import math import re import torch import torch.nn as nn from transformers import CLIPVisionModel def build_vision_tower(): vision_tower = 'openai/clip-vit-large-patch14-336' return CLIPVisionTower(vision_tower) def build_vision_projector(): projector_type = 'mlp2x_gelu' mm_hidden_size = 1024 hidden_size = 4096 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(mm_hidden_size, hidden_size)] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(hidden_size, hidden_size)) return nn.Sequential(*modules) if projector_type == 'identity': return IdentityMap() raise ValueError(f'Unknown projector type: {projector_type}') 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 CLIPVisionTower(nn.Module): def __init__(self, vision_tower): super().__init__() self.is_loaded = False self.is_resize_pos = False self.vision_tower_name = vision_tower self.select_layer = -1 self.select_feature = 'patch' self.load_model() self.resize_pos() def load_model(self): self.vision_tower = CLIPVisionModel.from_pretrained( self.vision_tower_name) self.vision_tower.requires_grad_(False) self.is_loaded = True def resize_pos(self): pos_embed_checkpoint = self.vision_tower.vision_model.embeddings.position_embedding.weight pos_embed_checkpoint = pos_embed_checkpoint.unsqueeze(0) orig_size = 24 new_size = 35 if pos_embed_checkpoint.shape[1] == new_size**2 + 1: self.is_resize_pos = True else: embedding_size = pos_embed_checkpoint.shape[-1] num_extra_tokens = 1 new_num = new_size**2 + num_extra_tokens print('Position interpolate from %dx%d to %dx%d' % (orig_size, orig_size, new_size, new_size)) extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute( 0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) new_pos_embed = new_pos_embed.squeeze(0) self.vision_tower.vision_model.embeddings.position_embedding = torch.nn.Embedding( new_num, 1024) self.vision_tower.vision_model.embeddings.position_embedding.weight = torch.nn.Parameter( new_pos_embed.to(pos_embed_checkpoint.dtype)) self.vision_tower.vision_model.embeddings.position_ids = torch.arange( new_num).expand((1, -1)) self.is_resize_pos = True def feature_select(self, image_forward_outs): image_features = image_forward_outs.hidden_states[self.select_layer] if self.select_feature == 'patch': image_features = image_features[:, 1:] elif self.select_feature == 'cls_patch': image_features = image_features else: raise ValueError( f'Unexpected select feature: {self.select_feature}') return image_features def forward(self, images): if not self.is_loaded: self.load_model() if type(images) is list: image_features = [] for image in images: image_forward_out = self.vision_tower( image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) image_feature = self.feature_select(image_forward_out).to( image.dtype) image_features.append(image_feature) else: image_forward_outs = self.vision_tower( images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) image_features = self.feature_select(image_forward_outs).to( images.dtype) return image_features @property def dummy_feature(self): return torch.zeros( 1, self.hidden_size, device=self.device, dtype=self.dtype) @property def dtype(self): return self.vision_tower.dtype @property def device(self): return self.vision_tower.device @property def config(self): if self.is_loaded: return self.vision_tower.config else: return self.cfg_only @property def hidden_size(self): return self.config.hidden_size @property def num_patches(self): return (self.config.image_size // self.config.patch_size)**2 class PLoRA(nn.Linear): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None, lora_r=8, lora_alpha=16, lora_dropout=0.05, lora_len=0, **kwargs) -> None: super().__init__(in_features, out_features, bias, device, dtype) self.lora_r = lora_r self.lora_alpha = lora_alpha self.lora_len = lora_len if lora_dropout > 0.: self.lora_dropout = nn.Dropout(p=lora_dropout) else: self.lora_dropout = lambda x: x self.lora_scaling = self.lora_alpha / self.lora_r self.Plora_A = nn.Linear( in_features, self.lora_r, bias=False, device=device, dtype=dtype) self.Plora_B = nn.Linear( self.lora_r, out_features, bias=False, device=device, dtype=dtype) self.reset_parameters() def reset_parameters(self): if hasattr(self, 'lora_A'): # initialize A the same way as the default for nn.Linear and B to zero nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) nn.init.zeros_(self.lora_B.weight) def forward(self, x, im_mask=None): res = super().forward(x) if im_mask is not None: if torch.sum(im_mask) > 0: part_x = x[im_mask] res[im_mask] += self.Plora_B( self.Plora_A( self.lora_dropout(part_x))) * self.lora_scaling else: part_x = x[:, :1] res[:, :1] += self.Plora_B( self.Plora_A(self.lora_dropout(part_x))) * 0 return res