281 lines
10 KiB
Python
281 lines
10 KiB
Python
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
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from dataclasses import dataclass
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from torch import nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.models import ModelMixin
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from diffusers.utils import BaseOutput
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from diffusers.models.attention import FeedForward, AdaLayerNorm
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from einops import rearrange, repeat
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@dataclass
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class Transformer3DModelOutput(BaseOutput):
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sample: torch.FloatTensor
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class Transformer3DModel(ModelMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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num_attention_heads: int = 16,
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attention_head_dim: int = 88,
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in_channels: Optional[int] = None,
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num_layers: int = 1,
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dropout: float = 0.0,
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norm_num_groups: int = 32,
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cross_attention_dim: Optional[int] = None,
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attention_bias: bool = False,
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activation_fn: str = "geglu",
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num_embeds_ada_norm: Optional[int] = None,
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use_linear_projection: bool = False,
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only_cross_attention: bool = False,
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upcast_attention: bool = False,
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add_audio_layer=False,
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):
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super().__init__()
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self.use_linear_projection = use_linear_projection
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self.num_attention_heads = num_attention_heads
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self.attention_head_dim = attention_head_dim
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inner_dim = num_attention_heads * attention_head_dim
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# Define input layers
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self.in_channels = in_channels
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self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
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if use_linear_projection:
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self.proj_in = nn.Linear(in_channels, inner_dim)
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else:
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self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
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# Define transformers blocks
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self.transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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inner_dim,
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num_attention_heads,
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attention_head_dim,
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dropout=dropout,
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cross_attention_dim=cross_attention_dim,
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activation_fn=activation_fn,
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num_embeds_ada_norm=num_embeds_ada_norm,
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attention_bias=attention_bias,
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upcast_attention=upcast_attention,
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add_audio_layer=add_audio_layer,
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)
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for d in range(num_layers)
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]
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)
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# Define output layers
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if use_linear_projection:
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self.proj_out = nn.Linear(in_channels, inner_dim)
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else:
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self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
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# Input
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assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
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video_length = hidden_states.shape[2]
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hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
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batch, channel, height, weight = hidden_states.shape
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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if not self.use_linear_projection:
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hidden_states = self.proj_in(hidden_states)
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inner_dim = hidden_states.shape[1]
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hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
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else:
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inner_dim = hidden_states.shape[1]
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hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
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hidden_states = self.proj_in(hidden_states)
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# Blocks
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for block in self.transformer_blocks:
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hidden_states = block(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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timestep=timestep,
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video_length=video_length,
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)
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# Output
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if not self.use_linear_projection:
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hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
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hidden_states = self.proj_out(hidden_states)
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else:
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hidden_states = self.proj_out(hidden_states)
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hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
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output = hidden_states + residual
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output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
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if not return_dict:
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return (output,)
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return Transformer3DModelOutput(sample=output)
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class BasicTransformerBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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dropout=0.0,
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cross_attention_dim: Optional[int] = None,
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activation_fn: str = "geglu",
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num_embeds_ada_norm: Optional[int] = None,
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attention_bias: bool = False,
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upcast_attention: bool = False,
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add_audio_layer=False,
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):
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super().__init__()
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self.use_ada_layer_norm = num_embeds_ada_norm is not None
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self.add_audio_layer = add_audio_layer
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self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
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self.attn1 = Attention(
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query_dim=dim,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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upcast_attention=upcast_attention,
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)
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# Cross-attn
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if add_audio_layer:
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self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
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self.attn2 = Attention(
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query_dim=dim,
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cross_attention_dim=cross_attention_dim,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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upcast_attention=upcast_attention,
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)
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else:
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self.attn2 = None
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# Feed-forward
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self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
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self.norm3 = nn.LayerNorm(dim)
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def forward(
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self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None
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):
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norm_hidden_states = (
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self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
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)
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hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
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if self.attn2 is not None and encoder_hidden_states is not None:
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if encoder_hidden_states.dim() == 4:
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encoder_hidden_states = rearrange(encoder_hidden_states, "b f s d -> (b f) s d")
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norm_hidden_states = (
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self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
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)
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hidden_states = (
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self.attn2(
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norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
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)
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+ hidden_states
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)
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# Feed-forward
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hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
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return hidden_states
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class Attention(nn.Module):
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def __init__(
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self,
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query_dim: int,
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cross_attention_dim: Optional[int] = None,
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heads: int = 8,
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dim_head: int = 64,
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dropout: float = 0.0,
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bias=False,
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upcast_attention: bool = False,
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upcast_softmax: bool = False,
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norm_num_groups: Optional[int] = None,
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):
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super().__init__()
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inner_dim = dim_head * heads
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cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
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self.upcast_attention = upcast_attention
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self.upcast_softmax = upcast_softmax
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self.scale = dim_head**-0.5
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self.heads = heads
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if norm_num_groups is not None:
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self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
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else:
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self.group_norm = None
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self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
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self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
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self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
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self.to_out = nn.ModuleList([])
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self.to_out.append(nn.Linear(inner_dim, query_dim))
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self.to_out.append(nn.Dropout(dropout))
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def split_heads(self, tensor):
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batch_size, seq_len, dim = tensor.shape
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tensor = tensor.reshape(batch_size, seq_len, self.heads, dim // self.heads)
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tensor = tensor.permute(0, 2, 1, 3)
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return tensor
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def concat_heads(self, tensor):
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batch_size, heads, seq_len, head_dim = tensor.shape
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tensor = tensor.permute(0, 2, 1, 3)
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tensor = tensor.reshape(batch_size, seq_len, heads * head_dim)
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return tensor
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def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
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if self.group_norm is not None:
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hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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query = self.to_q(hidden_states)
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query = self.split_heads(query)
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encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
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key = self.to_k(encoder_hidden_states)
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value = self.to_v(encoder_hidden_states)
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key = self.split_heads(key)
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value = self.split_heads(value)
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if attention_mask is not None:
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if attention_mask.shape[-1] != query.shape[1]:
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target_length = query.shape[1]
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attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
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attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
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# Use PyTorch native implementation of FlashAttention-2
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hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask)
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hidden_states = self.concat_heads(hidden_states)
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# linear proj
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hidden_states = self.to_out[0](hidden_states)
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# dropout
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hidden_states = self.to_out[1](hidden_states)
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return hidden_states
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