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, 249 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp
new file mode 100644
index 0000000000..d97af21a43
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp
@@ -0,0 +1,249 @@
+/*
+ * Copyright (c) 2019 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.
+ */
+
+#pragma once
+
+#include <algorithm>
+#include "winograd.hpp"
+#include "padding.hpp"
+#include "utils.hpp"
+
+#define MEMBERFN(RTYPE) template<\
+  int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols,\
+  typename TIn, typename TOut, WinogradRoots Roots\
+> RTYPE OutputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, TIn, TOut, Roots>
+
+#define Nx1MEMBERFN(RTYPE) template<\
+  int KernelRows, int InnerTileRows, typename TIn, typename TOut, WinogradRoots Roots\
+> RTYPE OutputTransform<KernelRows, 1, InnerTileRows, 1, TIn, TOut, Roots>
+
+namespace winograd
+{
+
+MEMBERFN()::OutputTransform(
+  const int n_batches,
+  const int n_rows,
+  const int n_cols,
+  const int n_channels
+) : _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels),
+    _matrix_base(nullptr),
+    _biases(nullptr),
+    _matrix_stride(0), _matrix_row_stride(0), _matrix_batch_stride(0),
+    _outptr(nullptr),
+    _tiles_M(iceildiv(n_rows, output_tile_rows)),
+    _tiles_N(iceildiv(n_cols, output_tile_cols)),
+    _out_col_stride(0), _out_row_stride(0), _out_batch_stride(0),
+    _working_space_col_stride(n_channels),
+    _working_space_row_stride(output_tile_cols * _working_space_col_stride),
+    _working_space(nullptr)
+{
+}
+
+MEMBERFN(void)::set_input_matrices(const void * const mptr, const int ldmatrix, const int ldrow)
+{
+  _matrix_base = static_cast<const TIn *>(mptr);
+  _matrix_stride = ldmatrix;
+  _matrix_row_stride = ldrow;
+  _matrix_batch_stride = _tiles_M * _tiles_N * ldrow;
+}
+
+MEMBERFN(void)::set_bias(const void * const bias)
+{
+  _biases = static_cast<const TOut *>(bias);
+}
+
+MEMBERFN(void)::set_output_tensor(void * const outptr)
+{
+  set_output_tensor(outptr, _n_channels);
+}
+
+MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldcol)
+{
+  set_output_tensor(outptr, _n_cols * ldcol, ldcol);
+}
+
+MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldrow, const int ldcol)
+{
+  set_output_tensor(outptr, _n_rows * ldrow, ldrow, ldcol);
+}
+
+MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldbatch, const int ldrow, const int ldcol)
+{
+  _outptr = static_cast<TOut *>(outptr);
+  _out_batch_stride = ldbatch;
+  _out_row_stride = ldrow;
+  _out_col_stride = ldcol;
+}
+
+Nx1MEMBERFN()::OutputTransform(
+  const int n_batches,
+  const int n_rows,
+  const int n_cols,
+  const int n_channels
+) : OutputTransform<1, KernelRows, 1, InnerTileRows, TIn, TOut, Roots>::OutputTransform(
+    n_batches, n_cols, n_rows, n_channels /* Transpose rows and columns */
+  )
+{
+}
+
+Nx1MEMBERFN(void)::set_output_tensor(void * const outptr)
+{
+  set_output_tensor(outptr, this->_n_channels);
+}
+
+Nx1MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldcol)
+{
+  set_output_tensor(outptr, this->_n_cols * ldcol, ldcol);
+}
+
+Nx1MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldrow, const int ldcol)
+{
+  set_output_tensor(outptr, this->_n_rows * ldrow, ldrow, ldcol);
+}
+
+Nx1MEMBERFN(void)::set_output_tensor(void * const outptr, const int ldbatch, const int ldrow, const int ldcol)
+{
+  // Transpose rows and columns
+  Base::set_output_tensor(outptr, ldbatch, ldcol, ldrow);
+}
+
+MEMBERFN(size_t)::get_working_space_size(const unsigned int nthreads) const
+{
+  return sizeof(TOut) * output_tile_rows * _working_space_row_stride * nthreads;
+}
+
