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