diff options
Diffstat (limited to 'src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp | 316 |
1 files changed, 169 insertions, 147 deletions
diff --git a/src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp b/src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp index 534076b97c..9bd1eae663 100644 --- a/src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp +++ b/src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.cpp @@ -26,9 +26,10 @@ #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" +#include "src/core/NEON/wrapper/wrapper.h" namespace arm_compute { @@ -38,37 +39,49 @@ namespace kernels { namespace { -Status validate_arguments_matrix_a_reduction(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) +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); + 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) + 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"); + 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) +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); + 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) + 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"); + 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) +void CpuGemmLowpMatrixAReductionKernel::configure(const ITensorInfo *src, + ITensorInfo *dst, + const GEMMLowpReductionKernelInfo &info) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); @@ -77,7 +90,7 @@ void CpuGemmLowpMatrixAReductionKernel::configure(const ITensorInfo *src, ITenso _scalar = info.scalar; _mul_by_scalar = info.mul_by_scalar; - switch(src->data_type()) + switch (src->data_type()) { case DataType::QASYMM8: _func = &CpuGemmLowpMatrixAReductionKernel::run_internal<uint8_t>; @@ -98,14 +111,18 @@ void CpuGemmLowpMatrixAReductionKernel::configure(const ITensorInfo *src, ITenso ICpuKernel::configure(win); } -Status CpuGemmLowpMatrixAReductionKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) +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) +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>; @@ -121,55 +138,58 @@ void CpuGemmLowpMatrixAReductionKernel::run_internal(const ITensor *src, ITensor 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; + 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])); + 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)); + 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); + 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)); + // 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)); - } + // 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]); - } + // 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); + // 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); + auto tmp = wrapper::vpadd(wrapper::vgethigh(vsum_row), wrapper::vgetlow(vsum_row)); + tmp = wrapper::vpadd(tmp, tmp); - sum_row += wrapper::vgetlane(tmp, 0); + sum_row += wrapper::vgetlane(tmp, 0); #endif // __aarch64__ - // Multiply by scalar if necessary - if(_mul_by_scalar) - { - sum_row *= _scalar; - } + // 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); + *(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) @@ -189,7 +209,9 @@ const char *CpuGemmLowpMatrixAReductionKernel::name() const return "CpuGemmLowpMatrixAReductionKernel"; } -void CpuGemmLowpMatrixBReductionKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) +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)); @@ -201,7 +223,7 @@ void CpuGemmLowpMatrixBReductionKernel::configure(const ITensorInfo *src, ITenso // Configure kernel window constexpr unsigned int num_elems_processed_per_iteration = 16; - switch(src->data_type()) + switch (src->data_type()) { case DataType::QASYMM8: _func = &CpuGemmLowpMatrixBReductionKernel::run_internal<uint8_t>; @@ -223,14 +245,19 @@ void CpuGemmLowpMatrixBReductionKernel::configure(const ITensorInfo *src, ITenso ICpuKernel::configure(win); } -Status CpuGemmLowpMatrixBReductionKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLowpReductionKernelInfo &info) +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) +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>; @@ -258,121 +285,116 @@ void CpuGemmLowpMatrixBReductionKernel::run_internal(const ITensor *src, ITensor Iterator inb(src, win_in); Iterator out(dst, win_out); - execute_window_loop(win_out, [&](const Coordinates & id) - { - if(id.x() > width_matrix_b) + execute_window_loop( + win_out, + [&](const Coordinates &id) { - return; - } + 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{}) - }; + // 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]); + 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)); + 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); + 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)); + 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); + // 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; + } - // Convert S8 to S16 - const typename wrapper::traits::neon_bitvector<TIAcc, wrapper::traits::BitWidth::W128>::type b0_b16[2] + // This for loop perfoms the leftover accumulations + for (; i < _k; ++i) { - wrapper::vmovl(wrapper::vgetlow(b0_b8)), - wrapper::vmovl(wrapper::vgethigh(b0_b8)) - }; + const auto b0_b8 = wrapper::vloadq(matrix_b + 0 * in_b_stride); - // 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])); + // 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))}; - matrix_b += in_b_stride; - } + // 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])); - // 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) + 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 { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) + auto left_over = width_matrix_b - id.x(); + for (auto k = 0; k < 4 && left_over; ++k) { - *(vector_sum_col + k * 4 + j) = sum_col[k][j]; + for (auto j = 0; j < 4 && left_over; ++j, --left_over) + { + *(vector_sum_col + k * 4 + j) = sum_col[k][j]; + } } } - } - }, - inb, out); + }, + inb, out); } void CpuGemmLowpMatrixBReductionKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) @@ -393,4 +415,4 @@ const char *CpuGemmLowpMatrixBReductionKernel::name() const } } // namespace kernels } // namespace cpu -} // namespace arm_compute
\ No newline at end of file +} // namespace arm_compute |