COMPMID-1623: NEWinograd reduce the number of output tiles.
Change-Id: I4d9240924fe483d2dd127ad6a4ae6f8066f61bd1
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/151893
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Andrew Mundy <andrew.mundy@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp
index cfd2029..ea842a4 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp
@@ -23,38 +23,33 @@
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
-namespace winograd
+namespace
{
-using Transform = WinogradGEMM<1, 2, 1, 7>::OutputTransform<float>;
-using TransformTransposed = WinogradGEMM<2, 1, 7, 1>::OutputTransform<float>;
-
-template <>
-template <>
-int Transform::ops_performed(const Tensor4DShape &shape)
-{
- (void) shape;
- return 0; // TODO
-}
-
-template <>
-template <>
-template <int pad_bottom, int pad_right>
-void Transform::process_tile(
+template <bool Specialized, int PadRight=0>
+void winograd_output_transform_2_7_fp32_process_tile(
const int n_channels,
const float* const matrix_base,
const int matrix_stride,
const float* const biases,
float* const output,
const int output_row_stride,
- const int output_col_stride
+ const int output_col_stride,
+ const int _pad_bottom,
+ const int _pad_right
)
{
(void) output_row_stride;
- constexpr int cells_j = output_tile_cols - pad_right;
+ (void) _pad_bottom;
+ constexpr int output_tile_cols = 2;
+ constexpr int inner_tile_cols = 8;
+
+ const int pad_right = Specialized ? PadRight : _pad_right;
+ const int cells_j = output_tile_cols - pad_right;
+
// Construct a map to the output cells
float *outptrs[cells_j];
@@ -149,22 +144,20 @@
}
}
}
+} // namespace (anonymous)
+
+namespace winograd
+{
+using Tiles = OutputTransformImplTiles<1, 7, 1, 8, float>;
template <>
+const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2_7_fp32_process_tile<true>;
+
template <>
-const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
-{
- {
- Transform::template process_tile<0, 0>,
- Transform::template process_tile<0, 1>,
- },
+const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
+ winograd_output_transform_2_7_fp32_process_tile<true, 1>
};
-
-template <>
-template <>
-const TransformTransposed::TileFn TransformTransposed::tile_fns[max_pad_bottom][max_pad_right] = {};
-
-template struct WinogradGEMM<1, 2, 1, 7>::OutputTransform<float>;
-template struct WinogradGEMM<2, 1, 7, 1>::OutputTransform<float>;
+template class OutputTransform<1, 7, 1, 8, float>;
+template class OutputTransform<7, 1, 8, 1, float>;
} // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp
index 3b3cda0..597b074 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp
@@ -23,59 +23,34 @@
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
-namespace winograd
+namespace
{
-using Transform = WinogradGEMM<2, 2, 3, 3>::OutputTransform<float>;
-
-template <>
-template <>
-int Transform::ops_performed(const Tensor4DShape &shape)
-{
- // NOTE: Cost in FLOPs rather than instructions or uops.
- const int tile_M = iceildiv(shape.n_rows, 2);
- const int tile_N = iceildiv(shape.n_cols, 2);
- return 24 * tile_M * tile_N * shape.n_channels;
-}
-
-/* F(2x2, 3x3) constructs 2x2 output tiles from a 3x3 convolution. Since we use
- * enough tiles to cover the output space each output tile may contain 0 or 1
- * padded values to the right and bottom columns or rows of the tile, e.g.:
- *
- * ___ ___
- * | | | X|
- * |___| |__X|
- *
- * ___ ___
- * | | | X|
- * |X_X| |X_X|
- *
- *
- * We provide a specialised output transform for each of these instances.
- * Consequently we below construct an array of the various padding options, the
- * array contains pointers to the specific implementations.
