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Preliminar support for sageattention3

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Panchovix 2025-07-25 12:00:11 -04:00 committed by GitHub
parent e6e5d33b35
commit a9d44d9869
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1 changed files with 57 additions and 15 deletions
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72
comfy/ldm/modules/attention.py
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@ -18,9 +18,24 @@ if model_management.xformers_enabled():
import xformers.ops import xformers.ops
if model_management.sage_attention_enabled(): if model_management.sage_attention_enabled():
sage_attention_available = False
SAGE_ATTENTION_3_AVAILABLE = False
try: try:
from sageattention import sageattn from sageattn import sageattn_blackwell
except ModuleNotFoundError as e: SAGE_ATTENTION_3_AVAILABLE = True
sage_attention_available = True
print("Found SageAttention3 (sageattn package)")
except ImportError:
try:
from sageattention import sageattn
sage_attention_available = True
print("Found SageAttention2 (sageattention package)")
except ModuleNotFoundError as e:
pass
if not sage_attention_available:
if e.name == "sageattention": if e.name == "sageattention":
logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
else: else:
@ -470,7 +485,6 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
return out return out
def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
if skip_reshape: if skip_reshape:
b, _, _, dim_head = q.shape b, _, _, dim_head = q.shape
@ -493,7 +507,46 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
mask = mask.unsqueeze(1) mask = mask.unsqueeze(1)
try: try:
out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) if SAGE_ATTENTION_3_AVAILABLE and dim_head < 256:
# SageAttention3 expects tensor layout as (batch, heads, seq_len, head_dim)
if tensor_layout == "NHD":
q_sa3, k_sa3, v_sa3 = map(lambda t: t.transpose(1, 2), (q, k, v))
else:
q_sa3, k_sa3, v_sa3 = q, k, v
out = sageattn_blackwell(q_sa3, k_sa3, v_sa3, attn_mask=mask, is_causal=False, per_block_mean=True)
# Convert back to expected layout
if tensor_layout == "HND":
if not skip_output_reshape:
out = out.transpose(1, 2).reshape(b, -1, heads * dim_head)
else:
if skip_output_reshape:
out = out.transpose(1, 2)
else:
out = out.transpose(1, 2).reshape(b, -1, heads * dim_head)
elif not SAGE_ATTENTION_3_AVAILABLE:
# Fall back to SageAttention2 if available
out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
if tensor_layout == "HND":
if not skip_output_reshape:
out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
)
else:
if skip_output_reshape:
out = out.transpose(1, 2)
else:
out = out.reshape(b, -1, heads * dim_head)
else:
# SageAttention3 is available but head_dim >= 256, fall back to pytorch
logging.warning(f"SageAttention3 doesn't support head_dim >= 256 (got {dim_head}), falling back to pytorch attention")
if tensor_layout == "NHD":
q, k, v = map(
lambda t: t.transpose(1, 2),
(q, k, v),
)
return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
except Exception as e: except Exception as e:
logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e)) logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
if tensor_layout == "NHD": if tensor_layout == "NHD":
@ -502,17 +555,6 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
(q, k, v), (q, k, v),
) )
return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape) return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
if tensor_layout == "HND":
if not skip_output_reshape:
out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
)
else:
if skip_output_reshape:
out = out.transpose(1, 2)
else:
out = out.reshape(b, -1, heads * dim_head)
return out return out