173 lines
7.4 KiB
C++
173 lines
7.4 KiB
C++
/*
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* Copyright (c) 2017-2020 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 "FullyConnectedLayer.h"
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#include "arm_compute/core/Types.h"
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#include "tests/validation/reference/UtilsQuantizedAsymm.h"
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#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
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#include <numeric>
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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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namespace
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{
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// Vector matrix multiply for floating point
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template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 >
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void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
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int rows_weights)
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{
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const T *src_ptr = src.data() + offset_src;
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const T *weights_ptr = weights.data();
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const TB *bias_ptr = bias.data();
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T *dst_ptr = dst.data() + offset_dst;
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#if defined(_OPENMP)
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#pragma omp parallel for
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#endif /* _OPENMP */
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for(int y = 0; y < rows_weights; ++y)
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{
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dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, &weights_ptr[cols_weights * y], static_cast<T>(0)) + bias_ptr[y];
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}
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}
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// Vector matrix multiply for quantized type
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template < typename T, typename TB, typename std::enable_if < (std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) &&std::is_same<TB, int32_t>::value, int >::type = 0 >
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void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst,
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int cols_weights, int rows_weights)
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{
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const T *src_ptr = src.data() + offset_src;
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const T *weights_ptr = weights.data();
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const TB *bias_ptr = bias.data();
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T *dst_ptr = dst.data() + offset_dst;
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const UniformQuantizationInfo iq_info = src.quantization_info().uniform();
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const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();
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const UniformQuantizationInfo oq_info = dst.quantization_info().uniform();
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const int input_offset = -iq_info.offset;
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const float input_scale = iq_info.scale;
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const int weights_offset = -wq_info.offset;
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const float weights_scale = wq_info.scale;
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const int output_offset = oq_info.offset;
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const float output_scale = oq_info.scale;
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int output_multiplier = 0;
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int output_shift = 0;
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const float multiplier = input_scale * weights_scale / output_scale;
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arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
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const int min = std::numeric_limits<T>::lowest();
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const int max = std::numeric_limits<T>::max();
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#if defined(_OPENMP)
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#pragma omp parallel for
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#endif /* _OPENMP */
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for(int y = 0; y < rows_weights; ++y)
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{
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// Reset accumulator
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int32_t acc = 0;
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for(int x = 0; x < cols_weights; ++x)
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{
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acc += (src_ptr[x] + input_offset) * (weights_ptr[x + y * cols_weights] + weights_offset);
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}
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// Accumulate the bias
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acc += bias_ptr[y];
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// Quantize down
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acc = quantize_down_scale_by_fixedpoint(acc, output_multiplier, output_shift, output_offset, min, max);
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// Store the result
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dst_ptr[y] = static_cast<T>(acc);
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}
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}
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} // namespace
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template <typename T, typename TB>
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SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &dst_shape, QuantizationInfo out_quant_info)
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{
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// if no explicit quantization has been set you the same as src
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if(out_quant_info == QuantizationInfo())
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{
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out_quant_info = src.quantization_info();
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}
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// Create reference
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SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, out_quant_info };
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// Sanity checks
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const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1);
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const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions;
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const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions);
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ARM_COMPUTE_UNUSED(num_batch_dimensions);
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ARM_COMPUTE_UNUSED(num_input_dimensions);
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ARM_COMPUTE_UNUSED(linear_input_size);
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ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size);
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ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x());
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ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x());
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// Compute reference
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const int cols_weights = weights.shape().x();
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const int rows_weights = weights.shape().y();
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const int num_batches = dst_shape.total_size_upper(1);
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for(int k = 0; k < num_batches; ++k)
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{
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const int offset_in = k * cols_weights;
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const int offset_out = k * rows_weights;
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vector_matrix_multiply<T>(src,
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weights,
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bias,
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dst,
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offset_in,
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offset_out,
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cols_weights,
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rows_weights);
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}
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return dst;
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}
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template SimpleTensor<float> fully_connected_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &dst_shape,
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QuantizationInfo out_quant_info);
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template SimpleTensor<half> fully_connected_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &dst_shape,
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QuantizationInfo out_quant_info);
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template SimpleTensor<uint8_t> fully_connected_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &dst_shape,
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QuantizationInfo out_quant_info);
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template SimpleTensor<int8_t> fully_connected_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &dst_shape,
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QuantizationInfo out_quant_info);
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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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