604 lines
24 KiB
Python
604 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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from fastvideo.v1.attention import DistributedAttention, LocalAttention
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from fastvideo.v1.configs.models.dits import WanVideoConfig
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from fastvideo.v1.configs.sample.wan import WanTeaCacheParams
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from fastvideo.v1.distributed.parallel_state import (
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get_sequence_model_parallel_world_size)
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from fastvideo.v1.forward_context import get_forward_context
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from fastvideo.v1.layers.layernorm import (LayerNormScaleShift, RMSNorm,
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ScaleResidual,
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ScaleResidualLayerNormScaleShift)
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from fastvideo.v1.layers.linear import ReplicatedLinear
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# from torch.nn import RMSNorm
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# TODO: RMSNorm ....
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from fastvideo.v1.layers.mlp import MLP
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from fastvideo.v1.layers.rotary_embedding import (_apply_rotary_emb,
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get_rotary_pos_embed)
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from fastvideo.v1.layers.visual_embedding import (ModulateProjection,
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PatchEmbed, TimestepEmbedder)
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from fastvideo.v1.models.dits.base import CachableDiT
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from fastvideo.v1.platforms import _Backend
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class WanImageEmbedding(torch.nn.Module):
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def __init__(self, in_features: int, out_features: int):
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super().__init__()
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self.norm1 = nn.LayerNorm(in_features)
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self.ff = MLP(in_features, in_features, out_features, act_type="gelu")
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self.norm2 = nn.LayerNorm(out_features)
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def forward(self,
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encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
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dtype = encoder_hidden_states_image.dtype
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hidden_states = self.norm1(encoder_hidden_states_image)
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hidden_states = self.ff(hidden_states)
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hidden_states = self.norm2(hidden_states).to(dtype)
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return hidden_states
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class WanTimeTextImageEmbedding(nn.Module):
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def __init__(
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self,
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dim: int,
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time_freq_dim: int,
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text_embed_dim: int,
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image_embed_dim: int | None = None,
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):
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super().__init__()
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self.time_embedder = TimestepEmbedder(
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dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
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self.time_modulation = ModulateProjection(dim,
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factor=6,
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act_layer="silu")
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self.text_embedder = MLP(text_embed_dim,
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dim,
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dim,
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bias=True,
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act_type="gelu_pytorch_tanh")
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self.image_embedder = None
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if image_embed_dim is not None:
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self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
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def forward(
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self,
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timestep: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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encoder_hidden_states_image: torch.Tensor | None = None,
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):
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temb = self.time_embedder(timestep)
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timestep_proj = self.time_modulation(temb)
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encoder_hidden_states = self.text_embedder(encoder_hidden_states)
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if encoder_hidden_states_image is not None:
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assert self.image_embedder is not None
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encoder_hidden_states_image = self.image_embedder(
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encoder_hidden_states_image)
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return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
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class WanSelfAttention(nn.Module):
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def __init__(self,
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dim: int,
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num_heads: int,
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window_size=(-1, -1),
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qk_norm=True,
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eps=1e-6,
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parallel_attention=False) -> None:
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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.window_size = window_size
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self.qk_norm = qk_norm
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self.eps = eps
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self.parallel_attention = parallel_attention
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# layers
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self.to_q = ReplicatedLinear(dim, dim)
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self.to_k = ReplicatedLinear(dim, dim)
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self.to_v = ReplicatedLinear(dim, dim)
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self.to_out = ReplicatedLinear(dim, dim)
