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Copy pathattention.py
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347 lines (280 loc) · 14.2 KB
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import math
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
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 (:obj:`int`): The number of channels in the input and output.
num_head_channels (:obj:`int`, *optional*):
The number of channels in each head. If None, then `num_heads` = 1.
num_groups (:obj:`int`, *optional*, defaults to 32): The number of groups to use for group norm.
rescale_output_factor (:obj:`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
eps (:obj:`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
"""
def __init__(
self,
channels: int,
num_head_channels: Optional[int] = None,
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=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)
def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
return new_projection
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)
# transpose
query_states = self.transpose_for_scores(query_proj)
key_states = self.transpose_for_scores(key_proj)
value_states = self.transpose_for_scores(value_proj)
# get scores
scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)
# compute attention output
hidden_states = torch.matmul(attention_probs, value_states)
hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
hidden_states = hidden_states.view(new_hidden_states_shape)
# 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 SpatialTransformer(nn.Module):
"""
Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
standard transformer action. Finally, reshape to image.
Parameters:
in_channels (:obj:`int`): The number of channels in the input and output.
n_heads (:obj:`int`): The number of heads to use for multi-head attention.
d_head (:obj:`int`): The number of channels in each head.
depth (:obj:`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (:obj:`float`, *optional*, defaults to 0.1): The dropout probability to use.
context_dim (:obj:`int`, *optional*): The number of context dimensions to use.
"""
def __init__(
self,
in_channels: int,
n_heads: int,
d_head: int,
depth: int = 1,
dropout: float = 0.0,
num_groups: int = 32,
context_dim: Optional[int] = None,
):
super().__init__()
self.n_heads = n_heads
self.d_head = d_head
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)
]
)
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
def _set_attention_slice(self, slice_size):
for block in self.transformer_blocks:
block._set_attention_slice(slice_size)
def forward(self, hidden_states, context=None):
# note: if no context is given, cross-attention defaults to self-attention
batch, channel, height, weight = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
hidden_states = self.proj_in(hidden_states)
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, channel)
for block in self.transformer_blocks:
hidden_states = block(hidden_states, context=context)
hidden_states = hidden_states.reshape(batch, height, weight, channel).permute(0, 3, 1, 2)
hidden_states = self.proj_out(hidden_states)
return hidden_states + residual
class BasicTransformerBlock(nn.Module):
r"""
A basic Transformer block.
Parameters:
dim (:obj:`int`): The number of channels in the input and output.
n_heads (:obj:`int`): The number of heads to use for multi-head attention.
d_head (:obj:`int`): The number of channels in each head.
dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
context_dim (:obj:`int`, *optional*): The size of the context vector for cross attention.
gated_ff (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use a gated feed-forward network.
checkpoint (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use checkpointing.
"""
def __init__(
self,
dim: int,
n_heads: int,
d_head: int,
dropout=0.0,
context_dim: Optional[int] = None,
gated_ff: bool = True,
checkpoint: bool = True,
):
super().__init__()
self.attn1 = CrossAttention(
query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = CrossAttention(
query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def _set_attention_slice(self, slice_size):
self.attn1._slice_size = slice_size
self.attn2._slice_size = slice_size
def forward(self, hidden_states, context=None):
hidden_states = hidden_states.contiguous() if hidden_states.device.type == "mps" else hidden_states
hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
hidden_states = self.attn2(self.norm2(hidden_states), context=context) + hidden_states
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
return hidden_states
class CrossAttention(nn.Module):
r"""
A cross attention layer.
Parameters:
query_dim (:obj:`int`): The number of channels in the query.
context_dim (:obj:`int`, *optional*):
The number of channels in the context. If not given, defaults to `query_dim`.
heads (:obj:`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
dim_head (:obj:`int`, *optional*, defaults to 64): The number of channels in each head.
dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
"""
def __init__(
self, query_dim: int, context_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: int = 0.0
):
super().__init__()
inner_dim = dim_head * heads
context_dim = context_dim if context_dim is not None else query_dim
self.scale = dim_head**-0.5
self.heads = 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._slice_size = None
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.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.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 forward(self, hidden_states, context=None, mask=None):
batch_size, sequence_length, _ = hidden_states.shape
query = self.to_q(hidden_states)
context = context if context is not None else hidden_states
key = self.to_k(context)
value = self.to_v(context)
dim = query.shape[-1]
query = self.reshape_heads_to_batch_dim(query)
key = self.reshape_heads_to_batch_dim(key)
value = self.reshape_heads_to_batch_dim(value)
# TODO(PVP) - mask is currently never used. Remember to re-implement when used
# attention, what we cannot get enough of
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
hidden_states = self._attention(query, key, value)
else:
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim)
return self.to_out(hidden_states)
def _attention(self, query, key, value):
attention_scores = torch.matmul(query, key.transpose(-1, -2)) * self.scale
attention_probs = attention_scores.softmax(dim=-1)
# compute attention output
hidden_states = torch.matmul(attention_probs, value)
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
return hidden_states
def _sliced_attention(self, query, key, value, sequence_length, dim):
batch_size_attention = query.shape[0]
hidden_states = torch.zeros(
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
)
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
for i in range(hidden_states.shape[0] // slice_size):
start_idx = i * slice_size
end_idx = (i + 1) * slice_size
attn_slice = torch.matmul(query[start_idx:end_idx], key[start_idx:end_idx].transpose(1, 2)) * self.scale
attn_slice = attn_slice.softmax(dim=-1)
attn_slice = torch.matmul(attn_slice, value[start_idx:end_idx])
hidden_states[start_idx:end_idx] = attn_slice
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
return hidden_states
class FeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
dim (:obj:`int`): The number of channels in the input.
dim_out (:obj:`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
mult (:obj:`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
glu (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use GLU activation.
dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
"""
def __init__(
self, dim: int, dim_out: Optional[int] = None, mult: int = 4, glu: bool = False, dropout: float = 0.0
):
super().__init__()
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
project_in = GEGLU(dim, inner_dim)
self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
def forward(self, hidden_states):
return self.net(hidden_states)
# feedforward
class GEGLU(nn.Module):
r"""
A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
Parameters:
dim_in (:obj:`int`): The number of channels in the input.
dim_out (:obj:`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 forward(self, hidden_states):
hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
return hidden_states * F.gelu(gate)
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