diff options
Diffstat (limited to 'src/core/cpu/kernels/CpuMulKernel.cpp')
| -rw-r--r-- | src/core/cpu/kernels/CpuMulKernel.cpp | 1729 |
1 files changed, 0 insertions, 1729 deletions
diff --git a/src/core/cpu/kernels/CpuMulKernel.cpp b/src/core/cpu/kernels/CpuMulKernel.cpp deleted file mode 100644 index 82ec322875..0000000000 --- a/src/core/cpu/kernels/CpuMulKernel.cpp +++ /dev/null @@ -1,1729 +0,0 @@ -/* - * Copyright (c) 2016-2021 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/CpuMulKernel.h" - -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.h" -#include "src/core/CPP/Validate.h" -#include "src/core/NEON/NEAsymm.h" -#include "src/core/NEON/NESymm.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> - -namespace arm_compute -{ -namespace cpu -{ -namespace kernels -{ -namespace -{ -const float scale255_constant = 1.f / 255.f; -const float32x4_t scale255_constant_f32q = vdupq_n_f32(scale255_constant); -const float32x4_t positive_round_f32q = vdupq_n_f32(0.5f); - -inline Status validate_arguments(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy) -{ - ARM_COMPUTE_UNUSED(overflow_policy); - ARM_COMPUTE_UNUSED(rounding_policy); - - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src1); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::S32, DataType::QSYMM16, DataType::F16, - DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::S32, DataType::QSYMM16, DataType::F16, - DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, - DataType::S16, DataType::QSYMM16, - DataType::S32, DataType::F16, DataType::F32); - if(is_data_type_quantized(src1->data_type()) || is_data_type_quantized(src2->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(overflow_policy == ConvertPolicy::WRAP, "ConvertPolicy cannot be WRAP if datatype is quantized"); - } - - if(dst->total_size() > 0) - { - const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - // clang-format off - ARM_COMPUTE_RETURN_ERROR_ON_MSG( - !(src1->data_type() == src2->data_type() && src2->data_type() == dst->data_type()) && - !(src1->data_type() == DataType::U8 && src2->data_type() == DataType::U8 && dst->data_type() == DataType::S16) && - !(src1->data_type() == DataType::U8 && src2->data_type() == DataType::S16 && dst->data_type() == DataType::S16) && - !(src1->data_type() == DataType::S16 && src2->data_type() == DataType::U8 && dst->data_type() == DataType::S16) && - !(src1->data_type() == DataType::S16 && src2->data_type() == DataType::U8 && dst->data_type() == DataType::S16) && - !(src1->data_type() == DataType::QSYMM16 && src2->data_type() == DataType::QSYMM16 && dst->data_type() == DataType::S32) - , "Invalid data type combination"); - // clang-format on - ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->data_type() == DataType::S16 && dst->data_type() == DataType::S32 && scale != 1.f, "Unsupported scale for QSYMM16 inputs and S32 dst"); - } - - if(std::abs(scale - scale255_constant) < 0.00001f) - { - ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_NEAREST_UP && rounding_policy != RoundingPolicy::TO_NEAREST_EVEN); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->data_type() == DataType::S32 && src2->data_type() == DataType::S32 && dst->data_type() == DataType::S32, - "Scale == 1/255 is not supported if input and dst are of data type S32"); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_ZERO); - - int exponent = 0; - const float normalized_mantissa = std::frexp(scale, &exponent); - - // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15 - // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14 - // Moreover, it will be negative as we deal with 1/2^n - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1)), "Scale value not supported (Should be 1/(2^n) or 1/255"); - } - - return Status{}; -} - -/* Scales a given vector by 1/255. - * - * @note This does not work for all cases. e.g. for float of 0.49999999999999994 and large floats. - * - * @param in Input vector to scale. - * @return Scaled dst rounded to nearest (round half up). - */ -inline int32x4_t scale255_S32_S32(int32x4_t in) -{ - // Scale - const float32x4_t tmp = vmulq_f32(vcvtq_f32_s32(in), scale255_constant_f32q); - // Round to nearest (round half up) - // Add +0.5 for all values - // Afterwards vcvt rounds toward zero - return vcvtq_s32_f32(vaddq_f32(tmp, positive_round_f32q)); -} - -inline uint16x8_t scale255_U16_U16(uint16x8_t in) -{ - const int32x4_t tmp_s1 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(in)))); - const int32x4_t tmp_s2 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(in)))); - return vreinterpretq_u16_s16(vcombine_s16(vmovn_s32(tmp_s2), vmovn_s32(tmp_s1))); -} - -template <typename T> -inline typename std::enable_if<std::is_same<T, int8_t>::value, int8x16_t>::type -vquantize(float32x4x4_t val, const UniformQuantizationInfo &info) -{ - return vquantize_signed(val, info); -} - -template <typename T> -inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8x16_t>::type -vquantize(float32x4x4_t val, const UniformQuantizationInfo &info) -{ - return vquantize(val, info); -} - -template <typename T> -void mul_saturate_quantized_8(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) -{ - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - const int window_step_x = 16 / sizeof(T); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - - const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform(); - const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset }; - - if(is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform(); - const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform(); - - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - - using ExactTagType = typename wrapper::traits::neon_vector<T, window_step_x>::tag_type; - - execute_window_loop(win, [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast<const T *>(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast<T *>(dst.ptr()); - - const auto broadcast_value = *reinterpret_cast<const T *>(broadcast_input.ptr()); - const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{}); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x); - - // Dequantize inputs - const float32x4x4_t in1_f32x4x4 = vdequantize(non_broadcast_v, non_broadcast_qinfo); - const float32x4x4_t in2_f32x4x4 = vdequantize(broadcast_value_vec, broadcast_qinfo); - - const float32x4x4_t out_f32x4x4 = - { - vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]), - vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]), - vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]), - vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]), - }; - - // Quantize dst - const auto result = vquantize<T>(out_f32x4x4, tmp_qua_info); - wrapper::vstore(output_ptr + x, result); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - // Dequantize inputs - const T src1 = *(non_broadcast_input_ptr + x); - const float tmp_in1 = Qasymm8QuantizationHelper<T>::dequantize(src1, non_broadcast_qinfo); - const float tmp_in2 = Qasymm8QuantizationHelper<T>::dequantize(broadcast_value, broadcast_qinfo); - const float tmp_f = tmp_in1 * tmp_in2; - - // Quantize dst - const auto tmp_qua = Qasymm8QuantizationHelper<T>::quantize(tmp_f, tmp_qua_info); - *(output_ptr + x) = tmp_qua; - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - const UniformQuantizationInfo input1_qua_info = src1->info()->quantization_info().uniform(); - const UniformQuantizationInfo input2_qua_info = src2->info()->quantization_info().uniform(); - - // Clear X Dimension on execution window as we handle manually - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const T *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const T *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<T *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto input1_q = wrapper::vloadq(input1_ptr + x); - const auto input2_q = wrapper::vloadq(input2_ptr + x); - - // Dequantize inputs - const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info); - const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info); - - const float32x4x4_t out_f32x4x4 = - { - vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]), - vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]), - vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]), - vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]), - }; - - // Quantize dst - const auto result = vquantize<T>(out_f32x4x4, tmp_qua_info); - wrapper::vstore(output_ptr + x, result); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - // Dequantize inputs - const T src1 = *(input1_ptr + x); - const T src2 = *(input2_ptr + x); - const float tmp_in1 = Qasymm8QuantizationHelper<T>::dequantize(src1, input1_qua_info); - const float tmp_in2 = Qasymm8QuantizationHelper<T>::dequantize(src2, input2_qua_info); - const float tmp_f = tmp_in1 * tmp_in2; - - // Quantize dst - const auto tmp_qua = Qasymm8QuantizationHelper<T>::quantize(tmp_f, tmp_qua_info); - *(output_ptr + x) = tmp_qua; - } - }, - input1, input2, dst); - } -} - -void mul_saturate_QSYMM16_QSYMM16_QSYMM16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) -{ - const UniformQuantizationInfo input1_qua_info = src1->info()->quantization_info().uniform(); - const UniformQuantizationInfo input2_qua_info = src2->info()->quantization_info().uniform(); - const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform(); - - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - const int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset }; - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<qsymm16_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const qsymm16x8x2_t input1_q = - { - { - vld1q_s16(input1_ptr + x), - vld1q_s16(input1_ptr + x + 8), - } - }; - const qsymm16x8x2_t input2_q = - { - { - vld1q_s16(input2_ptr + x), - vld1q_s16(input2_ptr + x + 8), - } - }; - - // Dequantize inputs - const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info); - const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info); - - const float32x4x4_t out_f32x4x4 = - { - vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]), - vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]), - vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]), - vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]), - }; - - const qsymm16x8x2_t result = vquantize_qsymm16(out_f32x4x4, tmp_qua_info); - vst1q_s16(output_ptr + x, result.val[0]); - vst1q_s16(output_ptr + x + 8, result.val[1]); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - // Dequantize inputs - float tmp_in1 = static_cast<float>(*(input1_ptr + x)) * input1_qua_info.scale; - float tmp_in2 = static_cast<float>(*(input2_ptr + x)) * input2_qua_info.scale; - float tmp_f = tmp_in1 * tmp_in2; - - // Quantize dst, lrintf() has same rounding mode as vcombine_s16 - int32_t tmp = lrintf(tmp_f / tmp_qua_info.scale); - qsymm16_t tmp_qua = static_cast<qsymm16_t>(tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp); - *(output_ptr + x) = tmp_qua; - } - }, - input1, input2, dst); -} - -void mul_QSYMM16_QSYMM16_S32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int scale) -{ - ARM_COMPUTE_UNUSED(scale); - - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - const int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<int32_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const qsymm16x8x2_t input1_q = - { - { - vld1q_s16(input1_ptr + x), - vld1q_s16(input1_ptr + x + 8), - } - }; - const qsymm16x8x2_t input2_q = - { - { - vld1q_s16(input2_ptr + x), - vld1q_s16(input2_ptr + x + 8), - } - }; - - const int32x4x4_t in1_s32 = - { - { - vmovl_s16(vget_low_s16(input1_q.val[0])), - vmovl_s16(vget_high_s16(input1_q.val[0])), - vmovl_s16(vget_low_s16(input1_q.val[1])), - vmovl_s16(vget_high_s16(input1_q.val[1])), - } - }; - const int32x4x4_t in2_s32 = - { - { - vmovl_s16(vget_low_s16(input2_q.val[0])), - vmovl_s16(vget_high_s16(input2_q.val[0])), - vmovl_s16(vget_low_s16(input2_q.val[1])), - vmovl_s16(vget_high_s16(input2_q.val[1])), - } - }; - - const int32x4x4_t result = - { - { - vmulq_s32(in1_s32.val[0], in2_s32.val[0]), - vmulq_s32(in1_s32.val[1], in2_s32.val[1]), - vmulq_s32(in1_s32.val[2], in2_s32.val[2]), - vmulq_s32(in1_s32.val[3], in2_s32.val[3]), - } - }; - - vst1q_s32(output_ptr + x, result.val[0]); - vst1q_s32(output_ptr + x + 4, result.val[1]); - vst1q_s32(output_ptr + x + 8, result.val[2]); - vst1q_s32(output_ptr + x + 12, result.val[3]); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x)); - *(output_ptr + x) = tmp; - } - }, - input1, input2, dst); -} - -template <bool is_scale255, bool is_sat> -void mul_U8_U8_U8(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) -{ - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - const int window_step_x = 16 / sizeof(uint8_t); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<uint8_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const uint8x16_t ta1 = wrapper::vloadq(input1_ptr + x); - const uint8x16_t ta2 = wrapper::vloadq(input2_ptr + x); - - uint16x8_t tmp1_high = vmovl_u8(vget_high_u8(ta1)); - const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2)); - uint16x8_t tmp1_low = vmovl_u8(vget_low_u8(ta1)); - const uint16x8_t tmp2_low = vmovl_u8(vget_low_u8(ta2)); - - tmp1_high = vmulq_u16(tmp1_high, tmp2_high); - tmp1_low = vmulq_u16(tmp1_low, tmp2_low); - - if(is_scale255) - { - tmp1_high = scale255_U16_U16(tmp1_high); - tmp1_low = scale255_U16_U16(tmp1_low); - } - else - { - const int16x8_t vn = vdupq_n_s16(-n); - - if(is_sat) - { - tmp1_high = vqshlq_u16(tmp1_high, vn); - tmp1_low = vqshlq_u16(tmp1_low, vn); - } - else - { - tmp1_high = vshlq_u16(tmp1_high, vn); - tmp1_low = vshlq_u16(tmp1_low, vn); - } - } - if(is_sat) - { - vst1q_u8(output_ptr, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high))); - } - else - { - vst1q_u8(output_ptr, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high))); - } - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - uint16_t tmp = static_cast<uint16_t>(*(input1_ptr + x)) * static_cast<uint16_t>(*(input2_ptr + x)); - - if(is_scale255) - { - float tmp_f = static_cast<float>(tmp) * scale255_constant; - tmp = static_cast<uint16_t>(tmp_f + 0.5f); - } - else - { - tmp >>= n; - } - if(is_sat && tmp > 255) - { - tmp = 255; - } - *(output_ptr + x) = static_cast<uint8_t>(tmp); - } - }, - input1, input2, dst); -} - -template <bool is_scale255, bool is_sat> -inline int16x8_t mul_S16_S16_S16_n_loop(const int16x8_t &src1, const int16x8_t &src2, int n) -{ - int32x4_t tmp1_high = vmovl_s16(vget_high_s16(src1)); - const int32x4_t tmp2_high = vmovl_s16(vget_high_s16(src2)); - int32x4_t tmp1_low = vmovl_s16(vget_low_s16(src1)); - const