import torch from ldm_patched.ldm.modules.attention import optimized_attention_for_device class CLIPAttention(torch.nn.Module): def __init__(self, embed_dim, heads, dtype, device, operations): super().__init__() self.heads = heads self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x, mask=None, optimized_attention=None): q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) out = optimized_attention(q, k, v, self.heads, mask) return self.out_proj(out) ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), "gelu": torch.nn.functional.gelu, } class CLIPMLP(torch.nn.Module): def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): super().__init__() self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) self.activation = ACTIVATIONS[activation] self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x): x = self.fc1(x) x = self.activation(x) x = self.fc2(x) return x class CLIPLayer(torch.nn.Module): def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) def forward(self, x, mask=None, optimized_attention=None): x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) x += self.mlp(self.layer_norm2(x)) return x class CLIPEncoder(torch.nn.Module): def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) def forward(self, x, mask=None, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None) if intermediate_output is not None: if intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output intermediate = None for i, l in enumerate(self.layers): x = l(x, mask, optimized_attention) if i == intermediate_output: intermediate = x.clone() return x, intermediate class CLIPEmbeddings(torch.nn.Module): def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None): super().__init__() self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, input_tokens): return self.token_embedding(input_tokens) + self.position_embedding.weight class CLIPTextModel_(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] super().__init__() self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): x = self.embeddings(input_tokens) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) if mask is not None: mask += causal_mask else: mask = causal_mask x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) x = self.final_layer_norm(x) if i is not None and final_layer_norm_intermediate: i = self.final_layer_norm(i) pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),] return x, i, pooled_output class CLIPTextModel(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.num_layers = config_dict["num_hidden_layers"] self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) self.dtype = dtype def get_input_embeddings(self): return self.text_model.embeddings.token_embedding def set_input_embeddings(self, embeddings): self.text_model.embeddings.token_embedding = embeddings def forward(self, *args, **kwargs): return self.text_model(*args, **kwargs) class CLIPVisionEmbeddings(torch.nn.Module): def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): super().__init__() self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) self.patch_embedding = operations.Conv2d( in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False, dtype=dtype, device=device ) num_patches = (image_size // patch_size) ** 2 num_positions = num_patches + 1 self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, pixel_values): embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) return torch.cat([self.class_embedding.expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight class CLIPVision(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations) self.pre_layrnorm = operations.LayerNorm(embed_dim) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.post_layernorm = operations.LayerNorm(embed_dim) def forward(self, pixel_values, attention_mask=None, intermediate_output=None): x = self.embeddings(pixel_values) x = self.pre_layrnorm(x) #TODO: attention_mask? x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) pooled_output = self.post_layernorm(x[:, 0, :]) return x, i, pooled_output class CLIPVisionModelProjection(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.vision_model = CLIPVision(config_dict, dtype, device, operations) self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) def forward(self, *args, **kwargs): x = self.vision_model(*args, **kwargs) out = self.visual_projection(x[2]) return (x[0], x[1], out)