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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer
import ftfy
import html
import string
import regex as re
def fp16_clamp(x):
if x.dtype == torch.float16 and torch.isinf(x).any():
clamp = torch.finfo(x.dtype).max - 1000
x = torch.clamp(x, min=-clamp, max=clamp)
return x
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(
math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
class T5LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super(T5LayerNorm, self).__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) +
self.eps)
if self.weight.dtype in [torch.float16, torch.bfloat16]:
x = x.type_as(self.weight)
return self.weight * x
class T5Attention(nn.Module):
def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
assert dim_attn % num_heads == 0
super(T5Attention, self).__init__()
self.dim = dim
self.dim_attn = dim_attn
self.num_heads = num_heads
self.head_dim = dim_attn // num_heads
# layers
self.q = nn.Linear(dim, dim_attn, bias=False)
self.k = nn.Linear(dim, dim_attn, bias=False)
self.v = nn.Linear(dim, dim_attn, bias=False)
self.o = nn.Linear(dim_attn, dim, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, x, context=None, mask=None, pos_bias=None):
"""
x: [B, L1, C].
context: [B, L2, C] or None.
mask: [B, L2] or [B, L1, L2] or None.
"""
# check inputs
context = x if context is None else context
b, n, c = x.size(0), self.num_heads, self.head_dim
# compute query, key, value
q = self.q(x).view(b, -1, n, c)
k = self.k(context).view(b, -1, n, c)
v = self.v(context).view(b, -1, n, c)
# attention bias
attn_bias = x.new_zeros(b, n, q.size(1), k.size(1))
if pos_bias is not None:
attn_bias += pos_bias
if mask is not None:
assert mask.ndim in [2, 3]
mask = mask.view(b, 1, 1,
-1) if mask.ndim == 2 else mask.unsqueeze(1)
attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min)
# compute attention (T5 does not use scaling)
attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias
attn = F.softmax(attn.float(), dim=-1).type_as(attn)
x = torch.einsum('bnij,bjnc->binc', attn, v)
# output
x = x.reshape(b, -1, n * c)
x = self.o(x)
x = self.dropout(x)
return x
class T5FeedForward(nn.Module):
def __init__(self, dim, dim_ffn, dropout=0.1):
super(T5FeedForward, self).__init__()
self.dim = dim
self.dim_ffn = dim_ffn
# layers
self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU())
self.fc1 = nn.Linear(dim, dim_ffn, bias=False)
self.fc2 = nn.Linear(dim_ffn, dim, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.fc1(x) * self.gate(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.dropout(x)
return x
class T5SelfAttention(nn.Module):
def __init__(self,
dim,
dim_attn,
dim_ffn,
num_heads,
num_buckets,
shared_pos=True,
dropout=0.1):
super(T5SelfAttention, self).__init__()
self.dim = dim
self.dim_attn = dim_attn
self.dim_ffn = dim_ffn
self.num_heads = num_heads
self.num_buckets = num_buckets
self.shared_pos = shared_pos
# layers
self.norm1 = T5LayerNorm(dim)
self.attn = T5Attention(dim, dim_attn, num_heads, dropout)
self.norm2 = T5LayerNorm(dim)
self.ffn = T5FeedForward(dim, dim_ffn, dropout)
self.pos_embedding = None if shared_pos else T5RelativeEmbedding(
num_buckets, num_heads, bidirectional=True)
def forward(self, x, mask=None, pos_bias=None):
e = pos_bias if self.shared_pos else self.pos_embedding(
x.size(1), x.size(1))
x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e))
x = fp16_clamp(x + self.ffn(self.norm2(x)))
return x
class T5RelativeEmbedding(nn.Module):
def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
super(T5RelativeEmbedding, self).__init__()
self.num_buckets = num_buckets
self.num_heads = num_heads
self.bidirectional = bidirectional
self.max_dist = max_dist
# layers
self.embedding = nn.Embedding(num_buckets, num_heads)
def forward(self, lq, lk):
device = self.embedding.weight.device
# rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \
# torch.arange(lq).unsqueeze(1).to(device)
rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \
torch.arange(lq, device=device).unsqueeze(1)
rel_pos = self._relative_position_bucket(rel_pos)
rel_pos_embeds = self.embedding(rel_pos)
rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze(
