Function Quantize

tensorflow/core/kernels/meta_support.cc:316–344

}

void Quantize(OpKernelContext* tf_context, const float* input, int count,
              float range_min, float range_max, quint8* output) {
#ifdef TENSORFLOW_USE_META
  mutex_lock library_lock(GetMutex());
  typedef gemmlowp::meta::Transform1DParams<float, uint8_t,
                                            gemmlowp::meta::Quantize>
      Params;

  Params params;
  params.input = reinterpret_cast<const float*>(input);
  params.output = reinterpret_cast<uint8_t*>(output);
  params.kernel.count = count;
  params.kernel.range_min = range_min;
  params.kernel.range_scale =
      CalculateOneOverRangeScale<uint8_t>(range_min, range_max);

  // After adding the range_offset the value is cast from float to uint.
  // The float to int/uint cast in NEON uses round toward 0. To keep the
  // rounding consistent with Eigen, which uses round toward closest, we can
  // add 0.5f and exploit the fact that we only operate on non negative values.
  // TODO(maciekc): fix the actual kernel in gemmlowp/meta
  params.kernel.range_offset =
      static_cast<float>(std::numeric_limits<uint8_t>::lowest()) + 0.5f;

  MultiThreadTransform1D<Params, 16>(tf_context, params);
#else
  LOG(FATAL) << "Quantize: Meta fastpath not supported.";
#endif
}

void QuantizedBiasAdd(OpKernelContext* tf_context, const quint8* input,
                      int input_count, const quint8* bias, int bias_count,