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"""
2025.3.17
2025.3.19
4.50.0
0.15.2
__UNSLOTH_VERSIONING__
"""
torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from peft.tuners.lora.tp_layer import (Any, __name__, nn, torch)
torch_addmm = torch.addmm
torch_add = torch.add
# @torch.compile(fullgraph = False, dynamic = True, options = torch_compile_options)
def lora_forward(result, lora_A, lora_B, dropout, x, scaling):
xA = dropout(x) @ lora_A.weight.t()
# output = result + scaling * xA @ lora_B.weight.t()
shape = result.shape
output = torch_addmm(
result.view(-1, shape[-1]),
xA.view(-1, xA.shape[-1]),
lora_B.weight.t(),
alpha = scaling,
beta = 1,
).view(shape)
bias = lora_B.bias
if bias is not None:
output = torch_add(
output,
bias,
alpha = scaling,
)
return output
pass
def unsloth_forward(self, x: torch.Tensor, *args: Any, **kwargs: Any):
adapter_names = kwargs.pop("adapter_names", None)
# If weight is used for matrix multiplication here, the final aggregation operation of the original
# parallel_linear layer will be missing, so we need to directly call its forward function to obtain the
# output of the original parallel_linear layer.
if self.disable_adapters:
if self.merged:
self.unmerge()
result, bias = self.base_layer(x, *args, **kwargs)
elif adapter_names is not None:
raise ValueError(f"{self.__class__.__name__} does not support mixed_batch_forward yet.")
elif self.merged:
result, bias = self.base_layer(x, *args, **kwargs)
else:
result, bias = self.base_layer(x, *args, **kwargs)
torch_result_dtype = result.dtype
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
if not torch.is_autocast_enabled(): result, x = result.to(lora_A.weight.dtype), x.to(lora_A.weight.dtype)
if not self.use_dora[active_adapter]:
return lora_forward(result, lora_A, lora_B, dropout, x, scaling)
else:
if isinstance(dropout, torch.nn.Identity) or not self.training:
base_result = result
else:
x = dropout(x)
base_result = None
result = result + self.lora_magnitude_vector[active_adapter](
x,
lora_A=lora_A,
lora_B=lora_B,
scaling=scaling,
base_layer=self.get_base_layer(),
base_result=base_result,
)
result = result.to(torch_result_dtype)
return result, bias
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