from torch import nn import torch.nn.functional as nnf from transformers import GPT2Tokenizer, GPT2LMHeadModel import torch from typing import Tuple, List, Union, Optional import numpy as np N = type(None) V = np.array ARRAY = np.ndarray ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]] VS = Union[Tuple[V, ...], List[V]] VN = Union[V, N] VNS = Union[VS, N] T = torch.Tensor TS = Union[Tuple[T, ...], List[T]] TN = Optional[T] TNS = Union[Tuple[TN, ...], List[TN]] TSN = Optional[TS] TA = Union[T, ARRAY] class ClipCaptionModel(nn.Module): def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None): embedding_text = self.gpt.transformer.wte(tokens) prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) if labels is not None: dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) labels = torch.cat((dummy_token, tokens), dim=1) out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) return out def __init__(self): super(ClipCaptionModel, self).__init__() self.prefix_length = 40 self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] self.clip_project = TransformerMapper(640, self.gpt_embedding_size, 40, 40, 8) class MLP(nn.Module): def forward(self, x: T) -> T: return self.model(x) def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): super(MLP, self).__init__() layers = [] for i in range(len(sizes) -1): layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) if i < len(sizes) - 2: layers.append(act()) self.model = nn.Sequential(*layers) class ClipCaptionPrefix(ClipCaptionModel): def parameters(self, recurse: bool = True): return self.clip_project.parameters() def train(self, mode: bool = True): super(ClipCaptionPrefix, self).train(mode) self.gpt.eval() return self class MlpTransformer(nn.Module): def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): super().__init__() out_d = out_d if out_d is not None else in_dim self.fc1 = nn.Linear(in_dim, h_dim) self.act = act self.fc2 = nn.Linear(h_dim, out_d) self.dropout = nn.Dropout(dropout) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.dropout(x) x = self.fc2(x) x = self.dropout(x) return x class MultiHeadAttention(nn.Module): def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): super().__init__() self.num_heads = num_heads head_dim = dim_self // num_heads self.scale = head_dim ** -0.5 self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) self.project = nn.Linear(dim_self, dim_self) self.dropout = nn.Dropout(dropout) def forward(self, x, y=None, mask=None): y = y if y is not None else x b, n, c = x.shape _, m, d = y.shape # b n h dh queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) # b m 2 h dh keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) keys, values = keys_values[:, :, 0], keys_values[:, :, 1] attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale if mask is not None: if mask.dim() == 2: mask = mask.unsqueeze(1) attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) attention = attention.softmax(dim=2) out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) out = self.project(out) return out, attention class TransformerLayer(nn.Module): def forward_with_attention(self, x, y=None, mask=None): x_, attention = self.attn(self.norm1(x), y, mask) x = x + x_ x = x + self.mlp(self.norm2(x)) return x, attention def forward(self, x, y=None, mask=None): x = x + self.attn(self.norm1(x), y, mask)[0] x = x + self.mlp(self.norm2(x)) return x def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim_self) self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) self.norm2 = norm_layer(dim_self) self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) class Transformer(nn.Module): def forward_with_attention(self, x, y=None, mask=None): attentions = [] for layer in self.layers: x, att = layer.forward_with_attention(x, y, mask) attentions.append(att) return x, attentions def forward(self, x, y=None, mask=None): for i, layer in enumerate(self.layers): if i % 2 == 0 and self.enc_dec: # cross x = layer(x, y) elif self.enc_dec: # self x = layer(x, x, mask) else: # self or cross x = layer(x, y, mask) return x def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): super(Transformer, self).__init__() dim_ref = dim_ref if dim_ref is not None else dim_self self.enc_dec = enc_dec if enc_dec: num_layers = num_layers * 2 layers = [] for i in range(num_layers): if i % 2 == 0 and enc_dec: # cross layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) elif enc_dec: # self layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) else: # self or cross layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) self.layers = nn.ModuleList(layers) class TransformerMapper(nn.Module): def forward(self, x): x = self.linear(x).view(x.shape[0], self.clip_length, -1) prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) prefix = torch.cat((x, prefix), dim=1) out = self.transformer(prefix)[:, self.clip_length:] return out def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): super(TransformerMapper, self).__init__() self.clip_length = clip_length self.transformer = Transformer(dim_embedding, 8, num_layers) self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)