startswith ( "1.12.0" ): # we need to use a patched version of AdamW to fix # and allow examples to succeed with torch 1.12.0 (this torch bug is fixed in 1.12.1) from fts_examples.patched_adamw import AdamW else : from import AdamWĬlass RteBoolqDataModule ( pl. Import os import warnings from datetime import datetime from typing import Any, Dict, List, Optional from packaging.version import Version import sentencepiece as sp # noqa: F401 # isort: split import datasets import pytorch_lightning as pl import torch from datasets import logging as datasets_logging from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_ import TensorBoardLogger from pytorch_lightning.utilities import rank_zero_warn from _scheduler import CosineAnnealingWarmRestarts from import DataLoader from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer from transformers import logging as transformers_logging from transformers.tokenization_utils_base import BatchEncoding if Version ( torch. It will be named after your LightningModule subclass with the suffix _ft_schedule.yaml. Fine-tuning phases are zero-indexed and executed in ascending order.įirst, generate the default schedule to Trainer.log_dir. To specify a fine-tuning schedule, it’s convenient to first generate the default schedule and then alter the thawed/unfrozen parameter groups associated with each fine-tuning phase as desired. Rates can be set as demonstrated in the next section. While the current version of FinetuningScheduler only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning Less computationally efficient than a user-defined fine-tuning schedule but is useful for exploring a model’s fine-tuning behavior and can serve as a good baseline for subsequent explicit schedule refinement. Using the default/implicitly generated schedule will likely be Schedule definition is facilitated via the gen_ft_schedule method which dumps a default fine-tuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as desired by the user and/or subsequently passed to the callback. For additional installation options, please see the Fine-Tuning Scheduler README.įrom pytorch_lightning import Trainer from finetuning_scheduler import FinetuningScheduler trainer = Trainer ( callbacks = ) The Default Fine-Tuning Schedule ¶ Once the finetuning-scheduler package is installed, the FinetuningScheduler callback is available for use transformers, sentencepiece), we installed the finetuning-scheduler package with the extra above. Setup is straightforward, just install from PyPI! Since this notebook-based example requires a few additional packages (e.g. It dramatically increases fine-tuning flexibilityĮxpedites and facilitates exploration of model tuning dynamicsĮnables marginal performance improvements of fine-tuned models Training with the extension is simple and confers a host of benefits: The Fine-Tuning Scheduler extension accelerates and enhances model experimentation with flexible fine-tuning schedules. ! pip install -quiet "torch>=1.8" "ipython" "setuptools=59.5.0" "pytorch-lightning>=1.4" "finetuning-scheduler>=0.2.0" "torchmetrics>=0.7" Scheduled Fine-Tuning with the Fine-Tuning Scheduler Extension ¶ Multi-agent Reinforcement Learning With WarpDrive.Finetune Transformers Models with PyTorch Lightning.PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.GPU and batched data augmentation with Kornia and PyTorch-Lightning.Tutorial 13: Self-Supervised Contrastive Learning with SimCLR.Tutorial 12: Meta-Learning - Learning to Learn.Tutorial 10: Autoregressive Image Modeling.Tutorial 9: Normalizing Flows for Image Modeling.Tutorial 7: Deep Energy-Based Generative Models.Tutorial 6: Basics of Graph Neural Networks.Tutorial 5: Transformers and Multi-Head Attention.Tutorial 4: Inception, ResNet and DenseNet.Tutorial 3: Initialization and Optimization.LightningLite (Stepping Stone to Lightning).Organize existing PyTorch into Lightning.
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