COMPMID-2156: Optimized dilated convolution for NEON.
Change-Id: I3a8abe8cc9637c8983d9bd69dcbaee1a15eac8d0
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1492
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
diff --git a/arm_compute/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp b/arm_compute/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp
new file mode 100644
index 0000000..2ef965b
--- /dev/null
+++ b/arm_compute/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp
@@ -0,0 +1,295 @@
+/*
+ * 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.
+ */
+
+#include "depthwise_dilated.hpp"
+#include "utils.hpp"
+
+#define MEMBERFN(TOUT) \
+ template <unsigned int OutputTileRows, unsigned int OutputTileColumns, \
+ unsigned int KernelRows, unsigned int KernelColumns, \
+ unsigned int StrideRows, unsigned int StrideColumns, typename TIn, \
+ typename TBias, typename TOut> \
+ TOUT DilatedDepthwiseConvolution<OutputTileRows, OutputTileColumns, \
+ KernelRows, KernelColumns, StrideRows, \
+ StrideColumns, TIn, TBias, TOut>
+
+namespace depthwise {
+
+MEMBERFN()
+::DilatedDepthwiseConvolution(const int n_batches, const int n_input_rows,
+ const int n_input_cols, const int n_channels,
+ const int dilation_factor,
+ nck::ActivationFunction activation,
+ const unsigned int padding_top,
+ const unsigned int padding_left,
+ const unsigned int padding_bottom,
+ const unsigned int padding_right)
+ : DilatedDepthwiseConvolution(
+ n_batches, n_input_rows, n_input_cols, n_channels, dilation_factor,
+ DilatedDepthwiseConvolution::get_output_size(
+ n_input_rows, padding_top, padding_bottom, dilation_factor),
+ DilatedDepthwiseConvolution::get_output_size(
+ n_input_cols, padding_left, padding_right, dilation_factor),
+ activation, padding_top, padding_left, padding_bottom,
+ padding_right) {}
+
+MEMBERFN()
+::DilatedDepthwiseConvolution(const int n_batches, const int n_input_rows,
+ const int n_input_cols, const int n_channels,
+ const int dilation_factor,
+ const int n_output_rows, const int n_output_cols,
+ nck::ActivationFunction activation,
+ const unsigned int padding_top,
+ const unsigned int padding_left,
+ const unsigned int, // padding_bottom
+ const unsigned int // padding_right
+ )
+ : DilatedDepthwiseConvolution(
+ n_batches, n_input_rows, n_input_cols, n_channels, dilation_factor,
+ n_output_rows, n_output_cols, activation, padding_top, padding_left,
+ 0, 0,
+ // Function which creates a new (standard) depthwise convolution
+ [](const int n_batches, const int n_input_rows,
+ const int n_input_cols, const int n_channels,
+ const int n_output_rows, const int n_output_cols,
+ const nck::ActivationFunction activation,
+ const unsigned int padding_top, const unsigned int padding_left,
+ const unsigned int padding_bottom,
+ const unsigned int padding_right) -> IDepthwiseConvolution * {
+ return new DepthwiseConvolution<
+ OutputTileRows, OutputTileColumns, KernelRows, KernelColumns,
+ StrideRows, StrideColumns, TIn, TBias, TOut>(
+ n_batches, n_input_rows, n_input_cols, n_channels,
+ n_output_rows, n_output_cols, activation, padding_top,
+ padding_left, padding_bottom, padding_right);
+ }) {}
+
+MEMBERFN()
+::DilatedDepthwiseConvolution(
+ const int n_batches, const int n_input_rows, const int n_input_cols,
+ const int n_channels, const int dilation_factor, const int n_output_rows,
+ const int n_output_cols, nck::ActivationFunction activation,
