Decouple data types of elementwise kernels
Partially implements: COMPMID-4003
Change-Id: Ie51e43e24fb9a6b5b96d13cdc3d72fbda027a68b
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4873
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
index 4d67ec3..b250465 100644
--- a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
+++ b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
@@ -26,1180 +26,131 @@
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "src/core/CPP/Validate.h"
-#include "src/core/NEON/NEAsymm.h"
-#include "src/core/NEON/NEFixedPoint.h"
-#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/NEON/kernels/elementwise/impl/elementwise_list.h"
+#include "src/core/NEON/kernels/elementwise/impl/elementwise_quantized_list.h"
#include "src/core/SVE/kernels/elementwise/impl/elementwise_list.h"
#include "src/core/SVE/kernels/elementwise/impl/elementwise_quantized_list.h"
+#include "src/core/common/Registrars.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.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)
+using ElementwiseSelector = std::add_pointer<bool(DataType)>::type;
+using UKernelType = NEElementwiseOperationKernel::ElementwiseFunction;
+struct ElementwiseKernel
{
- 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;
+ const char *name;
+ const ElementwiseSelector is_selected;
+ UKernelType *ukernel;
+};
+
+template <DataType dt>
+inline bool is_selected(DataType data_type)
+{
+ return dt == data_type;
}
-float32x4x4_t load_quantized_signed(const int8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale)
+template <DataType input_data_type, DataType output_data_type = input_data_type>
+static ElementwiseKernel generate_kernel(UKernelType *ukernel)
{
- 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;
-}
+ std::string kernel_name("op_");
+ kernel_name += string_from_data_type(input_data_type) + "_";
+ kernel_name += string_from_data_type(input_data_type) + "_";
+ kernel_name += string_from_data_type(output_data_type);
-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;
- 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>
-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 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>
-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_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>
-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_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>
-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 = 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);
- }
-}
-
-#if !defined(__ARM_FEATURE_SVE2)
-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);
- }
-}
-#endif /* !defined(__ARM_FEATURE_SVE2) */
-
-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>);
-}
-
-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 ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output,
- std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function)
-{
- std::string function_to_call("op_");
- function_to_call += string_from_data_type(input1->data_type()) + "_";
- function_to_call += string_from_data_type(input2->data_type()) + "_";
- function_to_call += string_from_data_type(output->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;
+ return { kernel_name.c_str(), is_selected<input_data_type>, ukernel };
}
template <ArithmeticOperation op>
std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)>
configure_arithm_func(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output)
{
- static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function =
+ ARM_COMPUTE_UNUSED(input2, output);
+ static ElementwiseKernel kernels[] =
{
#if defined(__ARM_FEATURE_SVE)
- { "op_F32_F32_F32", &arm_compute::cpu::sve::elementwise_arithmetic_op<op, float32_t> },
- { "op_S32_S32_S32", &arm_compute::cpu::sve::elementwise_arithmetic_op<op, int32_t> },
+ generate_kernel<DataType::F32>(REGISTER_FP32_SVE((arm_compute::cpu::sve::elementwise_arithmetic_op<op, float32_t>))),
+ generate_kernel<DataType::S32>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_arithmetic_op<op, int32_t>))),
#else /* defined(__ARM_FEATURE_SVE) */
- { "op_F32_F32_F32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float, 4>> },
- { "op_S32_S32_S32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int32_t, 4>> },
+ generate_kernel<DataType::F32>(REGISTER_FP32_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float, 4>>))),
+ generate_kernel<DataType::S32>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int32_t, 4>>))),
#endif /* defined(__ARM_FEATURE_SVE) */
#if defined(__ARM_FEATURE_SVE2)
- { "op_QASYMM8_QASYMM8_QASYMM8", &arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, uint8_t> },
- { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, int8_t> },
+ generate_kernel<DataType::QASYMM8>(REGISTER_QASYMM8_SVE((arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, uint8_t>))),
+ generate_kernel<DataType::QASYMM8_SIGNED>(REGISTER_QASYMM8_SIGNED_SVE((arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, int8_t>))),
#else /* defined(__ARM_FEATURE_SVE2) */
- { "op_QASYMM8_QASYMM8_QASYMM8", &elementwise_arithm_op_quantized<op> },
- { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &elementwise_arithm_op_quantized_signed<op> },