int32x4_t tmp2_low = vmovl_s16(vget_low_s16(src2)); - - tmp1_high = vmulq_s32(tmp1_high, tmp2_high); - tmp1_low = vmulq_s32(tmp1_low, tmp2_low); - - if(is_scale255) - { - tmp1_high = scale255_S32_S32(tmp1_high); - tmp1_low = scale255_S32_S32(tmp1_low); - } - else - { - // Right shift amount - const int32x4_t vn = vdupq_n_s32(-n); - // Left shift amount - const int32x4_t vnl = vdupq_n_s32(n); - // Calculate conversion bit - const uint32x4_t tmp1_high_u = vreinterpretq_u32_s32(tmp1_high); - const uint32x4_t tmp1_low_u = vreinterpretq_u32_s32(tmp1_low); - const uint32x4_t sign_high = vshrq_n_u32(tmp1_high_u, 31); - const uint32x4_t sign_low = vshrq_n_u32(tmp1_low_u, 31); - const int32x4_t sign_high_s = vreinterpretq_s32_u32(sign_high); - const int32x4_t sign_low_s = vreinterpretq_s32_u32(sign_low); - const int32x4_t convert_high = vsubq_s32(vshlq_s32(sign_high_s, vnl), sign_high_s); - const int32x4_t convert_low = vsubq_s32(vshlq_s32(sign_low_s, vnl), sign_low_s); - if(is_sat) - { - tmp1_high = vqshlq_s32(vaddq_s32(tmp1_high, convert_high), vn); - tmp1_low = vqshlq_s32(vaddq_s32(tmp1_low, convert_low), vn); - } - else - { - tmp1_high = vshlq_s32(vaddq_s32(tmp1_high, convert_high), vn); - tmp1_low = vshlq_s32(vaddq_s32(tmp1_low, convert_low), vn); - } - } - - if(is_sat) - { - return vcombine_s16(vqmovn_s32(tmp1_low), vqmovn_s32(tmp1_high)); - } - else - { - return vcombine_s16(vmovn_s32(tmp1_low), vmovn_s32(tmp1_high)); - } -} - -template <bool is_scale255, bool is_sat> -inline int16x8x2_t mul_S16_S16_S16_n_k(const int16x8x2_t &src1, const int16x8x2_t &src2, int n) -{ - const int16x8x2_t result = - { - { - // First 8 elements - mul_S16_S16_S16_n_loop<is_scale255, is_sat>(src1.val[0], src2.val[0], n), - // Second 8 elements - mul_S16_S16_S16_n_loop<is_scale255, is_sat>(src1.val[1], src2.val[1], n) - } - }; - - return result; -} - -template <bool is_scale255, bool is_sat> -void mul_S16_S16_S16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) -{ - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - const int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const int16_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<int16_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const int16x8x2_t ta1 = - { - { - vld1q_s16(input1_ptr + x), - vld1q_s16(input1_ptr + x + 8), - } - }; - const int16x8x2_t ta2 = - { - { - vld1q_s16(input2_ptr + x), - vld1q_s16(input2_ptr + x + 8), - } - }; - const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n); - - vst1q_s16(output_ptr + x, result.val[0]); - vst1q_s16(output_ptr + x + 8, result.val[1]); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x)); - - if(is_scale255) - { - float tmp_f = static_cast<float>(tmp) * scale255_constant; - - tmp = static_cast<int32_t>(tmp_f + 0.5f); - } - else - { - if(tmp >= 0) - { - tmp >>= n; - } - else - { - uint32_t mask = (1u << n) - 1; - tmp = (tmp + static_cast<int32_t>(mask)) >> n; - } - } - if(is_sat) - { - tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp); - } - *(output_ptr + x) = static_cast<int16_t>(tmp); - } - }, - input1, input2, dst); -} - -template <bool is_sat> -inline int32x4_t mul_S32_S32_S32_n_loop(const int32x4_t &src1, const int32x4_t &src2, int n) -{ - const int32x2_t input1_1 = vget_low_s32(src1); - const int32x2_t input2_1 = vget_low_s32(src2); - const int32x2_t input1_2 = vget_high_s32(src1); - const int32x2_t input2_2 = vget_high_s32(src2); - - int64x2_t tmp_1 = vmull_s32(input1_1, input2_1); - int64x2_t tmp_2 = vmull_s32(input1_2, input2_2); - - // Apply scaling, conversion and rounding (round to zero) - // Right shift amount - const int64x2_t vn = vdupq_n_s64(-n); - // Left shift amount - const int64x2_t vnl = vdupq_n_s64(n); - // Calculate conversion bit - const uint64x2_t tmp_1_u = vreinterpretq_u64_s64(tmp_1); - const uint64x2_t sign_1 = vshrq_n_u64(tmp_1_u, 63); - const int64x2_t sign_1_s = vreinterpretq_s64_u64(sign_1); - const int64x2_t convert_1 = vsubq_s64(vshlq_s64(sign_1_s, vnl), sign_1_s); - - const uint64x2_t tmp_2_u = vreinterpretq_u64_s64(tmp_2); - const uint64x2_t sign_2 = vshrq_n_u64(tmp_2_u, 63); - const int64x2_t sign_2_s = vreinterpretq_s64_u64(sign_2); - const int64x2_t convert_2 = vsubq_s64(vshlq_s64(sign_2_s, vnl), sign_2_s); - if(is_sat) - { - tmp_1 = vqshlq_s64(vaddq_s64(tmp_1, convert_1), vn); - tmp_2 = vqshlq_s64(vaddq_s64(tmp_2, convert_2), vn); - return vcombine_s32(vqmovn_s64(tmp_1), vqmovn_s64(tmp_2)); - } - else - { - tmp_1 = vshlq_s64(vaddq_s64(tmp_1, convert_1), vn); - tmp_2 = vshlq_s64(vaddq_s64(tmp_2, convert_2), vn); - return vcombine_s32(vmovn_s64(tmp_1), vmovn_s64(tmp_2)); - } -} - -template <bool is_sat> -inline int32x4x2_t mul_S32_S32_S32_n_k(const int32x4x2_t &src1, const int32x4x2_t &src2, int n) -{ - const int32x4x2_t result = - { - { - // First 4 elements - mul_S32_S32_S32_n_loop<is_sat>(src1.val[0], src2.val[0], n), - // Second 