import torch import torch.nn as nn import re from .pooler_projector import PoolerProjector class IdentityMap(nn.Module): def __init__(self): super().__init__() def forward(self, x, *args, **kwargs): return x @property def config(self): return {"mm_projector_type": "identity"} class SimpleResBlock(nn.Module): def __init__(self, channels): super().__init__() self.pre_norm = nn.LayerNorm(channels) self.proj = nn.Sequential(nn.Linear(channels, channels), nn.GELU(), nn.Linear(channels, channels)) def forward(self, x): x = self.pre_norm(x) return x + self.proj(x) def build_vision_projector(config, delay_load=False, **kwargs): projector_type = getattr(config, "mm_projector_type", "linear") if projector_type == "linear": return nn.Linear(config.mm_hidden_size, config.hidden_size) if projector_type == "pooler": return PoolerProjector(config, kwargs["vision_cfg"]) mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type) if mlp_gelu_match: mlp_depth = int(mlp_gelu_match.group(1)) modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(config.hidden_size, config.hidden_size)) return nn.Sequential(*modules) mlp_gelu_resnet_match = re.match(r"^mlp(\d+)x_res(\d+)x_gelu$", projector_type) if mlp_gelu_resnet_match: mlp_depth = int(mlp_gelu_resnet_match.group(1)) res_depth = int(mlp_gelu_resnet_match.group(2)) modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(config.hidden_size, config.hidden_size)) for _ in range(res_depth): modules.append(SimpleResBlock(config.hidden_size)) return nn.Sequential(*modules) if projector_type == "identity": return IdentityMap() raise ValueError(f"Unknown projector type: {projector_type}")