diff options
Diffstat (limited to 'src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp | 382 |
1 files changed, 0 insertions, 382 deletions
diff --git a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp deleted file mode 100644 index dfbfbd6fab..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp +++ /dev/null @@ -1,382 +0,0 @@ -/* - * Copyright (c) 2017-2021 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 "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h" - -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/KernelDescriptors.h" -#include "arm_compute/core/TensorInfo.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -namespace arm_compute -{ -namespace -{ -Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); - - if(output->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) != input->dimension(1), "Output vector must have length equal to the number of rows of the input matrix"); - } - return Status{}; -} -Status validate_arguments_matrix_b_reduction(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); - - if(output->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) != input->dimension(0), "Output vector must have length equal to the number of columns of the input matrix"); - } - return Status{}; -} -} // namespace - -INEGEMMLowpReductionKernel::INEGEMMLowpReductionKernel() - : _input(), _output(), _k(0), _scalar(0), _mul_by_scalar(false) -{ -} - -void NEGEMMLowpMatrixAReductionKernel::configure(const ITensor *mtx_a, ITensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row); - ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported"); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a->info(), vector_sum_row->info())); - _input = mtx_a; - _output = vector_sum_row; - _k = info.k; - _scalar = info.scalar; - _mul_by_scalar = info.mul_by_scalar; - - // Output auto initialization if not yet initialized - auto_init_if_empty(*_output->info(), TensorShape(_input->info()->dimension(1)), 1, DataType::S32); - - Window win = calculate_max_window(*_output->info(), Steps(1)); - - INEKernel::configure(win); -} - -Status NEGEMMLowpMatrixAReductionKernel::validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row)); - return Status{}; -} - -template <typename T> -void NEGEMMLowpMatrixAReductionKernel::run_internal(const arm_compute::Window &window) -{ - // Intermediate and final accumulator types - using TIAcc = wrapper::traits::promote_t<T>; - using TAcc = wrapper::traits::promote_t<TIAcc>; - - Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimY); - - Window win_input(collapsed_window); - win_input.set(Window::DimX, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator in(_input, win_input); - Iterator out(_output, collapsed_window); - - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - auto vsum_row = wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}); - TAcc sum_row = 0; - - const T *matrix_a = reinterpret_cast<const T *>((in.ptr() + id.x() * _input->info()->strides_in_bytes()[1] + id.y() * _input->info()->strides_in_bytes()[2])); - -#if __arm__ - asm volatile("PLD [%0, #128*4]" ::"r"(matrix_a)); -#endif /* __arm__ */ - - int i = 0; - // This for loop performs 16 accumulations - for(; i <= (_k - 16); i += 16) - { - const auto a0_d8 = wrapper::vloadq(matrix_a + i); - - // Partial accumulations in U16 - const auto tmp_sum0 = wrapper::vaddl(wrapper::vgetlow(a0_d8), wrapper::vgethigh(a0_d8)); - - // Accumulate to U32 - vsum_row = wrapper::vadd(vsum_row, wrapper::vpaddl(tmp_sum0)); - } - - // This for loop performs the leftover accumulations - for(; i < _k; ++i) - { - sum_row += static_cast<TAcc>(matrix_a[i]); - } - -#if defined(__aarch64__) - // Reduction operation available on 64 bit architectures only - sum_row += wrapper::vaddv(vsum_row); -#else // __aarch64__ - auto tmp = wrapper::vpadd(wrapper::vgethigh(vsum_row), wrapper::vgetlow(vsum_row)); - tmp = wrapper::vpadd(tmp, tmp); - - sum_row += wrapper::vgetlane(tmp, 0); -#endif // __aarch64__ - - // Multiply by scalar if necessary - if(_mul_by_scalar) - { - sum_row *= _scalar; - } - - *(reinterpret_cast<int *>(out.ptr())) = static_cast<int32_t>(sum_row); - }, - in, out); -} - -void NEGEMMLowpMatrixAReductionKernel::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); - - switch(_input->info()->data_type()) - { - case DataType::QASYMM8: - run_internal<uint8_t>(window); - break; - case DataType::QASYMM8_SIGNED: - case DataType::QSYMM8: - case DataType::QSYMM8_PER_CHANNEL: - run_internal<int8_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Unsupported data type"); - } -} - -void NEGEMMLowpMatrixBReductionKernel::configure(const ITensor *mtx_b, ITensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col); - ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported"); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b->info(), vector_sum_col->info())); - - _input = mtx_b; - _output = vector_sum_col; - _k = info.k; - _scalar = info.scalar; - _mul_by_scalar = info.mul_by_scalar; - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration = 16; - - // Output auto initialization if not yet initialized - auto_init_if_empty(*_output->info(), TensorShape(_input->info()->dimension(0)), 1, DataType::S32); - - // Configure kernel window - Window win = calculate_max_window_horizontal(*_output->info(), Steps(num_elems_processed_per_iteration)); - INEKernel::configure(win); -} - -Status NEGEMMLowpMatrixBReductionKernel::validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col)); - - return