Skip to content

use fused multiply-add pointwise ops in chroma #8279

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
May 30, 2025
Merged

use fused multiply-add pointwise ops in chroma #8279

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 9 additions & 11 deletions comfy/ldm/chroma/layers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,15 +80,13 @@ def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=N
(img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec

# prepare image for attention
img_modulated = self.img_norm1(img)
img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
img_qkv = self.img_attn.qkv(img_modulated)
img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)

# prepare txt for attention
txt_modulated = self.txt_norm1(txt)
txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
txt_qkv = self.txt_attn.qkv(txt_modulated)
txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
Expand All @@ -102,12 +100,12 @@ def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=N
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]

# calculate the img bloks
img = img + img_mod1.gate * self.img_attn.proj(img_attn)
img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))

# calculate the txt bloks
txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))

if txt.dtype == torch.float16:
txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
Expand Down Expand Up @@ -152,7 +150,7 @@ def __init__(

def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
mod = vec
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)

q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
Expand All @@ -162,7 +160,7 @@ def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
attn = attention(q, k, v, pe=pe, mask=attn_mask)
# compute activation in mlp stream, cat again and run second linear layer
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
x += mod.gate * output
x.addcmul_(mod.gate, output)
if x.dtype == torch.float16:
x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
return x
Expand All @@ -178,6 +176,6 @@ def forward(self, x: Tensor, vec: Tensor) -> Tensor:
shift, scale = vec
shift = shift.squeeze(1)
scale = scale.squeeze(1)
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
x = self.linear(x)
return x