| /* |
| * Copyright (c) 2017-2018 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/NELocallyConnectedMatrixMultiplyKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.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; |
| |
| namespace arm_compute |
| { |
| class Coordinates; |
| } // namespace arm_compute |
| |
| namespace |
| { |
| void vector_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| const auto width_matrix_b = static_cast<int>(output->info()->dimension(0)); |
| const auto in_b_stride = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type())); |
| const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0)); |
| |
| // The implementation computes 16 elements per iteration |
| const int window_start_x = 16 * info.thread_id; |
| const int window_step_x = 16 * info.num_threads; |
| // Make sure (window_end_x - window_start_x) is a multiple of window_step_x |
| const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x; |
| |
| Window win_out(window); |
| win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); |
| |
| Window win_a(window); |
| win_a.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator ina(input0, win_a); |
| Iterator out(output, win_out); |
| |
| execute_window_loop(win_out, [&](const Coordinates & id) |
| { |
| if(id.x() > width_matrix_b) |
| { |
| return; |
| } |
| |
| float16x8_t acc0 = vdupq_n_f16(0.f); |
| float16x8_t acc1 = vdupq_n_f16(0.f); |
| float16x8_t acc2 = vdupq_n_f16(0.f); |
| float16x8_t acc3 = vdupq_n_f16(0.f); |
| |
| auto vec_a = reinterpret_cast<const float16_t *>(ina.ptr()); |
| auto matrix_b = reinterpret_cast<const float16_t *>(input1->ptr_to_element(Coordinates(id[0], 0, id[1]))); |
| |
| const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a; |
| |
| for(; vec_a <= (vec_a_end_addr - 4);) |
| { |
| const float16x4_t a0l = vld1_f16(vec_a); |
| |
| float16x8_t b00 = vld1q_f16(matrix_b); |
| float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride); |
| float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride); |
| float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride); |
| |
| float16x8_t b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride); |
| float16x8_t b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride); |
| float16x8_t b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride); |
| float16x8_t b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride); |
| |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 0)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 0)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 0)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 0)); |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 1)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 1)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 1)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 1)); |
| |
| matrix_b += 2 * in_b_stride; |
| |
| b00 = vld1q_f16(matrix_b); |
| b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride); |
| b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride); |
| b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride); |
| b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride); |
| b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride); |
| b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride); |
| b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride); |
| |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 2)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 2)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 2)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 2)); |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 3)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 3)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 3)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 3)); |
| |
| vec_a += 4; |
| matrix_b += 2 * in_b_stride; |
| } |
| |
| for(; vec_a < vec_a_end_addr;) |
| { |
| const float16_t a0 = *vec_a; |
| const float16x8_t b00 = vld1q_f16(matrix_b); |
| const float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride); |
| const float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride); |
| const float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride); |
| |
| acc0 = vaddq_f16(acc0, vmulq_n_f16(b00, a0)); |
| acc1 = vaddq_f16(acc1, vmulq_n_f16(b01, a0)); |
| acc2 = vaddq_f16(acc2, vmulq_n_f16(b02, a0)); |
| acc3 = vaddq_f16(acc3, vmulq_n_f16(b03, a0)); |
| |
| vec_a += 1; |
| matrix_b += in_b_stride; |
| } |
| |
| const auto vec_out = reinterpret_cast<float16_t *>(out.ptr()); |
| |
| vst1q_f16(vec_out + 0, acc0); |
| vst1q_f16(vec_out + 8, acc1); |
| vst1q_f16(vec_out + 16, acc2); |
| vst1q_f16(vec_out + 24, acc3); |
| }, |
| ina, out); |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(input0); |
| ARM_COMPUTE_UNUSED(input1); |
| ARM_COMPUTE_UNUSED(output); |
| ARM_COMPUTE_UNUSED(window); |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR("Not supported, recompile with -march=armv8.2-a+fp16+simd."); |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| |
| void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info) |
| { |
| const auto width_matrix_b = static_cast<int>(output->info()->dimension(0)); |
| const auto in_b_stride = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type())); |
| const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0)); |
| |
| // The implementation computes 16 elements per iteration |
| const int window_start_x = 16 * info.thread_id; |
| const int window_step_x = 16 * info.num_threads; |
| // Make sure (window_end_x - window_start_x) is a multiple of window_step_x |
| const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x; |
| |
| Window win_out(window); |
| win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); |
| |
| Window win_a(window); |
| win_a.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| |
| Iterator ina(input0, win_a); |
| Iterator out(output, win_out); |
| |
