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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-07-12 16:30:41 +0100 |
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committer | Matthew Bentham <matthew.bentham@arm.com> | 2019-07-12 19:51:34 +0000 |
commit | cd0b8b521eb309af8cb84e1a1b031280b027c809 (patch) | |
tree | e19257b86286f8f7a6a43c1522781db9c15e1ea3 /src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp | |
parent | 51146c5006290541f029c534ed6a07cb8f579b21 (diff) | |
download | ComputeLibrary-cd0b8b521eb309af8cb84e1a1b031280b027c809.tar.gz |
COMPMID-2236: Move assembly implementation interfaces to src folder
Change-Id: I9d0493b64329e12120dce8cbe7cc19d90cea310a
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1536
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Matthew Bentham <matthew.bentham@arm.com>
Diffstat (limited to 'src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp')
-rw-r--r-- | src/core/NEON/kernels/convolution/depthwise/impl_dilated.hpp | 295 |
1 files changed, 295 insertions, 0 deletions
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 <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 |