From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Mon, 4 Sep 2017 18:44:23 +0100 Subject: COMPMID-344 Updated doxygen Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae --- .../kernels/NEDirectConvolutionLayerKernel.cpp | 817 +++++++++++++++++++++ 1 file changed, 817 insertions(+) create mode 100644 src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp (limited to 'src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp') diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp new file mode 100644 index 0000000000..d6088981aa --- /dev/null +++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp @@ -0,0 +1,817 @@ +/* + * Copyright (c) 2017 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 "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/NEFixedPoint.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Validate.h" + +#include +#include + +using namespace arm_compute; + +namespace +{ +template +float32x4_t internal_vld1q(const float *in); + +template <> +float32x4_t internal_vld1q<1>(const float *in) +{ + return vld1q_f32(in); +} + +template <> +float32x4_t internal_vld1q<2>(const float *in) +{ + const float32x4x2_t tmp = vld2q_f32(in); + return tmp.val[0]; +} + +template <> +float32x4_t internal_vld1q<3>(const float *in) +{ + const float32x4x3_t tmp = vld3q_f32(in); + return tmp.val[0]; +} + +template +qint8x8_t internal_vld1q(const qint8_t *in); + +template <> +qint8x8_t internal_vld1q<1>(const qint8_t *in) +{ + return vld1_qs8(in); +} + +template <> +qint8x8_t internal_vld1q<2>(const qint8_t *in) +{ + const qint8x8x2_t tmp = vld2_s8(in); + return tmp.val[0]; +} + +template <> +qint8x8_t internal_vld1q<3>(const qint8_t *in) +{ + const qint8x8x3_t tmp = vld3_s8(in); + return tmp.val[0]; +} + +template +qint16x8_t internal_vld1q(const qint16_t *in); + +template <> +qint16x8_t internal_vld1q<1>(const qint16_t *in) +{ + return vld1q_s16(in); +} + +inline float32x4_t internal_vdupq_n(float v) +{ + return vdupq_n_f32(v); +} + +inline qint8x8_t internal_vdupq_n(qint8_t v) +{ + return vdup_n_qs8(v); +} + +inline void internal_vst1q(float *p, const float32x4_t &v) +{ + vst1q_f32(p, v); +} + +inline void internal_vst1q(qint16_t *p, const qint16x8_t &v) +{ + vst1q_qs16(p, v); +} + +float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y, int fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + return vmulq_f32(x, y); +} + +qint16x8_t internal_vmull(const qint8x8_t &x, const qint8x8_t &y, int fixed_point_position) +{ + return vmull_qs8(x, y, fixed_point_position); +} + +inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z, int fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + return vmlaq_f32(x, y, z); +} + +inline qint16x8_t internal_vmlal(const qint16x8_t &x, const qint8x8_t &y, const qint8x8_t &z, int fixed_point_position) +{ + return vqmlal_qs8(x, y, z, fixed_point_position); +} + +template +class convolver_1x1 +{ +public: + static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) + { + const int input_stride_y = input->info()->strides_in_bytes().y(); + const int input_stride_z = input->info()->strides_in_bytes().z(); + const int output_stride_y = output->info()->strides_in_bytes().y(); + const int output_stride_z = output->info()->strides_in_bytes().z(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = output->info()->dimension(0); + const int output_h = output->info()->dimension(1); + const int range_z = window.z().end() - window.z().start(); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const int fixed_point_position = input->info()->fixed_point_position(); + + // setup output window for the iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); + window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY))); + window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), range_z)); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + + Iterator out(output, window_out); + Iterator in(input, window_in); + Iterator k(weights, window_k); + + const uint8_t *k_ptr = k.ptr(); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + /* + For a detailed explanation on how the algorithm works refer to template <> class convolver_3x3<1> + */ + const uint8_t *input_ptr = in.ptr(); + uint8_t *out_ptr = out.ptr(); + int ih = 0; + int oh = 0; + for(int oz = 0; oz < range_z; ++oz) + { + auto p_out_base = out_ptr + oz * output_stride_z; + // Step 1 + { + const auto k_val = reinterpret_cast(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w); + const auto vk = internal_vdupq_n(*k_val); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + const int offset_xy = ih * input_stride_y; + auto