mirror of
https://github.com/Wan-Video/Wan2.1.git
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514 lines
16 KiB
Python
514 lines
16 KiB
Python
# Modified from transformers.models.t5.modeling_t5
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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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from .tokenizers import HuggingfaceTokenizer
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__all__ = [
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'T5Model',
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'T5Encoder',
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'T5Decoder',
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'T5EncoderModel',
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]
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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, T5Model):
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nn.init.normal_(m.token_embedding.weight, std=1.0)
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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_(
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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(
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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) +
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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,
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-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,
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dim,
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dim_attn,
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dim_ffn,
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num_heads,
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num_buckets,
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shared_pos=True,
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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(
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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(
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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,
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dim,
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dim_attn,
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dim_ffn,
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num_heads,
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num_buckets,
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shared_pos=True,
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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(
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num_buckets, num_heads, bidirectional=False)
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def forward(self,
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x,
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mask=None,
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encoder_states=None,
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encoder_mask=None,
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pos_bias=None):
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e = pos_bias if self.shared_pos else self.pos_embedding(
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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(
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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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rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \
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torch.arange(lq, device=device).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(
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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 = max_exact + (torch.log(rel_pos.float() / max_exact) /
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math.log(self.max_dist / max_exact) *
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(num_buckets - max_exact)).long()
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rel_pos_large = torch.min(
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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 T5Encoder(nn.Module):
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def __init__(self,
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vocab,
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dim,
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dim_attn,
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dim_ffn,
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num_heads,
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num_layers,
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num_buckets,
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shared_pos=True,
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dropout=0.1):
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super(T5Encoder, 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) \
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else nn.Embedding(vocab, dim)
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self.pos_embedding = T5RelativeEmbedding(
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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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T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets,
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shared_pos, dropout) for _ in range(num_layers)
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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(self, ids, mask=None):
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x = self.token_embedding(ids)
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x = self.dropout(x)
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e = self.pos_embedding(x.size(1),
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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, 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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class T5Decoder(nn.Module):
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def __init__(self,
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vocab,
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dim,
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dim_attn,
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dim_ffn,
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num_heads,
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num_layers,
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num_buckets,
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shared_pos=True,
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dropout=0.1):
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super(T5Decoder, 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) \
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else nn.Embedding(vocab, dim)
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self.pos_embedding = T5RelativeEmbedding(
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num_buckets, num_heads, bidirectional=False) 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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T5CrossAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets,
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shared_pos, dropout) for _ in range(num_layers)
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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(self, ids, mask=None, encoder_states=None, encoder_mask=None):
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b, s = ids.size()
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# causal mask
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if mask is None:
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mask = torch.tril(torch.ones(1, s, s).to(ids.device))
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elif mask.ndim == 2:
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mask = torch.tril(mask.unsqueeze(1).expand(-1, s, -1))
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# layers
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x = self.token_embedding(ids)
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x = self.dropout(x)
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e = self.pos_embedding(x.size(1),
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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, mask, encoder_states, encoder_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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class T5Model(nn.Module):
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def __init__(self,
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vocab_size,
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dim,
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dim_attn,
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dim_ffn,
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num_heads,
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encoder_layers,
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decoder_layers,
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num_buckets,
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shared_pos=True,
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dropout=0.1):
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super(T5Model, self).__init__()
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self.vocab_size = vocab_size
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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.encoder_layers = encoder_layers
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self.decoder_layers = decoder_layers
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self.num_buckets = num_buckets
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# layers
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self.token_embedding = nn.Embedding(vocab_size, dim)
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self.encoder = T5Encoder(self.token_embedding, dim, dim_attn, dim_ffn,
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num_heads, encoder_layers, num_buckets,
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shared_pos, dropout)
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self.decoder = T5Decoder(self.token_embedding, dim, dim_attn, dim_ffn,
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num_heads, decoder_layers, num_buckets,
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shared_pos, dropout)
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self.head = nn.Linear(dim, vocab_size, bias=False)
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# initialize weights
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self.apply(init_weights)
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def forward(self, encoder_ids, encoder_mask, decoder_ids, decoder_mask):
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x = self.encoder(encoder_ids, encoder_mask)
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x = self.decoder(decoder_ids, decoder_mask, x, encoder_mask)
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x = self.head(x)
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return x
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def _t5(name,
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encoder_only=False,
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decoder_only=False,
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return_tokenizer=False,
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tokenizer_kwargs={},
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dtype=torch.float32,
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device='cpu',
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**kwargs):
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# sanity check
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assert not (encoder_only and decoder_only)
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# params
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if encoder_only:
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model_cls = T5Encoder
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kwargs['vocab'] = kwargs.pop('vocab_size')
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kwargs['num_layers'] = kwargs.pop('encoder_layers')
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_ = kwargs.pop('decoder_layers')
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elif decoder_only:
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model_cls = T5Decoder
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kwargs['vocab'] = kwargs.pop('vocab_size')
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kwargs['num_layers'] = kwargs.pop('decoder_layers')
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_ = kwargs.pop('encoder_layers')
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else:
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model_cls = T5Model
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# init model
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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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# init tokenizer
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if return_tokenizer:
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from .tokenizers import HuggingfaceTokenizer
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tokenizer = HuggingfaceTokenizer(f'google/{name}', **tokenizer_kwargs)
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return model, tokenizer
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else:
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return model
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def umt5_xxl(**kwargs):
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cfg = dict(
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vocab_size=256384,
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dim=4096,
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dim_attn=4096,
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dim_ffn=10240,
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num_heads=64,
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encoder_layers=24,
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decoder_layers=24,
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num_buckets=32,
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shared_pos=False,
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dropout=0.1)
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cfg.update(**kwargs)
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return _t5('umt5-xxl', **cfg)
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class T5EncoderModel:
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def __init__(
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self,
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text_len,
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dtype=torch.bfloat16,
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device=torch.cuda.current_device(),
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checkpoint_path=None,
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tokenizer_path=None,
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shard_fn=None,
|
|
):
|
|
self.text_len = text_len
|
|
self.dtype = dtype
|
|
self.device = device
|
|
self.checkpoint_path = checkpoint_path
|
|
self.tokenizer_path = tokenizer_path
|
|
|
|
# init model
|
|
model = umt5_xxl(
|
|
encoder_only=True,
|
|
return_tokenizer=False,
|
|
dtype=dtype,
|
|
device=device).eval().requires_grad_(False)
|
|
logging.info(f'loading {checkpoint_path}')
|
|
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
|
self.model = model
|
|
if shard_fn is not None:
|
|
self.model = shard_fn(self.model, sync_module_states=False)
|
|
else:
|
|
self.model.to(self.device)
|
|
# init tokenizer
|
|
self.tokenizer = HuggingfaceTokenizer(
|
|
name=tokenizer_path, seq_len=text_len, clean='whitespace')
|
|
|
|
def __call__(self, texts, device):
|
|
ids, mask = self.tokenizer(
|
|
texts, return_mask=True, add_special_tokens=True)
|
|
ids = ids.to(device)
|
|
mask = mask.to(device)
|
|
seq_lens = mask.gt(0).sum(dim=1).long()
|
|
context = self.model(ids, mask)
|
|
return [u[:v] for u, v in zip(context, seq_lens)]
|