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android / platform / external / ComputeLibrary / 975dfe175 / . / src / core / NEON / kernels / NEPixelWiseMultiplicationKernel.cpp
blob: 1dab5d955d560b2b5274e758def1aa584e9317ca [file] [log] [blame]
/*
* Copyright (c) 2016-2019 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/NEPixelWiseMultiplicationKernel.h"
#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEAsymm.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/NEON/NESymm.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include <arm_neon.h>
#include <climits>
#include <cmath>
#include <cstdint>
#include <cstdlib>
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#include <arm_fp16.h> // needed for float16_t
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
namespace arm_compute
{
class Coordinates;
namespace
{
const float scale255_constant = 1.f / 255.f;
const float32x4_t scale255_constant_f32q = vdupq_n_f32(scale255_constant);
const float32x4_t positive_round_f32q = vdupq_n_f32(0.5f);
constexpr unsigned int num_elems_processed_per_iteration = 16;
inline Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
{
ARM_COMPUTE_UNUSED(overflow_policy);
ARM_COMPUTE_UNUSED(rounding_policy);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input1);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8),
"Output can only be U8 if both inputs are U8");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->data_type() == DataType::QASYMM8 && input2->data_type() != DataType::QASYMM8,
"Input2 must be QASYMM8 if input1 is QASYMM8");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->data_type() != DataType::QASYMM8 && input2->data_type() == DataType::QASYMM8,
"Input1 must be QASYMM8 if input2 is QASYMM8");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->data_type() == DataType::QSYMM16 && input2->data_type() != DataType::QSYMM16,
"Input2 must be QSYMM16 if input1 is QSYMM16");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->data_type() != DataType::QSYMM16 && input2->data_type() == DataType::QSYMM16,
"Input1 must be QSYMM16 if input2 is QSYMM16");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(input1->data_type()) && overflow_policy == ConvertPolicy::WRAP,
"ConvertPolicy cannot be WRAP if datatype is quantized");
if(output->total_size() > 0)
{
if(is_data_type_quantized(output->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, output);
}
const TensorShape &out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
}
if(std::abs(scale - scale255_constant) < 0.00001f)
{
ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_NEAREST_UP && rounding_policy != RoundingPolicy::TO_NEAREST_EVEN);
}
else
{
ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_ZERO);
int exponent = 0;
const float normalized_mantissa = std::frexp(scale, &exponent);
// Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15
// frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14
// Moreover, it will be negative as we deal with 1/2^n
ARM_COMPUTE_RETURN_ERROR_ON_MSG(!((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1)), "Scale value not supported (Should be 1/(2^n) or 1/255");
}
return Status{};
}
inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
const ValidRegion &valid_region = broadcast_pair.second;
// Auto initialize output if not initialized
{
ARM_COMPUTE_UNUSED(set_shape_if_empty(*output, input1->tensor_shape()));
if(input1->data_type() == DataType::S16 || input2->data_type() == DataType::S16)
{
set_format_if_unknown(*output, Format::S16);
}
else if(input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32)
{
set_format_if_unknown(*output, Format::F32);
}
else if(input1->data_type() == DataType::F16 || input2->data_type() == DataType::F16)
{
set_format_if_unknown(*output, Format::F16);
}
else if(input1->data_type() == DataType::QASYMM8)
{
set_data_type_if_unknown(*output, DataType::QASYMM8);
}
else if(input1->data_type() == DataType::QSYMM16)
{
set_data_type_if_unknown(*output, DataType::QSYMM16);
}
}
// Configure kernel window
Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
Window win_input2 = win.broadcast_if_dimension_le_one(*input2);
AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
bool window_changed = update_window_and_padding(win_input1, input1_access)
|| update_window_and_padding(win_input2, input2_access)
|| update_window_and_padding(win, output_access);
output_access.set_valid_region(win, valid_region);
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, win);
}
/* Scales a given vector by 1/255.
*
* @note This does not work for all cases. e.g. for float of 0.49999999999999994 and large floats.
*
* @param in Input vector to scale.
* @return Scaled output rounded to nearest (round half up).
