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author | Michalis Spyrou <michalis.spyrou@arm.com> | 2017-11-23 12:10:21 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:41:04 +0000 |
commit | b7b31538eb9137e4d3e8de6d381dcbe9fc58df94 (patch) | |
tree | 7cca8c388cfb15867b9d92c6fd793ca1588b6526 /src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp | |
parent | 02bf80d4554cfc824a76008905921cb564bee999 (diff) | |
download | ComputeLibrary-b7b31538eb9137e4d3e8de6d381dcbe9fc58df94.tar.gz |
COMPMID-464 Implement Depthwise separable convolution on NEON
Change-Id: Iccd686be18381e96bcf09b14c7017c6dda0f38d8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/109824
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Diffstat (limited to 'src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp')
-rw-r--r-- | src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp | 131 |
1 files changed, 131 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp new file mode 100644 index 0000000000..c28dcf7463 --- /dev/null +++ b/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp @@ -0,0 +1,131 @@ +/* + * Copyright (c) 2016, 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/NEGEMMMatrixVectorMultiplyKernel.h" + +#include "arm_compute/core/AccessWindowStatic.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/Types.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include <arm_neon.h> +#include <cstddef> +#include <cstdint> +#include <tuple> + +using namespace arm_compute; + +NEGEMMMatrixVectorMultiplyKernel::NEGEMMMatrixVectorMultiplyKernel() + : _input0(nullptr), _input1(nullptr), _output(nullptr) +{ +} + +void NEGEMMMatrixVectorMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output); + ARM_COMPUTE_ERROR_ON(input0->info()->dimension(2) != input1->info()->dimension(1)); + + _input0 = input0; + _input1 = input1; + _output = output; + + // Configure kernel window + const unsigned int num_elems_read_per_iteration = 4; + + Window win = calculate_max_window(*input0->info(), Steps(num_elems_read_per_iteration)); + + AccessWindowHorizontal input0_access(input0->info(), 0, num_elems_read_per_iteration); + AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_read_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, 1); + + update_window_and_padding(win, input0_access, input1_access, output_access); + + _output->info()->set_valid_region(ValidRegion(Coordinates(), _output->info()->tensor_shape())); + + INEKernel::configure(win); +} + +void NEGEMMMatrixVectorMultiplyKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + + Window window_slice = window.first_slice_window_3D(); + + Window window_in(window); + Window window_weights(window_slice); + Window window_out(window); + + // Setup input0 slice + window_in.set(Window::DimX, Window::Dimension(0, _input0->info()->dimension(0), _input0->info()->dimension(0))); + window_in.set(Window::DimY, Window::Dimension(0, _input0->info()->dimension(1), 1)); + window_in.set(Window::DimZ, Window::Dimension(0, _input0->info()->dimension(2), 1)); + + // Setup input1 and output slice. Their dimensions are increased in the kernel. + window_weights.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_weights.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_weights.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + window_out.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_out.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Iterator in(_input0, window_in); + Iterator in2(_input1, window_weights); + Iterator out(_output, window_out); + + const int input_w = _input0->info()->dimension(0); + const int input_h = _input0->info()->dimension(1); + const int input_stride_x = _input0->info()->strides_in_bytes().x(); + const int weights_stride_x = _input1->info()->strides_in_bytes().x(); + const int weights_stride_y = _input1->info()->strides_in_bytes().y(); + const int output_stride_x = _output->info()->strides_in_bytes().x(); + + execute_window_loop(window_in, [&](const Coordinates & id) + { + // Get pointers + const uint8_t *const input_ptr = in.ptr(); + const uint8_t *const weights_ptr = in2.ptr() + id.z() * weights_stride_y; + auto output_ptr = reinterpret_cast<float *>(out.ptr() + (id.y() + id.z() * input_h) * output_stride_x); + + float32x4_t row_dot = vdupq_n_f32(0.f); + for(int i = 0; i < input_w; i += 4) + { + const auto input = vld1q_f32(reinterpret_cast<const float *>(input_ptr + i * input_stride_x)); + const auto weights = vld1q_f32(reinterpret_cast<const float *>(weights_ptr + i * weights_stride_x)); + row_dot = vaddq_f32(row_dot, vmulq_f32(input, weights)); + } + + auto temp = vadd_f32(vget_high_f32(row_dot), vget_low_f32(row_dot)); + temp = vpadd_f32(temp, temp); + + *output_ptr = vget_lane_f32(temp, 0); + }, + in, in2, out); +} |