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Signed-off-by: vladmandic <mandic00@live.com>
This commit is contained in:
vladmandic
2026-03-18 15:37:57 +01:00
parent c187aa706a
commit fb2f9ea650
11 changed files with 139 additions and 43 deletions
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modules/attention.py
+14 -4
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@@ -2,7 +2,7 @@ from functools import wraps
import torch
from modules import rocm
from modules.errors import log
from installer import install, installed
from installer import install, installed, torch_info
def set_dynamic_attention():
@@ -10,6 +10,7 @@ def set_dynamic_attention():
sdpa_pre_dyanmic_atten = torch.nn.functional.scaled_dot_product_attention
from modules.sd_hijack_dynamic_atten import dynamic_scaled_dot_product_attention
torch.nn.functional.scaled_dot_product_attention = dynamic_scaled_dot_product_attention
torch_info.set(attention='dynamic')
return sdpa_pre_dyanmic_atten
except Exception as err:
log.error(f'Torch attention: type="dynamic attention" {err}')
@@ -20,6 +21,7 @@ def set_triton_flash_attention(backend: str):
try:
if backend in {"rocm", "zluda"}: # flash_attn_triton_amd only works with AMD
from modules.flash_attn_triton_amd import interface_fa
sdpa_pre_triton_flash_atten = torch.nn.functional.scaled_dot_product_attention
@wraps(sdpa_pre_triton_flash_atten)
def sdpa_triton_flash_atten(query: torch.FloatTensor, key: torch.FloatTensor, value: torch.FloatTensor, attn_mask: torch.Tensor | None = None, dropout_p: float = 0.0, is_causal: bool = False, scale: float | None = None, enable_gqa: bool = False, **kwargs) -> torch.FloatTensor:
@@ -42,6 +44,7 @@ def set_triton_flash_attention(backend: str):
kwargs["enable_gqa"] = enable_gqa
return sdpa_pre_triton_flash_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
torch.nn.functional.scaled_dot_product_attention = sdpa_triton_flash_atten
torch_info.set(attention='triton')
log.debug('Torch attention: type="Triton Flash attention"')
except Exception as err:
log.error(f'Torch attention: type="Triton Flash attention" {err}')
@@ -78,6 +81,7 @@ def set_flex_attention():
return flex_attention(query, key, value, score_mod=score_mod, block_mask=block_mask, scale=scale, enable_gqa=enable_gqa)
torch.nn.functional.scaled_dot_product_attention = sdpa_flex_atten
torch_info.set(attention="flex")
log.debug('Torch attention: type="Flex attention"')
except Exception as err:
log.error(f'Torch attention: type="Flex attention" {err}')
@@ -93,6 +97,7 @@ def set_ck_flash_attention(backend: str, device: torch.device):
else:
install('flash-attn')
from flash_attn import flash_attn_func
sdpa_pre_flash_atten = torch.nn.functional.scaled_dot_product_attention
@wraps(sdpa_pre_flash_atten)
def sdpa_flash_atten(query: torch.FloatTensor, key: torch.FloatTensor, value: torch.FloatTensor, attn_mask: torch.Tensor | None = None, dropout_p: float = 0.0, is_causal: bool = False, scale: float | None = None, enable_gqa: bool = False, **kwargs) -> torch.FloatTensor:
@@ -120,6 +125,7 @@ def set_ck_flash_attention(backend: str, device: torch.device):
kwargs["enable_gqa"] = enable_gqa
return sdpa_pre_flash_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
torch.nn.functional.scaled_dot_product_attention = sdpa_flash_atten
torch_info.set(attention="flash")
log.debug('Torch attention: type="Flash attention"')
except Exception as err:
log.error(f'Torch attention: type="Flash attention" {err}')
@@ -174,6 +180,7 @@ def set_sage_attention(backend: str, device: torch.device):
kwargs["enable_gqa"] = enable_gqa
return sdpa_pre_sage_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
torch.nn.functional.scaled_dot_product_attention = sdpa_sage_atten
torch_info.set(attention="sage")
log.debug(f'Torch attention: type="Sage attention" backend={"cuda" if use_cuda_backend else "auto"}')
except Exception as err:
log.error(f'Torch attention: type="Sage attention" {err}')
@@ -208,19 +215,22 @@ def set_diffusers_attention(pipe, quiet:bool=False):
log.quiet(quiet, f'Setting model: attention="{shared.opts.cross_attention_optimization}"')
if shared.opts.cross_attention_optimization == "Disabled":
pass # do nothing
elif shared.opts.cross_attention_optimization == "Scaled-Dot-Product": # The default set by Diffusers
torch_info.set(attention="disabled")
elif shared.opts.cross_attention_optimization == "Scaled-Dot-Product": # The default set by Diffusers
torch_info.set(attention="sdpa")
# set_attn(pipe, p.AttnProcessor2_0(), name="Scaled-Dot-Product")
pass
elif shared.opts.cross_attention_optimization == "xFormers":
if hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
torch_info.set(attention="xformers")
pipe.enable_xformers_memory_efficient_attention()
else:
log.warning(f"Attention: xFormers is not compatible with {pipe.__class__.__name__}")
elif shared.opts.cross_attention_optimization == "Batch matrix-matrix":
torch_info.set(attention="bmm")
set_attn(pipe, p.AttnProcessor(), name="Batch matrix-matrix")
elif shared.opts.cross_attention_optimization == "Dynamic Attention BMM":
from modules.sd_hijack_dynamic_atten import DynamicAttnProcessorBMM
torch_info.set(attention="dynamic_bmm")
set_attn(pipe, DynamicAttnProcessorBMM(), name="Dynamic Attention BMM")
if shared.opts.attention_slicing != "Default" and hasattr(pipe, "enable_attention_slicing") and hasattr(pipe, "disable_attention_slicing"):