*/
inline int32x4_t scale255_S32_S32(int32x4_t in)
{
// Scale
const float32x4_t tmp = vmulq_f32(vcvtq_f32_s32(in), scale255_constant_f32q);
// Round to nearest (round half up)
// Add +0.5 for all values
// Afterwards vcvt rounds toward zero
return vcvtq_s32_f32(vaddq_f32(tmp, positive_round_f32q));
}
inline uint16x8_t scale255_U16_U16(uint16x8_t in)
{
const int32x4_t tmp_s1 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(in))));
const int32x4_t tmp_s2 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(in))));
return vreinterpretq_u16_s16(vcombine_s16(vmovn_s32(tmp_s2), vmovn_s32(tmp_s1)));
}
inline void mul_saturate_QASYMM8_QASYMM8_QASYMM8_n_opt(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale,
float32x4_t input1_vscale, int32x4_t input1_voffset, float32x4_t input2_vscale, int32x4_t input2_voffset, float32x4_t output_voffset, float32x4_t vinvscale)
{
const auto input1 = static_cast<const qasymm8_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const qasymm8_t *__restrict>(input2_ptr);
const auto output = static_cast<qasymm8_t *__restrict>(output_ptr);
const qasymm8x16_t input1_q = vld1q_u8(input1);
const qasymm8x16_t input2_q = vld1q_u8(input2);
// Dequantitize inputs
float32x4x4_t in1_f32x4x4;
float32x4x4_t in2_f32x4x4;
in1_f32x4x4.val[0] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(input1_q))))), input1_voffset)), input1_vscale);
in1_f32x4x4.val[1] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(input1_q))))), input1_voffset)), input1_vscale);
in1_f32x4x4.val[2] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(input1_q))))), input1_voffset)), input1_vscale);
in1_f32x4x4.val[3] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(input1_q))))), input1_voffset)), input1_vscale);
in2_f32x4x4.val[0] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(input2_q))))), input2_voffset)), input2_vscale);
in2_f32x4x4.val[1] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(input2_q))))), input2_voffset)), input2_vscale);
in2_f32x4x4.val[2] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(input2_q))))), input2_voffset)), input2_vscale);
in2_f32x4x4.val[3] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(input2_q))))), input2_voffset)), input2_vscale);
float32x4x4_t out_f32x4x4;
out_f32x4x4.val[0] = vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]);
out_f32x4x4.val[1] = vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]);
out_f32x4x4.val[2] = vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]);
out_f32x4x4.val[3] = vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]);
int32x4x4_t rf;
#ifdef __aarch64__
rf.val[0] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[0], vinvscale));
rf.val[1] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[1], vinvscale));
rf.val[2] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[2], vinvscale));
rf.val[3] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[3], vinvscale));
#else //__aarch64__
rf.val[0] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[0], vinvscale));
rf.val[1] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[1], vinvscale));
rf.val[2] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[2], vinvscale));
rf.val[3] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[3], vinvscale));
#endif //__aarch64__
const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1])));
const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3])));
vst1q_u8(output, vcombine_u8(pa, pb));
}
void mul_saturate_QSYMM16_QSYMM16_QSYMM16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale,
const UniformQuantizationInfo &input1_qua_info, const UniformQuantizationInfo &input2_qua_info, const UniformQuantizationInfo &output_qua_info)
{
const auto input1 = static_cast<const qsymm16_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const qsymm16_t *__restrict>(input2_ptr);
const auto output = static_cast<qsymm16_t *__restrict>(output_ptr);
const qsymm16x8x2_t input1_q = vld2q_s16(input1);
const qsymm16x8x2_t input2_q = vld2q_s16(input2);
// Dequantitize inputs
const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };
const float32x4x4_t out_f32x4x4 =
{
vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
};
const qsymm16x8x2_t result = vquantize_qsymm16(out_f32x4x4, tmp_qua_info);
vst2q_s16(output, result);
}
template <bool is_scale255, bool is_sat>
void mul_U8_U8_U8_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
{
