From cd0b8b521eb309af8cb84e1a1b031280b027c809 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 12 Jul 2019 16:30:41 +0100 Subject: COMPMID-2236: Move assembly implementation interfaces to src folder Change-Id: I9d0493b64329e12120dce8cbe7cc19d90cea310a Signed-off-by: Georgios Pinitas Reviewed-on: https://review.mlplatform.org/c/1536 Tested-by: Arm Jenkins Reviewed-by: Matthew Bentham --- .../kernels/convolution/depthwise/impl_base.hpp | 504 +++++++++++++++++++++ .../kernels/convolution/depthwise/impl_dilated.hpp | 295 ++++++++++++ 2 files changed, 799 insertions(+) create mode 100644 src/core/NEON/kernels/convolution/depthwise/impl_base.hpp create mode 100644 src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp (limited to 'src/core') diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp new file mode 100644 index 0000000000..b102a24250 --- /dev/null +++ b/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp @@ -0,0 +1,504 @@ +/* + * Copyright (c) 2018-2019 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. + */ + +/* + * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + * + * NOTE: Header to be included by implementation files only. + * + * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + */ + +#include +#include +#include "depthwise.hpp" +#include "padding.hpp" +#include "utils.hpp" + +#pragma once + +#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,\ + typename Derived\ +> TOUT DepthwiseConvolutionBase<\ + OutputTileRows, OutputTileColumns,\ + KernelRows, KernelColumns,\ + StrideRows, StrideColumns,\ + TIn, TBias, TOut, Derived\ +> + +using namespace neon_convolution_kernels; + +namespace depthwise +{ + +template +struct PackParameters +{ + static void execute( + unsigned int n_channels, + void *buffer, + const void *weights, + unsigned int weight_row_stride, + unsigned int weight_col_stride, + const void *biases + ); +}; + +const unsigned int CHANNEL_BLOCK = 16; + +MEMBERFN(int)::get_output_size( + const int dim_size, const unsigned int padding_before, const unsigned int padding_after +) +{ + return iceildiv(dim_size + padding_before + padding_after - KernelRows + 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); +} + +MEMBERFN()::DepthwiseConvolutionBase( + const int n_batches, + const int n_input_rows, + const int n_input_cols, + const int n_channels, + ActivationFunction activation, + const unsigned int padding_top, + const unsigned int padding_left, + const unsigned int padding_bottom, + const unsigned int padding_right +) : DepthwiseConvolutionBase( + n_batches, n_input_rows, n_input_cols, n_channels, + get_output_size(n_input_rows, padding_top, padding_bottom), + get_output_size(n_input_cols, padding_left, padding_right), + activation, + padding_top, padding_left, padding_bottom, padding_right + ) +{ +} + +MEMBERFN()::DepthwiseConvolutionBase( + 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, + ActivationFunction activation, + const unsigned int padding_top, + const unsigned int padding_left, + const unsigned int padding_bottom, + const unsigned int padding_right +) : _input(nullptr), _output(nullptr), + _packed_parameters(nullptr), + _working_space(nullptr), + _n_batches(n_batches), + _n_input_rows(n_input_rows), + _n_input_cols(n_input_cols), + _n_channels(n_channels), + _n_output_rows(n_output_rows), + _n_output_cols(n_output_cols), + _n_tile_rows(iceildiv(_n_output_rows, output_tile_rows)), + _n_tile_cols(iceildiv(_n_output_cols, output_tile_cols)), + _padding_top(padding_top), + _padding_left(padding_left), + _padding_bottom(padding_bottom), + _padding_right(padding_right), + _activation(activation), + _input_col_stride(0), _input_row_stride(0), _input_batch_stride(0), + _output_col_stride(0), _output_row_stride(0), _output_batch_stride(0) +{ +} + +MEMBERFN(void)::set_input(const void* const inptr) +{ + set_input(inptr, _n_channels); +} + +MEMBERFN(void)::set_input(const void* const inptr, const int ld_col) +{ + set_input(inptr, _n_input_cols * ld_col, ld_col); +} + +MEMBERFN(void)::set_input(const void* const inptr, const int ld_row, const int ld_col) +{ + set_input(inptr, _n_input_rows * ld_row, ld_row, ld_col); +} + +MEMBERFN(void)::set_input(const void* const inptr, const int ld_batch, const int ld_row, const int ld_col) +{ + _input = static_cast(inptr); + _input_batch_stride = ld_batch; + _input_row_stride = ld_row; + _input_col_stride = ld_col; +} + +MEMBERFN(void)::set_output(void* const outptr) +{ + set_output(outptr, _n_channels); +} + +MEMBERFN(void)::set_output(void* const outptr, const int ld_col) +{ + set_output(outptr, _n_output_cols * ld_col, ld_col); +} + +MEMBERFN(void)::set_output(void* const outptr, const int ld_row, const int ld_col) +{ + set_output(outptr, _n_output_rows * ld_row, ld_row, ld_col); +} + +MEMBERFN(void)::set_output(void* const outptr, const int ld_batch, const int ld_row, const int ld_col) +{ + _output = static_cast(outptr); + _output_batch_stride = ld_batch; + _output_row_stride = ld_row; + _output_col_stride = ld_col; +} + +MEMBERFN(size_t)::get_packed_params_size(void) const +{ + return _n_channels * (sizeof(TIn)*KernelRows*KernelColumns + sizeof(TBias)); +} + +MEMBERFN(void)::set_packed_params_buffer(void *buffer) +{ + _packed_parameters = buffer; +} + +MEMBERFN(void)::pack_params(const void *weights, const void *biases) const +{ + static_cast(this)->pack_params(_packed_parameters, weights, biases); +} + +MEMBERFN(void)::pack_params(void *buffer, const void *weights, const void *biases) const +{ + const unsigned int weight_col_stride = _n_channels; + const unsigned int weight_row_stride = KernelColumns * weight_col_stride; + static_cast(this)->pack_params( + buffer, weights, weight_row_stride, weight_col_stride, biases + ); +} + +MEMBERFN(void)::pack_params( + void * const buffer, + const void * const weights, + const unsigned int weight_row_stride, + const unsigned int weight_col_stride, + const void * const biases +) const +{ + static_cast(this)->_pack_params( + buffer, weights, weight_row_stride, weight_col_stride, biases + ); +} + +MEMBERFN(void)::_pack_params( + void * const buffer, + const void * const weights, + const unsigned int weight_row_stride, + const unsigned int weight_col_stride, + const void * const biases +) const +{ + // Default implementation + PackParameters::execute( + _n_channels, buffer, weights, weight_row_stride, weight_col_stride, biases + ); +} + +MEMBERFN(size_t)::get_working_space_size(const unsigned int nthreads) const +{ + return nthreads * ( + _get_input_working_space_size() + _get_output_working_space_size() + ); +} + +MEMBERFN(void)::set_working_space(void *buffer) +{ + _working_space = buffer; +} + +MEMBERFN(size_t)::_get_input_working_space_size(void) const +{ + return sizeof(TIn) * _n_channels; +} + +MEMBERFN(size_t)::_get_output_working_space_size(void) const +{ + return sizeof(TOut) * _n_channels; +} + +MEMBERFN(void *)::_get_input_working_space(const unsigned int threadid) const +{ + return static_cast(_working_space) + threadid * ( + _get_input_working_space_size() + _get_output_working_space_size() + ); +} + +MEMBERFN(void *)::_get_output_working_space(const unsigned int threadid) const +{ + return static_cast(_get_input_working_space(threadid)) + _get_input_working_space_size(); +} + +MEMBERFN(unsigned int)::get_window() const +{ + // Parallelise over blocks of channels. + return iceildiv(_n_channels, CHANNEL_BLOCK); +} + +MEMBERFN(void)::run( + const unsigned int start, + const unsigned int stop, + const unsigned int threadid +) +{ + // Clear the input padding buffer + TIn *buf = static_cast(_get_input_working_space(threadid)); + const TIn pad_value = static_cast(this)->_input_padding_value(); + for (int n = 0; n < _n_channels; n++) + { + buf[n] = pad_value; + } + + // Parallelise over blocks of channels + const auto start_channel = CHANNEL_BLOCK * start; + const auto stop_channel = std::min(_n_channels, CHANNEL_BLOCK * stop); + + // Compute top and bottom padding for input and output + const int input_pad_top = _padding_top; + const int input_pad_left = _padding_left; + constexpr int tile_overlap = kernel_rows - stride_rows; + + // Perform the convolution by calling `process_tile_row` for each tile row in + // each batch. + for (int batch = 0; batch < _n_batches; batch++) + { + const TIn* const inptr_batch = _input + batch*_input_batch_stride; + TOut* const outptr_batch = _output + batch*_output_batch_stride; + + // Loop over rows of tiles + for (int tile_i = 0; tile_i < _n_tile_rows; tile_i++) + { + // Pointer to the row + const int input_row_offset = (tile_i == 0) ? 0 : input_pad_top; + const TIn* const inptr_row = (inptr_batch + ((inner_tile_rows - tile_overlap)*tile_i - input_row_offset)*_input_row_stride); + TOut* const outptr_row = outptr_batch + output_tile_rows * tile_i * _output_row_stride; + + // Input padding (top + bottom) for the row + const int input_row_top = tile_i*(inner_tile_rows - tile_overlap) - input_pad_top; + const int input_row_bottom = input_row_top + inner_tile_rows; + const int input_row_pad_top = (tile_i == 0) ? input_pad_top : 0; + const int input_row_pad_bottom = std::max(0, input_row_bottom - _n_input_rows); + + // Output padding (bottom) for the row + const int output_row_bottom = (tile_i + 1)*output_tile_rows; + const int output_row_pad_bottom = std::max(0, output_row_bottom - _n_output_rows); + + // Get the offset into the packed parameters + const auto params_ptr = static_cast(_packed_parameters) + + start_channel*(sizeof(TIn)*KernelRows*KernelColumns + sizeof(TBias)); + + // Process the row + process_tile_row( + threadid, + stop_channel - start_channel, + params_ptr, + inptr_row + start_channel, + outptr_row + start_channel, + input_row_pad_top, input_pad_left, input_row_pad_bottom, + output_row_pad_bottom, + _n_tile_cols, _n_input_cols, _n_output_cols + ); + } + } +} + +MEMBERFN(void)::process_tile_row( + const unsigned int threadid, + const