class NgramBlock(nn.Module):
    requires_input_ids = True

    def __init__(self, config, ngram):
        """
        parameter size 4d^2
        """
        super().__init__()
        self.ln_1 = RMSNorm(config.d_model, eps=1e-5)
        
        self.attn = Ngram(config, ngram)
        self.ln_2 = RMSNorm(config.d_model, eps=1e-5)

        mlp_hidden = config.d_model 
        self.mlp = nn.Sequential(
            nn.Linear(config.d_model, mlp_hidden),
            nn.SiLU(),
            nn.Linear(mlp_hidden, config.d_model),
        )

        self.resid_dropout = nn.Dropout(config.resid_pdrop)
    

    def forward(self, x, input_ids):
        # attention/rnn
        x_att = self.attn(self.ln_1(x), input_ids)
        x = x + self.resid_dropout(x_att)
        # ffn
        x_mlp = self.mlp(self.ln_2(x))
        x = x + self.resid_dropout(x_mlp)
        return x