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Commit 1a4bd9e9 authored by comfyanonymous's avatar comfyanonymous
Browse files

Refactor the attention functions. 

There's no reason for the whole CrossAttention object to be repeated when
only the operation in the middle changes.
parent 8cc75c64
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Showing with 201 additions and 380 deletions
comfy/ldm/modules/attention.py
View file @ 1a4bd9e9
......@@ -94,95 +94,41 @@ def zero_module(module):
def Normalize(in_channels, dtype=None, device=None):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
def attention_basic(q, k, v, heads, mask=None):
h = heads
scale = (q.shape[-1] // heads) ** -0.5
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
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_
class CrossAttentionBirchSan(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * scale
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
del q, k
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
query = self.to_q(x)
context = default(context, x)
key = self.to_k(context)
if value is not None:
value = self.to_v(value)
else:
value = self.to_v(context)
out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return out
del context, x
query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
def attention_sub_quad(query, key, value, heads, mask=None):
scale = (query.shape[-1] // heads) ** -0.5
query = query.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
key_t = key.transpose(1,2).unflatten(1, (heads, -1)).flatten(end_dim=1)
del key
value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
value = value.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
dtype = query.dtype
upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
......@@ -230,54 +176,19 @@ class CrossAttentionBirchSan(nn.Module):
query_chunk_size=query_chunk_size,
kv_chunk_size=kv_chunk_size,
kv_chunk_size_min=kv_chunk_size_min,
use_checkpoint=self.training,
use_checkpoint=False,
upcast_attention=upcast_attention,
)
hidden_states = hidden_states.to(dtype)
hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
out_proj, dropout = self.to_out
hidden_states = out_proj(hidden_states)
hidden_states = dropout(hidden_states)
hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
return hidden_states
class CrossAttentionDoggettx(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.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.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
k_in = self.to_k(context)
if value is not None:
v_in = self.to_v(value)
del value
else:
v_in = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
def attention_split(q, k, v, heads, mask=None):
scale = (q.shape[-1] // heads) ** -0.5
h = heads
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
......@@ -310,9 +221,9 @@ class CrossAttentionDoggettx(nn.Module):
end = i + slice_size
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
else:
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
first_op_done = True
s2 = s1.softmax(dim=-1).to(v.dtype)
......@@ -339,115 +250,66 @@ class CrossAttentionDoggettx(nn.Module):
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return r2
return self.to_out(r2)
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.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.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
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))
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
del q, k
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', sim, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
class MemoryEfficientCrossAttention(nn.Module):
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.heads = heads
self.dim_head = dim_head
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
def attention_xformers(q, k, v, heads, mask=None):
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.reshape(b, t.shape[1], heads, -1)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.reshape(b * heads, t.shape[1], -1)
.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=self.attention_op)
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.reshape(b, heads, out.shape[1], -1)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
.reshape(b, out.shape[1], -1)
)
return self.to_out(out)
return out
def attention_pytorch(q, k, v, heads, mask=None):
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = map(
lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
if exists(mask):
raise NotImplementedError
out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
)
return out
optimized_attention = attention_basic
if model_management.xformers_enabled():
print("Using xformers cross attention")
optimized_attention = attention_xformers
elif model_management.pytorch_attention_enabled():
print("Using pytorch cross attention")
optimized_attention = attention_pytorch
else:
if args.use_split_cross_attention:
print("Using split optimization for cross attention")
optimized_attention = attention_split
else:
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
optimized_attention = attention_sub_quad
class CrossAttentionPytorch(nn.Module):
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
......@@ -461,7 +323,6 @@ class CrossAttentionPytorch(nn.Module):
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
q = self.to_q(x)
......@@ -473,36 +334,9 @@ class CrossAttentionPytorch(nn.Module):
else:
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
if exists(mask):
raise NotImplementedError
out = (
out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head)
)
out = optimized_attention(q, k, v, self.heads, mask)
return self.to_out(out)
if model_management.xformers_enabled():
print("Using xformers cross attention")
CrossAttention = MemoryEfficientCrossAttention
elif model_management.pytorch_attention_enabled():
print("Using pytorch cross attention")
CrossAttention = CrossAttentionPytorch
else:
if args.use_split_cross_attention:
print("Using split optimization for cross attention")
CrossAttention = CrossAttentionDoggettx
else:
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
CrossAttention = CrossAttentionBirchSan
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
......
comfy/ldm/modules/diffusionmodules/model.py
View file @ 1a4bd9e9
......@@ -6,7 +6,6 @@ import numpy as np
from einops import rearrange
from typing import Optional, Any
from ..attention import MemoryEfficientCrossAttention
from comfy import model_management
import comfy.ops
......@@ -352,15 +351,6 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
out = self.proj_out(out)
return x+out
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
def forward(self, x, context=None, mask=None):
b, c, h, w = x.shape
x = rearrange(x, 'b c h w -> b (h w) c')
out = super().forward(x, context=context, mask=mask)
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
return x + out
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
if model_management.xformers_enabled_vae() and attn_type == "vanilla":
......@@ -376,9 +366,6 @@ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
return MemoryEfficientAttnBlock(in_channels)
elif attn_type == "vanilla-pytorch":
return MemoryEfficientAttnBlockPytorch(in_channels)
elif type == "memory-efficient-cross-attn":
attn_kwargs["query_dim"] = in_channels
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
......
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