darkpowerxo
7357458e45
Implement production-grade PEFT adapters targeting the 2.5 architecture: - LoRALinear: low-rank A/B decomposition with scaling (alpha/rank) - DoRALinear: weight-decomposed LoRA (magnitude + direction) - inject_adapters(): freezes base weights, wraps target nn.Linear modules - Supports fused QKV (qkv_proj), attention output, and FFN layers - num_adapter_layers controls how many top layers get adapters (0=all) - target_modules selects 'all', 'attention', or 'ffn' - merge_adapters(): folds adapter deltas back into base nn.Linear - save/load_adapter_weights(): safetensors adapter-only checkpoints - PEFTConfig dataclass with all hyperparameters References: LoRA — https://arxiv.org/abs/2106.09685 DoRA — https://arxiv.org/abs/2402.09353
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PEFT (LoRA/DoRA) fine-tuning pipeline for TimesFM 2.5."""
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from .adapters import (
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DoRALinear,
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LoRALinear,
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get_adapter_params,
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inject_adapters,
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load_adapter_weights,
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merge_adapters,
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save_adapter_weights,
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)
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from .config import PEFTConfig
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from .data import TimeSeriesDataset
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from .trainer import PEFTTrainer
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__all__ = [
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"PEFTConfig",
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"PEFTTrainer",
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"TimeSeriesDataset",
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"LoRALinear",
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"DoRALinear",
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"inject_adapters",
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"merge_adapters",
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"save_adapter_weights",
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"load_adapter_weights",
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"get_adapter_params",
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]
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