chore: add .gitignore

unsloth_compiled_cache/LoraParallelLinear_peft_forward.py

@@ -0,0 +1,92 @@
+"""
+2026.2.1
+2026.2.1
+4.57.6
+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)
+
+        result = result.to(torch_result_dtype)
+    return result, bias