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authorMichalis Spyrou <michalis.spyrou@arm.com>2021-07-01 12:20:56 +0100
committerMichalis Spyrou <michalis.spyrou@arm.com>2021-07-13 13:42:25 +0000
commit96f977e43f452a75f2658b820791cb3d3da9c0a3 (patch)
treefe279f0573d871c051bb49acf4b83f50b29a1647
parent04b39e8e56112dabf6f5746117354680a9985841 (diff)
downloadComputeLibrary-96f977e43f452a75f2658b820791cb3d3da9c0a3.tar.gz
Port NEWinogradConvolutionLayer
Rename to CpuWinogradConv2d Allow memory to be injected externally Change-Id: I1f0a26ea533e326a7c63df86e708895c31752a39 Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5926 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Diffstat
-rw-r--r--Android.bp3
-rw-r--r--arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h59
-rw-r--r--docs/user_guide/release_version_and_change_log.dox12
-rw-r--r--filelist.json5
-rw-r--r--src/core/NEON/NEKernels.h1
-rw-r--r--src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp (renamed from src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp)282
-rw-r--r--src/core/cpu/kernels/CpuWinogradConv2dKernel.h (renamed from src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h)180
-rw-r--r--src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp754
-rw-r--r--src/runtime/cpu/operators/CpuWinogradConv2d.cpp848
-rw-r--r--src/runtime/cpu/operators/CpuWinogradConv2d.h137
-rw-r--r--tests/SConscript1
-rw-r--r--tests/validation/NEON/ConvolutionLayer.cpp103
12 files changed, 1385 insertions, 1000 deletions
diff --git a/Android.bp b/Android.bp
index 8b6dd8392c..0778b636ce 100644
--- a/Android.bp
+++ b/Android.bp
@@ -178,7 +178,6 @@ cc_library_static {
"src/core/NEON/kernels/NEStridedSliceKernel.cpp",
"src/core/NEON/kernels/NETileKernel.cpp",
"src/core/NEON/kernels/NEWeightsReshapeKernel.cpp",
- "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp",
"src/core/NEON/kernels/arm_conv/depthwise/depthwise_fp16.cpp",
"src/core/NEON/kernels/arm_conv/depthwise/depthwise_fp32.cpp",
"src/core/NEON/kernels/arm_conv/depthwise/depthwise_s8q.cpp",
@@ -285,6 +284,7 @@ cc_library_static {
"src/core/cpu/kernels/CpuSoftmaxKernel.cpp",
"src/core/cpu/kernels/CpuSubKernel.cpp",
"src/core/cpu/kernels/CpuTransposeKernel.cpp",
+ "src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp",
"src/core/cpu/kernels/activation/neon/fp16.cpp",
"src/core/cpu/kernels/activation/neon/fp32.cpp",
"src/core/cpu/kernels/activation/neon/qasymm8.cpp",
@@ -653,6 +653,7 @@ cc_library_static {
"src/runtime/cpu/operators/CpuSoftmax.cpp",
"src/runtime/cpu/operators/CpuSub.cpp",
"src/runtime/cpu/operators/CpuTranspose.cpp",
+ "src/runtime/cpu/operators/CpuWinogradConv2d.cpp",
"src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp",
"src/runtime/gpu/cl/operators/ClActivation.cpp",
"src/runtime/gpu/cl/operators/ClAdd.cpp",
diff --git a/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h
index b02c4ed5b7..2a49f2be59 100644
--- a/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h
@@ -27,11 +27,6 @@
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CPP/functions/CPPPermute.h"
-#include "arm_compute/runtime/MemoryGroup.h"
-#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
-#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
-
#include "arm_compute/runtime/Tensor.h"
#include <memory>
@@ -40,13 +35,12 @@ namespace arm_compute
{
// Forward declarations
class ITensor;
-class ICPPKernel;
/** Basic function to simulate a convolution layer. This function calls the following kernels:
*
- * -# @ref NEWinogradLayerTransformWeightsKernel (executed only once in the first call to the run() method )
- * -# @ref NEWinogradLayerTransformInputKernel
- * -# @ref NEWinogradLayerTransformOutputKernel
+ * -# @ref cpu::CpuWinogradConv2dTransformWeightsKernel (executed only once in the first call to the run() method )
+ * -# @ref cpu::CpuWinogradConv2dTransformInputKernel
+ * -# @ref cpu::CpuWinogradConv2dTransformOutputKernel
* -# @ref cpu::CpuGemmAssemblyDispatch
* -# @ref CPPPermute (three times: weights, input and output)
*
@@ -57,12 +51,16 @@ class NEWinogradConvolutionLayer : public IFunction
public:
/** Constructor */
NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEWinogradConvolutionLayer(NEWinogradConvolutionLayer &&) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEWinogradConvolutionLayer &operator=(NEWinogradConvolutionLayer &&) = delete;
- /** Default destructor */
- ~NEWinogradConvolutionLayer() = default;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ NEWinogradConvolutionLayer(const NEWinogradConvolutionLayer &) = delete;
+ /** Default move constructor */
+ NEWinogradConvolutionLayer(NEWinogradConvolutionLayer &&) = default;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ NEWinogradConvolutionLayer &operator=(const NEWinogradConvolutionLayer &) = delete;
+ /** Default move assignment operator */
+ NEWinogradConvolutionLayer &operator=(NEWinogradConvolutionLayer &&) = default;
+ /** Destructor */
+ ~NEWinogradConvolutionLayer();
/** Set the input and output tensors.
*
@@ -105,36 +103,9 @@ public:
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradConvolutionLayer(const NEWinogradConvolutionLayer &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradConvolutionLayer &operator=(const NEWinogradConvolutionLayer &) = delete;
-
private:
- MemoryGroup _memory_group;
- NEGEMM _gemm_function;
- std::unique_ptr<ICPPKernel> _transform_input_kernel;
- std::unique_ptr<ICPPKernel> _transform_output_kernel;
- std::unique_ptr<ICPPKernel> _transform_weights_kernel;
- NEActivationLayer _activationlayer_function;
-
- CPPPermute _permute_input;
- CPPPermute _permute_weights;
- CPPPermute _permute_output;
- Tensor _input_transformed;
- Tensor _output_transformed;
- Tensor _input_workspace;
- Tensor _output_workspace;
- Tensor _kernel_storage;
- Tensor _input_nhwc;
- Tensor _output_nhwc;
- Tensor _weights_hwio;
- const ITensor *_input;
- const ITensor *_weights;
- ITensor *_output;
- bool _is_prepared;
- bool _is_activationlayer_enabled;
- DataLayout _data_layout;
+ struct Impl;
+ std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H */
diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox
index 78b60976d7..93fbfb01af 100644
--- a/docs/user_guide/release_version_and_change_log.dox
+++ b/docs/user_guide/release_version_and_change_log.dox
@@ -639,9 +639,9 @@ v20.05 Public major release
- Removed NEDepthwiseConvolutionLayerOptimized
- Added support for Winograd 3x3,4x4 on Arm® Neon™ FP16:
- @ref NEWinogradConvolutionLayer
- - @ref NEWinogradLayerTransformInputKernel
- - @ref NEWinogradLayerTransformOutputKernel
- - @ref NEWinogradLayerTransformWeightsKernel
+ - CpuWinogradConv2dTransformInputKernel
+ - CpuWinogradConv2dTransformOutputKernel
+ - CpuWinogradConv2dTransformWeightsKernel
- Added CLCompileContext
- Added Arm® Neon™ GEMM kernel with 2D window support
@@ -1171,9 +1171,9 @@ v18.02 Public major release
- Added name() method to all kernels.
- Added support for Winograd 5x5.
