class nbla::MinMaxQuantize

template<typename T>
class MinMaxQuantize : public nbla::BaseFunction<float, bool, bool, bool, float>

MinMaxQuantize quantizes values in integer representation.

Inputs:

  • N-D array of input

  • N-D array of minimum quantization range (modified during forward execution)

  • N-D array of maximum quantization range (modified during forward execution)

  • N-D array of minimum quantization level

  • N-D array of maximum quantization level execution)

Template Parameters:

T – Data type for computation.

Param decay:

Decay rate for the exponential moving average.

Param x_min_max:

Use the min and max of x to compute quantization ranges.

Param ema:

Use the exponential moving average for the min and max quantization. ranges.

Param ste_fine_grained:

Straight Through Estimator is fine-grained or not.

Public Functions

inline virtual shared_ptr<Function> copy() const

Copy another instance of Function with the same context.

inline virtual int min_inputs()

Get minimum number of inputs.

This is meant to be used in setup function with in_types which is used to get maximum number of inputs.

inline virtual int min_outputs()

Get minimum number of outputs.

This is meant to be used in setup function with out_types which is used to get max number of outputs.

inline virtual vector<dtypes> in_types()

Get input dtypes.

Last in_type will be used repeatedly if size of in_types is smaller than size of inputs

inline virtual vector<dtypes> out_types()

Get output dtypes.

Last out_type will be used repeatedly if size of out_types is smaller than size of outputs

inline virtual vector<string> allowed_array_classes()

Get array classes that are allowed to be specified by Context.

inline virtual string name()

Get function name in string.

inline virtual bool grad_depends_output_data(int i, int o) const

Dependency flag for checking if in-grad depends on out-data.

Checking if i-th input’ gradient computation requires o-th output’s data or not.

Note

If any of inputs requires an output variable data when computing its gradient, this function must be overridden to return appropriate boolean value. Otherwise, backward computation will be incorrect.

Parameters:

  • i – [in] Input variable index.

  • o – [in] Output variable index.