in_val = reinterpret_cast(input_ptr + (0 * input_stride_z + offset_xy)); + auto p_out = reinterpret_cast(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration) + { + internal_vst1q(p_out, internal_vmull(vk, internal_vld1q(in_val), fixed_point_position)); + } + } + } + // Step 2 + for(int p = 1; p < kernel_depth; ++p) + { + const auto k_val = reinterpret_cast(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w); + const auto vk = internal_vdupq_n(*k_val); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + const int offset_xy = ih * input_stride_y; + auto in_val = reinterpret_cast(input_ptr + p * input_stride_z + offset_xy); + auto p_out = reinterpret_cast(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration) + { + internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q(in_val), fixed_point_position)); + } + } + } + } + }, + in, out); + } +}; + +inline float32x4x3_t load_matrix_row(const float *ptr) +{ + const float32x4x3_t r = + { + { + vld1q_dup_f32(ptr), + vld1q_dup_f32(1 + ptr), + vld1q_dup_f32(2 + ptr) + } + }; + return r; +} +inline qint8x8x3_t load_matrix_row(const qint8_t *ptr) +{ + /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes: + r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */ + const qint8x8x3_t r = + { + { + vld1_dup_qs8(ptr), + vld1_dup_qs8(1 + ptr), + vld1_dup_qs8(2 + ptr) + } + }; + return r; +} + +template +float32x4x2_t convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position); + +template <> +inline float32x4x2_t convolve_3x3<1>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + + const float32x4x3_t vtop = + { + { + vld1q_f32(in_top), + vld1q_f32(in_top + 4), + vld1q_f32(in_top + 8) + } + }; + const float32x4x3_t vmid = + { + { + vld1q_f32(in_mid), + vld1q_f32(in_mid + 4), + vld1q_f32(in_mid + 8) + } + }; + const float32x4x3_t vlow = + { + { + vld1q_f32(in_low), + vld1q_f32(in_low + 4), + vld1q_f32(in_low + 8) + } + }; + float32x4x2_t out = + { + { + vmulq_f32(vtop.val[0], m0.val[0]), + vmulq_f32(vtop.val[1], m0.val[0]) + } + }; + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]); + out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]); + out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]); + out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]); + out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]); + return out; +} + +template <> +inline float32x4x2_t convolve_3x3<2>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position) +{ + float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3); + return out; +} + +template <> +inline float32x4x2_t convolve_3x3<3>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position) +{ + float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); + out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); + return out; +} + +template +qint16x8x2_t convolve_3x3(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position); + +template <> +inline qint16x8x2_t convolve_3x3<1>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + + const qint8x8x3_t vtop = + { + { + vld1_qs8(in_top), + vld1_qs8(in_top + 8), + vld1_qs8(in_top + 16) + } + }; + const qint8x8x3_t vmid = + { + { + vld1_qs8(in_mid), + vld1_qs8(in_mid + 8), + vld1_qs8(in_mid + 16) + } + }; + const qint8x8x3_t vlow = + { + { + vld1_qs8(in_low), + vld1_qs8(in_low + 8), + vld1_qs8(in_low + 16) + } + }; + qint16x8x2_t out = + { + { + vmull_qs8(vtop.val[0], m0.val[0], fixed_point_position), + vmull_qs8(vtop.val[1], m0.val[0], fixed_point_position) + } + }; + out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 1), m0.val[1], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 2), m0.val[2], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vmid.val[0], m1.val[0], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 1), m1.val[1], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 2), m1.val[2], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vlow.val[0], m2.val[0], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 1), m2.val[1], fixed_point_position); + out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 2), m2.val[2], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 1), m0.val[1], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 2), m0.val[2], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vmid.val[1], m1.val[0], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 1), m1.val[1], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 2), m1.val[2], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vlow.val[1], m2.val[0], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 