diff options
Diffstat (limited to 'src/core/cpu/kernels/elementwise')
8 files changed, 0 insertions, 2256 deletions
diff --git a/src/core/cpu/kernels/elementwise/neon/elementwise_list.h b/src/core/cpu/kernels/elementwise/neon/elementwise_list.h deleted file mode 100644 index 43e44be5e2..0000000000 --- a/src/core/cpu/kernels/elementwise/neon/elementwise_list.h +++ /dev/null @@ -1,486 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_NEON_KERNELS_ELEMENTWISE_LIST_H -#define SRC_CORE_NEON_KERNELS_ELEMENTWISE_LIST_H - -#include "src/core/NEON/NEAsymm.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/WindowHelpers.h" - -namespace arm_compute -{ -namespace cpu -{ -template <typename InputScalarType, typename OutputScalarType, typename InputVectorType> -void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, - OutputScalarType (*scalar_func)(const InputScalarType &, const InputScalarType &), - int (*broadcast_func)(int, int, int, const InputScalarType *, const InputScalarType &, OutputScalarType *, const bool), - int (*neon_func)(int, int, int, const InputScalarType *, const InputScalarType *, OutputScalarType *)) -{ - // 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 = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 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 = in1->info()->tensor_shape().x() != in2->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 ? 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 &) - { - auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); - const auto non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr()); - const InputScalarType broadcast_value = *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr()); - - int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_value, output_ptr, !is_broadcast_input_2); - for(; x < window_end_x; ++x) - { - const auto a = *(non_broadcast_input_ptr + x); - *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a, !is_broadcast_input_2 ? a : broadcast_value); - } - }, - 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 &) - { - auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); - const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr()); - - int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr); - for(; x < window_end_x; ++x) - { - const auto a = *(input1_ptr + x); - const auto b = *(input2_ptr + x); - *(output_ptr + x) = (*scalar_func)(a, b); - } - }, - input1, input2, output); - } -} - -template <ArithmeticOperation op, typename ScalarType> -inline ScalarType elementwise_arithm_op_scalar(const ScalarType &a, const ScalarType &b) -{ - auto res = ScalarType(0); - - switch(op) - { - case ArithmeticOperation::MAX: - res = std::max(a, b); - break; - case ArithmeticOperation::MIN: - res = std::min(a, b); - break; - case ArithmeticOperation::SQUARED_DIFF: - { - res = (a - b) * (a - b); - break; - } - case ArithmeticOperation::PRELU: - { - res = (a > 0 ? a : a * b); - break; - } - case ArithmeticOperation::DIV: - { - res = a / b; - if(std::is_integral<ScalarType>::value) - { - res = (b == 0) ? 0 : res; - if(static_cast<int32_t>(a) % static_cast<int32_t>(b) != 0 && ((a < 0) != (b < 0))) - { - --res; - } - } - break; - } - case ArithmeticOperation::POWER: - { - res = std::pow(a, b); - break; - } - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - return res; -} - -template <ArithmeticOperation op, typename VectorType> -inline typename VectorType::type elementwise_arithm_op(const typename VectorType::type &a, const typename VectorType::type &b) -{ - using vec_type = typename VectorType::type; - using scalar_type = typename VectorType::scalar_type; - using tag_type = typename VectorType::tag_type; - - vec_type res = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{}); - - switch(op) - { - case ArithmeticOperation::MAX: - res = wrapper::vmax(a, b); - break; - case ArithmeticOperation::MIN: - res = wrapper::vmin(a, b); - break; - case ArithmeticOperation::SQUARED_DIFF: - { - const vec_type tmp = wrapper::vsub(a, b); - res = wrapper::vmul(tmp, tmp); - break; - } - case ArithmeticOperation::PRELU: - { - const vec_type zero = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{}); - const vec_type tmp = wrapper::vmul(a, b); - const auto gt = wrapper::vcgt(a, zero); - - res = wrapper::vbsl(gt, a, tmp); - break; - } - - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - - return res; -} - -template <> -inline int32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<int32_t, 4>>(const int32x4_t &a, const int32x4_t &b) -{ - return vcvtq_s32_f32(vfloorq_f32(wrapper::vdiv(vcvtq_f32_s32(a), vcvtq_f32_s32(b)))); -} - -template <> -inline float32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b) -{ - return wrapper::vdiv(a, b); -} - -template <> -inline float32x4_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b) -{ - return wrapper::vpow(a, b); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template <> -inline float16x8_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b) -{ - return wrapper::vdiv(a, b); -} - -template <> -inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b) -{ - return wrapper::vpow(a, b); -} -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - -template <ArithmeticOperation op, typename ScalarType, typename VectorType> -inline typename VectorType::type elementwise_arithm_op_broadcast(const typename VectorType::type &a, const ScalarType &broadcast_value, const bool reorder) -{ - using tag_type = typename VectorType::tag_type; - using vec_type = typename VectorType::type; - - vec_type broadcast_vector = wrapper::vdup_n(broadcast_value, tag_type{}); - return elementwise_arithm_op<op, VectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector); -} - -template <ArithmeticOperation op, typename ScalarType, typename VectorType> -inline int elementwise_arithm_op_loop(int window_start_x, int window_end_x, int window_step_x, - const ScalarType *input1_ptr, const ScalarType *input2_ptr, ScalarType *output_ptr) -{ - 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); - wrapper::vstore(output_ptr + x, elementwise_arithm_op<op, VectorType>(a, b)); - } - return x; -} - -template <ArithmeticOperation op, typename ScalarType, typename VectorType> -inline int elementwise_arithm_op_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, - const ScalarType *non_broadcast_input_ptr, const ScalarType &broadcast_value, ScalarType *output_ptr, const bool reorder) -{ - 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)); - wrapper::vstore(output_ptr + x, elementwise_arithm_op_broadcast<op, ScalarType, VectorType>(a, broadcast_value, reorder)); - } - return x; -} - -template <ArithmeticOperation op, typename VectorType> -void elementwise_arithm_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - using scalar_type = typename VectorType::scalar_type; - - elementwise_op<scalar_type, scalar_type, VectorType>(in1, in2, out, window, - &elementwise_arithm_op_scalar<op, scalar_type>, - &elementwise_arithm_op_broadcast_loop<op, scalar_type, VectorType>, - &elementwise_arithm_op_loop<op, scalar_type, VectorType>); -} - -template <ComparisonOperation op, typename InputScalarType> -inline uint8_t elementwise_comp_op_scalar(const InputScalarType &a, const InputScalarType &b) -{ - bool res = false; - - switch(op) - { - case ComparisonOperation::Equal: - res = (a == b); - break; - case ComparisonOperation::NotEqual: - res = (a != b); - break; - case ComparisonOperation::Greater: - res = (a > b); - break; - case ComparisonOperation::GreaterEqual: - res = (a >= b); - break; - case ComparisonOperation::Less: - res = (a < b); - break; - case ComparisonOperation::LessEqual: - res = (a <= b); - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - return res ? ~static_cast<uint8_t>(0) : static_cast<uint8_t>(0); -} - -template <ComparisonOperation op, typename InputVectorType, typename OutputVectorType> -inline OutputVectorType elementwise_comp_op(const InputVectorType &a, const InputVectorType &b) -{ - OutputVectorType res = { 0, 0, 0, 0 }; - - switch(op) - { - case ComparisonOperation::Equal: - res = wrapper::vceq(a, b); - break; - case ComparisonOperation::NotEqual: - res = wrapper::vnot(wrapper::vceq(a, b)); - break; - case