- NEPermuteKernel / @ref NEPermute
- - @ref NEWinogradLayerTransformInputKernel / NEWinogradLayer
- - @ref NEWinogradLayerTransformOutputKernel / NEWinogradLayer
- - @ref NEWinogradLayerTransformWeightsKernel / NEWinogradLayer
+ - CpuWinogradConv2dTransformInputKernel / NEWinogradLayer
+ - CpuWinogradConv2dTransformOutputKernel / NEWinogradLayer
+ - CpuWinogradConv2dTransformWeightsKernel / NEWinogradLayer
- Renamed NEWinogradLayerKernel into NEWinogradLayerBatchedGEMMKernel
- New GLES kernels / functions:
- GCTensorShiftKernel / GCTensorShift
diff --git a/filelist.json b/filelist.json
index b8c1203374..3b549b2483 100644
--- a/filelist.json
+++ b/filelist.json
@@ -1682,8 +1682,11 @@
"Permute"
],
"files": {
+ "operator": [
+ "src/runtime/cpu/operators/CpuWinogradConv2d.cpp"
+ ],
"kernel": [
- "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp",
+ "src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp",
"src/core/NEON/kernels/convolution/winograd/padding.cpp",
"src/core/NEON/kernels/convolution/winograd/winograd.cpp",
"src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_1x8_fp32_fp32_integers.cpp",
diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h
index cd09544d31..6c6c51dd87 100644
--- a/src/core/NEON/NEKernels.h
+++ b/src/core/NEON/NEKernels.h
@@ -66,6 +66,5 @@
#include "src/core/NEON/kernels/NEStridedSliceKernel.h"
#include "src/core/NEON/kernels/NETileKernel.h"
#include "src/core/NEON/kernels/NEWeightsReshapeKernel.h"
-#include "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
#endif /* ARM_COMPUTE_NEKERNELS_H */
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp
index be34980663..74b031b226 100644
--- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
+++ b/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp
@@ -21,7 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
+#include "src/core/cpu/kernels/CpuWinogradConv2dKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
@@ -39,6 +39,8 @@
namespace arm_compute
{
+namespace cpu
+{
//Batched Gemms
namespace
@@ -175,7 +177,7 @@ std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(IT
}
} // namespace
-Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights)
+Status ICpuWinogradConv2dTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
@@ -189,7 +191,7 @@ Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const
+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>(
@@ -198,89 +200,94 @@ unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTile
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel()
- : _transform(nullptr), _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0)
+CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformWeightsKernel()
+ : _transform(nullptr), _num_output_channels(0), _matrix_stride(0)
{
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const
+int CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const
{
return WinogradConv::get_kernel_matrix_stride(num_input_channels, num_output_channels);
}
#ifndef DOXYGEN_SKIP_THIS
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const ITensor *weights_hwio,
- ITensor *output,
- const int matrix_stride, /** Stride across matrices in the output. */
- const int num_output_channels, /** Number of filters. */
- const int num_input_channels) /** Number of channels in each filter. */
-{
- _weights_hwio = weights_hwio;
- _output = output;
- _matrix_stride = matrix_stride;
- _num_output_channels = num_output_channels;
- _num_input_channels = num_input_channels;
+void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
+ const ITensorInfo *weights_hwio,
+ ITensorInfo *output,
+ const int matrix_stride, /** Stride across matrices in the output. */
+ const int num_output_channels, /** Number of filters. */
+ const int num_input_channels) /** Number of channels in each filter. */
+{
+ ARM_COMPUTE_UNUSED(weights_hwio, output);
+
_transform = std::make_unique<WeightsTransform>(num_output_channels, num_input_channels);
+ _num_output_channels = num_output_channels;
+ _matrix_stride = matrix_stride;
Window win;
auto win_last = _transform->get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
- INEKernel::configure(win);
+ ICpuKernel::configure(win);
}
#endif /* DOXYGEN_SKIP_THIS */
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
+void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON(tensors.empty());
+
const size_t fst = window.x().start();
const size_t lst = window.x().end();
- _transform->set_weight_tensor(_weights_hwio->buffer());
+
+ const ITensor *weights_hwio = tensors.get_const_tensor(TensorType::ACL_SRC);
+ ITensor *output = tensors.get_tensor(TensorType::ACL_DST);
+
+ _transform->set_weight_tensor(weights_hwio->buffer());
const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK);
- _transform->set_output_matrices(_output->buffer(), _matrix_stride, matrix_row_stride);
- _transform->set_working_space(_output->buffer());
+ _transform->set_output_matrices(output->buffer(), _matrix_stride, matrix_row_stride);
+ _transform->set_working_space(output->buffer());
_transform->run(fst, lst);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-bool NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
+bool CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
{
return false;
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
- const WinogradInfo &winograd_info)
+Status CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
+ const WinogradInfo &winograd_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first);
return Status{};
}
-template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>;
-template class NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>;
-template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>;
-template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>;
-template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>;
-template class NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>;
-template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>;
-template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>;
-template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>;
+template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>;
+template class CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
// Input transform
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
+unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
int num_batches, /* Number of batches in the input tensor. */
int num_channels, /* Number of feature maps in the input tensor. */
int num_rows, /* Number of rows in each feature map. */
@@ -296,13 +303,13 @@ unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCo
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
+unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
{
return _transform->get_working_space_size(num_threads) / sizeof(T);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
+int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
int num_batches, /* Number of batches in the input tensor. */
int num_channels, /* Number of feature maps in the input tensor. */
int num_rows, /* Number of rows in each feature map. */
@@ -313,38 +320,32 @@ int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kerne
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel()
- : _transform(nullptr), _input_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0), _padding_top(), _padding_left(),
- _padding_right(), _padding_bottom(), _workspace(nullptr)
+CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformInputKernel()
+ : _transform(nullptr), _num_channels(0), _matrix_stride(0)
{
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const ITensor *input_nhwc,
- const int num_batches, /* Number of batches in input tensor. */
- const int num_rows, /* Number of rows in input tensor. */
- const int num_cols, /* Number of columns in input tensor. */
- const int num_channels, /* Number of channels in input tensor. */
- const PaddingType padding, /* Padding type. */
- ITensor *output, /* Base of output matrices. */
- const int matrix_stride, /* Stride between output matrices. */
- ITensor *workspace)
-{
- _input_nhwc = input_nhwc;
- _num_batches = num_batches;
- _num_rows = num_rows;
- _num_cols = num_cols;
+void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
+ const ITensorInfo *input_nhwc,
+ const int num_batches, /* Number of batches in input tensor. */
+ const int num_rows, /* Number of rows in input tensor. */
+ const int num_cols, /* Number of columns in input tensor. */
+ const int num_channels, /* Number of channels in input tensor. */
+ const PaddingType padding, /* Padding type. */
+ ITensorInfo *output, /* Base of output matrices. */
+ const int matrix_stride, /* Stride between output matrices. */
+ ITensorInfo *workspace)
+{
+ ARM_COMPUTE_UNUSED(input_nhwc, output, matrix_stride, workspace);
+
_num_channels = num_channels;
- _padding = padding;
- _output = output;
_matrix_stride = matrix_stride;
- _workspace = workspace;
- _padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0;
- _padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0;
- _padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0;
- _padding_right = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0;
+ const int padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0;
+ const int padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0;
+ const int padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0;
+ const int padding_right = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0;
_transform = std::make_unique<InputTransform>(
KernelRows,
@@ -353,37 +354,41 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern
num_rows,
num_cols,
num_channels,
- _padding_top, /**< Padding to apply to the top of the image. */
- _padding_left, /**< Padding to apply to the left of the image. */
- _padding_bottom, /**< Padding to apply to the bottom of the image. */
- _padding_right /**< Padding to apply to the right of the image. */
+ padding_top, /**< Padding to apply to the top of the image. */
+ padding_left, /**< Padding to apply to the left of the image. */
+ padding_bottom, /**< Padding to apply to the bottom of the image. */
+ padding_right /**< Padding to apply to the right of the image. */
);
Window win;
auto win_last = _transform->get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
- INEKernel::configure(win);
+ ICpuKernel::configure(win);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
+void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_NULLPTR(_workspace);
-
- const int element_size_in_bytes = _input_nhwc->info()->element_size();
- const int input_col_stride = _input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
- const int input_row_stride = _input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
- const int input_batch_stride = _input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
- const auto input_nhwc_ptr = reinterpret_cast<const T *>(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes());
- auto output_ptr = reinterpret_cast<T *>(_output->buffer() + _output->info()->offset_first_element_in_bytes());
+ ARM_COMPUTE_ERROR_ON(tensors.empty());
+
+ const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC);
+ const ITensor *workspace = tensors.get_const_tensor(TensorType::ACL_INT);
+ ITensor *output = tensors.get_tensor(TensorType::ACL_DST);
+
+ const int element_size_in_bytes = input_nhwc->info()->element_size();
+ const int input_col_stride = input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
+ const int input_row_stride = input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
+ const int input_batch_stride = input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
+ const auto input_nhwc_ptr = reinterpret_cast<const T *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes());
+ auto output_ptr = reinterpret_cast<T *>(output->buffer() + output->info()->offset_first_element_in_bytes());
ARM_COMPUTE_ERROR_ON_NULLPTR(output_ptr);
_transform->set_input_tensor(input_nhwc_ptr, input_batch_stride, input_row_stride, input_col_stride);
_transform->set_output_matrices(output_ptr, _matrix_stride, _num_channels);
- _transform->set_working_space(_workspace->buffer());
+ _transform->set_working_space(workspace->buffer());
// The code below cannot be moved to configure because biases hasn't been allocated at that point
const size_t fst = window.x().start();
@@ -392,7 +397,8 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
+Status CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
+ const WinogradInfo &winograd_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first);
@@ -400,25 +406,25 @@ Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Ke
return Status{};
}
-template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>;
-template class NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>;
-template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>;
-template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>;
-template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>;
+template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>;
