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review.mlplatform.org / ml / ComputeLibrary / ebcebf1dee7f8314976b1e0cabd62b4cf893d765 / . / src / core / NEON / kernels / NEPixelWiseMultiplicationKernel.cpp
blob: 8d17651f375fb4b02344fdd79b7cd50a7ae56308 [file] [log] [blame]
/*
* Copyright (c) 2016-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h"
#include "arm_compute/core/TensorInfo.h"
#include "src/core/CPP/Validate.h"
#include "src/core/NEON/NEAsymm.h"
#include "src/core/NEON/NESymm.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include <arm_neon.h>
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#include <arm_fp16.h> // needed for float16_t
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
namespace arm_compute
{
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);
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::QASYMM8_SIGNED, DataType::S16, DataType::S32, DataType::QSYMM16, DataType::F16,
DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::S32, DataType::QSYMM16, DataType::F16,
DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
DataType::S16, DataType::QSYMM16,
DataType::S32, DataType::F16, DataType::F32);
if(is_data_type_quantized(input1->data_type()) || is_data_type_quantized(input2->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(overflow_policy == ConvertPolicy::WRAP, "ConvertPolicy cannot be WRAP if datatype is quantized");
}
if(output->total_size() > 0)
{
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");
// clang-format off
ARM_COMPUTE_RETURN_ERROR_ON_MSG(
!(input1->data_type() == input2->data_type() && input2->data_type() == output->data_type()) &&
!(input1->data_type() == DataType::U8 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16) &&
!(input1->data_type() == DataType::U8 && input2->data_type() == DataType::S16 && output->data_type() == DataType::S16) &&
!(input1->data_type() == DataType::S16 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16) &&
!(input1->data_type() == DataType::S16 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16) &&
!(input1->data_type() == DataType::QSYMM16 && input2->data_type() == DataType::QSYMM16 && output->data_type() == DataType::S32)
, "Invalid data type combination");
// clang-format on
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->data_type() == DataType::S16 && output->data_type() == DataType::S32 && scale != 1.f, "Unsupported scale for QSYMM16 inputs and S32 output");
}
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);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->data_type() == DataType::S32 && input2->data_type() == DataType::S32 && output->data_type() == DataType::S32,
"Scale == 1/255 is not supported if input and output are of data type S32");
}
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{};
}
/* 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)));
}
template <typename T>
inline typename std::enable_if<std::is_same<T, int8_t>::value, int8x16_t>::type
vquantize(float32x4x4_t val, const UniformQuantizationInfo &info)
{
return vquantize_signed(val, info);
}
template <typename T>
inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8x16_t>::type
vquantize(float32x4x4_t val, const UniformQuantizationInfo &info)
{
return vquantize(val, info);
}
template <typename T>
void mul_saturate_quantized_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
const int window_step_x = 16 / sizeof(T);
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform();
const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };
if(is_broadcast_across_x)
{
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform();
const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
// 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);
using ExactTagType = typename wrapper::traits::neon_vector<T, window_step_x>::tag_type;
execute_window_loop(win, [&](const Coordinates &)
