| /* |
| * 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/cpu/kernels/CpuGemmLowpMatrixReductionKernel.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 cpu |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| Status validate_arguments_matrix_a_reduction(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); |
| |
| if(dst->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->dimension(0) != src->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 *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); |
| |
| if(dst->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->dimension(0) != src->dimension(0), "Output vector must have length equal to the number of columns of the input matrix"); |
| } |
| return Status{}; |
| } |
| } // namespace |
| |
| void CpuGemmLowpMatrixAReductionKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) |
| { |
| // Perform validate step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(src, dst, info)); |
| _k = info.k; |
| _scalar = info.scalar; |
| _mul_by_scalar = info.mul_by_scalar; |
| |
| switch(src->data_type()) |
| { |
| case DataType::QASYMM8: |
| _func = &CpuGemmLowpMatrixAReductionKernel::run_internal<uint8_t>; |
| break; |
| case DataType::QASYMM8_SIGNED: |
| case DataType::QSYMM8: |
| case DataType::QSYMM8_PER_CHANNEL: |
| _func = &CpuGemmLowpMatrixAReductionKernel::run_internal<int8_t>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*dst, TensorShape(src->dimension(1)), 1, DataType::S32); |
| |
| Window win = calculate_max_window(*dst, Steps(1)); |
| ICpuKernel::configure(win); |
| } |
| |
| Status CpuGemmLowpMatrixAReductionKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(src, dst, info)); |
| return Status{}; |
| } |
| |
| template <typename T> |
| void CpuGemmLowpMatrixAReductionKernel::run_internal(const ITensor *src, ITensor *dst, 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(src, win_input); |
| Iterator out(dst, 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() * src->info()->strides_in_bytes()[1] + id.y() * src->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 CpuGemmLowpMatrixAReductionKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); |
| |
| auto src = tensors.get_const_tensor(TensorType::ACL_SRC); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| (this->*_func)(src, dst, window); |
| } |
| |
| const char *CpuGemmLowpMatrixAReductionKernel::name() const |
| { |
| return "CpuGemmLowpMatrixAReductionKernel"; |
| } |
| |
| void CpuGemmLowpMatrixBReductionKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(src, dst, info)); |
| |
| _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; |
| |
| switch(src->data_type()) |
| { |
| case DataType::QASYMM8: |
| _func = &CpuGemmLowpMatrixBReductionKernel::run_internal<uint8_t>; |
| break; |
| case DataType::QASYMM8_SIGNED: |
| case DataType::QSYMM8: |
| case DataType::QSYMM8_PER_CHANNEL: |
| _func = &CpuGemmLowpMatrixBReductionKernel::run_internal<int8_t>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*dst, TensorShape(src->dimension(0)), 1, DataType::S32); |
| |
| // Configure kernel window |
| Window win = calculate_max_window_horizontal(*dst, Steps(num_elems_processed_per_iteration)); |
| ICpuKernel::configure(win); |
| } |
| |
| Status CpuGemmLowpMatrixBReductionKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(src, dst, info)); |
| return Status{}; |
| } |
| |
| template <typename T> |
| void CpuGemmLowpMatrixBReductionKernel::run_internal(const ITensor *src, ITensor *dst, 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>(src->info()->dimension(0)); |
| const auto in_b_stride = static_cast<int>(src->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(src, win_in); |
| Iterator out(dst, 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() * src->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 CpuGemmLowpMatrixBReductionKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); |
| |
| auto src = tensors.get_const_tensor(TensorType::ACL_SRC); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| (this->*_func)(src, dst, window, info); |
| } |
| |
| const char *CpuGemmLowpMatrixBReductionKernel::name() const |
| { |
| return "CpuGemmLowpMatrixBReductionKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |