wehub-resource-sync
341 lines
13 KiB
Python
Executable File
341 lines
13 KiB
Python
Executable File
# Modified from https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/t5.py
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import math
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from typing import Optional
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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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from diffusers.configuration_utils import ConfigMixin
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from diffusers.loaders.single_file_model import FromOriginalModelMixin
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from diffusers.models.modeling_utils import ModelMixin
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def fp16_clamp(x):
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if x.dtype == torch.float16 and torch.isinf(x).any():
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clamp = torch.finfo(x.dtype).max - 1000
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x = torch.clamp(x, min=-clamp, max=clamp)
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return x
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def init_weights(m):
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if isinstance(m, T5LayerNorm):
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nn.init.ones_(m.weight)
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elif isinstance(m, T5FeedForward):
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nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5)
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nn.init.normal_(m.fc1.weight, std=m.dim**-0.5)
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nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5)
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elif isinstance(m, T5Attention):
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nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn) ** -0.5)
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nn.init.normal_(m.k.weight, std=m.dim**-0.5)
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nn.init.normal_(m.v.weight, std=m.dim**-0.5)
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nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn) ** -0.5)
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elif isinstance(m, T5RelativeEmbedding):
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nn.init.normal_(m.embedding.weight, std=(2 * m.num_buckets * m.num_heads) ** -0.5)
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class GELU(nn.Module):
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def forward(self, x):
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return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
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class T5LayerNorm(nn.Module):
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def __init__(self, dim, eps=1e-6):
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super(T5LayerNorm, self).__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def forward(self, x):
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x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + self.eps)
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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x = x.type_as(self.weight)
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return self.weight * x
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class T5Attention(nn.Module):
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def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
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assert dim_attn % num_heads == 0
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super(T5Attention, self).__init__()
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self.dim = dim
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self.dim_attn = dim_attn
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self.num_heads = num_heads
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self.head_dim = dim_attn // num_heads
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# layers
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self.q = nn.Linear(dim, dim_attn, bias=False)
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self.k = nn.Linear(dim, dim_attn, bias=False)
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self.v = nn.Linear(dim, dim_attn, bias=False)
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self.o = nn.Linear(dim_attn, dim, bias=False)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x, context=None, mask=None, pos_bias=None):
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"""
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x: [B, L1, C].
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context: [B, L2, C] or None.
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mask: [B, L2] or [B, L1, L2] or None.
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"""
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# check inputs
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context = x if context is None else context
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b, n, c = x.size(0), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.q(x).view(b, -1, n, c)
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k = self.k(context).view(b, -1, n, c)
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v = self.v(context).view(b, -1, n, c)
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# attention bias
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attn_bias = x.new_zeros(b, n, q.size(1), k.size(1))
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if pos_bias is not None:
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attn_bias += pos_bias
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if mask is not None:
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assert mask.ndim in [2, 3]
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mask = mask.view(b, 1, 1, -1) if mask.ndim == 2 else mask.unsqueeze(1)
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attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min)
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# compute attention (T5 does not use scaling)
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attn = torch.einsum("binc,bjnc->bnij", q, k) + attn_bias
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attn = F.softmax(attn.float(), dim=-1).type_as(attn)
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x = torch.einsum("bnij,bjnc->binc", attn, v)
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# output
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x = x.reshape(b, -1, n * c)
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x = self.o(x)
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x = self.dropout(x)
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return x
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class T5FeedForward(nn.Module):
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def __init__(self, dim, dim_ffn, dropout=0.1):
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super(T5FeedForward, self).__init__()
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self.dim = dim
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self.dim_ffn = dim_ffn
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# layers
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self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU())
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self.fc1 = nn.Linear(dim, dim_ffn, bias=False)
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self.fc2 = nn.Linear(dim_ffn, dim, bias=False)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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x = self.fc1(x) * self.gate(x)
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x = self.dropout(x)
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x = self.fc2(x)
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x = self.dropout(x)
