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Fix typo: change Upample to Upsample. #23838

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Merged
fs-eire merged 1 commit into from
Mar 1, 2025
Merged

Fix typo: change Upample to Upsample. #23838

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20 changes: 10 additions & 10 deletions onnxruntime/core/providers/cuda/tensor/upsample.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -290,16 +290,16 @@ Status Upsample<T>::BaseCompute(OpKernelContext* context,
scales_div[i] = fast_divmod(gsl::narrow_cast<int>(ceil(scales[i])));
}

UpampleImpl(Stream(context),
mode_,
rank,
(UpsampleMode::LINEAR == mode_) ? (rank == 2 ? X_dims[0] : X_dims[2]) : 0,
input_strides,
output_div_pitches,
scales_div,
reinterpret_cast<const CudaT*>(X->Data<T>()),
reinterpret_cast<CudaT*>(Y->MutableData<T>()),
output_count);
UpsampleImpl(Stream(context),
mode_,
rank,
(UpsampleMode::LINEAR == mode_) ? (rank == 2 ? X_dims[0] : X_dims[2]) : 0,
input_strides,
output_div_pitches,
scales_div,
reinterpret_cast<const CudaT*>(X->Data<T>()),
reinterpret_cast<CudaT*>(Y->MutableData<T>()),
output_count);
}

return Status::OK();
Expand Down
94 changes: 47 additions & 47 deletions onnxruntime/core/providers/cuda/tensor/upsample_impl.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -8,12 +8,12 @@ namespace onnxruntime {
namespace cuda {

template <typename T, int RANK>
__global__ void _UpampleNearestKernel(const TArray<int64_t> input_pitches,
const TArray<fast_divmod> output_div_pitches,
const TArray<fast_divmod> scales_div,
const T* __restrict__ input_data,
T* __restrict__ output_data,
const size_t N) {
__global__ void _UpsampleNearestKernel(const TArray<int64_t> input_pitches,
const TArray<fast_divmod> output_div_pitches,
const TArray<fast_divmod> scales_div,
const T* __restrict__ input_data,
T* __restrict__ output_data,
const size_t N) {
CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id, N);
CUDA_LONG input_index = 0;
CUDA_LONG output_index = id;
Expand All @@ -36,13 +36,13 @@ __global__ void _UpampleNearestKernel(const TArray<int64_t> input_pitches,
// This is the common use-case where the 4-D input (batched multi-channel images)
// is usually of shape [N, C, H, W] and the scales are [1.0, 1.0, height_scale, width_scale]
template <typename T>
__global__ void _UpampleBilinear4DInputKernel(const int64_t input_dim2,
const TArray<int64_t> input_pitches,
const TArray<fast_divmod> output_div_pitches,
const TArray<fast_divmod> scales_div,
const T* __restrict__ input_data,
T* __restrict__ output_data,
const size_t N) {
__global__ void _UpsampleBilinear4DInputKernel(const int64_t input_dim2,
const TArray<int64_t> input_pitches,
const TArray<fast_divmod> output_div_pitches,
const TArray<fast_divmod> scales_div,
const T* __restrict__ input_data,
T* __restrict__ output_data,
const size_t N) {
CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id, N);
CUDA_LONG input_index = 0;

Expand Down Expand Up @@ -95,13 +95,13 @@ __global__ void _UpampleBilinear4DInputKernel(const int64_t input_dim2,

// The following method supports a 2-D input in 'Linear mode'
template <typename T>
__global__ void _UpampleBilinear2DInputKernel(const int64_t input_dim0,
const TArray<int64_t> input_pitches,
const TArray<fast_divmod> output_div_pitches,
const TArray<fast_divmod> scales_div,
const T* __restrict__ input_data,
T* __restrict__ output_data,
const size_t N) {
__global__ void _UpsampleBilinear2DInputKernel(const int64_t input_dim0,
const TArray<int64_t> input_pitches,
const TArray<fast_divmod> output_div_pitches,
const TArray<fast_divmod> scales_div,
const T* __restrict__ input_data,
T* __restrict__ output_data,
const size_t N) {
CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id, N);
CUDA_LONG input_index = 0;