4 elements - mul_S32_S32_S32_n_loop<is_sat>(src1.val[1], src2.val[1], n) - } - }; - - return result; -} - -template <bool is_sat> -void mul_S32_S32_S32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) -{ - // Create input windows - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - const int window_step_x = 8; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - - if(is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast<const int32_t *>(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast<int32_t *>(dst.ptr()); - - const int32_t broadcast_value = *reinterpret_cast<const int32_t *>(broadcast_input.ptr()); - const auto broadcast_value_vec = vdupq_n_s32(broadcast_value); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const int32x4x2_t broadcast_v = - { - { - broadcast_value_vec, - broadcast_value_vec, - } - }; - const int32x4x2_t non_broadcast_v = - { - { - vld1q_s32(non_broadcast_input_ptr + x), - vld1q_s32(non_broadcast_input_ptr + x + 4), - } - }; - const int32x4x2_t result = mul_S32_S32_S32_n_k<is_sat>(broadcast_v, non_broadcast_v, n); - - vst1q_s32(output_ptr + x, result.val[0]); - vst1q_s32(output_ptr + x + 4, result.val[1]); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int64_t tmp = static_cast<int64_t>(broadcast_value) * static_cast<int64_t>(*(non_broadcast_input_ptr + x)); - - if(tmp >= 0) - { - tmp >>= n; - } - else - { - uint64_t mask = ((uint64_t)1u << n) - 1; - tmp = (tmp + static_cast<int64_t>(mask)) >> n; - } - if(is_sat) - { - tmp = utility::clamp<int64_t, int32_t>(tmp); - } - *(output_ptr + x) = static_cast<int32_t>(tmp); - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - // Clear X Dimension on execution window as we handle manually - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const int32_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const int32_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<int32_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const int32x4x2_t ta1 = - { - { - vld1q_s32(input1_ptr + x), - vld1q_s32(input1_ptr + x + 4), - } - }; - const int32x4x2_t ta2 = - { - { - vld1q_s32(input2_ptr + x), - vld1q_s32(input2_ptr + x + 4), - } - }; - const int32x4x2_t result = mul_S32_S32_S32_n_k<is_sat>(ta1, ta2, n); - - vst1q_s32(output_ptr + x, result.val[0]); - vst1q_s32(output_ptr + x + 4, result.val[1]); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int64_t tmp = static_cast<int64_t>(*(input1_ptr + x)) * static_cast<int64_t>(*(input2_ptr + x)); - - if(tmp >= 0) - { - tmp >>= n; - } - else - { - uint64_t mask = ((uint64_t)1u << n) - 1; - tmp = (tmp + static_cast<int64_t>(mask)) >> n; - } - if(is_sat) - { - tmp = utility::clamp<int64_t, int32_t>(tmp); - } - *(output_ptr + x) = static_cast<int32_t>(tmp); - } - }, - input1, input2, dst); - } -} - -void mul_F32_F32_F32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) -{ - // Create input windows - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - constexpr int window_step_x = 16 / sizeof(float); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - - using ExactTagType = typename wrapper::traits::neon_vector<float, window_step_x>::tag_type; - - if(is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast<float *>(dst.ptr()); - - const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr()); - const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{}); - const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{}); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x); - auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec); - wrapper::vstore(output_ptr + x, res); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const auto non_broadcast_v = *(non_broadcast_input_ptr + x); - *(output_ptr + x) = broadcast_value * non_broadcast_v * scale; - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - // Clear X Dimension on execution window as we handle manually - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<float *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto ta1 = wrapper::vloadq(input1_ptr + x); - const auto ta2 = wrapper::vloadq(input2_ptr + x); - const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{}); - const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec); - wrapper::vstore(output_ptr + x, res); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const auto ta1 = *(input1_ptr + x); - const auto ta2 = *(input2_ptr + x); - *(output_ptr + x) = ta1 * ta2 * scale; - } - }, - input1, input2, dst); - } -} - -void c_mul_F32_F32_F32_n(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window) -{ - // Create input windows - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - constexpr int window_step_x = 8 / sizeof(float); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - - using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type; - - if(is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast<float *>(dst.ptr()); - - const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto a = wrapper::vloadq(non_broadcast_input_ptr + 2 * x); - float32x4_t b = vdupq_n_f32(broadcast_value); - - const float32x4_t mask = { -1.0f, 1.0f, -1.0f, 1.0f }; - const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{}); - const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{}); - const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{}); - const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{}); - - const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10); - const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11); - - float32x4_t res = wrapper::vmul(tmp0, b); - b = wrapper::vmul(b, mask); - - res = wrapper::vmla(res, tmp1, b); - wrapper::vstore(output_ptr + 2 * x, res); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const auto non_broadcast_value0 = *(non_broadcast_input_ptr + 2 * x); - const auto non_broadcast_value1 = *(non_broadcast_input_ptr + 2 * x + 1); - auto res1 = broadcast_value * (non_broadcast_value0 - non_broadcast_value1); - auto res2 = broadcast_value * (non_broadcast_value1 + non_broadcast_value0); - *(output_ptr + 2 * x) = res1; - *(output_ptr + 2 * x + 1) = res2; - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - // Clear X Dimension on execution window as we handle manually - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<float *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4_t a = wrapper::vloadq(input1_ptr + 2 * x); - float32x4_t b = wrapper::vloadq(input2_ptr + 2 * x); - - const float32x4_t mask = { -1.0f, 1.0f, -1.0f, 1.0f }; - const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{}); - const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{}); - const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{}); - const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{}); - - const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10); - const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11); - - float32x4_t res = wrapper::vmul(tmp0, b); - - b = wrapper::vrev64(b); - b = wrapper::vmul(b, mask); - - res = wrapper::vmla(res, tmp1, b); - wrapper::vstore(output_ptr + 2 * x, res); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const auto a0 = *(input1_ptr + 2 * x); - const auto a1 = *(input1_ptr + 2 * x + 1); - const auto b0 = *(input2_ptr + 2 * x); - const auto b1 = *(input2_ptr + 2 * x + 1); - auto res1 = a0 * b0 - a1 * b1; - auto res2 = a0 * b1 + a1 * b0; - *(output_ptr + 2 * x) = res1; - *(output_ptr + 2 * x + 1) = res2; - } - }, - input1, input2, dst); - } -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -void mul_F16_F16_F16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) -{ - // Create input windows - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - constexpr int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - if(is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - execute_window_loop(win, [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast<const float16_t *>(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr()); - const auto broadcast_value = *reinterpret_cast<const float16_t *>(broadcast_input.ptr()); - const float16x8x2_t broadcast_value_vec = - { - { - vdupq_n_f16(broadcast_value), - vdupq_n_f16(broadcast_value), - } - }; - const auto scale_vec = vdupq_n_f16(scale); - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float16x8x2_t non_broadcast_v = - { - { - vld1q_f16(non_broadcast_input_ptr + x), - vld1q_f16(non_broadcast_input_ptr + x + 8), - } - }; - const float16x8x2_t result = - { - { - vmulq_f16(vmulq_f16(broadcast_value_vec.val[0], non_broadcast_v.val[0]), scale_vec), - vmulq_f16(vmulq_f16(broadcast_value_vec.val[1], non_broadcast_v.val[1]), scale_vec), - } - }; - vst1q_f16(output_ptr + x, result.val[0]); - vst1q_f16(output_ptr + x + 8, result.val[1]); - } - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const auto non_broadcast_v = *(non_broadcast_input_ptr + x); - *(output_ptr + x) = broadcast_value * non_broadcast_v * scale; - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr()); - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float16x8x2_t ta1 = - { - { - vld1q_f16(input1_ptr + x), - vld1q_f16(input1_ptr + x + 8), - } - }; - const float16x8x2_t ta2 = - { - { - vld1q_f16(input2_ptr + x), - vld1q_f16(input2_ptr + x + 8), - } - }; - const float16x8_t scale_vec = vdupq_n_f16(scale); - const float16x8x2_t result = - { - { - vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec), - vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec), - } - }; - vst1q_f16(output_ptr + x, result.val[0]); - vst1q_f16(output_ptr + x + 8, result.val[1]); - } - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const auto ta1 = *(input1_ptr + x); - const auto ta2 = *(input2_ptr + x); - *(output_ptr + x) = ta1 * ta2 * scale; - } - }, - input1, input2, dst); - } -} -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - -template <bool is_scale255, bool is_sat> -void mul_U8_U8_S16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) -{ - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - const int window_step_x = 16 / sizeof(uint8_t); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<int16_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const uint8x16_t bv = wrapper::vloadq(input2_ptr + x); - const uint8x16_t av = wrapper::vloadq(input1_ptr + x); - - uint16x8_t tmp_low = vmovl_u8(vget_low_u8(av)); - uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av)); - tmp_low = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv))); - tmp_high = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv))); - - if(is_scale255) - { - tmp_low = scale255_U16_U16(tmp_low); - tmp_high = scale255_U16_U16(tmp_high); - } - else - { - const int16x8_t vn = vdupq_n_s16(-n); - - if(is_sat) - { - tmp_low = vqshlq_u16(tmp_low, vn); - tmp_high = vqshlq_u16(tmp_high, vn); - } - else - { - tmp_low = vshlq_u16(tmp_low, vn); - tmp_high = vshlq_u16(tmp_high, vn); - } - } - - if(is_sat) - { - static const uint16x8_t max = vdupq_n_u16(SHRT_MAX); - - tmp_low = vminq_u16(tmp_low, max); - tmp_high = vminq_u16(tmp_high, max); - } - - vst1q_s16(output_ptr + x, vreinterpretq_s16_u16(tmp_low)); - vst1q_s16(output_ptr + x + 8, vreinterpretq_s16_u16(tmp_high)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x)); - - if(is_scale255) - { - float tmp_f = static_cast<float>(tmp) * scale255_constant; - tmp = static_cast<int32_t>(tmp_f + 0.5f); - } - else - { - tmp >>= n; - } - - if(is_sat) - { - tmp = (tmp > SHRT_MAX) ? SHRT_MAX : tmp; - } - - *(output_ptr + x) = static_cast<int16_t>(tmp); - } - }, - input1, input2, dst); -} - -template <bool is_scale255, bool is_sat> -void mul_S16_U8_S16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) -{ - // Create input windows - Window win = window; - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - const int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - execute_window_loop(win, [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr()); - const auto output_ptr = reinterpret_cast<int16_t *>(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const int16x8x2_t ta1 = - { - { - vld1q_s16(input1_ptr + x), - vld1q_s16(input1_ptr + x + 8), - } - }; - const uint8x8x2_t ta2u = - { - { - vld1_u8(input2_ptr + x), - vld1_u8(input2_ptr + x + 8), - } - }; - const int16x8x2_t ta2 = - { - { - vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])), - vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1])) - } - }; - - const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n); - - vst1q_s16(output_ptr + x, result.val[0]); - vst1q_s16(output_ptr + x + 8, result.val[1]); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x)); - - if(is_scale255) - { - float tmp_f = static_cast<float>(tmp) * scale255_constant; - - tmp = static_cast<int32_t>(tmp_f + 0.5f); - } - else - { - if(tmp >= 0) - { - tmp >>= n; - } - else - { - uint32_t mask = (1u << n) - 1; - tmp = (tmp + static_cast<int32_t>(mask)) >> n; - } - } - if(is_sat) - { - tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp); - } - *(output_ptr + x) = static_cast<int16_t>(tmp); - } - }, - input1, input2, dst); -} - -template <bool is_scale255, bool is_sat> -void mul_U8_S16_S16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) -{ - // Simply swap the two input buffers - mul_S16_U8_S16<is_scale255, is_sat>(src2, src1, out, window, n); -} -} // namespace - -void CpuMulKernel::configure(ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy) -{ - ARM_COMPUTE_UNUSED(rounding_policy); - ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src1, src2, dst, scale, overflow_policy, rounding_policy)); - - const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); - - // Auto initialize dst if not initialized - set_shape_if_empty(*dst, out_shape); - - _scale = scale; - _scale_exponent = 0; - _func_quantized = nullptr; - _func_int = nullptr; - _func_float = nullptr; - - bool is_scale_255 = false; - // Check and validate scaling factor - if(std::abs(scale - scale255_constant) < 0.00001f) - { - is_scale_255 = true; - } - else - { - int exponent = 0; - - std::frexp(scale, &exponent); - - // Store the positive exponent. We know that we compute 1/2^n - // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5 - _scale_exponent = std::abs(exponent - 1); - } - - const DataType dt_input1 = src1->data_type(); - const DataType dt_input2 = src2->data_type(); - const DataType dt_output = dst->data_type(); - const bool is_sat = (overflow_policy == ConvertPolicy::SATURATE); - - switch(dt_input1) - { - case DataType::QASYMM8: - if(dt_input2 == DataType::QASYMM8 && dt_output == DataType::QASYMM8) - { - _func_quantized = &mul_saturate_quantized_8<uint8_t>; - } - break; - case DataType::QASYMM8_SIGNED: - if(dt_input2 == DataType::QASYMM8_SIGNED) - { - _func_quantized = &mul_saturate_quantized_8<int8_t>; - ; - } - break; - case DataType::QSYMM16: - if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::QSYMM16) - { - _func_quantized = &mul_saturate_QSYMM16_QSYMM16_QSYMM16; - } - else if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::S32) - { - _func_int = &mul_QSYMM16_QSYMM16_S32; - } - break; - case DataType::S16: - if(DataType::U8 == dt_input2 && DataType::S16 == dt_output) - { - if(is_scale_255) - { - _func_int = is_sat ? &mul_S16_U8_S16<true, true> : &mul_S16_U8_S16<true, false>; - } - else - { - _func_int = is_sat ? &mul_S16_U8_S16<false, true> : &mul_S16_U8_S16<false, false>; - } - } - if(DataType::S16 == dt_input2 && DataType::S16 == dt_output) - { - if(is_scale_255) - { - _func_int = is_sat ? &mul_S16_S16_S16<true, true> : &mul_S16_S16_S16<true, false>; - } - else - { - _func_int = is_sat ? &mul_S16_S16_S16<false, true> : &mul_S16_S16_S16<false, false>; - } - } - break; - case DataType::S32: - if(DataType::S32 == dt_input2 && DataType::S32 == dt_output) - { - _func_int = is_sat ? &mul_S32_S32_S32<true> : &mul_S32_S32_S32<false>; - } - break; - case DataType::U8: - if(DataType::U8 == dt_input2 && DataType::U8 == dt_output) - { - if(is_scale_255) - { - _func_int = is_sat ? &mul_U8_U8_U8<true, true> : &mul_U8_U8_U8<true, false>; - } - else - { - _func_int = is_sat ? &mul_U8_U8_U8<false, true> : &mul_U8_U8_U8<false, false>; - } - } - else if(DataType::U8 == dt_input2 && DataType::S16 == dt_output) - { - if(is_scale_255) - { - _func_int = is_sat ? &mul_U8_U8_S16<true, true> : &mul_U8_U8_S16<true, false>; - } - else - { - _func_int = is_sat ? &mul_U8_U8_S16<false, true> : &mul_U8_U8_S16<false, false>; - } - } - else if(DataType::S16 == dt_input2 && DataType::S16 == dt_output) - { - if(is_scale_255) - { - _func_int = is_sat ? &mul_U8_S16_S16<true, true> : &mul_U8_S16_S16<true, false>; - } - else - { - _func_int = is_sat ? &mul_U8_S16_S16<false, true> : &mul_U8_S16_S16<false, false>; - } - } - break; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func_float = &mul_F16_F16_F16; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::F32: - _func_float = &mul_F32_F32_F32; - break; - default: - ARM_COMPUTE_ERROR("You called with the wrong img formats"); - } - - // Configure kernel window - Window win = calculate_max_window(out_shape); - - ICpuKernel::configure(win); -} - -Status CpuMulKernel::validate(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float scale, ConvertPolicy overflow_policy, - RoundingPolicy rounding_policy) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src1, src2, dst, scale, overflow_policy, rounding_policy)); - - return Status{}; -} - -void CpuMulKernel::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 src1 = tensors.get_const_tensor(TensorType::ACL_SRC_0); - auto src2 = tensors.get_const_tensor(TensorType::ACL_SRC_1); - auto dst = tensors.get_tensor(TensorType::ACL_DST); - - if(_func_quantized != nullptr) - { - (*_func_quantized)(src1, src2, dst, window, _scale); - } - else if(_func_int != nullptr) - { - (*_func_int)(src1, src2, dst, window, _scale_exponent); - } - else - { - ARM_COMPUTE_ERROR_ON(_func_float == nullptr); - (*_func_float)(src1, src2, dst, window, _scale); - } -} -const char *CpuMulKernel::name() const -{ - return "CpuMulKernel"; -} -namespace -{ -Status validate_arguments_complex(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 2, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, 2, DataType::F32); - - const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - - // Validate in case of configured dst - if(dst->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 2, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst"); - } - - return Status{}; -} -} // namespace - -void CpuComplexMulKernel::configure(ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(src1, src2, dst)); - - const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); - - // Auto initialize dst if not initialized - const TensorInfo out_info(out_shape, src1->num_channels(), src1->data_type()); - auto_init_if_empty(*dst, out_info); - - // Configure kernel window - Window win = calculate_max_window(out_shape); - - ICpuKernel::configure(win); -} - -Status CpuComplexMulKernel::validate(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(src1, src2, dst)); - - return Status{}; -} - -void CpuComplexMulKernel::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 src1 = tensors.get_const_tensor(TensorType::ACL_SRC_0); - auto src2 = tensors.get_const_tensor(TensorType::ACL_SRC_1); - auto dst = tensors.get_tensor(TensorType::ACL_DST); - - c_mul_F32_F32_F32_n(src1, src2, dst, window); -} - -const char *CpuComplexMulKernel::name() const -{ - return "CpuComplexMulKernel"; -} -} // namespace kernels -} // namespace cpu -} // namespace arm_compute |