1), m2.val[1], fixed_point_position); + out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 2), m2.val[2], fixed_point_position); + return out; +} + +template <> +inline qint16x8x2_t convolve_3x3<2>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position) +{ + qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 2), out.val[0], 1); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 4), out.val[0], 2); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 3); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 0), out.val[0], 4); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 2), out.val[0], 5); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 4), out.val[0], 6); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 6), out.val[0], 7); + return out; +} + +template <> +inline qint16x8x2_t convolve_3x3<3>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position) +{ + qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 3), out.val[0], 1); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 2); + out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 1), out.val[0], 3); + return out; +} + +template +void store_results(float *buffer, const float32x4x2_t &values); + +template <> +void store_results<1>(float *buffer, const float32x4x2_t &values) +{ + vst1q_f32(buffer, values.val[0]); + vst1q_f32(buffer + 4, values.val[1]); +} + +template <> +void store_results<2>(float *buffer, const float32x4x2_t &values) +{ + vst1q_f32(buffer, values.val[0]); +} + +template <> +void store_results<3>(float *buffer, const float32x4x2_t &values) +{ + vst1_f32(buffer, vget_low_f32(values.val[0])); +} + +template +void store_results(qint16_t *buffer, const qint16x8x2_t &values); + +template <> +void store_results<1>(qint16_t *buffer, const qint16x8x2_t &values) +{ + vst1q_qs16(buffer, values.val[0]); + vst1q_qs16(buffer + 8, values.val[1]); +} + +template <> +void store_results<2>(qint16_t *buffer, const qint16x8x2_t &values) +{ + vst1q_qs16(buffer, values.val[0]); +} + +template <> +void store_results<3>(qint16_t *buffer, const qint16x8x2_t &values) +{ + vst1_qs16(buffer, vget_low_s16(values.val[0])); +} + +template +void accumulate_results(float *buffer, const float32x4x2_t &values); + +template <> +void accumulate_results<1>(float *buffer, const float32x4x2_t &values) +{ + vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0])); + vst1q_f32(buffer + 4, vaddq_f32(vld1q_f32(buffer + 4), values.val[1])); +} + +template <> +void accumulate_results<2>(float *buffer, const float32x4x2_t &values) +{ + vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0])); +} + +template <> +void accumulate_results<3>(float *buffer, const float32x4x2_t &values) +{ + vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0]))); +} + +template +void accumulate_results(qint16_t *buffer, const qint16x8x2_t &values); + +template <> +void accumulate_results<1>(qint16_t *buffer, const qint16x8x2_t &values) +{ + vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0])); + vst1q_qs16(buffer + 8, vqaddq_qs16(vld1q_qs16(buffer + 8), values.val[1])); +} + +template <> +void accumulate_results<2>(qint16_t *buffer, const qint16x8x2_t &values) +{ + vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0])); +} + +template <> +void accumulate_results<3>(qint16_t *buffer, const qint16x8x2_t &values) +{ + vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0]))); +} + +template +int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration); + +template <> +int get_input_num_elems_processed<1>(unsigned int num_elems_written_per_iteration) +{ + return num_elems_written_per_iteration; +} + +template <> +int get_input_num_elems_processed<2>(unsigned int num_elems_written_per_iteration) +{ + return num_elems_written_per_iteration << 1; +} + +template <> +int get_input_num_elems_processed<3>(unsigned int num_elems_written_per_iteration) +{ + return num_elems_written_per_iteration * 3; +} + +template +class convolver_3x3 +{ +public: + static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) + { + ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); + const int input_stride_x = input->info()->strides_in_bytes().x(); + const int input_stride_y = input->info()->strides_in_bytes().y(); + const int input_stride_z = input->info()->strides_in_bytes().z(); + const int output_stride_y = output->info()->strides_in_bytes().y(); + const int output_stride_z = output->info()->strides_in_bytes().z(); + const int kernel_stride_x = weights->info()->strides_in_bytes().x(); + const int kernel_stride_y = weights->info()->strides_in_bytes().y(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = output->info()->dimension(0); + const