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import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from functools import partial
from ..common import (
checkpoint,
exists,
default,
)
from ..basics import zero_module
import comfy.ops
ops = comfy.ops.disable_weight_init
from comfy import model_management
from comfy.ldm.modules.attention import optimized_attention, optimized_attention_masked
if model_management.xformers_enabled():
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
else:
XFORMERS_IS_AVAILBLE = False
class RelativePosition(nn.Module):
""" https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
def __init__(self, num_units, max_relative_position):
super().__init__()
self.num_units = num_units
self.max_relative_position = max_relative_position
self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
nn.init.xavier_uniform_(self.embeddings_table)
def forward(self, length_q, length_k):
device = self.embeddings_table.device
range_vec_q = torch.arange(length_q, device=device)
range_vec_k = torch.arange(length_k, device=device)
distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
final_mat = distance_mat_clipped + self.max_relative_position
final_mat = final_mat.long()
embeddings = self.embeddings_table[final_mat]
return embeddings
# TODO Add native Comfy optimized attention.
class CrossAttention(nn.Module):
def __init__(
self,
query_dim,
context_dim=None,
heads=8,
dim_head=64,
dropout=0.,
relative_position=False,
temporal_length=None,
video_length=None,
image_cross_attention=False,
image_cross_attention_scale=1.0,
image_cross_attention_scale_learnable=False,
text_context_len=77,
device=None,
dtype=None,
operations=ops
):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head**-0.5
self.heads = heads
self.dim_head = dim_head
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, device=device, dtype=dtype),
nn.Dropout(dropout)
)
self.relative_position = relative_position
if self.relative_position:
assert(temporal_length is not None)
self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
else:
## only used for spatial attention, while NOT for temporal attention
if XFORMERS_IS_AVAILBLE and temporal_length is None:
self.forward = self.efficient_forward
else:
self.forward = self.comfy_efficient_forward
self.video_length = video_length
self.image_cross_attention = image_cross_attention
self.image_cross_attention_scale = image_cross_attention_scale
self.text_context_len = text_context_len
self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
if self.image_cross_attention:
self.to_k_ip = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
self.to_v_ip = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
if image_cross_attention_scale_learnable:
self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) )
def comfy_efficient_forward(self, x, context=None, mask=None, *args, **kwargs):
spatial_self_attn = (context is None)
k_ip, v_ip, out_ip = None, None, None
h = self.heads
q = self.to_q(x)
context = default(context, x)
if self.image_cross_attention and not spatial_self_attn:
context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
k = self.to_k(context)
v = self.to_v(context)
k_ip = self.to_k_ip(context_image)
v_ip = self.to_v_ip(context_image)
else:
if not spatial_self_attn:
context = context[:,:self.text_context_len,:]
k = self.to_k(context)
v = self.to_v(context)
out = optimized_attention(q, k, v, h)
if exists(mask):
## feasible for causal attention mask only
out = optimized_attention_masked(q, k, v, h)
## for image cross-attention
if k_ip is not None:
q = rearrange(q, 'b n (h d) -> (b h) n d', h=h)
k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
del k_ip
sim_ip = sim_ip.softmax(dim=-1)
out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
if out_ip is not None:
if self.image_cross_attention_scale_learnable:
out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
else:
out = out + self.image_cross_attention_scale * out_ip
return self.to_out(out)
def forward(self, x, context=None, mask=None):
spatial_self_attn = (context is None)
k_ip, v_ip, out_ip = None, None, None
h = self.heads
q = self.to_q(x)
context = default(context, x)
if self.image_cross_attention and not spatial_self_attn:
context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
k = self.to_k(context)
v = self.to_v(context)
k_ip = self.to_k_ip(context_image)
v_ip = self.to_v_ip(context_image)
else:
# Assumed Spatial Attention (b c h w)
if not spatial_self_attn:
context = context[:,:self.text_context_len,:]
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
if self.relative_position:
len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