0) # [1, N, Lq, Lk]
return rel_pos_embeds.contiguous()
def _relative_position_bucket(self, rel_pos):
# preprocess
if self.bidirectional:
num_buckets = self.num_buckets // 2
rel_buckets = (rel_pos > 0).long() * num_buckets
rel_pos = torch.abs(rel_pos)
else:
num_buckets = self.num_buckets
rel_buckets = 0
rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos))
# embeddings for small and large positions
max_exact = num_buckets // 2
rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) /
math.log(self.max_dist / max_exact) *
(num_buckets - max_exact)).long()
rel_pos_large = torch.min(
rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1))
rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large)
return rel_buckets
def init_weights(m):
if isinstance(m, T5LayerNorm):
nn.init.ones_(m.weight)
elif isinstance(m, T5FeedForward):
nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5)
nn.init.normal_(m.fc1.weight, std=m.dim**-0.5)
nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5)
elif isinstance(m, T5Attention):
nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5)
nn.init.normal_(m.k.weight, std=m.dim**-0.5)
nn.init.normal_(m.v.weight, std=m.dim**-0.5)
nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5)
elif isinstance(m, T5RelativeEmbedding):
nn.init.normal_(
m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5)
class WanTextEncoder(torch.nn.Module):
def __init__(self,
vocab=256384,
dim=4096,
dim_attn=4096,
dim_ffn=10240,
num_heads=64,
num_layers=24,
num_buckets=32,
shared_pos=False,
dropout=0.1):
super(WanTextEncoder, self).__init__()
self.dim = dim
self.dim_attn = dim_attn
self.dim_ffn = dim_ffn
self.num_heads = num_heads
self.num_layers = num_layers
self.num_buckets = num_buckets
self.shared_pos = shared_pos
# layers
self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \
else nn.Embedding(vocab, dim)
self.pos_embedding = T5RelativeEmbedding(
num_buckets, num_heads, bidirectional=True) if shared_pos else None
self.dropout = nn.Dropout(dropout)
self.blocks = nn.ModuleList([
T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets,
shared_pos, dropout) for _ in range(num_layers)
])
self.norm = T5LayerNorm(dim)
# initialize weights
self.apply(init_weights)
def forward(self, ids, mask=None):
x = self.token_embedding(ids)
x = self.dropout(x)
e = self.pos_embedding(x.size(1),
x.size(1)) if self.shared_pos else None
for block in self.blocks:
x = block(x, mask, pos_bias=e)
x = self.norm(x)
x = self.dropout(x)
return x
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def whitespace_clean(text):
text = re.sub(r'\s+', ' ', text)
text = text.strip()
return text
def canonicalize(text, keep_punctuation_exact_string=None):
text = text.replace('_', ' ')
if keep_punctuation_exact_string:
text = keep_punctuation_exact_string.join(
part.translate(str.maketrans('', '', string.punctuation))
for part in text.split(keep_punctuation_exact_string))
else:
text = text.translate(str.maketrans('', '', string.punctuation))
text = text.lower()
text = re.sub(r'\s+', ' ', text)
return text.strip()
class HuggingfaceTokenizer:
def __init__(self, name, seq_len=None, clean=None, **kwargs):
assert clean in (None, 'whitespace', 'lower', 'canonicalize')
self.name = name
self.seq_len = seq_len
self.clean = clean
# init tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs)
self.vocab_size = self.tokenizer.vocab_size
def __call__(self, sequence, **kwargs):
return_mask = kwargs.pop('return_mask', False)
# arguments
_kwargs = {'return_tensors': 'pt'}
if self.seq_len is not None:
_kwargs.update({
'padding': 'max_length',
'truncation': True,
'max_length': self.seq_len
})
_kwargs.update(**kwargs)
# tokenization
if isinstance(sequence, str):
sequence = [sequence]
if self.clean:
sequence = [self._clean(u) for u in sequence]
ids = self.tokenizer(sequence, **_kwargs)
# output
if return_mask:
return ids.input_ids, ids.attention_mask
else:
return ids.input_ids
def _clean(self, text):
if self.clean == 'whitespace':
text = whitespace_clean(basic_clean(text))
elif self.clean == 'lower':
text = whitespace_clean(basic_clean(text)).lower()
elif self.clean == 'canonicalize':
text = canonicalize(basic_clean(text))
return text |