{
const auto non_broadcast_input_ptr = reinterpret_cast<const T *>(non_broadcast_input.ptr());
const auto output_ptr = reinterpret_cast<T *>(output.ptr());
const auto broadcast_value = *reinterpret_cast<const T *>(broadcast_input.ptr());
const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
// Dequantize inputs
const float32x4x4_t in1_f32x4x4 = vdequantize(non_broadcast_v, non_broadcast_qinfo);
const float32x4x4_t in2_f32x4x4 = vdequantize(broadcast_value_vec, broadcast_qinfo);
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]),
};
// Quantize output
const auto result = vquantize<T>(out_f32x4x4, tmp_qua_info);
wrapper::vstore(output_ptr + x, result);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
// Dequantize inputs
const T in1 = *(non_broadcast_input_ptr + x);
const float tmp_in1 = Qasymm8QuantizationHelper<T>::dequantize(in1, non_broadcast_qinfo);
const float tmp_in2 = Qasymm8QuantizationHelper<T>::dequantize(broadcast_value, broadcast_qinfo);
const float tmp_f = tmp_in1 * tmp_in2;
// Quantize output
const auto tmp_qua = Qasymm8QuantizationHelper<T>::quantize(tmp_f, tmp_qua_info);
*(output_ptr + x) = tmp_qua;
}
},
broadcast_input, non_broadcast_input, output);
}
else
{
const UniformQuantizationInfo input1_qua_info = in1->info()->quantization_info().uniform();
const UniformQuantizationInfo input2_qua_info = in2->info()->quantization_info().uniform();
// Clear X Dimension on execution window as we handle manually
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const T *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const T *>(input2.ptr());
const auto output_ptr = reinterpret_cast<T *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto input1_q = wrapper::vloadq(input1_ptr + x);
const auto input2_q = wrapper::vloadq(input2_ptr + x);
// Dequantize inputs
const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
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]),
};
// Quantize output
const auto result = vquantize<T>(out_f32x4x4, tmp_qua_info);
wrapper::vstore(output_ptr + x, result);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
// Dequantize inputs
const T in1 = *(input1_ptr + x);
const T in2 = *(input2_ptr + x);
const float tmp_in1 = Qasymm8QuantizationHelper<T>::dequantize(in1, input1_qua_info);
const float tmp_in2 = Qasymm8QuantizationHelper<T>::dequantize(in2, input2_qua_info);
const float tmp_f = tmp_in1 * tmp_in2;
// Quantize output
const auto tmp_qua = Qasymm8QuantizationHelper<T>::quantize(tmp_f, tmp_qua_info);
*(output_ptr + x) = tmp_qua;
}
},
input1, input2, output);
}
}
void mul_saturate_QSYMM16_QSYMM16_QSYMM16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
const UniformQuantizationInfo input1_qua_info = in1->info()->quantization_info().uniform();
const UniformQuantizationInfo input2_qua_info = in2->info()->quantization_info().uniform();
const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform();
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<qsymm16_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const qsymm16x8x2_t input1_q =
{
{
vld1q_s16(input1_ptr + x),
vld1q_s16(input1_ptr + x + 8),
}
};
const qsymm16x8x2_t input2_q =
{
{
vld1q_s16(input2_ptr + x),
vld1q_s16(input2_ptr + x + 8),
}
};
// Dequantize inputs
const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
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);
vst1q_s16(output_ptr + x, result.val[0]);
vst1q_s16(output_ptr + x + 8, result.val[1]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
// Dequantize inputs
float tmp_in1 = static_cast<float>(*(input1_ptr + x)) * input1_qua_info.scale;
float tmp_in2 = static_cast<float>(*(input2_ptr + x)) * input2_qua_info.scale;
float tmp_f = tmp_in1 * tmp_in2;
// Quantize output, lrintf() has same rounding mode as vcombine_s16
int32_t tmp = lrintf(tmp_f / tmp_qua_info.scale);
