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model_management: lazy-cache aimdo_tensor

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These tensors cosntructed from aimdo-allocations are CPU expensive to
make on the pytorch side. Add a cache version that will be valid with
signature match to fast path past whatever torch is doing.
This commit is contained in:
Rattus
2026-02-11 23:23:47 +10:00
parent 89dc4a8df8
commit f7aebddcf6
2 changed files with 13 additions and 5 deletions
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10
comfy/model_management.py
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@@ -1213,9 +1213,13 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str
signature = comfy_aimdo.model_vbar.vbar_fault(weight._v)
if signature is not None:
raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
if not comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
if comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
raw_tensor = weight._v_tensor
v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
else:
raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
weight._v_tensor = raw_tensor
weight._v_signature = signature
#Send it over
v_tensor.copy_(weight, non_blocking=non_blocking)

8
comfy/ops.py
View File

@@ -86,9 +86,13 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ])
signature = comfy_aimdo.model_vbar.vbar_fault(s._v)
if signature is not None:
xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)
if signature is not None:
if resident:
xfer_dest = s._v_tensor
else:
xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
s._v_tensor = xfer_dest
if not resident:
cast_dest = None