int n_channels, + const void* const packed_params, + const TIn* const inptr, + TOut* const outptr, + const int row_pad_in_top, + const int row_pad_in_left, + const int row_pad_in_bottom, + const int row_pad_out_bottom, + const int n_tiles, + const int n_input_cols, + const int n_output_cols +) +{ + constexpr int tile_overlap = kernel_cols - stride_cols; + + // Loop over columns of tiles + for (int tile_j = 0; tile_j < n_tiles; tile_j++) + { + // Input padding (left + right) for the tile + const int t_pad_in_left = (tile_j == 0) ? row_pad_in_left : 0; + const int t_in_start = tile_j*(inner_tile_cols - tile_overlap) - row_pad_in_left; + const int t_in_end = t_in_start + inner_tile_cols; + const int t_pad_in_right = std::max(0, t_in_end - n_input_cols); + + // Output padding (right) for the tile + const int t_out_end = (tile_j + 1) * output_tile_cols; + const int t_pad_out_right = std::max(0, t_out_end - n_output_cols); + + // Get pointers into the inputs and outputs + const int col_offset = (tile_j == 0) ? 0 : row_pad_in_left; + const TIn* const inptr_col = (inptr + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*_input_col_stride); + TOut* const outptr_col = outptr + tile_j * output_tile_cols * _output_col_stride; + + // Process just this tile + process_tile( + threadid, n_channels, packed_params, inptr_col, outptr_col, + row_pad_in_top, t_pad_in_left, row_pad_in_bottom, t_pad_in_right, // Input paddings + row_pad_out_bottom, t_pad_out_right // Output paddings + ); + } +} + +MEMBERFN(TIn)::_input_padding_value(void) const +{ + return static_cast(0); +} + +MEMBERFN(void)::process_tile( + const unsigned int threadid, + const int n_channels, + const void* const packed_params, + const TIn* const inptr, + TOut* const outptr, + const int pad_in_top, + const int pad_in_left, + const int pad_in_bottom, + const int pad_in_right, + const int pad_out_bottom, + const int pad_out_right +) +{ + Derived * dthis = static_cast(this); + const bool pad_input = pad_in_top || pad_in_left || pad_in_bottom || pad_in_right; + const bool pad_output = pad_out_bottom || pad_out_right; + + if (!pad_input && !pad_output) + { + switch(_activation) + { + case ActivationFunction::ReLU: + dthis->template execute_tile( + n_channels, packed_params, + inptr, _input_row_stride, _input_col_stride, + outptr, _output_row_stride, _output_col_stride + ); + break; + case ActivationFunction::ReLU6: + dthis->template execute_tile( + n_channels, packed_params, + inptr, _input_row_stride, _input_col_stride, + outptr, _output_row_stride, _output_col_stride + ); + break; + default: + dthis->template execute_tile( + n_channels, packed_params, + inptr, _input_row_stride, _input_col_stride, + outptr, _output_row_stride, _output_col_stride + ); + break; + } + } + else + { + // Create arrays of input and output pointers, pointing padded elements to + // the working space padding buffers provided. + const TIn *inptrs[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + if (i < pad_in_top || (inner_tile_rows - pad_in_bottom) <= i || + j < pad_in_left || (inner_tile_cols - pad_in_right) <= j) + { + // Padded input + inptrs[i][j] = static_cast(_get_input_working_space(threadid)); + } + else + { + inptrs[i][j] = inptr + (i - pad_in_top)*_input_row_stride + (j - pad_in_left)*_input_col_stride; + } + } + } + + TOut *outptrs[output_tile_rows][output_tile_cols]; + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + if (i < (output_tile_rows - pad_out_bottom) && + j < (output_tile_cols - pad_out_right)) + { + outptrs[i][j] = outptr + i*_output_row_stride + j*_output_col_stride; + } + else + { + outptrs[i][j] = static_cast(_get_output_working_space(threadid)); + } + } + } + + switch(_activation) + { + case ActivationFunction::ReLU: + dthis->template execute_tile( + n_channels, packed_params, inptrs, outptrs + ); + break; + case ActivationFunction::ReLU6: + dthis->template execute_tile( + n_channels, packed_params, inptrs, outptrs + ); + break; + default: + dthis->template execute_tile( + n_channels, packed_params, inptrs, outptrs + ); + break; + } + } +} + +MEMBERFN(int)::n_channels(void) const +{ + return _n_channels; +} + +} // namespace depthwise diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp new file mode 100644 index 0000000000..2ef965ba4b --- /dev/null +++ b/src/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 \ + TOUT DilatedDepthwiseConvolution + +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 + 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(padding_top)), + _padding_left(static_cast(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(i * StrideRows) < _padding_top) + ? iceildiv(_padding_top - i * StrideRows, _dilation_factor) * + _dilation_factor + : 0); + const TIn *const inptr_i = + static_cast(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(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(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 -- cgit v1.2.1