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from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
class Attention(nn.Module):
r"""
A cross attention layer.
Parameters:
query_dim (`int`):
The number of channels in the query.
cross_attention_dim (`int`, *optional*):
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
heads (`int`, *optional*, defaults to 8):
The number of heads to use for multi-head attention.
dim_head (`int`, *optional*, defaults to 64):
The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
bias (`bool`, *optional*, defaults to False):
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
upcast_attention (`bool`, *optional*, defaults to False):
Set to `True` to upcast the attention computation to `float32`.
upcast_softmax (`bool`, *optional*, defaults to False):
Set to `True` to upcast the softmax computation to `float32`.
cross_attention_norm (`str`, *optional*, defaults to `None`):
The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use for the group norm in the cross attention.
added_kv_proj_dim (`int`, *optional*, defaults to `None`):
The number of channels to use for the added key and value projections. If `None`, no projection is used.
norm_num_groups (`int`, *optional*, defaults to `None`):
The number of groups to use for the group norm in the attention.
spatial_norm_dim (`int`, *optional*, defaults to `None`):
The number of channels to use for the spatial normalization.
out_bias (`bool`, *optional*, defaults to `True`):
Set to `True` to use a bias in the output linear layer.
scale_qk (`bool`, *optional*, defaults to `True`):
Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
only_cross_attention (`bool`, *optional*, defaults to `False`):
Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
`added_kv_proj_dim` is not `None`.
eps (`float`, *optional*, defaults to 1e-5):
An additional value added to the denominator in group normalization that is used for numerical stability.
rescale_output_factor (`float`, *optional*, defaults to 1.0):
A factor to rescale the output by dividing it with this value.
residual_connection (`bool`, *optional*, defaults to `False`):
Set to `True` to add the residual connection to the output.
_from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
Set to `True` if the attention block is loaded from a deprecated state dict.
processor (`AttnProcessor`, *optional*, defaults to `None`):
The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
`AttnProcessor` otherwise.
"""
def __init__(
self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
bias: bool = False,
upcast_attention: bool = False,
upcast_softmax: bool = False,
cross_attention_norm: Optional[str] = None,
cross_attention_norm_num_groups: int = 32,
added_kv_proj_dim: Optional[int] = None,
norm_num_groups: Optional[int] = None,
out_bias: bool = True,
scale_qk: bool = True,
only_cross_attention: bool = False,
eps: float = 1e-5,
rescale_output_factor: float = 1.0,
residual_connection: bool = False,
_from_deprecated_attn_block: bool = False,
processor: Optional["AttnProcessor"] = None,
out_dim: int = None,
):
super().__init__()
self.inner_dim = out_dim if out_dim is not None else dim_head * heads
self.query_dim = query_dim
self.cross_attention_dim = (
cross_attention_dim if cross_attention_dim is not None else query_dim
)
self.upcast_attention = upcast_attention
self.upcast_softmax = upcast_softmax
self.rescale_output_factor = rescale_output_factor
self.residual_connection = residual_connection
self.dropout = dropout
self.fused_projections = False
self.out_dim = out_dim if out_dim is not None else query_dim
# we make use of this private variable to know whether this class is loaded
# with an deprecated state dict so that we can convert it on the fly
self._from_deprecated_attn_block = _from_deprecated_attn_block
self.scale_qk = scale_qk
self.scale = dim_head**-0.5 if self.scale_qk else 1.0
self.heads = out_dim // dim_head if out_dim is not None else heads
# for slice_size > 0 the attention score computation
# is split across the batch axis to save memory
# You can set slice_size with `set_attention_slice`
self.sliceable_head_dim = heads
self.added_kv_proj_dim = added_kv_proj_dim
self.only_cross_attention = only_cross_attention
if self.added_kv_proj_dim is None and self.only_cross_attention:
raise ValueError(
"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
)
if norm_num_groups is not None:
self.group_norm = nn.GroupNorm(
num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True
)
else:
self.group_norm = None
self.spatial_norm = None
if cross_attention_norm is None:
self.norm_cross = None
elif cross_attention_norm == "layer_norm":
self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
elif cross_attention_norm == "group_norm":
if self.added_kv_proj_dim is not None:
# The given `encoder_hidden_states` are initially of shape
# (batch_size, seq_len, added_kv_proj_dim) before being projected
# to (batch_size, seq_len, cross_attention_dim). The norm is applied
# before the projection, so we need to use `added_kv_proj_dim` as
# the number of channels for the group norm.
norm_cross_num_channels = added_kv_proj_dim
else:
norm_cross_num_channels = self.cross_attention_dim
self.norm_cross = nn.GroupNorm(
num_channels=norm_cross_num_channels,
num_groups=cross_attention_norm_num_groups,
eps=1e-5,
affine=True,
)
else:
raise ValueError(
f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
)
linear_cls = nn.Linear
self.linear_cls = linear_cls
self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
if not self.only_cross_attention:
# only relevant for the `AddedKVProcessor` classes
self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
else:
self.to_k = None
self.to_v = None
if self.added_kv_proj_dim is not None:
self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
self.to_out = nn.ModuleList([])
self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias))
self.to_out.append(nn.Dropout(dropout))
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
if processor is None:
processor = (
AttnProcessor2_0()
if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
else AttnProcessor()
)
self.set_processor(processor)
def set_processor(self, processor: "AttnProcessor") -> None:
self.processor = processor
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
**cross_attention_kwargs,
) -> torch.Tensor:
r"""
The forward method of the `Attention` class.