const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
const auto output = static_cast<uint8_t *__restrict>(output_ptr);
const uint8x16_t ta1 = vld1q_u8(input1);
const uint8x16_t ta2 = vld1q_u8(input2);
uint16x8_t tmp1_high = vmovl_u8(vget_high_u8(ta1));
const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2));
uint16x8_t tmp1_low = vmovl_u8(vget_low_u8(ta1));
const uint16x8_t tmp2_low = vmovl_u8(vget_low_u8(ta2));
tmp1_high = vmulq_u16(tmp1_high, tmp2_high);
tmp1_low = vmulq_u16(tmp1_low, tmp2_low);
if(is_scale255)
{
tmp1_high = scale255_U16_U16(tmp1_high);
tmp1_low = scale255_U16_U16(tmp1_low);
}
else
{
const int16x8_t vn = vdupq_n_s16(-n);
if(is_sat)
{
tmp1_high = vqshlq_u16(tmp1_high, vn);
tmp1_low = vqshlq_u16(tmp1_low, vn);
}
else
{
tmp1_high = vshlq_u16(tmp1_high, vn);
tmp1_low = vshlq_u16(tmp1_low, vn);
}
}
if(is_sat)
{
vst1q_u8(output, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high)));
}
else
{
vst1q_u8(output, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high)));
}
}
template <bool is_scale255, bool is_sat>
inline int16x8_t mul_S16_S16_S16_n_loop(const int16x8_t &input1, const int16x8_t &input2, int n)
{
int32x4_t tmp1_high = vmovl_s16(vget_high_s16(input1));
const int32x4_t tmp2_high = vmovl_s16(vget_high_s16(input2));
int32x4_t tmp1_low = vmovl_s16(vget_low_s16(input1));
const int32x4_t tmp2_low = vmovl_s16(vget_low_s16(input2));
tmp1_high = vmulq_s32(tmp1_high, tmp2_high);
tmp1_low = vmulq_s32(tmp1_low, tmp2_low);
if(is_scale255)
{
tmp1_high = scale255_S32_S32(tmp1_high);
tmp1_low = scale255_S32_S32(tmp1_low);
}
else
{
// Right shift amount
const int32x4_t vn = vdupq_n_s32(-n);
// Left shift amount
const int32x4_t vnl = vdupq_n_s32(n);
// Calculate conversion bit
const uint32x4_t tmp1_high_u = vreinterpretq_u32_s32(tmp1_high);
const uint32x4_t tmp1_low_u = vreinterpretq_u32_s32(tmp1_low);
const uint32x4_t sign_high = vshrq_n_u32(tmp1_high_u, 31);
const uint32x4_t sign_low = vshrq_n_u32(tmp1_low_u, 31);
const int32x4_t sign_high_s = vreinterpretq_s32_u32(sign_high);
const int32x4_t sign_low_s = vreinterpretq_s32_u32(sign_low);
const int32x4_t convert_high = vsubq_s32(vshlq_s32(sign_high_s, vnl), sign_high_s);
const int32x4_t convert_low = vsubq_s32(vshlq_s32(sign_low_s, vnl), sign_low_s);
if(is_sat)
{
tmp1_high = vqshlq_s32(vaddq_s32(tmp1_high, convert_high), vn);
tmp1_low = vqshlq_s32(vaddq_s32(tmp1_low, convert_low), vn);
}
else
{
tmp1_high = vshlq_s32(vaddq_s32(tmp1_high, convert_high), vn);
tmp1_low = vshlq_s32(vaddq_s32(tmp1_low, convert_low), vn);
}
}
if(is_sat)
{
return vcombine_s16(vqmovn_s32(tmp1_low), vqmovn_s32(tmp1_high));
}
else
{
return vcombine_s16(vmovn_s32(tmp1_low), vmovn_s32(tmp1_high));
}
}
template <bool is_scale255, bool is_sat>
inline int16x8x2_t mul_S16_S16_S16_n_k(const int16x8x2_t &input1, const int16x8x2_t &input2, int n)
{
const int16x8x2_t result =
{
{
// First 8 elements
mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[0], input2.val[0], n),
// Second 8 elements
mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[1], input2.val[1], n)
}
};
return result;
}
template <bool is_scale255, bool is_sat>
void mul_S16_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
{
const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const int16_t *__restrict>(input2_ptr);
const auto output = static_cast<int16_t *__restrict>(output_ptr);
const int16x8x2_t ta1 = vld2q_s16(input1);
const int16x8x2_t ta2 = vld2q_s16(input2);
const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
vst2q_s16(output, result);
}
void mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale)
{
const auto input1 = static_cast<const float *__restrict>(input1_ptr);
const auto input2 = static_cast<const float *__restrict>(input2_ptr);
const auto output = static_cast<float *__restrict>(output_ptr);
const float32x4x4_t ta1 = vld4q_f32(input1);
const float32x4x4_t ta2 = vld4q_f32(input2);
const float32x4_t scale_vec = vdupq_n_f32(scale);
const float32x4x4_t result =
{
{
vmulq_f32(vmulq_f32(ta1.val[0], ta2.val[0]), scale_vec),
vmulq_f32(vmulq_f32(ta1.val[1], ta2.val[1]), scale_vec),
vmulq_f32(vmulq_f32(ta1.val[2], ta2.val[2]), scale_vec),
vmulq_f32(vmulq_f32(ta1.val[3], ta2.val[3]), scale_vec)
}
};
vst4q_f32(output, result);
}
void c_mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr)
{
const auto input1 = static_cast<const float *__restrict>(input1_ptr);