+template class CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>;
+template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>;
+template class CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>;
+template class CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>;
-template class NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>;
-template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>;
-template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>;
-template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>;
+template class CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>;
+template class CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>;
+template class CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>;
+template class CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>;
+template class CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
// Output transform
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
+unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
int num_batches, /* Number of batches in the output tensor. */
int num_rows, /* Number of rows in each feature map of the input tensor. */
int num_cols, /* Number of columns in each feature map of the input tensor. */
@@ -434,20 +440,19 @@ unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileC
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel()
- : _transform(nullptr), _biases(nullptr), _transformed_output(nullptr), _workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0),
- _num_cols(0), _num_channels(0)
+CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformOutputKernel()
+ : _transform(nullptr), _matrix_stride(0), _matrix_row_stride(0)
{
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
+unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
{
return _transform->get_working_space_size(num_threads) / sizeof(T);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
+int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
int num_batches, /* Number of batches in the output tensor. */
int num_rows, /* Number of rows in each feature map of the input tensor. */
int num_cols, /* Number of columns in each feature map of the input tensor. */
@@ -458,7 +463,7 @@ int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Kern
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape(
+std::pair<unsigned int, unsigned int> CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape(
int num_rows, /* Number of rows in each feature map of the input tensor. */
int num_cols, /* Number of columns in each feature map of the input tensor. */
bool padding_same) const
@@ -467,54 +472,52 @@ std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, Ou
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const ITensor *biases,
- const ITensor *transformed_output,
+void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
+ const ITensorInfo *biases,
+ const ITensorInfo *transformed_output,
const int matrix_stride,
- ITensor *output_nhwc,
+ ITensorInfo *output_nhwc,
const int num_batches,
const int num_rows,
const int num_cols,
const int num_channels,
- ITensor *workspace,
+ ITensorInfo *workspace,
const arm_gemm::Activation &activation)
{
- _biases = biases;
- _workspace = workspace;
- _transformed_output = transformed_output;
- _matrix_stride = matrix_stride;
- _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK);
- _output_nhwc = output_nhwc;
- _num_batches = num_batches;
- _num_rows = num_rows;
- _num_cols = num_cols;
- _num_channels = num_channels;
+ ARM_COMPUTE_UNUSED(biases, transformed_output, output_nhwc, num_batches, num_rows, num_cols, workspace, activation);
+
+ _matrix_stride = matrix_stride;
+ _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK);
+
// We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
_transform = std::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels, activation);
Window win;
auto win_last = _transform->get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
- INEKernel::configure(win);
+ ICpuKernel::configure(win);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
+void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
- ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_NULLPTR(_workspace);
- ARM_COMPUTE_ERROR_ON_NULLPTR(_transformed_output);
- ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc);
-
- const int out_batch_stride = _output_nhwc->info()->strides_in_bytes()[3] / sizeof(T);
- const int out_row_stride = _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T);
- const int out_col_stride = _output_nhwc->info()->strides_in_bytes()[1] / sizeof(T);
-
- _transform->set_input_matrices(_transformed_output->buffer(), _matrix_stride, _matrix_row_stride);
- _transform->set_bias((_biases ? reinterpret_cast<T *>(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr));
- _transform->set_output_tensor(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride);
- _transform->set_working_space(_workspace->buffer());
+ ARM_COMPUTE_ERROR_ON(tensors.empty());
+
+ const ITensor *biases = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ const ITensor *transformed_output = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+ ITensor *workspace = tensors.get_tensor(TensorType::ACL_INT);
+ ITensor *dst_nhwc = tensors.get_tensor(TensorType::ACL_DST);
+
+ const int out_batch_stride = dst_nhwc->info()->strides_in_bytes()[3] / sizeof(T);
+ const int out_row_stride = dst_nhwc->info()->strides_in_bytes()[2] / sizeof(T);
+ const int out_col_stride = dst_nhwc->info()->strides_in_bytes()[1] / sizeof(T);
+
+ _transform->set_input_matrices(transformed_output->buffer(), _matrix_stride, _matrix_row_stride);
+ _transform->set_bias((biases ? reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()) : nullptr));
+ _transform->set_output_tensor(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride);
+ _transform->set_working_space(workspace->buffer());
+
// The code below cannot be moved to configure because biases hasn't been allocated at that point
const size_t fst = window.x().start();
const size_t lst = window.x().end();
@@ -522,8 +525,8 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
- const WinogradInfo &winograd_info)
+Status CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+ const WinogradInfo &winograd_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), output->clone().get(), winograd_info).first);
@@ -531,18 +534,19 @@ Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, K
return Status{};
}
-template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>;
-template class NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>;
-template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>;
-template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>;
-template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>;
-template class NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>;
-template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>;
-template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>;
-template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>;
+template class CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>;
+template class CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+} // namespace cpu
} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/src/core/cpu/kernels/CpuWinogradConv2dKernel.h
index 75d257de4b..b5a29ffd02 100644
--- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
+++ b/src/core/cpu/kernels/CpuWinogradConv2dKernel.h
@@ -21,22 +21,21 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H
-#define ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H
+#ifndef ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
+#define ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
-#include "src/core/NEON/INEKernel.h"
#include "src/core/NEON/kernels/convolution/common/convolution.hpp"
#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
+#include "src/core/cpu/ICpuKernel.h"
#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
namespace arm_compute
{
-// Forward declarations
-class ITensor;
-
+namespace cpu
+{
/** Interface for the kernel to perform Winograd input transform. */
-class INEWinogradLayerTransformInputKernel : public INEKernel
+class ICpuWinogradConv2dTransformInputKernel : public ICpuKernel
{
public:
/** Get the working space required to perform the transformation.
@@ -87,30 +86,30 @@ public:
* @param[in] matrix_stride Stride between output matrices.
* @param[in] workspace Tensor to be used as the working space during the computation.
*/
- virtual void configure(const ITensor *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
- const PaddingType padding, ITensor *output, const int matrix_stride, ITensor *workspace) = 0;
+ virtual void configure(const ITensorInfo *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
+ const PaddingType padding, ITensorInfo *output, const int matrix_stride, ITensorInfo *workspace) = 0;
/** Destructor */
- virtual ~INEWinogradLayerTransformInputKernel()
+ virtual ~ICpuWinogradConv2dTransformInputKernel()
{
}
};
/** Kernel to perform Winograd input transform. */
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel
+class CpuWinogradConv2dTransformInputKernel : public ICpuWinogradConv2dTransformInputKernel
{
public:
/** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformInputKernel(const NEWinogradLayerTransformInputKernel &) = delete;
+ CpuWinogradConv2dTransformInputKernel(const CpuWinogradConv2dTransformInputKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformInputKernel &operator=(const NEWinogradLayerTransformInputKernel &) = delete;
+ CpuWinogradConv2dTransformInputKernel &operator=(const CpuWinogradConv2dTransformInputKernel &) = delete;
/** Allow instances of this class to be moved */
- NEWinogradLayerTransformInputKernel(NEWinogradLayerTransformInputKernel &&) = default;
+ CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = default;
/** Allow instances of this class to be moved */
- NEWinogradLayerTransformInputKernel &operator=(NEWinogradLayerTransformInputKernel &&) = default;
+ CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = default;
/** Default destructor */
- ~NEWinogradLayerTransformInputKernel() = default;
+ ~CpuWinogradConv2dTransformInputKernel() = default;
/** Determine how much memory (in units of TIn) to allocate for the
* transformed input.
@@ -160,11 +159,11 @@ public:
bool same_padding) const override;
/** Default constructor */
- NEWinogradLayerTransformInputKernel();
+ CpuWinogradConv2dTransformInputKernel();
const char *name() const override
{
- return "NEWinogradLayerTransformInputKernel";
+ return "CpuWinogradConv2dTransformInputKernel";
}
/** Configure the output transform kernel.
@@ -180,25 +179,25 @@ public:
* @param[in] workspace Tensor to be used as the working space during the computation.
*/
void configure(
- const ITensor *input_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- const PaddingType padding,
- ITensor *output,
- const int matrix_stride,
- ITensor *workspace) override;
+ const ITensorInfo *input_nhwc,
+ const int num_batches,
+ const int num_rows,
+ const int num_cols,
+ const int num_channels,
+ const PaddingType padding,
+ ITensorInfo *output,
+ const int matrix_stride,
+ ITensorInfo *workspace) override;
// Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
/** Winograd base kernel */
using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
/** Winograd convolution kernel */
using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformInputKernel
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformInputKernel
*
* @param[in] input First tensor input info. Data types supported: F16/F32.
* @param[in] output Output tensor info. Data types supported: same as @p input.
@@ -212,23 +211,12 @@ private:
using InputTransform = typename WinogradBase::template InputTransform<T, T>;
std::unique_ptr<InputTransform> _transform{ nullptr };
- const ITensor *_input_nhwc;
- int _num_batches; /**< Number of batches in input tensor. */
- int _num_rows; /**< Number of rows in input tensor. */
- int _num_cols; /**< Number of columns in input tensor. */
- int _num_channels; /**< Number of channels in input tensor. */
- PaddingType _padding; /**< Padding type. */
- ITensor *_output; /**< Base of output matrices. */
- int _matrix_stride; /**< Stride between output matrices. */
- int _padding_top; /**< Padding to apply to the top of the image. */
- int _padding_left; /**< Padding to apply to the left of the image. */
- int _padding_right; /**< Padding to apply to the right of the image. */
- int _padding_bottom; /**< Padding to apply to the bottom of the image. */
- ITensor *_workspace;
+ int _num_channels; /**< Number of channels in input tensor. */
+ int _matrix_stride; /**< Stride between output matrices. */
};
/** Interface for the kernel to perform Winograd output transform. */
-class INEWinogradLayerTransformOutputKernel : public INEKernel
+class ICpuWinogradConv2dTransformOutputKernel : public ICpuKernel
{
public:
/** Get the working space required to perform the transformation.