qsymm16_t tmp_qua = static_cast<qsymm16_t>(tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
*(output_ptr + x) = tmp_qua;
}
},
input1, input2, output);
}
void mul_QSYMM16_QSYMM16_S32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int scale)
{
ARM_COMPUTE_UNUSED(scale);
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<int32_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const qsymm16x8x2_t input1_q =
{
{
vld1q_s16(input1_ptr + x),
vld1q_s16(input1_ptr + x + 8),
}
};
const qsymm16x8x2_t input2_q =
{
{
vld1q_s16(input2_ptr + x),
vld1q_s16(input2_ptr + x + 8),
}
};
const int32x4x4_t in1_s32 =
{
{
vmovl_s16(vget_low_s16(input1_q.val[0])),
vmovl_s16(vget_high_s16(input1_q.val[0])),
vmovl_s16(vget_low_s16(input1_q.val[1])),
vmovl_s16(vget_high_s16(input1_q.val[1])),
}
};
const int32x4x4_t in2_s32 =
{
{
vmovl_s16(vget_low_s16(input2_q.val[0])),
vmovl_s16(vget_high_s16(input2_q.val[0])),
vmovl_s16(vget_low_s16(input2_q.val[1])),
vmovl_s16(vget_high_s16(input2_q.val[1])),
}
};
const int32x4x4_t result =
{
{
vmulq_s32(in1_s32.val[0], in2_s32.val[0]),
vmulq_s32(in1_s32.val[1], in2_s32.val[1]),
vmulq_s32(in1_s32.val[2], in2_s32.val[2]),
vmulq_s32(in1_s32.val[3], in2_s32.val[3]),
}
};
vst1q_s32(output_ptr + x, result.val[0]);
vst1q_s32(output_ptr + x + 4, result.val[1]);
vst1q_s32(output_ptr + x + 8, result.val[2]);
vst1q_s32(output_ptr + x + 12, result.val[3]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
*(output_ptr + x) = tmp;
}
},
input1, input2, output);
}
template <bool is_scale255, bool is_sat>
void mul_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16 / sizeof(uint8_t);
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const uint8x16_t ta1 = wrapper::vloadq(input1_ptr + x);
const uint8x16_t ta2 = wrapper::vloadq(input2_ptr + x);
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_ptr, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high)));
}
else
{
vst1q_u8(output_ptr, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high)));
}
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
uint16_t tmp = static_cast<uint16_t>(*(input1_ptr + x)) * static_cast<uint16_t>(*(input2_ptr + x));
if(is_scale255)
{
float tmp_f = static_cast<float>(tmp) * scale255_constant;
tmp = static_cast<uint16_t>(tmp_f + 0.5f);
}
else
{
tmp >>= n;
}
if(is_sat && tmp > 255)
{
tmp = 255;
}
*(output_ptr + x) = static_cast<uint8_t>(tmp);
}
},
input1, input2, output);
}
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(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const int16_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const int16x8x2_t ta1 =
{
{
vld1q_s16(input1_ptr + x),
vld1q_s16(input1_ptr + x + 8),
}
};
const int16x8x2_t ta2 =
{
{
vld1q_s16(input2_ptr + x),
vld1q_s16(input2_ptr + x + 8),
}
};
const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
vst1q_s16(output_ptr + x, result.val[0]);
vst1q_s16(output_ptr + x + 8, result.val[1]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
if(is_scale255)
{
float tmp_f = static_cast<float>(tmp) * scale255_constant;
tmp = static_cast<int32_t>(tmp_f + 0.5f);
}
else
{
if(tmp >= 0)
{
tmp >>= n;
}
else
{
uint32_t mask = (1u << n) - 1;
tmp = (tmp + static_cast<int32_t>(mask)) >> n;
}
}
if(is_sat)
{
tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
}
*(output_ptr + x) = static_cast<int16_t>(tmp);
}
},
input1, input2, output);
}
template <bool is_sat>
inline int32x4_t mul_S32_S32_S32_n_loop(const int32x4_t &input1, const int32x4_t &input2, int n)
{
const int32x2_t input1_1 = vget_low_s32(input1);
const int32x2_t input2_1 = vget_low_s32(input2);
const int32x2_t input1_2 = vget_high_s32(input1);
const int32x2_t input2_2 = vget_high_s32(input2);
int64x2_t tmp_1 = vmull_s32(input1_1, input2_1);
int64x2_t tmp_2 = vmull_s32(input1_2, input2_2);
// Apply scaling, conversion and rounding (round to zero)
// Right shift amount
const int64x2_t vn = vdupq_n_s64(-n);
// Left shift amount