const auto input2 = static_cast<const float *__restrict>(input2_ptr);
const auto output = static_cast<float *__restrict>(output_ptr);
const float32x4_t a = wrapper::vloadq(input1);
float32x4_t b = wrapper::vloadq(input2);
using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type;
const float32x4_t mask = { -1.0f, 1.0f, -1.0f, 1.0f };
const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{});
const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{});
const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{});
const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{});
const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10);
const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11);
float32x4_t res = wrapper::vmul(tmp0, b);
b = wrapper::vrev64(b);
b = wrapper::vmul(b, mask);
res = wrapper::vmla(res, tmp1, b);
wrapper::vstore(output, res);
}
void mul_F16_F16_F16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale)
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
const auto input1 = static_cast<const float16_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const float16_t *__restrict>(input2_ptr);
const auto output = static_cast<float16_t *__restrict>(output_ptr);
const float16x8x2_t ta1 = vld2q_f16(input1);
const float16x8x2_t ta2 = vld2q_f16(input2);
const float16x8_t scale_vec = vdupq_n_f16(scale);
const float16x8x2_t result =
{
{
vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
}
};
vst2q_f16(output, result);
#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
ARM_COMPUTE_UNUSED(input1_ptr);
ARM_COMPUTE_UNUSED(input2_ptr);
ARM_COMPUTE_UNUSED(output_ptr);
ARM_COMPUTE_UNUSED(scale);
ARM_COMPUTE_ERROR("Not supported. Recompile the library with arch=arm64-v8.2-a.");
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
}
template <bool is_scale255, bool is_sat>
void mul_U8_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
{
const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
const auto output = static_cast<int16_t *__restrict>(output_ptr);
const uint8x16_t bv = vld1q_u8(input2);
const uint8x16_t av = vld1q_u8(input1);
uint16x8_t tmp_low = vmovl_u8(vget_low_u8(av));
uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av));
tmp_low = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv)));
tmp_high = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv)));
if(is_scale255)
{
tmp_low = scale255_U16_U16(tmp_low);
tmp_high = scale255_U16_U16(tmp_high);
}
else
{
const int16x8_t vn = vdupq_n_s16(-n);
if(is_sat)
{
tmp_low = vqshlq_u16(tmp_low, vn);
tmp_high = vqshlq_u16(tmp_high, vn);
}
else
{
tmp_low = vshlq_u16(tmp_low, vn);
tmp_high = vshlq_u16(tmp_high, vn);
}
}
if(is_sat)
{
static const uint16x8_t max = vdupq_n_u16(SHRT_MAX);
tmp_low = vminq_u16(tmp_low, max);
tmp_high = vminq_u16(tmp_high, max);
}
vst1q_s16(output, vreinterpretq_s16_u16(tmp_low));
vst1q_s16(output + 8, vreinterpretq_s16_u16(tmp_high));
}
template <bool is_scale255, bool is_sat>
void mul_S16_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
{
const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr);
const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
const auto output = static_cast<int16_t *__restrict>(output_ptr);
const int16x8x2_t ta1 = vld2q_s16(input1);
const uint8x8x2_t ta2u = vld2_u8(input2);
const int16x8x2_t ta2 =
{
{
vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])),
vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1]))
}
};
const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
vst2q_s16(output, result);
}
template <bool is_scale255, bool is_sat>
void mul_U8_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
{
// Simply swap the two input buffers
mul_S16_U8_S16_n<is_scale255, is_sat>(input2_ptr, input1_ptr, output_ptr, n);
}
} // namespace
NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel()
: _func_float(nullptr), _func_int(nullptr), _func_quantized(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 }, _run_optimized_qasymm8(false)
{
}
void NEPixelWiseMultiplicationKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
{
ARM_COMPUTE_UNUSED(rounding_policy);
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), scale, overflow_policy, rounding_policy));
// Configure kernel window
auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
_input1 = input1;
_input2 = input2;
_output = output;
_scale = scale;
_scale_exponent = 0;
_func_quantized = nullptr;
_func_int = nullptr;
_func_float = nullptr;
