Introduction to the Fine-Tuning Scheduler

The FinetuningScheduler callback accelerates and enhances foundational model experimentation with flexible fine-tuning schedules. Training with the FinetuningScheduler callback is simple and confers a host of benefits:

  • it dramatically increases fine-tuning flexibility

  • expedites and facilitates exploration of model tuning dynamics

  • enables marginal performance improvements of fine-tuned models


If you’re exploring using the FinetuningScheduler, this is a great place to start! You may also find the notebook-based tutorial useful and for those using the LightningCLI, there is a CLI-based example at the bottom of this introduction.


Setup is straightforward, just install from PyPI!

pip install finetuning-scheduler

Additional installation options (from source etc.) are discussed under “Additional installation options” in the README


Fundamentally, the FinetuningScheduler callback enables multi-phase, scheduled fine-tuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize foundational model knowledge retention while allowing (typically upper layers of) the model to optimally adapt to new tasks during transfer learning 1 2 3 .

FinetuningScheduler orchestrates the gradual unfreezing of models via a fine-tuning schedule that is either implicitly generated (the default) or explicitly provided by the user (more computationally efficient). fine-tuning phase transitions are driven by FTSEarlyStopping criteria (a multi-phase extension of EarlyStopping), user-specified epoch transitions or a composition of the two (the default mode). A FinetuningScheduler training session completes when the final phase of the schedule has its stopping criteria met. See Early Stopping for more details on that callback’s configuration.

Basic Usage

If no fine-tuning schedule is user-provided, FinetuningScheduler will generate a default schedule and proceed to fine-tune according to the generated schedule, using default FTSEarlyStopping and FTSCheckpoint callbacks with monitor=val_loss.

from pytorch_lightning import Trainer
from finetuning_scheduler import FinetuningScheduler

trainer = Trainer(callbacks=[FinetuningScheduler()])

The Default Fine-Tuning Schedule

Schedule definition is facilitated via gen_ft_schedule() 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. Using the default/implicitly generated schedule will often be less computationally efficient than a user-defined fine-tuning schedule but can often serve as a good baseline for subsequent explicit schedule refinement and will marginally outperform many explicit schedules.

Specifying a Fine-Tuning Schedule

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. Fine-tuning phases are zero-indexed and executed in ascending order. In addition to being zero-indexed, fine-tuning phase keys should be contiguous and either integers or convertible to integers via int().

  1. First, generate the default schedule to Trainer.log_dir. It will be named after your LightningModule subclass with the suffix _ft_schedule.yaml.

from pytorch_lightning import Trainer
from finetuning_scheduler import FinetuningScheduler

trainer = Trainer(callbacks=[FinetuningScheduler(gen_ft_sched_only=True)])
  1. Alter the schedule as desired.

Changing the generated schedule for this boring model…

 1  0:
 2      params:
 3      - layer.3.bias
 4      - layer.3.weight
 5  1:
 6      params:
 7      - layer.2.bias
 8      - layer.2.weight
 9  2:
10      params:
11      - layer.1.bias
12      - layer.1.weight
13  3:
14      params:
15      - layer.0.bias
16      - layer.0.weight

… to have three fine-tuning phases instead of four:

 1  0:
 2      params:
 3      - layer.3.bias
 4      - layer.3.weight
 5  1:
 6      params:
 7      - layer.2.*
 8      - layer.1.bias
 9      - layer.1.weight
10  2:
11      params:
12      - layer.0.*
  1. Once the fine-tuning schedule has been altered as desired, pass it to FinetuningScheduler to commence scheduled training:

from pytorch_lightning import Trainer
from finetuning_scheduler import FinetuningScheduler

trainer = Trainer(callbacks=[FinetuningScheduler(ft_schedule="/path/to/my/schedule/my_schedule.yaml")])


For each fine-tuning phase, FinetuningScheduler will unfreeze/freeze parameters as directed in the explicitly specified or implicitly generated schedule. Prior to beginning the first phase of training (phase 0), FinetuningScheduler will inspect the optimizer to determine if the user has manually initialized the optimizer with parameters that are non-trainable or otherwise altered the parameter trainability states from that expected of the configured phase 0.

When manually configuring the optimizer initially, one should normally ensure non-trainable parameters are filtered out to (among other reasons) avoid triggering a parameter collision error in pytorch during a future training phase, e.g.:

1  def configure_optimizers(self):
2      parameters = list(filter(lambda x: x.requires_grad, self.parameters()))
3      optimizer = torch.optim.SGD(parameters, lr=1e-3, weight_decay=self.weight_decay)
4      ...

EarlyStopping and Epoch-Driven Phase Transition Criteria

By default, FTSEarlyStopping and epoch-driven transition criteria are composed. If a max_transition_epoch is specified for a given phase, the next finetuning phase will begin at that epoch unless FTSEarlyStopping criteria are met first. If epoch_transitions_only is True, FTSEarlyStopping will not be used and transitions will be exclusively epoch-driven.


Use of regex expressions can be convenient for specifying more complex schedules. Also, a per-phase base_max_lr can be specified:

 1 0:
 2   params: # the parameters for each phase definition can be fully specified
 3   - model.classifier.bias
 4   - model.classifier.weight
 5   max_transition_epoch: 3
 6 1:
 7   params: # or specified via a regex
 8   - model.albert.pooler.*
 9 2:
10   params:
11   - model.albert.encoder.*.ffn_output.*
12   max_transition_epoch: 9
13   lr: 1e-06 # per-phase maximum learning rates can be specified
14 3:
15   params: # both approaches to parameter specification can be used in the same phase
16   - model.albert.encoder.*.(ffn\.|attention|full*).*
17   - model.albert.encoder.embedding_hidden_mapping_in.bias
18   - model.albert.encoder.embedding_hidden_mapping_in.weight
19   - model.albert.embeddings.*

For a practical end-to-end example of using FinetuningScheduler in implicit versus explicit modes, see scheduled fine-tuning for SuperGLUE below or the notebook-based tutorial.

