From 7362f0de41305eccb4b2b9b606647ffe318d32b7 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 18 Oct 2017 17:58:22 +0100 Subject: COMPMID-464 Implement Depthwise convolution 3x3 on NEON Change-Id: Ie4e1803a52afac6b6c597c6e551729dad2347cd1 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92607 Tested-by: Kaizen Reviewed-by: Pablo Tello --- .../kernels/NEDepthwiseConvolution3x3Kernel.cpp | 186 ++++++++++ .../kernels/NEDirectConvolutionLayerKernel.cpp | 373 +-------------------- 2 files changed, 188 insertions(+), 371 deletions(-) create mode 100644 src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp (limited to 'src/core/NEON') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp new file mode 100644 index 0000000000..62aa934f26 --- /dev/null +++ b/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp @@ -0,0 +1,186 @@ +/* + * 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/NEDepthwiseConvolution3x3Kernel.h" +#include "arm_compute/core/NEON/kernels/convolution/NEDirectConvolutionDetail.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/AccessWindowTranspose.h" +#include "arm_compute/core/Coordinates.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/INEKernel.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +using namespace arm_compute; +using namespace arm_compute::detail; + +NEDepthwiseConvolution3x3Kernel::NEDepthwiseConvolution3x3Kernel() + : _border_size(0), _input(), _output(), _weights(), _conv_info() +{ +} + +BorderSize NEDepthwiseConvolution3x3Kernel::border_size() const +{ + return _border_size; +} + +void NEDepthwiseConvolution3x3Kernel::configure(const ITensor *input, ITensor *output, const ITensor *weights, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3); + + std::pair expected_output = scaled_dimensions(input->info()->tensor_shape().x(), input->info()->tensor_shape().y(), + weights->info()->tensor_shape().x(), weights->info()->tensor_shape().y(), + conv_info); + + ARM_COMPUTE_UNUSED(expected_output); + ARM_COMPUTE_ERROR_ON(expected_output.first != output->info()->tensor_shape().x()); + ARM_COMPUTE_ERROR_ON(expected_output.second != output->info()->tensor_shape().y()); + + _input = input; + _output = output; + _weights = weights; + _conv_info = conv_info; + const unsigned int conv_stride_x = conv_info.stride().first; + const unsigned int conv_pad_x = conv_info.pad().first; + const unsigned int conv_pad_y = conv_info.pad().second; + + ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3); + + const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; + _border_size = BorderSize(conv_pad_y, conv_pad_x); + + // Configure kernel window + Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration)); + + AccessWindowStatic input_access(input->info(), -conv_pad_x, -conv_pad_y, input->info()->dimension(0) + _border_size.right, input->info()->dimension(1) + _border_size.bottom); + AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1)); + 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())); + + INEKernel::configure(win); +} + +template +class convolver_3x3 +{ +public: + static void convolve(const Window &window, unsigned int num_elems_written_per_iteration, + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) + { + const int input_stride_x = input->info()->strides_in_bytes().x(); + const int input_stride_y = input->info()->strides_in_bytes().y(); + const int output_stride_y = output->info()->strides_in_bytes().y(); + const int kernel_stride_y = weights->info()->strides_in_bytes().y(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int output_w = output->info()->dimension(0); + const int output_h = output->info()->dimension(1); + const int delta_input = get_input_num_elems_processed(num_elems_written_per_iteration); + 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()); + + // 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))); + + // setup input window for the iterator + Window window_in = window; + // we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0 + window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + + Window window_k = calculate_max_window(*weights->info(), Steps(1u)); + + Iterator in(input, window_in); + Iterator out(output, window_out); + Iterator w(weights, window_k); + + const uint8_t *weights_ptr = w.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; + int ih = 0; + int oh = 0; + + const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z; + const auto ptr_weights_r0 = reinterpret_cast(ptr_weights_base); + const auto ptr_weights_r1 = reinterpret_cast(ptr_weights_base + kernel_stride_y); + const auto ptr_weights_r2 = reinterpret_cast(ptr_weights_base + kernel_stride_y * 2); + const auto vw_r0 = load_matrix_row(ptr_weights_r0); + const auto vw_r1 = load_matrix_row(ptr_weights_r1); + const auto vw_r2 = load_matrix_row(ptr_weights_r2); + + for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) + { + auto in_top = reinterpret_cast(input_ptr + (ih + 0) * input_stride_y); + auto in_mid = reinterpret_cast(input_ptr + (ih + 1) * input_stride_y); + auto in_low = reinterpret_cast(input_ptr + (ih + 2) * input_stride_y); + auto p_out = reinterpret_cast(out.ptr() + 