const int64x2_t vnl = vdupq_n_s64(n);
// Calculate conversion bit
const uint64x2_t tmp_1_u = vreinterpretq_u64_s64(tmp_1);
const uint64x2_t sign_1 = vshrq_n_u64(tmp_1_u, 63);
const int64x2_t sign_1_s = vreinterpretq_s64_u64(sign_1);
const int64x2_t convert_1 = vsubq_s64(vshlq_s64(sign_1_s, vnl), sign_1_s);
const uint64x2_t tmp_2_u = vreinterpretq_u64_s64(tmp_2);
const uint64x2_t sign_2 = vshrq_n_u64(tmp_2_u, 63);
const int64x2_t sign_2_s = vreinterpretq_s64_u64(sign_2);
const int64x2_t convert_2 = vsubq_s64(vshlq_s64(sign_2_s, vnl), sign_2_s);
if(is_sat)
{
tmp_1 = vqshlq_s64(vaddq_s64(tmp_1, convert_1), vn);
tmp_2 = vqshlq_s64(vaddq_s64(tmp_2, convert_2), vn);
return vcombine_s32(vqmovn_s64(tmp_1), vqmovn_s64(tmp_2));
}
else
{
tmp_1 = vshlq_s64(vaddq_s64(tmp_1, convert_1), vn);
tmp_2 = vshlq_s64(vaddq_s64(tmp_2, convert_2), vn);
return vcombine_s32(vmovn_s64(tmp_1), vmovn_s64(tmp_2));
}
}
template <bool is_sat>
inline int32x4x2_t mul_S32_S32_S32_n_k(const int32x4x2_t &input1, const int32x4x2_t &input2, int n)
{
const int32x4x2_t result =
{
{
// First 4 elements
mul_S32_S32_S32_n_loop<is_sat>(input1.val[0], input2.val[0], n),
// Second 4 elements
mul_S32_S32_S32_n_loop<is_sat>(input1.val[1], input2.val[1], n)
}
};
return result;
}
template <bool is_sat>
void mul_S32_S32_S32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Create input windows
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
const int window_step_x = 8;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
if(is_broadcast_across_x)
{
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
// Clear X Dimension on execution window as we handle manually
non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
Iterator output(out, win);
execute_window_loop(win, [&](const Coordinates &)
{
const auto non_broadcast_input_ptr = reinterpret_cast<const int32_t *>(non_broadcast_input.ptr());
const auto output_ptr = reinterpret_cast<int32_t *>(output.ptr());
const int32_t broadcast_value = *reinterpret_cast<const int32_t *>(broadcast_input.ptr());
const auto broadcast_value_vec = vdupq_n_s32(broadcast_value);
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const int32x4x2_t broadcast_v =
{
{
broadcast_value_vec,
broadcast_value_vec,
}
};
const int32x4x2_t non_broadcast_v =
{
{
vld1q_s32(non_broadcast_input_ptr + x),
vld1q_s32(non_broadcast_input_ptr + x + 4),
}
};
const int32x4x2_t result = mul_S32_S32_S32_n_k<is_sat>(broadcast_v, non_broadcast_v, n);
vst1q_s32(output_ptr + x, result.val[0]);
vst1q_s32(output_ptr + x + 4, result.val[1]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
int64_t tmp = static_cast<int64_t>(broadcast_value) * static_cast<int64_t>(*(non_broadcast_input_ptr + x));
if(tmp >= 0)
{
tmp >>= n;
}
else
{
uint64_t mask = (1u << n) - 1;
tmp = (tmp + static_cast<int64_t>(mask)) >> n;
}
if(is_sat)
{
tmp = utility::clamp<int64_t, int32_t>(tmp);
}
*(output_ptr + x) = static_cast<int32_t>(tmp);
}
},
broadcast_input, non_broadcast_input, output);
}
else
{
// Clear X Dimension on execution window as we handle manually
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const int32_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const int32_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<int32_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const int32x4x2_t ta1 =
{
{
vld1q_s32(input1_ptr + x),
vld1q_s32(input1_ptr + x + 4),
}
};
const int32x4x2_t ta2 =
{
{
vld1q_s32(input2_ptr + x),
vld1q_s32(input2_ptr + x + 4),
}
};
const int32x4x2_t result = mul_S32_S32_S32_n_k<is_sat>(ta1, ta2, n);
vst1q_s32(output_ptr + x, result.val[0]);
vst1q_s32(output_ptr + x + 4, result.val[1]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
int64_t tmp = static_cast<int64_t>(*(input1_ptr + x)) * static_cast<int64_t>(*(input2_ptr + x));
if(tmp >= 0)
{
tmp >>= n;
}
else
{
uint64_t mask = (1u << n) - 1;