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self.norm_q = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_k = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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# Scaled dot product attention
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self.attn = LocalAttention(
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num_heads=num_heads,
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head_size=self.head_dim,
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dropout_rate=0,
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softmax_scale=None,
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causal=False,
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supported_attention_backends=(_Backend.FLASH_ATTN,
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_Backend.TORCH_SDPA))
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def forward(self, x: torch.Tensor, context: torch.Tensor,
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context_lens: int):
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r"""
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Args:
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x(Tensor): Shape [B, L, num_heads, C / num_heads]
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seq_lens(Tensor): Shape [B]
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grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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"""
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pass
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class WanT2VCrossAttention(WanSelfAttention):
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def forward(self, x, context, context_lens):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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context(Tensor): Shape [B, L2, C]
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context_lens(Tensor): Shape [B]
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"""
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b, n, d = x.size(0), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
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k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
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v = self.to_v(context)[0].view(b, -1, n, d)
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# compute attention
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x = self.attn(q, k, v)
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# output
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x = x.flatten(2)
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x, _ = self.to_out(x)
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return x
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class WanI2VCrossAttention(WanSelfAttention):
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def __init__(
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self,
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dim: int,
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num_heads: int,
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window_size=(-1, -1),
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qk_norm=True,
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eps=1e-6,
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supported_attention_backends: tuple[_Backend, ...] | None = None
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) -> None:
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super().__init__(dim, num_heads, window_size, qk_norm, eps,
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supported_attention_backends)
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self.add_k_proj = ReplicatedLinear(dim, dim)
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self.add_v_proj = ReplicatedLinear(dim, dim)
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self.norm_added_k = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_added_q = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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def forward(self, x, context, context_lens):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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context(Tensor): Shape [B, L2, C]
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context_lens(Tensor): Shape [B]
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"""
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context_img = context[:, :257]
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context = context[:, 257:]
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b, n, d = x.size(0), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
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k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
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v = self.to_v(context)[0].view(b, -1, n, d)
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k_img = self.norm_added_k(self.add_k_proj(context_img)[0]).view(
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b, -1, n, d)
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v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
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img_x = self.attn(q, k_img, v_img)
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# compute attention
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x = self.attn(q, k, v)
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# output
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x = x.flatten(2)
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img_x = img_x.flatten(2)
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x = x + img_x
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x, _ = self.to_out(x)
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return x
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class WanTransformerBlock(nn.Module):
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def __init__(self,
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dim: int,
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ffn_dim: int,
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num_heads: int,
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qk_norm: str = "rms_norm_across_heads",
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cross_attn_norm: bool = False,
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eps: float = 1e-6,
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added_kv_proj_dim: int | None = None,
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supported_attention_backends: tuple[_Backend, ...]
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prefix: str = ""):
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super().__init__()
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# 1. Self-attention
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self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
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self.to_q = ReplicatedLinear(dim, dim, bias=True)
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self.to_k = ReplicatedLinear(dim, dim, bias=True)
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self.to_v = ReplicatedLinear(dim, dim, bias=True)
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self.to_out = ReplicatedLinear(dim, dim, bias=True)
