.. _program_listing_file_src_optimizers_quantizer.h:

Program Listing for File quantizer.h
====================================

|exhale_lsh| :ref:`Return to documentation for file <file_src_optimizers_quantizer.h>` (``src/optimizers/quantizer.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #pragma once
   
   #include "common/options.h"
   #include "functional/functional.h"
   #include "graph/expression_graph.h"
   #include "tensors/backend.h"
   #include "tensors/tensor.h"
   #include "tensors/tensor_allocator.h"
   #include "tensors/tensor_operators.h"
   
   namespace marian {
   
   /* Class to implement quantization of all the parameters in a model graph
    * This class handles the required error-feedback mechanism internally.
    * Example:
    *   auto mq = New<ModelQuantizer>(options_);
    *   mq->quantize(graph_);
    *
    * Parameters in graph_ will be quantized every time quantize is called.
    * The internal error-residual is also updated each quantize call,
    * therefore, use the same ModelQuantizer object to quantize the same graph.
    */
   class ModelQuantizer {
   public:
     ModelQuantizer(Ptr<Options> options)
         : bits_{options->get<size_t>("quantize-bits")},
           optSteps_{options->get<size_t>("quantize-optimization-steps")},
           quantBias_{options->get<bool>("quantize-biases")},
           logQuant_{options->get<bool>("quantize-log-based")} {}
   
     void quantize(Ptr<ExpressionGraph> graph);
   
   protected:
     void quantizeImpl(Tensor t);
   
     size_t bits_;
     size_t optSteps_;
     bool  quantBias_;
     bool logQuant_;
     bool isFirstError_;
   
     std::vector<Ptr<TensorAllocator>> allocators_;
   
     Tensor errorResidual_; // Tensor to store the error-residual
     Tensor delta_; // temporary Tensor for storing q to calculate optimal S
     Tensor tempVar_; // single element Tensor for Reduce swap variable
   };
   }  // namespace marian