Status{}; -} - -template <typename T> -void NEGEMMLowpMatrixBReductionKernel::run_internal(const Window &window, const ThreadInfo &info) -{ - // Intermediate and final accumulator types - using TIAcc = wrapper::traits::promote_t<T>; - using TAcc = wrapper::traits::promote_t<TIAcc>; - - Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimY); - const auto vec_scalar = wrapper::vdup_n(static_cast<TAcc>(_scalar), wrapper::traits::vector_128_tag{}); - - const auto width_matrix_b = static_cast<int>(_input->info()->dimension(0)); - const auto in_b_stride = static_cast<int>(_input->info()->strides_in_bytes()[1]); - - // 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(collapsed_window); - win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); - - Window win_in(win_out); - win_in.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator inb(_input, win_in); - Iterator out(_output, win_out); - - execute_window_loop(win_out, [&](const Coordinates & id) - { - if(id.x() > width_matrix_b) - { - return; - } - - // Note: Since the input is unsigned char, we can safely use unsigned int for the accumulation - typename wrapper::traits::neon_bitvector<TAcc, wrapper::traits::BitWidth::W128>::type sum_col[4] = - { - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}) - }; - - const auto *matrix_b = reinterpret_cast<const T *>(inb.ptr() + id.y() * _input->info()->strides_in_bytes()[2]); - -#if __arm__ - asm volatile("PLD [%0, #128*4]" ::"r"(matrix_b)); - asm volatile("PLD [%0, #128*4]" ::"r"(matrix_b + in_b_stride)); -#endif /* __arm__ */ - - int i = 0; - // This for loop performs 4 accumulations - for(; i <= (_k - 4); i += 4) - { - const auto b0_u8 = wrapper::vloadq(matrix_b + 0 * in_b_stride); - const auto b1_u8 = wrapper::vloadq(matrix_b + 1 * in_b_stride); - const auto b2_u8 = wrapper::vloadq(matrix_b + 2 * in_b_stride); - const auto b3_u8 = wrapper::vloadq(matrix_b + 3 * in_b_stride); - -#if __arm__ - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 1 * in_b_stride)); - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 2 * in_b_stride)); - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 3 * in_b_stride)); - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 4 * in_b_stride)); -#endif /* __arm__ */ - - // Partial accumulation in 16bit - typename wrapper::traits::neon_bitvector<TIAcc, wrapper::traits::BitWidth::W128>::type tmp_sum[2] = - { - wrapper::vdup_n(static_cast<TIAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TIAcc>(0), wrapper::traits::vector_128_tag{}) - }; - - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b1_u8)); - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b0_u8)); - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b2_u8)); - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b3_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b0_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b1_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b2_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b3_u8)); - - // Accumulate to 32bit - sum_col[0] = wrapper::vaddw(sum_col[0], wrapper::vgetlow(tmp_sum[0])); - sum_col[1] = wrapper::vaddw(sum_col[1], wrapper::vgethigh(tmp_sum[0])); - sum_col[2] = wrapper::vaddw(sum_col[2], wrapper::vgetlow(tmp_sum[1])); - sum_col[3] = wrapper::vaddw(sum_col[3], wrapper::vgethigh(tmp_sum[1])); - - matrix_b += 4 * in_b_stride; - } - - // This for loop perfoms the leftover accumulations - for(; i < _k; ++i) - { - const auto b0_b8 = wrapper::vloadq(matrix_b + 0 * in_b_stride); - - // Convert S8 to S16 - const typename wrapper::traits::neon_bitvector<TIAcc, wrapper::traits::BitWidth::W128>::type b0_b16[2] - { - wrapper::vmovl(wrapper::vgetlow(b0_b8)), - wrapper::vmovl(wrapper::vgethigh(b0_b8)) - }; - - // Accumulate to 32bit - sum_col[0] = wrapper::vaddw(sum_col[0], wrapper::vgetlow(b0_b16[0])); - sum_col[1] = wrapper::vaddw(sum_col[1], wrapper::vgethigh(b0_b16[0])); - sum_col[2] = wrapper::vaddw(sum_col[2], wrapper::vgetlow(b0_b16[1])); - sum_col[3] = wrapper::vaddw(sum_col[3], wrapper::vgethigh(b0_b16[1])); - - matrix_b += in_b_stride; - } - - // Multiply by scalar if necessary - if(_mul_by_scalar) - { - sum_col[0] = wrapper::vmul(sum_col[0], vec_scalar); - sum_col[1] = wrapper::vmul(sum_col[1], vec_scalar); - sum_col[2] = wrapper::vmul(sum_col[2], vec_scalar); - sum_col[3] = wrapper::vmul(sum_col[3], vec_scalar); - } - - auto vector_sum_col = reinterpret_cast<int32_t *>(out.ptr()); - if(id.x() + 16 < width_matrix_b) - { - wrapper::vstore(vector_sum_col + 0, wrapper::vreinterpret(sum_col[0])); - wrapper::vstore(vector_sum_col + 4, wrapper::vreinterpret(sum_col[1])); - wrapper::vstore(vector_sum_col + 8, wrapper::vreinterpret(sum_col[2])); - wrapper::vstore(vector_sum_col + 12, wrapper::vreinterpret(sum_col[3])); - } - else - { - auto left_over = width_matrix_b - id.x(); - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(vector_sum_col + k * 4 + j) = sum_col[k][j]; - } - } - } - }, - inb, out); -} - -void NEGEMMLowpMatrixBReductionKernel::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); - - switch(_input->info()->data_type()) - { - case DataType::QASYMM8: - run_internal<uint8_t>(window, info); - break; - case DataType::QASYMM8_SIGNED: - case DataType::QSYMM8: - case DataType::QSYMM8_PER_CHANNEL: - run_internal<int8_t>(window, info); - break; - default: - ARM_COMPUTE_ERROR("Unsupported data type"); - } -} -} // namespace arm_compute |