#!/usr/bin/env python
# ******************************************************************************
# 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.
# 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.
# ******************************************************************************
__all__ = ["WeightQuantizer"]
import tensorflow as tf
from ...tensors import QTensor, QFloat, FixedPoint
from ..recorders import QFloatRecorder, FixedPointRecorder
from .quantizers import Quantizer
[docs]@tf.keras.utils.register_keras_serializable()
class WeightQuantizer(Quantizer):
"""A uniform quantizer that converts a float Tensor to a QFloat representation.
In order, the WeightQuantizer:
- evaluates the scales required to align the values on optimal ranges for FixedPoint
quantization,
- quantizes the rescaled Tensor as a FixedPoint and returns a QFloat.
Args:
bitwidth (int, optional): the quantization bitwidth, defaults to 4.
signed (bool, optional): whether the quantizer expects signed values or unsigned.
Defaults to True.
axis (int, optional): the quantization range is a scalar (None) or a vector corresponding to
the given axis. Defaults to -1.
fp_quantizer (bool, optional): True to enable FixedPoint quantization, QFloat otherwise.
Defaults to False.
"""
def __init__(self, bitwidth=4, signed=True, axis=-1, fp_quantizer=False, **kwargs):
super().__init__(bitwidth, signed, **kwargs)
self.axis = axis
self.fp_quantizer = fp_quantizer
qw_name = self.name + "/qweights"
if self.fp_quantizer:
self.qweights = FixedPointRecorder(qw_name)
else:
self.qweights = QFloatRecorder(qw_name)
[docs] def build(self, input_shape):
"""Build the layer.
Args:
input_shape (list): the shape of input tensor.
"""
super().build(input_shape)
# Convert axis to a list of int
if self.axis is not None and len(input_shape) > 1:
axis_list = list(range(len(input_shape)))
positive_axis = self.axis if self.axis >= 0 else len(axis_list) + self.axis
if positive_axis < 0 or positive_axis > len(axis_list) - 1:
raise ValueError(f"Axis {self.axis} is not valid in {self.name}.")
axis_list.pop(positive_axis)
self._axis = axis_list
else:
self._axis = None
[docs] def call(self, inputs):
"""Quantize the float inputs
The quantization is done in two steps:
1. Compute the quantization ranges,
2. Quantize the inputs.
Args:
inputs (tf.Tensor): the inputs tensor.
Returns:
:obj:`QFloat`: the quantized tensor.
"""
if isinstance(inputs, QTensor):
raise ValueError(
f"{type(inputs)} input is not supported. WeightQuantizer only accepts float"
" inputs.")
# Compute the quantization ranges from the inputs
ranges = tf.math.reduce_max(tf.math.abs(inputs), self._axis)
if self.axis in [-2, 2] and inputs.shape[-1] == 1 and len(inputs.shape) > 2:
# Expand the shape of the ranges so that it is broacastable on the inputs
ranges = tf.expand_dims(ranges, -1)
if self.fp_quantizer:
# Build a FixedPoint when scale is disabled
frac_bits = tf.stop_gradient(FixedPoint.max_frac_bits(self.value_bits, ranges))
qweights = FixedPoint.quantize(inputs, self.value_bits, frac_bits)
else:
# Evaluate the scales to align on the optimal quantization ranges
scales = QFloat.optimal_scales(ranges, self.value_bits)
# Clip scales lower bound to avoid quantizing very tiny values. Minimum is defined
# as 1e-6, value low enough to be considered as zero.
scales = tf.maximum(scales, 1e-6)
# Since we use the optimal quantization ranges [-int_max -1, int_max], the inner
# FixedPoint can be quantized with exactly zero fractional bits
qweights = QFloat.quantize(inputs, self.value_bits, scales, 0.)
# Record the quantized weights (it does nothing if recording is disabled)
self.qweights(qweights)
return qweights
[docs] def get_config(self):
"""Get the config of the layer.
Returns:
dict: the config of the layer.
"""
config = super().get_config()
config.update({"axis": self.axis, "fp_quantizer": self.fp_quantizer})
return config