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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.aqlm import (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):
# note: logic differs from default Linear because merging is not supported
result = self.base_layer(x)
if self.disable_adapters:
return result
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]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
x = self._cast_input_dtype(x, lora_A.weight.dtype)
output = lora_B(lora_A(dropout(x)))
if requires_conversion:
output = output.to(expected_dtype)
output = output * scaling
result += output
return result
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