100 lines
4.1 KiB
C++
100 lines
4.1 KiB
C++
/*
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* Copyright (c) 2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "QLSTMLayerNormalization.h"
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#include "ArithmeticOperations.h"
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#include "MeanStdDevNormalizationLayer.h"
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#include "PixelWiseMultiplication.h"
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#include "arm_compute/core/utils/misc/Utility.h"
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#include "src/core/utils/quantization/AsymmHelpers.cpp"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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SimpleTensor<int16_t> qlstm_layer_normalization(const SimpleTensor<int16_t> &src, const SimpleTensor<int16_t> &weight, const SimpleTensor<int32_t> &bias)
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{
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ARM_COMPUTE_ERROR_ON(src.shape().num_dimensions() > 2);
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SimpleTensor<int16_t> output{ src.shape(), DataType::QSYMM16 };
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const auto wq_info = weight.quantization_info().uniform();
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int output_multiplier{};
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int output_shift{};
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const auto s = quantization::calculate_quantized_multiplier(wq_info.scale, &output_multiplier, &output_shift);
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output_shift *= -1;
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if(!bool(s))
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{
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output_multiplier = 0;
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output_shift = 0;
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}
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const uint32_t num_batch = src.shape()[1];
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const uint32_t num_input = src.shape()[0];
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for(uint32_t batch_idx = 0; batch_idx < num_batch; ++batch_idx)
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{
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int64_t sum{};
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int64_t sum_sq{};
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for(uint32_t input_idx = 0; input_idx < num_input; ++input_idx)
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{
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const auto index = batch_idx * num_input + input_idx;
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const auto val = static_cast<int32_t>(src[index]);
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sum += val;
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sum_sq += val * val;
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}
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const auto temp = static_cast<int64_t>(0x100000) / num_input;
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const auto mean = sum * 1024 / static_cast<int64_t>(num_input);
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const auto variance = ((sum_sq * temp) - (mean * mean)) / 0x100000;
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int32_t stddev_invsqrt_mul{};
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int32_t stddev_invsqrt_shift{};
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quantization::get_invsqrt_quantized_multiplier_exp(variance, -1, stddev_invsqrt_mul, stddev_invsqrt_shift);
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for(uint32_t input_idx = 0; input_idx < num_input; ++input_idx)
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{
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const auto index = batch_idx * num_input + input_idx;
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const auto val = static_cast<int32_t>(src[index]);
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const auto shifted = (val << 10) - mean;
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const auto rescaled = quantization::multiply_by_quantized_multiplier(shifted, stddev_invsqrt_mul, stddev_invsqrt_shift);
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const int64_t weighted = rescaled * weight[input_idx] + bias[input_idx];
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const auto reverse_shifted = static_cast<int32_t>((weighted + 512) >> 10);
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auto out_val = quantization::multiply_by_quantized_multiplier(reverse_shifted, output_multiplier, output_shift + 12);
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out_val = arm_compute::utility::clamp<decltype(out_val), int16_t>(out_val, std::numeric_limits<int16_t>::min());
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output[index] = static_cast<int16_t>(out_val);
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}
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}
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return output;
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}
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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