diff options
| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-20 17:26:45 +0100 |
|---|---|---|
| committer | SiCong Li <sicong.li@arm.com> | 2021-08-24 09:00:23 +0000 |
| commit | 87a74effff65f6fa1b0e565818e02c3b414ae1cf (patch) | |
| tree | 0c5d63bbbcc285232959a5a4134f282a980ab4bf /src/core/cpu/kernels | |
| parent | 511771fbe0a74e6d9dfd37ba9b4926a8315ec7aa (diff) | |
| download | ComputeLibrary-87a74effff65f6fa1b0e565818e02c3b414ae1cf.tar.gz | |
Re-use auxiliary memory withing CpuWinogradConv2d operators
Input/Output transformation operations are independent and done in
different time-steps of the algorithm, this memory can be re-used
between this transformation stages.
Moreover, reduce the allocation when extracting workspace sizes for
Winograd trasformations. There is a mix return of sizes in bytes and
elements, thus ensure the correct is in place. storage_size() member
functions return elements while working_space() function bytes.
Resolves: COMPMID-4781
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I705445ba7ca818cead48369db3cacd49684c7192
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6145
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Diffstat (limited to 'src/core/cpu/kernels')
| -rw-r--r-- | src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp b/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp index 5620d36e2c..9456f96354 100644 --- a/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp +++ b/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp @@ -194,8 +194,8 @@ template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, in unsigned int CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const { const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels); - return static_cast<unsigned int>( - WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels)); + // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T + return static_cast<unsigned int>(WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T)); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -297,7 +297,8 @@ unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTile // Construct shapes for the input and kernel tensors. const Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels); const KernelShape kern_shape(1, KernelRows, KernelCols, num_channels); - return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding)); + // Return the size, converted into units of TIn + return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding) / sizeof(T)); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -432,8 +433,9 @@ unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTil // Construct shapes for the input and kernel tensors. const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1); const KernelShape kern_shape(num_output_channels, KernelRows, KernelCols, 1); + // Return the size, converted into units of TOut return static_cast<unsigned int>( - WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels)); + WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels) / sizeof(T)); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |