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https://github.com/Wan-Video/Wan2.1.git
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543 lines
16 KiB
Python
543 lines
16 KiB
Python
# Modified from ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip''
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import logging
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as T
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from .attention import flash_attention
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from .tokenizers import HuggingfaceTokenizer
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from .xlm_roberta import XLMRoberta
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__all__ = [
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'XLMRobertaCLIP',
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'clip_xlm_roberta_vit_h_14',
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'CLIPModel',
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]
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def pos_interpolate(pos, seq_len):
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if pos.size(1) == seq_len:
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return pos
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else:
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src_grid = int(math.sqrt(pos.size(1)))
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tar_grid = int(math.sqrt(seq_len))
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n = pos.size(1) - src_grid * src_grid
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return torch.cat([
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pos[:, :n],
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F.interpolate(
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pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute(
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0, 3, 1, 2),
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size=(tar_grid, tar_grid),
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mode='bicubic',
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align_corners=False).flatten(2).transpose(1, 2)
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],
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dim=1)
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class QuickGELU(nn.Module):
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def forward(self, x):
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return x * torch.sigmoid(1.702 * x)
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class LayerNorm(nn.LayerNorm):
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def forward(self, x):
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return super().forward(x.float()).type_as(x)
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class SelfAttention(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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causal=False,
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attn_dropout=0.0,
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proj_dropout=0.0):
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assert dim % num_heads == 0
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.causal = causal
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self.attn_dropout = attn_dropout
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self.proj_dropout = proj_dropout
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# layers
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self.to_qkv = nn.Linear(dim, dim * 3)
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self.proj = nn.Linear(dim, dim)
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def forward(self, x):
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"""
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x: [B, L, C].
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"""
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b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
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# compute query, key, value
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q, k, v = self.to_qkv(x).view(b, s, 3, n, d).unbind(2)
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# compute attention
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p = self.attn_dropout if self.training else 0.0
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x = flash_attention(q, k, v, dropout_p=p, causal=self.causal, version=2)
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x = x.reshape(b, s, c)
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# output
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x = self.proj(x)
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x = F.dropout(x, self.proj_dropout, self.training)
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return x
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class SwiGLU(nn.Module):
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def __init__(self, dim, mid_dim):
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super().__init__()
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self.dim = dim
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self.mid_dim = mid_dim
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# layers
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self.fc1 = nn.Linear(dim, mid_dim)
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self.fc2 = nn.Linear(dim, mid_dim)
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self.fc3 = nn.Linear(mid_dim, dim)
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def forward(self, x):
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x = F.silu(self.fc1(x)) * self.fc2(x)
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x = self.fc3(x)
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return x
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class AttentionBlock(nn.Module):
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def __init__(self,
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dim,
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mlp_ratio,
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num_heads,
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post_norm=False,
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causal=False,
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activation='quick_gelu',
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attn_dropout=0.0,
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proj_dropout=0.0,
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norm_eps=1e-5):
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assert activation in ['quick_gelu', 'gelu', 'swi_glu']
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super().__init__()
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self.dim = dim
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self.mlp_ratio = mlp_ratio
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self.num_heads = num_heads
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self.post_norm = post_norm
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self.causal = causal
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self.norm_eps = norm_eps
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# layers
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self.norm1 = LayerNorm(dim, eps=norm_eps)
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self.attn = SelfAttention(dim, num_heads, causal, attn_dropout,
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proj_dropout)
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self.norm2 = LayerNorm(dim, eps=norm_eps)
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if activation == 'swi_glu':
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self.mlp = SwiGLU(dim, int(dim * mlp_ratio))
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else:
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self.mlp = nn.Sequential(
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nn.Linear(dim, int(dim * mlp_ratio)),
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QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
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nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
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def forward(self, x):
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if self.post_norm:
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x = x + self.norm1(self.attn(x))
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x = x + self.norm2(self.mlp(x))
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else:
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x = x + self.attn(self.norm1(x))
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x = x + self.mlp(self.norm2(x))
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return x
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class AttentionPool(nn.Module):
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def __init__(self,
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dim,
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mlp_ratio,
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num_heads,
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activation='gelu',
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proj_dropout=0.0,
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norm_eps=1e-5):
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assert dim % num_heads == 0
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super().__init__()
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self.dim = dim
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self.mlp_ratio = mlp_ratio
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.proj_dropout = proj_dropout
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self.norm_eps = norm_eps
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# layers
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gain = 1.0 / math.sqrt(dim)
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self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
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self.to_q = nn.Linear(dim, dim)
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self.to_kv = nn.Linear(dim, dim * 2)
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self.proj = nn.Linear(dim, dim)
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self.norm = LayerNorm(dim, eps=norm_eps)
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self.mlp = nn.Sequential(
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nn.Linear(dim, int(dim * mlp_ratio)),
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QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
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nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
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def forward(self, x):
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"""
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x: [B, L, C].
