/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_CORE_KERNELS_MKL_QUANTIZED_CONV_OPS_H_
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#define TENSORFLOW_CORE_KERNELS_MKL_QUANTIZED_CONV_OPS_H_
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#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
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#include "tensorflow/core/framework/tensor.h"
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#ifdef INTEL_MKL
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namespace tensorflow {
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template <class T>
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float MklFloatForOneQuantizedLevel(float range_min, float range_max) {
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int64 highest = static_cast<int64>(Eigen::NumTraits<T>::highest());
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int64 lowest = static_cast<int64>(Eigen::NumTraits<T>::lowest());
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// Adjusting for having a symmetric range.
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// for example: for 8-bit [-127, 127] as opposed to [-128, 127].
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if (lowest < -highest) ++lowest;
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const float float_for_one_quantized_level =
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(range_max - range_min) / (highest - lowest);
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return float_for_one_quantized_level;
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}
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template <class T1, class T2, class T3>
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void MklQuantizationRangeForMultiplication(float min_a, float max_a,
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float min_b, float max_b,
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float* min_c, float* max_c) {
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const float a_float_for_one_quant_level =
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MklFloatForOneQuantizedLevel<T1>(min_a, max_a);
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const float b_float_for_one_quant_level =
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MklFloatForOneQuantizedLevel<T2>(min_b, max_b);
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const int64 c_highest = static_cast<int64>(Eigen::NumTraits<T3>::highest());
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const int64 c_lowest = static_cast<int64>(Eigen::NumTraits<T3>::lowest());
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const float c_float_for_one_quant_level =
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a_float_for_one_quant_level * b_float_for_one_quant_level;
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*min_c = c_float_for_one_quant_level * c_lowest;
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*max_c = c_float_for_one_quant_level * c_highest;
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}
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template <class T1, class T2, class T3>
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void MklQuantizationRangeForMultiplication(float min_a, float max_a,
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const Tensor& min_b_vector,
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const Tensor& max_b_vector,
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Tensor** min_c_vector,
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Tensor** max_c_vector) {
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DCHECK(min_b_vector.NumElements() == (*min_c_vector)->NumElements());
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DCHECK(max_b_vector.NumElements() == (*max_c_vector)->NumElements());
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size_t n_channel = min_b_vector.NumElements();
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const int64 c_highest = static_cast<int64>(Eigen::NumTraits<T3>::highest());
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const int64 c_lowest = static_cast<int64>(Eigen::NumTraits<T3>::lowest());
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const float* min_b = min_b_vector.flat<float>().data();
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const float* max_b = max_b_vector.flat<float>().data();
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float* min_c = (*min_c_vector)->flat<float>().data();
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float* max_c = (*max_c_vector)->flat<float>().data();
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#pragma omp parallel for
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for (size_t n = 0; n < n_channel; ++n) {
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float a_float_for_one_quant_level =
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MklFloatForOneQuantizedLevel<T1>(min_a, max_a);
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float b_float_for_one_quant_level =
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MklFloatForOneQuantizedLevel<T2>(min_b[n], max_b[n]);
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float c_float_for_one_quant_level =
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a_float_for_one_quant_level * b_float_for_one_quant_level;
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min_c[n] = c_float_for_one_quant_level * c_lowest;
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max_c[n] = c_float_for_one_quant_level * c_highest;
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}
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}
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} // namespace tensorflow
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#endif // INTEL_MKL
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#endif // TENSORFLOW_CORE_KERNELS_MKL_QUANTIZED_CONV_OPS_H_
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