ComparisonOperation::Greater: - res = wrapper::vcgt(a, b); - break; - case ComparisonOperation::GreaterEqual: - res = wrapper::vcge(a, b); - break; - case ComparisonOperation::Less: - res = wrapper::vcgt(b, a); - break; - case ComparisonOperation::LessEqual: - res = wrapper::vcge(b, a); - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - - return res; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType, typename OutputVectorType> -inline OutputVectorType elementwise_comp_op_broadcast(const InputVectorType &a, const InputScalarType &broadcast_value, const bool reorder) -{ - InputVectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag()); - return elementwise_comp_op<op, InputVectorType, OutputVectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector); -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -inline int elementwise_comp_op_broadcast_8_loop(int window_start_x, int window_end_x, int window_step_x, - const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint8x16_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder); - wrapper::vstore(output_ptr + x, a); - } - return x; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -inline int elementwise_comp_op_broadcast_16_loop(int window_start_x, int window_end_x, int window_step_x, - const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint16x8_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder); - wrapper::vstore(output_ptr + x, wrapper::vmovn(a)); - } - return x; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -inline int elementwise_comp_op_broadcast_32_loop(int window_start_x, int window_end_x, int window_step_x, - const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x), broadcast_value, reorder); - const auto b = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x + 4), broadcast_value, reorder); - wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(a), wrapper::vmovn(b)))); - } - if(x <= window_end_x - 4) - { - const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder); - for(int i = 0; i < 4; i++) - { - *(output_ptr + x + i) = wrapper::vgetlane(a, i); - } - x = +4; - } - return x; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -inline int elementwise_comp_op_8_loop(int window_start_x, int window_end_x, int window_step_x, - const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr) -{ - 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 auto res = elementwise_comp_op<op, InputVectorType, uint8x16_t>(a, b); - wrapper::vstore(output_ptr + x, res); - } - return x; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -inline int elementwise_comp_op_16_loop(int window_start_x, int window_end_x, int window_step_x, - const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr) -{ - 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 auto res = elementwise_comp_op<op, InputVectorType, uint16x8_t>(a, b); - wrapper::vstore(output_ptr + x, wrapper::vmovn(res)); - } - return x; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -inline int elementwise_comp_op_32_loop(int window_start_x, int window_end_x, int window_step_x, - const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - auto a = wrapper::vloadq(input1_ptr + x); - auto b = wrapper::vloadq(input2_ptr + x); - const auto res = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b); - a = wrapper::vloadq(input1_ptr + x + 4); - b = wrapper::vloadq(input2_ptr + x + 4); - const auto res2 = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b); - wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(res), wrapper::vmovn(res2)))); - } - if(x <= window_end_x - 4) - { - const auto a = wrapper::vloadq(input1_ptr + x); - const auto b = wrapper::vloadq(input2_ptr + x); - const auto res = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b); - for(int i = 0; i < 4; i++) - { - *(output_ptr + x + i) = wrapper::vgetlane(res, i); - } - x = +4; - } - return x; -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -void elementwise_comp_op_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window, - &elementwise_comp_op_scalar<op, InputScalarType>, - &elementwise_comp_op_broadcast_8_loop<op, InputScalarType, InputVectorType>, - &elementwise_comp_op_8_loop<op, InputScalarType, InputVectorType>); -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -void elementwise_comp_op_16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window, - &elementwise_comp_op_scalar<op, InputScalarType>, - &elementwise_comp_op_broadcast_16_loop<op, InputScalarType, InputVectorType>, - &elementwise_comp_op_16_loop<op, InputScalarType, InputVectorType>); -} - -template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> -void elementwise_comp_op_32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window, - &elementwise_comp_op_scalar<op, InputScalarType>, - &elementwise_comp_op_broadcast_32_loop<op, InputScalarType, InputVectorType>, - &elementwise_comp_op_32_loop<op, InputScalarType, InputVectorType>); -} -} // namesapce cpu -} // namespace arm_compute - -#endif /* SRC_CORE_NEON_KERNELS_ELEMENTWISE_LIST_H */
\ No newline at end of file diff --git a/src/core/cpu/kernels/elementwise/neon/elementwise_quantized_list.h b/src/core/cpu/kernels/elementwise/neon/elementwise_quantized_list.h deleted file mode 100644 index 1ff4632f5c..0000000000 --- a/src/core/cpu/kernels/elementwise/neon/elementwise_quantized_list.h +++ /dev/null @@ -1,654 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_NEON_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H -#define SRC_CORE_NEON_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H - -#include "src/core/cpu/kernels/elementwise/neon/elementwise_list.h" - -namespace arm_compute -{ -namespace cpu -{ -float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale) -{ - qasymm8x16_t x = vld1q_u8(input1_ptr); - const float32x4x4_t out = - { - { - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale), - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale), - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale), - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale), - } - }; - return out; -} - -float32x4x4_t load_quantized_signed(const int8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale) -{ - qasymm8x16_signed_t x = vld1q_s8(input1_ptr); - const float32x4x4_t out = - { - { - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale), - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale), - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale), - vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale), - } - }; - return out; -} - -void store_quantized(uint8_t *output_ptr, const uint32x4x4_t &out) -{ - const uint8x8_t pa = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[0]), vqmovn_u32(out.val[1]))); - const uint8x8_t pb = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[2]), vqmovn_u32(out.val[3]))); - vst1q_u8(output_ptr, vcombine_u8(pa, pb)); -} - -void store_quantized(uint8_t *output_ptr, const int32x4x4_t &out) -{ - const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1]))); - const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3]))); - vst1q_u8(output_ptr, vcombine_u8(pa, pb)); -} - -void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale) -{ - int32x4x4_t out = - { - { - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)), - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)), - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)), - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)), - } - }; - store_quantized(output_ptr, out); -} - -void store_quantized_signed(int8_t *output_ptr, const int32x4x4_t &out) -{ - const int8x8_t pa = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1]))); - const int8x8_t pb = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3]))); - vst1q_s8(output_ptr, vcombine_s8(pa, pb)); -} - -void store_quantized_signed(int8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale) -{ - int32x4x4_t out = - { - { - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)), - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)), - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)), - vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)), - } - }; - store_quantized_signed(output_ptr, out); -} - -template <ArithmeticOperation op> -inline uint8_t elementwise_arithm_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo) -{ - return quantize_qasymm8(elementwise_arithm_op_scalar<op>(a, b), qinfo); -} - -template <ArithmeticOperation op> -inline int8_t elementwise_arithm_op_quantized_signed_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo) -{ - return quantize_qasymm8_signed(elementwise_arithm_op_scalar<op>(a, b), qinfo); -} - -template <ArithmeticOperation op> -inline float32x4x4_t elementwise_arithm_op(const float32x4x4_t &a, const float32x4x4_t &b) -{ - using neon_vector_float = wrapper::traits::neon_vector<float, 4>; - float32x4x4_t out = - { - { - elementwise_arithm_op<op, neon_vector_float>(a.val[0], b.val[0]), - elementwise_arithm_op<op, neon_vector_float>(a.val[1], b.val[1]), - elementwise_arithm_op<op, neon_vector_float>(a.val[2], b.val[2]), - elementwise_arithm_op<op, neon_vector_float>(a.val[3], b.val[3]), - } - }; - return out; -} - -template <ComparisonOperation op> -inline uint8_t elementwise_comp_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo) -{ - ARM_COMPUTE_UNUSED(qinfo); - return elementwise_comp_op_scalar<op>(a, b); -} - -template <ComparisonOperation op> -inline uint32x4x4_t elementwise_comp_op(const float32x4x4_t &a, const float32x4x4_t &b) -{ - uint32x4x4_t out = - { - { - elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[0], b.val[0]), - elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[1], b.val[1]), - elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[2], b.val[2]), - elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[3], b.val[3]) - } - }; - return out; -} - -template <ArithmeticOperation op> -inline int elementwise_arithm_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x, - const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr, - int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, - float32x4_t voffseto, float32x4_t invvscaleo) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get inputs and compute output - const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1); - const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2); - const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf); - store_quantized(output_ptr + x, rf, voffseto, invvscaleo); - } - return x; -} - -template <ArithmeticOperation op> -inline int elementwise_arithm_op_quantized_singed_loop(int window_start_x, int window_end_x, int window_step_x, - const int8_t *input1_ptr, const int8_t *input2_ptr, int8_t *output_ptr, - int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, - float32x4_t voffseto, float32x4_t invvscaleo) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get inputs and compute output - const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1); - const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2); - const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf); - store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo); - } - return x; -} - -template <ArithmeticOperation op> -inline int elementwise_arithm_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, - const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr, - int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, - float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); - const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); - store_quantized(output_ptr + x, rf, voffseto, invvscaleo); - } - return x; -} -template <ArithmeticOperation op> -inline int elementwise_arithm_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, - const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, int8_t *output_ptr, - int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, - float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) -{ - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); - const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); - store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo); - } - return x; -} - -template <ComparisonOperation op> -inline int elementwise_comp_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x, - const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr, - int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, - float32x4_t voffseto, float32x4_t invvscaleo) -{ - ARM_COMPUTE_UNUSED(voffseto, invvscaleo); - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1); - const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2); - const uint32x4x4_t rf = elementwise_comp_op<op>(af, bf); - store_quantized(output_ptr + x, rf); - } - return x; -} - -template <ComparisonOperation op> -inline int elementwise_comp_op_quantized_signed_loop(int window_start_x, int window_end_x, int window_step_x, - const int8_t *input1_ptr, const int8_t *input2_ptr, uint8_t *output_ptr, - int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, - float32x4_t voffseto, float32x4_t invvscaleo) -{ - ARM_COMPUTE_UNUSED(voffseto, invvscaleo); - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1); - const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2); - const uint32x4x4_t rf = elementwise_comp_op<op>(af, bf); - store_quantized(output_ptr + x, rf); - } - return x; -} - -template <ComparisonOperation op> -inline int elementwise_comp_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, - const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr, - int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, - float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) -{ - ARM_COMPUTE_UNUSED(voffseto, invvscaleo); - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); - const uint32x4x4_t rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); - store_quantized(output_ptr + x, rf); - } - return x; -} - -template <ComparisonOperation op> -inline int elementwise_comp_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, - const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr, - int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, - float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) -{ - ARM_COMPUTE_UNUSED(voffseto, invvscaleo); - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); - const uint32x4x4_t rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); - store_quantized(output_ptr + x, rf); - } - return x; -} - -void elementwise_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, - uint8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo), - int (*broadcast_func)(int, int, int, const uint8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t, - float32x4_t, float32x4_t, const bool), - int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *, - int32x4_t, int32x4_t, float32x4_t, float32x4_t, - float32x4_t, float32x4_t)) -{ - // 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 = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); - - const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform(); - - // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from zero) - const float32x4_t voffseto = vdupq_n_f32(output_qinfo.offset + 0.5f); - const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale); - - if(is_broadcast_across_x) - { - // Select the broadcast input on the X axis - 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 int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset); - const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale); - - // 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 float32x4x4_t broadcast_vector = vdequantize(vdupq_n_u8(broadcast_value), broadcast_qinfo); - - int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr, - voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2); - for(; x < window_end_x; ++x) - { - const float afs = dequantize_qasymm8(*(non_broadcast_input_ptr + x), non_broadcast_qinfo); - const float bfs = dequantize_qasymm8(broadcast_value, broadcast_qinfo); - *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo); - } - }, - broadcast_input, non_broadcast_input, output); - } - else - { - const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform(); - const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform(); - - // Input1 quantization info - const int32x4_t voffset1 = vdupq_n_s32(input1_qinfo.offset); - const float32x4_t vscale1 = vdupq_n_f32(input1_qinfo.scale); - - // Input2 quantization info - const int32x4_t voffset2 = vdupq_n_s32(input2_qinfo.offset); - const float32x4_t vscale2 = vdupq_n_f32(input2_qinfo.scale); - - // 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()); - - int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2, - vscale1, vscale2, voffseto, invvscaleo); - for(; x < window_end_x; ++x) - { - const float afs = dequantize_qasymm8(*(input1_ptr + x), input1_qinfo); - const float bfs = dequantize_qasymm8(*(input2_ptr + x), input2_qinfo); - *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo); - } - }, - input1, input2, output); - } -} - -void elementwise_comp_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, - uint8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo), - int (*broadcast_func)(int, int, int, const int8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t, - float32x4_t, float32x4_t, const bool), - int (*neon_func)(int, int, int, const int8_t *, const int8_t *, uint8_t *, - int32x4_t, int32x4_t, float32x4_t, float32x4_t, - float32x4_t, float32x4_t)) -{ - // 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 = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); - - const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform(); - - const float32x4_t voffseto = vdupq_n_f32(output_qinfo.offset); - const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale); - - if(is_broadcast_across_x) - { - // Select the broadcast input on the X axis - 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 int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset); - const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale); - - // 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<uint8_t *>(output.ptr()); - - const int8_t broadcast_value = *reinterpret_cast<const int8_t *>(broadcast_input.ptr()); - const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo); - - int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr, - voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2); - for(; x < window_end_x; ++x) - { - const float afs = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo); - const float bfs = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo); - *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo); - } - }, - broadcast_input, non_broadcast_input, output); - } - else - { - const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform(); - const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform(); - - // Input1 quantization info - const int32x4_t voffset1 = vdupq_n_s32(input1_qinfo.offset); - const float32x4_t vscale1 = vdupq_n_f32(input1_qinfo.scale); - - // Input2 quantization info - const int32x4_t voffset2 = vdupq_n_s32(input2_qinfo.offset); - const float32x4_t vscale2 = vdupq_n_f32(input2_qinfo.scale); - - // 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<uint8_t *>(output.ptr()); - - int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2, - vscale1, vscale2, voffseto, invvscaleo); - for(; x < window_end_x; ++x) - { - const float afs = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo); - const float bfs = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo); - *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo); - } - }, - input1, input2, output); - } -} - -void elementwise_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, - int8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo), - int (*broadcast_func)(int, int, int, const int8_t *, float32x4x4_t, int8_t *, int32x4_t, float32x4_t, - float32x4_t, float32x4_t, const bool), - int (*neon_func)(int, int, int, const int8_t *, const int8_t *, int8_t *, - int32x4_t, int32x4_t, float32x4_t, float32x4_t, - float32x4_t, float32x4_t)) -{ - // 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 = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); - - const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform(); - - const float32x4_t voffseto = vdupq_n_f32(output_qinfo.offset); - const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale); - - if(is_broadcast_across_x) - { - // Select the broadcast input on the X axis - 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 int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset); - const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale); - - // 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 float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo); - - int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr, - voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2); - for(; x < window_end_x; ++x) - { - const float afs = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo); - const float bfs = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo); - *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo); - } - }, - broadcast_input, non_broadcast_input, output); - } - else - { - const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform(); - const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform(); - - // Input1 quantization info - const int32x4_t voffset1 = vdupq_n_s32(input1_qinfo.offset); - const float32x4_t vscale1 = vdupq_n_f32(input1_qinfo.scale); - - // Input2 quantization info - const int32x4_t voffset2 = vdupq_n_s32(input2_qinfo.offset); - const float32x4_t vscale2 = vdupq_n_f32(input2_qinfo.scale); - - // 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()); - - int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2, - vscale1, vscale2, voffseto, invvscaleo); - for(; x < window_end_x; ++x) - { - const float afs = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo); - const float bfs = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo); - *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo); - } - }, - input1, input2, output); - } -} - -template <ArithmeticOperation op> -void elementwise_arithm_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_op_quantized(in1, in2, out, window, &elementwise_arithm_op_quantized_scalar<op>, - &elementwise_arithm_op_quantized_broadcast_loop<op>, - &elementwise_arithm_op_quantized_loop<op>); -} -template <ArithmeticOperation op> -void elementwise_arithm_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_op_quantized_signed(in1, in2, out, window, &elementwise_arithm_op_quantized_signed_scalar<op>, - &elementwise_arithm_op_quantized_signed_broadcast_loop<op>, - &elementwise_arithm_op_quantized_singed_loop<op>); -} - -template <ComparisonOperation op> -void elementwise_comp_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_op_quantized(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>, - &elementwise_comp_op_quantized_broadcast_loop<op>, - &elementwise_comp_op_quantized_loop<op>); -} - -template <ComparisonOperation op> -void elementwise_comp_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - elementwise_comp_quantized_signed(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>, - &elementwise_comp_op_quantized_signed_broadcast_loop<op>, - &elementwise_comp_op_quantized_signed_loop<op>); -} -} // namespace cpu -} // namespace arm_compute - -#endif /* SRC_CORE_NEON_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H */ diff --git a/src/core/cpu/kernels/elementwise/neon/elementwise_unary_list.h b/src/core/cpu/kernels/elementwise/neon/elementwise_unary_list.h deleted file mode 100644 index 307e95fae9..0000000000 --- a/src/core/cpu/kernels/elementwise/neon/elementwise_unary_list.h +++ /dev/null @@ -1,116 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_NEON_KERNELS_ELEMENTWISE_UNARY_LIST_H -#define SRC_CORE_NEON_KERNELS_ELEMENTWISE_UNARY_LIST_H - -#include "arm_compute/core/Types.h" -#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" - -namespace arm_compute -{ -namespace cpu -{ -template <typename ScalarType> -inline ScalarType elementwise_op_scalar_imp(ElementWiseUnary op, const ScalarType &a) -{ - switch(op) - { - case ElementWiseUnary::RSQRT: - return 1 / sqrt(a); - case ElementWiseUnary::EXP: - return std::exp(a); - case ElementWiseUnary::NEG: - return -a; - case ElementWiseUnary::LOG: - return std::log(a); - case ElementWiseUnary::ABS: - return std::abs(a); - case ElementWiseUnary::ROUND: - return support::cpp11::nearbyint(a); - case