k2 = self.relative_position_k(len_q, len_k)
sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check
sim += sim2
del k
if exists(mask):
## feasible for causal attention mask only
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b i j -> (b h) i j', h=h)
sim.masked_fill_(~(mask>0.5), max_neg_value)
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
out = torch.einsum('b i j, b j d -> b i d', sim, v)
if self.relative_position:
v2 = self.relative_position_v(len_q, len_v)
out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check
out += out2
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
## for image cross-attention
if k_ip is not None:
k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
del k_ip
sim_ip = sim_ip.softmax(dim=-1)
out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
if out_ip is not None:
if self.image_cross_attention_scale_learnable:
out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
else:
out = out + self.image_cross_attention_scale * out_ip
return self.to_out(out)
def efficient_forward(self, x, context=None, mask=None):
spatial_self_attn = (context is None)
k_ip, v_ip, out_ip = None, None, None
q = self.to_q(x)
context = default(context, x)
if self.image_cross_attention and not spatial_self_attn:
context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
k = self.to_k(context)
v = self.to_v(context)
k_ip = self.to_k_ip(context_image)
v_ip = self.to_v_ip(context_image)
else:
if not spatial_self_attn:
context = context[:,:self.text_context_len,:]
k = self.to_k(context)
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(q, k, v),
)
# actually compute the attention, what we cannot get enough of
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
## for image cross-attention
if k_ip is not None:
k_ip, v_ip = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(k_ip, v_ip),
)
out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None)
out_ip = (
out_ip.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
)
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
)
if out_ip is not None:
if self.image_cross_attention_scale_learnable:
out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
else:
out = out + self.image_cross_attention_scale * out_ip
return self.to_out(out)
class BasicTransformerBlock(nn.Module):
def __init__(
self,
dim,
n_heads,
d_head,
dropout=0.,
context_dim=None,
gated_ff=True,
checkpoint=True,
disable_self_attn=False,
attention_cls=None,
video_length=None,
inner_dim=None,
image_cross_attention=False,
image_cross_attention_scale=1.0,
image_cross_attention_scale_learnable=False,
switch_temporal_ca_to_sa=False,
text_context_len=77,
ff_in=None,
device=None,
dtype=None,
operations=ops
):
super().__init__()
attn_cls = CrossAttention if attention_cls is None else attention_cls
self.ff_in = ff_in or inner_dim is not None
if self.ff_in:
self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
self.ff_in = FeedForward(
dim,
dim_out=inner_dim,
dropout=dropout,
glu=gated_ff,
dtype=dtype,
device=device,
operations=operations
)
if inner_dim is None:
inner_dim = dim
self.is_res = inner_dim == dim
self.disable_self_attn = disable_self_attn
self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
context_dim=None, device=device, dtype=dtype if self.disable_self_attn else None)
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, device=device, dtype=dtype)
self.attn2 = attn_cls(
query_dim=dim,
context_dim=context_dim,
heads=n_heads,
dim_head=d_head,
dropout=dropout,
video_length=video_length,
image_cross_attention=image_cross_attention,
image_cross_attention_scale=image_cross_attention_scale,
image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
text_context_len=text_context_len,
device=device,
dtype=dtype
)
self.image_cross_attention = image_cross_attention
self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
self.norm3 = operations.LayerNorm(dim, device=device, dtype=dtype)
self.n_heads = n_heads
self.d_head = d_head
self.checkpoint = checkpoint
self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
def forward(self, x, context=None, mask=None, **kwargs):
## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
if context is not None:
input_tuple = (x, context)
if mask is not None:
forward_mask = partial(self._forward, mask=mask)
return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)
def _forward(self, x, context=None, mask=None, transformer_options={}):
extra_options = {}
block = transformer_options.get("block", None)
block_index = transformer_options.get("block_index", 0)
transformer_patches = {}
transformer_patches_replace = {}
for k in transformer_options:
