def __init_

def init()

in pytorch_transformers/modeling_transfo_xl.py [0:0]
69 lines of code
18 McCabe index (conditional complexity)

    def __init__(self, config):
        super(TransfoXLModel, self).__init__(config)
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states

        self.n_token = config.n_token

        self.d_embed = config.d_embed
        self.d_model = config.d_model
        self.n_head = config.n_head
        self.d_head = config.d_head

        self.word_emb = AdaptiveEmbedding(config.n_token, config.d_embed, config.d_model, config.cutoffs, 
                                          div_val=config.div_val)

        self.drop = nn.Dropout(config.dropout)

        self.n_layer = config.n_layer

        self.tgt_len = config.tgt_len
        self.mem_len = config.mem_len
        self.ext_len = config.ext_len
        self.max_klen = config.tgt_len + config.ext_len + config.mem_len

        self.attn_type = config.attn_type

        if not config.untie_r:
            self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
            self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))

        self.layers = nn.ModuleList()
        if config.attn_type == 0: # the default attention
            for i in range(config.n_layer):
                self.layers.append(
                    RelPartialLearnableDecoderLayer(
                        config.n_head, config.d_model, config.d_head, config.d_inner, config.dropout,
                        tgt_len=config.tgt_len, ext_len=config.ext_len, mem_len=config.mem_len,
                        dropatt=config.dropatt, pre_lnorm=config.pre_lnorm,
                        r_w_bias=None if config.untie_r else self.r_w_bias,
                        r_r_bias=None if config.untie_r else self.r_r_bias,
                        output_attentions=self.output_attentions)
                )
        elif config.attn_type == 1: # learnable embeddings
            for i in range(config.n_layer):
                self.layers.append(
                    RelLearnableDecoderLayer(
                        config.n_head, config.d_model, config.d_head, config.d_inner, config.dropout,
                        tgt_len=config.tgt_len, ext_len=config.ext_len, mem_len=config.mem_len,
                        dropatt=config.dropatt, pre_lnorm=config.pre_lnorm,
                        r_w_bias=None if config.untie_r else self.r_w_bias,
                        r_r_bias=None if config.untie_r else self.r_r_bias,
                        output_attentions=self.output_attentions)
                )
        elif config.attn_type in [2, 3]: # absolute embeddings
            for i in range(config.n_layer):
                self.layers.append(
                    DecoderLayer(
                        config.n_head, config.d_model, config.d_head, config.d_inner, config.dropout,
                        dropatt=config.dropatt, pre_lnorm=config.pre_lnorm,
                        r_w_bias=None if config.untie_r else self.r_w_bias,
                        r_r_bias=None if config.untie_r else self.r_r_bias,
                        output_attentions=self.output_attentions)
                )

        self.same_length = config.same_length
        self.clamp_len = config.clamp_len

        if self.attn_type == 0: # default attention
            self.pos_emb = PositionalEmbedding(self.d_model)
        elif self.attn_type == 1: # learnable
            self.r_emb = nn.Parameter(torch.FloatTensor(
                    self.n_layer, self.max_klen, self.n_head, self.d_head))
            self.r_bias = nn.Parameter(torch.FloatTensor(
                    self.n_layer, self.max_klen, self.n_head))
        elif self.attn_type == 2: # absolute standard
            self.pos_emb = PositionalEmbedding(self.d_model)
        elif self.attn_type == 3: # absolute deeper SA
            self.r_emb = nn.Parameter(torch.FloatTensor(
                    self.n_layer, self.max_klen, self.n_head, self.d_head))

        self.apply(self.init_weights)
def __init__()

def init()
