast<double>(GetFirstElement<T>(startShapeInitializer));
auto limit = static_cast<double>(GetFirstElement<T>(limitShapeInitializer));
auto delta = static_cast<double>(GetFirstElement<T>(deltaShapeInitializer));
if (delta == 0) {
fail_shape_inference("delta in Range operator can not be zero!");
}
return static_cast<int64_t>(ceil((1.0 * (limit - start)) / delta));

2
onnxruntime/core/providers/cuda/gpu_data_transfer.h
View file

@ -22,6 +22,8 @@ class GPUDataTransfer : public IDataTransfer {
bool CanCopy(const OrtDevice& src_device, const OrtDevice& dst_device) const override;
// Dumpen MSVC warning about not fully overriding
using IDataTransfer::CopyTensor;
common::Status CopyTensor(const Tensor& src, Tensor& dst, int exec_queue_id) const override;
cudaStream_t GetStream(int queue_id) const {

6
onnxruntime/core/providers/cuda/tensor/resize_impl.cu
View file

@ -461,9 +461,9 @@ void ResizeNearestImpl(
float cubic_coeff_a,
CudaFunctionOriginalCoordinate transform_coordinate,
CudaFunctionNearestPixel calc_nearest_pixel,
int64_t* prefix_dim_sum,
int64_t* /* prefix_dim_sum */,
NearestMappingInfo* dims_mapping) {
int blocksPerGrid = (int)(ceil(static_cast<float>(N) / GridDim::maxThreadsPerBlock));
int blocksPerGrid = static_cast<int>(ceil(static_cast<float>(N) / GridDim::maxThreadsPerBlock));
bool could2d = rank >= 2 &&
transform_coordinate != GetDeviceOriginalCoordinateFunc(ResizeCoordinateTransformationMode::TF_CROP_AND_RESIZE) &&
@ -472,7 +472,7 @@ void ResizeNearestImpl(
int64_t output_height = output_shape[rank - 2];
int64_t output_width = output_shape[rank - 1];
fast_divmod div_output_image = (rank > 2) ? output_div_pitches[rank - 3] : fast_divmod(output_height * output_width);
int blocksPerDimsMappingGrid = (int)(ceil((output_height + output_width) / 32.0));
int blocksPerDimsMappingGrid = static_cast<int>(ceil((output_height + output_width) / 32.0));
_ResizeNearestMappingKernel2D<T><<<blocksPerDimsMappingGrid, 32, 0>>>(
input_shape[rank - 2], input_shape[rank - 1],