Implement multi-fidelity optimization

[sgbaird]

No description provided.

[sgbaird]

Need to set fidelity row to hidden for now

[Abrikosoff]

I've been trying to implement this in the Service API: Ax Issue 2514. Might be useful here (if I can verify its correctness!)

[sgbaird]

I've been trying to implement this in the Service API: Ax Issue 2514. Might be useful here (if I can verify its correctness!)

Thanks! I have https://github.com/sgbaird/honegumi/blob/main/scripts%2Frefreshers%2Fcontinuous_multi_fidelity.py though I'm not sure if this is the most straightforward way with the most recent Ax version. Will take a look!

[sgbaird]

Fidelity not complete, but at least removed from visibility for now.

[Abrikosoff]

In the end I was able to make the following work (for multiobjectives):

from botorch.test_functions.multi_objective_multi_fidelity import MOMFBraninCurrin
import torch

tkwargs = {  # Tkwargs is a dictionary contaning data about data type and data device
    "dtype": torch.double,
    "device": torch.device("cuda" if torch.cuda.is_available() else "cpu"),
}

BC = MOMFBraninCurrin(negate=True).to(**tkwargs)
dim_x = BC.dim
dim_y = BC.num_objectives

ref_point = torch.zeros(dim_y, **tkwargs)

n_INIT = 2  # Initialization budget in terms of the number of full-fidelity evaluations

from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import Models
from botorch.acquisition.multi_objective import qExpectedHypervolumeImprovement
from ax.models.torch.botorch_modular.surrogate import Surrogate
from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP

gs = GenerationStrategy(
    steps=[
        # Quasi-random initialization step
        GenerationStep(
            model=Models.SOBOL,
            num_trials=1,  # How many trials should be produced from this generation step
        ),
        # Bayesian optimization step using the custom acquisition function
        GenerationStep(
            model=Models.BOTORCH_MODULAR,
            num_trials=-1,  # No limitation on how many trials should be produced from this step
            # For `BOTORCH_MODULAR`, we pass in kwargs to specify what surrogate or acquisition function to use.
            model_kwargs={
                # "botorch_acqf_class": qHypervolumeKnowledgeGradient,
                "botorch_acqf_class": qExpectedHypervolumeImprovement,
                "surrogate": Surrogate(SingleTaskMultiFidelityGP),
            },
            model_gen_kwargs={
                                "model_gen_options": {
                                    "acqf_kwargs": {
                                        # "ref_point": ref_point,
                                        "data_fidelities": [2],
                    }
                }
            }  
        ),
    ]
)

import torch
from ax.service.ax_client import AxClient
from ax.service.utils.instantiation import ObjectiveProperties
from botorch.test_functions import Branin


# # Initialize the client - AxClient offers a convenient API to control the experiment
ax_client = AxClient(generation_strategy=gs)

ax_client.create_experiment(
    name="hartmann_test_experiment",
    parameters=[
        {
            "name": "x1",
            "type": "range",
            "bounds": [0.0, 1.0],
            "value_type": "float",  # Optional, defaults to inference from type of "bounds".
            "log_scale": False,  # Optional, defaults to False.
        },
        {
            "name": "x2",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
        # {
        #     "name": "x3",
        #     "type": "range",
        #     "bounds": [0.0, 1.0],
        # },
        {
            "name": "x4",
            "type": "range",
            "bounds": [0.0, 1.0],
            "is_fidelity": True,
            "target_value": 1.0,  
        },
    ],
    # Multi-objective optimization, using augmented Hartmann function (6D+1D).
    objectives={
        "a": ObjectiveProperties(minimize=False, threshold=BC.ref_point[0]),
        "b": ObjectiveProperties(minimize=False, threshold=BC.ref_point[1]),
    },
    # parameter_constraints=["x1 + x2 <= 2.0"],  # Optional.
    # outcome_constraints=["l2norm <= 1.25"],  # Optional.
)

def evaluate(parameters):
    evaluation = BC(
        torch.tensor([parameters.get("x1"), parameters.get("x2"), parameters.get("x4")])
    )
    return {"a": (evaluation[0].item(), 0.0), "b": (evaluation[1].item(), 0.0)}

NUM_EVALS = 40
# with fast_smoke_test():
for i in range(NUM_EVALS):
    parameters, trial_index = ax_client.get_next_trial()
    # Local evaluation here can be replaced with deployment to external system.
    ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))

Not sure if this is helpful in anyway, or even if it is correct, but I'm putting it here for completeness. Also I still wasn't able to make the MF-HVKG work with multiple objectives (linked above), so that's kind of ongoing.