+MEMBERFN(void)::set_working_space(void * const buffer)
+{
+  _working_space = static_cast<TOut *>(buffer);
+}
+
+MEMBERFN(unsigned int)::get_window(void) const
+{
+  return iceildiv(_n_channels, WINDOW_BLOCK);
+}
+
+MEMBERFN(void)::run(
+  const unsigned int start,
+  const unsigned int stop,
+  const unsigned int threadid
+)
+{
+  // Determine the channels on which to work
+  if (start >= get_window())
+  {
+    return;  // No work to do beyond the end of the window
+  }
+  const unsigned int start_channel = start * WINDOW_BLOCK;
+  const unsigned int stop_channel = std::min<unsigned int>(_n_channels, stop * WINDOW_BLOCK);
+  const unsigned int n_channels = stop_channel - start_channel;
+
+  const auto matrix_tile_col_stride = _matrix_row_stride;
+  const auto matrix_tile_row_stride = _tiles_N * matrix_tile_col_stride;
+
+  const TOut* const bptr = (_biases == nullptr) ? nullptr : _biases + start_channel;
+
+  // Loop over batches
+  for (int batch = 0; batch < _n_batches; batch++)
+  {
+    const TIn* const matrix_batch = _matrix_base + start_channel + batch * _matrix_batch_stride;
+    TOut* const outptr_batch = _outptr + start_channel + batch * _out_batch_stride;
+
+    for (int tile_i = 0; tile_i < _tiles_M; tile_i++)
+    {
+      // Compute properties of the row of output tiles
+      const int row_pad_bottom = std::max(0, (tile_i + 1)*output_tile_rows - _n_rows);
+      const TIn* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride;
+      TOut* const outptr_row = outptr_batch + tile_i * output_tile_rows * _out_row_stride;
+
+      for (int tile_j = 0; tile_j < _tiles_N; tile_j++)
+      {
+        // Compute property of this specific tile
+        const int tile_pad_right = std::max(0, (tile_j + 1)*output_tile_cols - _n_cols);
+        const TIn* const matrix_tile = matrix_tile_row + tile_j * matrix_tile_col_stride;
+        TOut* const outptr_tile = outptr_row + tile_j * output_tile_cols * _out_col_stride;
+
+        // Perform the transformation
+        if (row_pad_bottom || tile_pad_right)
+        {
+          transform_cropped_tile(
+            threadid, n_channels, outptr_tile, matrix_tile, bptr,
+            row_pad_bottom, tile_pad_right
+          );
+        }
+        else
+        {
+          transform_uncropped_tile(
+            threadid, n_channels, outptr_tile, matrix_tile, bptr
+          );
+        }
+      }
+    }
+  }
+}
+
+MEMBERFN(void)::transform_uncropped_tile(
+  const unsigned int /* threadid unused */,
+  const int n_channels,
+  TOut * const outptr,
+  const TIn * const inptr,
+  const TOut * const biases
+)
+{
+  transform_tile(
+    n_channels, inptr, _matrix_stride, biases,
+    outptr, _out_row_stride, _out_col_stride
+  );
+}
+
+MEMBERFN(void)::transform_cropped_tile(
+  const unsigned int threadid,
+  const int n_channels,
+  TOut * const outptr,
+  const TIn * const inptr,
+  const TOut * const biases,
+  const int pad_bottom,
+  const int pad_right
+)
+{
+  // Transform into working space and then copy the relevant section out.
+  TOut *wsptr = static_cast<TOut *>(get_working_space(threadid));
+  transform_tile(
+    n_channels, inptr, _matrix_stride, biases,
+    wsptr, _working_space_row_stride, _working_space_col_stride
+  );
+
+  padding::crop_and_copy_tile(
+    output_tile_rows, output_tile_cols, n_channels,
+    wsptr, _working_space_row_stride, _working_space_col_stride,
+    outptr, _out_row_stride, _out_col_stride,
+    0u, 0u, pad_bottom, pad_right
+  );
+}
+
+MEMBERFN(void *)::get_working_space(const unsigned int threadid) const
+{
+  return _working_space + output_tile_rows * _working_space_row_stride * threadid;
+}
+
+}  // namespace winograd