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https://github.com/ostris/ai-toolkit.git
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163 lines
4.5 KiB
Python
163 lines
4.5 KiB
Python
import torch
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import torch.nn as nn
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from toolkit.models.zipper_resampler import ContextualAlphaMask
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# Conv1d MLP
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# MLP that can alternately be used as a conv1d on dim 1
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class MLPC(nn.Module):
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def __init__(
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self,
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in_dim,
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out_dim,
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hidden_dim,
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do_conv=False,
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use_residual=True
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):
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super().__init__()
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self.do_conv = do_conv
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if use_residual:
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assert in_dim == out_dim
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# dont normalize if using conv
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if not do_conv:
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self.layernorm = nn.LayerNorm(in_dim)
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if do_conv:
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self.fc1 = nn.Conv1d(in_dim, hidden_dim, 1)
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self.fc2 = nn.Conv1d(hidden_dim, out_dim, 1)
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else:
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self.fc1 = nn.Linear(in_dim, hidden_dim)
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self.fc2 = nn.Linear(hidden_dim, out_dim)
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self.use_residual = use_residual
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self.act_fn = nn.GELU()
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def forward(self, x):
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residual = x
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if not self.do_conv:
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x = self.layernorm(x)
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x = self.fc1(x)
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x = self.act_fn(x)
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x = self.fc2(x)
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if self.use_residual:
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x = x + residual
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return x
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class ZipperBlock(nn.Module):
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def __init__(
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self,
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in_size,
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in_tokens,
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out_size,
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out_tokens,
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hidden_size,
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hidden_tokens,
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):
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super().__init__()
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self.in_size = in_size
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self.in_tokens = in_tokens
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self.out_size = out_size
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self.out_tokens = out_tokens
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self.hidden_size = hidden_size
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self.hidden_tokens = hidden_tokens
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# permute to (batch_size, out_size, in_tokens)
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self.zip_token = MLPC(
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in_dim=self.in_tokens,
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out_dim=self.out_tokens,
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hidden_dim=self.hidden_tokens,
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do_conv=True, # no need to permute
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use_residual=False
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)
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# permute to (batch_size, out_tokens, out_size)
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# in shpae: (batch_size, in_tokens, in_size)
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self.zip_size = MLPC(
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in_dim=self.in_size,
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out_dim=self.out_size,
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hidden_dim=self.hidden_size,
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use_residual=False
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)
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def forward(self, x):
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x = self.zip_token(x)
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x = self.zip_size(x)
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return x
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# CLIPFusionModule
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# Fuses any size of vision and text embeddings into a single embedding.
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# remaps tokens and vectors.
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class CLIPFusionModule(nn.Module):
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def __init__(
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self,
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text_hidden_size: int = 768,
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text_tokens: int = 77,
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vision_hidden_size: int = 1024,
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vision_tokens: int = 257,
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num_blocks: int = 1,
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):
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super(CLIPFusionModule, self).__init__()
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self.text_hidden_size = text_hidden_size
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self.text_tokens = text_tokens
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self.vision_hidden_size = vision_hidden_size
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self.vision_tokens = vision_tokens
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self.resampler = ZipperBlock(
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in_size=self.vision_hidden_size,
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in_tokens=self.vision_tokens,
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out_size=self.text_hidden_size,
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out_tokens=self.text_tokens,
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hidden_size=self.vision_hidden_size * 2,
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hidden_tokens=self.vision_tokens * 2
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)
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self.zipper_blocks = torch.nn.ModuleList([
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ZipperBlock(
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in_size=self.text_hidden_size * 2,
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in_tokens=self.text_tokens,
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out_size=self.text_hidden_size,
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out_tokens=self.text_tokens,
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hidden_size=self.text_hidden_size * 2,
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hidden_tokens=self.text_tokens * 2
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) for i in range(num_blocks)
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])
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self.ctx_alpha = ContextualAlphaMask(
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dim=self.text_hidden_size,
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)
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self.alpha = nn.Parameter(torch.zeros([text_tokens]) + 0.01)
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def forward(self, text_embeds, vision_embeds):
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# text_embeds = (batch_size, 77, 768)
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# vision_embeds = (batch_size, 257, 1024)
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# output = (batch_size, 77, 768)
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vision_embeds = self.resampler(vision_embeds)
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x = vision_embeds
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for i, block in enumerate(self.zipper_blocks):
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res = x
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x = torch.cat([text_embeds, x], dim=-1)
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x = block(x)
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x = x + res
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# alpha mask
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ctx_alpha = self.ctx_alpha(text_embeds)
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# reshape alpha to (1, 77, 1)
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alpha = self.alpha.unsqueeze(0).unsqueeze(-1)
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x = ctx_alpha * x * alpha
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x = x + text_embeds
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return x
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