COMPMID-784: Winograd tramsforms refactoring
1) Removed the example files winograd_layer.hpp/cpp
2) Teplatized winograd transform kernels
Change-Id: I7045fa0b801b9d30a11275914aaa2dafd254aed2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118332
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h
index ea6c8d8..97532f3 100644
--- a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h
@@ -25,104 +25,93 @@
#define __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__
#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/NEON/kernels/winograd/batched_blocked_gemm.hpp"
#include "arm_compute/core/NEON/kernels/winograd/convolution.hpp"
#include "arm_compute/core/NEON/kernels/winograd/tensor.hpp"
+#include "arm_compute/core/NEON/kernels/winograd/winograd_gemm.hpp"
namespace arm_compute
{
class ITensor;
-class NEWinogradLayerKernel;
-class NEWinogradLayerTransformInputKernel;
-class NEWinogradLayerTransformWeightsKernel;
-class Winograd3x3F32 final
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerTransformInputKernel : public INEKernel
{
public:
- /** Create a new Winograd convolution layer.
+ /** Determine how much memory (in units of TIn) to allocate for the
+ * transformed input.
*
- * @param[in] n_batches Number of batches in the input and output tensors.
- * @param[in] n_input_channels Number of feature maps in a batch of the input tensor.
- * @param[in] n_input_rows Number of rows in a feature map of the input tensor.
- * @param[in] n_input_cols Number of columns in a feature map of the input tensor.
- * @param[in] n_output_channels Number of feature maps in the output tensor.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- * @param[in] weights Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps.
- * @param[out] weights_storage Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size
- * @param[in] input Pointer to NHWC ordered input tensor, in the spatial domain.
- * @param[out] winograd_input Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`.
- * @param[in] biases Pointer to the biases vector.
- * @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain.
- * @param[out] winograd_output Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`.
+ * @param[in] n_batches Number of batches in the input tensor.
+ * @param[in] n_channels Number of feature maps in the input tensor.
+ * @param[in] n_rows Number of rows in each feature map.
+ * @param[in] n_cols Number of columns in each feature map.
+ * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
*/
- friend class NEWinogradLayerKernel;
- friend class NEWinogradLayerTransformInputKernel;
- friend class NEWinogradLayerTransformOutputKernel;
- friend class NEWinogradLayerTransformWeightsKernel;
+ static unsigned int get_input_storage_size(
+ int n_batches,
+ int n_channels,
+ int n_rows,
+ int n_cols,
+ bool same_padding);
- Winograd3x3F32(
- const int n_batches,
- const int n_input_channels,
- const int n_input_rows,
- const int n_input_cols,
- const int n_output_channels,
- const bool same_padding,
- const float *const weights,
- float *const weights_storage,
- const float *const input,
- float *const winograd_input,
- float *const output,
- float *const winograd_output);
-
- ~Winograd3x3F32();
-
-private:
- class Private;
- std::unique_ptr<Private> _pimpl;
-};
-
-class INEWinogradLayerTransformKernel : public INEKernel
-{
-public:
- /** Constructor */
- INEWinogradLayerTransformKernel();
-
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- INEWinogradLayerTransformKernel(const INEWinogradLayerTransformKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- INEWinogradLayerTransformKernel &operator=(const INEWinogradLayerTransformKernel &) = delete;
- /** Allow instances of this class to be moved */
- INEWinogradLayerTransformKernel(INEWinogradLayerTransformKernel &&) = default;
- /** Allow instances of this class to be moved */
- INEWinogradLayerTransformKernel &operator=(INEWinogradLayerTransformKernel &&) = default;
-
- virtual ~INEWinogradLayerTransformKernel() = default;
-
- /** Initialise the kernel
- *
- * @param[in] convolver A pointer to the winograd convolver, this object must have been configured and is ready to execute 16 GEMMS .
- */
- virtual void configure(Winograd3x3F32 *convolver);
-
-protected:
- Winograd3x3F32 *_convolver;
-};
-
-class NEWinogradLayerTransformInputKernel final : public INEWinogradLayerTransformKernel
-{
-public:
+ NEWinogradLayerTransformInputKernel();
const char *name() const override
{
return "NEWinogradLayerTransformInputKernel";
}
+
+ /** Configure the output transform kernel.
+ *
+ * @param[in] input Input tensor data
+ * @param[in] n_batches Number of batches in input tensor.
+ * @param[in] n_rows Number of rows in input tensor.
+ * @param[in] n_cols Number of columns in input tensor.
+ * @param[in] n_channels Number of channels in input tensor.
+ * @param[in] padding Padding type.
+ * @param[out] output Base of output matrices.
+ * @param[in] matrix_stride Stride between output matrices.
+ */
+ void configure(
+ const float *const input,
+ const int n_batches,
+ const int n_rows,
+ const int n_cols,
+ const int n_channels,
+ const PaddingType padding,
+ float *const output,
+ const int matrix_stride);
+
// Inherited methods overridden:
- void configure(Winograd3x3F32 *convolver) override;
void run(const Window &window, const ThreadInfo &info) override;
bool is_parallelisable() const override;
+
+private:
+ using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelCols, KernelCols>;
+ using WinogradConv = typename WinogradBase::template Convolution<float, float>;
+ using InputTransform = typename WinogradBase::template InputTransform<float>;
+ std::unique_ptr<InputTransform> _transform;
};
-class NEWinogradLayerTransformOutputKernel final : public INEKernel
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerTransformOutputKernel : public INEKernel
{
public:
+ /** Determine how much memory (in units of TOut) to allocate for the
+ * (Winograd domain) output.