tmp = (tmp + static_cast<int64_t>(mask)) >> n;
}
if(is_sat)
{
tmp = utility::clamp<int64_t, int32_t>(tmp);
}
*(output_ptr + x) = static_cast<int32_t>(tmp);
}
},
input1, input2, output);
}
}
void mul_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
// Create input windows
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
constexpr int window_step_x = 16 / sizeof(float);
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);
using ExactTagType = typename wrapper::traits::neon_vector<float, window_step_x>::tag_type;
if(is_broadcast_across_x)
{
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
// Clear X Dimension on execution window as we handle manually
non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
Iterator output(out, win);
execute_window_loop(win, [&](const Coordinates &)
{
const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
const auto output_ptr = reinterpret_cast<float *>(output.ptr());
const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr());
const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});
const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec);
wrapper::vstore(output_ptr + x, res);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
*(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
}
},
broadcast_input, non_broadcast_input, output);
}
else
{
// Clear X Dimension on execution window as we handle manually
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
const auto output_ptr = reinterpret_cast<float *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto ta1 = wrapper::vloadq(input1_ptr + x);
const auto ta2 = wrapper::vloadq(input2_ptr + x);
const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec);
wrapper::vstore(output_ptr + x, res);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const auto ta1 = *(input1_ptr + x);
const auto ta2 = *(input2_ptr + x);
*(output_ptr + x) = ta1 * ta2 * scale;
}
},
input1, input2, output);
}
}
void c_mul_F32_F32_F32_n(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
// Create input windows
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
constexpr int window_step_x = 8 / sizeof(float);
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 &)
{
const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
const auto output_ptr = reinterpret_cast<float *>(output.ptr());
const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr());
int x = window_start_x;
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const auto broadcast_value0 = *(non_broadcast_input_ptr + 2 * x);
const auto broadcast_value1 = *(non_broadcast_input_ptr + 2 * x + 1);
auto res1 = broadcast_value * (broadcast_value0 - broadcast_value1);
auto res2 = broadcast_value * (broadcast_value1 + broadcast_value0);
*(output_ptr + 2 * x) = res1;
*(output_ptr + 2 * x + 1) = res2;
}
},
broadcast_input, non_broadcast_input, output);
}
else
{
// Clear X Dimension on execution window as we handle manually
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
const auto output_ptr = reinterpret_cast<float *>(output.ptr());
using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type;
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const float32x4_t a = wrapper::vloadq(input1_ptr + 2 * x);
float32x4_t b = wrapper::vloadq(input2_ptr + 2 * x);
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_ptr + 2 * x, res);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const auto a0 = *(input1_ptr + 2 * x);
const auto a1 = *(input1_ptr + 2 * x + 1);
const auto b0 = *(input2_ptr + 2 * x);
const auto b1 = *(input2_ptr + 2 * x + 1);
auto res1 = a0 * b0 - a1 * b1;
auto res2 = a0 * b1 + a1 * b0;
*(output_ptr + 2 * x) = res1;
*(output_ptr + 2 * x + 1) = res2;
}
},
input1, input2, output);
}
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
void mul_F16_F16_F16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<float16_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const float16x8x2_t ta1 =
