logs/unsloth_compiled_cache/LoraParallelLinear_peft_forward.py

"""
2026.5.1
2026.5.2
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/>.


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': 32, '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}
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.tp_layer import (Any, __name__, 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: 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)
            return lora_forward(result, lora_A, lora_B, dropout, x, scaling).to(torch_result_dtype)

        result = result.to(torch_result_dtype)
    return result, bias