Source code for quantizeml.layers.quantizers.quantizers

#!/usr/bin/env python
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# Copyright 2022 Brainchip Holdings Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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__all__ = ["Quantizer", "Dequantizer"]

import tensorflow as tf
from keras.layers import Layer

from ...tensors import QTensor, QFloat
from ..recorders import TensorRecorder

[docs]class Quantizer(Layer): """The base class for all quantizers. The bitwidth defines the number of quantization levels on which the values will be quantized. For a quantizer that accepts unsigned values, the maximum quantization level is :math:`2 ^ {bitwidth} - 1`. For a quantizer that accepts signed values, we lose one bit of precision to store the sign. When the quantizer is signed, the quantization interval is asymmetric around zero (i.e range: :math:`[- 2 ^ {bitwidth - 1}, 2 ^ {bitwidth - 1} - 1]`). Args: bitwidth (int): the quantization bitwidth. signed (bool, optional): whether the quantizer expects signed values or unsigned. Defaults to True. """ def __init__(self, bitwidth, signed=True, **kwargs): min_bitwidth = 2 if signed else 1 if not isinstance(bitwidth, int) or bitwidth < min_bitwidth: raise ValueError( f"Bitwidth should be an int >= {min_bitwidth}, currently {bitwidth}") self.bitwidth = bitwidth self.signed = signed self.value_bits = bitwidth - 1 if signed else bitwidth super().__init__(**kwargs) def get_config(self): """Get the config of the layer. Returns: dict: the config of the layer. """ config = super().get_config() config.update({"bitwidth": self.bitwidth}) config.update({"signed": self.signed}) return config
[docs]@tf.keras.utils.register_keras_serializable() class Dequantizer(Layer): """ Layer that allows to dequantize its inputs. """ scales: list = None frac_bits: list = None def _build_records(self, inputs): def _build(x): record_fb = record_scale = None # From Keras documentation, any variable creation taking place # in call should be wrapped with tf.init_scope with tf.init_scope(): if isinstance(x, QTensor): record_fb = TensorRecorder( + "/record_fb") if isinstance(x, QFloat): record_scale = TensorRecorder( + "/record_scale") return record_fb, record_scale if self.frac_bits is not None: # Nothing to do return if not isinstance(inputs, (tuple, list)): # Manage single inputs self.frac_bits, self.scales = _build(inputs) return self.frac_bits = [] self.scales = [] with tf.init_scope(): for x in inputs: frac_bits, scales = _build(x) self.frac_bits.append(frac_bits) self.scales.append(scales)
[docs] def call(self, inputs): """Convert QTensor inputs to float. Args: inputs (tf.Tensor or :obj:`QTensor`): the inputs tensor(s). Returns: tf.Tensor: the dequantized tensor(s). """ def dequantize(x, frac_bits_recorder=None, scales_recorder=None): if isinstance(x, QTensor): if frac_bits_recorder is not None: frac_bits_recorder(x.fp.frac_bits if isinstance(x, QFloat) else x.frac_bits) if scales_recorder is not None: scales_recorder(x.scales) return x.to_float() return x # Build records self._build_records(inputs) # Apply dequantizer if isinstance(inputs, (list, tuple)): return [dequantize(x, fb, scales) for x, fb, scales in zip(inputs, self.frac_bits, self.scales)] return dequantize(inputs, self.frac_bits, self.scales)