hypervision

Requirements

$ pip install transformers lightning scikit-learn tensorboard lit-nlp

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About supervision

• supervision contains LightningModule-based classes for model architecture and training. modeling has basic model kits, which comprises a couple of model (a subclass of model.LightningModuleBase) and its configuration (a subclass of config.ModelConfigBase)

• It is very encouraged to make your own model kits under modeling. You can easily place activation, objective, learning rate scheduler and whatever you want to put in your model in the configuration class. (See SentenceClassificationConfig as an example.)

• Once you defined your own model and config classes, (see SentenceClassificationModel and SentenceClassificationConfig) you can simply instantiate a model, which is actually pl.LightningModule at the core, by passing a config of that model you've just implemented.

• Please be noticed that the config.ModelConfigBase holds all of pretrained artifacts (AutoConfig, AutoTokenizer and AutoModel) from transformers at first. Then model.LightningModuleBase will automatically load the pretrained artifacts from model config object you've just passed to.

from modeling.sentence_classifier_kit import SentenceClassificationConfig, SentenceClassificationModel

config = SentenceClassificationConfig(
    pretrained_model_name_or_path='klue/bert-base',  # AutoTokenizer & AutoModel are prepared to be fed to model later.
    num_classes=2,
    batch_size=32,
    learning_rate=1e-5,
    pooling_strategy='cls',
    max_seq_length='max'
)

model = SentenceClassificationModel(config)  # model initiated with pretrained artifacts from config.

• data has all codes for training and evaluation data. You need to define custom DatasetBase, (a subclass of Dataset from torch) DataModuleBase (a subclass of pl.LightningDataModule) and custom collator function. (See collator.BaselineCSVsCollator as an example.)

Basic training with supervision

• You can see detailed, working code example of single training event at supervision/training.py.

import pytorch_lightning as pl
from modeling.sentence_classifier_kit import SentenceClassificationConfig, SentenceClassificationModel
from data.datamodule import BaselineCSVsDataModule
 
# loading model & data
config = SentenceClassificationConfig(**model_params)           # custom model config
model = SentenceClassificationModel(config)                     # pl.LightningModule
datamodule = BaselineCSVsDataModule(**datamodule_params)        # pl.LightningDataModule
 
# trainer
trainer = pl.Trainer(**trainer_params)
trainer.fit(model, datamodule)

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About hypervision

• hypervision.session has two classes for hyperparameter tuning. HypervisionSession is a supervisor (or 'hypervisor') of all subordinate supervised learning sessions (SupervisionSession) those who has a distinct set of hyperparameters required by config, model, datamodule and trainer at every individual session.

• You can easily set up a hyperparameter tuning loop based on grid search method. Just put in the supervision codes under the for loop which is motivated by HypervisionSession.supervision_sessions.

Basic hyperparameter tuning with hypervision

• You can see detailed, working code example of hyperparameter tuning at hypervision/tuning.py.

import pytorch_lightning as pl
from hypervision.session import HypervisionSession
from modeling.sentence_classifier_kit import SentenceClassificationConfig, SentenceClassificationModel
from data.datamodule import BaselineCSVsDataModule

hypervisor = HypervisionSession(session_name='DEMO')
 
for session in hypervisor.supervision_sessions:
    # begin of supervision session: it will internally set up callbacks and tensorboard logger.
    session.initiate()
     
    # put your supervision codes under the loop.
    config = SentenceClassificationConfig(**session.model_params)         # custom model config
    model = SentenceClassificationModel(config)                           # pl.LightningModule
    datamodule = BaselineCSVsDataModule(**session.datamodule_params)      # pl.LightningDataModule
     
    trainer = pl.Trainer(**session.trainer_params)
    trainer.fit(model, datamodule)
 
    # end of supervision session: wrapping up with best score and checkpoint are registered.
    session.terminate()
 
best_model = hypervisor.best_scored_session  # end of hyperparameter tuning loop.

• ... or simply use with.

for session in hypervisor.supervision_sessions:
    with session:
        config = SentenceClassificationConfig(**session.model_params)         # custom model config
        model = SentenceClassificationModel(config)                           # pl.LightningModule
        datamodule = BaselineCSVsDataModule(**session.datamodule_params)      # pl.LightningDataModule
         
        trainer = pl.Trainer(**session.trainer_params)
        trainer.fit(model, datamodule)

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How to load finetuned model

from modeling.sentence_classifier_kit import SentenceClassificationConfig, SentenceClassificationModel

config = SentenceClassificationConfig(pretrained_model_name_or_path='klue/bert-base', num_classes=2)
# It will not load immediately bare pretrained model from HuggingFace Hub until requested.

model = SentenceClassificationModel.load_from_checkpoint(
    checkpoint_path='../hypervision/checkpoints/YOUR_AWESOME_CHECKPOINT.ckpt',
    map_location={'cuda:0': 'cuda:0'},
    **{'config': config}
)