- */
-template <>
-template <>
-template <int pad_bottom, int pad_right>
-void Transform::process_tile(
+template <bool Specialized, int PadBottom=0, int PadRight=0>
+void winograd_output_transform_2x2_3x3_fp32_process_tile(
const int n_channels,
const float* const matrix_base,
const int matrix_stride,
const float* const biases,
float* const output,
const int output_row_stride,
- const int output_col_stride
+ const int output_col_stride,
+ const int _pad_bottom,
+ const int _pad_right
)
{
- constexpr int cells_i = 2 - pad_bottom;
- constexpr int cells_j = 2 - pad_right;
+ constexpr int OutputTileRows = 2, OutputTileCols = 2;
+ const int pad_bottom = Specialized ? PadBottom : _pad_bottom;
+ const int pad_right = Specialized ? PadRight : _pad_right;
+
+ const int cells_i = OutputTileRows - pad_bottom;
+ const int cells_j = OutputTileCols - pad_right;
// Construct a map to the output cells
- float *outptrs[cells_i][cells_j];
+ float *outptrs[OutputTileRows][OutputTileCols];
for (int i = 0; i < cells_i; i++)
{
for (int j = 0; j < cells_j; j++)
@@ -373,19 +348,28 @@
}
}
-template <>
-template <>
-const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+} // namespace (anonymous)
+
+namespace winograd
{
- {
- Transform::template process_tile<0, 0>, // No padding
- Transform::template process_tile<0, 1>, // Right padding
- },
- {
- Transform::template process_tile<1, 0>, // Bottom padding
- Transform::template process_tile<1, 1>, // Bottom and right padding
- }
+using Tiles = OutputTransformImplTiles<3, 3, 4, 4, float>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_2x2_3x3_fp32_process_tile<false>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2x2_3x3_fp32_process_tile<true>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = {
+ winograd_output_transform_2x2_3x3_fp32_process_tile<true, 1, 0>
};
-template struct WinogradGEMM<2, 2, 3, 3>::OutputTransform<float>;
+template <>
+const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
+ winograd_output_transform_2x2_3x3_fp32_process_tile<true, 0, 1>
+};
+
+template class OutputTransform<3, 3, 4, 4, float>;
} // namespace winograd
+
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp
index cafce95..60d7181 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp
@@ -23,57 +23,34 @@
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
-namespace winograd
+namespace
{
-using Transform = WinogradGEMM<2, 2, 5, 5>::OutputTransform<float>;
-
-template <>
-template <>
-int Transform::ops_performed(const Tensor4DShape &shape)
-{
- (void) shape;
- return 0; // TODO
-}
-
-/* F(2x2, 5x5) constructs 2x2 output tiles from a 5x5 convolution. Since we use
- * enough tiles to cover the output space each output tile may contain 0 or 1
- * padded values to the right and bottom columns or rows of the tile, e.g.:
- *
- * ___ ___
- * | | | X|
- * |___| |__X|
- *
- * ___ ___
- * | | | X|
- * |X_X| |X_X|
- *
- *
- * We provide a specialised output transform for each of these instances.
- * Consequently we below construct an array of the various padding options, the
- * array contains pointers to the specific implementations.
