File size: 2,940 Bytes
9014afd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
"""
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.bnb 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, *args, **kwargs) -> torch.Tensor:
adapter_names = kwargs.pop("adapter_names", None)
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif adapter_names is not None:
result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
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)
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
output = 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,
)
if requires_conversion:
output = output.to(expected_dtype)
result = result + output
return result
|