@@ -294,44 +282,44 @@ public:
* @param[in] activation Activation to be used
*/
virtual void configure(
- const ITensor *biases,
- const ITensor *transformed_output,
+ const ITensorInfo *biases,
+ const ITensorInfo *transformed_output,
const int matrix_stride,
- ITensor *output_nhwc,
+ ITensorInfo *output_nhwc,
const int num_batches,
const int num_rows,
const int num_cols,
const int num_channels,
- ITensor *workspace,
+ ITensorInfo *workspace,
const arm_gemm::Activation &activation) = 0;
- virtual ~INEWinogradLayerTransformOutputKernel()
+ virtual ~ICpuWinogradConv2dTransformOutputKernel()
{
}
};
/** Kernel to perform Winograd output transform. */
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerTransformOutputKernel : public INEWinogradLayerTransformOutputKernel
+class CpuWinogradConv2dTransformOutputKernel : public ICpuWinogradConv2dTransformOutputKernel
{
public:
const char *name() const override
{
- return "NEWinogradLayerTransformOutputKernel";
+ return "CpuWinogradConv2dTransformOutputKernel";
}
/** Constructor */
- NEWinogradLayerTransformOutputKernel();
+ CpuWinogradConv2dTransformOutputKernel();
/** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformOutputKernel(const NEWinogradLayerTransformOutputKernel &) = delete;
+ CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformOutputKernel &operator=(const NEWinogradLayerTransformOutputKernel &) = delete;
+ CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
/** Allow instances of this class to be moved */
- NEWinogradLayerTransformOutputKernel(NEWinogradLayerTransformOutputKernel &&) = default;
+ CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = default;
/** Allow instances of this class to be moved */
- NEWinogradLayerTransformOutputKernel &operator=(NEWinogradLayerTransformOutputKernel &&) = default;
+ CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = default;
/** Default destructor */
- ~NEWinogradLayerTransformOutputKernel() = default;
+ ~CpuWinogradConv2dTransformOutputKernel() = default;
// Inherited methods overridden:
/** Determine how much memory (in units of TOut) to allocate for the
@@ -395,20 +383,20 @@ public:
* @param[in] activation Activation to be used
*/
void configure(
- const ITensor *biases,
- const ITensor *transformed_output,
+ const ITensorInfo *biases,
+ const ITensorInfo *transformed_output,
const int matrix_stride,
- ITensor *output_nhwc,
+ ITensorInfo *output_nhwc,
const int num_batches,
const int num_rows,
const int num_cols,
const int num_channels,
- ITensor *workspace,
+ ITensorInfo *workspace,
const arm_gemm::Activation &activation) override;
- void run(const Window &window, const ThreadInfo &info) override;
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformOutputKernel
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformOutputKernel
*
* @param[in] input Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
* @param[in] bias Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
@@ -425,35 +413,27 @@ private:
using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
std::unique_ptr<OutputTransform> _transform{ nullptr };
- const ITensor *_biases;
- const ITensor *_transformed_output;
- ITensor *_workspace;
int _matrix_stride;
int _matrix_row_stride;
- ITensor *_output_nhwc;
- int _num_batches;
- int _num_rows;
- int _num_cols;
- int _num_channels;
};
/** Interface for the kernel to perform Winograd weights transform. */
-class INEWinogradLayerTransformWeightsKernel : public INEKernel
+class ICpuWinogradConv2dTransformWeightsKernel : public ICpuKernel
{
public:
/** Prevent instances of this class from being copied (As this class contains pointers) */
- INEWinogradLayerTransformWeightsKernel(const INEWinogradLayerTransformWeightsKernel &) = default;
+ ICpuWinogradConv2dTransformWeightsKernel(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
- INEWinogradLayerTransformWeightsKernel &operator=(const INEWinogradLayerTransformWeightsKernel &) = default;
+ ICpuWinogradConv2dTransformWeightsKernel &operator=(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
/** Allow instances of this class to be moved */
- INEWinogradLayerTransformWeightsKernel(INEWinogradLayerTransformWeightsKernel &&) = default;
+ ICpuWinogradConv2dTransformWeightsKernel(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
/** Allow instances of this class to be moved */
- INEWinogradLayerTransformWeightsKernel &operator=(INEWinogradLayerTransformWeightsKernel &&) = default;
+ ICpuWinogradConv2dTransformWeightsKernel &operator=(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
- INEWinogradLayerTransformWeightsKernel()
+ ICpuWinogradConv2dTransformWeightsKernel()
{
}
- virtual ~INEWinogradLayerTransformWeightsKernel()
+ virtual ~ICpuWinogradConv2dTransformWeightsKernel()
{
}
/** Determine how much memory (in units of T) to allocate for the
@@ -476,16 +456,16 @@ public:
/** Configure the weights transform kernel.
*
- * @param[in] weights_hwio Pointer to the weights tensor
+ * @param[in] weights_hwio Pointer to the weights tensor info
* @param[out] output Pointer to working space for the output tensor in the Winograd domain.
* @param[in] matrix_stride Stride across matrices in the output workspace.
* @param[in] num_output_channels Number of filters.
* @param[in] num_input_channels Number of channels in each filter.
*/
- virtual void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
+ virtual void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
- /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
*
* @param[in] input First tensor input info. Data types supported: F16/F32.
* @param[in] weights Weights tensor info. Data types supported: same as @p input.
@@ -497,28 +477,28 @@ public:
/** Kernel to perform Winograd weights transform. */
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel
+class CpuWinogradConv2dTransformWeightsKernel final : public ICpuWinogradConv2dTransformWeightsKernel
{
public:
/** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformWeightsKernel(const NEWinogradLayerTransformWeightsKernel &) = delete;
+ CpuWinogradConv2dTransformWeightsKernel(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformWeightsKernel &operator=(const NEWinogradLayerTransformWeightsKernel &) = delete;
+ CpuWinogradConv2dTransformWeightsKernel &operator=(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
/** Allow instances of this class to be moved */
- NEWinogradLayerTransformWeightsKernel(NEWinogradLayerTransformWeightsKernel &&) = default;
+ CpuWinogradConv2dTransformWeightsKernel(CpuWinogradConv2dTransformWeightsKernel &&) = default;
/** Allow instances of this class to be moved */
- NEWinogradLayerTransformWeightsKernel &operator=(NEWinogradLayerTransformWeightsKernel &&) = default;
+ CpuWinogradConv2dTransformWeightsKernel &operator=(CpuWinogradConv2dTransformWeightsKernel &&) = default;
/** Default destructor */
- ~NEWinogradLayerTransformWeightsKernel() = default;
+ ~CpuWinogradConv2dTransformWeightsKernel() = default;
/** Default constructor. */
- NEWinogradLayerTransformWeightsKernel();
+ CpuWinogradConv2dTransformWeightsKernel();
const char *name() const override
{
- return "NEWinogradLayerTransformWeightsKernel";
+ return "CpuWinogradConv2dTransformWeightsKernel";
}
- /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
*
* @param[in] input Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
* kernel_x must be 3 and equal to kernel_y. Data types supported: F16/F32.
@@ -534,13 +514,13 @@ public:
#ifndef DOXYGEN_SKIP_THIS
/** Configure the weights transform kernel.
*
- * @param[in] weights_hwio Pointer to the weights tensor
+ * @param[in] weights_hwio Pointer to the weights tensor info
* @param[out] output Pointer to working space for the output tensor in the Winograd domain.
* @param[in] matrix_stride Stride across matrices in the output workspace.
* @param[in] num_output_channels Number of filters.
* @param[in] num_input_channels Number of channels in each filter.
*/
- void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
+ void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
#endif /* DOXYGEN_SKIP_THIS */
/** Determine how much memory (in units of T) to allocate for the
@@ -561,7 +541,7 @@ public:
* @return Stride expressed in bytes.
*/
int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
- void run(const Window &window, const ThreadInfo &info) override;
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
bool is_parallelisable() const override;
private:
@@ -570,16 +550,13 @@ private:
using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
std::unique_ptr<WeightsTransform> _transform{ nullptr };
- const ITensor *_weights_hwio;
- ITensor *_output;
- int _matrix_stride;
int _num_output_channels;
- int _num_input_channels;
+ int _matrix_stride;
};
/** Kernel to perform Winograd. */
template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerConfiguration
+class CpuWinogradConv2dConfiguration
{
public:
/** Winograd base kernel */
@@ -588,10 +565,11 @@ public:
using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
- using TransformInputKernel = NEWinogradLayerTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformWeightsKernel = NEWinogradLayerTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformOutputKernel = NEWinogradLayerTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ using TransformInputKernel = CpuWinogradConv2dTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ using TransformWeightsKernel = CpuWinogradConv2dTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ using TransformOutputKernel = CpuWinogradConv2dTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
};
+} // namespace cpu
} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H*/
+#endif /*ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H*/
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
index 57950d5126..745179c050 100644
--- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -24,754 +24,94 @@
#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h"
#include "arm_compute/core/Error.h"
+#include "arm_compute/core/ITensorPack.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "src/core/CPP/Validate.h"
-#include "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
+#include "src/core/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/runtime/cpu/operators/CpuWinogradConv2d.h"
#include "src/core/NEON/kernels/convolution/common/utils.hpp"
#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp"
namespace arm_compute
{
-namespace
-{
-inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
-
- if(input->data_type() == DataType::F32)
- {
- if(input_dims.width > 4 && input_dims.height > 4)
- {
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
- }
- }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(input->data_type() == DataType::F16)
- {
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
- }
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_5x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, output, winograd_info)));
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, output, winograd_info)));
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, output, winograd_info)));
-
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info)));
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info)));
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info)));
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info)));
-
- if(act_info.enabled())
- {
- NEActivationLayer::validate(output, nullptr, act_info);
- }
- return Status{};
-}
+using namespace arm_compute::experimental;
-inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
+struct NEWinogradConvolutionLayer::Impl
{
- const DataLayout data_layout = input->info()->data_layout();
- const int in_width = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
- const int in_height = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
- const int in_channels = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
- const int in_batches = input->info()->dimension(3);
-
- return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
-}
-
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
-{
- ARM_COMPUTE_UNUSED(output);
- ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
- if(biases != nullptr)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
- }
- return INEWinogradLayerTransformWeightsKernel::validate(input, weights);
-}
-
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
-{
- Size2D output_tile = Size2D{};
- if(kernel_dims == Size2D(3U, 3U))
- {
- output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
- if(data_type == DataType::F16)
- {
- output_tile = Size2D(4U, 4U);
- }
- }
- else if(kernel_dims == Size2D(5U, 5U))
- {
- output_tile = Size2D(2U, 2U);
- }
- else if(kernel_dims == Size2D(1U, 3U))
- {
- output_tile = Size2D(1U, 6U);
- }
- else if(kernel_dims == Size2D(3U, 1U))
- {
- output_tile = Size2D(6U, 1U);
- }
- else if(kernel_dims == Size2D(1U, 5U))
- {
- output_tile = Size2D(1U, 4U);
- }
- else if(kernel_dims == Size2D(5U, 1U))
- {
- output_tile = Size2D(4U, 1U);
- }
- else if(kernel_dims == Size2D(7U, 1U))
- {
- output_tile = Size2D(2U, 1U);
- }
- else if(kernel_dims == Size2D(1U, 7U))
- {
- output_tile = Size2D(1U, 2U);
- }
- return output_tile;
-}
-
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type)
-{
- // Check if we want to configure a Winograd configuration which requires fast math
- using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
- const std::vector<WinogradConfiguration> fast_math_winograd_f16 =
- {
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3))
- };
-
- const std::vector<WinogradConfiguration> fast_math_winograd_f32 =
- {
- WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
- };
-
- auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
- std::pair<int, int>(kernel_size.width, kernel_size.height));
-
- switch(data_type)
- {
- case DataType::F16:
- return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end();
- case DataType::F32:
- return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end();
- default:
- return false;
- }
-}
-
-inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
-{
- return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
-}
-
-arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info)
-{
- switch(act_info.activation())
- {
- case ActivationLayerInfo::ActivationFunction::RELU:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b());
- }
- case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b());
- }
- default:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::None);
- }
- }
-}
-} //namespace
+ MemoryGroup memory_group{};
+ std::unique_ptr<cpu::CpuWinogradConv2d> op{ nullptr };
+ ITensorPack run_pack{};
+ ITensorPack prep_pack{};
+ WorkspaceData<Tensor> workspace{};
+ experimental::MemoryRequirements aux_mem_req{};
+ const ITensor *original_weights{ nullptr };
+ bool is_prepared{ false };
+ bool is_activationlayer_enabled{ false };
+ DataLayout data_layout{};
+};
NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
- : _memory_group(memory_manager), _gemm_function(memory_manager), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _activationlayer_function(),
- _permute_input(), _permute_weights(), _permute_output(), _input_transformed(), _output_transformed(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),
- _weights_hwio(), _input(), _weights(), _output(), _is_prepared(false), _is_activationlayer_enabled(false), _data_layout()
+ : _impl(std::make_unique<Impl>())
{
+ _impl->memory_group = MemoryGroup(std::move(memory_manager));
}
+NEWinogradConvolutionLayer::~NEWinogradConvolutionLayer() = default;
+
void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info,
bool enable_fast_math)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info));
-
- // Get indices for the width and height
- _data_layout = input->info()->data_layout();
- const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
- const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
- const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
-
- const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx));
- const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx));
- const DataType data_type = input->info()->data_type();
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
-
- // Check if the Winograd configuration requires fast math
- if(!enable_fast_math)
- {
- ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
- "This Winograd configuration requires enable_fast_math=true");
- }
+ _impl->original_weights = weights;
+ _impl->op = std::make_unique<cpu::CpuWinogradConv2d>();
+ _impl->op->configure(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info, act_info, enable_fast_math);
- _weights = weights;
- _input = input;
- _output = output;
- _is_prepared = false;
-
- int n_gemms = 1;
- int N_BLOCK = 1; // Size of block used by GEMM.