- */
-template <>
-template <>
-template <int pad_bottom, int pad_right>
-void Transform::process_tile(
+template <bool Specialized, int PadBottom=0, int PadRight=0>
+void winograd_output_transform_2x2_5x5_fp32_process_tile(
const int n_channels,
const float* const matrix_base,
const int matrix_stride,
const float* const biases,
float* const output,
const int output_row_stride,
- const int output_col_stride
+ const int output_col_stride,
+ const int _pad_bottom,
+ const int _pad_right
)
{
- constexpr int cells_i = 2 - pad_bottom;
- constexpr int cells_j = 2 - pad_right;
+ constexpr int OutputTileRows = 2, OutputTileCols = 2;
+ const int pad_bottom = Specialized ? PadBottom : _pad_bottom;
+ const int pad_right = Specialized ? PadRight : _pad_right;
+
+ const int cells_i = 2 - pad_bottom;
+ const int cells_j = 2 - pad_right;
// Construct a map to the output cells
- float *outptrs[cells_i][cells_j];
+ float *outptrs[OutputTileRows][OutputTileCols];
for (int i = 0; i < cells_i; i++)
{
for (int j = 0; j < cells_j; j++)
@@ -365,19 +342,28 @@
}
}
-template <>
-template <>
-const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+} // namespace (anonymous)
+
+namespace winograd
{
- {
- Transform::template process_tile<0, 0>, // No padding
- Transform::template process_tile<0, 1>, // Right padding
- },
- {
- Transform::template process_tile<1, 0>, // Bottom padding
- Transform::template process_tile<1, 1>, // Bottom and right padding
- }
+using Tiles = OutputTransformImplTiles<5, 5, 6, 6, float>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_2x2_5x5_fp32_process_tile<false>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2x2_5x5_fp32_process_tile<true>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = {
+ winograd_output_transform_2x2_5x5_fp32_process_tile<true, 1, 0>
};
-template struct WinogradGEMM<2, 2, 5, 5>::OutputTransform<float>;
+template <>
+const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
+ winograd_output_transform_2x2_5x5_fp32_process_tile<true, 0, 1>
+};
+
+template class OutputTransform<5, 5, 6, 6, float>;
} // namespace winograd
+
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp
index 2417f52..911759b 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp
@@ -23,38 +23,32 @@
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
-namespace winograd
+namespace
{
-using Transform = WinogradGEMM<1, 4, 1, 5>::OutputTransform<float>;
-using TransformTransposed = WinogradGEMM<4, 1, 5, 1>::OutputTransform<float>;
-
-template <>
-template <>
-int Transform::ops_performed(const Tensor4DShape &shape)
-{
- (void) shape;
- return 0; // TODO
-}
-
-template <>
-template <>
-template <int pad_bottom, int pad_right>
-void Transform::process_tile(
+template <bool Specialized, int PadRight=0>
+void winograd_output_transform_4_5_fp32_process_tile(
const int n_channels,
const float* const matrix_base,
const int matrix_stride,
const float* const biases,
float* const output,
const int output_row_stride,
- const int output_col_stride
+ const int output_col_stride,
+ const int _pad_bottom,
+ const int _pad_right
)
{
(void) output_row_stride;
- constexpr int cells_j = output_tile_cols - pad_right;
+ (void) _pad_bottom;
+ constexpr int output_tile_cols = 4;
+ constexpr int inner_tile_cols = 8;
+
+ const int pad_right = Specialized ? PadRight : _pad_right;
+ const int cells_j = output_tile_cols - pad_right;
// Construct a map to the output cells
float *outptrs[cells_j];
@@ -156,23 +150,22 @@
}
}
-template <>
-template <>
-const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+} // namespace (anonymous)
+
+namespace winograd
{
- {
- Transform::template process_tile<0, 0>,
- Transform::template process_tile<0, 1>,
- Transform::template process_tile<0, 2>,
- Transform::template process_tile<0, 3>,
- },
+using Tiles = OutputTransformImplTiles<1, 5, 1, 8, float>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_4_5_fp32_process_tile<true>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
+ winograd_output_transform_4_5_fp32_process_tile<true, 1>,
+ winograd_output_transform_4_5_fp32_process_tile<true, 2>,
+ winograd_output_transform_4_5_fp32_process_tile<true, 3>
};
-template <>
-template <>
-const TransformTransposed::TileFn TransformTransposed::tile_fns[max_pad_bottom][max_pad_right] = {};
-
-
-template struct WinogradGEMM<1, 4, 1, 5>::OutputTransform<float>;
-template struct WinogradGEMM<4, 1, 5, 1>::OutputTransform<float>;
+template class OutputTransform<1, 5, 1, 8, float>;
+template class OutputTransform<5, 1, 8, 1, float>;
} // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp
index cd3bdef..15cc04b 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp
@@ -23,73 +23,34 @@
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
-namespace winograd
+namespace
{
-using Transform = WinogradGEMM<4, 4, 3, 3>::OutputTransform<float>;
-
-template <>
-template <>
-int Transform::ops_performed(const Tensor4DShape &shape)
-{
- // NOTE: Cost in FLOPs rather than instructions or uops.