_run_optimized_qasymm8 = false;
bool is_scale_255 = false;
// Check and validate scaling factor
if(std::abs(scale - scale255_constant) < 0.00001f)
{
is_scale_255 = true;
}
else
{
int exponent = 0;
std::frexp(scale, &exponent);
// Store the positive exponent. We know that we compute 1/2^n
// Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5
_scale_exponent = std::abs(exponent - 1);
}
const DataType dt_input1 = input1->info()->data_type();
const DataType dt_input2 = input2->info()->data_type();
const DataType dt_output = output->info()->data_type();
const bool is_sat = (overflow_policy == ConvertPolicy::SATURATE);
if(dt_input1 == DataType::QASYMM8 && dt_input2 == DataType::QASYMM8)
{
_run_optimized_qasymm8 = true;
}
else if(dt_input1 == DataType::QSYMM16 && dt_input2 == DataType::QSYMM16)
{
_func_quantized = &mul_saturate_QSYMM16_QSYMM16_QSYMM16_n;
}
else if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::U8 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_U8_U8_U8_n<true, true> : &mul_U8_U8_U8_n<true, false>;
}
else
{
_func_int = is_sat ? &mul_U8_U8_U8_n<false, true> : &mul_U8_U8_U8_n<false, false>;
}
}
else if(DataType::S16 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_S16_S16_S16_n<true, true> : &mul_S16_S16_S16_n<true, false>;
}
else
{
_func_int = is_sat ? &mul_S16_S16_S16_n<false, true> : &mul_S16_S16_S16_n<false, false>;
}
}
else if(DataType::S16 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_S16_U8_S16_n<true, true> : &mul_S16_U8_S16_n<true, false>;
}
else
{
_func_int = is_sat ? &mul_S16_U8_S16_n<false, true> : &mul_S16_U8_S16_n<false, false>;
}
}
else if(DataType::U8 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_U8_S16_S16_n<true, true> : &mul_U8_S16_S16_n<true, false>;
}
else
{
_func_int = is_sat ? &mul_U8_S16_S16_n<false, true> : &mul_U8_S16_S16_n<false, false>;
}
}
else if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_U8_U8_S16_n<true, true> : &mul_U8_U8_S16_n<true, false>;
}
else
{
_func_int = is_sat ? &mul_U8_U8_S16_n<false, true> : &mul_U8_U8_S16_n<false, false>;
}
}
else if(DataType::F16 == dt_input1 && DataType::F16 == dt_input2 && DataType::F16 == dt_output)
{
_func_float = &mul_F16_F16_F16_n;
_func_int = nullptr;
}
else if(DataType::F32 == dt_input1 && DataType::F32 == dt_input2 && DataType::F32 == dt_output)
{
_func_float = &mul_F32_F32_F32_n;
_func_int = nullptr;
}
else
{
ARM_COMPUTE_ERROR("You called with the wrong img formats");
}
INEKernel::configure(win_config.second);
}
Status NEPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy,
RoundingPolicy rounding_policy)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, scale, overflow_policy, rounding_policy));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first);
return Status{};
}
void NEPixelWiseMultiplicationKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
const TensorShape &in_shape1 = _input1->info()->tensor_shape();
const TensorShape &in_shape2 = _input2->info()->tensor_shape();
const TensorShape &out_shape = _output->info()->tensor_shape();
bool can_collapse = true;
if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
{
can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d)
{
can_collapse = (in_shape1[d] == in_shape2[d]);
}
}
bool has_collapsed = false;
Window collapsed = can_collapse ? window.collapse_if_possible(INEKernel::window(), Window::DimZ, &has_collapsed) : window;
const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
Window slice = collapsed.first_slice_window_3D();
Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
Iterator input1(_input1, slice_input1);
Iterator input2(_input2, slice_input2);
Iterator output(_output, slice);
if(is_data_type_quantized(_input1->info()->data_type()))
{
if(_run_optimized_qasymm8)
{
const int32x4_t input1_voffset = vdupq_n_s32(_input1->info()->quantization_info().uniform().offset);
const float32x4_t input1_vscale = vdupq_n_f32(_input1->info()->quantization_info().uniform().scale);
const int32x4_t input2_voffset = vdupq_n_s32(_input2->info()->quantization_info().uniform().offset);
const float32x4_t input2_vscale = vdupq_n_f32(_input2->info()->quantization_info().uniform().scale);
const float32x4_t output_voffset = vdupq_n_f32(static_cast<float>(_output->info()->quantization_info().uniform().offset));
const float output_scale = _output->info()->quantization_info().uniform().scale;
const float32x4_t vinvscale = vdupq_n_f32(1.f / (output_scale / _scale));