if k == "patches":
transformer_patches = transformer_options[k]
elif k == "patches_replace":
transformer_patches_replace = transformer_options[k]
else:
extra_options[k] = transformer_options[k]
extra_options["n_heads"] = self.n_heads
extra_options["dim_head"] = self.d_head
if self.ff_in:
x_skip = x
x = self.ff_in(self.norm_in(x))
if self.is_res:
x += x_skip
n = self.norm1(x)
if self.disable_self_attn:
context_attn1 = context
else:
context_attn1 = None
value_attn1 = None
if "attn1_patch" in transformer_patches:
patch = transformer_patches["attn1_patch"]
if context_attn1 is None:
context_attn1 = n
value_attn1 = context_attn1
for p in patch:
n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
if block is not None:
transformer_block = (block[0], block[1], block_index)
else:
transformer_block = None
attn1_replace_patch = transformer_patches_replace.get("attn1", {})
block_attn1 = transformer_block
if block_attn1 not in attn1_replace_patch:
block_attn1 = block
if block_attn1 in attn1_replace_patch:
if context_attn1 is None:
context_attn1 = n
value_attn1 = n
n = self.attn1.to_q(n)
context_attn1 = self.attn1.to_k(context_attn1)
value_attn1 = self.attn1.to_v(value_attn1)
n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
n = self.attn1.to_out(n)
else:
n = self.attn1(n, context=context_attn1, value=value_attn1)
if "attn1_output_patch" in transformer_patches:
patch = transformer_patches["attn1_output_patch"]
for p in patch:
n = p(n, extra_options)
x += n
if "middle_patch" in transformer_patches:
patch = transformer_patches["middle_patch"]
for p in patch:
x = p(x, extra_options)
if self.attn2 is not None:
n = self.norm2(x)
if self.switch_temporal_ca_to_sa:
context_attn2 = n
else:
context_attn2 = context
value_attn2 = None
if "attn2_patch" in transformer_patches:
patch = transformer_patches["attn2_patch"]
value_attn2 = context_attn2
for p in patch:
n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
attn2_replace_patch = transformer_patches_replace.get("attn2", {})
block_attn2 = transformer_block
if block_attn2 not in attn2_replace_patch:
block_attn2 = block
if block_attn2 in attn2_replace_patch:
if value_attn2 is None:
value_attn2 = context_attn2
n = self.attn2.to_q(n)
context_attn2 = self.attn2.to_k(context_attn2)
value_attn2 = self.attn2.to_v(value_attn2)
n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
n = self.attn2.to_out(n)
else:
n = self.attn2(n, context=context_attn2, value=value_attn2)
if "attn2_output_patch" in transformer_patches:
patch = transformer_patches["attn2_output_patch"]
for p in patch:
n = p(n, extra_options)
x += n
if self.is_res:
x_skip = x
x = self.ff(self.norm3(x))
if self.is_res:
x += x_skip
return x
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data in spatial axis.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
NEW: use_linear for more efficiency instead of the 1x1 convs
"""
def __init__(
self,
in_channels,
n_heads,
d_head,
depth=1,
dropout=0.,
context_dim=None,
use_checkpoint=True,
disable_self_attn=False,
use_linear=False,
video_length=None,
image_cross_attention=False,
image_cross_attention_scale_learnable=False,
device=None,
dtype=None,
operations=ops
):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, device=device, dtype=dtype)
if not use_linear:
self.proj_in = opeations.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
else:
self.proj_in = operations.Linear(in_channels, inner_dim, device=device, dtype=dtype)
attention_cls = None
self.transformer_blocks = nn.ModuleList([
BasicTransformerBlock(
inner_dim,
n_heads,
d_head,
dropout=dropout,
context_dim=context_dim,
disable_self_attn=disable_self_attn,
checkpoint=use_checkpoint,
attention_cls=attention_cls,
video_length=video_length,
image_cross_attention=image_cross_attention,
image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
device=device,
dtype=dtype
) for d in range(depth)
])
if not use_linear:
self.proj_out = zero_module(operations.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype))
else:
self.proj_out = zero_module(operations.Linear(inner_dim, in_channels, device=device, dtype=dtype))
self.use_linear = use_linear
def forward(self, x, context=None, transformer_options={}, **kwargs):
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
if self.use_linear:
x = self.proj_in(x)
for i, block in enumerate(self.transformer_blocks):
transformer_options['block_index'] = i
x = block(x, context=context, **kwargs)
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
if not self.use_linear:
x = self.proj_out(x)
return x + x_in
class TemporalTransformer(nn.Module):
"""
Transformer block for image-like data in temporal axis.
First, reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
"""
def __init__(
self,
in_channels,
n_heads,
d_head,
depth=1,
dropout=0.,
context_dim=None,
use_checkpoint=True,
use_linear=False,
only_self_att=True,
causal_attention=False,
causal_block_size=1,
relative_position=False,
temporal_length=None,
device=None,
dtype=None,
operations=ops
):
super().__init__()
self.only_self_att = only_self_att
self.relative_position = relative_position
self.causal_attention = causal_attention
self.causal_block_size = causal_block_size
if only_self_att:
context_dim = None
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, device=device, dtype=dtype)
self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0).to(device, dtype)
if not use_linear:
self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0).to(device, dtype)
else:
self.proj_in = operations.Linear(in_channels, inner_dim, device=device, dtype=dtype)
if relative_position:
assert(temporal_length is not None)
attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length, device=device, dtype=dtype)
else:
attention_cls = partial(CrossAttention, temporal_length=temporal_length, device=device, dtype=dtype)
if self.causal_attention:
assert(temporal_length is not None)
self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
if self.only_self_att:
context_dim = None
self.transformer_blocks = nn.ModuleList([
BasicTransformerBlock(
inner_dim,
n_heads,
d_head,
dropout=dropout,
context_dim=context_dim,
attention_cls=attention_cls,
checkpoint=use_checkpoint,
device=device,
dtype=dtype
) for d in range(depth)
])
if not use_linear:
self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0).to(device, dtype))
else:
self.proj_out = zero_module(operations.Linear(inner_dim, in_channels, device=device, dtype=dtype))
self.use_linear = use_linear
def forward(self, x, context=None):
b, c, t, h, w = x.shape
x_in = x
x = self.norm(x)
x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous()
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, 'bhw c t -> bhw t c').contiguous()
if self.use_linear:
x = self.proj_in(x)
temp_mask = None
if self.causal_attention:
# slice the from mask map
temp_mask = self.mask[:,:t,:t].to(x.device)
if temp_mask is not None:
mask = temp_mask.to(x.device)
mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w)
else:
mask = None
if self.only_self_att:
## note: if no context is given, cross-attention defaults to self-attention
for i, block in enumerate(self.transformer_blocks):
x = block(x, mask=mask)
x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
else:
x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous()
for i, block in enumerate(self.transformer_blocks):
# calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
for j in range(b):
context_j = repeat(
context[j],
't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous()
## note: causal mask will not applied in cross-attention case
x[j] = block(x[j], context=context_j)
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous()
if not self.use_linear:
x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous()
x = self.proj_out(x)
x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous()
return x + x_in
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out, device=None, dtype=None, operations=ops):
super().__init__()
self.proj = operations.Linear(dim_in, dim_out * 2, device=device, dtype=dtype)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., device=None, dtype=None, operations=ops):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
operations.Linear(dim, inner_dim, device=device, dtype=dtype),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
operations.Linear(inner_dim, dim_out, device=device, dtype=dtype)
)
def forward(self, x):
return self.net(x)
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32, device=None, dtype=None, operations=ops):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = operations.Conv2d(dim, hidden_dim * 3, 1, bias = False, device=device, dtype=dtype)
self.to_out = operations.Conv2d(hidden_dim, dim, 1, device=device, dtype=dtype)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
return self.to_out(out)
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels, device=None, dtype=None, operations=ops):
super().__init__()
self.in_channels = in_channels
self.norm = operations.GroupNorm(
num_groups=32,
num_channels=in_channels,
eps=1e-6,
affine=True,
device=device,
dtype=dtype
)
self.q = operations.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0,
device=device,
dtype=dtype
)
self.k = operations.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0,
device=device,
dtype=dtype
)
self.v = operations.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0,
device=device,
dtype=dtype
)
self.proj_out = operations.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0,
device=device,
dtype=dtype
)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = rearrange(v, 'b c h w -> b c (h w)')
w_ = rearrange(w_, 'b i j -> b j i')
h_ = torch.einsum('bij,bjk->bik', v, w_)
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
return x+h_
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