/* * Copyright (c) 2016-2022 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/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 namespace { #if defined(ENABLE_FP32_KERNELS) static constexpr size_t default_mws_N1_fp32_neon = 22447; static constexpr size_t default_mws_V1_fp32_neon = 38982; #endif /* ENABLE_FP32_KERNELS */ static constexpr size_t default_mws_other_platforms_1d_tensor = 10240; } 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 inline typename std::enable_if::value, int8x16_t>::type vquantize(float32x4x4_t val, const UniformQuantizationInfo &info) { return vquantize_signed(val, info); } template inline typename std::enable_if::value, uint8x16_t>::type vquantize(float32x4x4_t val, const UniformQuantizationInfo &info) { return vquantize(val, info); } template 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(window.x().start()); const auto window_end_x = static_cast(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::tag_type; execute_window_loop( win, [&](const Coordinates &) { const auto non_broadcast_input_ptr = reinterpret_cast(non_broadcast_input.ptr()); const auto output_ptr = reinterpret_cast(dst.ptr()); const auto broadcast_value = *reinterpret_cast(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(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::dequantize(src1, non_broadcast_qinfo); const float tmp_in2 = Qasymm8QuantizationHelper::dequantize(broadcast_value, broadcast_qinfo); const float tmp_f = tmp_in1 * tmp_in2; // Quantize dst const auto tmp_qua = Qasymm8QuantizationHelper::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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(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::dequantize(src1, input1_qua_info); const float tmp_in2 = Qasymm8QuantizationHelper::dequantize(src2, input2_qua_info); const float tmp_f = tmp_in1 * tmp_in2; // Quantize dst const auto tmp_qua = Qasymm8QuantizationHelper::quantize(tmp_f, tmp_qua_info); *(output_ptr + x) = tmp_qua; } }, input1, input2, dst); } } bool mul_q8_neon_fixedpoint_possible(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, float scale) { const auto iq0 = src0->quantization_info().uniform(); const auto iq1 = src1->quantization_info().uniform(); const auto oq = dst->quantization_info().uniform(); const auto multiplier = ((iq0.scale * iq1.scale) / oq.scale) * scale; if(multiplier < -8191.f || multiplier > 8191.f) { //The multiplier cannot be stored as a 14.18 signed fixed-point number return false; } const auto offset_out = float(oq.offset); const auto max_result = multiplier * (256) * (256) + offset_out; if(max_result > 8191.f) { //It might not be possible to store the result as a 14.18 signed fixed-point number. return false; } return true; } template void mul_q8_neon_fixedpoint(const ITensor *src0, const ITensor *src1, ITensor *dst, const Window &window, float scale) { const auto in0_info = src0->info(); const auto in1_info = src1->info(); const auto &in0_shape = in0_info->tensor_shape(); const auto &in1_shape = in1_info->tensor_shape(); // Create input windows. Window in0_win = window.broadcast_if_dimension_le_one(in0_shape); Window in1_win = window.broadcast_if_dimension_le_one(in1_shape); // Clear the x dimension on the execution window as we process the whole row each iteration. Window win = window; win.set(Window::DimX, Window::Dimension(0, 1, 1)); constexpr int window_step_x = 16; const auto window_start_x = window.x().start(); const auto window_end_x = window.x().end(); const auto is_broadcast_across_x = in0_shape.x() != in1_shape.x(); const auto iq0_info = in0_info->quantization_info().uniform(); const auto iq1_info = in1_info->quantization_info().uniform(); const auto oq_info = dst->info()->quantization_info().uniform(); const auto in0_offset = iq0_info.offset; const auto in1_offset = iq1_info.offset; const auto out_offset = oq_info.offset; const auto multiplier = ((iq0_info.scale * iq1_info.scale) / oq_info.scale) * scale; constexpr int32_t two_pwr18i = 262144; constexpr float two_pwr18f = 262144.f; const auto in0_offset_16p0 = static_cast(in0_offset); const auto in1_offset_16p0 = static_cast(in1_offset); const auto out_offset_14p18 = static_cast(out_offset * two_pwr18i); const auto multiplier_14p18 = static_cast(multiplier * two_pwr18f); if(is_broadcast_across_x) { // Prefix: a = non-broadcast, b = broadcast. const auto is_broadcast_input_1 = in1_win.x().step() == 0; auto a_win = is_broadcast_input_1 ? in0_win : in1_win; auto b_win = is_broadcast_input_1 ? in1_win : in0_win; const auto a_tensor = is_broadcast_input_1 ? src0 : src1; const auto b_tensor = is_broadcast_input_1 ? src1 : src0; const auto a_offset_16p0 = is_broadcast_input_1 ? in0_offset_16p0 : in1_offset_16p0; const auto b_offset_16p0 = is_broadcast_input_1 ? in1_offset : in0_offset; #ifndef __aarch64__ const auto a_offset = is_broadcast_input_1 ? in0_offset : in1_offset; const auto b_offset = is_broadcast_input_1 ? in1_offset : in0_offset; #endif //__aarch64__ const auto a_voffset_16p0 = wrapper::vdup_n(a_offset_16p0, wrapper::traits::vector_64_tag()); // Clear the x dimension on the execution window as we process the whole row each iteration. a_win.set(Window::DimX, Window::Dimension(0, 1, 1)); Iterator a_input_it(a_tensor, a_win); Iterator b_input_it(b_tensor, b_win); Iterator out_it(dst, win); execute_window_loop( win, [&](const Coordinates &) { const auto a_ptr = reinterpret_cast(a_input_it.ptr()); const auto b_ptr = reinterpret_cast(b_input_it.ptr()); const auto out_ptr = reinterpret_cast(out_it.ptr()); const auto b_val = *b_ptr; const auto b_offseted_32p0 = static_cast(b_val - b_offset_16p0); const auto b_voffseted_32p0 = wrapper::vdup_n(b_offseted_32p0, wrapper::traits::vector_128_tag()); const auto vmultiplier_14p18 = wrapper::vdup_n(multiplier_14p18, wrapper::traits::vector_128_tag()); const auto voffsetout_14p18 = wrapper::vdup_n(out_offset_14p18, wrapper::traits::vector_128_tag()); int x = window_start_x; for(; x <= (window_end_x - window_step_x); x += window_step_x) { // Load the inputs. const auto a_vin_8p0 = wrapper::vloadq(a_ptr + x); // Widen the non-broadcast elements to signed 16-bit regardless of the input signedness. const auto a_vin_16p0_0 = wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(a_vin_8p0))); const auto a_vin_16p0_1 = wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(a_vin_8p0))); const auto voffseted_32p0_00 = wrapper::vsubl(wrapper::vgetlow(a_vin_16p0_0), a_voffset_16p0); const auto voffseted_32p0_01 = wrapper::vsubl(wrapper::vgethigh(a_vin_16p0_0), a_voffset_16p0); const auto voffseted_32p0_10 = wrapper::vsubl(wrapper::vgetlow(a_vin_16p0_1), a_voffset_16p0); const auto voffseted_32p0_11 = wrapper::vsubl(wrapper::vgethigh(a_vin_16p0_1), a_voffset_16p0); const auto vinnermul_32p0_00 = wrapper::vmul(voffseted_32p0_00, b_voffseted_32p0); const auto vinnermul_32p0_01 = wrapper::vmul(voffseted_32p0_01, b_voffseted_32p0); const auto vinnermul_32p0_10 = wrapper::vmul(voffseted_32p0_10, b_voffseted_32p0); const auto vinnermul_32p0_11 = wrapper::vmul(voffseted_32p0_11, b_voffseted_32p0); const auto vout_14p18_00 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_00, vmultiplier_14p18); const auto vout_14p18_01 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_01, vmultiplier_14p18); const auto vout_14p18_10 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_10, vmultiplier_14p18); const auto vout_14p18_11 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_11, vmultiplier_14p18); // These shift rights are to revert the multiplication by twopwr18. Hard limit of a maximum shift by 8 requires multiple shift instructions to achieve this. const auto vout_15p1_00 