Resuming Scheduled Fine-Tuning Training Sessions

Resumption of scheduled fine-tuning training is identical to the continuation of other training sessions with the caveat that the provided checkpoint must have been saved by a FinetuningScheduler session. FinetuningScheduler uses FTSCheckpoint (an extension of ModelCheckpoint) to maintain schedule state with special metadata.

from pytorch_lightning import Trainer
from finetuning_scheduler import FinetuningScheduler

trainer = Trainer(callbacks=[FinetuningScheduler()], ckpt_path="some/path/to/my_checkpoint.ckpt")

Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions.


By default ( restore_best is True), FinetuningScheduler will attempt to restore the best available checkpoint before fine-tuning depth transitions.

trainer = Trainer(

Note that similar to the behavior of ModelCheckpoint, (specifically this PR), when resuming training with a different FTSCheckpoint dirpath from the provided checkpoint, the new training session’s checkpoint state will be re-initialized at the resumption depth with the provided checkpoint being set as the best checkpoint.

Fine-Tuning All The Way Down!

There are plenty of options for customizing FinetuningScheduler’s behavior, see scheduled fine-tuning for SuperGLUE below for examples of composing different configurations.


Custom or officially unsupported strategies and lr schedulers can be used by setting allow_untested to True.

Some officially unsupported strategies may work unaltered and are only unsupported due to the Fine-Tuning Scheduler project’s lack of CI/testing resources for that strategy (e.g. single_tpu). Most unsupported strategies and schedulers, however, are currently unsupported because they require varying degrees of modification to be compatible.

For instance, with respect to strategies, deepspeed requires an add_param_group method, tpu_spawn an override of the current broadcast method to include python objects.

Regarding lr schedulers, ChainedScheduler and SequentialLR are examples of schedulers not currently supported due to the configuration complexity and semantic conflicts supporting them would introduce. If a supported torch lr scheduler does not meet your requirements, one can always subclass a supported lr scheduler and modify it as required (e.g. LambdaLR is especially useful for this). PRs are also always welcome!

Example: Scheduled Fine-Tuning For SuperGLUE

A demonstration of the scheduled fine-tuning callback FinetuningScheduler using the RTE and BoolQ tasks of the SuperGLUE benchmark and the LightningCLI is available under ./fts_examples/.

Since this CLI-based example requires a few additional packages (e.g. transformers, sentencepiece), you should install them using the [examples] extra:

pip install finetuning-scheduler['examples']

There are three different demo schedule configurations composed with shared defaults (./config/fts_defaults.yaml) provided for the default ‘rte’ task. Note DDP (with auto-selected GPUs) is the default configuration so ensure you adjust the configuration files referenced below as desired for other configurations.

Note there will likely be minor variations in training paths and performance as packages (e.g. transformers, datasets, finetuning-scheduler itself etc.) evolve. The precise package versions and salient environmental configuration used in the building of this tutorial is available in the tensorboard summaries, logs and checkpoints referenced below if you’re interested.

# Generate a baseline without scheduled fine-tuning enabled:
python fit --config config/nofts_baseline.yaml

# Train with the default fine-tuning schedule:
python fit --config config/fts_implicit.yaml

# Train with a non-default fine-tuning schedule:
python fit --config config/fts_explicit.yaml

All three training scenarios use identical configurations with the exception of the provided fine-tuning schedule. See the tensorboard experiment summaries and table below for a characterization of the relative computational and performance tradeoffs associated with these FinetuningScheduler configurations.

FinetuningScheduler expands the space of possible fine-tuning schedules and the composition of more sophisticated schedules can yield marginal fine-tuning performance gains. That stated, it should be emphasized the primary utility of FinetuningScheduler is to grant greater fine-tuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3’s implicit training run, a critical tuning transition point is immediately apparent:

Our val_loss begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our schedule, in phase 17 we’re beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! Though beyond the scope of this documentation, it might be worth investigating these dynamics further and FinetuningScheduler allows one to do just that quite easily.

In addition to the tensorboard experiment summaries, full logs/schedules for all three scenarios are available as well as the checkpoints produced in the scenarios (caution, ~3.5GB).

Example Scenario
Fine-Tuning Schedule




RTE Accuracy
(0.81, 0.84, 0.85)

Note that though this example is intended to capture a common usage scenario, substantial variation is expected among use cases and models. In summary, FinetuningScheduler provides increased fine-tuning flexibility that can be useful in a variety of contexts from exploring model tuning behavior to maximizing performance.

FinetuningScheduler Explicit Loss Animation


The FinetuningScheduler callback is currently in beta.



Howard, J., & Ruder, S. (2018). Fine-tuned Language Models for Text Classification. ArXiv, abs/1801.06146.


Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019). An embarrassingly simple approach for transfer learning from pretrained language models. arXiv preprint arXiv:1902.10547.


Peters, M. E., Ruder, S., & Smith, N. A. (2019). To tune or not to tune? adapting pretrained representations to diverse tasks. arXiv preprint arXiv:1903.05987.

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