-
- std::unique_ptr<INEWinogradLayerTransformInputKernel> transform_input_kernel;
- std::unique_ptr<INEWinogradLayerTransformWeightsKernel> transform_weights_kernel;
- std::unique_ptr<INEWinogradLayerTransformOutputKernel> transform_output_kernel;
-
- if(data_type == DataType::F32)
- {
- if(kernel_size == Size2D(3, 3))
- {
- if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
- {
- using config = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- using config = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- }
- else if(kernel_size == Size2D(5, 5))
- {
- using config = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 3))
- {
- using config = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(3, 1))
- {
- using config = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 5))
- {
- using config = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(5, 1))
- {
- using config = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 7))
- {
- using config = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(7, 1))
- {
- using config = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
- }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(data_type == DataType::F16)
- {
- if(kernel_size == Size2D(3, 3))
- {
- using config = NEWinogradLayerConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
- }
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
-
- const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
- const bool use_same_padding = use_padding_type == PADDING_SAME;
-
- // Get convolved dimensions
- const int in_channels = input->info()->dimension(channel_idx);
- const int out_channels = output->info()->dimension(channel_idx);
-
- const Tensor4DShape in_shape(internal_get_input_shape(input));
- const size_t data_type_size = input->info()->element_size();
- // Get the memory required to instantiate a new Winograd operator.
- constexpr size_t storage_alignment = 64;
-
- // Kernel Storage
- const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels,
- in_channels)
- * data_type_size;
-
- // Input storage
- const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols,
- use_same_padding)
- * data_type_size;
-
- // Output storage
- const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;
- const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);
- const int output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);
- const auto output_shape = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
- const int input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-
- // Configure GEMM
- const int tile_rows = iceildiv(output_shape.first, output_tile.height);
- const int tile_cols = iceildiv(output_shape.second, output_tile.width);
- const int m = in_shape.n_batches * tile_rows * tile_cols;
- const int k = in_shape.n_channels;
- const int n = out_channels;
- const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
- const int output_matrix_row_stride = kernel_matrix_row_stride;
-
- TensorShape a_shape(k, m, 1, n_gemms);
- Strides a_strides(data_type_size);
- a_strides.set(1, a_strides[0] * k);
- //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
- a_strides.set(2, 0);
- a_strides.set(3, data_type_size * input_matrix_stride);
-
- TensorShape b_shape(n, k, n_gemms);
- Strides b_strides(data_type_size);
- b_strides.set(1, data_type_size * kernel_matrix_row_stride);
- b_strides.set(2, data_type_size * kernel_matrix_stride);
-
- TensorShape d_shape(n, m, 1, n_gemms);
- Strides d_strides(data_type_size);
- d_strides.set(1, data_type_size * output_matrix_row_stride);
- //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
- d_strides.set(2, 0);
- d_strides.set(3, data_type_size * output_matrix_stride);
-
- TensorInfo a_info{};
- TensorInfo b_info{};
- TensorInfo d_info{};
- a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
- b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
- d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
-
- _input_transformed.allocator()->init(a_info, storage_alignment);
- _kernel_storage.allocator()->init(b_info, storage_alignment);
- _output_transformed.allocator()->init(d_info, storage_alignment);
-
- // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
- TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
- _output->info()->dimension(1), _output->info()->dimension(3)),
- 1, _output->info()->data_type());
- _output_nhwc.allocator()->init(info);
-
- const ITensor *input_to_use = _input;
- ITensor *output_to_use = _output;
- PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
- const unsigned int max_num_threads = NEScheduler::get().num_threads();
-
- // Configure the kernel to transform the input tensor from NCHW -> NHWC
- if(_data_layout == DataLayout::NCHW)
- {
- _memory_group.manage(&_input_nhwc);
- _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
- input_to_use = &_input_nhwc;
- weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
- }
-
- // Configure input transform kernel
- _memory_group.manage(&_input_transformed);
- _memory_group.manage(&_input_workspace);
- transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
- &_input_transformed, input_matrix_stride, &_input_workspace);
- const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
- TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, _input->info()->data_type());
- _input_workspace.allocator()->init(input_workspace_info);
- _input_workspace.allocator()->allocate();
- if(_data_layout == DataLayout::NCHW)
- {
- _input_nhwc.allocator()->allocate();
- }
-
- // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
- _permute_weights.configure(weights, &_weights_hwio, weights_permutation_vector);
- transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
-
- // Configure GEMM function
- _memory_group.manage(&_output_transformed);
- _gemm_function.configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
- _input_transformed.allocator()->allocate();
-
- // Configure output transform function
- // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
- if(_data_layout == DataLayout::NCHW)
- {
- _memory_group.manage(&_output_nhwc);
- output_to_use = &_output_nhwc;
- }
- const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info);
-
- transform_output_kernel->configure(biases,
- &_output_transformed,
- output_matrix_stride,
- output_to_use,
- in_shape.n_batches,
- output_shape.first,
- output_shape.second,
- out_channels,
- &_output_workspace,
- activation);
-
- const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
- TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, _output->info()->data_type());
- _output_workspace.allocator()->init(output_workspace_info);
- _output_workspace.allocator()->allocate();
- _output_transformed.allocator()->allocate();
-
- // Reorder the convoluted output to ACL's ordering NCHW
- if(_data_layout == DataLayout::NCHW)
- {
- _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
- _output_nhwc.allocator()->allocate();
- }
-
- _transform_input_kernel = std::move(transform_input_kernel);
- _transform_weights_kernel = std::move(transform_weights_kernel);
- _transform_output_kernel = std::move(transform_output_kernel);
-
- //Configure Activation Layer
- _is_activationlayer_enabled = act_info.enabled() && !fuse_function_supported(act_info);
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.configure(_output, nullptr, act_info);
- }
+ _impl->aux_mem_req = _impl->op->workspace();
+ _impl->run_pack = { { ACL_SRC_0, input }, { ACL_SRC_1, weights }, { ACL_SRC_2, biases }, { ACL_DST, output } };
+ _impl->prep_pack = { { ACL_SRC_1, weights }, { ACL_SRC_2, biases } };
+ _impl->workspace = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
}
void NEWinogradConvolutionLayer::run()
{
prepare();
- MemoryGroupResourceScope scope_mg(_memory_group);
-
- if(_data_layout == DataLayout::NCHW)
- {
- //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
- _permute_input.run();
- }
-
- // Transform input tensor to the winograd domain
- NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX);
-
- //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
- _gemm_function.run();
-
- // Transform output tensor to the spatial domain
- NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX);
-
- if(_data_layout == DataLayout::NCHW)
- {
- // Reorder the convoluted output to ACL's ordering NCHW
- _permute_output.run();
- }
-
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.run();
- }
+ MemoryGroupResourceScope scope_mg(_impl->memory_group);
+ _impl->op->run(_impl->run_pack);
}
Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info, bool enable_fast_math)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));
-
- // Get indices for the width and height
- const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-
- // Input shape, kernel size and output tile
- const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height));
- const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
- const DataType data_type = input->data_type();
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
-
- // Check if the Winograd configuration requires fast math
- if(!enable_fast_math)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
- "This Winograd configuration requires enable_fast_math=true");
- }
-
- const WinogradInfo winograd_info = WinogradInfo(output_tile,
- kernel_size,
- input_dims,
- conv_info,
- input->data_layout());
-
- // Validate input transform
- const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
- const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape);
- // Validate filter transform
- const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
- const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape);
- // Validate batched matrix multiply
- TensorShape batched_mm_output_shape = input0.tensor_shape();
- batched_mm_output_shape[0] = input1.tensor_shape()[0];
- const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape);
-
- if(kernel_size == Size2D(3, 3))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_3x3(input_dims, input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(5, 5))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_5x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- if(kernel_size == Size2D(3, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_3x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 3))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x3(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(5, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_5x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 5))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(7, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_7x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 7))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x7(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
