simulations/models/quantized_net.py

from numpy import (
    array,
    zeros,
    dot,
    median,
    log2,
    linspace,
    argmin,
    abs,
)
from scipy.linalg import norm
from tensorflow.keras.backend import function as Kfunction
from tensorflow.keras.models import Model, clone_model
from collections import namedtuple
from typing import List, Generator
from time import time

from models.autoencoder import Autoencoder

QuantizedNeuron = namedtuple("QuantizedNeuron", ["layer_idx", "neuron_idx", "q"])
QuantizedFilter = namedtuple(
    "QuantizedFilter", ["layer_idx", "filter_idx", "channel_idx", "q_filtr"]
)
SegmentedData = namedtuple("SegmentedData", ["wX_seg", "qX_seg"])


class QuantizedNeuralNetwork:
    def __init__(
            self,
            network: Model,
            batch_size: int,
            get_data: Generator[array, None, None],
            logger=None,
            ignore_layers=[],
            bits=log2(3),
            alphabet_scalar=1,
    ):

        self.get_data = get_data

        # The pre-trained network.
        self.trained_net = network

        # This copies the network structure but not the weights.
        if isinstance(network, Autoencoder):
            # The pre-trained network.
            self.trained_net_layers = network.all_layers
            self.quantized_net = Autoencoder(network.N, network.channel, bipolar=network.bipolar)
            self.quantized_net.set_weights(network.get_weights())
            self.quantized_net_layers = self.quantized_net.all_layers
            # self.quantized_net.layers = self.quantized_net.layers
        else:
            # The pre-trained network.
            self.trained_net_layers = network.layers
            self.quantized_net = clone_model(network)
            # Set all the weights to be the same a priori.
            self.quantized_net.set_weights(network.get_weights())
            self.quantized_net_layers = self.quantized_net.layers

        self.batch_size = batch_size

        self.alphabet_scalar = alphabet_scalar

        # Create a dictionary encoding which layers are Dense, and what their dimensions are.
        self.layer_dims = {
            layer_idx: layer.get_weights()[0].shape
            for layer_idx, layer in enumerate(network.layers)
            if layer.__class__.__name__ == "Dense"
        }

        # This determines the alphabet. There will be 2**bits atoms in our alphabet.
        self.bits = bits

        # Construct the (unscaled) alphabet. Layers will scale this alphabet based on the
        # distribution of that layer's weights.
        self.alphabet = linspace(-1, 1, num=int(round(2 ** (bits))))

        self.logger = logger

        self.ignore_layers = ignore_layers

    def _log(self, msg: str):
        if self.logger:
            self.logger.info(msg)
        else:
            print(msg)

    def _bit_round(self, t: float, rad: float) -> float:
        """Rounds a quantity to the nearest atom in the (scaled) quantization alphabet.

        Parameters
        -----------
        t : float
            The value to quantize.
        rad : float
            Scaling factor for the quantization alphabet.

        Returns
        -------
        bit : float
            The quantized value.
        """

        # Scale the alphabet appropriately.
        layer_alphabet = rad * self.alphabet
        return layer_alphabet[argmin(abs(layer_alphabet - t))]

    def _quantize_weight(
            self, w: float, u: array, X: array, X_tilde: array, rad: float
    ) -> float:
        """Quantizes a single weight of a neuron.

        Parameters
        -----------
        w : float
            The weight.
        u : array ,
            Residual vector.
        X : array
            Vector from the analog network's random walk.
        X_tilde : array
            Vector from the quantized network's random walk.
        rad : float
            Scaling factor for the quantization alphabet.

        Returns
        -------
        bit : float
            The quantized value.
        """

        if norm(X_tilde, 2) < 10 ** (-16):
            return 0

        if abs(dot(X_tilde, u)) < 10 ** (-10):
            return self._bit_round(w, rad)

        return self._bit_round(dot(X_tilde, u + w * X) / (norm(X_tilde, 2) ** 2), rad)

    def _quantize_neuron(
            self,
            layer_idx: int,
            neuron_idx: int,
            wX: array,
            qX: array,
            rad=1,
    ) -> QuantizedNeuron:
        """Quantizes a single neuron in a Dense layer.

        Parameters
        -----------
        layer_idx : int
            Index of the Dense layer.
        neuron_idx : int,
            Index of the neuron in the Dense layer.
        wX : array
            Layer input for the analog convolutional neural network.
        qX : array
            Layer input for the quantized convolutional neural network.
        rad : float
            Scaling factor for the quantization alphabet.