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self.attn1 = DistributedAttention(
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num_heads=num_heads,
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head_size=dim // num_heads,
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causal=False,
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supported_attention_backends=supported_attention_backends,
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prefix=f"{prefix}.attn1")
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self.hidden_dim = dim
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self.num_attention_heads = num_heads
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dim_head = dim // num_heads
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if qk_norm == "rms_norm":
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self.norm_q = RMSNorm(dim_head, eps=eps)
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self.norm_k = RMSNorm(dim_head, eps=eps)
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elif qk_norm == "rms_norm_across_heads":
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# LTX applies qk norm across all heads
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self.norm_q = RMSNorm(dim, eps=eps)
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self.norm_k = RMSNorm(dim, eps=eps)
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else:
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print("QK Norm type not supported")
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raise Exception
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assert cross_attn_norm is True
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self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(
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dim,
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norm_type="layer",
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eps=eps,
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elementwise_affine=True,
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dtype=torch.float32)
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# 2. Cross-attention
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if added_kv_proj_dim is not None:
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# I2V
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self.attn2 = WanI2VCrossAttention(dim,
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num_heads,
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qk_norm=qk_norm,
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eps=eps)
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else:
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# T2V
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self.attn2 = WanT2VCrossAttention(dim,
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num_heads,
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qk_norm=qk_norm,
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eps=eps)
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self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
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dim,
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norm_type="layer",
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eps=eps,
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elementwise_affine=False,
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dtype=torch.float32)
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# 3. Feed-forward
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self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
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self.mlp_residual = ScaleResidual()
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self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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temb: torch.Tensor,
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freqs_cis: tuple[torch.Tensor, torch.Tensor],
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) -> torch.Tensor:
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if hidden_states.dim() == 4:
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hidden_states = hidden_states.squeeze(1)
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bs, seq_length, _ = hidden_states.shape
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orig_dtype = hidden_states.dtype
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assert orig_dtype != torch.float32
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e = self.scale_shift_table + temb.float()
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shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
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6, dim=1)
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assert shift_msa.dtype == torch.float32
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# 1. Self-attention
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norm_hidden_states = (self.norm1(hidden_states.float()) *
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(1 + scale_msa) + shift_msa).to(orig_dtype)
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query, _ = self.to_q(norm_hidden_states)
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key, _ = self.to_k(norm_hidden_states)
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value, _ = self.to_v(norm_hidden_states)
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if self.norm_q is not None:
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query = self.norm_q.forward_native(query)
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if self.norm_k is not None:
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key = self.norm_k.forward_native(key)
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query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
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key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
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value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
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# Apply rotary embeddings
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cos, sin = freqs_cis
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query, key = _apply_rotary_emb(query, cos, sin,
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is_neox_style=False), _apply_rotary_emb(
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key, cos, sin, is_neox_style=False)
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attn_output, _ = self.attn1(query, key, value)
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attn_output = attn_output.flatten(2)
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attn_output, _ = self.to_out(attn_output)
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attn_output = attn_output.squeeze(1)
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null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
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norm_hidden_states, hidden_states = self.self_attn_residual_norm(
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hidden_states, attn_output, gate_msa, null_shift, null_scale)
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norm_hidden_states, hidden_states = norm_hidden_states.to(
