chore: add .gitignore

unsloth_compiled_cache/BatchNorm1d.py

@@ -0,0 +1,117 @@
+"""
+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/>.
+
+
+import os
+import torch
+import importlib.util
+import math
+if importlib.util.find_spec("unsloth_studio") is None:
+    UNSLOTH_STUDIO_ENABLED = False
+else:
+    UNSLOTH_STUDIO_ENABLED = os.environ.get("UNSLOTH_STUDIO_DISABLED", "0") == "0"
+pass
+from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
+import math
+
+UNSLOTH_ENABLE_LOGGING = os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") == "1"
+UNSLOTH_ENABLE_CCE = os.environ.get("UNSLOTH_ENABLE_CCE", "1") == "1"
+UNSLOTH_COMPILE_DISABLE = os.environ.get("UNSLOTH_COMPILE_DISABLE", "0") in ("1", "partial",)
+
+import logging
+logger_compiler = logging.getLogger(__name__)
+if UNSLOTH_ENABLE_LOGGING:
+    logger_compiler.setLevel(logging.DEBUG)
+
+global INFERENCE_RUNS
+INFERENCE_RUNS = 0
+
+try:
+    import torch._dynamo.eval_frame as torch_dynamo_eval_frame
+    torch_dynamo_eval_frame._stance.stance
+    torch_compiler_set_stance = torch.compiler.set_stance
+except:
+    torch_dynamo_eval_frame = None
+    torch_compiler_set_stance = None
+pass
+
+from unsloth_zoo import DEVICE_TYPE_TORCH, DEVICE_COUNT
+
+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 typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
+from transformers.models.gemma3.modeling_gemma3 import (nn)
+
+def forward(self, input: Tensor) -> Tensor:
+    self._check_input_dim(input)
+
+    # exponential_average_factor is set to self.momentum
+    # (when it is available) only so that it gets updated
+    # in ONNX graph when this node is exported to ONNX.
+    if self.momentum is None:
+        exponential_average_factor = 0.0
+    else:
+        exponential_average_factor = self.momentum
+
+    if self.training and self.track_running_stats:
+        # TODO: if statement only here to tell the jit to skip emitting this when it is None
+        if self.num_batches_tracked is not None:  # type: ignore[has-type]
+            self.num_batches_tracked.add_(1)  # type: ignore[has-type]
+            if self.momentum is None:  # use cumulative moving average
+                exponential_average_factor = 1.0 / float(self.num_batches_tracked)
+            else:  # use exponential moving average
+                exponential_average_factor = self.momentum
+
+    r"""
+    Decide whether the mini-batch stats should be used for normalization rather than the buffers.
+    Mini-batch stats are used in training mode, and in eval mode when buffers are None.
+    """
+    if self.training:
+        bn_training = True
+    else:
+        bn_training = (self.running_mean is None) and (self.running_var is None)
+
+    r"""
+    Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
+    passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
+    used for normalization (i.e. in eval mode when buffers are not None).
+    """
+    return F.batch_norm(
+        input,
+        # If buffers are not to be tracked, ensure that they won't be updated
+        (
+            self.running_mean
+            if not self.training or self.track_running_stats
+            else None
+        ),
+        self.running_var if not self.training or self.track_running_stats else None,
+        self.weight,
+        self.bias,
+        bn_training,
+        exponential_average_factor,
+        self.eps,
+    ).to(input.dtype).to(input.dtype)