- }
+ return cpu::CpuWinogradConv2d::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math);
}
void NEWinogradConvolutionLayer::prepare()
{
- if(!_is_prepared)
+ if(!_impl->is_prepared)
{
- // Permute weights
- _weights_hwio.allocator()->allocate();
- _permute_weights.run();
- _weights->mark_as_unused();
+ _impl->op->prepare(_impl->prep_pack);
+ _impl->original_weights->mark_as_unused();
- // Transform weights
- _kernel_storage.allocator()->allocate();
- NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
- _weights_hwio.allocator()->free();
-
- _gemm_function.prepare();
- if(!_kernel_storage.is_used())
+ // Release temporary tensors that are only used in prepare stage
+ for(auto &ws : _impl->workspace)
{
- _kernel_storage.allocator()->free();
+ const int slot = ws.first;
+ for(auto &m : _impl->aux_mem_req)
+ {
+ if(m.slot == slot && m.lifetime == MemoryLifetime::Prepare)
+ {
+ auto tensor = ws.second.get();
+ tensor->allocator()->free();
+ break;
+ }
+ }
}
- _is_prepared = true;
+ _impl->is_prepared = true;
}
}
} // namespace arm_compute
diff --git a/src/runtime/cpu/operators/CpuWinogradConv2d.cpp b/src/runtime/cpu/operators/CpuWinogradConv2d.cpp
new file mode 100644
index 0000000000..bf105d5880
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuWinogradConv2d.cpp
@@ -0,0 +1,848 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/runtime/cpu/operators/CpuWinogradConv2d.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/FunctionDescriptors.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/kernels/convolution/common/utils.hpp"
+#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp"
+#include "src/core/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/runtime/cpu/operators/CpuActivation.h"
+#include "src/runtime/cpu/operators/CpuPermute.h"
+#include "src/runtime/cpu/operators/CpuWinogradConv2d.h"
+#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h"
+
+#include "support/Cast.h"
+
+#include <set>
+
+namespace arm_compute
+{
+namespace cpu
+{
+using namespace arm_compute::experimental;
+using namespace arm_compute::utils::cast;
+
+namespace
+{
+arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info)
+{
+ switch(act_info.activation())
+ {
+ case ActivationLayerInfo::ActivationFunction::RELU:
+ {
+ return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b());
+ }
+ case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
+ {
+ return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b());
+ }
+ default:
+ {
+ return arm_gemm::Activation(arm_gemm::Activation::Type::None);
+ }
+ }
+}
+
+inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+
+ if(input->data_type() == DataType::F32)
+ {
+ if(input_dims.width > 4 && input_dims.height > 4)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ }
+ }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ else if(input->data_type() == DataType::F16)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ }
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Status validate_kernel_5x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, output, winograd_info)));
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info)));
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info)));
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+ const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+
+ if(act_info.enabled())
+ {
+ CpuActivation::validate(output, nullptr, act_info);
+ }
+ return Status{};
+}
+
+inline Tensor4DShape internal_get_input_shape(const ITensorInfo *input)
+{
+ const DataLayout data_layout = input->data_layout();
+ const int in_width = input->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
+ const int in_height = input->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
+ const int in_channels = input->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
+ const int in_batches = input->dimension(3);
+
+ return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
+}
+
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+ ARM_COMPUTE_UNUSED(output);
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
+ if(biases != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ }
+ return ICpuWinogradConv2dTransformWeightsKernel::validate(input, weights);
+}
+Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
+{
+ Size2D output_tile = Size2D{};
+ if(kernel_dims == Size2D(3U, 3U))
+ {
+ output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
+ if(data_type == DataType::F16)
+ {
+ output_tile = Size2D(4U, 4U);
+ }
+ }
+ else if(kernel_dims == Size2D(5U, 5U))
+ {
+ output_tile = Size2D(2U, 2U);
+ }
+ else if(kernel_dims == Size2D(1U, 3U))
+ {
+ output_tile = Size2D(1U, 6U);
+ }
+ else if(kernel_dims == Size2D(3U, 1U))
+ {
+ output_tile = Size2D(6U, 1U);
+ }
+ else if(kernel_dims == Size2D(1U, 5U))
+ {
+ output_tile = Size2D(1U, 4U);
+ }
+ else if(kernel_dims == Size2D(5U, 1U))
+ {
+ output_tile = Size2D(4U, 1U);
+ }
+ else if(kernel_dims == Size2D(7U, 1U))
+ {
+ output_tile = Size2D(2U, 1U);
+ }
+ else if(kernel_dims == Size2D(1U, 7U))
+ {
+ output_tile = Size2D(1U, 2U);
+ }
+ return output_tile;
+}
+
+bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type)
+{
+ // Check if we want to configure a Winograd configuration which requires fast math
+ using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
+
+ const std::vector<WinogradConfiguration> fast_math_winograd_f16 =
+ {
+ WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3))
+ };
+
+ const std::vector<WinogradConfiguration> fast_math_winograd_f32 =
+ {
+ WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
+ WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
+ };
+
+ auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
+ std::pair<int, int>(kernel_size.width, kernel_size.height));
+
+ switch(data_type)
+ {
+ case DataType::F16:
+ return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end();
+ case DataType::F32:
+ return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end();
+ default:
+ return false;
+ }
+}
+
+inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
+{
+ return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
+}
+
+} // namespace
+
+CpuWinogradConv2d::CpuWinogradConv2d()
+ : _gemm_function(std::make_unique<CpuGemm>()),
+ _activation_func(std::make_unique<CpuActivation>()),
+ _permute_input(std::make_unique<CpuPermute>()),
+ _permute_output(std::make_unique<CpuPermute>()),
+ _permute_weights(std::make_unique<CpuPermute>()),
+ _transform_input_kernel(nullptr),
+ _transform_weights_kernel(nullptr),
+ _transform_output_kernel(nullptr),
+ _data_layout(),
+ _aux_mem(AuxTensorIdx::Count),
+ _input_nhwc(),
+ _output_nhwc(),
+ _input_workspace(),
+ _kernel_storage(),
+ _output_workspace(),
+ _input_transformed(),
+ _output_transformed(),
+ _weights_hwio(),
+ _run_activation(false),
+ _is_prepared(false)
+{
+}
+
+CpuWinogradConv2d::~CpuWinogradConv2d() = default;
+
+void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst,
+ const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
+
+ // Get indices for the width and height
+ _data_layout = src->data_layout();
+ const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+
+ const Size2D input_dims = Size2D(src->dimension(width_idx), src->dimension(height_idx));
+ const Size2D kernel_size = Size2D(weights->dimension(width_idx), weights->dimension(height_idx));
+ const DataType data_type = src->data_type();
+ const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+
+ // Check if the Winograd configuration requires fast math
+ if(!enable_fast_math)
+ {
+ ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
+ "This Winograd configuration requires enable_fast_math=true");
+ }
+
+ _is_prepared = false;
+
+ std::unique_ptr<ICpuWinogradConv2dTransformInputKernel> transform_input_kernel;
+ std::unique_ptr<ICpuWinogradConv2dTransformWeightsKernel> transform_weights_kernel;
+ std::unique_ptr<ICpuWinogradConv2dTransformOutputKernel> transform_output_kernel;
+
+ int n_gemms = 1;
+ int N_BLOCK = 1; // Size of block used by GEMM.
+ if(data_type == DataType::F32)
+ {
+ if(kernel_size == Size2D(3, 3))
+ {
+ if(src->dimension(width_idx) > 4 && src->dimension(height_idx) > 4)
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 4, 4, 3, 3>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 3, 3>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ }
+ else if(kernel_size == Size2D(5, 5))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 5, 5>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(1, 3))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 6, 1, 3, 1>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(3, 1))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 1, 6, 1, 3>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(1, 5))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 4, 1, 5, 1>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(5, 1))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 1, 4, 1, 5>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(1, 7))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 2, 1, 7, 1>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(7, 1))
+ {
+ using config = CpuWinogradConv2dConfiguration<float, float, 1, 2, 1, 7>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Not supported.");
+ }
+ }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ else if(data_type == DataType::F16)
+ {
+ if(kernel_size == Size2D(3, 3))
+ {
+ using config = CpuWinogradConv2dConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
+ transform_input_kernel = std::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Not supported.");
+ }
+ }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ else
+ {
+ ARM_COMPUTE_ERROR("Not supported.");
+ }
+
+ const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
+ const bool use_same_padding = use_padding_type == PADDING_SAME;
+
+ // Get convolved dimensions
+ const int in_channels = src->dimension(channel_idx);
+ const int out_channels = dst->dimension(channel_idx);
+
+ const Tensor4DShape in_shape(internal_get_input_shape(src));
+ const size_t data_type_size = src->element_size();
+ // Get the memory required to instantiate a new Winograd operator.