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orig_dtype), hidden_states.to(orig_dtype)
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# 2. Cross-attention
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attn_output = self.attn2(norm_hidden_states,
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context=encoder_hidden_states,
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context_lens=None)
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norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
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hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
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norm_hidden_states, hidden_states = norm_hidden_states.to(
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orig_dtype), hidden_states.to(orig_dtype)
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# 3. Feed-forward
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ff_output = self.ffn(norm_hidden_states)
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hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
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hidden_states = hidden_states.to(orig_dtype)
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return hidden_states
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class WanTransformer3DModel(CachableDiT):
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_fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
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_compile_conditions = WanVideoConfig()._compile_conditions
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_supported_attention_backends = WanVideoConfig(
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)._supported_attention_backends
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_param_names_mapping = WanVideoConfig()._param_names_mapping
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def __init__(self, config: WanVideoConfig) -> None:
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super().__init__(config=config)
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inner_dim = config.num_attention_heads * config.attention_head_dim
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self.hidden_size = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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self.in_channels = config.in_channels
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self.out_channels = config.out_channels
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self.num_channels_latents = config.num_channels_latents
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self.patch_size = config.patch_size
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self.text_len = config.text_len
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# 1. Patch & position embedding
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self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
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embed_dim=inner_dim,
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patch_size=config.patch_size,
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flatten=False)
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# 2. Condition embeddings
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self.condition_embedder = WanTimeTextImageEmbedding(
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dim=inner_dim,
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time_freq_dim=config.freq_dim,
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text_embed_dim=config.text_dim,
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image_embed_dim=config.image_dim,
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)
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# 3. Transformer blocks
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self.blocks = nn.ModuleList([
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WanTransformerBlock(inner_dim,
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config.ffn_dim,
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config.num_attention_heads,
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config.qk_norm,
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config.cross_attn_norm,
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config.eps,
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config.added_kv_proj_dim,
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self._supported_attention_backends,
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prefix=f"{config.prefix}.blocks.{i}")
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for i in range(config.num_layers)
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])
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# 4. Output norm & projection
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self.norm_out = LayerNormScaleShift(inner_dim,
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norm_type="layer",
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eps=config.eps,
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elementwise_affine=False,
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dtype=torch.float32)
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self.proj_out = nn.Linear(
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inner_dim, config.out_channels * math.prod(config.patch_size))
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self.scale_shift_table = nn.Parameter(
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torch.randn(1, 2, inner_dim) / inner_dim**0.5)
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self.gradient_checkpointing = False
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self.__post_init__()
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def forward(self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor | list[torch.Tensor],
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timestep: torch.LongTensor,
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encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
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| None = None,
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guidance=None,
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**kwargs) -> torch.Tensor:
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forward_batch = get_forward_context().forward_batch
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enable_teacache = forward_batch is not None and forward_batch.enable_teacache
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orig_dtype = hidden_states.dtype
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if not isinstance(encoder_hidden_states, torch.Tensor):
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encoder_hidden_states = encoder_hidden_states[0]
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if isinstance(encoder_hidden_states_image,
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list) and len(encoder_hidden_states_image) > 0:
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encoder_hidden_states_image = encoder_hidden_states_image[0]
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else:
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encoder_hidden_states_image = None
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batch_size, num_channels, num_frames, height, width = hidden_states.shape
