Tuning API in Katib for LLMs

[andreyvelich]

Tuning API in Katib for LLMs

[tariq-hasan]

Having worked through the Python SDK and examples for training operator and Katib I have further ideas on an appropriate implementation of the tuning API in Katib for LLMs.

It appears that the current implementation of the tune API for Katib Python SDK leverages a mandatory objective function to define the trial specification as a batch job. That said the higher-level interface to the existing API for Katib is meant to fine-tune pre-trained models on custom datasets.

The following are some important points to note:

• This implementation is in the context of LLMs
• The objective function has already been defined in the trainer module (along with a pre-requisite storage initializer module)
• The trainer and storage initializer modules are used to define containers for the master and workers in the specification for PyTorchJob as part of the implementation of the train API for training operator.

Following the example implementation of a Katib experiment using PyTorchJob we would therefore need to modify the tune API to take in either a combination of objective and parameters or a combination of model_provider_parameters, dataset_provider_parameters and train_parameters.

In the former case the code would default to defining a Katib experiment using a batch job in the trial specification. In the latter case the code would define a Katib experiment using a PyTorchJob in the trial specification. This PyTorchJob would define an init container and an app container for the master and use the same app container for the workers.

[tariq-hasan]

I presume that the initiative here is motivated by the recent trend in the ML space to fine-tune pre-trained models (LLMs or otherwise) using custom datasets instead of training bare models from scratch.

This requires that the interface provided to users for training and hyperparameter tuning needs to be enriched.

Training (training-operator):

The train function takes the following arguments and is essentially an abstraction over the create_job function that enables model fine-tuning.

• System parameters: Number of workers and number of resources (GPUs) per worker
• Model parameters: Model provider and repository details
• Dataset parameters: Dataset provider and dataset details
• Training parameters: Training-specific parameters e.g. learning rate, etc.

trainingClient.train(
   num_workers=1,
   num_procs_per_worker = 1,
   resources_per_worker={"gpu": "2", "cpu":8, "memory": "16Gi"},
   HuggingFaceModelParams(model='hf://openchat/openchat_3.5', access_token = "hf_..." ),
   S3DatasetParams(dataset= 's3://doc-example-bucket1/train_dataset', eval_dataset = "s3://doc-example-bucket1/eval_dataset", access_token = "s3 access token", region="us-west-2"),
   HuggingFaceTrainParams(learning_rate=0.1, transformerClass="Trainer", peft_config = {})
)

Hyperparameter tuning (Katib):

Taking inspiration from the design in training-operator I would think that the higher-level interface in Katib would be an abstraction over the tune function and would still allow users to specify function parameters such as hyperparameters, algorithm name, evaluation metric, etc. but that the objective function would be replaced by model provider and dataset provider.

katib_client.tune(
    name=exp_name,
    objective=train_mnist_model, # Objective function.
    parameters=parameters, # HyperParameters to tune.
    algorithm_name="cmaes", # Algorithm to use.
    objective_metric_name="accuracy", # Katib is going to optimize "accuracy".
    additional_metric_names=["loss"], # Katib is going to collect these metrics in addition to the objective metric.
    max_trial_count=12, # Trial Threshold.
    parallel_trial_count=2,
)

I presume then that the difference with this example implementation would just be that train_mnist_model is replaced with a model provider and a dataset provider that forms the basis for the hyperparameter tuning.

[andreyvelich]

/assign @helenxie-bit

[google-oss-prow]

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