Args:
hidden_states (`torch.Tensor`):
The hidden states of the query.
encoder_hidden_states (`torch.Tensor`, *optional*):
The hidden states of the encoder.
attention_mask (`torch.Tensor`, *optional*):
The attention mask to use. If `None`, no mask is applied.
**cross_attention_kwargs:
Additional keyword arguments to pass along to the cross attention.
Returns:
`torch.Tensor`: The output of the attention layer.
"""
# The `Attention` class can call different attention processors / attention functions
# here we simply pass along all tensors to the selected processor class
# For standard processors that are defined here, `**cross_attention_kwargs` is empty
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
batch_size, seq_len, dim = tensor.shape
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 head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
reshaped to `[batch_size * heads, seq_len, dim // heads]`.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
batch_size, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3)
if out_dim == 3:
tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
return tensor
def get_attention_scores(
self,
query: torch.Tensor,
key: torch.Tensor,
attention_mask: torch.Tensor = None,
) -> torch.Tensor:
r"""
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): The key tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
Returns:
`torch.Tensor`: The attention probabilities/scores.
"""
dtype = query.dtype
if self.upcast_attention:
query = query.float()
key = key.float()
if attention_mask is None:
baddbmm_input = torch.empty(
query.shape[0],
query.shape[1],
key.shape[1],
dtype=query.dtype,
device=query.device,
)
beta = 0
else:
baddbmm_input = attention_mask
beta = 1
attention_scores = torch.baddbmm(
baddbmm_input,
query,
key.transpose(-1, -2),
beta=beta,
alpha=self.scale,
)
del baddbmm_input
if self.upcast_softmax:
attention_scores = attention_scores.float()
attention_probs = attention_scores.softmax(dim=-1)
del attention_scores
attention_probs = attention_probs.to(dtype)
return attention_probs
def prepare_attention_mask(
self,
attention_mask: torch.Tensor,
target_length: int,
batch_size: int,
out_dim: int = 3,
) -> torch.Tensor:
r"""
Prepare the attention mask for the attention computation.
Args:
attention_mask (`torch.Tensor`):
The attention mask to prepare.
target_length (`int`):
The target length of the attention mask. This is the length of the attention mask after padding.
batch_size (`int`):
The batch size, which is used to repeat the attention mask.
out_dim (`int`, *optional*, defaults to `3`):
The output dimension of the attention mask. Can be either `3` or `4`.
Returns:
`torch.Tensor`: The prepared attention mask.
"""
head_size = self.heads
if attention_mask is None:
return attention_mask
current_length: int = attention_mask.shape[-1]
if current_length != target_length:
if attention_mask.device.type == "mps":
# HACK: MPS: Does not support padding by greater than dimension of input tensor.
# Instead, we can manually construct the padding tensor.
padding_shape = (
attention_mask.shape[0],
attention_mask.shape[1],
target_length,
)
padding = torch.zeros(
padding_shape,
dtype=attention_mask.dtype,
device=attention_mask.device,
)
attention_mask = torch.cat([attention_mask, padding], dim=2)
else:
# TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
# we want to instead pad by (0, remaining_length), where remaining_length is:
# remaining_length: int = target_length - current_length
# TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
if out_dim == 3:
if attention_mask.shape[0] < batch_size * head_size:
attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
elif out_dim == 4:
attention_mask = attention_mask.unsqueeze(1)
attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
return attention_mask
def norm_encoder_hidden_states(
self, encoder_hidden_states: torch.Tensor
) -> torch.Tensor:
r"""
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
Returns:
`torch.Tensor`: The normalized encoder hidden states.
"""
assert (
self.norm_cross is not None
), "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
if isinstance(self.norm_cross, nn.LayerNorm):
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
elif isinstance(self.norm_cross, nn.GroupNorm):
# Group norm norms along the channels dimension and expects
# input to be in the shape of (N, C, *). In this case, we want
# to norm along the hidden dimension, so we need to move
# (batch_size, sequence_length, hidden_size) ->
# (batch_size, hidden_size, sequence_length)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
else:
assert False
return encoder_hidden_states
@torch.no_grad()
def fuse_projections(self, fuse=True):
is_cross_attention = self.cross_attention_dim != self.query_dim
device = self.to_q.weight.data.device
dtype = self.to_q.weight.data.dtype
if not is_cross_attention:
# fetch weight matrices.
concatenated_weights = torch.cat(
[self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data]
)
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0]
# create a new single projection layer and copy over the weights.
self.to_qkv = self.linear_cls(
in_features, out_features, bias=False, device=device, dtype=dtype
)
self.to_qkv.weight.copy_(concatenated_weights)
else:
concatenated_weights = torch.cat(
[self.to_k.weight.data, self.to_v.weight.data]
)
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0]
self.to_kv = self.linear_cls(
in_features, out_features, bias=False, device=device, dtype=dtype
)
self.to_kv.weight.copy_(concatenated_weights)
self.fused_projections = fuse
class AttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
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)
batch_size, sequence_length, _ = (
hidden_states.shape
if encoder_hidden_states is None
else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(
attention_mask, sequence_length, batch_size
)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
1, 2
)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(
encoder_hidden_states
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class AttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
"""
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: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
) -> 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)
batch_size, sequence_length, _ = (
hidden_states.shape
if encoder_hidden_states is None
else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(
attention_mask, sequence_length, batch_size
)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(
batch_size, attn.heads, -1, attention_mask.shape[-1]
)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
1, 2
)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(
encoder_hidden_states
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
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)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, 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)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
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