+ constexpr size_t storage_alignment = 64;
+
+ // Kernel Storage
+ const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels,
+ in_channels)
+ * data_type_size;
+
+ // Input storage
+ const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols,
+ use_same_padding)
+ * data_type_size;
+
+ // Output storage
+ const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;
+ const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);
+ const int output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);
+ const auto output_shape = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
+ const int input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
+
+ // Configure GEMM
+ const int tile_rows = iceildiv(output_shape.first, output_tile.height);
+ const int tile_cols = iceildiv(output_shape.second, output_tile.width);
+ const int m = in_shape.n_batches * tile_rows * tile_cols;
+ const int k = in_shape.n_channels;
+ const int n = out_channels;
+ const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
+ const int output_matrix_row_stride = kernel_matrix_row_stride;
+
+ TensorShape a_shape(k, m, 1, n_gemms);
+ Strides a_strides(data_type_size);
+ a_strides.set(1, a_strides[0] * k);
+ //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
+ a_strides.set(2, 0);
+ a_strides.set(3, data_type_size * input_matrix_stride);
+
+ TensorShape b_shape(n, k, n_gemms);
+ Strides b_strides(data_type_size);
+ b_strides.set(1, data_type_size * kernel_matrix_row_stride);
+ b_strides.set(2, data_type_size * kernel_matrix_stride);
+
+ TensorShape d_shape(n, m, 1, n_gemms);
+ Strides d_strides(data_type_size);
+ d_strides.set(1, data_type_size * output_matrix_row_stride);
+ //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
+ d_strides.set(2, 0);
+ d_strides.set(3, data_type_size * output_matrix_stride);
+
+ TensorInfo a_info{};
+ TensorInfo b_info{};
+ TensorInfo d_info{};
+ a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
+ b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
+ d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
+
+ _input_transformed = a_info;
+ _kernel_storage = b_info;
+ _output_transformed = d_info;
+
+ // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+ TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
+ dst->dimension(1), dst->dimension(3)),
+ 1, dst->data_type());
+ _output_nhwc = info;
+
+ const ITensorInfo *input_to_use = src;
+ ITensorInfo *output_to_use = dst;
+ PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
+ const unsigned int max_num_threads = NEScheduler::get().num_threads();
+
+ // Configure the kernel to transform the input tensor from NCHW -> NHWC
+ if(_data_layout == DataLayout::NCHW)
+ {
+ _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
+ _aux_mem[PermutedInput] = MemoryInfo(offset_int_vec(PermutedInput), MemoryLifetime::Temporary, src->total_size());
+ input_to_use = &_input_nhwc;
+ weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
+ }
+
+ // Configure input transform kernel
+ transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
+ &_input_transformed, input_matrix_stride, &_input_workspace);
+ const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
+ TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, src->data_type());
+ _input_workspace = input_workspace_info;
+
+ // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
+ _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
+ transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
+
+ // Configure GEMM function
+ _gemm_function->configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
+
+ // Configure output transform function
+ // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
+ if(_data_layout == DataLayout::NCHW)
+ {
+ output_to_use = &_output_nhwc;
+ }
+ const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info);
+
+ transform_output_kernel->configure(biases,
+ &_output_transformed,
+ output_matrix_stride,
+ output_to_use,
+ in_shape.n_batches,
+ output_shape.first,
+ output_shape.second,
+ out_channels,
+ &_output_workspace,
+ activation);
+
+ const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
+ TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, dst->data_type());
+ _output_workspace = output_workspace_info;
+
+ // Reorder the convoluted output to ACL's ordering NCHW
+ if(_data_layout == DataLayout::NCHW)
+ {
+ _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
+ _aux_mem[PermutedOutput] = MemoryInfo(offset_int_vec(PermutedOutput), MemoryLifetime::Temporary, dst->total_size());
+ }
+
+ _transform_input_kernel = std::move(transform_input_kernel);
+ _transform_weights_kernel = std::move(transform_weights_kernel);
+ _transform_output_kernel = std::move(transform_output_kernel);
+
+ //Configure Activation Layer
+ _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
+ if(_run_activation)
+ {
+ _activation_func->configure(dst, nullptr, act_info);
+ }
+
+ auto asm_mem_req = _gemm_function->workspace();
+ _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
+ _aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
+ _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
+ _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
+ _aux_mem[TempResult] = asm_mem_req[TempResult];
+
+ _aux_mem[InputTransformed] = MemoryInfo(offset_int_vec(InputTransformed), MemoryLifetime::Persistent, input_storage_size, storage_alignment);
+ _aux_mem[InputWorkspace] = MemoryInfo(offset_int_vec(InputWorkspace), MemoryLifetime::Persistent, input_workspace_size);
+ if(_aux_mem[Pretranspose].size > 0)
+ {
+ // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch
+ _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
+ }
+ else
+ {
+ _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Persistent, _weights_hwio.total_size());
+ }
+ _aux_mem[WeightsTransformed] = MemoryInfo(offset_int_vec(WeightsTransformed), MemoryLifetime::Persistent, kernel_storage_size, storage_alignment);
+ _aux_mem[OutputTransformed] = MemoryInfo(offset_int_vec(OutputTransformed), MemoryLifetime::Persistent, output_storage_size, storage_alignment);
+ _aux_mem[OutputWorkspace] = MemoryInfo(offset_int_vec(OutputWorkspace), MemoryLifetime::Persistent, output_workspace_size);
+}
+
+Status CpuWinogradConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+ const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));
+
+ // Get indices for the width and height
+ const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+
+ // Input shape, kernel size and output tile
+ const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height));
+ const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
+ const DataType data_type = input->data_type();
+ const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+
+ // Check if the Winograd configuration requires fast math
+ if(!enable_fast_math)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
+ "This Winograd configuration requires enable_fast_math=true");
+ }
+
+ const WinogradInfo winograd_info = WinogradInfo(output_tile,
+ kernel_size,
+ input_dims,
+ conv_info,
+ input->data_layout());
+
+ // Validate input transform
+ const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
+ const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape);
+ // Validate filter transform
+ const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
+ const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape);
+ // Validate batched matrix multiply
+ TensorShape batched_mm_output_shape = input0.tensor_shape();
+ batched_mm_output_shape[0] = input1.tensor_shape()[0];
+ const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape);
+
+ if(kernel_size == Size2D(3, 3))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
+ return validate_kernel_3x3(input_dims, input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else if(kernel_size == Size2D(5, 5))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
+ return validate_kernel_5x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ if(kernel_size == Size2D(3, 1))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
+ return validate_kernel_3x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else if(kernel_size == Size2D(1, 3))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
+ return validate_kernel_1x3(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else if(kernel_size == Size2D(5, 1))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
+ return validate_kernel_5x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else if(kernel_size == Size2D(1, 5))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
+ return validate_kernel_1x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else if(kernel_size == Size2D(7, 1))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
+ return validate_kernel_7x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else if(kernel_size == Size2D(1, 7))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
+ return validate_kernel_1x7(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
+ }
+}
+
+void CpuWinogradConv2d::run(ITensorPack &tensors)
+{
+ prepare(tensors);
+
+ auto a = tensors.get_const_tensor(ACL_SRC_0);
+ auto c = tensors.get_const_tensor(ACL_SRC_2);
+ auto d = tensors.get_tensor(ACL_DST);
+
+ CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
+ CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
+ CpuAuxTensorHandler input_transformed(offset_int_vec(InputTransformed), _input_transformed, tensors, true);
+ CpuAuxTensorHandler input_workspace(offset_int_vec(InputWorkspace), _input_workspace, tensors, true);
+
+ const bool is_nchw = _data_layout == DataLayout::NCHW;
+ if(is_nchw)
+ {
+ //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
+ ITensorPack pack{ { ACL_SRC, a }, { ACL_DST, input_nhwc.get() } };
+ _permute_input->run(pack);
+ }
+
+ // Transform input tensor to the winograd domain
+ ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : a }, { ACL_DST, input_transformed.get() }, { ACL_INT, input_workspace.get() } };
+ NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, _transform_input_kernel->window(), transform_input_pack);
+
+ CpuAuxTensorHandler output_transformed(offset_int_vec(OutputTransformed), _output_transformed, tensors, true);
+ CpuAuxTensorHandler weights_transformed(offset_int_vec(WeightsTransformed), _kernel_storage, tensors, true);
+
+ // Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
+ ITensorPack gemm_pack{ { ACL_SRC, input_transformed.get() }, { ACL_SRC_1, weights_transformed.get() }, { ACL_DST, output_transformed.get() } };
+ _gemm_function->run(gemm_pack);
+
+ // Transform output tensor to the spatial domain
+ CpuAuxTensorHandler output_workspace(offset_int_vec(OutputWorkspace), _output_workspace, tensors, true);
+ ITensorPack transform_output_pack{ { ACL_SRC_0, c }, { ACL_SRC_1, output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : d }, { ACL_INT, output_workspace.get() } };
+ NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, _transform_output_kernel->window(), transform_output_pack);
+
+ if(is_nchw)
+ {
+ // Reorder the convoluted output to ACL's ordering NCHW
+ ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, d } };
+ _permute_output->run(pack);
+ }
+
+ if(_run_activation)
+ {
+ ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } };
+ _activation_func->run(pack);
+ }
+}
+
+void CpuWinogradConv2d::prepare(ITensorPack &tensors)
+{
+ if(!_is_prepared)
+ {
+ // Permute weights
+ const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1);
+ ITensor *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));
+ ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux);
+
+ CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
+ ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
+ _permute_weights->run(permute_tensors);
+
+ // Transform weights
+ ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(WeightsTransformed)));
+ ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
+
+ CpuAuxTensorHandler transformed_weights(_kernel_storage, *weights_transf);
+ ITensorPack transform_tensors{ { ACL_SRC, permuted_weights.get() }, { ACL_DST, transformed_weights.get() } };
+ NEScheduler::get().schedule_op(_transform_weights_kernel.get(), Window::DimX, _transform_weights_kernel->window(), transform_tensors);
+
+ CpuAuxTensorHandler input_transformed(offset_int_vec(InputTransformed), _input_transformed, tensors, true);
+ CpuAuxTensorHandler output_transformed(offset_int_vec(OutputTransformed), _output_transformed, tensors, true);
+ ITensorPack gemm_pack = tensors;
+ gemm_pack.add_const_tensor(ACL_SRC_0, input_transformed.get());
+ gemm_pack.add_const_tensor(ACL_SRC_1, transformed_weights.get());
+ _gemm_function->prepare(gemm_pack);
+
+ _is_prepared = true;
+ }
+}
+
+experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
+{
+ return _aux_mem;
+}
+} // namespace cpu
+} // namespace arm_compute \ No newline at end of file
diff --git a/src/runtime/cpu/operators/CpuWinogradConv2d.h b/src/runtime/cpu/operators/CpuWinogradConv2d.h
new file mode 100644
index 0000000000..14c61f7355
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuWinogradConv2d.h
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CPU_WINOGRAD_CONV2D_KERNEL_H
+#define ARM_COMPUTE_CPU_WINOGRAD_CONV2D_KERNEL_H
+
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/FunctionDescriptors.h"
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/runtime/cpu/ICpuOperator.h"
+#include "src/runtime/cpu/operators/CpuActivation.h"
+#include "src/runtime/cpu/operators/CpuGemm.h"
+#include "src/runtime/cpu/operators/CpuPermute.h"
+#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+class CpuWinogradConv2d : public ICpuOperator
+{
+public:
+ /** Constructor */
+ CpuWinogradConv2d();
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuWinogradConv2d);
+ /** Destructor */
+ ~CpuWinogradConv2d();
+
+ /** Set the input and output tensors.