+ generate_kernel<DataType::QASYMM8>(REGISTER_QASYMM8_NEON((arm_compute::cpu::elementwise_arithm_op_quantized<op>))),
+ generate_kernel<DataType::QASYMM8_SIGNED>(REGISTER_QASYMM8_SIGNED_NEON((arm_compute::cpu::elementwise_arithm_op_quantized_signed<op>))),
#endif /* defined(__ARM_FEATURE_SVE2) */
- { "op_S16_S16_S16", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>> },
- };
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#if defined(__ARM_FEATURE_SVE)
- map_function["op_F16_F16_F16"] = &arm_compute::cpu::sve::elementwise_arithmetic_op<op, float16_t>;
+ generate_kernel<DataType::F16>(REGISTER_FP16_SVE((arm_compute::cpu::sve::elementwise_arithmetic_op<op, float16_t>))),
#else /* defined(__ARM_FEATURE_SVE) */
- map_function["op_F16_F16_F16"] = &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float16_t, 8>>;
+ generate_kernel<DataType::F16>(REGISTER_FP16_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float16_t, 8>>))),
#endif /* defined(__ARM_FEATURE_SVE) */
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ generate_kernel<DataType::S16>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>>))),
+ };
- return configure_func(input1, input2, output, map_function);
+ for(const auto &uk : kernels)
+ {
+ if(uk.is_selected(input1->data_type()))
+ {
+ return uk.ukernel;
+ }
+ }
+
+ return nullptr;
}
template <ComparisonOperation op>
std::function<void(const ITensor *input1, const ITensor *input2, ITensor *output, const Window &window)>
configure_comp_func(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output)
{
- static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function =
+ ARM_COMPUTE_UNUSED(input2, output);
+ static ElementwiseKernel kernels[] =
{
#if defined(__ARM_FEATURE_SVE)
- { "op_U8_U8_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, uint8_t> },
- { "op_F32_F32_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, float> },
- { "op_S16_S16_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, int16_t> },
- { "op_S32_S32_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, int32_t> },
+ generate_kernel<DataType::U8, DataType::U8>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, uint8_t>))),
+ generate_kernel<DataType::F32, DataType::U8>(REGISTER_FP32_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, float>))),
+ generate_kernel<DataType::S16, DataType::U8>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, int16_t>))),
+ generate_kernel<DataType::S32, DataType::U8>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, int32_t>))),
#else /* defined(__ARM_FEATURE_SVE) */
- { "op_U8_U8_U8", &elementwise_comp_op_8<op, uint8_t, uint8x16_t> },
- { "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> },
+ generate_kernel<DataType::U8, DataType::U8>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_comp_op_8<op, uint8_t, uint8x16_t>))),
+ generate_kernel<DataType::F32, DataType::U8>(REGISTER_FP32_NEON((arm_compute::cpu::elementwise_comp_op_32<op, float, float32x4_t>))),
+ generate_kernel<DataType::S16, DataType::U8>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_comp_op_16<op, int16_t, int16x8_t>))),
+ generate_kernel<DataType::S32, DataType::U8>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_comp_op_32<op, int32_t, int32x4_t>))),
#endif /* defined(__ARM_FEATURE_SVE) */
#if defined(__ARM_FEATURE_SVE2)
- { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_U8", &arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, int8_t> },
- { "op_QASYMM8_QASYMM8_U8", &arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, uint8_t> }
+ generate_kernel<DataType::QASYMM8_SIGNED, DataType::U8>(REGISTER_QASYMM8_SIGNED_SVE((arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, int8_t>))),
+ generate_kernel<DataType::QASYMM8, DataType::U8>(REGISTER_QASYMM8_SVE((arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, uint8_t>))),
#else /* defined(__ARM_FEATURE_SVE2) */
- { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_U8", &elementwise_comp_op_quantized_signed<op> },
- { "op_QASYMM8_QASYMM8_U8", &elementwise_comp_op_quantized<op> }
+ generate_kernel<DataType::QASYMM8_SIGNED, DataType::U8>(REGISTER_QASYMM8_SIGNED_NEON((arm_compute::cpu::elementwise_comp_op_quantized_signed<op>))),
+ generate_kernel<DataType::QASYMM8, DataType::U8>(REGISTER_QASYMM8_NEON((arm_compute::cpu::elementwise_comp_op_quantized<op>))),
#endif /* defined(__ARM_FEATURE_SVE2) */
- };
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#if defined(__ARM_FEATURE_SVE)
- map_function["op_F16_F16_U8"] = &arm_compute::cpu::sve::elementwise_comparison_op<op, float16_t>;
+ generate_kernel<DataType::F16, DataType::U8>(REGISTER_FP16_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, float16_t>))),
#else /* defined(__ARM_FEATURE_SVE) */
- map_function["op_F16_F16_U8"] = &elementwise_comp_op_16<op, float16_t, float16x8_t>;
+ generate_kernel<DataType::F16, DataType::U8>(REGISTER_FP16_NEON((arm_compute::cpu::elementwise_comp_op_16<op, float16_t, float16x8_t>))),
#endif /* defined(__ARM_FEATURE_SVE) */
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ };
- return configure_func(input1, input2, output, map_function);
+ for(const auto &uk : kernels)
+ {
+ if(uk.is_selected(input1->data_type()))
+ {
+ return uk.ukernel;
+ }
+ }
+
+ return nullptr;
}
} // namespace