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from functools import partial
import torch
import torch.nn as nn
from typing import Any, Dict, Optional, Tuple, Union
import torch.nn.functional as F
assert hasattr(F, "scaled_dot_product_attention")
from diffusers.models.attention import Attention, FeedForward
from diffusers.models.attention_processor import CogVideoXAttnProcessor2_0, JointAttnProcessor2_0
class CogVideoXBlock(nn.Module):
r"""
Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`):
The number of channels in each head.
time_embed_dim (`int`):
The number of channels in timestep embedding.
dropout (`float`, defaults to `0.0`):
The dropout probability to use.
activation_fn (`str`, defaults to `"gelu-approximate"`):
Activation function to be used in feed-forward.
attention_bias (`bool`, defaults to `False`):
Whether or not to use bias in attention projection layers.
qk_norm (`bool`, defaults to `True`):
Whether or not to use normalization after query and key projections in Attention.
norm_elementwise_affine (`bool`, defaults to `True`):
Whether to use learnable elementwise affine parameters for normalization.
norm_eps (`float`, defaults to `1e-5`):
Epsilon value for normalization layers.
final_dropout (`bool` defaults to `False`):
Whether to apply a final dropout after the last feed-forward layer.
ff_inner_dim (`int`, *optional*, defaults to `None`):
Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
ff_bias (`bool`, defaults to `True`):
Whether or not to use bias in Feed-forward layer.
attention_out_bias (`bool`, defaults to `True`):
Whether or not to use bias in Attention output projection layer.
"""
def __init__(
self,
dim: int,
num_heads: int,
# num_attention_heads: int,
# attention_head_dim: int,
# time_embed_dim: int,
dropout: float = 0.0,
activation_fn: str = "gelu-approximate",
attention_bias: bool = False,
qk_norm: bool = True,
norm_elementwise_affine: bool = True,
eps: float = 1e-5,
# norm_eps: float = 1e-5,
final_dropout: bool = True,
ff_inner_dim: Optional[int] = None,
ff_bias: bool = True,
attention_out_bias: bool = True,
):
super().__init__()
norm_eps = eps
num_attention_heads = num_heads
attention_head_dim = dim // num_attention_heads
assert attention_head_dim * num_attention_heads == dim
# 1. Self Attention
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps, bias=True)
self.norm1_context = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps, bias=True)
self.attn1 = Attention(
query_dim=dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
qk_norm="layer_norm" if qk_norm else None,
eps=1e-6,
bias=attention_bias,
out_bias=attention_out_bias,
processor=CogVideoXAttnProcessor2_0(),
)
# 2. Feed Forward
self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps, bias=True)
self.norm2_context = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps, bias=True)
self.ff = FeedForward(
dim,
dropout=dropout,
activation_fn=activation_fn,
final_dropout=final_dropout,
inner_dim=ff_inner_dim,
bias=ff_bias,
)
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor = None,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
) -> torch.Tensor:
text_seq_length = encoder_hidden_states.size(1)
# norm & modulate
# norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
# hidden_states, encoder_hidden_states, temb
# )
norm_hidden_states = self.norm1(hidden_states)
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states)
# attention
attn_hidden_states, attn_encoder_hidden_states = self.attn1(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
)
hidden_states = hidden_states + attn_hidden_states
encoder_hidden_states = encoder_hidden_states + attn_encoder_hidden_states
# norm & modulate
# norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
# hidden_states, encoder_hidden_states, temb
# )
norm_hidden_states = self.norm2(hidden_states)
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
# feed-forward
norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
ff_output = self.ff(norm_hidden_states)
hidden_states = hidden_states + ff_output[:, text_seq_length:]
encoder_hidden_states = encoder_hidden_states + ff_output[:, :text_seq_length]
return hidden_states, encoder_hidden_states
def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
# "feed_forward_chunk_size" can be used to save memory
if hidden_states.shape[chunk_dim] % chunk_size != 0:
raise ValueError(
f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
)
num_chunks = hidden_states.shape[chunk_dim] // chunk_size
ff_output = torch.cat(
[ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
dim=chunk_dim,
)
return ff_output
class QKNormJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size = encoder_hidden_states.shape[0]
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
# attention
query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
return hidden_states, encoder_hidden_states
class SD3JointTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
processing of `context` conditions.
"""
def __init__(
self,
dim: int,
num_heads: int,
eps: float,
# num_attention_heads: int,
# attention_head_dim: int,
context_pre_only: bool = False,
qk_norm: Optional[str] = None,
use_dual_attention: bool = False,
):
super().__init__()
num_attention_heads = num_heads
attention_head_dim = dim // num_attention_heads
assert attention_head_dim * num_attention_heads == dim
self.use_dual_attention = use_dual_attention
self.context_pre_only = context_pre_only
# context_norm_type = "ada_norm_continous" if context_pre_only else "ada_norm_zero"
# if use_dual_attention:
# self.norm1 = SD35AdaLayerNormZeroX(dim)
# else:
# self.norm1 = AdaLayerNormZero(dim)
self.norm1 = nn.LayerNorm(dim)
# if context_norm_type == "ada_norm_continous":
# self.norm1_context = AdaLayerNormContinuous(
# dim, dim, elementwise_affine=False, eps=1e-6, bias=True, norm_type="layer_norm"
# )
# elif context_norm_type == "ada_norm_zero":
# self.norm1_context = AdaLayerNormZero(dim)
# else:
# raise ValueError(
# f"Unknown context_norm_type: {context_norm_type}, currently only support `ada_norm_continous`, `ada_norm_zero`"
# )
# self.norm1_context = AdaLayerNormZero(dim)
self.norm1_context = nn.LayerNorm(dim)
processor = JointAttnProcessor2_0()
self.attn = Attention(
query_dim=dim,
cross_attention_dim=None,
added_kv_proj_dim=dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
context_pre_only=context_pre_only,
bias=True,
processor=processor,
qk_norm=qk_norm,
eps=eps,
)
if use_dual_attention:
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
bias=True,
processor=processor,
qk_norm=qk_norm,
eps=eps,
)
else:
self.attn2 = None
self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
if not context_pre_only:
self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
else:
self.norm2_context = None
self.ff_context = None
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
# Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self, hidden_states: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor, temb: torch.FloatTensor=None
):
# if self.use_dual_attention:
# norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(
# hidden_states, emb=temb
# )
# else:
# norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
# if self.context_pre_only:
# norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
# else:
# norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
# encoder_hidden_states, emb=temb
# )
norm_hidden_states = self.norm1(hidden_states)
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states)
# Attention.
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states, encoder_hidden_states=norm_encoder_hidden_states
)
# Process attention outputs for the `hidden_states`.
# attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = hidden_states + attn_output
if self.use_dual_attention:
attn_output2 = self.attn2(hidden_states=norm_hidden_states)
# attn_output2 = gate_msa2.unsqueeze(1) * attn_output2
hidden_states = hidden_states + attn_output2
norm_hidden_states = self.norm2(hidden_states)
# norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
# ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = hidden_states + ff_output
# Process attention outputs for the `encoder_hidden_states`.
if self.context_pre_only:
encoder_hidden_states = None
else:
# context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
encoder_hidden_states = encoder_hidden_states + context_attn_output
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
# norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
context_ff_output = _chunked_feed_forward(
self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size
)
else:
context_ff_output = self.ff_context(norm_encoder_hidden_states)
# encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
encoder_hidden_states = encoder_hidden_states + context_ff_output
return hidden_states, encoder_hidden_states |