execute_window_loop(collapsed, [&](const Coordinates &)
{
mul_saturate_QASYMM8_QASYMM8_QASYMM8_n_opt(input1.ptr(), input2.ptr(), output.ptr(), _scale,
input1_vscale, input1_voffset, input2_vscale, input2_voffset, output_voffset, vinvscale);
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
},
input1, input2, output);
}
else
{
execute_window_loop(collapsed, [&](const Coordinates &)
{
(*_func_quantized)(input1.ptr(), input2.ptr(), output.ptr(), _scale,
_input1->info()->quantization_info().uniform(), _input2->info()->quantization_info().uniform(), _output->info()->quantization_info().uniform());
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
},
input1, input2, output);
}
}
else if(_func_int != nullptr)
{
execute_window_loop(collapsed, [&](const Coordinates &)
{
(*_func_int)(input1.ptr(), input2.ptr(), output.ptr(), _scale_exponent);
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
},
input1, input2, output);
}
else
{
ARM_COMPUTE_ERROR_ON(_func_float == nullptr);
execute_window_loop(collapsed, [&](const Coordinates &)
{
(*_func_float)(input1.ptr(), input2.ptr(), output.ptr(), _scale);
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
},
input1, input2, output);
}
}
BorderSize NEPixelWiseMultiplicationKernel::border_size() const
{
const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
return BorderSize{ 0, border, 0, 0 };
}
namespace
{
constexpr unsigned int num_elems_processed_per_iteration_complex = 2;
Status validate_arguments_complex(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 2, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 2, DataType::F32);
const TensorShape &out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
// Validate in case of configured output
if(output->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 2, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output");
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
const TensorShape &out_shape = broadcast_pair.first;
const ValidRegion &valid_region = broadcast_pair.second;
// Auto initialize output if not initialized
const TensorInfo out_info(out_shape, input1->num_channels(), input1->data_type());
auto_init_if_empty(*output, out_info);
Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration_complex));
Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
Window win_input2 = win.broadcast_if_dimension_le_one(*input2);
AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_complex);
AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration_complex);
AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_complex);
bool window_changed = update_window_and_padding(win_input1, input1_access)
|| update_window_and_padding(win_input2, input2_access)
|| update_window_and_padding(win, output_access);
output_access.set_valid_region(win, valid_region);
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, win);
}
} // namespace
NEComplexPixelWiseMultiplicationKernel::NEComplexPixelWiseMultiplicationKernel()
: _input1(nullptr), _input2(nullptr), _output(nullptr)
{
}
void NEComplexPixelWiseMultiplicationKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1->info(), input2->info(), output->info()));
// Configure kernel window
auto win_config = validate_and_configure_window_complex(input1->info(), input2->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
_input1 = input1;
_input2 = input2;
_output = output;
// Create kernel
INEKernel::configure(win_config.second);
}
Status NEComplexPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(input1, input2, output));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_complex(input1->clone().get(), input2->clone().get(), output->clone().get()).first);
return Status{};
}
void NEComplexPixelWiseMultiplicationKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
Iterator input1(_input1, window.broadcast_if_dimension_le_one(_input1->info()->tensor_shape()));
Iterator input2(_input2, window.broadcast_if_dimension_le_one(_input2->info()->tensor_shape()));
Iterator output(_output, window);
execute_window_loop(window, [&](const Coordinates &)
{
c_mul_F32_F32_F32_n(input1.ptr(), input2.ptr(), output.ptr());
},
input1, input2, output);
}
BorderSize NEComplexPixelWiseMultiplicationKernel::border_size() const
{
const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration_complex - 1U, replicateSize);
return { 0, border, 0, 0 };
}
} // namespace arm_compute