| /* |
| * Copyright (c) 2016-2020 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 "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include <map> |
| #include <string> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| template <typename T> |
| void add_same(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy policy, const Window &window) |
| { |
| /** NEON vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; |
| |
| // Create input windows |
| Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(in2->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(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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); |
| |
| 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 ? in2 : in1; |
| const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| |
| // 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 output(out, win); |
| |
| 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 *>(output.ptr()); |
| |
| const T broadcast_value = *reinterpret_cast<const T *>(broadcast_input.ptr()); |
| const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{}); |
| |
| // Compute S 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); |
| const auto res = (policy == ConvertPolicy::SATURATE) ? wrapper::vqadd(broadcast_value_vec, non_broadcast_v) : wrapper::vadd(broadcast_value_vec, non_broadcast_v); |
| 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) = (policy == ConvertPolicy::SATURATE) ? wrapper::add_sat(broadcast_value, non_broadcast_v) : broadcast_value + non_broadcast_v; |
| } |
| }, |
| broadcast_input, non_broadcast_input, output); |
| } |
| 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(in1, input1_win); |
| Iterator input2(in2, input2_win); |
| Iterator output(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 *>(output.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto val1 = wrapper::vloadq(input1_ptr + x); |
| const auto val2 = wrapper::vloadq(input2_ptr + x); |
| const auto res = (policy == ConvertPolicy::SATURATE) ? wrapper::vqadd(val1, val2) : wrapper::vadd(val1, val2); |
| wrapper::vstore(output_ptr + x, res); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const auto val1 = *(input1_ptr + x); |
| const auto val2 = *(input2_ptr + x); |
| *(output_ptr + x) = (policy == ConvertPolicy::SATURATE) ? wrapper::add_sat(val1, val2) : val1 + val2; |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| void add_QASYMM8_QASYMM8_QASYMM8(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(policy); |
| |
| // Create input windows |
| Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(in2->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 = 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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); |
| |
| const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform(); |
| |
| const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale); |
| const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale); |
| const float32x4_t invvscaleo = vdupq_n_f32(1.f / oq_info.scale); |
| const int32x4_t voffset1 = vdupq_n_s32(iq1_info.offset); |
| const int32x4_t voffset2 = vdupq_n_s32(iq2_info.offset); |
| const float32x4_t voffseto = vdupq_n_f32(oq_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 ? in2 : in1; |
| const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 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 output(out, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto non_broadcast_input_ptr = reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr()); |
| const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr()); |
| |
| const uint8_t broadcast_value = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr()); |
| const uint8x16_t broadcast_value_vec = vdupq_n_u8(broadcast_value); |
| |
| const float32x4x4_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(broadcast_value_vec))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(broadcast_value_vec))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(broadcast_value_vec))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(broadcast_value_vec))))), voffset2)), vscale2), |
| } |
| }; |
| const float bfs = static_cast<int32_t>(broadcast_value - broadcast_qinfo.offset) * broadcast_qinfo.scale; |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const uint8x16_t a = vld1q_u8(non_broadcast_input_ptr + x); |
| const float32x4x4_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]))); |
| const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3]))); |
| vst1q_u8(output_ptr + x, vcombine_u8(pa, pb)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>(*(non_broadcast_input_ptr + x) - non_broadcast_qinfo.offset) * non_broadcast_qinfo.scale; |
| *(output_ptr + x) = quantize_qasymm8((afs + bfs), oq_info); |
| } |
| }, |
| broadcast_input, non_broadcast_input, output); |
| } |
| 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(in1, input1_win); |
| Iterator input2(in2, input2_win); |
| Iterator output(out, win); |
| |
| 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 *>(output.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const uint8x16_t a = vld1q_u8(input1_ptr + x); |
| const uint8x16_t b = vld1q_u8(input2_ptr + x); |
| |
| const float32x4x4_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1), |
| } |
| }; |
| |
| const float32x4x4_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(b))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(b))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(b))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(b))))), voffset2)), vscale2), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]))); |
