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