Expand Down Expand Up @@ -147,44 +147,44 @@ __global__ void _UpampleBilinear2DInputKernel(const int64_t input_dim0,
}

template <typename T>
void UpampleImpl(cudaStream_t stream,
const onnxruntime::UpsampleMode upsample_mode,
const size_t rank,
const int64_t input_dim2,
const TArray<int64_t>& input_pitches,
const TArray<fast_divmod>& output_div_pitches,
const TArray<fast_divmod>& scales_div,
const T* input_data,
T* output_data,
const size_t N) {
void UpsampleImpl(cudaStream_t stream,
const onnxruntime::UpsampleMode upsample_mode,
const size_t rank,
const int64_t input_dim2,
const TArray<int64_t>& input_pitches,
const TArray<fast_divmod>& output_div_pitches,
const TArray<fast_divmod>& scales_div,
const T* input_data,
T* output_data,
const size_t N) {
int blocksPerGrid = (int)(ceil(static_cast<float>(N) / GridDim::maxThreadsPerBlock));
if (onnxruntime::UpsampleMode::NN == upsample_mode) {
if (rank == 4) {
_UpampleNearestKernel<T, 4><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
_UpsampleNearestKernel<T, 4><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
input_pitches, output_div_pitches, scales_div,
input_data, output_data, N);
} else if (rank == 3) {
_UpampleNearestKernel<T, 3><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
_UpsampleNearestKernel<T, 3><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
input_pitches, output_div_pitches, scales_div,
input_data, output_data, N);
} else if (rank == 2) {
_UpampleNearestKernel<T, 2><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
_UpsampleNearestKernel<T, 2><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
input_pitches, output_div_pitches, scales_div,
input_data, output_data, N);
} else if (rank == 1) {
_UpampleNearestKernel<T, 1><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
_UpsampleNearestKernel<T, 1><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
input_pitches, output_div_pitches, scales_div,
input_data, output_data, N);
} else {
ORT_THROW("Unsupported rank by the Upsample CUDA kernel. Input rank: ", rank);
}
} else if (onnxruntime::UpsampleMode::LINEAR == upsample_mode) {
if (rank == 4) {
_UpampleBilinear4DInputKernel<T><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
_UpsampleBilinear4DInputKernel<T><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
input_dim2, input_pitches, output_div_pitches, scales_div,
input_data, output_data, N);
} else if (rank == 2) {
_UpampleBilinear2DInputKernel<T><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
_UpsampleBilinear2DInputKernel<T><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, stream>>>(
input_dim2, input_pitches, output_div_pitches, scales_div,
input_data, output_data, N);
} else {
Expand All @@ -197,17 +197,17 @@ void UpampleImpl(cudaStream_t stream,
}
}

#define SPECIALIZED_IMPL(T) \
template void UpampleImpl<T>(cudaStream_t stream, \
const onnxruntime::UpsampleMode upsample_mode, \
const size_t rank, \
const int64_t input_dim2, \
const TArray<int64_t>& input_pitches, \
const TArray<fast_divmod>& output_div_pitches, \
const TArray<fast_divmod>& scales_div, \
const T* input_data, \
T* output_data, \
const size_t N);
#define SPECIALIZED_IMPL(T) \
template void UpsampleImpl<T>(cudaStream_t stream, \
const onnxruntime::UpsampleMode upsample_mode, \
const size_t rank, \
const int64_t input_dim2, \
const TArray<int64_t>& input_pitches, \
const TArray<fast_divmod>& output_div_pitches, \
const TArray<fast_divmod>& scales_div, \
const T* input_data, \
T* output_data, \
const size_t N);

SPECIALIZED_IMPL(float)
SPECIALIZED_IMPL(double)
Expand Down
20 changes: 10 additions & 10 deletions onnxruntime/core/providers/cuda/tensor/upsample_impl.h
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,16 +11,16 @@ namespace onnxruntime {
namespace cuda {

template <typename T>
void UpampleImpl(cudaStream_t stream,
const onnxruntime::UpsampleMode upsample_mode,
const size_t rank,
const int64_t input_dim2,
const TArray<int64_t>& input_pitches,
const TArray<fast_divmod>& output_div_pitches,
const TArray<fast_divmod>& scales_div,
const T* input_data,
T* output_data,
const size_t N);
void UpsampleImpl(cudaStream_t stream,
const onnxruntime::UpsampleMode upsample_mode,
const size_t rank,
const int64_t input_dim2,
const TArray<int64_t>& input_pitches,
const TArray<fast_divmod>& output_div_pitches,
const TArray<fast_divmod>& scales_div,
const T* input_data,
T* output_data,
const size_t N);

} // namespace cuda
} // namespace onnxruntime
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