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return x
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class T5SelfAttention(nn.Module):
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def __init__(self, dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos=True, dropout=0.1):
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super(T5SelfAttention, self).__init__()
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self.dim = dim
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self.dim_attn = dim_attn
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self.dim_ffn = dim_ffn
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self.num_heads = num_heads
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self.num_buckets = num_buckets
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self.shared_pos = shared_pos
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# layers
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self.norm1 = T5LayerNorm(dim)
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self.attn = T5Attention(dim, dim_attn, num_heads, dropout)
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self.norm2 = T5LayerNorm(dim)
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self.ffn = T5FeedForward(dim, dim_ffn, dropout)
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self.pos_embedding = None if shared_pos else T5RelativeEmbedding(num_buckets, num_heads, bidirectional=True)
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def forward(self, x, mask=None, pos_bias=None):
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e = pos_bias if self.shared_pos else self.pos_embedding(x.size(1), x.size(1))
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x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e))
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x = fp16_clamp(x + self.ffn(self.norm2(x)))
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return x
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class T5CrossAttention(nn.Module):
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def __init__(self, dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos=True, dropout=0.1):
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super(T5CrossAttention, self).__init__()
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self.dim = dim
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self.dim_attn = dim_attn
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self.dim_ffn = dim_ffn
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self.num_heads = num_heads
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self.num_buckets = num_buckets
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self.shared_pos = shared_pos
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# layers
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self.norm1 = T5LayerNorm(dim)
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self.self_attn = T5Attention(dim, dim_attn, num_heads, dropout)
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self.norm2 = T5LayerNorm(dim)
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self.cross_attn = T5Attention(dim, dim_attn, num_heads, dropout)
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self.norm3 = T5LayerNorm(dim)
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self.ffn = T5FeedForward(dim, dim_ffn, dropout)
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self.pos_embedding = None if shared_pos else T5RelativeEmbedding(num_buckets, num_heads, bidirectional=False)
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def forward(self, x, mask=None, encoder_states=None, encoder_mask=None, pos_bias=None):
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e = pos_bias if self.shared_pos else self.pos_embedding(x.size(1), x.size(1))
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x = fp16_clamp(x + self.self_attn(self.norm1(x), mask=mask, pos_bias=e))
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x = fp16_clamp(x + self.cross_attn(self.norm2(x), context=encoder_states, mask=encoder_mask))
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x = fp16_clamp(x + self.ffn(self.norm3(x)))
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return x
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class T5RelativeEmbedding(nn.Module):
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def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
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super(T5RelativeEmbedding, self).__init__()
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self.num_buckets = num_buckets
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self.num_heads = num_heads
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self.bidirectional = bidirectional
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self.max_dist = max_dist
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# layers
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self.embedding = nn.Embedding(num_buckets, num_heads)
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def forward(self, lq, lk):
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device = self.embedding.weight.device
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# rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \
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# torch.arange(lq).unsqueeze(1).to(device)
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if torch.device(type="meta") != device:
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rel_pos = torch.arange(lk, device=device).unsqueeze(0) - torch.arange(lq, device=device).unsqueeze(1)
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else:
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rel_pos = torch.arange(lk).unsqueeze(0) - torch.arange(lq).unsqueeze(1)
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rel_pos = self._relative_position_bucket(rel_pos)
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rel_pos_embeds = self.embedding(rel_pos)
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rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze(0) # [1, N, Lq, Lk]
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return rel_pos_embeds.contiguous()
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def _relative_position_bucket(self, rel_pos):
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# preprocess
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if self.bidirectional:
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num_buckets = self.num_buckets // 2
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rel_buckets = (rel_pos > 0).long() * num_buckets
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rel_pos = torch.abs(rel_pos)
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else:
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num_buckets = self.num_buckets
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rel_buckets = 0
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rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos))
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# embeddings for small and large positions
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max_exact = num_buckets // 2
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rel_pos_large = (
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max_exact
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+ (
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torch.log(rel_pos.float() / max_exact) / math.log(self.max_dist / max_exact) * (num_buckets - max_exact)
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).long()
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)
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rel_pos_large = torch.min(rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1))
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rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large)
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return rel_buckets
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class WanT5EncoderModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