+ *
+ * @param[in] n_batches Number of batches in the output tensor.
+ * @param[in] n_rows Number of rows in each feature map of the input tensor.
+ * @param[in] n_cols Number of columns in each feature map of the input tensor.
+ * @param[in] n_output_channels Number of feature maps in the output tensor.
+ * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
+ */
+ static unsigned int get_output_storage_size(
+ int n_batches,
+ int n_rows,
+ int n_cols,
+ int n_output_channels,
+ bool same_padding);
+
const char *name() const override
{
return "NEWinogradLayerTransformOutputKernel";
@@ -167,6 +156,10 @@
bool is_parallelisable() const override;
private:
+ using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ using WinogradConv = typename WinogradBase::template Convolution<float, float>;
+ using OutputTransform = typename WinogradBase::template OutputTransform<float>;
+
const ITensor *_biases;
const float *_output_workspace;
int _matrix_stride;
@@ -178,22 +171,61 @@
int _n_channels;
};
-class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformKernel
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerTransformWeightsKernel final : public INEKernel
{
public:
+ /** Determine how much memory (in units of TIn) to allocate for the
+ * transformed weights.
+ *
+ * @param[in] n_output_channels Number of output feature maps.
+ * @param[in] n_input_channels Number of input feature maps.
+ */
+ static unsigned int get_weight_storage_size(int n_output_channels, int n_input_channels);
+
+ NEWinogradLayerTransformWeightsKernel();
const char *name() const override
{
return "NEWinogradLayerTransformWeightsKernel";
}
+ /** Configure the output transform kernel.
+ *
+ * @param[in] weights_hwio Pointer to the weights tensor
+ * @param[in] 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] n_output_channels Number of filters.
+ * @param[in] n_input_channels Number of channels in each filter.
+ */
+ void configure(
+ const ITensor *weights_hwio,
+ float *const output,
+ const int matrix_stride,
+ const int n_output_channels,
+ const int n_input_channels);
+
// Inherited methods overridden:
- void configure(Winograd3x3F32 *convolver) override;
+
void run(const Window &window, const ThreadInfo &info) override;
bool is_parallelisable() const override;
+
+private:
+ using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ using WinogradConv = typename WinogradBase::template Convolution<float, float>;
+ using WeightsTransform = typename WinogradBase::template WeightsTransform<float>;
+ std::unique_ptr<WeightsTransform> _transform;
};
-class NEWinogradLayerKernel final : public INEKernel
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerKernel : public INEKernel
{
public:
+ using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ using WinogradConv = typename WinogradBase::template Convolution<float, float>;
+ using MultiGEMM = winograd::BatchedBlockedGemm<WinogradConv::M_BLOCK, WinogradConv::N_BLOCK, float, float>;
+
+ static const int _output_tile_rows = OutputTileRows;
+ static const int _output_tile_cols = OutputTileCols;
+
const char *name() const override
{
return "NEWinogradLayerKernel";
@@ -214,57 +246,38 @@
/** Initialise the kernel
*
- * @param[in] convolver A pointer to the winograd convolver, this object must have been configured and is ready to execute 16 GEMMS .
+ * @param[in] n_gemms Number of GEMMs to compute.
+ * @param[in] M in_shape.n_batches * tile_rows * tile_cols.
+ * @param[in] K Number of channels in the input tensor.
+ * @param[in] N Number of channels in the output tensor.
+ * @param[in] a_matrix_stride Stride between input matrices.
+ * @param[in] a_row_stride Row stride inside input matrix.
+ * @param[in] b_matrix_stride Stride between weights matrices.
+ * @param[in] b_row_stride Row stride inside the weights matrix.
+ * @param[in] c_matrix_stride Stride between output matrices.
+ * @param[in] c_row_stride Row stride inside the output matrix.
+ * @param[out] a_ptr Input workspace.
+ * @param[out] b_ptr Kernel workspace.
+ * @param[out] c_ptr Output workspace.
*/
- void configure(Winograd3x3F32 *convolver);
+ void configure(
+ const unsigned int n_gemms,
+ const int M, const int K, const int N,
+ const int a_matrix_stride,
+ const int a_row_stride,
+ const int b_matrix_stride,
+ const int b_row_stride,
+ const int c_matrix_stride,
+ const int c_row_stride,
+ const float *const a_ptr,
+ const float *const b_ptr,
+ float *const c_ptr);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed weights.
- *
- * @param[in] n_output_channels Number of output feature maps.
- * @param[in] n_input_channels Number of input feature maps.
- */
- static unsigned int get_weight_storage_size(
- const int n_output_channels,
- const int n_input_channels);
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] n_batches Number of batches in the input tensor.
- * @param[in] n_channels Number of feature maps in the input tensor.
- * @param[in] n_rows Number of rows in each feature map.
- * @param[in] n_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- */
- static unsigned int get_input_storage_size(
- const int n_batches,
- const int n_channels,
- const int n_rows,
- const int n_cols,
- const bool same_padding);
-
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- *
- * @param[in] n_batches Number of batches in the output tensor.
- * @param[in] n_rows Number of rows in each feature map of the input tensor.
- * @param[in] n_cols Number of columns in each feature map of the input tensor.
- * @param[in] n_output_channels Number of feature maps in the output tensor.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- */
- static unsigned int get_output_storage_size(
- const int n_batches,
- const int n_rows,
- const int n_cols,
- const int n_output_channels,
- const bool same_padding);
-
-protected:
- Winograd3x3F32 *_convolver;
+private:
+ std::unique_ptr<MultiGEMM> _gemms;
};
} // namespace arm_compute