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2026.5.3
2026.5.5
5.5.0
0.24.0
__UNSLOTH_VERSIONING__
"""
# Unsloth auto generated code
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
try:
from peft.tuners.lora.layer import VARIANT_KWARG_KEYS
except ImportError:
VARIANT_KWARG_KEYS = ['alora_offsets']
torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False, 'debug': False, 'dce': True, 'memory_planning': True, 'coordinate_descent_tuning': False, 'trace.graph_diagram': False, 'compile_threads': 28, 'group_fusion': True, 'disable_progress': True, 'verbose_progress': False, 'triton.multi_kernel': 0, 'triton.use_block_ptr': False, 'triton.enable_persistent_tma_matmul': True, 'triton.autotune_at_compile_time': False, 'triton.cooperative_reductions': False, 'cuda.compile_opt_level': '-O2', 'cuda.enable_cuda_lto': True, 'combo_kernels': False, 'benchmark_combo_kernel': True, 'combo_kernel_foreach_dynamic_shapes': True}
import torch._dynamo
@torch._dynamo.disable
def _call_8bit_base_layer(base_layer, x, *args, **kwargs):
return base_layer(x, *args, **kwargs)
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from unsloth_zoo.temporary_patches.common import torch_compile
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from peft.tuners.lora.bnb import (VARIANT_KWARG_KEYS, 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):
# Use result.dtype (bfloat16 from base layer) since x may have been cast to float32
# by _cast_input_dtype when autocast is disabled
target_dtype = result.dtype
xA = dropout(x).to(target_dtype) @ lora_A.weight.to(target_dtype).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.to(target_dtype).t(),
alpha = scaling,
beta = 1,
).view(shape)
bias = lora_B.bias
if bias is not None:
output = torch_add(
output,
bias.to(target_dtype),
alpha = scaling,
)
return output
pass
def unsloth_forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
adapter_names = kwargs.pop("adapter_names", None)
variant_kwargs = {k: kwargs.pop(k, None) for k in VARIANT_KWARG_KEYS} # don't pass these to base_layer
if self.disable_adapters:
if self.merged:
self.unmerge()
result = _call_8bit_base_layer(self.base_layer, x, *args, **kwargs)
elif adapter_names is not None:
result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **variant_kwargs, **kwargs)
elif self.merged:
result = _call_8bit_base_layer(self.base_layer, x, *args, **kwargs)
else:
result = _call_8bit_base_layer(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 active_adapter not in self.lora_variant: # vanilla LoRA
return lora_forward(result, lora_A, lora_B, dropout, x, scaling).to(result.dtype)
if requires_conversion:
output = output.to(expected_dtype)
result = result + output
else:
result = self.lora_variant[active_adapter].forward(
self,
active_adapter=active_adapter,
x=x,
result=result,
**variant_kwargs,
**kwargs,
)
if requires_conversion:
result = result.to(expected_dtype)
return result
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