+ const unsigned int padding_top, const unsigned int padding_left,
+ const unsigned int, // padding_bottom
+ const unsigned int, // padding_right
+ std::function<IDepthwiseConvolution *(
+ int, int, int, int, int, int, nck::ActivationFunction, unsigned int,
+ unsigned int, unsigned int, unsigned int)>
+ subconvfn // Function to create a new convolution
+ )
+ : _dilation_factor(dilation_factor), _n_input_rows(n_input_rows),
+ _n_input_cols(n_input_cols), _n_channels(n_channels),
+ _padding_top(static_cast<int>(padding_top)),
+ _padding_left(static_cast<int>(padding_left)),
+ _n_output_rows(n_output_rows), _n_output_cols(n_output_cols),
+ _convs(_dilation_factor) {
+ // Instantiate the base convolutions
+ for (int i = 0; i < _dilation_factor; i++) {
+ // Compute properties of this row of base convolutions
+ const int row_top =
+ i * StrideRows - _padding_top; // -ve values are in the padding
+ const int row_pad_top =
+ row_top < 0 ? iceildiv(-row_top, dilation_factor) : 0;
+
+ const int _n_input_rows = iceildiv(n_input_rows - i, dilation_factor);
+ const int _n_output_rows = iceildiv(n_output_rows - i, dilation_factor);
+
+ for (int j = 0; j < _dilation_factor; j++) {
+ // Compute properties of the base convolution
+ const int col_left =
+ j * StrideColumns - padding_left; // -ve values are in the padding
+ const int col_pad_left =
+ col_left < 0 ? iceildiv(-col_left, dilation_factor) : 0;
+
+ const int _n_input_cols = iceildiv(n_input_cols - j, dilation_factor);
+ const int _n_output_cols = iceildiv(n_output_cols - j, dilation_factor);
+
+ // Create new depthwise convolution engine and include it in the vector
+ // of engines. The new depthwise convolution engine is created by calling
+ // the delegate function we received as an argument.
+ _convs[i].emplace_back(subconvfn(
+ n_batches, _n_input_rows, _n_input_cols, n_channels, _n_output_rows,
+ _n_output_cols, activation,
+ // Note: since we have computed the output tensor size we don't need
+ // to explicitly provide bottom and right padding values to the
+ // depthwise convolution.
+ row_pad_top, col_pad_left, 0, 0));
+ }
+ }
+}
+
+MEMBERFN(void)::set_input(const void *const inptr) {
+ set_input(inptr, _n_channels);
+}
+
+MEMBERFN(void)::set_input(const void *const inptr, const int ldcol) {
+ set_input(inptr, _n_input_cols * ldcol, ldcol);
+}
+
+MEMBERFN(void)
+::set_input(const void *const inptr, const int ldrow, const int ldcol) {
+ set_input(inptr, _n_input_rows * ldrow, ldrow, ldcol);
+}
+
+MEMBERFN(void)
+::set_input(const void *const inptr, const int ldbatch, const int ldrow,
+ const int ldcol) {
+ // Compute dilated strides
+ const int ldrow_dilated = ldrow * _dilation_factor;
+ const int ldcol_dilated = ldcol * _dilation_factor;
+
+ // Pass input parameters on to base convolutions
+ for (int i = 0; i < _dilation_factor; i++) {
+ const int top_pos =
+ i * StrideRows - _padding_top +
+ ((static_cast<int>(i * StrideRows) < _padding_top)
+ ? iceildiv(_padding_top - i * StrideRows, _dilation_factor) *
+ _dilation_factor
+ : 0);
+ const TIn *const inptr_i =
+ static_cast<const TIn *>(inptr) + top_pos * ldrow;
+
+ for (int j = 0; j < _dilation_factor; j++) {
+ int left_pos = j * StrideColumns - _padding_left;
+ while (left_pos < 0)
+ left_pos += _dilation_factor;
+
+ // Modify the pointer to point to the first element of the dilated input
+ // tensor, then set the input for this convolution engine.
