| /* |
| * 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 "src/core/NEON/kernels/NEArithmeticSubtractionKernel.h" |
| |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.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" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| template <typename T> |
| inline typename std::enable_if<std::is_same<T, int8_t>::value, int8_t>::type |
| quantize(float val, const QuantizationInfo &info) |
| { |
| return quantize_qasymm8_signed(val, info); |
| } |
| |
| template <typename T> |
| inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type |
| quantize(float val, const QuantizationInfo &info) |
| { |
| return quantize_qasymm8(val, info); |
| } |
| |
| template <typename T> |
| void sub_same(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat) |
| { |
| /** 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); |
| |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| 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); |
| auto res = is_sat ? wrapper::vqsub(broadcast_value_vec, non_broadcast_v) : wrapper::vsub(broadcast_value_vec, non_broadcast_v); |
| if(is_broadcast_input_2) |
| { |
| res = wrapper::vmul(res, wrapper::vdup_n(static_cast<T>(-1), ExactTagType{})); |
| } |
| 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); |
| auto res = is_sat ? wrapper::sub_sat(broadcast_value, non_broadcast_v) : broadcast_value - non_broadcast_v; |
| if(is_broadcast_input_2) |
| { |
| res = static_cast<T>(-1) * res; |
| } |
| |
| *(output_ptr + x) = res; |
| } |
| }, |
| 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 = is_sat ? wrapper::vqsub(val1, val2) : wrapper::vsub(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) = is_sat ? wrapper::sub_sat(val1, val2) : val1 - val2; |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| template <typename T> |
| void sub_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat) |
| { |
| ARM_COMPUTE_UNUSED(is_sat); |
| |
| // 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 invvscaleo = vdupq_n_f32(1.f / oq_info.scale); |
| 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(); |
| const float32x4_t vscale1 = is_broadcast_input_2 ? vdupq_n_f32(iq1_info.scale) : vdupq_n_f32(iq2_info.scale); |
| const float32x4_t vscale2 = is_broadcast_input_2 ? vdupq_n_f32(iq2_info.scale) : vdupq_n_f32(iq1_info.scale); |
| const int32x4_t voffset1 = is_broadcast_input_2 ? vdupq_n_s32(iq1_info.offset) : vdupq_n_s32(iq2_info.offset); |
| const int32x4_t voffset2 = is_broadcast_input_2 ? vdupq_n_s32(iq2_info.offset) : vdupq_n_s32(iq1_info.offset); |
| |
| // 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 auto broadcast_value = *reinterpret_cast<const T *>(broadcast_input.ptr()); |
| const auto broadcast_value_vec = wrapper::vdup_n(static_cast<T>(broadcast_value), wrapper::traits::vector_128_tag{}); |
| |
| const float32x4x4_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgetlow(broadcast_value_vec))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgetlow(broadcast_value_vec))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgethigh(broadcast_value_vec))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgethigh(broadcast_value_vec))))), voffset2)), vscale2), |
| } |
| }; |
| |
| // Compute S 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 + x); |
| |
| const float32x4x4_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgetlow(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgetlow(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgethigh(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgethigh(a))))), voffset1)), vscale1), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64_ |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[0], af.val[0]) : vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[1], af.val[1]) : vsubq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[2], af.val[2]) : vsubq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[3], af.val[3]) : vsubq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[0], af.val[0]) : vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[1], af.val[1]) : vsubq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[2], af.val[2]) : vsubq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, !is_broadcast_input_2 ? vsubq_f32(bf.val[3], af.val[3]) : vsubq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const auto pa = wrapper::vqmov<T>(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]))); |
| const auto pb = wrapper::vqmov<T>(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3]))); |
| wrapper::vstore(output_ptr + x, wrapper::vcombine(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; |
| const float bfs = static_cast<int32_t>(broadcast_value - broadcast_qinfo.offset) * broadcast_qinfo.scale; |