{
{
vld1q_f16(input1_ptr + x),
vld1q_f16(input1_ptr + x + 8),
}
};
const float16x8x2_t ta2 =
{
{
vld1q_f16(input2_ptr + x),
vld1q_f16(input2_ptr + x + 8),
}
};
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),
}
};
vst1q_f16(output_ptr + x, result.val[0]);
vst1q_f16(output_ptr + x + 8, result.val[1]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const auto ta1 = *(input1_ptr + x);
const auto ta2 = *(input2_ptr + x);
*(output_ptr + x) = ta1 * ta2 * scale;
}
},
input1, input2, output);
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
template <bool is_scale255, bool is_sat>
void mul_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16 / sizeof(uint8_t);
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const uint8x16_t bv = wrapper::vloadq(input2_ptr + x);
const uint8x16_t av = wrapper::vloadq(input1_ptr + x);
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_ptr + x, vreinterpretq_s16_u16(tmp_low));
vst1q_s16(output_ptr + x + 8, vreinterpretq_s16_u16(tmp_high));
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
if(is_scale255)
{
float tmp_f = static_cast<float>(tmp) * scale255_constant;
tmp = static_cast<int32_t>(tmp_f + 0.5f);
}
else
{
tmp >>= n;
}
if(is_sat)
{
tmp = (tmp > SHRT_MAX) ? SHRT_MAX : tmp;
}
*(output_ptr + x) = static_cast<int16_t>(tmp);
}
},
input1, input2, output);
}
template <bool is_scale255, bool is_sat>
void mul_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Create input windows
Window win = window;
Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(in1, input1_win);
Iterator input2(in2, input2_win);
Iterator output(out, win);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
execute_window_loop(win, [&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const int16x8x2_t ta1 =
{
{
vld1q_s16(input1_ptr + x),
vld1q_s16(input1_ptr + x + 8),
}
};
const uint8x8x2_t ta2u =
{
{
vld1_u8(input2_ptr + x),
vld1_u8(input2_ptr + x + 8),
}
};
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);
vst1q_s16(output_ptr + x, result.val[0]);
vst1q_s16(output_ptr + x + 8, result.val[1]);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
if(is_scale255)
{
float tmp_f = static_cast<float>(tmp) * scale255_constant;
tmp = static_cast<int32_t>(tmp_f + 0.5f);
}
else
{
if(tmp >= 0)
{
tmp >>= n;
}
else
{
uint32_t mask = (1u << n) - 1;
tmp = (tmp + static_cast<int32_t>(mask)) >> n;
}
}
if(is_sat)
{
tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
}
*(output_ptr + x) = static_cast<int16_t>(tmp);
}
},
input1, input2, output);
}
template <bool is_scale255, bool is_sat>
void mul_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Simply swap the two input buffers
mul_S16_U8_S16<is_scale255, is_sat>(in2, in1, out, window, n);
}
} // namespace
NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel()
: _func_float(nullptr), _func_int(nullptr), _func_quantized(nullptr), _scale{ 0 }, _scale_exponent{ 0 }
{
}
void NEPixelWiseMultiplicationKernel::configure(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *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, input2, output, scale, overflow_policy, rounding_policy));
const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
const TensorShape &out_shape = broadcast_pair.first;
const ValidRegion &valid_region = broadcast_pair.second;
// Auto initialize output if not initialized
set_shape_if_empty(*output, out_shape);
_scale = scale;
_scale_exponent = 0;
_func_quantized = nullptr;
_func_int = nullptr;
_func_float = nullptr;
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->data_type();
const DataType dt_input2 = input2->data_type();
const DataType dt_output = output->data_type();
const bool is_sat = (overflow_policy == ConvertPolicy::SATURATE);
switch(dt_input1)
{
case DataType::QASYMM8:
if(dt_input2 == DataType::QASYMM8 && dt_output == DataType::QASYMM8)
{