+ *
+ * Valid data layouts:
+ * - NHWC
+ * - NCHW
+ *
+ * Valid data type configurations:
+ * |src0 |src1 |src2 |dst |
+ * |:--------------|:--------------|:------|:--------------|
+ * |F16 |F16 |F16 |F16 |
+ * |F32 |F32 |F32 |F32 |
+ *
+ * @param[in] src Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
+ * while every optional dimension from 4 and above represent a batch of inputs.
+ * Data types supported: F16/F32.
+ * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+ * Currently only 3x3 and 5x5 kernels are supported.
+ * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[out] dst Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * Data types supported: Same as @p input.
+ * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
+ * @param[in] act_info (Optional) Activation layer information in case of a fused activation.
+ * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
+ * available which may introduce a drop of accuracy as well. Default is false
+ */
+ void configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info,
+ const ActivationLayerInfo &act_info = ActivationLayerInfo(),
+ bool enable_fast_math = false);
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2d
+ *
+ * Similar to CpuWinogradConv2d::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const ActivationLayerInfo &act_info = ActivationLayerInfo(),
+ bool enable_fast_math = false);
+
+ // Inherited methods overridden:
+ void run(ITensorPack &tensors) override;
+ void prepare(ITensorPack &constants) override;
+ experimental::MemoryRequirements workspace() const override;
+
+private:
+ enum AuxTensorIdx
+ {
+ GemmWorkspace = 0,
+ Pretranspose,
+ InterleavedLHS,
+ TransposedRHS,
+ TempResult,
+ PermutedInput,
+ InputTransformed,
+ InputWorkspace,
+ PermutedOutput,
+ PermutedWeights,
+ WeightsTransformed,
+ OutputTransformed,
+ OutputWorkspace,
+ Count
+ };
+
+ std::unique_ptr<CpuGemm> _gemm_function;
+ std::unique_ptr<CpuActivation> _activation_func;
+ std::unique_ptr<CpuPermute> _permute_input;
+ std::unique_ptr<CpuPermute> _permute_output;
+ std::unique_ptr<CpuPermute> _permute_weights;
+ std::unique_ptr<ICPPKernel> _transform_input_kernel;
+ std::unique_ptr<ICPPKernel> _transform_weights_kernel;
+ std::unique_ptr<ICPPKernel> _transform_output_kernel;
+
+ DataLayout _data_layout;
+ experimental::MemoryRequirements _aux_mem{ Count };
+ TensorInfo _input_nhwc;
+ TensorInfo _output_nhwc;
+ TensorInfo _input_workspace;
+ TensorInfo _kernel_storage;
+ TensorInfo _output_workspace;
+ TensorInfo _input_transformed;
+ TensorInfo _output_transformed;
+ TensorInfo _weights_hwio;
+ bool _run_activation;
+ bool _is_prepared;
+};
+} // namespace cpu
+} // namespace arm_compute
+
+#endif /* ARM_COMPUTE_CPU_WINOGRAD_CONV2D_KERNEL_H */
diff --git a/tests/SConscript b/tests/SConscript
index df7a6c5686..29ed7394d3 100644
--- a/tests/SConscript
+++ b/tests/SConscript
@@ -134,6 +134,7 @@ if env['neon']:
filter_pattern = test_env['test_filter']
files_benchmark += Glob('benchmark/NEON/*/' + filter_pattern)
files_benchmark += Glob('benchmark/NEON/' + filter_pattern)
+ test_env.Append(CPPPATH = ["#/src/core/cpu/kernels/assembly/"])
if env['external_tests_dir']:
files_benchmark += Glob(env['external_tests_dir'] + '/tests/benchmark/NEON/' + filter_pattern)
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index be01655a86..04ecb03077 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -30,6 +30,7 @@
#include "arm_compute/runtime/TensorAllocator.h"
#include "src/core/helpers/MemoryHelpers.h"
#include "src/runtime/cpu/operators/CpuGemmDirectConv2d.h"
+#include "src/runtime/cpu/operators/CpuWinogradConv2d.h"
#include "tests/NEON/Accessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/LargeConvolutionLayerDataset.h"
@@ -158,6 +159,108 @@ using NEWinogradConvolutionLayerMixedDataLayoutFixture = WinogradConvolutionLaye
template <typename T>
using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T, T, false>;
+/** Test case for memory injection in @ref cpu::CpuWinogradConv2d.
+ *
+ * Configure the operator once and inject memory at run-time in multiple executions.
+ *
+ * Checks performed in order:
+ * - Both runs compute the same output
+ */
+TEST_CASE(MemoryInjection, framework::DatasetMode::ALL)
+{
+ auto winograd = std::make_unique<cpu::CpuWinogradConv2d>();
+ const auto src_info = TensorInfo(TensorShape(8U, 8U, 32U), 1, DataType::F32);
+ const auto w_info = TensorInfo(TensorShape(1U), 1, DataType::F32);
+ const auto b_info = TensorInfo(TensorShape(1U, 3U, 32U, 1U), 1, DataType::F32);
+ auto dst_info = TensorInfo(TensorShape(8U, 6U, 1U), 1, DataType::F32);
+ const PadStrideInfo pad_info{};
+
+ winograd->configure(&src_info, &b_info, &w_info, &dst_info, pad_info);
+
+ // telhs are newly created every call of this lambda function
+ auto a = create_tensor<Tensor>(src_info);
+ auto b = create_tensor<Tensor>(b_info);
+ auto c = create_tensor<Tensor>(w_info);
+ a.allocator()->allocate();
+ b.allocator()->allocate();
+ c.allocator()->allocate();
+
+ ITensorPack run_pack{ { TensorType::ACL_SRC_0, &a }, { TensorType::ACL_SRC_1, &b }, { TensorType::ACL_SRC_2, &c } };
+ ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &b }, { TensorType::ACL_SRC_2, &c } };
+
+ auto mg = MemoryGroup{};
+ auto ws = manage_workspace<Tensor>(winograd->workspace(), mg, run_pack, prep_pack);
+ auto run_conv = [&]() -> Tensor
+ {
+ auto dst = create_tensor<Tensor>(dst_info);
+ dst.allocator()->allocate();
+
+ run_pack.add_tensor(TensorType::ACL_DST, &dst);
+ library->fill_tensor_value(Accessor(a), 1.f);
+ library->fill_tensor_value(Accessor(b), 2.f);
+ library->fill_tensor_value(Accessor(c), 3.f);
+
+ // This operator is configured once and captured by this lambda.
+ winograd->prepare(prep_pack);
+ winograd->run(run_pack);
+ return dst;
+ };
+
+ auto result_0 = run_conv();
+ auto result_1 = run_conv();
+
+ for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i)
+ {
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
+ }
+}
+
+/** Test case for memory injection in @ref NEWinogradConvolutionLayer.
+ *
+ * Make sure @ref NEWinogradConvolutionLayer still works through injecting the memory at configure time using the old API.
+ *
+ * Checks performed in order:
+ * - Both runs compute the same output
+ */
+TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL)
+{
+ auto gemm = std::make_unique<NEWinogradConvolutionLayer>();
+ const auto src_info = TensorInfo(TensorShape(8U, 8U, 32U), 1, DataType::F32);
+ const auto w_info = TensorInfo(TensorShape(1U), 1, DataType::F32);
+ const auto b_info = TensorInfo(TensorShape(1U, 3U, 32U, 1U), 1, DataType::F32);
+ auto dst_info = TensorInfo(TensorShape(8U, 6U, 1U), 1, DataType::F32);
+ const PadStrideInfo pad_info{};
+
+ auto run_conv = [&]()
+ {
+ auto src = create_tensor<Tensor>(src_info);
+ auto w = create_tensor<Tensor>(w_info);
+ auto b = create_tensor<Tensor>(b_info);
+ auto dst = create_tensor<Tensor>(dst_info);
+
+ gemm->configure(&src, &b, &w, &dst, pad_info);
+
+ src.allocator()->allocate();
+ b.allocator()->allocate();
+ w.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ library->fill_tensor_value(Accessor(src), 1.f);
+ library->fill_tensor_value(Accessor(b), 2.f);
+ library->fill_tensor_value(Accessor(w), 3.f);
+ gemm->run();
+ return dst;
+ };
+
+ auto result_0 = run_conv();
+ auto result_1 = run_conv();
+
+ for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i)
+ {
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
+ }
+}
+
TEST_SUITE(FP32)
TEST_SUITE(Conv1x3)
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-09 19:26:08 +0000