        Returns
        -------
        QuantizedNeuron: NamedTuple
            A tuple with the layer and neuron index, as well as the quantized neuron.
        """

        N_ell = wX.shape[1]
        u = zeros(self.batch_size)
        w = self.trained_net_layers[layer_idx].get_weights()[0][:, neuron_idx]
        q = zeros(N_ell)
        for t in range(N_ell):
            q[t] = self._quantize_weight(w[t], u, wX[:, t], qX[:, t], rad)
            u += w[t] * wX[:, t] - q[t] * qX[:, t]

        return QuantizedNeuron(layer_idx=layer_idx, neuron_idx=neuron_idx, q=q)

    def _get_layer_data(self, layer_idx: int, hf=None):
        """Gets the input data for the layer at a given index.

        Parameters
        -----------
        layer_idx : int
            Index of the layer.
        hf: hdf5 File object in write mode.
            If provided, will write output to hdf5 file instead of returning directly.

        Returns
        -------
        tuple: (array, array)
            A tuple of arrays, with the first entry being the input for the analog network
            and the latter being the input for the quantized network.
        """

        layer = self.trained_net_layers[layer_idx]
        layer_data_shape = layer.input_shape[1:] if layer.input_shape[0] is None else layer.input_shape
        wX = zeros((self.batch_size, *layer_data_shape))
        qX = zeros((self.batch_size, *layer_data_shape))
        if layer_idx == 0:
            for sample_idx in range(self.batch_size):
                try:
                    wX[sample_idx, :] = next(self.get_data)
                except StopIteration:
                    # No more samples!
                    break
            qX = wX
        else:
            # Define functions which will give you the output of the previous hidden layer
            # for both networks.
            prev_trained_output = Kfunction(
                [self.trained_net_layers[0].input],
                [self.trained_net_layers[layer_idx - 1].output],
            )
            prev_quant_output = Kfunction(
                [self.quantized_net_layers[0].input],
                [self.quantized_net_layers[layer_idx - 1].output],
            )
            input_layer = self.trained_net_layers[0]
            input_shape = input_layer.input_shape[1:] if input_layer.input_shape[0] is None else input_layer.input_shape
            batch = zeros((self.batch_size, *input_shape))

            # TODO: Add hf option here. Feed batches of data through rather than all at once. You may want
            # to reconsider how much memory you preallocate for batch, wX, and qX.
            feed_foward_batch_size = 500
            ctr = 0
            for sample_idx in range(self.batch_size):
                try:
                    batch[sample_idx, :] = next(self.get_data)
                except StopIteration:
                    # No more samples!
                    break

            wX = prev_trained_output([batch])[0]
            qX = prev_quant_output([batch])[0]

        return (wX, qX)

    def _update_weights(self, layer_idx: int, Q: array):
        """Updates the weights of the quantized neural network given a layer index and
        quantized weights.

        Parameters
        -----------
        layer_idx : int
            Index of the Conv2D layer.
        Q : array
            The quantized weights.
        """

        # Update the quantized network. Use the same bias vector as in the analog network for now.
        if self.trained_net_layers[layer_idx].use_bias:
            bias = self.trained_net_layers[layer_idx].get_weights()[1]
            self.quantized_net_layers[layer_idx].set_weights([Q, bias])
        else:
            self.quantized_net_layers[layer_idx].set_weights([Q])

    def _quantize_layer(self, layer_idx: int):
        """Quantizes a Dense layer of a multi-layer perceptron.

        Parameters
        -----------
        layer_idx : int
            Index of the Dense layer.
        """

        W = self.trained_net_layers[layer_idx].get_weights()[0]
        N_ell, N_ell_plus_1 = W.shape
        # Placeholder for the weight matrix in the quantized network.
        Q = zeros(W.shape)
        N_ell_plus_1 = W.shape[1]
        wX, qX = self._get_layer_data(layer_idx)

        # Set the radius of the alphabet.
        rad = self.alphabet_scalar * median(abs(W.flatten()))

        for neuron_idx in range(N_ell_plus_1):
            self._log(f"\tQuantizing neuron {neuron_idx} of {N_ell_plus_1}...")
            tic = time()
            qNeuron = self._quantize_neuron(layer_idx, neuron_idx, wX, qX, rad)
            Q[:, neuron_idx] = qNeuron.q

            self._log(f"\tdone. {time() - tic :.2f} seconds.")

            self._update_weights(layer_idx, Q)

    def quantize_network(self):
        """Quantizes all Dense layers that are not specified by the list of ignored layers."""

        # This must be done sequentially.
        for layer_idx, layer in enumerate(self.trained_net_layers):
            if (
                    layer.__class__.__name__ == "Dense"
                    and layer_idx not in self.ignore_layers
            ):
                # Only quantize dense layers.
                self._log(f"Quantizing layer {layer_idx}...")
                self._quantize_layer(layer_idx)
                self._log(f"done. {layer_idx}...")