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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Callable, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils.import_utils import is_xformers_available
from .cross_attention import CrossAttention
from .embeddings import CombinedTimestepLabelEmbeddings
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
Uses three q, k, v linear layers to compute attention.
Parameters:
channels (`int`): The number of channels in the input and output.
num_head_channels (`int`, *optional*):
The number of channels in each head. If None, then `num_heads` = 1.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for group norm.
rescale_output_factor (`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
eps (`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
"""
# IMPORTANT;TODO(Patrick, William) - this class will be deprecated soon. Do not use it anymore
def __init__(
self,
channels: int,
num_head_channels: Optional[int] = None,
norm_num_groups: int = 32,
rescale_output_factor: float = 1.0,
eps: float = 1e-5,
):
super().__init__()
self.channels = channels
self.num_heads = channels // num_head_channels if num_head_channels is not None else 1
self.num_head_size = num_head_channels
self.group_norm = nn.GroupNorm(num_channels=channels, num_groups=norm_num_groups, eps=eps, affine=True)
# define q,k,v as linear layers
self.query = nn.Linear(channels, channels)
self.key = nn.Linear(channels, channels)
self.value = nn.Linear(channels, channels)
self.rescale_output_factor = rescale_output_factor
self.proj_attn = nn.Linear(channels, channels, 1)
self._use_memory_efficient_attention_xformers = False
self._attention_op = None
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.num_heads
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
return tensor
def reshape_batch_dim_to_heads(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.num_heads
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
def set_use_memory_efficient_attention_xformers(
self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
):
if use_memory_efficient_attention_xformers:
if not is_xformers_available():
raise ModuleNotFoundError(
(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers"
),
name="xformers",
)
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
" only available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
)
except Exception as e:
raise e
self._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
self._attention_op = attention_op
def forward(self, hidden_states):
residual = hidden_states
batch, channel, height, width = hidden_states.shape
# norm
hidden_states = self.group_norm(hidden_states)
hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
# proj to q, k, v
query_proj = self.query(hidden_states)
key_proj = self.key(hidden_states)
value_proj = self.value(hidden_states)
scale = 1 / math.sqrt(self.channels / self.num_heads)
query_proj = self.reshape_heads_to_batch_dim(query_proj)
key_proj = self.reshape_heads_to_batch_dim(key_proj)
value_proj = self.reshape_heads_to_batch_dim(value_proj)
if self._use_memory_efficient_attention_xformers:
# Memory efficient attention
hidden_states = xformers.ops.memory_efficient_attention(
query_proj, key_proj, value_proj, attn_bias=None, op=self._attention_op
)
hidden_states = hidden_states.to(query_proj.dtype)
else:
attention_scores = torch.baddbmm(
torch.empty(
query_proj.shape[0],
query_proj.shape[1],
key_proj.shape[1],
dtype=query_proj.dtype,
device=query_proj.device,
),
query_proj,
key_proj.transpose(-1, -2),
beta=0,
alpha=scale,
)
attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)
hidden_states = torch.bmm(attention_probs, value_proj)
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
# compute next hidden_states
hidden_states = self.proj_attn(hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
# res connect and rescale
hidden_states = (hidden_states + residual) / self.rescale_output_factor
return hidden_states
class BasicTransformerBlock(nn.Module):
r"""
A basic Transformer block.
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.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
num_embeds_ada_norm (:
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
attention_bias (:
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
norm_elementwise_affine: bool = True,
norm_type: str = "layer_norm",
final_dropout: bool = False,
):
super().__init__()
self.only_cross_attention = only_cross_attention
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
)
# 1. Self-Attn
self.attn1 = CrossAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
)
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
# 2. Cross-Attn
if cross_attention_dim is not None:
self.attn2 = CrossAttention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
) # is self-attn if encoder_hidden_states is none
else:
self.attn2 = None
if self.use_ada_layer_norm:
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
elif self.use_ada_layer_norm_zero:
self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
else:
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
if cross_attention_dim is not None:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
self.norm2 = (
AdaLayerNorm(dim, num_embeds_ada_norm)
if self.use_ada_layer_norm
else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
)
else:
self.norm2 = None
# 3. Feed-forward
self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
def forward(
self,
hidden_states,
encoder_hidden_states=None,
timestep=None,
attention_mask=None,
cross_attention_kwargs=None,
class_labels=None,
):
if self.use_ada_layer_norm:
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.use_ada_layer_norm_zero:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
)
else:
norm_hidden_states = self.norm1(hidden_states)
# 1. Self-Attention
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
if self.use_ada_layer_norm_zero:
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = attn_output + hidden_states
if self.attn2 is not None:
norm_hidden_states = (
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
)
# 2. Cross-Attention
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self.use_ada_layer_norm_zero:
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
ff_output = self.ff(norm_hidden_states)
if self.use_ada_layer_norm_zero:
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = ff_output + hidden_states
return hidden_states
class FeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
dim (`int`): The number of channels in the input.
dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
"""
def __init__(
self,
dim: int,
dim_out: Optional[int] = None,
mult: int = 4,
dropout: float = 0.0,
activation_fn: str = "geglu",
final_dropout: bool = False,
):
super().__init__()
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
act_fn = GELU(dim, inner_dim)
if activation_fn == "gelu-approximate":
act_fn = GELU(dim, inner_dim, approximate="tanh")
elif activation_fn == "geglu":
act_fn = GEGLU(dim, inner_dim)
elif activation_fn == "geglu-approximate":
act_fn = ApproximateGELU(dim, inner_dim)
self.net = nn.ModuleList([])
# project in
self.net.append(act_fn)
# project dropout
self.net.append(nn.Dropout(dropout))
# project out
self.net.append(nn.Linear(inner_dim, dim_out))
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(dropout))
def forward(self, hidden_states):
for module in self.net:
hidden_states = module(hidden_states)
return hidden_states
class GELU(nn.Module):
r"""
GELU activation function with tanh approximation support with `approximate="tanh"`.
"""
def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out)
self.approximate = approximate
def gelu(self, gate):
if gate.device.type != "mps":
return F.gelu(gate, approximate=self.approximate)
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype)
def forward(self, hidden_states):
hidden_states = self.proj(hidden_states)
hidden_states = self.gelu(hidden_states)
return hidden_states
class GEGLU(nn.Module):
r"""
A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channels in the output.
"""
def __init__(self, dim_in: int, dim_out: int):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def gelu(self, gate):
if gate.device.type != "mps":
return F.gelu(gate)
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
def forward(self, hidden_states):
hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
return hidden_states * self.gelu(gate)
class ApproximateGELU(nn.Module):
"""
The approximate form of Gaussian Error Linear Unit (GELU)
For more details, see section 2: https://arxiv.org/abs/1606.08415
"""
def __init__(self, dim_in: int, dim_out: int):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out)
def forward(self, x):
x = self.proj(x)
return x * torch.sigmoid(1.702 * x)
class AdaLayerNorm(nn.Module):
"""
Norm layer modified to incorporate timestep embeddings.
"""
def __init__(self, embedding_dim, num_embeddings):
super().__init__()
self.emb = nn.Embedding(num_embeddings, embedding_dim)
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)
def forward(self, x, timestep):
emb = self.linear(self.silu(self.emb(timestep)))
scale, shift = torch.chunk(emb, 2)
x = self.norm(x) * (1 + scale) + shift
return x
class AdaLayerNormZero(nn.Module):
"""
Norm layer adaptive layer norm zero (adaLN-Zero).
"""
def __init__(self, embedding_dim, num_embeddings):
super().__init__()
self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
def forward(self, x, timestep, class_labels, hidden_dtype=None):
emb = self.linear(self.silu(self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)
x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class AdaGroupNorm(nn.Module):
"""
GroupNorm layer modified to incorporate timestep embeddings.
"""
def __init__(
self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5
):
super().__init__()
self.num_groups = num_groups
self.eps = eps
self.act = None
if act_fn == "swish":
self.act = lambda x: F.silu(x)
elif act_fn == "mish":
self.act = nn.Mish()
elif act_fn == "silu":
self.act = nn.SiLU()
elif act_fn == "gelu":
self.act = nn.GELU()
self.linear = nn.Linear(embedding_dim, out_dim * 2)
def forward(self, x, emb):
if self.act:
emb = self.act(emb)
emb = self.linear(emb)
emb = emb[:, :, None, None]
scale, shift = emb.chunk(2, dim=1)
x = F.group_norm(x, self.num_groups, eps=self.eps)
x = x * (1 + scale) + shift
return x