COMPMID-2063: New Winograd implementation

Refactoring of winograd code reducing the size of the binaries
about 8X.

Change-Id: If8845bda324573e1a5cf436f354ac8603e88a92e
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/959
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Anthony Barbier <Anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 0 insertions, 240 deletions

diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
deleted file mode 100644
index a7de2fd3e5..0000000000
--- a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
+++ /dev/null
@@ -1,240 +0,0 @@
-/*
- * Copyright (c) 2017 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 <cstring>
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.hpp"
-using namespace winograd;
-
-/** Get the output shape of a convolution. */
-template <int kr, int kc, int itr, int itc>
-template <typename TOut, typename TIn>
-Tensor4DShape WinogradGEMM<kr, kc, itr, itc>::Convolution<TOut, TIn>::get_output_shape(
-  const KernelShape &kernel_shape,
-  const Tensor4DShape &in_shape,
-  const PaddingType padding
-)
-{
-  return Tensor4DShape {
-    in_shape.n_batches,
-    (padding == PADDING_SAME) ? in_shape.n_rows : in_shape.n_rows - (kernel_rows - 1),
-    (padding == PADDING_SAME) ? in_shape.n_cols : in_shape.n_cols - (kernel_cols - 1),
-    kernel_shape.n_output_channels,
-    in_shape.ordering
-  };
-}
-
-/* Get the memory required to transform the kernel.
- */
-template <int kernel_rows, int kernel_cols,
-          int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_transform_working_size(const KernelShape &shape)
-{
-  if (shape.ordering == HWIO)
-  {
-    // Kernel is already in the correct order, so no additional memory is
-    // required.
-    return 0;
-  }
-  else
-  {
-    // Need to re-order the kernel into HWIO form, require enough space to
-    // represent the tensor.
-    return sizeof(TIn) * shape.size();
-  }
-}
-
-/** Get the memory required to store the kernel transformed into the
- * Winograd domain.
- */
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_storage_size(const KernelShape &shape)
-{
-  return N_GEMMS * get_kernel_matrix_size(shape);
-}
-
-
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_input_storage_size(
-  const KernelShape &kernel_shape,
-  const Tensor4DShape &input_shape,
-  const PaddingType padding
-)
-{
-  return N_GEMMS * get_input_matrix_size(kernel_shape, input_shape, padding);
-}
-
-
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_output_storage_size(
-  const KernelShape &kernel_shape,
-  const Tensor4DShape &input_shape,
-  const PaddingType padding
-)
-{
-  return N_GEMMS * get_output_matrix_size(kernel_shape, input_shape, padding);
-}
-
-
-/** Get the memory required to apply a Winograd operator to some input.
- */
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_working_space_size(
-  const KernelShape &kernel_shape,
-  const Tensor4DShape &input_shape,
-  const PaddingType padding_type
-)
-{
-  const auto output_shape = get_output_shape(kernel_shape, input_shape, padding_type);
-
-  // Get the memory required to store the matrices
-  const size_t matrix_sizes = N_GEMMS * (
-    get_input_matrix_size(kernel_shape, input_shape, padding_type) +
-    get_output_matrix_size(kernel_shape, input_shape, padding_type)
-  );
-
-  // Add additional space to re-order the input and output if the input tensor
-  // is not in NHWC format.
-  if (input_shape.ordering == NHWC)
-  {
-    return matrix_sizes;  // No extra spacing required
-  }
-  else  // NCHW, must reorder the input and output tensors
-  {
-    // We only need to re-order the input or output at any one time, so request
-    // enough memory to do the largest of these.
-    const size_t extra_memory = std::max(
-      sizeof(TIn) * input_shape.size(),
-      sizeof(TOut) * output_shape.size()
-    );
-    return matrix_sizes + extra_memory;
-  }
-}
-
-
-/* Get the memory required by a single "input" matrix.
- */
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_input_matrix_size(
-  const KernelShape &kernel_shape,
-  const Tensor4DShape &input_shape,
-  const PaddingType padding_type
-)
-{
-  return get_input_matrix_stride(kernel_shape, input_shape, padding_type) * sizeof(TIn);
-}
-
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_input_matrix_stride(
-  const KernelShape &kernel_shape,
-  const Tensor4DShape &input_shape,
-  const PaddingType padding_type
-)
-{
-  // Compute shape for the GEMM
-  const auto output_shape = get_output_shape(kernel_shape, input_shape, padding_type);
-  const int tile_rows = iceildiv(output_shape.n_rows, output_tile_rows);
-  const int tile_cols = iceildiv(output_shape.n_cols, output_tile_cols);
-  const int M = roundup(input_shape.n_batches * tile_rows * tile_cols, M_BLOCK);
-  const int K = kernel_shape.n_input_channels;
-
-  return M * K;
-}
-
-
-/* Get the memory required by a single "output" matrix.
- */
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_output_matrix_size(
-    const KernelShape &kernel_shape,
-    const Tensor4DShape &input_shape,
-    const PaddingType padding_type
-)
-{
-  return get_output_matrix_stride(kernel_shape, input_shape, padding_type) * sizeof(TOut);
-}
-
-
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_output_matrix_stride(
-    const KernelShape &kernel_shape,
-    const Tensor4DShape &input_shape,
-    const PaddingType padding_type
-)
-{
-  // Compute shape for the GEMM
-  const auto output_shape = get_output_shape(kernel_shape, input_shape, padding_type);
-  const int tile_rows = iceildiv(output_shape.n_rows, output_tile_rows);
-  const int tile_cols = iceildiv(output_shape.n_cols, output_tile_cols);
-  const int M = roundup(tile_rows * tile_cols, M_BLOCK);
-  const int N = roundup(kernel_shape.n_output_channels, N_BLOCK);
-
-  return input_shape.n_batches * M * N;
-}
-
-
-/* Get the memory required by a single "kernel" matrix.
- */
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-size_t WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_matrix_size(const KernelShape &shape)
-{
-  return sizeof(TIn) * get_kernel_matrix_stride(shape);
-}
-
-template <int kernel_rows, int kernel_cols, int output_tile_rows, int output_tile_cols>
-template <typename TOut, typename TIn>
-int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::Convolution<TOut, TIn>::get_kernel_matrix_stride(const KernelShape &shape)
-{
-  const int K = shape.n_input_channels;
-  const int N = roundup(shape.n_output_channels, N_BLOCK);
-  return K * N;
-}
-
-
-// Instantiate required implementations
-template class WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>;
-template class WinogradGEMM<4, 4, 3, 3>::Convolution<float, float>;
-
-template class WinogradGEMM<1, 6, 1, 3>::Convolution<float, float>;
-template class WinogradGEMM<6, 1, 3, 1>::Convolution<float, float>;
-
-template class WinogradGEMM<2, 2, 5, 5>::Convolution<float, float>;
-
-template class WinogradGEMM<1, 4, 1, 5>::Convolution<float, float>;
-template class WinogradGEMM<4, 1, 5, 1>::Convolution<float, float>;
-
-template class WinogradGEMM<1, 2, 1, 7>::Convolution<float, float>;
-template class WinogradGEMM<2, 1, 7, 1>::Convolution<float, float>;
-
-
-