| from functools import cache |
|
|
| SUPPORT_QUANT = False |
| try: |
| from bitsandbytes.nn import LinearNF4, Linear8bitLt, LinearFP4 |
|
|
| SUPPORT_QUANT = True |
| except Exception: |
| import torch.nn as nn |
|
|
| class LinearNF4(nn.Linear): |
| pass |
|
|
| class Linear8bitLt(nn.Linear): |
| pass |
|
|
| class LinearFP4(nn.Linear): |
| pass |
|
|
|
|
| try: |
| from quanto.nn import QLinear, QConv2d, QLayerNorm |
|
|
| SUPPORT_QUANT = True |
| except Exception: |
| import torch.nn as nn |
|
|
| class QLinear(nn.Linear): |
| pass |
|
|
| class QConv2d(nn.Conv2d): |
| pass |
|
|
| class QLayerNorm(nn.LayerNorm): |
| pass |
|
|
|
|
| try: |
| from optimum.quanto.nn import ( |
| QLinear as QLinearOpt, |
| QConv2d as QConv2dOpt, |
| QLayerNorm as QLayerNormOpt, |
| ) |
|
|
| SUPPORT_QUANT = True |
| except Exception: |
| import torch.nn as nn |
|
|
| class QLinearOpt(nn.Linear): |
| pass |
|
|
| class QConv2dOpt(nn.Conv2d): |
| pass |
|
|
| class QLayerNormOpt(nn.LayerNorm): |
| pass |
|
|
|
|
| from ..logging import logger |
|
|
|
|
| QuantLinears = ( |
| Linear8bitLt, |
| LinearFP4, |
| LinearNF4, |
| QLinear, |
| QConv2d, |
| QLayerNorm, |
| QLinearOpt, |
| QConv2dOpt, |
| QLayerNormOpt, |
| ) |
|
|
|
|
| @cache |
| def log_bypass(): |
| return logger.warning( |
| "Using bnb/quanto/optimum-quanto with LyCORIS will enable force-bypass mode." |
| ) |
|
|
|
|
| @cache |
| def log_suspect(): |
| return logger.warning( |
| "Non-native Linear detected but bypass_mode is not set. " |
| "Automatically using force-bypass mode to avoid possible issues. " |
| "Please set bypass_mode=False explicitly if there are no quantized layers." |
| ) |
|
|