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"""
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b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.to_q(self.cls_embedding).view(1, 1, n, d).expand(b, -1, -1, -1)
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k, v = self.to_kv(x).view(b, s, 2, n, d).unbind(2)
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# compute attention
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x = flash_attention(q, k, v, version=2)
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x = x.reshape(b, 1, c)
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# output
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x = self.proj(x)
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x = F.dropout(x, self.proj_dropout, self.training)
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# mlp
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x = x + self.mlp(self.norm(x))
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return x[:, 0]
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class VisionTransformer(nn.Module):
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def __init__(self,
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image_size=224,
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patch_size=16,
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dim=768,
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mlp_ratio=4,
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out_dim=512,
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num_heads=12,
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num_layers=12,
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pool_type='token',
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pre_norm=True,
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post_norm=False,
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activation='quick_gelu',
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attn_dropout=0.0,
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proj_dropout=0.0,
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embedding_dropout=0.0,
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norm_eps=1e-5):
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if image_size % patch_size != 0:
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print(
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'[WARNING] image_size is not divisible by patch_size',
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flush=True)
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assert pool_type in ('token', 'token_fc', 'attn_pool')
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out_dim = out_dim or dim
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super().__init__()
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_patches = (image_size // patch_size)**2
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self.dim = dim
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self.mlp_ratio = mlp_ratio
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self.out_dim = out_dim
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.pool_type = pool_type
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self.post_norm = post_norm
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self.norm_eps = norm_eps
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# embeddings
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gain = 1.0 / math.sqrt(dim)
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self.patch_embedding = nn.Conv2d(
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3,
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dim,
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kernel_size=patch_size,
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stride=patch_size,
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bias=not pre_norm)
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if pool_type in ('token', 'token_fc'):
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self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
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self.pos_embedding = nn.Parameter(gain * torch.randn(
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1, self.num_patches +
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(1 if pool_type in ('token', 'token_fc') else 0), dim))
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self.dropout = nn.Dropout(embedding_dropout)
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# transformer
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self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None
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self.transformer = nn.Sequential(*[
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AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False,
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activation, attn_dropout, proj_dropout, norm_eps)
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for _ in range(num_layers)
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])
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self.post_norm = LayerNorm(dim, eps=norm_eps)
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# head
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if pool_type == 'token':
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self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
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elif pool_type == 'token_fc':
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self.head = nn.Linear(dim, out_dim)
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elif pool_type == 'attn_pool':
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self.head = AttentionPool(dim, mlp_ratio, num_heads, activation,
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proj_dropout, norm_eps)
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def forward(self, x, interpolation=False, use_31_block=False):
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b = x.size(0)
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# embeddings
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x = self.patch_embedding(x).flatten(2).permute(0, 2, 1)
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if self.pool_type in ('token', 'token_fc'):
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x = torch.cat([self.cls_embedding.expand(b, -1, -1), x], dim=1)
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if interpolation:
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e = pos_interpolate(self.pos_embedding, x.size(1))
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else:
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e = self.pos_embedding
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x = self.dropout(x + e)
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if self.pre_norm is not None:
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x = self.pre_norm(x)
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# transformer
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if use_31_block:
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x = self.transformer[:-1](x)
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return x
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else:
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x = self.transformer(x)
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return x
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class XLMRobertaWithHead(XLMRoberta):
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def __init__(self, **kwargs):
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self.out_dim = kwargs.pop('out_dim')
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super().__init__(**kwargs)
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# head
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mid_dim = (self.dim + self.out_dim) // 2
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self.head = nn.Sequential(
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nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(),
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nn.Linear(mid_dim, self.out_dim, bias=False))
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def forward(self, ids):
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# xlm-roberta
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x = super().forward(ids)
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# average pooling
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mask = ids.ne(self.pad_id).unsqueeze(-1).to(x)
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x = (x * mask).sum(dim=1) / mask.sum(dim=1)
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# head
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x = self.head(x)
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return x
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class XLMRobertaCLIP(nn.Module):
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def __init__(self,
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embed_dim=1024,
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image_size=224,
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patch_size=14,
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vision_dim=1280,
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vision_mlp_ratio=4,
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vision_heads=16,
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vision_layers=32,
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vision_pool='token',
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vision_pre_norm=True,
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vision_post_norm=False,
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activation='gelu',
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vocab_size=250002,
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max_text_len=514,
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type_size=1,
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pad_id=1,
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text_dim=1024,
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text_heads=16,
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text_layers=24,
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text_post_norm=True,
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text_dropout=0.1,
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attn_dropout=0.0,
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proj_dropout=0.0,
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embedding_dropout=0.0,
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norm_eps=1e-5):
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super().__init__()
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self.embed_dim = embed_dim
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self.image_size = image_size
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self.patch_size = patch_size
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self.vision_dim = vision_dim
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self.vision_mlp_ratio = vision_mlp_ratio
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self.vision_heads = vision_heads
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self.vision_layers = vision_layers
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self.vision_pre_norm = vision_pre_norm
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self.vision_post_norm = vision_post_norm
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self.activation = activation
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self.vocab_size = vocab_size
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self.max_text_len = max_text_len
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self.type_size = type_size
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self.pad_id = pad_id
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self.text_dim = text_dim
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self.text_heads = text_heads
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self.text_layers = text_layers
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self.text_post_norm = text_post_norm
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self.norm_eps = norm_eps
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# models
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self.visual = VisionTransformer(
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image_size=image_size,
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patch_size=patch_size,
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dim=vision_dim,
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mlp_ratio=vision_mlp_ratio,
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out_dim=embed_dim,
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num_heads=vision_heads,
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num_layers=vision_layers,
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pool_type=vision_pool,
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pre_norm=vision_pre_norm,
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post_norm=vision_post_norm,
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activation=activation,
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attn_dropout=attn_dropout,
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proj_dropout=proj_dropout,
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embedding_dropout=embedding_dropout,
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norm_eps=norm_eps)
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self.textual = XLMRobertaWithHead(
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vocab_size=vocab_size,
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max_seq_len=max_text_len,
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type_size=type_size,
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pad_id=pad_id,
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dim=text_dim,
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out_dim=embed_dim,
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num_heads=text_heads,
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num_layers=text_layers,
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post_norm=text_post_norm,
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dropout=text_dropout)
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self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
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def forward(self, imgs, txt_ids):
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"""
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imgs: [B, 3, H, W] of torch.float32.