| *(output_ptr + x) = quantize<T>(is_broadcast_input_2 ? afs - bfs : bfs - afs, out->info()->quantization_info()); |
| } |
| }, |
| broadcast_input, non_broadcast_input, output); |
| } |
| else |
| { |
| const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale); |
| const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale); |
| const int32x4_t voffset1 = vdupq_n_s32(iq1_info.offset); |
| const int32x4_t voffset2 = vdupq_n_s32(iq2_info.offset); |
| |
| // 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 a = wrapper::vloadq(input1_ptr + x); |
| const auto b = wrapper::vloadq(input2_ptr + x); |
| |
| const float32x4x4_t af = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgetlow(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgetlow(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgethigh(a))))), voffset1)), vscale1), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgethigh(a))))), voffset1)), vscale1), |
| } |
| }; |
| |
| const float32x4x4_t bf = |
| { |
| { |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgetlow(b))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgetlow(b))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vgethigh(b))))), voffset2)), vscale2), |
| vmulq_f32(vcvtq_f32_s32(vsubq_s32(wrapper::vreinterpret(wrapper::vmovl(wrapper::vgethigh(wrapper::vmovl(wrapper::vgethigh(b))))), voffset2)), vscale2), |
| } |
| }; |
| |
| const int32x4x4_t rf = |
| { |
| { |
| #ifdef __aarch64__ |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtnq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[2], bf.val[2]), invvscaleo)), |
| vcvtq_s32_f32(vmlaq_f32(voffseto, vsubq_f32(af.val[3], bf.val[3]), invvscaleo)), |
| #endif //__aarch64__ |
| } |
| }; |
| |
| const auto pa = wrapper::vqmov<T>(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]))); |
| const auto pb = wrapper::vqmov<T>(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3]))); |
| wrapper::vstore(output_ptr + x, wrapper::vcombine(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<T>((afs - bfs), out->info()->quantization_info()); |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| void sub_QSYMM16_QSYMM16_QSYMM16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat) |
| { |
| ARM_COMPUTE_UNUSED(is_sat); |
| |
| // 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(is_broadcast_input_2 ? vsubq_f32(bf.val[0], af.val[0]) : vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmulq_f32(is_broadcast_input_2 ? vsubq_f32(bf.val[1], af.val[1]) : vsubq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmulq_f32(is_broadcast_input_2 ? vsubq_f32(bf.val[0], af.val[0]) : vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmulq_f32(is_broadcast_input_2 ? vsubq_f32(bf.val[1], af.val[1]) : vsubq_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(is_broadcast_input_2 ? (bfs - afs) : (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(vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtnq_s32_f32(vmulq_f32(vsubq_f32(af.val[1], bf.val[1]), invvscaleo)), |
| #else //__aarch64__ |
| vcvtq_s32_f32(vmulq_f32(vsubq_f32(af.val[0], bf.val[0]), invvscaleo)), |
| vcvtq_s32_f32(vmulq_f32(vsubq_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 sub_S16_U8_S16_impl(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat, bool is_swapped) |
| { |
| // 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(!is_sat) |
| { |
| // 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))); |
| const auto res = is_swapped ? wrapper::vsub(vin2, vin1) : wrapper::vsub(vin1, vin2); |
| wrapper::vstore(output_ptr + x, res); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const auto res = is_swapped ? static_cast<int16_t>(*(input2_ptr + x)) - *(input1_ptr + x) : *(input1_ptr + x) - static_cast<int16_t>(*(input2_ptr + x)); |
| *(output_ptr + x) = res; |
| } |
| } |
| 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))); |
| const auto res = is_swapped ? wrapper::vqsub(vin2, vin1) : wrapper::vqsub(vin1, vin2); |
| wrapper::vstore(output_ptr + x, res); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const auto res = is_swapped ? wrapper::sub_sat(static_cast<int16_t>(*(input2_ptr + x)), *(input1_ptr + x)) : wrapper::sub_sat(*(input1_ptr + x), static_cast<int16_t>(*(input2_ptr + x))); |
| *(output_ptr + x) = res; |
| } |
| } |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat) |
| { |
| sub_S16_U8_S16_impl(in1, in2, out, window, is_sat, false); |
| } |
| |
| void sub_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat) |
| { |
| // Swap arguments |
| sub_S16_U8_S16_impl(in2, in1, out, window, is_sat, true); |
| } |
| |
| void sub_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, bool is_sat) |
| { |
| // 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(!is_sat) |
| { |