| const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3]))); |
| vst1q_u8(output_ptr + x, vcombine_u8(pa, pb)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>((*(input1_ptr + x)) - iq1_info.offset) * iq1_info.scale; |
| const float bfs = static_cast<int32_t>((*(input2_ptr + x)) - iq2_info.offset) * iq2_info.scale; |
| *(output_ptr + x) = quantize_qasymm8((afs + bfs), out->info()->quantization_info()); |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| void add_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(policy); |
| |
| // Create input windows |
| Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(in2->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 = 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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); |
| |
| const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform(); |
| |
| const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale); |
| const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale); |
| const float32x4_t invvscaleo = vdupq_n_f32(1.f / oq_info.scale); |
| const int32x4_t voffset1 = vdupq_n_s32(iq1_info.offset); |
| const int32x4_t voffset2 = vdupq_n_s32(iq2_info.offset); |
| const float32x4_t voffseto = vdupq_n_f32(oq_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 ? in2 : in1; |
| const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 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 output(out, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr()); |
| const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr()); |
| |
| const int8_t broadcast_value = *reinterpret_cast<const int8_t *>(broadcast_input.ptr()); |
| const int8x16_t broadcast_value_vec = vdupq_n_s8(broadcast_value); |
| |
| const float32x4x4_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(broadcast_value_vec)))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(broadcast_value_vec)))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(broadcast_value_vec)))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(broadcast_value_vec)))), voffset2)), vscale2), |
| } |
| }; |
| const float bfs = static_cast<int32_t>(broadcast_value - broadcast_qinfo.offset) * broadcast_qinfo.scale; |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const int8x16_t a = vld1q_s8(non_broadcast_input_ptr + x); |
| const float32x4x4_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a)))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(a)))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(a)))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(a)))), voffset1)), vscale1), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const int8x8_t pa = vqmovn_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]))); |
| const int8x8_t pb = vqmovn_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3]))); |
| vst1q_s8(output_ptr + x, vcombine_s8(pa, pb)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>(*(non_broadcast_input_ptr + x) - non_broadcast_qinfo.offset) * non_broadcast_qinfo.scale; |
| *(output_ptr + x) = quantize_qasymm8_signed((afs + bfs), oq_info); |
| } |
| }, |
| broadcast_input, non_broadcast_input, output); |
| } |
| 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(in1, input1_win); |
| Iterator input2(in2, input2_win); |
| Iterator output(out, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr()); |
| const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr()); |
| const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const int8x16_t a = vld1q_s8(input1_ptr + x); |
| const int8x16_t b = vld1q_s8(input2_ptr + x); |
| |
| const float32x4x4_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a)))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(a)))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(a)))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(a)))), voffset1)), vscale1), |
| } |
| }; |
| |
| const float32x4x4_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(b)))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(b)))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(b)))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(b)))), voffset2)), vscale2), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const int8x8_t pa = vqmovn_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]))); |
| const int8x8_t pb = vqmovn_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3]))); |
| vst1q_s8(output_ptr + x, vcombine_s8(pa, pb)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>((*(input1_ptr + x)) - iq1_info.offset) * iq1_info.scale; |
| const float bfs = static_cast<int32_t>((*(input2_ptr + x)) - iq2_info.offset) * iq2_info.scale; |
| *(output_ptr + x) = quantize_qasymm8_signed((afs + bfs), out->info()->quantization_info()); |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| void add_QSYMM16_QSYMM16_QSYMM16(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(policy); |
| |
| // Create input windows |
| Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(in2->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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); |
| |
| const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform(); |
| |
| const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale); |
| const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale); |
| const float32x4_t invvscaleo = vdupq_n_f32(1.f / oq_info.scale); |
| |
| 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 ? in2 : in1; |
| const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 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 output(out, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto non_broadcast_input_ptr = reinterpret_cast<const int16_t *>(non_broadcast_input.ptr()); |