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_00)); const auto vout_15p1_01 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_01)); const auto vout_15p1_10 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_10)); const auto vout_15p1_11 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_11)); const auto vout_15p1_0 = wrapper::vcombine( vout_15p1_00, vout_15p1_01); const auto vout_15p1_1 = wrapper::vcombine( vout_15p1_10, vout_15p1_11); const auto out_ptr = reinterpret_cast(out_it.ptr()); const auto vout_8p0 = wrapper::vcombine( wrapper::vqrshrn<2>(vout_15p1_0), wrapper::vqrshrn<2>(vout_15p1_1)); wrapper::vstore(out_ptr + x, vout_8p0); } //Process the left-over elements. for(; x < window_end_x; ++x) { #ifdef __aarch64__ out_ptr[x] = wrapper::vqrshrn<2>(wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>((multiplier_14p18 * (int32_t(a_ptr[x]) - a_offset_16p0) * (int32_t( b_val) - b_offset_16p0)) + out_offset_14p18))); #else //__aarch64__ out_ptr[x] = utility::clamp(support::cpp11::lround(multiplier * ((float(a_ptr[x]) - a_offset) * (float(b_val) - b_offset)) + float(out_offset))); #endif //__aarch64__ } }, a_input_it, b_input_it, out_it); } else { const auto voffset0_16p0 = wrapper::vdup_n(in0_offset_16p0, wrapper::traits::vector_64_tag()); const auto voffset1_16p0 = wrapper::vdup_n(in1_offset_16p0, wrapper::traits::vector_64_tag()); const auto voffsetout_14p18 = wrapper::vdup_n(out_offset_14p18, wrapper::traits::vector_128_tag()); const auto vmultiplier_14p18 = wrapper::vdup_n(multiplier_14p18, wrapper::traits::vector_128_tag()); // Clear the x dimension on the execution window as we process the whole row each iteration. in0_win.set(Window::DimX, Window::Dimension(0, 1, 1)); in1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); Iterator in0_it(src0, in0_win); Iterator in1_it(src1, in1_win); Iterator out_it(dst, win); execute_window_loop( win, [&](const Coordinates &) { const auto in0_ptr = reinterpret_cast(in0_it.ptr()); const auto in1_ptr = reinterpret_cast(in1_it.ptr()); const auto out_ptr = reinterpret_cast(out_it.ptr()); int x = window_start_x; for(; x <= (window_end_x - window_step_x); x += window_step_x) { // Load the inputs. const auto vin0_8p0 = wrapper::vloadq(in0_ptr + x); const auto vin1_8p0 = wrapper::vloadq(in1_ptr + x); // Widen the input elements to signed 16-bit regardless of the input signedness. const auto vin0_16p0_0 = wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(vin0_8p0))); const auto vin0_16p0_1 = wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(vin0_8p0))); const auto vin1_16p0_0 = wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(vin1_8p0))); const auto vin1_16p0_1 = wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(vin1_8p0))); const auto voffseted0_32p0_00 = wrapper::vsubl(wrapper::vgetlow(vin0_16p0_0), voffset0_16p0); const auto voffseted0_32p0_01 = wrapper::vsubl(wrapper::vgethigh(vin0_16p0_0), voffset0_16p0); const auto voffseted0_32p0_10 = wrapper::vsubl(wrapper::vgetlow(vin0_16p0_1), voffset0_16p0); const auto voffseted0_32p0_11 = wrapper::vsubl(wrapper::vgethigh(vin0_16p0_1), voffset0_16p0); const auto voffseted1_32p0_00 = wrapper::vsubl(wrapper::vgetlow(vin1_16p0_0), voffset1_16p0); const auto voffseted1_32p0_01 = wrapper::vsubl(wrapper::vgethigh(vin1_16p0_0), voffset1_16p0); const auto voffseted1_32p0_10 = wrapper::vsubl(wrapper::vgetlow(vin1_16p0_1), voffset1_16p0); const auto voffseted1_32p0_11 = wrapper::vsubl(wrapper::vgethigh(vin1_16p0_1), voffset1_16p0); const auto vinnermul_32p0_00 = wrapper::vmul(voffseted0_32p0_00, voffseted1_32p0_00); const auto vinnermul_32p0_01 = wrapper::vmul(voffseted0_32p0_01, voffseted1_32p0_01); const auto vinnermul_32p0_10 = wrapper::vmul(voffseted0_32p0_10, voffseted1_32p0_10); const auto vinnermul_32p0_11 = wrapper::vmul(voffseted0_32p0_11, voffseted1_32p0_11); const auto vout_14p18_00 