Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

Fix offload/backup

Browse Source
This commit is contained in:
AI-Casanova
2024-09-29 00:21:47 -05:00
parent a8d31d085b
commit 6a49db472b
2 changed files with 29 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
extensions-builtin/Lora/lora_patches.py
+5
View File
@@ -47,6 +47,11 @@ class LoraPatches:
def undo(self):
if not self.active or shared.opts.lora_force_diffusers:
return
try:
import bitsandbytes
self.Linear4bit_forward = patches.undo(__name__, bitsandbytes.nn.Linear4bit, 'forward')
except:
pass
if "Model" in shared.opts.optimum_quanto_weights or "Text Encoder" in shared.opts.optimum_quanto_weights:
from optimum import quanto # pylint: disable=no-name-in-module
self.QLinear_forward = patches.undo(__name__, quanto.nn.QLinear, 'forward') # pylint: disable=E1128, attribute-defined-outside-init
extensions-builtin/Lora/networks.py
+24 -3
View File
@@ -296,6 +296,12 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
elif hasattr(self, "qweight") and hasattr(self, "freeze"):
self.weight = torch.nn.Parameter(weights_backup.to(self.weight.device, copy=True))
self.freeze()
elif getattr(self.weight, "quant_type", None) is not None:
import bitsandbytes
device = self.weight.device
self.weight = bitsandbytes.nn.Params4bit(weights_backup, quant_state=self.weight.quant_state,
quant_type=self.weight.quant_type, blocksize=self.weight.blocksize)
self.weight.to(device)
else:
self.weight.copy_(weights_backup)
if bias_backup is not None:
@@ -330,6 +336,15 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
raise RuntimeError("no backup weights found and current weights are not unchanged")
if isinstance(self, torch.nn.MultiheadAttention):
weights_backup = (self.in_proj_weight.clone().to(devices.cpu), self.out_proj.weight.clone().to(devices.cpu))
elif getattr(self.weight, "quant_type", None) == "nf4" or getattr(self.weight, "quant_type", None) == "nf4":
# weights_backup = self.weight.__deepcopy__("")
import bitsandbytes
with devices.inference_context():
weights_backup = bitsandbytes.functional.dequantize_4bit(self.weight,
quant_state=self.weight.quant_state,
quant_type=self.weight.quant_type,
blocksize=self.weight.blocksize,
).to(devices.cpu)
else:
weights_backup = self.weight.clone().to(devices.cpu)
self.network_weights_backup = weights_backup
@@ -356,11 +371,17 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
if len(weight.shape) == 4 and weight.shape[1] == 9:
# inpainting model. zero pad updown to make channel[1] 4 to 9
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) # pylint: disable=not-callable
if getattr(self.weight, "quant_type", None) == "nf4":
if getattr(self.weight, "quant_type", None) == "nf4" or self.weight.numel() != updown.numel():
import bitsandbytes
device = self.weight.device
weight = bitsandbytes.functional.dequantize_4bit(self.weight, quant_state=self.weight.quant_state, quant_type=self.weight.quant_type, blocksize=self.weight.blocksize)
self.weight = bitsandbytes.nn.Params4bit(weight + updown)
weight = bitsandbytes.functional.dequantize_4bit(self.weight,
quant_state=self.weight.quant_state,
quant_type=self.weight.quant_type,
blocksize=self.weight.blocksize)
self.weight = bitsandbytes.nn.Params4bit(weight + updown,
quant_state=self.weight.quant_state,
quant_type=self.weight.quant_type,
blocksize=self.weight.blocksize)
self.weight.to(device)
else:
self.weight = torch.nn.Parameter(weight + updown)