ElementWiseUnary::SIN: - return std::sin(a); - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } -} - -template <typename ScalarType, typename VectorType> -inline VectorType elementwise_op_imp(ElementWiseUnary op, const VectorType &a) -{ - switch(op) - { - case ElementWiseUnary::RSQRT: - return wrapper::vinvsqrt(a); - case ElementWiseUnary::EXP: - return wrapper::vexpq(a); - case ElementWiseUnary::NEG: - return wrapper::vneg(a); - case ElementWiseUnary::LOG: - return wrapper::vlog(a); - case ElementWiseUnary::ABS: - return wrapper::vabs(a); - case ElementWiseUnary::ROUND: - return wrapper::vround(a); - case ElementWiseUnary::SIN: - return wrapper::vsin(a); - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } -} - -template <typename ScalarType> -void elementwise_op(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op) -{ - const int window_step_x = 16 / sizeof(ScalarType); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input(in, win); - Iterator output(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - auto output_ptr = reinterpret_cast<ScalarType *>(output.ptr()); - const auto input_ptr = reinterpret_cast<const ScalarType *>(input.ptr()); - - int x = window_start_x; - for(; x <= window_end_x - window_step_x; x += window_step_x) - { - wrapper::vstore(output_ptr + x, elementwise_op_imp<ScalarType>(op, wrapper::vloadq(input_ptr + x))); - } - for(; x < window_end_x; ++x) - { - *(output_ptr + x) = elementwise_op_scalar_imp(op, *(input_ptr + x)); - } - }, - input, output); -} - -} // namespace cpu -} // namespace arm_compute - -#endif // SRC_CORE_NEON_KERNELS_ELEMENTWISE_UNARY_LIST_H
\ No newline at end of file diff --git a/src/core/cpu/kernels/elementwise/sve/elementwise.cpp b/src/core/cpu/kernels/elementwise/sve/elementwise.cpp deleted file mode 100644 index 58ebb28fe5..0000000000 --- a/src/core/cpu/kernels/elementwise/sve/elementwise.cpp +++ /dev/null @@ -1,311 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#if defined(__ARM_FEATURE_SVE) -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/Types.h" -#include "src/core/cpu/kernels/elementwise/sve/elementwise_list.h" -#include <arm_sve.h> - -namespace arm_compute -{ -namespace cpu -{ -using namespace arm_compute::wrapper; - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -struct LoopArguments -{ - OperatorType op; - const InputScalarType *input1_ptr; - const InputScalarType *input2_ptr; - OutputScalarType *output_ptr; -}; - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -struct BroadcastLoopArguments -{ - OperatorType op; - const InputScalarType *input1_ptr; - InputScalarType broadcast_value; - OutputScalarType *output_ptr; - bool reorder; -}; - -template <typename InputScalarType, typename OutputScalarType> -void arithmetic_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) -{ - const auto in1 = svld1(pg, args.input1_ptr); - const auto in2 = svld1(pg, args.input2_ptr); - const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op); - svst1(pg, args.output_ptr, res); -} - -template <typename InputScalarType, typename OutputScalarType> -void arithmetic_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) -{ - const auto non_broadcast_vector = svld1(pg, args.input1_ptr); - const auto broadcast_vector = svdup_n(args.broadcast_value); - const auto in1 = args.reorder ? broadcast_vector : non_broadcast_vector; - const auto in2 = args.reorder ? non_broadcast_vector : broadcast_vector; - const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op); - svst1(pg, args.output_ptr, res); -} - -template <typename InputScalarType, typename OutputScalarType> -void comparison_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) -{ - const auto in1 = svld1(pg, args.input1_ptr); - const auto in2 = svld1(pg, args.input2_ptr); - const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op); - const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg); - svst1(output_pg, args.output_ptr, res); -} - -template <typename InputScalarType, typename OutputScalarType> -void comparison_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) -{ - const auto non_broadcast_vector = svld1(pg, args.input1_ptr); - const auto broadcast_vector = svdup_n(args.broadcast_value); - const auto in1 = args.reorder ? broadcast_vector : non_broadcast_vector; - const auto in2 = args.reorder ? non_broadcast_vector : broadcast_vector; - const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op); - const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg); - svst1(output_pg, args.output_ptr, res); -} - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -using LoopFuncType = void (*)(svbool_t, const LoopArguments<InputScalarType, OutputScalarType, OperatorType> &); - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -using BroadcastLoopFuncType = void (*)(svbool_t, const BroadcastLoopArguments<InputScalarType, OutputScalarType, OperatorType> &); - -template <typename InputVectorType, typename OutputVectorType, typename OperatorType, - typename InputScalarType = typename sve_scalar<InputVectorType>::type, - typename OutputScalarType = typename sve_scalar<OutputVectorType>::type> -void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, - OperatorType op, - LoopFuncType<InputScalarType, OutputScalarType, OperatorType> func, - BroadcastLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func) -{ - const auto all_true_pg = svptrue<InputScalarType>(); - - // 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 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 = in1->info()->tensor_shape().x() != in2->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 ? 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 &) - { - auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); - const auto non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr()); - const InputScalarType broadcast_value = *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr()); - - int x = window_start_x; - - svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x); - do - { - broadcast_func(pg, - { - op, - non_broadcast_input_ptr + x, - broadcast_value, - output_ptr + x, - !is_broadcast_input_2 - }); - x += svcnt<InputScalarType>(); - pg = svwhilelt<InputScalarType>(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - }, - 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 &) - { - auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); - const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr()); - - int x = window_start_x; - - svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x); - do - { - func(pg, - { - op, - input1_ptr + x, - input2_ptr + x, - output_ptr + x - }); - x += svcnt<InputScalarType>(); - pg = svwhilelt<InputScalarType>(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - }, - input1, input2, output); - } -} - -template <ArithmeticOperation op, typename ScalarType> -void elementwise_arithmetic_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - using VectorType = typename sve_vector<ScalarType>::type; - - elementwise_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op, - &arithmetic_op_loop<ScalarType, ScalarType>, - &arithmetic_op_broadcast_loop<ScalarType, ScalarType>); -} - -template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t> -void elementwise_comparison_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - static_assert(sizeof(InputScalarType) >= sizeof(OutputScalarType), "input data type's width should be equal to or greater than output data type's width"); - using InputVectorType = typename sve_vector<InputScalarType>::type; - using OutputVectorType = typename sve_vector<OutputScalarType>::type; - - elementwise_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op, - &comparison_op_loop<InputScalarType, OutputScalarType>, - &comparison_op_broadcast_loop<InputScalarType, OutputScalarType>); -} - -template <> -svint32_t elementwise_pow<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b) -{ - return svcvt_s32_z(pg, svpow_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b))); -} - -template <> -svint32_t elementwise_div<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b) -{ - return svcvt_s32_z(pg, svdiv_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b))); -} - -template <> -svint16_t elementwise_div<svint16_t>(svbool_t &pg, const svint16_t &a, const svint16_t &b) -{ - ARM_COMPUTE_UNUSED(pg, a, b); - ARM_COMPUTE_ERROR("Not supported"); -} - -template void elementwise_arithmetic_op<ArithmeticOperation::MAX, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::MAX, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::MAX, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::MAX, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_arithmetic_op<ArithmeticOperation::MIN, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::MIN, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::MIN, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::MIN, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_arithmetic_op<ArithmeticOperation::DIV, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::DIV, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::DIV, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::DIV, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_arithmetic_op<ArithmeticOperation::POWER, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::POWER, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::POWER, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_arithmetic_op<ArithmeticOperation::POWER, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_comparison_op<ComparisonOperation::Equal, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Equal, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Equal, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Equal, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Equal, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_comparison_op<ComparisonOperation::NotEqual, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::NotEqual, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::NotEqual, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::NotEqual, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::NotEqual, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_comparison_op<ComparisonOperation::Greater, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Greater, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Greater, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Greater, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Greater, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_comparison_op<ComparisonOperation::Less, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Less, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Less, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Less, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::Less, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template void elementwise_comparison_op<ComparisonOperation::LessEqual, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::LessEqual, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::LessEqual, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::LessEqual, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -template void elementwise_comparison_op<ComparisonOperation::LessEqual, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -} // namespace cpu -} // namespace arm_compute -#endif /* defined(__ARM_FEATURE_SVE) */
\ No newline at end of file diff --git a/src/core/cpu/kernels/elementwise/sve/elementwise_list.h b/src/core/cpu/kernels/elementwise/sve/elementwise_list.h deleted file mode 100644 index a92a8648a8..0000000000 --- a/src/core/cpu/kernels/elementwise/sve/elementwise_list.h +++ /dev/null @@ -1,171 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H -#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H -#if defined(ENABLE_SVE) -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/Traits.h" -#include "src/core/NEON/SVEMath.h" -#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" -#include "src/core/NEON/wrapper/svtraits.h" -#include "src/core/cpu/kernels/elementwise/sve/elementwise_list.h" -#include <arm_sve.h> - -namespace arm_compute -{ -namespace cpu -{ -using namespace arm_compute::wrapper; - -template <typename VectorType> -VectorType elementwise_pow(svbool_t &pg, const VectorType &a, const VectorType &b) -{ - return svpow_z(pg, a, b); -} - -template <typename VectorType> -VectorType elementwise_div(svbool_t &pg, const VectorType &a, const VectorType &b) -{ - return svdiv_z(pg, a, b); -} - -template <uint32_t bytewidth> -svbool_t narrow_to_byte_predicate(svbool_t pg) -{ - const auto all_false = svpfalse(); - - switch(bytewidth) - { - case 8: - pg = svuzp1_b32(pg, all_false); - /* fall through */ - case 4: - pg = svuzp1_b16(pg, all_false); - /* fall through */ - case 2: - pg = svuzp1_b8(pg, all_false); - /* fall through */ - default: - break; - } - return pg; -} - -template <typename VectorType> -VectorType elementwise_arithmetic_op(svbool_t &pg, const VectorType &a, const VectorType &b, ArithmeticOperation op) -{ - using ScalarType = typename wrapper::sve_scalar<VectorType>::type; - VectorType res{}; - - switch(op) - { - case ArithmeticOperation::MAX: - res = svmax_z(pg, a, b); - break; - case ArithmeticOperation::MIN: - res = svmin_z(pg, a, b); - break; - case ArithmeticOperation::SQUARED_DIFF: - { - const auto tmp = svsub_z(pg, a, b); - res = svmul_z(pg, tmp, tmp); - break; - } - case ArithmeticOperation::PRELU: - { - const auto zero = svdup_n(ScalarType(0)); - const auto tmp = svmul_z(pg, a, b); - const auto gt = svcmpgt(pg, a, zero); - res = svsel(gt, a, tmp); - break; - } - case ArithmeticOperation::DIV: - { - res = elementwise_div(pg, a, b); - break; - } - case ArithmeticOperation::POWER: - { - res = elementwise_pow(pg, a, b); - break; - } - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - - return res; -} - -template <typename InputVectorType, typename OutputVectorType> -OutputVectorType elementwise_comparison_op(svbool_t &pg, const InputVectorType &a, const InputVectorType &b, ComparisonOperation op) -{ - svbool_t selection_vector{}; - - switch(op) - { - case ComparisonOperation::Equal: - selection_vector = svcmpeq(pg, a, b); - break; - case ComparisonOperation::NotEqual: - selection_vector = svcmpne(pg, a, b); - break; - case ComparisonOperation::Greater: - selection_vector = svcmpgt(pg, a, b); - break; - case ComparisonOperation::GreaterEqual: - selection_vector = svcmpge(pg, a, b); - break; - case ComparisonOperation::Less: - selection_vector = svcmplt(pg, a, b); - break; - case ComparisonOperation::LessEqual: - selection_vector = svcmple(pg, a, b); - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - - using InputScalarType = typename wrapper::sve_scalar<InputVectorType>::type; - selection_vector = narrow_to_byte_predicate<sizeof(InputScalarType)>(selection_vector); - - using OutputScalarType = typename wrapper::sve_scalar<OutputVectorType>::type; - const auto false_vector = svdup_n(static_cast<OutputScalarType>((uint32_t)0)); - const auto true_vector = svdup_n(static_cast<OutputScalarType>(~(uint32_t)0)); - auto ret = svsel(selection_vector, true_vector, false_vector); - - return ret; -} - -template <ArithmeticOperation op, typename ScalarType> -void elementwise_arithmetic_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); - -template <ComparisonOperation op, typename ScalarType, typename OutputScalarType = uint8_t> -void elementwise_comparison_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); -} // namespace cpu -} // namespace arm_compute -#endif // defined(ENABLE_SVE) -#endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H */ diff --git a/src/core/cpu/kernels/elementwise/sve/elementwise_quantized_list.h b/src/core/cpu/kernels/elementwise/sve/elementwise_quantized_list.h deleted file mode 100644 index 6c5524e284..0000000000 --- a/src/core/cpu/kernels/elementwise/sve/elementwise_quantized_list.h +++ /dev/null @@ -1,366 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H -#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H - -#if defined(__ARM_FEATURE_SVE2) - -#include "src/core/NEON/wrapper/svtraits.h" -#include "src/core/cpu/kernels/elementwise/sve/elementwise_list.h" - -namespace arm_compute -{ -namespace cpu -{ -using namespace arm_compute::wrapper; - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -struct QuantizedLoopArguments -{ - OperatorType op; - const InputScalarType *input1_ptr; - const InputScalarType *input2_ptr; - OutputScalarType *output_ptr; - - const svint32_t &in1_offset; - const svint32_t &in2_offset; - const svint32_t &out_offset; - const svfloat32_t &in1_scale; - const svfloat32_t &in2_scale; - const svfloat32_t &out_scale; -}; - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -struct BroadcastQuantizedLoopArguments -{ - OperatorType op; - const InputScalarType *input1_ptr; - float broadcast_value; - OutputScalarType *output_ptr; - bool reorder; - - const svint32_t &in1_offset; - const svint32_t &out_offset; - const svfloat32_t &in1_scale; - const svfloat32_t &out_scale; -}; - -svfloat32x4_t load_quantized(const int8_t *ptr, svbool_t pg, const svint32_t &offset, const svfloat32_t &scale) -{ - auto x = svld1(pg, ptr); - - const auto widened = svcreate4( - svmovlb(svmovlb(x)), - svmovlt(svmovlb(x)), - svmovlb(svmovlt(x)), - svmovlt(svmovlt(x))); - - pg = svptrue_b8(); - - return svcreate4( - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 0), offset)), scale), - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 1), offset)), scale), - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 2), offset)), scale), - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 3), offset)), scale)); -} - -svfloat32x4_t load_quantized(const uint8_t *ptr, svbool_t pg, const svint32_t &offset, const svfloat32_t &scale) -{ - auto x = svld1(pg, ptr); - - //vprint(x); - - const auto widened = svcreate4( - svmovlb(svmovlb(x)), - svmovlt(svmovlb(x)), - svmovlb(svmovlt(x)), - svmovlt(svmovlt(x))); - - pg = svptrue_b8(); - - return svcreate4( - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 0)), offset)), scale), - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 1)), offset)), scale), - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 2)), offset)), scale), - svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 3)), offset)), scale)); -} - -void store_quantized(uint8_t *ptr, svbool_t pg, svfloat32x4_t data, const svint32_t &offset, const svfloat32_t &inv_scale) -{ - const auto quantized = svcreate4( - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 0), inv_scale))), offset), - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 1), inv_scale))), offset), - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 2), inv_scale))), offset), - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 3), inv_scale))), offset)); - - const auto narrowed_bottom = svqxtunt(svqxtunb(svget4(quantized, 0)), svget4(quantized, 1)); - const auto narrowed_top = svqxtunt(svqxtunb(svget4(quantized, 2)), svget4(quantized, 3)); - const auto narrowed = svqxtnt(svqxtnb(narrowed_bottom), narrowed_top); - svst1(pg, ptr, narrowed); -} - -void store_quantized(int8_t *ptr, svbool_t pg, svfloat32x4_t data, const svint32_t &offset, const svfloat32_t &inv_scale) -{ - const auto quantized = svcreate4( - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 0), inv_scale))), offset), - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 1), inv_scale))), offset), - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 2), inv_scale))), offset), - svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 3), inv_scale))), offset)); - - const auto narrowed_bottom = svqxtnt(svqxtnb(svget4(quantized, 0)), svget4(quantized, 1)); - const auto narrowed_top = svqxtnt(svqxtnb(svget4(quantized, 2)), svget4(quantized, 3)); - const auto narrowed = svqxtnt(svqxtnb(narrowed_bottom), narrowed_top); - - svst1(pg, ptr, narrowed); -} - -template <typename InputScalarType, typename OutputScalarType> -inline void arithmetic_op_quantized_loop(svbool_t pg, const QuantizedLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) -{ - const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); - const auto in2 = load_quantized(args.input2_ptr, pg, args.in2_offset, args.in2_scale); - - const auto result = svcreate4( - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 0), svget4(in2, 0), args.op), - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 1), svget4(in2, 1), args.op), - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 2), svget4(in2, 2), args.op), - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 3), svget4(in2, 3), args.op)); - - store_quantized(args.output_ptr, pg, result, args.out_offset, args.out_scale); -} - -template <typename InputScalarType, typename OutputScalarType> -inline void arithmetic_op_broadcast_quantized_loop(svbool_t pg, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) -{ - const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); - const auto in2 = svcreate4( - svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value)); - - const auto &af = args.reorder ? in2 : in1; - const auto &bf = args.reorder ? in1 : in2; - - const auto result = svcreate4( - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 0), svget4(bf, 0), args.op), - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 1), svget4(bf, 1), args.op), - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 2), svget4(bf, 2), args.op), - elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 3), svget4(bf, 3), args.op)); - - store_quantized(args.output_ptr, pg, result, args.out_offset, args.out_scale); -} - -template <typename InputScalarType, typename OutputScalarType> -inline void comparison_op_quantized_loop(svbool_t pg, const QuantizedLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) -{ - const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); - const auto in2 = load_quantized(args.input2_ptr, pg, args.in2_offset, args.in2_scale); - - using OutputVectorType = typename wrapper::traits::sve_vector<OutputScalarType>::type; - - const auto result = svcreate4( - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 0), svget4(in2, 0), args.op), - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 1), svget4(in2, 1), args.op), - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 2), svget4(in2, 2), args.op), - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 3), svget4(in2, 3), args.op)); - - const auto zipped_bottom = svzip1(svget4(result, 0), svget4(result, 1)); - const auto zipped_top = svzip1(svget4(result, 2), svget4(result, 3)); - const auto zipped = svzip1(zipped_bottom, zipped_top); - svst1(pg, args.output_ptr, zipped); -} - -template <typename InputScalarType, typename OutputScalarType> -inline void comparison_op_broadcast_quantized_loop(svbool_t pg, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) -{ - const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); - const auto in2 = svcreate4( - svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value)); - - const auto &af = args.reorder ? in2 : in1; - const auto &bf = args.reorder ? in1 : in2; - - using OutputVectorType = typename wrapper::traits::sve_vector<OutputScalarType>::type; - - const auto result = svcreate4( - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 0), svget4(bf, 0), args.op), - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 1), svget4(bf, 1), args.op), - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 2), svget4(bf, 2), args.op), - elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 3), svget4(bf, 3), args.op)); - - const auto zipped_bottom = svzip1(svget4(result, 0), svget4(result, 1)); - const auto zipped_top = svzip1(svget4(result, 2), svget4(result, 3)); - const auto zipped = svzip1(zipped_bottom, zipped_top); - svst1(pg, args.output_ptr, zipped); -} - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -using LoopQuantizedFuncType = void (*)(svbool_t, const QuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> &); - -template <typename InputScalarType, typename OutputScalarType, typename OperatorType> -using BroadcastQuantizedLoopFuncType = void (*)(svbool_t, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> &); - -template <typename InputVectorType, typename OutputVectorType, typename OperatorType, - typename InputScalarType = typename wrapper::sve_scalar<InputVectorType>::type, - typename OutputScalarType = typename wrapper::sve_scalar<OutputVectorType>::type> -void elementwise_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, - OperatorType op, - LoopQuantizedFuncType<InputScalarType, OutputScalarType, OperatorType> func, - BroadcastQuantizedLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func) -{ - const auto all_true_pg = wrapper::svptrue<InputScalarType>(); - - // 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 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 = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); - - const auto output_voffset = svdup_n(out->info()->quantization_info().uniform().offset); - const auto output_vscale = svdup_n(1.f / out->info()->quantization_info().uniform().