| const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr()); |
| |
| const int16_t broadcast_value = *reinterpret_cast<const int16_t *>(broadcast_input.ptr()); |
| const int16x8_t broadcast_value_vec = vdupq_n_s16(broadcast_value); |
| |
| const float32x4x2_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(broadcast_value_vec))), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(broadcast_value_vec))), vscale2), |
| } |
| }; |
| const float bfs = static_cast<int32_t>(broadcast_value) * broadcast_qinfo.scale; |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const int16x8_t a = vld1q_s16(non_broadcast_input_ptr + x); |
| const float32x4x2_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(a))), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(a))), vscale1), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmulq_f32(vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmulq_f32(vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const int16x8_t pa = vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1])); |
| vst1q_s16(output_ptr + x, pa); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>(*(non_broadcast_input_ptr + x)) * non_broadcast_qinfo.scale; |
| *(output_ptr + x) = quantize_qsymm16((afs + bfs), oq_info); |
| } |
| }, |
| broadcast_input, non_broadcast_input, output); |
| } |
| 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(in1, input1_win); |
| Iterator input2(in2, input2_win); |
| Iterator output(out, win); |
| |
| 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 *>(output.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const int16x8_t a = vld1q_s16(input1_ptr + x); |
| const int16x8_t b = vld1q_s16(input2_ptr + x); |
| |
| const float32x4x2_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(a))), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(a))), vscale1), |
| } |
| }; |
| |
| const float32x4x2_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(b))), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(b))), vscale2), |
| } |
| }; |
| |
| const int32x4x2_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmulq_f32(vaddq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmulq_f32(vaddq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const int16x8_t pa = vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1])); |
| vst1q_s16(output_ptr + x, pa); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>((*(input1_ptr + x))) * iq1_info.scale; |
| const float bfs = static_cast<int32_t>((*(input2_ptr + x))) * iq2_info.scale; |
| *(output_ptr + x) = quantize_qsymm16((afs + bfs), out->info()->quantization_info()); |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| void add_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window) |
| { |
| // Create input windows |
| Window win = window; |
| Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(in2->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(in1, input1_win); |
| Iterator input2(in2, input2_win); |
| Iterator output(out, win); |
| |
| 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()); |
| |
| 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 *>(output.ptr()); |
| |
| if(policy == ConvertPolicy::WRAP) |
| { |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vin1 = wrapper::vloadq(input1_ptr + x); |
| const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x))); |
| wrapper::vstore(output_ptr + x, wrapper::vadd(vin1, vin2)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(output_ptr + x) = *(input1_ptr + x) + static_cast<int16_t>(*(input2_ptr + x)); |
| } |
| } |
| else |
| { |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vin1 = wrapper::vloadq(input1_ptr + x); |
| const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x))); |
| wrapper::vstore(output_ptr + x, wrapper::vqadd(vin1, vin2)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(output_ptr + x) = wrapper::add_sat(*(input1_ptr + x), static_cast<int16_t>(*(input2_ptr + x))); |
| } |
| } |
| }, |
| input1, input2, output); |
| } |
| |
| inline void add_U8_S16_S16(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy, const Window &window) |
| { |
| // Simply swap the two input buffers: |
| add_S16_U8_S16(input2, input1, output, policy, window); |
| } |
| |
| void add_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window) |
| { |
| // Create input windows |
| Window win = window; |
| Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(in2->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(in1, input1_win); |
| Iterator input2(in2, input2_win); |
| Iterator output(out, win); |
| |
| 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()); |
| |
| 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 *>(output.ptr()); |
| |
| if(policy == ConvertPolicy::WRAP) |
| { |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vin1 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input1_ptr + x))); |
| const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x))); |
| wrapper::vstore(output_ptr + x, wrapper::vadd(vin1, vin2)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(output_ptr + x) = static_cast<int16_t>(*(input1_ptr + x)) + static_cast<int16_t>(*(input2_ptr + x)); |
| } |
| } |
| else |
| { |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vin1 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input1_ptr + x))); |
| const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x))); |
| wrapper::vstore(output_ptr + x, wrapper::vqadd(vin1, vin2)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(output_ptr + x) = wrapper::add_sat(static_cast<int16_t>(*(input1_ptr + x)), |