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- mean: [0.48145466, 0.4578275, 0.40821073]
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- std: [0.26862954, 0.26130258, 0.27577711]
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txt_ids: [B, L] of torch.long.
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Encoded by data.CLIPTokenizer.
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"""
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xi = self.visual(imgs)
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xt = self.textual(txt_ids)
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return xi, xt
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def param_groups(self):
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groups = [{
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'params': [
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p for n, p in self.named_parameters()
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if 'norm' in n or n.endswith('bias')
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],
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'weight_decay': 0.0
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}, {
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'params': [
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p for n, p in self.named_parameters()
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if not ('norm' in n or n.endswith('bias'))
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]
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}]
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return groups
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def _clip(pretrained=False,
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pretrained_name=None,
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model_cls=XLMRobertaCLIP,
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return_transforms=False,
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return_tokenizer=False,
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tokenizer_padding='eos',
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dtype=torch.float32,
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device='cpu',
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**kwargs):
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# init a model on device
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with torch.device(device):
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model = model_cls(**kwargs)
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# set device
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model = model.to(dtype=dtype, device=device)
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output = (model,)
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# init transforms
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if return_transforms:
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# mean and std
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if 'siglip' in pretrained_name.lower():
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mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
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else:
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mean = [0.48145466, 0.4578275, 0.40821073]
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std = [0.26862954, 0.26130258, 0.27577711]
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# transforms
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transforms = T.Compose([
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T.Resize((model.image_size, model.image_size),
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interpolation=T.InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=mean, std=std)
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])
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output += (transforms,)
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return output[0] if len(output) == 1 else output
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def clip_xlm_roberta_vit_h_14(
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pretrained=False,
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pretrained_name='open-clip-xlm-roberta-large-vit-huge-14',
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**kwargs):
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cfg = dict(
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embed_dim=1024,
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image_size=224,
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patch_size=14,
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vision_dim=1280,
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vision_mlp_ratio=4,
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vision_heads=16,
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vision_layers=32,
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vision_pool='token',
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activation='gelu',
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vocab_size=250002,
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max_text_len=514,
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type_size=1,
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pad_id=1,
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text_dim=1024,
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text_heads=16,
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text_layers=24,
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text_post_norm=True,
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text_dropout=0.1,
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attn_dropout=0.0,
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proj_dropout=0.0,
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embedding_dropout=0.0)
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cfg.update(**kwargs)
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return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg)
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class CLIPModel:
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def __init__(self, dtype, device, checkpoint_path, tokenizer_path):
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self.dtype = dtype
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self.device = device
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self.checkpoint_path = checkpoint_path
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self.tokenizer_path = tokenizer_path
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|
|
|
# init model
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|
self.model, self.transforms = clip_xlm_roberta_vit_h_14(
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pretrained=False,
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|
return_transforms=True,
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|
return_tokenizer=False,
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|
dtype=dtype,
|
|
device=device)
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|
self.model = self.model.eval().requires_grad_(False)
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|
logging.info(f'loading {checkpoint_path}')
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|
self.model.load_state_dict(
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|
torch.load(checkpoint_path, map_location='cpu'))
|
|
|
|
# init tokenizer
|
|
self.tokenizer = HuggingfaceTokenizer(
|
|
name=tokenizer_path,
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|
seq_len=self.model.max_text_len - 2,
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|
clean='whitespace')
|
|
|
|
def visual(self, videos):
|
|
# preprocess
|
|
size = (self.model.image_size,) * 2
|
|
videos = torch.cat([
|
|
F.interpolate(
|
|
u.transpose(0, 1),
|
|
size=size,
|
|
mode='bicubic',
|
|
align_corners=False) for u in videos
|
|
])
|
|
videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5))
|
|
|
|
# forward
|
|
with torch.cuda.amp.autocast(dtype=self.dtype):
|
|
out = self.model.visual(videos, use_31_block=True)
|
|
return out
|