int output_h = output->info()->dimension(1); + const int num_planes_z = window.z().end() - window.z().start(); + const int delta_input = get_input_num_elems_processed(num_elems_written_per_iteration); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_x = std::get<0>(conv_info.pad()); + const unsigned int conv_pad_y = std::get<1>(conv_info.pad()); + const int fixed_point_position = input->info()->fixed_point_position(); + + // setup output window for the iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); + window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY))); + window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), num_planes_z)); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + + Iterator out(output, window_out); + Iterator in(input, window_in); + Iterator k(weights, window_k); + + const uint8_t *k_ptr = k.ptr(); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y; + uint8_t *out_ptr = out.ptr(); + int ih = 0; + int oh = 0; + /* + Each thread executing this kernel computes one or more output's volume planes. + + Let's say the 3rd dimension of the output volume is 32, the first thread will compute the output for Z = [0,7], the second thread will compute the output for Z = [8,15], + the third thread [16,24] and the fourth thread [25,31]. + + The algorithm outer loop iterates over Z, P, Y, X where P is the depth/3rd dimension of each kernel. This order is not arbitrary, the main benefit of this + is that we setup the neon registers containing the kernerl's values only once and then compute each XY using the preloaded registers as opposed as doing this for every XY value. + + The algorithm does not require allocating any additional memory amd computes the results directly in-place in two stages: + 1) Convolve plane 0 with kernel 0 and initialize the corresponding output plane with these values. + 2) Convolve the remaining planes and accumulate the results in the output's plane which has been initialized in step 1. + */ + + for(int oz = 0; oz < num_planes_z; ++oz) + { + uint8_t *p_out_base = out_ptr + oz * output_stride_z; + // Step 1 + { + const auto ptr_k_r0 = reinterpret_cast(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r1 = reinterpret_cast(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r2 = reinterpret_cast(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x); + const auto vk_r0 = load_matrix_row(ptr_k_r0); + const auto vk_r1 = load_matrix_row(ptr_k_r1); + const auto vk_r2 = load_matrix_row(ptr_k_r2); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_top = reinterpret_cast(input_ptr + 0 * input_stride_z + (ih + 0) * input_stride_y); + auto in_mid = reinterpret_cast(input_ptr + 0 * input_stride_z + (ih + 1) * input_stride_y); + auto in_low = reinterpret_cast(input_ptr + 0 * input_stride_z + (ih + 2) * input_stride_y); + auto p_out = reinterpret_cast(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, + in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) + { + auto vres = convolve_3x3(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position); + store_results(p_out, vres); + } + } + } + // Step 2 + for(int p = 1; p < kernel_depth; ++p) + { + const auto ptr_k_r0 = reinterpret_cast(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r1 = reinterpret_cast(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x); + const auto ptr_k_r2 = reinterpret_cast(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x); + const auto vk_r0 = load_matrix_row(ptr_k_r0); + const auto vk_r1 = load_matrix_row(ptr_k_r1); + const auto vk_r2 = load_matrix_row(ptr_k_r2); + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_top = reinterpret_cast(input_ptr + p * input_stride_z + (ih + 0) * input_stride_y); + auto in_mid = reinterpret_cast(input_ptr + p * input_stride_z + (ih + 1) * input_stride_y); + auto in_low = reinterpret_cast(input_ptr + p * input_stride_z + (ih + 2) * input_stride_y); + auto p_out = reinterpret_cast(p_out_base + oh * output_stride_y); + for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, + in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) + { + auto vres = convolve_3x3(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position); + accumulate_results(p_out, vres); + } + } + } + } + }, + in, out); + } +}; + +template +inline void convolve_1x1(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) +{ + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + switch(conv_stride_x) + { + case 1: + convolver_1x1::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); + break; + case 2: + convolver_1x1::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); + break; + case 3: + convolver_1x1::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } +} + +template +inline void convolve_3x3(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) +{ + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + switch(conv_stride_x) + { + case 1: + convolver_3x3::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); + break; + case 2: + convolver_3x3::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); + break; + case 3: + convolver_3x3::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } +} +} // namespace + +NEDirectConvolutionLayerKernel::NEDirectConvolutionLayerKernel() + : _input(nullptr), _weights(nullptr), _output(nullptr), _conv_info(), _border_size(0), _kernel_size(0), _num_elems_read_per_iteration(0), _num_elems_written_per_iteration(0) +{ +} + +BorderSize NEDirectConvolutionLayerKernel::border_size() const +{ + return _border_size; +} + +void NEDirectConvolutionLayerKernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS16, DataType::F32); + ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())), + "Pad > 0 not supported for 1x1 weights"); + ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1), + "Pad > 1 not supported for 3x3 weights"); + ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported."); + + const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); + const unsigned int conv_pad_x = std::get<0>(conv_info.pad()); + const unsigned int conv_pad_y = std::get<1>(conv_info.pad()); + + _input = input; + _weights = weights; + _output = output; + _conv_info = conv_info; + _kernel_size = weights->info()->dimension(0); + _border_size = BorderSize(conv_pad_y, conv_pad_x); + + Window win = calculate_max_window(*output->info()); + + switch(_kernel_size) + { + case 1: + { + _num_elems_written_per_iteration = (input->info()->data_type() == DataType::QS8) ? 8 : 4; + _num_elems_read_per_iteration = conv_stride_x * _num_elems_written_per_iteration; + + win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration)); + AccessWindowHorizontal input_access(input->info(), 0, _num_elems_read_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration); + update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + break; + } + case 3: + { + if(input->info()->data_type() == DataType::F32) + { + _num_elems_read_per_iteration = 12; + _num_elems_written_per_iteration = 16 >> conv_stride_x; + } + else + { + _num_elems_read_per_iteration = 24; + _num_elems_written_per_iteration = 32 >> conv_stride_x; + } + + // Calculate right and bottom border + const unsigned int conv_stride_y = std::get<1>(_conv_info.stride()); + const int input_width = input->info()->dimension(0); + const int input_height = input->info()->dimension(1); + const int upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * conv_stride_x + _kernel_size), _num_elems_read_per_iteration) - conv_pad_x - input_width; + const int upper_bound_h = ((output->info()->dimension(1) - 1) * conv_stride_y - conv_pad_y + _kernel_size) - input_height; + _border_size.right = std::max(upper_bound_w, static_cast(_kernel_size)); + _border_size.bottom = std::max(upper_bound_h, static_cast(_kernel_size)); + + // Create window and update padding + win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration)); + AccessWindowStatic input_access(input->info(), -conv_pad_x, -conv_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); + AccessWindowStatic weights_access(weights->info(), 0, 0, _kernel_size, _kernel_size); + AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration); + update_window_and_padding(win, input_access, weights_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + break; + } + default: + { + ARM_COMPUTE_ERROR("Not implemented"); + break; + } + } + + INEKernel::configure(win); +} + +void NEDirectConvolutionLayerKernel::run(const Window &window) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); + + const int kernel_size = _weights->info()->dimension(0); + + switch(kernel_size) + { + case 1: + { + if(_input->info()->data_type() == DataType::QS8) + { + convolve_1x1(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + } + else + { + convolve_1x1(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + } + break; + } + case 3: + { + if(_input->info()->data_type() == DataType::QS8) + { + convolve_3x3(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + } + else + { + convolve_3x3(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + } + break; + } + default: + { + ARM_COMPUTE_ERROR("Only kernel sizes 1x1 and 3x3 are supported."); + break; + } + } +} -- cgit v1.2.1