| static_cast<int16_t>(*(input2_ptr + x))); |
| } |
| } |
| }, |
| input1, input2, output); |
| } |
| |
| Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_UNUSED(policy); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32); |
| |
| const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((input1.tensor_shape().x() != input2.tensor_shape().x()) && ((input1.data_type() != input2.data_type()) || (input1.data_type() != output.data_type()) |
| || (input2.data_type() != output.data_type())), |
| "Broadcasting across width is supported on configurations where all tensors have the same data type"); |
| |
| // Validate in case of configured output |
| if(output.total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32 && output.data_type() == DataType::F32) |
| && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16 && output.data_type() == DataType::F16) |
| && !(input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8 && output.data_type() == DataType::QASYMM8) |
| && !(input1.data_type() == DataType::QASYMM8_SIGNED && input2.data_type() == DataType::QASYMM8_SIGNED && output.data_type() == DataType::QASYMM8_SIGNED) |
| && !(input1.data_type() == DataType::QSYMM16 && input2.data_type() == DataType::QSYMM16 && output.data_type() == DataType::QSYMM16), |
| "You called addition with the wrong image formats"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), |
| "Wrong shape for output"); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) |
| { |
| const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); |
| const TensorShape &out_shape = broadcast_pair.first; |
| const ValidRegion &valid_region = broadcast_pair.second; |
| |
| // Auto initialize output if not initialized |
| { |
| set_shape_if_empty(output, out_shape); |
| |
| if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) |
| { |
| set_format_if_unknown(output, Format::S16); |
| } |
| else if(input1.data_type() == DataType::F16 || input2.data_type() == DataType::F16) |
| { |
| set_format_if_unknown(output, Format::F16); |
| } |
| else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) |
| { |
| set_format_if_unknown(output, Format::F32); |
| } |
| else if(input1.data_type() == DataType::QASYMM8 || input2.data_type() == DataType::QASYMM8) |
| { |
| set_data_type_if_unknown(output, DataType::QASYMM8); |
| } |
| else if(input1.data_type() == DataType::QASYMM8_SIGNED || input2.data_type() == DataType::QASYMM8_SIGNED) |
| { |
| set_data_type_if_unknown(output, DataType::QASYMM8_SIGNED); |
| } |
| else if(input1.data_type() == DataType::QSYMM16 || input2.data_type() == DataType::QSYMM16) |
| { |
| set_data_type_if_unknown(output, DataType::QSYMM16); |
| } |
| } |
| |
| Window win = calculate_max_window(valid_region, Steps()); |
| |
| // NEArithmeticAdditionKernel doesn't need padding so update_window_and_padding() can be skipped |
| Coordinates coord; |
| coord.set_num_dimensions(output.num_dimensions()); |
| output.set_valid_region(valid_region); |
| return std::make_pair(Status{}, win); |
| } |
| } // namespace |
| |
| NEArithmeticAdditionKernel::NEArithmeticAdditionKernel() |
| : _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _policy() |
| { |
| } |
| |
| void NEArithmeticAdditionKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy)); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| |
| static std::map<std::string, AddFunction *> map_function = |
| { |
| { "add_wrap_QASYMM8_QASYMM8_QASYMM8", &add_QASYMM8_QASYMM8_QASYMM8 }, |
| { "add_saturate_QASYMM8_QASYMM8_QASYMM8", &add_QASYMM8_QASYMM8_QASYMM8 }, |
| { "add_wrap_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &add_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED }, |
| { "add_saturate_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &add_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED }, |
| { "add_wrap_QSYMM16_QSYMM16_QSYMM16", &add_QSYMM16_QSYMM16_QSYMM16 }, |
| { "add_saturate_QSYMM16_QSYMM16_QSYMM16", &add_QSYMM16_QSYMM16_QSYMM16 }, |
| { "add_wrap_U8_U8_U8", &add_same<uint8_t> }, |
| { "add_saturate_U8_U8_U8", &add_same<uint8_t> }, |
| { "add_wrap_S16_U8_S16", &add_S16_U8_S16 }, |
| { "add_saturate_S16_U8_S16", &add_S16_U8_S16 }, |
| { "add_wrap_U8_S16_S16", &add_U8_S16_S16 }, |
| { "add_saturate_U8_S16_S16", &add_U8_S16_S16 }, |
| { "add_wrap_U8_U8_S16", &add_U8_U8_S16 }, |
| { "add_saturate_U8_U8_S16", &add_U8_U8_S16 }, |
| { "add_wrap_S16_S16_S16", &add_same<int16_t> }, |
| { "add_saturate_S16_S16_S16", &add_same<int16_t> }, |
| { "add_wrap_F32_F32_F32", &add_same<float> }, |
| { "add_saturate_F32_F32_F32", &add_same<float> }, |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| { "add_wrap_F16_F16_F16", &add_same<float16_t> }, |
| { "add_saturate_F16_F16_F16", &add_same<float16_t> }, |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| }; |
| |
| _input1 = input1; |
| _input2 = input2; |
| _output = output; |
| _policy = policy; |
| |
| std::string function_to_call("add_"); |
| function_to_call += policy == ConvertPolicy::WRAP ? "wrap_" : "saturate_"; |
| function_to_call += string_from_data_type(input1->info()->data_type()) + "_"; |
| function_to_call += string_from_data_type(input2->info()->data_type()) + "_"; |
| function_to_call += string_from_data_type(output->info()->data_type()); |
| |
| auto it = map_function.find(function_to_call); |
| |
| if(it != map_function.end()) |
| { |
| _func = it->second; |
| } |
| |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); |
| |
| return Status{}; |
| } |
| |
| void NEArithmeticAdditionKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (*_func)(_input1, _input2, _output, _policy, window); |
| } |
| } // namespace arm_compute |