wan/models/wan_text_encoder.py

# Modified from https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/t5.py
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin
from diffusers.loaders.single_file_model import FromOriginalModelMixin
from diffusers.models.modeling_utils import ModelMixin


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


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 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 T5CrossAttention(nn.Module):
    def __init__(self,
                 dim,
                 dim_attn,
                 dim_ffn,
                 num_heads,
                 num_buckets,
                 shared_pos=True,
                 dropout=0.1):
        super(T5CrossAttention, 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.self_attn = T5Attention(dim, dim_attn, num_heads, dropout)
        self.norm2 = T5LayerNorm(dim)
        self.cross_attn = T5Attention(dim, dim_attn, num_heads, dropout)
        self.norm3 = T5LayerNorm(dim)
        self.ffn = T5FeedForward(dim, dim_ffn, dropout)
        self.pos_embedding = None if shared_pos else T5RelativeEmbedding(
            num_buckets, num_heads, bidirectional=False)

    def forward(self,
                x,
                mask=None,
                encoder_states=None,
                encoder_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.self_attn(self.norm1(x), mask=mask, pos_bias=e))
        x = fp16_clamp(x + self.cross_attn(
            self.norm2(x), context=encoder_states, mask=encoder_mask))
        x = fp16_clamp(x + self.ffn(self.norm3(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)
        if torch.device(type="meta") != device:
            rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \
                torch.arange(lq, device=device).unsqueeze(1)
        else:
            rel_pos = torch.arange(lk).unsqueeze(0) - \
                torch.arange(lq).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

class WanT5EncoderModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
    def __init__(self,
                 vocab,
                 dim,
                 dim_attn,
                 dim_ffn,
                 num_heads,
                 num_layers,
                 num_buckets,
                 shared_pos=True,
                 dropout=0.1):
        super(WanT5EncoderModel, 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, 
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ):
        x = self.token_embedding(input_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, attention_mask, pos_bias=e)
        x = self.norm(x)
        x = self.dropout(x)
        return (x, )
    
    @classmethod
    def from_pretrained(cls, pretrained_model_path, additional_kwargs={}, low_cpu_mem_usage=False, torch_dtype=torch.bfloat16):
        def filter_kwargs(cls, kwargs):
            import inspect
            sig = inspect.signature(cls.__init__)
            valid_params = set(sig.parameters.keys()) - {'self', 'cls'}
            filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params}
            return filtered_kwargs
    
        if low_cpu_mem_usage:
            try:
                import re

                from diffusers.models.modeling_utils import \
                    load_model_dict_into_meta
                from diffusers.utils import is_accelerate_available
                if is_accelerate_available():
                    import accelerate
                
                # Instantiate model with empty weights
                with accelerate.init_empty_weights():
                    model = cls(**filter_kwargs(cls, additional_kwargs))

                param_device = "cpu"
                if pretrained_model_path.endswith(".safetensors"):
                    from safetensors.torch import load_file
                    state_dict = load_file(pretrained_model_path)
                else:
                    state_dict = torch.load(pretrained_model_path, map_location="cpu")
                # move the params from meta device to cpu
                missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
                if len(missing_keys) > 0:
                    raise ValueError(
                        f"Cannot load {cls} from {pretrained_model_path} because the following keys are"
                        f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
                        " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
                        " those weights or else make sure your checkpoint file is correct."
                    )

                unexpected_keys = load_model_dict_into_meta(
                    model,
                    state_dict,
                    device=param_device,
                    dtype=torch_dtype,
                    model_name_or_path=pretrained_model_path,
                )

                if cls._keys_to_ignore_on_load_unexpected is not None:
                    for pat in cls._keys_to_ignore_on_load_unexpected:
                        unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

                if len(unexpected_keys) > 0:
                    print(
                        f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
                    )
                return model
            except Exception as e:
                print(
                    f"The low_cpu_mem_usage mode is not work because {e}. Use low_cpu_mem_usage=False instead."
                )