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Diffstat (limited to 'src/core/NEON/kernels/convolution/depthwise/impl_u8_s32.hpp')
| -rw-r--r-- | src/core/NEON/kernels/convolution/depthwise/impl_u8_s32.hpp | 315 |
1 files changed, 0 insertions, 315 deletions
diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_u8_s32.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_u8_s32.hpp deleted file mode 100644 index d0d8de538d..0000000000 --- a/src/core/NEON/kernels/convolution/depthwise/impl_u8_s32.hpp +++ /dev/null @@ -1,315 +0,0 @@ -/* - * Copyright (c) 2018 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 "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp" - -#pragma once - -namespace depthwise -{ -// Partial specialisation for U8 to S32 -template <int OutputTileRows, int OutputTileCols, - int KernelRows, int KernelCols, - int StrideRows, int StrideCols> -struct DepthwiseConvolutionImpl<OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols, uint8_t, int32_t> -{ - typedef DepthwiseConvolution< - OutputTileRows, OutputTileCols, - KernelRows, KernelCols, - StrideRows, StrideCols, - uint8_t, int32_t - > DWC; - - template < - bool Specialize=false, // Specialize (or not) the method - int InPadTop=0, // If specialized, top padding - int InPadLeft=0, // If specialized, left padding - int InPadBottom=0, // If specialized, bottom padding - int InPadRight=0, // If specialized, right padding - int OutPadBottom=0, // If specialized, bottom output padding - int OutPadRight=0 // If specialized, bottom right padding - > - static void process_tile( - const int n_channels, - const uint8_t* const weights, - const int weight_row_stride, - const int weight_col_stride, - const uint8_t* const inptr, - const int in_row_stride, - const int in_col_stride, - int32_t* const outptr, - const int out_row_stride, - const int out_col_stride, - const int in_pad_top=0, - const int in_pad_left=0, - const int in_pad_bottom=0, - const int in_pad_right=0, - const int out_pad_bottom=0, - const int out_pad_right=0, - const int input_offset=0, - const int weights_offset=0); -}; - - -template <int OTR, int OTC, int KR, int KC, int SR, int SC> -template < - bool Specialize, - int InPadTop, int InPadLeft, int InPadBottom, int InPadRight, - int OutPadBottom, int OutPadRight -> -void DepthwiseConvolutionImpl<OTR, OTC, KR, KC, SR, SC, uint8_t, int32_t>::process_tile( - const int n_channels, - const uint8_t *__restrict__ const weights, - const int weight_row_stride, - const int weight_col_stride, - const uint8_t *__restrict__ const inptr, - const int in_row_stride, - const int in_col_stride, - int32_t *__restrict__ const outptr, - const int out_row_stride, - const int out_col_stride, - const int _in_pad_top, - const int _in_pad_left, - const int _in_pad_bottom, - const int _in_pad_right, - const int _out_pad_bottom, - const int _out_pad_right, - const int _input_offset, - const int _weights_offset -) -{ - constexpr auto inner_tile_rows = DWC::inner_tile_rows; - constexpr auto inner_tile_cols = DWC::inner_tile_cols; - constexpr auto kernel_rows = DWC::kernel_rows; - constexpr auto kernel_cols = DWC::kernel_cols; - constexpr auto output_tile_rows = DWC::output_tile_rows; - constexpr auto output_tile_cols = DWC::output_tile_cols; - constexpr auto stride_rows = DWC::stride_rows; - constexpr auto stride_cols = DWC::stride_cols; - - // Extract parameters - const int in_pad_top = Specialize ? InPadTop : _in_pad_top; - const int in_pad_left = Specialize ? InPadLeft : _in_pad_left; - const int in_pad_bottom = Specialize ? InPadBottom : _in_pad_bottom; - const int in_pad_right = Specialize ? InPadRight : _in_pad_right; - const int out_pad_bottom = Specialize ? OutPadBottom : _out_pad_bottom; - const int out_pad_right = Specialize ? OutPadRight : _out_pad_right; - - // Compute valid ranges of the tile - const int in_cells_i = inner_tile_rows - in_pad_bottom; - const int in_cells_j = inner_tile_cols - in_pad_right; - const int out_cells_i = output_tile_rows - out_pad_bottom; - const int out_cells_j = output_tile_cols - out_pad_right; - - // Instantiate pointers - const uint8_t* __restrict__ inptr_base = inptr; - const uint8_t* __restrict__ wptr_base = weights; - int32_t* __restrict__ outptr_base = outptr; - - // Perform the depthwise convolution - int channels_remaining = n_channels; -#ifdef __aarch64__ - const int32x4_t v_input_offset = vdupq_n_s32(_input_offset); - const int32x4_t v_weights_offset = vdupq_n_s32(_weights_offset); - for (; channels_remaining >= 16; channels_remaining -= 16) - { - // Load input tile - int32x4x4_t u[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - const uint8_t* const inptr_row = inptr_base + (i - in_pad_top)*in_row_stride; - for (int j = 0; j < inner_tile_cols; j++) - { - if (i < in_pad_top || in_cells_i <= i || - j < in_pad_left || in_cells_j <= j) - { - u[i][j].val[0] = vdupq_n_s32(0); - u[i][j].val[1] = vdupq_n_s32(0); - u[i][j].val[2] = vdupq_n_s32(0); - u[i][j].val[3] = vdupq_n_s32(0); - } - else - { - const uint8x16_t uv = vld1q_u8(inptr_row + (j - in_pad_left)*in_col_stride); - u[i][j].val[0] = vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_low_u8(uv))))); - u[i][j].val[1] = vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_low_u8(uv))))); - u[i][j].val[2] = vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_high_u8(uv))))); - u[i][j].val[3] = vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_high_u8(uv))))); - } - } - } - inptr_base += 16; - - // Load weights tile - int32x4x4_t w[kernel_rows][kernel_cols]; - for (int i = 0; i < kernel_rows; i++) - { - const uint8_t* const wptr_row = wptr_base + i*weight_row_stride; - for (int j = 0; j < kernel_cols; j++) - { - const uint8x16_t wv = vld1q_u8(wptr_row + j*weight_col_stride); - w[i][j].val[0] = vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_low_u8(wv))))); - w[i][j].val[1] = vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_low_u8(wv))))); - w[i][j].val[2] = vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_high_u8(wv))))); - w[i][j].val[3] = vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_high_u8(wv))))); - } - } - wptr_base += 16; - - // Perform the convolution - int32x4x4_t v[output_tile_rows][output_tile_cols]; - for (int out_i = 0; out_i < out_cells_i; out_i++) - { - for (int out_j = 0; out_j < out_cells_j; out_j++) - { - // Base co-ordinate - const int base_i = out_i * stride_rows; - const int base_j = out_j * stride_cols; - - // Fill the accumulator - for (int in_i = 0; in_i < kernel_rows; in_i++) - { - const int i = base_i + in_i; - for (int in_j = 0; in_j < kernel_cols; in_j++) - { - const int j = base_j + in_j; - if (in_i == 0 && in_j == 0) - { - // v[out_i][out_j] = w[in_i][in_j] * u[i][j]; - v[out_i][out_j].val[0] = vmulq_s32(w[in_i][in_j].val[0], u[i][j].val[0]); - v[out_i][out_j].val[1] = vmulq_s32(w[in_i][in_j].val[1], u[i][j].val[1]); - v[out_i][out_j].val[2] = vmulq_s32(w[in_i][in_j].val[2], u[i][j].val[2]); - v[out_i][out_j].val[3] = vmulq_s32(w[in_i][in_j].val[3], u[i][j].val[3]); - } - else - { - // v[out_i][out_j] += w[in_i][in_j] * u[i][j]; - v[out_i][out_j].val[0] = vmlaq_s32(v[out_i][out_j].val[0], w[in_i][in_j].val[0], u[i][j].val[0]); - v[out_i][out_j].val[1] = vmlaq_s32(v[out_i][out_j].val[1], w[in_i][in_j].val[1], u[i][j].val[1]); - v[out_i][out_j].val[2] = vmlaq_s32(v[out_i][out_j].val[2], w[in_i][in_j].val[2], u[i][j].val[2]); - v[out_i][out_j].val[3] = vmlaq_s32(v[out_i][out_j].val[3], w[in_i][in_j].val[3], u[i][j].val[3]); - } - } - } - } - } - - // Store the output tile - for (int i = 0; i < out_cells_i; i++) - { - int32_t* const outptr_row = outptr_base + i*out_row_stride; - for (int j = 0; j < out_cells_j; j++) - { - vst1q_s32(outptr_row + j*out_col_stride, v[i][j].val[0]); - vst1q_s32(outptr_row + j*out_col_stride + 4, v[i][j].val[1]); - vst1q_s32(outptr_row + j*out_col_stride + 8, v[i][j].val[2]); - vst1q_s32(outptr_row + j*out_col_stride + 12, v[i][j].val[3]); - } - } - outptr_base += 16; - } -#endif // __aarch64__ - for (; channels_remaining; channels_remaining--) - { - // Load input tile - int32_t u[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - const uint8_t* const inptr_row = inptr_base + (i - in_pad_top)*in_row_stride; - for (int j = 0; j < inner_tile_cols; j++) - { - if (i < in_pad_top || in_cells_i <= i || - j < in_pad_left || in_cells_j <= j) - { - u[i][j] = static_cast<uint8_t>(0); - } - else - { - u[i][j] = static_cast<int32_t >(*(inptr_row + (j - in_pad_left)*in_col_stride)) + _input_offset; - } - } - } - inptr_base++; - - // Load weights tile - int32_t w[kernel_rows][kernel_cols]; - for (int i = 0; i < kernel_rows; i++) - { - const uint8_t* const wptr_row = wptr_base + i*weight_row_stride; - for (int j = 0; j < kernel_cols; j++) - { - w[i][j] = static_cast<int32_t >(*(wptr_row + j*weight_col_stride)) + _weights_offset; - } - } - wptr_base++; - - // Perform the convolution - int32_t v[output_tile_rows][output_tile_cols]; - for (int out_i = 0; out_i < out_cells_i; out_i++) - { - for (int out_j = 0; out_j < out_cells_j; out_j++) - { - // Clear the accumulator - v[out_i][out_j] = static_cast<int32_t>(0); - - // Base co-ordinate - const int base_i = out_i * stride_rows; - const int base_j = out_j * stride_cols; - - // Fill the accumulator - for (int in_i = 0; in_i < kernel_rows; in_i++) - { - const int i = base_i + in_i; - for (int in_j = 0; in_j < kernel_cols; in_j++) - { - const int j = base_j + in_j; - v[out_i][out_j] += w[in_i][in_j] * u[i][j]; - } - } - } - } - - // Store the output tile - for (int i = 0; i < out_cells_i; i++) - { - int32_t* const outptr_row = outptr_base + i*out_row_stride; - for (int j = 0; j < out_cells_j; j++) - { - *(outptr_row + j*out_col_stride) = v[i][j]; - } - } - outptr_base++; - } -} - -} // namespace depthwise |