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p_t, p_h, p_w = self.patch_size
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post_patch_num_frames = num_frames // p_t
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post_patch_height = height // p_h
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post_patch_width = width // p_w
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# Get rotary embeddings
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d = self.hidden_size // self.num_attention_heads
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rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
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freqs_cos, freqs_sin = get_rotary_pos_embed(
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(post_patch_num_frames * get_sequence_model_parallel_world_size(),
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post_patch_height, post_patch_width),
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self.hidden_size,
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self.num_attention_heads,
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rope_dim_list,
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dtype=torch.float64,
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rope_theta=10000)
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freqs_cos = freqs_cos.to(hidden_states.device)
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freqs_sin = freqs_sin.to(hidden_states.device)
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freqs_cis = (freqs_cos.float(),
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freqs_sin.float()) if freqs_cos is not None else None
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hidden_states = self.patch_embedding(hidden_states)
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hidden_states = hidden_states.flatten(2).transpose(1, 2)
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temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
|
|
timestep, encoder_hidden_states, encoder_hidden_states_image)
|
|
timestep_proj = timestep_proj.unflatten(1, (6, -1))
|
|
|
|
if encoder_hidden_states_image is not None:
|
|
encoder_hidden_states = torch.concat(
|
|
[encoder_hidden_states_image, encoder_hidden_states], dim=1)
|
|
|
|
assert encoder_hidden_states.dtype == orig_dtype
|
|
|
|
# 4. Transformer blocks
|
|
# if caching is enabled, we might be able to skip the forward pass
|
|
should_skip_forward = self.should_skip_forward_for_cached_states(
|
|
timestep_proj=timestep_proj, temb=temb)
|
|
|
|
if should_skip_forward:
|
|
hidden_states = self.retrieve_cached_states(hidden_states)
|
|
else:
|
|
# if teacache is enabled, we need to cache the original hidden states
|
|
if enable_teacache:
|
|
original_hidden_states = hidden_states.clone()
|
|
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
for block in self.blocks:
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
block, hidden_states, encoder_hidden_states,
|
|
timestep_proj, freqs_cis)
|
|
else:
|
|
for block in self.blocks:
|
|
hidden_states = block(hidden_states, encoder_hidden_states,
|
|
timestep_proj, freqs_cis)
|
|
|
|
# if teacache is enabled, we need to cache the original hidden states
|
|
if enable_teacache:
|
|
self.maybe_cache_states(hidden_states, original_hidden_states)
|
|
|
|
# 5. Output norm, projection & unpatchify
|
|
shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
|
|
dim=1)
|
|
hidden_states = self.norm_out(hidden_states.float(), shift, scale)
|
|
hidden_states = self.proj_out(hidden_states)
|
|
|
|
hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
|
|
post_patch_height,
|
|
post_patch_width, p_t, p_h, p_w,
|
|
-1)
|
|
hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
|
|
output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
|
|
|
|
return output
|
|
|
|
def maybe_cache_states(self, hidden_states: torch.Tensor,
|
|
original_hidden_states: torch.Tensor) -> None:
|
|
if self.is_even:
|
|
self.previous_residual_even = hidden_states.squeeze(
|
|
0) - original_hidden_states
|
|
else:
|
|
self.previous_residual_odd = hidden_states.squeeze(
|
|
0) - original_hidden_states
|
|
|
|
def should_skip_forward_for_cached_states(self, **kwargs) -> bool:
|
|
|
|
forward_context = get_forward_context()
|
|
forward_batch = forward_context.forward_batch
|
|
if forward_batch is None or not forward_batch.enable_teacache:
|
|
return False
|
|
teacache_params = forward_batch.teacache_params
|
|
assert teacache_params is not None, "teacache_params is not initialized"
|
|
assert isinstance(
|
|
teacache_params,
|
|
WanTeaCacheParams), "teacache_params is not a WanTeaCacheParams"
|
|
current_timestep = forward_context.current_timestep
|
|
num_inference_steps = forward_batch.num_inference_steps
|
|
|
|
# initialize the coefficients, cutoff_steps, and ret_steps
|
|
coefficients = teacache_params.coefficients
|
|
use_ret_steps = teacache_params.use_ret_steps
|
|
cutoff_steps = teacache_params.get_cutoff_steps(num_inference_steps)
|
|
ret_steps = teacache_params.ret_steps
|
|
teacache_thresh = teacache_params.teacache_thresh
|
|
|
|
if current_timestep == 0:
|
|
self.cnt = 0
|
|
|
|
timestep_proj = kwargs["timestep_proj"]
|
|
temb = kwargs["temb"]
|
|
modulated_inp = timestep_proj if use_ret_steps else temb
|
|
|
|
if self.cnt % 2 == 0: # even -> condition
|
|
self.is_even = True
|
|
if self.cnt < ret_steps or self.cnt >= cutoff_steps:
|
|
self.should_calc_even = True
|
|
self.accumulated_rel_l1_distance_even = 0
|
|
else:
|
|
assert self.previous_e0_even is not None, "previous_e0_even is not initialized"
|
|
assert self.accumulated_rel_l1_distance_even is not None, "accumulated_rel_l1_distance_even is not initialized"
|
|
rescale_func = np.poly1d(coefficients)
|
|
self.accumulated_rel_l1_distance_even += rescale_func(
|
|
((modulated_inp - self.previous_e0_even).abs().mean() /
|
|
self.previous_e0_even.abs().mean()).cpu().item())
|
|
if self.accumulated_rel_l1_distance_even < teacache_thresh:
|
|
self.should_calc_even = False
|
|
else:
|
|
self.should_calc_even = True
|
|
self.accumulated_rel_l1_distance_even = 0
|
|
self.previous_e0_even = modulated_inp.clone()
|
|
|
|
else: # odd -> unconditon
|
|
self.is_even = False
|
|
if self.cnt < ret_steps or self.cnt >= cutoff_steps:
|
|
self.should_calc_odd = True
|
|
self.accumulated_rel_l1_distance_odd = 0
|
|
else:
|
|
assert self.previous_e0_odd is not None, "previous_e0_odd is not initialized"
|
|
assert self.accumulated_rel_l1_distance_odd is not None, "accumulated_rel_l1_distance_odd is not initialized"
|
|
rescale_func = np.poly1d(coefficients)
|
|
self.accumulated_rel_l1_distance_odd += rescale_func(
|
|
((modulated_inp - self.previous_e0_odd).abs().mean() /
|
|
self.previous_e0_odd.abs().mean()).cpu().item())
|
|
if self.accumulated_rel_l1_distance_odd < teacache_thresh:
|
|
self.should_calc_odd = False
|
|
else:
|
|
self.should_calc_odd = True
|
|
self.accumulated_rel_l1_distance_odd = 0
|
|
self.previous_e0_odd = modulated_inp.clone()
|
|
self.cnt += 1
|
|
should_skip_forward = False
|
|
if self.is_even:
|
|
if not self.should_calc_even:
|
|
should_skip_forward = True
|
|
else:
|
|
if not self.should_calc_odd:
|
|
should_skip_forward = True
|
|
|
|
return should_skip_forward
|
|
|
|
def retrieve_cached_states(self,
|
|
hidden_states: torch.Tensor) -> torch.Tensor:
|
|
if self.is_even:
|
|
return hidden_states + self.previous_residual_even
|
|
else:
|
|
return hidden_states + self.previous_residual_odd
|