| execute_window_loop(win_out, [&](const Coordinates & id) |
| { |
| if(id.x() > width_matrix_b) |
| { |
| return; |
| } |
| |
| float32x4_t acc0 = vdupq_n_f32(0.f); |
| float32x4_t acc1 = vdupq_n_f32(0.f); |
| float32x4_t acc2 = vdupq_n_f32(0.f); |
| float32x4_t acc3 = vdupq_n_f32(0.f); |
| |
| auto vec_a = reinterpret_cast<const float *>(ina.ptr()); |
| auto matrix_b = reinterpret_cast<const float *>(input1->ptr_to_element(Coordinates(id[0], 0, id[1]))); |
| |
| #if __arm__ |
| asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a))); |
| asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b))); |
| asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + in_b_stride))); |
| #endif /* __arm__ */ |
| |
| const float *vec_a_end_addr = vec_a + num_elems_vec_a; |
| |
| for(; vec_a <= (vec_a_end_addr - 4);) |
| { |
| float32x2_t a0l = vld1_f32(vec_a); |
| |
| float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride); |
| float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride); |
| float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride); |
| float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride); |
| |
| float32x4_t b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride); |
| float32x4_t b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride); |
| float32x4_t b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride); |
| float32x4_t b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride); |
| |
| #if __arm__ |
| asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a))); |
| asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 1 * in_b_stride))); |
| asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 2 * in_b_stride))); |
| asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 3 * in_b_stride))); |
| asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 4 * in_b_stride))); |
| #endif /* __arm__ */ |
| |
| acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0); |
| acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0); |
| acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0); |
| acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0); |
| |
| acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1); |
| acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1); |
| acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1); |
| acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1); |
| |
| vec_a += 2; |
| matrix_b += 2 * in_b_stride; |
| |
| a0l = vld1_f32(vec_a); |
| |
| b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride); |
| b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride); |
| b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride); |
| b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride); |
| |
| b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride); |
| b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride); |
| b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride); |
| b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride); |
| |
| acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0); |
| acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0); |
| acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0); |
| acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0); |
| |
| acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1); |
| acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1); |
| acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1); |
| acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1); |
| |
| vec_a += 2; |
| matrix_b += 2 * in_b_stride; |
| } |
| |
| for(; vec_a < vec_a_end_addr;) |
| { |
| const float a0 = *vec_a; |
| |
| const float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride); |
| const float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride); |
| const float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride); |
| const float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride); |
| |
| acc0 = vmlaq_n_f32(acc0, b00, a0); |
| acc1 = vmlaq_n_f32(acc1, b01, a0); |
| acc2 = vmlaq_n_f32(acc2, b02, a0); |
| acc3 = vmlaq_n_f32(acc3, b03, a0); |
| |
| vec_a += 1; |
| matrix_b += in_b_stride; |
| } |
| |
| const auto vec_out = reinterpret_cast<float *>(out.ptr()); |
| |
| vst1q_f32(vec_out + 0, acc0); |
| vst1q_f32(vec_out + 4, acc1); |
| vst1q_f32(vec_out + 8, acc2); |
| vst1q_f32(vec_out + 12, acc3); |
| }, |
| ina, out); |
| } |
| |
| 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::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1)); |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output) |
| { |
| const unsigned int num_elems_processed_per_iteration_x = 16; |
| |
| Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x)); |
| |
| AccessWindowHorizontal input0_access(input0, 0, num_elems_processed_per_iteration_x); |
| AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_x); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x); |
| |
| bool window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| |
| return std::make_tuple(err, win); |
| } |
| } // namespace |
| |
| NELocallyConnectedMatrixMultiplyKernel::NELocallyConnectedMatrixMultiplyKernel() |
| : _input0(nullptr), _input1(nullptr), _output(nullptr) |
| { |
| } |
| |
| void NELocallyConnectedMatrixMultiplyKernel::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; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info()); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| INEKernel::configure(std::get<1>(win_config)); |
| } |
| |
| Status NELocallyConnectedMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()))); |
| |
| return Status{}; |
| } |
| |
| void NELocallyConnectedMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| switch(_input0->info()->data_type()) |
| { |
| case DataType::F16: |
| { |
| vector_matrix_multiply_f16(_input0, _input1, _output, window, info); |
| break; |
| } |
| case DataType::F32: |
| { |
| vector_matrix_multiply_f32(_input0, _input1, _output, window, info); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| } |