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 auto non_broadcast_qinfo = is_broadcast_input_2 ? in1->info()->quantization_info() : in2->info()->quantization_info(); - const auto broadcast_qinfo = is_broadcast_input_2 ? in2->info()->quantization_info() : in1->info()->quantization_info(); - - const auto non_broadcast_voffset = svdup_n(non_broadcast_qinfo.uniform().offset); - const auto non_broadcast_vscale = svdup_n(non_broadcast_qinfo.uniform().scale); - - // 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 &) - { - auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); - const auto non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr()); - const InputScalarType broadcast_value = *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr()); - - int x = window_start_x; - - svbool_t pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x); - do - { - const auto args = BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> - { - op, - non_broadcast_input_ptr + x, - Qasymm8QuantizationHelper<InputScalarType>::dequantize(broadcast_value, broadcast_qinfo), - output_ptr + x, - !is_broadcast_input_2, - non_broadcast_voffset, output_voffset, - non_broadcast_vscale, output_vscale - }; - broadcast_func(pg, args); - x += wrapper::svcnt<InputScalarType>(); - pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - }, - 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); - - const auto in1_voffset = svdup_n(in1->info()->quantization_info().uniform().offset); - const auto in1_vscale = svdup_n(in1->info()->quantization_info().uniform().scale); - - const auto in2_voffset = svdup_n(in2->info()->quantization_info().uniform().offset); - const auto in2_vscale = svdup_n(in2->info()->quantization_info().uniform().scale); - - execute_window_loop(win, [&](const Coordinates &) - { - auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); - const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr()); - const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr()); - - int x = window_start_x; - - svbool_t pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x); - do - { - const auto args = QuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> - { - op, - input1_ptr + x, - input2_ptr + x, - output_ptr + x, - in1_voffset, in2_voffset, output_voffset, - in1_vscale, in2_vscale, output_vscale - }; - func(pg, args); - x += wrapper::svcnt<InputScalarType>(); - pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - }, - input1, input2, output); - } -} - -template <ArithmeticOperation op, typename ScalarType> -void elementwise_arithmetic_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - using VectorType = typename wrapper::traits::sve_vector<ScalarType>::type; - elementwise_quantized_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op, - &arithmetic_op_quantized_loop<ScalarType, ScalarType>, - &arithmetic_op_broadcast_quantized_loop<ScalarType, ScalarType>); -} - -template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t> -void elementwise_comparison_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) -{ - static_assert(sizeof(InputScalarType) >= sizeof(OutputScalarType), "input data type's width should be equal to or greater than output data type's width"); - using InputVectorType = typename wrapper::traits::sve_vector<InputScalarType>::type; - using OutputVectorType = typename wrapper::traits::sve_vector<OutputScalarType>::type; - elementwise_quantized_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op, - &comparison_op_quantized_loop<InputScalarType, OutputScalarType>, - &comparison_op_broadcast_quantized_loop<InputScalarType, OutputScalarType>); -} -} // namespace cpu -} // namespace arm_compute - -#endif /* defined(__ARM_FEATURE_SVE2) */ -#endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H */
\ No newline at end of file diff --git a/src/core/cpu/kernels/elementwise/sve/elementwise_unary.cpp b/src/core/cpu/kernels/elementwise/sve/elementwise_unary.cpp deleted file mode 100644 index ddf1febd66..0000000000 --- a/src/core/cpu/kernels/elementwise/sve/elementwise_unary.cpp +++ /dev/null @@ -1,113 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#if defined(__ARM_FEATURE_SVE) -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/Traits.h" -#include "src/core/NEON/SVEMath.h" -#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" -#include <arm_sve.h> - -namespace arm_compute -{ -namespace cpu -{ -template <typename ScalarType, typename VectorType> -inline typename std::enable_if<utils::traits::is_floating_point<ScalarType>::value, VectorType>::type elementwise_op_sve_imp(svbool_t pg, ElementWiseUnary op, const VectorType &a) -{ - switch(op) - { - case ElementWiseUnary::RSQRT: - return svinvsqrt(pg, a); - case ElementWiseUnary::EXP: - return wrapper::svexp_z(pg, a); - case ElementWiseUnary::NEG: - return svneg_z(pg, a); - case ElementWiseUnary::LOG: - return wrapper::svlog_z(pg, a); - case ElementWiseUnary::ABS: - return svabs_z(pg, a); - case ElementWiseUnary::ROUND: - return svrintn_z(pg, a); - case ElementWiseUnary::SIN: - return wrapper::svsin_z(pg, a); - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED"); - } -} - -template <typename ScalarType, typename VectorType> -inline typename std::enable_if<std::is_integral<ScalarType>::value, VectorType>::type elementwise_op_sve_imp(svbool_t pg, ElementWiseUnary op, const VectorType &a) -{ - switch(op) - { - case ElementWiseUnary::NEG: - return svneg_z(pg, a); - case ElementWiseUnary::ABS: - return svabs_z(pg, a); - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED"); - } -} - -template <typename ScalarType> -void elementwise_sve_op(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op) -{ - const auto all_true_pg = wrapper::svptrue<ScalarType>(); - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input(in, win); - Iterator output(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - auto output_ptr = reinterpret_cast<ScalarType *>(output.ptr()); - const auto input_ptr = reinterpret_cast<const ScalarType *>(input.ptr()); - int x = window_start_x; - - svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x); - do - { - const auto vin = svld1(pg, input_ptr + x); - svst1(pg, output_ptr + x, elementwise_op_sve_imp<ScalarType, decltype(vin)>(pg, op, vin)); - x += wrapper::svcnt<ScalarType>(); - pg = wrapper::svwhilelt<ScalarType>(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - }, - input, output); -} - -template void elementwise_sve_op<float16_t>(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); -template void elementwise_sve_op<float32_t>(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); -template void elementwise_sve_op<int32_t>(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); -} // namespace cpu -} // namespace arm_compute -#endif /* defined(__ARM_FEATURE_SVE) */
\ No newline at end of file diff --git a/src/core/cpu/kernels/elementwise/sve/elementwise_unary_list.h b/src/core/cpu/kernels/elementwise/sve/elementwise_unary_list.h deleted file mode 100644 index 63490421e9..0000000000 --- a/src/core/cpu/kernels/elementwise/sve/elementwise_unary_list.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef SRC_CORE_SVE_KERNELS_ELEMENTWISE_UNARY_LIST_H -#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_UNARY_LIST_H - -#include "arm_compute/core/Types.h" -#if defined(ENABLE_SVE) - -namespace arm_compute -{ -namespace cpu -{ -template <typename ScalarType> -void elementwise_sve_op(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); -} // namespace cpu -} // namespace arm_compute -#endif // defined(ENABLE_SVE) -#endif // SRC_CORE_NEON_KERNELS_ELEMENTWISE_UNARY_LIST_H
\ No newline at end of file |