- const int tile_M = iceildiv(shape.n_rows, 4);
- const int tile_N = iceildiv(shape.n_cols, 4);
- return 170 * tile_M * tile_N * shape.n_channels;
-}
-
-/* F(4x4, 3x3) constructs 4x4 output tiles from a 3x3 convolution. Since we use
- * enough tiles to cover the output space each output tile may contain up to 3
- * padded values to the right and bottom columns or rows of the tile, e.g.:
-*
-* ________ ________ ________ ________
-* | | | X| | X X| | X X X|
-* | | | X| | X X| | X X X|
-* | | | X| | X X| | X X X|
-* |_______| |______X| |____X_X| |__X_X_X|
-*
-* ________ ________ ________ ________
-* | | | X| | X X| | X X X|
-* | | | X| | X X| | X X X|
-* | | | X| | X X| | X X X|
-* |X_X_X_X| |X_X_X_X| |X_X_X_X| |X_X_X_X|
-*
-* ________ ________ ________ ________
-* | | | X| | X X| | X X X|
-* | | | X| | X X| | X X X|
-* |X X X X| |X X X X| |X X X X| |X X X X|
-* |X_X_X_X| |X_X_X_X| |X_X_X_X| |X_X_X_X|
-*
-* ________ ________ ________ ________
-* | | | X| | X X| | X X X|
-* |X X X X| |X X X X| |X X X X| |X X X X|
-* |X X X X| |X X X X| |X X X X| |X X X X|
-* |X_X_X_X| |X_X_X_X| |X_X_X_X| |X_X_X_X|
-*
-*
-* We provide a specialised output transform for each of these instances.
-*/
-template <>
-template <>
-template <int pad_bottom, int pad_right>
-void Transform::process_tile(
+template <bool Specialized, int PadBottom=0, int PadRight=0>
+void winograd_output_transform_4x4_3x3_fp32_process_tile(
const int n_channels,
const float* const matrix_base,
const int matrix_stride,
const float* const biases,
float* const output,
const int output_row_stride,
- const int output_col_stride
+ const int output_col_stride,
+ const int _pad_bottom,
+ const int _pad_right
)
{
- constexpr int cells_i = 4 - pad_bottom;
- constexpr int cells_j = 4 - pad_right;
+ const int pad_bottom = Specialized ? PadBottom : _pad_bottom;
+ const int pad_right = Specialized ? PadRight : _pad_right;
+ constexpr int TileRows = 4, TileCols = 4;
+
+ const int cells_i = TileRows - pad_bottom;
+ const int cells_j = TileCols - pad_right;
// Construct a map to the output cells
- float *outptrs[cells_i][cells_j];
+ float *outptrs[TileRows][TileCols];
for (int i = 0; i < cells_i; i++)
{
for (int j = 0; j < cells_j; j++)
@@ -437,35 +398,31 @@
}
}
-template <>
-template <>
-const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+} // namespace (anonymous)
+
+namespace winograd
{
- {
- Transform::template process_tile<0, 0>,
- Transform::template process_tile<0, 1>,
- Transform::template process_tile<0, 2>,
- Transform::template process_tile<0, 3>,
- },
- {
- Transform::template process_tile<1, 0>,
- Transform::template process_tile<1, 1>,
- Transform::template process_tile<1, 2>,
- Transform::template process_tile<1, 3>,
- },
- {
- Transform::template process_tile<2, 0>,
- Transform::template process_tile<2, 1>,
- Transform::template process_tile<2, 2>,
- Transform::template process_tile<2, 3>,
- },
- {
- Transform::template process_tile<3, 0>,
- Transform::template process_tile<3, 1>,
- Transform::template process_tile<3, 2>,
- Transform::template process_tile<3, 3>,
- }
+using Tiles = OutputTransformImplTiles<3, 3, 6, 6, float>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_4x4_3x3_fp32_process_tile<false>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_4x4_3x3_fp32_process_tile<true>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = {
