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Make last PR not crash comfy on old pytorch. (#9324)

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comfyanonymous 2025-08-13 12:12:41 -07:00 committed by GitHub
parent 3da5a07510
commit 9df8792d4b
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4 changed files with 27 additions and 17 deletions
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2
comfy/ldm/hunyuan3d/vae.py
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@ -178,7 +178,7 @@ class FourierEmbedder(nn.Module):
class CrossAttentionProcessor: class CrossAttentionProcessor:
def __call__(self, attn, q, k, v): def __call__(self, attn, q, k, v):
out = ops.scaled_dot_product_attention(q, k, v) out = comfy.ops.scaled_dot_product_attention(q, k, v)
return out return out

4
comfy/ldm/modules/attention.py
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@ -448,7 +448,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
mask = mask.unsqueeze(1) mask = mask.unsqueeze(1)
if SDP_BATCH_LIMIT >= b: if SDP_BATCH_LIMIT >= b:
out = ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
if not skip_output_reshape: if not skip_output_reshape:
out = ( out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head) out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@ -461,7 +461,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
if mask.shape[0] > 1: if mask.shape[0] > 1:
m = mask[i : i + SDP_BATCH_LIMIT] m = mask[i : i + SDP_BATCH_LIMIT]
out[i : i + SDP_BATCH_LIMIT] = ops.scaled_dot_product_attention( out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention(
q[i : i + SDP_BATCH_LIMIT], q[i : i + SDP_BATCH_LIMIT],
k[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT],
v[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT],

2
comfy/ldm/modules/diffusionmodules/model.py
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@ -285,7 +285,7 @@ def pytorch_attention(q, k, v):
) )
try: try:
out = ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = out.transpose(2, 3).reshape(orig_shape) out = out.transpose(2, 3).reshape(orig_shape)
except model_management.OOM_EXCEPTION: except model_management.OOM_EXCEPTION:
logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")

24
comfy/ops.py
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@ -23,18 +23,32 @@ from comfy.cli_args import args, PerformanceFeature
import comfy.float import comfy.float
import comfy.rmsnorm import comfy.rmsnorm
import contextlib import contextlib
from torch.nn.attention import SDPBackend, sdpa_kernel
cast_to = comfy.model_management.cast_to #TODO: remove once no more references
def scaled_dot_product_attention(q, k, v, *args, **kwargs):
return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
try:
if torch.cuda.is_available():
from torch.nn.attention import SDPBackend, sdpa_kernel
SDPA_BACKEND_PRIORITY = [ SDPA_BACKEND_PRIORITY = [
SDPBackend.FLASH_ATTENTION, SDPBackend.FLASH_ATTENTION,
SDPBackend.EFFICIENT_ATTENTION, SDPBackend.EFFICIENT_ATTENTION,
SDPBackend.MATH, SDPBackend.MATH,
] ]
if torch.cuda.is_available():
SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION) SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION)
@sdpa_kernel(backends=SDPA_BACKEND_PRIORITY, set_priority=True)
def scaled_dot_product_attention(q, k, v, *args, **kwargs):
return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
except (ModuleNotFoundError, TypeError):
logging.warning("Could not set sdpa backend priority.")
cast_to = comfy.model_management.cast_to #TODO: remove once no more references
def cast_to_input(weight, input, non_blocking=False, copy=True): def cast_to_input(weight, input, non_blocking=False, copy=True):
return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
@ -258,10 +272,6 @@ class disable_weight_init:
else: else:
raise ValueError(f"unsupported dimensions: {dims}") raise ValueError(f"unsupported dimensions: {dims}")
@staticmethod
@sdpa_kernel(backends=SDPA_BACKEND_PRIORITY, set_priority=True)
def scaled_dot_product_attention(q, k, v, *args, **kwargs):
return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
class manual_cast(disable_weight_init): class manual_cast(disable_weight_init):
class Linear(disable_weight_init.Linear): class Linear(disable_weight_init.Linear):