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, vw_r0, vw_r1, vw_r2, 0); + store_results(p_out, vres); + } + } + }, + in, out); + } +}; + +void NEDepthwiseConvolution3x3Kernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_UNUSED(info); + + const unsigned int conv_stride_x = _conv_info.stride().first; + const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; + + switch(conv_stride_x) + { + case 1: + convolver_3x3<1>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + break; + case 2: + convolver_3x3<2>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + break; + case 3: + convolver_3x3<3>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + break; + default: + ARM_COMPUTE_ERROR("Not implemented"); + } +} diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp index 60a3a1b636..bf23c4b2d2 100644 --- a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp +++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp @@ -22,6 +22,7 @@ * SOFTWARE. */ #include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h" +#include "arm_compute/core/NEON/kernels/convolution/NEDirectConvolutionDetail.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/Error.h" @@ -37,6 +38,7 @@ #include using namespace arm_compute; +using namespace arm_compute::detail; namespace { @@ -428,122 +430,6 @@ public: }; #ifdef ARM_COMPUTE_AARCH64_V8_2 -inline float16x8x3_t load_matrix_row(const float16_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 float16x8x3_t r = - { - { - vld1q_dup_f16(ptr), - vld1q_dup_f16(1 + ptr), - vld1q_dup_f16(2 + ptr) - } - }; - return r; -} - -template -float16x8x2_t convolve_3x3(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - int fixed_point_position); - -template <> -float16x8x2_t convolve_3x3<1>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - int fixed_point_position) -{ - ARM_COMPUTE_UNUSED(fixed_point_position); - - const float16x8x3_t vtop = - { - { - vld1q_f16(in_top), - vld1q_f16(in_top + 8), - vld1q_f16(in_top + 16) - } - }; - const float16x8x3_t vmid = - { - { - vld1q_f16(in_mid), - vld1q_f16(in_mid + 8), - vld1q_f16(in_mid + 16) - } - }; - const float16x8x3_t vlow = - { - { - vld1q_f16(in_low), - vld1q_f16(in_low + 8), - vld1q_f16(in_low + 16) - } - }; - float16x8x2_t out = - { - { - vmulq_f16(vtop.val[0], m0.val[0]), - vmulq_f16(vtop.val[1], m0.val[0]) - } - }; - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 1), m0.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 2), m0.val[2])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vmid.val[0], m1.val[0])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 1), m1.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 2), m1.val[2])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vlow.val[0], m2.val[0])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 1), m2.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 2), m2.val[2])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 1), m0.val[1])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 2), m0.val[2])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vmid.val[1], m1.val[0])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 1), m1.val[1])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 2), m1.val[2])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vlow.val[1], m2.val[0])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 1), m2.val[1])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 2), m2.val[2])); - return out; -} - -template <> -inline float16x8x2_t convolve_3x3<2>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - int fixed_point_position) -{ - float16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 2), out.val[0], 1); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 0), out.val[0], 2); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 2), out.val[0], 3); - return out; -} - -template <> -inline float16x8x2_t convolve_3x3<3>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - int fixed_point_position) -{ - float16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 3), out.val[0], 1); - return out; -} - -template -void store_results(float16_t *buffer, const float16x8x2_t &values); - -template <> -void store_results<1>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1q_f16(buffer, values.val[0]); - vst1q_f16(buffer + 8, values.val[1]); -} - -template <> -void store_results<2>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1q_f16(buffer, values.val[0]); -} - -template <> -void store_results<3>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1_f16(buffer, vget_low_f16(values.val[0])); -} template void accumulate_results(float16_t *buffer, const float16x8x2_t &values); @@ -569,33 +455,6 @@ void accumulate_results<3>(float16_t *buffer, const float16x8x2_t &values) #endif /* ARM_COMPUTE_AARCH64_V8_2 */ -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_5x5(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position); @@ -749,213 +608,6 @@ inline float32x4x2_t convolve_5x5<3>(const float *in_0, const float *in_1, const return out; } -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); @@ -1000,27 +652,6 @@ 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 { -- cgit v1.2.1