#!/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__ = ["QuantizedDense"]
import tensorflow as tf
import keras
from .layers_base import (register_quantize_target, rescale_outputs, tensor_inputs,
neural_layer_init, register_aligned_inputs)
from ..tensors import QTensor
[docs]@register_quantize_target(keras.layers.Dense)
@register_aligned_inputs
@tf.keras.utils.register_keras_serializable()
class QuantizedDense(keras.layers.Dense):
"""A Dense layer that operates on quantized inputs and weights
Args:
quant_config (dict, optional): the serialized quantization configuration. Defaults to None.
"""
@neural_layer_init(False)
def __init__(self, *args, quant_config=None, **kwargs):
# Limit buffer bitwidth to 27 for HW constraint
self.quant_config['buffer_bitwidth'] = min(28, self.quant_config['buffer_bitwidth'])
self.buffer_bitwidth = self.quant_config['buffer_bitwidth'] - 1
@tensor_inputs([QTensor, tf.Tensor])
@rescale_outputs
def call(self, inputs):
# Quantize the weights
kernel = self.weight_quantizer(self.kernel)
outputs = tf.matmul(inputs, kernel)
if self.use_bias:
# Quantize and align biases
bias = self.bias_quantizer(self.bias, outputs)
outputs = tf.add(outputs, bias)
return outputs
def get_config(self):
config = super().get_config()
config["quant_config"] = self.quant_config
return config