diff options
Diffstat (limited to 'src/core/NEON/kernels/NEElementwiseOperationKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEElementwiseOperationKernel.cpp | 1307 |
1 files changed, 0 insertions, 1307 deletions
diff --git a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp deleted file mode 100644 index 7b2b5e4f19..0000000000 --- a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp +++ /dev/null @@ -1,1307 +0,0 @@ -/* - * Copyright (c) 2018-2020 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/core/NEON/kernels/NEElementwiseOperationKernel.h" - -#include "arm_compute/core/CPP/Validate.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/NEON/NEAsymm.h" -#include "arm_compute/core/NEON/NEFixedPoint.h" -#include "arm_compute/core/NEON/wrapper/wrapper.h" - -#include <arm_neon.h> -#include <map> - -namespace arm_compute -{ -namespace -{ -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, 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; - break; - } - case ArithmeticOperation::POWER: - { - res = std::pow(a, b); - break; - } - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - return res; -} - -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, 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 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> -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 <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 <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> -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, 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> -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 <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 <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> -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, 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> -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, 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> -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, 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> -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; -} - -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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); - - if(is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; - - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator output(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - 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); - } -} - -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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); - - 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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); - - 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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0); - - 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 <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>); -} - -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 <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>); -} - -std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)> -configure_func(const ITensor *input1, const ITensor *input2, ITensor *output, - std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function) -{ - std::string function_to_call("op_"); - function_to_call += string_from_data_type(input1->info()->data_type()) + "_"; - function_to_call += string_from_data_type(input2->info()->data_type()) + "_"; - function_to_call += string_from_data_type(output->info()->data_type()); - - auto it = map_function.find(function_to_call); - - if(it != map_function.end()) - { - auto func = it->second; - return [func](const ITensor * input1, const ITensor * input2, ITensor * output, const Window & window) - { - func(input1, input2, output, window); - }; - } - return nullptr; -} - -template <ArithmeticOperation op> -std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)> -configure_arithm_func(const ITensor *input1, const ITensor *input2, ITensor *output) -{ - static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function = - { - { "op_F32_F32_F32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float, 4>> }, - { "op_S16_S16_S16", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>> }, - { "op_S32_S32_S32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int32_t, 4>> }, - { "op_QASYMM8_QASYMM8_QASYMM8", &elementwise_arithm_op_quantized<op> }, - { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &elementwise_arithm_op_quantized_signed<op> } - }; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - map_function["op_F16_F16_F16"] = &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float16_t, 8>>; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - - return configure_func(input1, input2, output, map_function); -} - -template <ComparisonOperation op> -std::function<void(const ITensor *input1, const ITensor *input2, ITensor *output, const Window &window)> -configure_comp_func(const ITensor *input1, const ITensor *input2, ITensor *output) -{ - static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function = - { - { "op_F32_F32_U8", &elementwise_comp_op_32<op, float, float32x4_t> }, - { "op_S16_S16_U8", &elementwise_comp_op_16<op, int16_t, int16x8_t> }, - { "op_S32_S32_U8", &elementwise_comp_op_32<op, int32_t, int32x4_t> }, - { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_U8", &elementwise_comp_op_quantized_signed<op> }, - { "op_QASYMM8_QASYMM8_U8", &elementwise_comp_op_quantized<op> } - }; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - map_function["op_F16_F16_U8"] = &elementwise_comp_op_16<op, float16_t, float16x8_t>; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - - return configure_func(input1, input2, output, map_function); -} -} // namespace - -NEElementwiseOperationKernel::NEElementwiseOperationKernel() - : _function(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr) -{ -} - -Status NEElementwiseOperationKernel::validate_arguments_common(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::F16, DataType::S32, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); - - const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - - // Validate in case of configured output - if(output.total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), - "Wrong shape for output"); - } - - return Status{}; -} - -void NEElementwiseOperationKernel::configure_common(const ITensor *input1, const ITensor *input2, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); - - // Configure kernel window - const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info()); - const TensorShape &out_shape = broadcast_pair.first; - const ValidRegion &valid_region = broadcast_pair.second; - - // Auto initialize output if not initialized - auto_init_if_empty(*output->info(), out_shape, 1, input1->info()->data_type()); - - Window win = calculate_max_window(valid_region); - - _input1 = input1; - _input2 = input2; - _output = output; - - INEKernel::configure(win); -} - -void NEElementwiseOperationKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info, window); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - ARM_COMPUTE_ERROR_ON(_function == nullptr); - _function(_input1, _input2, _output, window); -} - -/** Arithmetic operators (min, max, squared_diff) */ - -void NEArithmeticOperationKernel::configure(ArithmeticOperation op, const ITensor *input1, const ITensor *input2, ITensor *output) -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info())); - configure_common(input1, input2, output); - switch(op) - { - case ArithmeticOperation::MAX: - _function = configure_arithm_func<ArithmeticOperation::MAX>(input1, input2, output); - break; - case ArithmeticOperation::MIN: - _function = configure_arithm_func<ArithmeticOperation::MIN>(input1, input2, output); - break; - case ArithmeticOperation::SQUARED_DIFF: - _function = configure_arithm_func<ArithmeticOperation::SQUARED_DIFF>(input1, input2, output); - break; - case ArithmeticOperation::PRELU: - _function = configure_arithm_func<ArithmeticOperation::PRELU>(input1, input2, output); - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } -} - -Status NEArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) -{ - // Validate in case of configured output - if(output.total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output); - } - return validate_arguments_common(input1, input2, output); -} - -Status NEArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - ARM_COMPUTE_UNUSED(op); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output)); - return Status{}; -} - -/** The division operator */ - -void NEDivisionOperationKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output) -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info())); - configure_common(input1, input2, output); - _function = configure_arithm_func<ArithmeticOperation::DIV>(input1, input2, output); -} - -Status NEDivisionOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::F16, DataType::F32); - return NEArithmeticOperationKernel::validate_arguments(input1, input2, output); -} - -Status NEDivisionOperationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output)); - return Status{}; -} - -/** The power operator */ -void NEPowerOperationKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output) -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info())); - configure_common(input1, input2, output); - _function = configure_arithm_func<ArithmeticOperation::POWER>(input1, input2, output); -} - -Status NEPowerOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::F16, DataType::F32); - return NEArithmeticOperationKernel::validate_arguments(input1, input2, output); -} - -Status NEPowerOperationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output)); - return Status{}; -} - -/** Comparison operators (equal, not equal, less than, greater than, less than or equal, greater than or equal) */ - -void NEComparisonOperationKernel::configure(ComparisonOperation op, const ITensor *input1, const ITensor *input2, ITensor *output) -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info())); - configure_common(input1, input2, output); - switch(op) - { - case ComparisonOperation::Equal: - _function = configure_comp_func<ComparisonOperation::Equal>(input1, input2, output); - break; - case ComparisonOperation::NotEqual: - _function = configure_comp_func<ComparisonOperation::NotEqual>(input1, input2, output); - break; - case ComparisonOperation::Greater: - _function = configure_comp_func<ComparisonOperation::Greater>(input1, input2, output); - break; - case ComparisonOperation::GreaterEqual: - _function = configure_comp_func<ComparisonOperation::GreaterEqual>(input1, input2, output); - break; - case ComparisonOperation::Less: - _function = configure_comp_func<ComparisonOperation::Less>(input1, input2, output); - break; - case ComparisonOperation::LessEqual: - _function = configure_comp_func<ComparisonOperation::LessEqual>(input1, input2, output); - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } -} - -Status NEComparisonOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) -{ - // Validate in case of configured output - if(output.total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8); - } - return validate_arguments_common(input1, input2, output); -} - -Status NEComparisonOperationKernel::validate(ComparisonOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - ARM_COMPUTE_UNUSED(op); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output)); - return Status{}; -} -} // namespace arm_compute |