_func_quantized = &mul_saturate_quantized_8<uint8_t>;
}
break;
case DataType::QASYMM8_SIGNED:
if(dt_input2 == DataType::QASYMM8_SIGNED)
{
_func_quantized = &mul_saturate_quantized_8<int8_t>;
;
}
break;
case DataType::QSYMM16:
if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::QSYMM16)
{
_func_quantized = &mul_saturate_QSYMM16_QSYMM16_QSYMM16;
}
else if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::S32)
{
_func_int = &mul_QSYMM16_QSYMM16_S32;
}
break;
case DataType::S16:
if(DataType::U8 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_S16_U8_S16<true, true> : &mul_S16_U8_S16<true, false>;
}
else
{
_func_int = is_sat ? &mul_S16_U8_S16<false, true> : &mul_S16_U8_S16<false, false>;
}
}
if(DataType::S16 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_S16_S16_S16<true, true> : &mul_S16_S16_S16<true, false>;
}
else
{
_func_int = is_sat ? &mul_S16_S16_S16<false, true> : &mul_S16_S16_S16<false, false>;
}
}
break;
case DataType::S32:
if(DataType::S32 == dt_input2 && DataType::S32 == dt_output)
{
_func_int = is_sat ? &mul_S32_S32_S32<true> : &mul_S32_S32_S32<false>;
}
break;
case DataType::U8:
if(DataType::U8 == dt_input2 && DataType::U8 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_U8_U8_U8<true, true> : &mul_U8_U8_U8<true, false>;
}
else
{
_func_int = is_sat ? &mul_U8_U8_U8<false, true> : &mul_U8_U8_U8<false, false>;
}
}
else if(DataType::U8 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_U8_U8_S16<true, true> : &mul_U8_U8_S16<true, false>;
}
else
{
_func_int = is_sat ? &mul_U8_U8_S16<false, true> : &mul_U8_U8_S16<false, false>;
}
}
else if(DataType::S16 == dt_input2 && DataType::S16 == dt_output)
{
if(is_scale_255)
{
_func_int = is_sat ? &mul_U8_S16_S16<true, true> : &mul_U8_S16_S16<true, false>;
}
else
{
_func_int = is_sat ? &mul_U8_S16_S16<false, true> : &mul_U8_S16_S16<false, false>;
}
}
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
_func_float = &mul_F16_F16_F16;
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
case DataType::F32:
_func_float = &mul_F32_F32_F32;
break;
default:
ARM_COMPUTE_ERROR("You called with the wrong img formats");
}
// Configure kernel window
Coordinates coord;
coord.set_num_dimensions(output->num_dimensions());
output->set_valid_region(valid_region);
Window win = calculate_max_window(valid_region, Steps());
INEKernel::configure(win);
}
Status 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));
return Status{};
}
void NEPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
auto input1 = tensors.get_const_tensor(TensorType::ACL_SRC_0);
auto input2 = tensors.get_const_tensor(TensorType::ACL_SRC_1);
auto output = tensors.get_tensor(TensorType::ACL_DST);
if(_func_quantized != nullptr)
{
(*_func_quantized)(input1, input2, output, window, _scale);
}
else if(_func_int != nullptr)
{
(*_func_int)(input1, input2, output, window, _scale_exponent);
}
else
{
ARM_COMPUTE_ERROR_ON(_func_float == nullptr);
(*_func_float)(input1, input2, output, window, _scale);
}
}
namespace
{
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{};
}
} // namespace
void NEComplexPixelWiseMultiplicationKernel::configure(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1, input2, 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);
// Configure kernel window
Coordinates coord;
coord.set_num_dimensions(output->num_dimensions());
output->set_valid_region(valid_region);
Window win = calculate_max_window(valid_region, Steps());
INEKernel::configure(win);
}
Status 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));
return Status{};
}
void NEComplexPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
auto input1 = tensors.get_const_tensor(TensorType::ACL_SRC_0);
auto input2 = tensors.get_const_tensor(TensorType::ACL_SRC_1);
auto output = tensors.get_tensor(TensorType::ACL_DST);
c_mul_F32_F32_F32_n(input1, input2, output, window);
}
} // namespace arm_compute