def __init_

def init()

in deepseek_vl2/models/modeling_deepseek_vl_v2.py [0:0]
40 lines of code
4 McCabe index (conditional complexity)

    def __init__(self, config: DeepseekVLV2Config):
        super().__init__(config)

        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"

        # ----------- vision encoder ------------
        vision_config = config.vision_config
        self.vision = VisionTransformer(
            img_size=vision_config.image_size,
            patch_size=vision_config.patch_size,
            embed_dim=vision_config.width,
            depth=vision_config.layers,
            num_heads=vision_config.heads,
            mlp_ratio=vision_config.mlp_ratio,
            class_token=vision_config.class_token,
            global_pool=vision_config.global_pool,
            ignore_head=vision_config.ignore_head,
            weight_init=vision_config.weight_init,
            num_classes=0,
            deterministic=vision_config.deterministic,
            num_recomputing_layers=vision_config.num_recomputing_layers
        )

        # ----------- vl projector ------------
        projector_config = config.projector_config
        self.projector = MlpProjector(projector_config)

        # image token format 形式
        # FIXME 目前tile tag & global_view_pos的默认取值都是之前的实验策略；后续应当去掉默认取值，改为没有取值就raise error
        self.tile_tag = config.tile_tag
        self.global_view_pos = config.global_view_pos

        # 用于format image token sequence的特殊token
        embed_std = 1 / torch.sqrt(torch.tensor(projector_config.n_embed, dtype=torch.float32))
        if self.tile_tag == "2D":
            # <|view_separator|>, <|\n|>
            self.image_newline = nn.Parameter(torch.randn(projector_config.n_embed) * embed_std)
            # fix the typo: view_seperater
            self.view_seperator = nn.Parameter(torch.randn(projector_config.n_embed) * embed_std)
        elif self.tile_tag == "1D":
            # <|tile_x|>, <|tile_global|>
            candidate_resolutions = config.candidate_resolutions
            if len(candidate_resolutions) == 0:
                raise ValueError(
                    f"len(candidate_resolutions) should be larger than 0, but got {len(candidate_resolutions)}")
            tile_variants_num = len(candidate_resolutions)
            self.tile_indicators = nn.Parameter(
                torch.randn(size=(tile_variants_num + 1, config.aligner.params.n_embed)) * embed_std
            )
        else:
            raise ValueError(f"tile tag should be either 1D or 2D, but got {self.tile_tag}")

        # ----------- language model ------------
        language_config = config.language_config
        self.language = DeepseekV2ForCausalLM(language_config)
def __init__()

def init()
