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/*
* Copyright (c) 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 "QLSTMLayerNormalization.h"
#include "ArithmeticOperations.h"
#include "MeanStdDevNormalizationLayer.h"
#include "PixelWiseMultiplication.h"
#include "arm_compute/core/utils/misc/Utility.h"
#include "src/core/utils/quantization/AsymmHelpers.cpp"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
SimpleTensor<int16_t> qlstm_layer_normalization(const SimpleTensor<int16_t> &src, const SimpleTensor<int16_t> &weight, const SimpleTensor<int32_t> &bias)
{
ARM_COMPUTE_ERROR_ON(src.shape().num_dimensions() > 2);
SimpleTensor<int16_t> output{ src.shape(), DataType::QSYMM16 };
const auto wq_info = weight.quantization_info().uniform();
int output_multiplier{};
int output_shift{};
const auto s = quantization::calculate_quantized_multiplier(wq_info.scale, &output_multiplier, &output_shift);
output_shift *= -1;
if(!bool(s))
{
output_multiplier = 0;
output_shift = 0;
}
const uint32_t num_batch = src.shape()[1];
const uint32_t num_input = src.shape()[0];
for(uint32_t batch_idx = 0; batch_idx < num_batch; ++batch_idx)
{
int64_t sum{};
int64_t sum_sq{};
for(uint32_t input_idx = 0; input_idx < num_input; ++input_idx)
{
const auto index = batch_idx * num_input + input_idx;
const auto val = static_cast<int32_t>(src[index]);
sum += val;
sum_sq += val * val;
}
const auto temp = static_cast<int64_t>(0x100000) / num_input;
const auto mean = sum * 1024 / static_cast<int64_t>(num_input);
const auto variance = ((sum_sq * temp) - (mean * mean)) / 0x100000;
int32_t stddev_invsqrt_mul{};
int32_t stddev_invsqrt_shift{};
quantization::get_invsqrt_quantized_multiplier_exp(variance, -1, stddev_invsqrt_mul, stddev_invsqrt_shift);
for(uint32_t input_idx = 0; input_idx < num_input; ++input_idx)
{
const auto index = batch_idx * num_input + input_idx;
const auto val = static_cast<int32_t>(src[index]);
const auto shifted = (val << 10) - mean;
const auto rescaled = quantization::multiply_by_quantized_multiplier(shifted, stddev_invsqrt_mul, stddev_invsqrt_shift);
const int64_t weighted = rescaled * weight[input_idx] + bias[input_idx];
const auto reverse_shifted = static_cast<int32_t>((weighted + 512) >> 10);
auto out_val = quantization::multiply_by_quantized_multiplier(reverse_shifted, output_multiplier, output_shift + 12);
out_val = arm_compute::utility::clamp<decltype(out_val), int16_t>(out_val, std::numeric_limits<int16_t>::min());
output[index] = static_cast<int16_t>(out_val);
}
}
return output;
}
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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