+ const void *const inptr_ij = inptr_i + left_pos * ldcol;
+ _convs[i][j]->set_input(inptr_ij, ldbatch, ldrow_dilated, ldcol_dilated);
+ }
+ }
+}
+
+MEMBERFN(void)::set_output(void *const outptr) {
+ set_output(outptr, _n_channels);
+}
+
+MEMBERFN(void)::set_output(void *const outptr, const int ldcol) {
+ set_output(outptr, _n_output_cols * ldcol, ldcol);
+}
+
+MEMBERFN(void)
+::set_output(void *const outptr, const int ldrow, const int ldcol) {
+ set_output(outptr, _n_output_rows * ldrow, ldrow, ldcol);
+}
+
+MEMBERFN(void)
+::set_output(void *const outptr, const int ldbatch, const int ldrow,
+ const int ldcol) {
+ // Compute dilated strides
+ const int ldrow_dilated = ldrow * _dilation_factor;
+ const int ldcol_dilated = ldcol * _dilation_factor;
+
+ // Pass input parameters on to base convolutions
+ for (int i = 0; i < _dilation_factor; i++) {
+ for (int j = 0; j < _dilation_factor; j++) {
+ // Modify the pointer to point to the first element of the dilated input
+ // tensor, then set the input for this convolution engine.
+ void *const outptr_ij =
+ static_cast<TOut *>(outptr) + i * ldrow + j * ldcol;
+ _convs[i][j]->set_output(outptr_ij, ldbatch, ldrow_dilated,
+ ldcol_dilated);
+ }
+ }
+}
+
+MEMBERFN(int)
+::get_output_size(const int dim_size, const unsigned int padding_before,
+ const unsigned int padding_after, const int dilation_factor) {
+ const int input_size =
+ dim_size + static_cast<int>(padding_before + padding_after);
+ const int window_size = (KernelRows - 1) * dilation_factor + 1;
+ return iceildiv(input_size - window_size + 1, StrideRows);
+}
+
+MEMBERFN(int)
+::output_size(const int dim_size, const unsigned int padding_before,
+ const unsigned int padding_after) const {
+ return get_output_size(dim_size, padding_before, padding_after,
+ _dilation_factor);
+}
+
+MEMBERFN(size_t)::get_packed_params_size(void) const {
+ return _convs[0][0]->get_packed_params_size();
+}
+
+MEMBERFN(void)::set_packed_params_buffer(void *buffer) {
+ // Set the buffer for all convolution engines
+ for (auto &&row : _convs) {
+ for (auto &&conv : row) {
+ conv->set_packed_params_buffer(buffer);
+ }
+ }
+}
+
+MEMBERFN(void)
+::pack_params(const void *const weights, const void *const biases) const {
+ _convs[0][0]->pack_params(weights, biases);
+}
+
+MEMBERFN(void)
+::pack_params(void *const buffer, const void *const weights,
+ const void *const biases) const {
+ _convs[0][0]->pack_params(buffer, weights, biases);
+}
+
+MEMBERFN(void)
+::pack_params(void *const buffer, const void *const weights,
+ const unsigned int ldrow, const unsigned int ldcol,
+ const void *const biases) const {
+ _convs[0][0]->pack_params(buffer, weights, ldrow, ldcol, biases);
+}
+
+MEMBERFN(size_t)::get_working_space_size(unsigned int nthreads) const {
+ return _convs[0][0]->get_working_space_size(nthreads);
+}
+
+MEMBERFN(void)::set_working_space(void *const ws) {
+ // Use the same working space set for all contained depthwise engines.
+ for (auto &&row : _convs) {
+ for (auto &&conv : row) {
+ conv->set_working_space(ws);
+ }
+ }
+}
+
+MEMBERFN(unsigned int)::get_window(void) const {
+ return _convs[0][0]->get_window();
+}
+
+MEMBERFN(void)
+::run(const unsigned int start, const unsigned int stop,
+ const unsigned int threadid) {
+ // Run each contained convolution in turn
+ for (auto &&row : _convs) {
+ for (auto &&conv : row) {
+ conv->run(start, stop, threadid);
+ }
+ }
+}
+
+} // namespace depthwise