| /* |
| * Copyright (c) 2016-2019 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/CPP/Validate.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> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input0); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); |
| if(is_data_type_quantized_asymmetric(input0->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->num_dimensions() == input1->num_dimensions()); |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(2) != input1->dimension(1)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(DataLayoutDimension::HEIGHT) != output->dimension(DataLayoutDimension::HEIGHT)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(DataLayoutDimension::WIDTH) != output->dimension(DataLayoutDimension::WIDTH)); |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output) |
| { |
| const unsigned int num_elems_read_per_iteration = 16 / input0->element_size(); |
| |
| Window win = calculate_max_window(*input0, Steps(num_elems_read_per_iteration)); |
| |
| AccessWindowHorizontal input0_access(input0, 0, num_elems_read_per_iteration); |
| AccessWindowHorizontal input1_access(input1, 0, num_elems_read_per_iteration); |
| AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); |
| |
| bool window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); |
| |
| output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| template <typename I0, typename I1, typename O> |
| 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); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| template <> |
| void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<half, half, half>(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<__fp16 *>(out.ptr() + (id.y() + id.z() * input_h) * output_stride_x); |
| |
| float16x8_t row_dot = vdupq_n_f16(0.f); |
| for(int i = 0; i < input_w; i += 8) |
| { |
| const auto input = vld1q_f16(reinterpret_cast<const __fp16 *>(input_ptr + i * input_stride_x)); |
| const auto weights = vld1q_f16(reinterpret_cast<const __fp16 *>(weights_ptr + i * weights_stride_x)); |
| row_dot = vaddq_f16(row_dot, vmulq_f16(input, weights)); |
| } |
| |
| auto temp = vadd_f16(vget_high_f16(row_dot), vget_low_f16(row_dot)); |
| temp = vpadd_f16(temp, temp); |
| temp = vpadd_f16(temp, temp); |
| |
| *output_ptr = vget_lane_f16(temp, 0); |
| }, |
| in, in2, out); |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| template <> |
| void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<float, float, float>(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<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); |
| } |
| |
| template <> |
| void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<uint8_t, uint8_t, int32_t>(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().uniform().offset; |
| const int weights_offset = -_input1->info()->quantization_info().uniform().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<int32_t *>(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<const uint8_t *>(input_ptr + i * input_stride_x)); |
| const auto weights = vld1q_u8(reinterpret_cast<const uint8_t *>(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); |
| } |
| |
| template <> |
| void NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<int8_t, int8_t, int32_t>(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().uniform().offset; |
| const int weights_offset = -_input1->info()->quantization_info().uniform().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<int32_t *>(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_s8(reinterpret_cast<const int8_t *>(input_ptr + i * input_stride_x)); |
| const auto weights = vld1q_s8(reinterpret_cast<const int8_t *>(weights_ptr + i * weights_stride_x)); |
| |
| // Add offsets |
| const int32x4x4_t input_s32 = |
| { |
| { |
| vaddw_s16(v_input_offset, vget_low_s16(vmovl_s8(vget_low_s8(input)))), |
| vaddw_s16(v_input_offset, vget_high_s16(vmovl_s8(vget_low_s8(input)))), |
| vaddw_s16(v_input_offset, vget_low_s16(vmovl_s8(vget_high_s8(input)))), |
| vaddw_s16(v_input_offset, vget_high_s16(vmovl_s8(vget_high_s8(input)))) |
| } |
| }; |
| const int32x4x4_t weights_s32 = |
| { |
| { |
| vaddw_s16(v_weights_offset, vget_low_s16(vmovl_s8(vget_low_s8(weights)))), |
| vaddw_s16(v_weights_offset, vget_high_s16(vmovl_s8(vget_low_s8(weights)))), |
| vaddw_s16(v_weights_offset, vget_low_s16(vmovl_s8(vget_high_s8(weights)))), |
| vaddw_s16(v_weights_offset, vget_high_s16(vmovl_s8(vget_high_s8(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); |
| } |
| |
| 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_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info())); |
| |
| _input0 = input0; |
| _input1 = input1; |
| _output = output; |
| |
| // Set appropriate function to run |
| switch(input0->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| _func = &NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<uint8_t, uint8_t, int32_t>; |
| break; |
| case DataType::QASYMM8_SIGNED: |
| _func = &NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<int8_t, int8_t, int32_t>; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = &NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<half, half, half>; |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::F32: |
| _func = &NEGEMMMatrixVectorMultiplyKernel::matrix_vector_multiply<float, float, float>; |
| 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); |
| |
| auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEGEMMMatrixVectorMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first); |
| return Status{}; |
| } |
| |
| 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); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| 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)); |
| |
| (this->*_func)(window_in, window_weights, window_out); |
| } |
| } // namespace arm_compute |