| // 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::vsub(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::vqsub(vin1, vin2)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(output_ptr + x) = wrapper::sub_sat(static_cast<int16_t>(*(input1_ptr + x)), |
| static_cast<int16_t>(*(input2_ptr + x))); |
| } |
| } |
| }, |
| input1, input2, output); |
| } |
| |
| inline 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::QSYMM16, DataType::S16, DataType::S32, DataType::F16, |
| DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM16, DataType::S16, DataType::S32, DataType::F16, |
| DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM16, DataType::S16, DataType::S32, 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.data_type() == DataType::U8 && input2.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8) |
| && !(input1.data_type() == DataType::QASYMM8_SIGNED && input2.data_type() == DataType::QASYMM8_SIGNED) |
| && !(input1.data_type() == DataType::QSYMM16 && input2.data_type() == DataType::QSYMM16) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S32 && input2.data_type() == DataType::S32) |
| && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32) |
| && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16), |
| "You called subtract with the wrong image formats"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| (input1.data_type() == DataType::QASYMM8_SIGNED && input2.data_type() == DataType::QASYMM8_SIGNED && policy == ConvertPolicy::WRAP) |
| || (input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8 && policy == ConvertPolicy::WRAP) |
| || (input1.data_type() == DataType::QSYMM16 && input2.data_type() == DataType::QSYMM16 && policy == ConvertPolicy::WRAP), |
| "Convert policy cannot be WRAP if datatype is quantized"); |
| |
| // 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::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) |
| && !(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::S32 && input2.data_type() == DataType::S32 && output.data_type() == DataType::S32) |
| && !(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), |
| "You called subtract 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{}; |
| } |
| } // namespace |
| |
| NEArithmeticSubtractionKernel::NEArithmeticSubtractionKernel() |
| : _func(nullptr), _policy(ConvertPolicy::WRAP) |
| { |
| } |
| |
| void NEArithmeticSubtractionKernel::configure(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1, *input2, *output, policy)); |
| |
| _policy = policy; |
| |
| 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); |
| |
| switch(input1->data_type()) |
| { |
| case DataType::U8: |
| if(input2->data_type() == DataType::U8 && output->data_type() == DataType::U8) |
| { |
| _func = &sub_same<uint8_t>; |
| } |
| else if(input2->data_type() == DataType::U8 && output->data_type() == DataType::S16) |
| { |
| _func = &sub_U8_U8_S16; |
| } |
| else |
| { |
| _func = &sub_U8_S16_S16; |
| } |
| break; |
| case DataType::QASYMM8: |
| _func = &sub_quantized<uint8_t>; |
| set_data_type_if_unknown(*output, DataType::QASYMM8); |
| break; |
| case DataType::QASYMM8_SIGNED: |
| _func = &sub_quantized<int8_t>; |
| set_data_type_if_unknown(*output, DataType::QASYMM8_SIGNED); |
| break; |
| case DataType::S16: |
| if(input2->data_type() == DataType::U8) |
| { |
| _func = &sub_S16_U8_S16; |
| } |
| else |
| { |
| _func = &sub_same<int16_t>; |
| } |
| set_format_if_unknown(*output, Format::S16); |
| break; |
| case DataType::QSYMM16: |
| _func = &sub_QSYMM16_QSYMM16_QSYMM16; |
| set_data_type_if_unknown(*output, DataType::QSYMM16); |
| break; |
| case DataType::S32: |
| _func = &sub_same<int32_t>; |
| set_format_if_unknown(*output, Format::S32); |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = &sub_same<float16_t>; |
| set_format_if_unknown(*output, Format::F16); |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::F32: |
| _func = &sub_same<float>; |
| set_format_if_unknown(*output, Format::F32); |
| break; |
| default: |
| _func = nullptr; |
| } |
| |
| // NEArithmeticSubtractionKernel 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); |
| Window win = calculate_max_window(valid_region, Steps()); |
| |
| INEKernel::configure(win); |
| } |
| |
| Status NEArithmeticSubtractionKernel::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)); |
| |
| return Status{}; |
| } |
| |
| void NEArithmeticSubtractionKernel::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(INEKernel::window(), window); |
| // Dispatch kernel |
| (*_func)(tensors.get_const_tensor(TensorType::ACL_SRC_0), |
| tensors.get_const_tensor(TensorType::ACL_SRC_1), |
| tensors.get_tensor(TensorType::ACL_DST), |
| window, |
| (_policy == ConvertPolicy::SATURATE)); |
| } |
| } // namespace arm_compute |