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_00, vmultiplier_14p18); const auto vout_14p18_01 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_01, vmultiplier_14p18); const auto vout_14p18_10 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_10, vmultiplier_14p18); const auto vout_14p18_11 = wrapper::vmla(voffsetout_14p18, vinnermul_32p0_11, vmultiplier_14p18); // These shift rights are to revert the multiplication by twopwr18. Hard limit of a maximum shift by 8 requires multiple shift instructions to achieve this. const auto vout_14p2_00 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_00)); const auto vout_14p2_01 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_01)); const auto vout_14p2_10 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_10)); const auto vout_14p2_11 = wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>(vout_14p18_11)); const auto vout_14p2_0 = wrapper::vcombine( vout_14p2_00, vout_14p2_01); const auto vout_14p2_1 = wrapper::vcombine( vout_14p2_10, vout_14p2_11); const auto vout_8p0 = wrapper::vcombine( wrapper::vqrshrn<2>(vout_14p2_0), wrapper::vqrshrn<2>(vout_14p2_1)); wrapper::vstore(out_ptr + x, vout_8p0); } //Process the left-over elements. for(; x < window_end_x; ++x) { #ifdef __aarch64__ out_ptr[x] = wrapper::vqrshrn<2>(wrapper::vqrshrn_ex<8, ScalarType>(wrapper::vshrq_n<8>((multiplier_14p18 * (int32_t(in0_ptr[x]) - in0_offset_16p0) * (int32_t( in1_ptr[x]) - in1_offset_16p0)) + out_offset_14p18))); #else //__aarch64__ out_ptr[x] = utility::clamp(support::cpp11::lround(multiplier * ((float(in0_ptr[x]) - in0_offset) * (float(in1_ptr[x]) - in1_offset)) + float(out_offset))); #endif //__aarch64__ } }, in0_it, in1_it, out_it); } } 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(window.x().start()); const auto window_end_x = static_cast(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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(*(input1_ptr + x)) * input1_qua_info.scale; float tmp_in2 = static_cast(*(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(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(window.x().start()); const auto window_end_x = static_cast(window.x().end()); execute_window_loop( win, [&](const Coordinates &) { const auto input1_ptr = reinterpret_cast(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(*(input1_ptr + x)) * static_cast(*(input2_ptr + x)); *(output_ptr + x) = tmp; } }, input1, input2, dst); } template 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(window.x().start()); const auto window_end_x = static_cast(window.x().end()); execute_window_loop( win, [&](const Coordinates &) { const auto input1_ptr = reinterpret_cast(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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 + x, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high))); } else { vst1q_u8(output_ptr + x, 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(*(input1_ptr + x)) * static_cast(*(input2_ptr + x)); if(is_scale255) { float tmp_f = static_cast(tmp) * scale255_constant; tmp = static_cast(tmp_f + 0.5f); } else { tmp >>= n; } if(is_sat && tmp > 255) { tmp = 255; } *(output_ptr + x) = static_cast(tmp); } }, input1, input2, dst); } template 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 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(src1.val[0], src2.val[0], n), // Second 8 elements mul_S16_S16_S16_n_loop(src1.val[1], src2.val[1], n) } }; return result; } template 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(window.x().start()); const auto window_end_x = static_cast(window.x().end()); execute_window_loop( win, [&](const Coordinates &) { const auto input1_ptr = reinterpret_cast(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(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(*(input1_ptr + x)) * static_cast(*(input2_ptr + x)); if(is_scale255) { float tmp_f = static_cast(tmp) * scale255_constant; tmp = static_cast(tmp_f + 0.5f); } else { if(tmp >= 0) { tmp >>= n; } else { uint32_t mask = (1u << n) - 1; tmp = (tmp + static_cast(mask)) >> n; } } if(is_sat) { tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp); } *(output_ptr + x) = static_cast(tmp); } }, input1, input2, dst); } template 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 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(src1.val[0], src2.val[0], n), // Second 4 elements mul_S32_S32_S32_n_loop(src1.val[1], src2.val[1], n) } }; return result; } template 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(window.x().start()); const auto window_end_x = static_cast(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(non_broadcast_input.ptr()); const auto output_ptr = reinterpret_cast(dst.ptr()); const int32_t broadcast_value = *reinterpret_cast(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(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(broadcast_value) * static_cast(*(non_broadcast_input_ptr + x)); if(tmp >= 0) { tmp >>= n; } else { uint64_t mask = ((uint64_t)1u << n) - 1; tmp = (tmp + static_cast(mask)) >> n; } if(is_sat) { tmp = utility::clamp(tmp); } *(output_ptr + x) = static_cast(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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(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(*(input1_ptr + x)) * static_cast(*(input2_ptr + x)); if(tmp >= 0) { tmp >>= n; } else { uint64_t mask = ((uint64_t)1u << n) - 1; tmp = (tmp + static_cast(mask)) >> n; } if(is_sat) { tmp = utility::clamp(tmp); } *(output_ptr + x) = static_cast(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(window.x().start()); const auto window_end_x = static_cast(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::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(non_broadcast_input.ptr()); const auto output_ptr = reinterpret_cast(dst.ptr()); const float broadcast_value = *reinterpret_cast(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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(window.x().start()); const auto window_end_x = static_cast(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::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(non_broadcast_input.ptr()); const auto output_ptr = reinterpret_cast(dst.ptr()); const float broadcast_value = *reinterpret_cast(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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(window.x().start()); const auto window_end_x = static_cast(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(non_broadcast_input.ptr()); const auto output_ptr = reinterpret_cast(dst.ptr()); const auto broadcast_value = *reinterpret_cast(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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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 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(window.x().start()); const auto window_end_x = static_cast(window.x().end()); execute_window_loop( win, [&](const Coordinates &) { const auto input1_ptr = reinterpret_cast(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(*(input1_ptr + x)) * static_cast(*(input2_ptr + x)); if(is_scale255) { float tmp_f = static_cast(tmp) * scale255_constant; tmp = static_cast(tmp_f + 0.5f); } else { tmp >>= n; } if(is_sat) { tmp = (tmp > SHRT_MAX) ? SHRT_MAX : tmp; } *(output_ptr + x) = static_cast(tmp); } }, input1, input2, dst); } template 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(window.x().start()); const auto window_end_x = static_cast(window.x().end()); execute_window_loop( win, [&](const Coordinates &) { const auto input1_ptr = reinterpret_cast(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(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(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(*(input1_ptr + x)) * static_cast(*(input2_ptr + x)); if(is_scale255) { float tmp_f = static_cast(tmp) * scale255_constant; tmp = static_cast(tmp_f + 0.5f); } else { if(tmp >= 0) { tmp >>= n; } else { uint32_t mask = (1u << n) - 1; tmp = (tmp + static_cast(mask)) >> n; } } if(is_sat) { tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp); } *(output_ptr + x) = static_cast(tmp); } }, input1, input2, dst); } template 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(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) { if(mul_q8_neon_fixedpoint_possible(src1, src2, dst, scale)) { _func_quantized = &mul_q8_neon_fixedpoint; } else { _func_quantized = &mul_saturate_quantized_8; } } break; case DataType::QASYMM8_SIGNED: if(dt_input2 == DataType::QASYMM8_SIGNED) { if(mul_q8_neon_fixedpoint_possible(src1, src2, dst, scale)) { _func_quantized = &mul_q8_neon_fixedpoint; } else { _func_quantized = &mul_saturate_quantized_8; } } 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 : &mul_S16_U8_S16; } else { _func_int = is_sat ? &mul_S16_U8_S16 : &mul_S16_U8_S16; } } if(DataType::S16 == dt_input2 && DataType::S16 == dt_output) { if(is_scale_255) { _func_int = is_sat ? &mul_S16_S16_S16 : &mul_S16_S16_S16; } else { _func_int = is_sat ? &mul_S16_S16_S16 : &mul_S16_S16_S16; } } break; case DataType::S32: if(DataType::S32 == dt_input2 && DataType::S32 == dt_output) { _func_int = is_sat ? &mul_S32_S32_S32 : &mul_S32_S32_S32; } 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 : &mul_U8_U8_U8; } else { _func_int = is_sat ? &mul_U8_U8_U8 : &mul_U8_U8_U8; } } else if(DataType::U8 == dt_input2 && DataType::S16 == dt_output) { if(is_scale_255) { _func_int = is_sat ? &mul_U8_U8_S16 : &mul_U8_U8_S16; } else { _func_int = is_sat ? &mul_U8_U8_S16 : &mul_U8_U8_S16; } } else if(DataType::S16 == dt_input2 && DataType::S16 == dt_output) { if(is_scale_255) { _func_int = is_sat ? &mul_U8_S16_S16 : &mul_U8_S16_S16; } else { _func_int = is_sat ? &mul_U8_S16_S16 : &mul_U8_S16_S16; } } 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; std::tie(win, _split_dimension) = calculate_squashed_or_max_window(*src1, *src2); ICpuKernel::configure(win); } size_t CpuMulKernel::get_mws(const CPUInfo &platform, size_t thread_count) const { ARM_COMPUTE_UNUSED(thread_count); #if defined(ENABLE_FP32_KERNELS) if(this->_func_float == &mul_F32_F32_F32) { size_t mws = ICPPKernel::default_mws; if(platform.get_cpu_model() == CPUModel::N1) { mws = default_mws_N1_fp32_neon; } else if(platform.get_cpu_model() == CPUModel::V1) { mws = default_mws_V1_fp32_neon; } else { if(_split_dimension == Window::DimX) { // Don't split the work load too small if the tensor has been reinterpreted as 1D. // This number is loosely chosen as threading overhead in each platform varies wildly. return default_mws_other_platforms_1d_tensor; } return default_mws; } // tensor is 1D or was re-interpreted as 1D if(this->window().shape().num_dimensions() == 1) { return mws; } else { // scale mws down by the number of elements along all the dimensions (x, z, w, etc) except the one // that we parallelize along (the y dimension). This allows for parallelization when the Y_SIZE is small // but the other sizes are large, which boosts performance. mws = static_cast(mws / (this->window().num_iterations_total() / this->window().num_iterations(1))); return std::max(static_cast(1), mws); } } #else /* ENABLE_FP32_KERNELS */ ARM_COMPUTE_UNUSED(platform); #endif /* ENABLE_FP32_KERNELS */ if(_split_dimension == Window::DimX) { // Don't split the work load too small if the tensor has been reinterpreted as 1D. // This number is loosely chosen as threading overhead in each platform varies wildly. return default_mws_other_platforms_1d_tensor; } return default_mws; } 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