+ winograd_output_transform_4x4_3x3_fp32_process_tile<true, 1, 0>,
+ winograd_output_transform_4x4_3x3_fp32_process_tile<true, 2, 0>,
+ winograd_output_transform_4x4_3x3_fp32_process_tile<true, 3, 0>,
};
-template struct WinogradGEMM<4, 4, 3, 3>::OutputTransform<float>;
+template <>
+const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
+ winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 1>,
+ winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 2>,
+ winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 3>,
+};
+
+template class OutputTransform<3, 3, 6, 6, float>;
} // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp
index 16667cc..58bed71 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp
@@ -23,38 +23,32 @@
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
-namespace winograd
+namespace
{
-using Transform = WinogradGEMM<1, 6, 1, 3>::OutputTransform<float>;
-using TransformTransposed = WinogradGEMM<6, 1, 3, 1>::OutputTransform<float>;
-
-template <>
-template <>
-int Transform::ops_performed(const Tensor4DShape &shape)
-{
- (void) shape;
- return 0; // TODO
-}
-
-template <>
-template <>
-template <int pad_bottom, int pad_right>
-void Transform::process_tile(
+template <bool Specialized, int PadRight=0>
+void winograd_output_transform_6_3_fp32_process_tile(
const int n_channels,
const float* const matrix_base,
const int matrix_stride,
const float* const biases,
float* const output,
const int output_row_stride,
- const int output_col_stride
+ const int output_col_stride,
+ const int _pad_bottom,
+ const int _pad_right
)
{
(void) output_row_stride;
- constexpr int cells_j = output_tile_cols - pad_right;
+ (void) _pad_bottom;
+ constexpr int output_tile_cols = 6;
+ constexpr int inner_tile_cols = 8;
+
+ const int pad_right = Specialized ? PadRight : _pad_right;
+ const int cells_j = output_tile_cols - pad_right;
// Construct a map to the output cells
float *outptrs[cells_j];
@@ -162,25 +156,24 @@
}
}
-template <>
-template <>
-const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+} // namespace (anonymous)
+
+namespace winograd
{
- {
- Transform::template process_tile<0, 0>,
- Transform::template process_tile<0, 1>,
- Transform::template process_tile<0, 2>,
- Transform::template process_tile<0, 3>,
- Transform::template process_tile<0, 4>,
- Transform::template process_tile<0, 5>,
- },
+using Tiles = OutputTransformImplTiles<1, 3, 1, 8, float>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_6_3_fp32_process_tile<true>;
+
+template <>
+const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
+ winograd_output_transform_6_3_fp32_process_tile<true, 1>,
+ winograd_output_transform_6_3_fp32_process_tile<true, 2>,
+ winograd_output_transform_6_3_fp32_process_tile<true, 3>,
+ winograd_output_transform_6_3_fp32_process_tile<true, 4>,
+ winograd_output_transform_6_3_fp32_process_tile<true, 5>,
};
-template <>
-template <>
-const TransformTransposed::TileFn TransformTransposed::tile_fns[max_pad_bottom][max_pad_right] = {};
-
-
-template struct WinogradGEMM<1, 6, 1, 3>::OutputTransform<float>;
-template struct WinogradGEMM<6, 1, 3, 1>::OutputTransform<float>;
+template class OutputTransform<1, 3, 1, 8, float>;
+template class OutputTransform<3, 1, 8, 1, float>;
} // namespace winograd