/* * Copyright (c) 2016-2018 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. */ #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 #include #include #include using namespace arm_compute; template void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply(const Window &window_in, const Window &window_w, const Window &window_out) { ARM_COMPUTE_ERROR("Unsupported data types"); ARM_COMPUTE_UNUSED(window_in); ARM_COMPUTE_UNUSED(window_w); ARM_COMPUTE_UNUSED(window_out); } namespace arm_compute { template <> void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply(const Window &window_in, const Window &window_w, const Window &window_out) { Iterator in(_input0, window_in); Iterator in2(_input1, window_w); 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(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(input_ptr + i * input_stride_x)); const auto weights = vld1q_f32(reinterpret_cast(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); } template <> void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply(const Window &window_in, const Window &window_w, const Window &window_out) { Iterator in(_input0, window_in); Iterator in2(_input1, window_w); Iterator out(_output, window_out); const int input_offset = -_input0->info()->quantization_info().offset; const int weights_offset = -_input1->info()->quantization_info().offset; 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(); const int read_step = 16 / _input0->info()->element_size(); const int32x4_t v_input_offset = vdupq_n_s32(input_offset); const int32x4_t v_weights_offset = vdupq_n_s32(weights_offset); 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(out.ptr() + (id.y() + id.z() * input_h) * output_stride_x); int32x4_t row_dot = vdupq_n_s32(0); for(int i = 0; i < input_w; i += read_step) { // Read values const auto input = vld1q_u8(reinterpret_cast(input_ptr + i * input_stride_x)); const auto weights = vld1q_u8(reinterpret_cast(weights_ptr + i * weights_stride_x)); // Add offsets const int32x4x4_t input_s32 = { { vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_low_u8(input))))), vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_low_u8(input))))), vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_high_u8(input))))), vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_high_u8(input))))) } }; const int32x4x4_t weights_s32 = { { vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_low_u8(weights))))), vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_low_u8(weights))))), vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vget_high_u8(weights))))), vaddw_s16(v_weights_offset, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(vget_high_u8(weights))))) } }; // Dot row_dot = vaddq_s32(row_dot, vmulq_s32(input_s32.val[0], weights_s32.val[0])); row_dot = vaddq_s32(row_dot, vmulq_s32(input_s32.val[1], weights_s32.val[1])); row_dot = vaddq_s32(row_dot, vmulq_s32(input_s32.val[2], weights_s32.val[2])); row_dot = vaddq_s32(row_dot, vmulq_s32(input_s32.val[3], weights_s32.val[3])); } // Reduction auto temp = vadd_s32(vget_high_s32(row_dot), vget_low_s32(row_dot)); temp = vpadd_s32(temp, temp); *output_ptr = vget_lane_s32(temp, 0); }, in, in2, out); } } //namespace arm_compute NEGEMMMatrixVectorMultiplyKernel::NEGEMMMatrixVectorMultiplyKernel() : _func(nullptr), _input0(nullptr), _input1(nullptr), _output(nullptr), _border_size(0) { } BorderSize NEGEMMMatrixVectorMultiplyKernel::border_size() const { return _border_size; } void NEGEMMMatrixVectorMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input0->info()->data_type()) && (output->info()->data_type() != DataType::S32)); ARM_COMPUTE_ERROR_ON(input0->info()->dimension(2) != input1->info()->dimension(1)); _input0 = input0; _input1 = input1; _output = output; // Set appropriate function to run switch(input0->info()->data_type()) { case DataType::QASYMM8: _func = &NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply; break; case DataType::F32: _func = &NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply; break; default: ARM_COMPUTE_ERROR("Unsupported data type"); } // Configure kernel window const unsigned int num_elems_read_per_iteration = 16 / _input0->info()->element_size(); const unsigned int border_x = ceil_to_multiple(input0->info()->dimension(0), num_elems_read_per_iteration) - input0->info()->dimension(0); _border_size = BorderSize(0, border_x); 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); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); 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)); if(_func != nullptr) { (this->*_func)(window_in, window_weights, window_out); } }