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def __init__(self, vocab, dim, dim_attn, dim_ffn, num_heads, num_layers, num_buckets, shared_pos=True, dropout=0.1):
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super(WanT5EncoderModel, self).__init__()
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self.dim = dim
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self.dim_attn = dim_attn
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self.dim_ffn = dim_ffn
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.num_buckets = num_buckets
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self.shared_pos = shared_pos
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# layers
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self.token_embedding = vocab if isinstance(vocab, nn.Embedding) else nn.Embedding(vocab, dim)
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self.pos_embedding = T5RelativeEmbedding(num_buckets, num_heads, bidirectional=True) if shared_pos else None
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self.dropout = nn.Dropout(dropout)
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self.blocks = nn.ModuleList(
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[
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T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos, dropout)
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for _ in range(num_layers)
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]
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)
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self.norm = T5LayerNorm(dim)
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# initialize weights
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self.apply(init_weights)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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):
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x = self.token_embedding(input_ids)
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x = self.dropout(x)
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e = self.pos_embedding(x.size(1), x.size(1)) if self.shared_pos else None
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for block in self.blocks:
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x = block(x, attention_mask, pos_bias=e)
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x = self.norm(x)
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x = self.dropout(x)
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return (x,)
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@classmethod
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def from_pretrained(
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cls, pretrained_model_path, additional_kwargs={}, low_cpu_mem_usage=False, torch_dtype=torch.bfloat16
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):
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def filter_kwargs(cls, kwargs):
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import inspect
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sig = inspect.signature(cls.__init__)
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valid_params = set(sig.parameters.keys()) - {"self", "cls"}
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filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params}
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return filtered_kwargs
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if low_cpu_mem_usage:
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try:
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import re
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from diffusers.models.modeling_utils import load_model_dict_into_meta
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from diffusers.utils import is_accelerate_available
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if is_accelerate_available():
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import accelerate
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# Instantiate model with empty weights
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with accelerate.init_empty_weights():
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model = cls(**filter_kwargs(cls, additional_kwargs))
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param_device = "cpu"
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if pretrained_model_path.endswith(".safetensors"):
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from safetensors.torch import load_file
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state_dict = load_file(pretrained_model_path)
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else:
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state_dict = torch.load(pretrained_model_path, map_location="cpu")
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# move the params from meta device to cpu
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missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
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if len(missing_keys) > 0:
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raise ValueError(
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f"Cannot load {cls} from {pretrained_model_path} because the following keys are"
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f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
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" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
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" those weights or else make sure your checkpoint file is correct."
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)
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unexpected_keys = load_model_dict_into_meta(
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model,
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state_dict,
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device=param_device,
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dtype=torch_dtype,
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model_name_or_path=pretrained_model_path,
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)
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if cls._keys_to_ignore_on_load_unexpected is not None:
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for pat in cls._keys_to_ignore_on_load_unexpected:
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unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
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if len(unexpected_keys) > 0:
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print(
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f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
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)
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return model
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except Exception as e:
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print(f"The low_cpu_mem_usage mode is not work because {e}. Use low_cpu_mem_usage=False instead.")
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model = cls(**filter_kwargs(cls, additional_kwargs))
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if pretrained_model_path.endswith(".safetensors"):
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from safetensors.torch import load_file
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state_dict = load_file(pretrained_model_path)
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else:
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state_dict = torch.load(pretrained_model_path, map_location="cpu")
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m, u = model.load_state_dict(state_dict, strict=False)
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print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
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print(m, u)
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return model
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