| /* |
| * 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); |
| AccessWindowStatic output_access(output->info(), 0, 0, output->info()->dimension(0), output->info()->dimension(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); |
| } |