Model.py

"""
@author: Yoni Choukroun, choukroun.yoni@gmail.com
Error Correction Code Transformer
https://arxiv.org/abs/2203.14966
"""
from torch.nn import LayerNorm
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import copy
import logging
from Codes import sign_to_bin


def clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])


class Encoder(nn.Module):
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)
        if N > 1:
            self.norm2 = LayerNorm(layer.size)

    def forward(self, x, mask):
        for idx, layer in enumerate(self.layers, start=1):
            x = layer(x, mask)
            if idx == len(self.layers)//2 and len(self.layers) > 1:
                x = self.norm2(x)
        return self.norm(x)


class SublayerConnection(nn.Module):
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        return x + self.dropout(sublayer(self.norm(x)))


class EncoderLayer(nn.Module):
    def __init__(self, size, self_attn, feed_forward, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x, mask):
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.feed_forward)


class MultiHeadedAttention(nn.Module):
    def __init__(self, h, d_model, dropout=0.1):
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        self.d_k = d_model // h
        self.h = h
        self.linears = clones(nn.Linear(d_model, d_model), 4)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        nbatches = query.size(0)
        query, key, value = \
            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
             for l, x in zip(self.linears, (query, key, value))]

        x, self.attn = self.attention(query, key, value, mask=mask)

        x = x.transpose(1, 2).contiguous() \
            .view(nbatches, -1, self.h * self.d_k)
        return self.linears[-1](x)

    def attention(self, query, key, value, mask=None):
        d_k = query.size(-1)
        scores = torch.matmul(query, key.transpose(-2, -1)) \
            / math.sqrt(d_k)
        if mask is not None:
            scores = scores.masked_fill(mask, -1e9)
        p_attn = F.softmax(scores, dim=-1)
        if self.dropout is not None:
            p_attn = self.dropout(p_attn)
        return torch.matmul(p_attn, value), p_attn


class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.w_2(self.dropout(F.gelu(self.w_1(x))))

############################################################


class ECC_Transformer(nn.Module):
    def __init__(self, args, dropout=0):
        super(ECC_Transformer, self).__init__()
        ####
        code = args.code
        c = copy.deepcopy
        attn = MultiHeadedAttention(args.h, args.d_model)
        ff = PositionwiseFeedForward(args.d_model, args.d_model*4, dropout)

        self.src_embed = torch.nn.Parameter(torch.empty(
            (code.n + code.pc_matrix.size(0), args.d_model)))
        self.decoder = Encoder(EncoderLayer(
            args.d_model, c(attn), c(ff), dropout), args.N_dec)
        self.oned_final_embed = torch.nn.Sequential(
            *[nn.Linear(args.d_model, 1)])
        self.out_fc = nn.Linear(code.n + code.pc_matrix.size(0), code.n)

        self.get_mask(code)
        logging.info(f'Mask:\n {self.src_mask}')
        ###
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

    def forward(self, magnitude, syndrome):
        emb = torch.cat([magnitude, syndrome], -1).unsqueeze(-1)
        emb = self.src_embed.unsqueeze(0) * emb
        emb = self.decoder(emb, self.src_mask)
        return self.out_fc(self.oned_final_embed(emb).squeeze(-1))

    def loss(self, z_pred, z2, y):
        loss = F.binary_cross_entropy_with_logits(
            z_pred, sign_to_bin(torch.sign(z2)))
        x_pred = sign_to_bin(torch.sign(-z_pred * torch.sign(y)))
        return loss, x_pred

    def get_mask(self, code, no_mask=False):
        if no_mask:
            self.src_mask = None
            return

        def build_mask(code):
            mask_size = code.n + code.pc_matrix.size(0)
            mask = torch.eye(mask_size, mask_size)
            for ii in range(code.pc_matrix.size(0)):
                idx = torch.where(code.pc_matrix[ii] > 0)[0]
                for jj in idx:
                    for kk in idx:
                        if jj != kk:
                            mask[jj, kk] += 1
                            mask[kk, jj] += 1
                            mask[code.n + ii, jj] += 1
                            mask[jj, code.n + ii] += 1
            src_mask = ~ (mask > 0).unsqueeze(0).unsqueeze(0)
            return src_mask
        src_mask = build_mask(code)
        mask_size = code.n + code.pc_matrix.size(0)
        a = mask_size ** 2
        logging.info(
            f'Self-Attention Sparsity Ratio={100 * torch.sum((src_mask).int()) / a:0.2f}%, Self-Attention Complexity Ratio={100 * torch.sum((~src_mask).int())//2 / a:0.2f}%')
        self.register_buffer('src_mask', src_mask)


############################################################
############################################################

if __name__ == '__main__':
    pass