Source code for botorch.models.converter

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

Utilities for converting between different models.

from __future__ import annotations

from copy import deepcopy
from typing import Dict, Optional, Set, Tuple

import torch
from botorch.exceptions import UnsupportedError
from botorch.models.gp_regression import FixedNoiseGP, HeteroskedasticSingleTaskGP
from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel
from botorch.models.model_list_gp_regression import ModelListGP
from botorch.models.transforms.input import InputTransform
from torch import Tensor
from torch.nn import Module

def _get_module(module: Module, name: str) -> Module:
    """Recursively get a sub-module from a module.

        module: A `torch.nn.Module`.
        name: The name of the submodule to return, in the form of a period-delinated
            string: `sub_module.subsub_module.[...].leaf_module`.

        The requested sub-module.

        >>> gp = SingleTaskGP(train_X, train_Y)
        >>> noise_prior = _get_module(gp, "likelihood.noise_covar.noise_prior")
    current = module
    if name != "":
        for a in name.split("."):
            current = getattr(current, a)
    return current

def _check_compatibility(models: ModelListGP) -> None:
    """Check if a ModelListGP can be converted."""
    # check that all submodules are of the same type
    for modn, mod in models[0].named_modules():
        mcls = mod.__class__
        if not all(isinstance(_get_module(m, modn), mcls) for m in models[1:]):
            raise UnsupportedError(
                "Sub-modules must be of the same type across models."

    # check that each model is a BatchedMultiOutputGPyTorchModel
    if not all(isinstance(m, BatchedMultiOutputGPyTorchModel) for m in models):
        raise UnsupportedError(
            "All models must be of type BatchedMultiOutputGPyTorchModel."

    # TODO: Add support for HeteroskedasticSingleTaskGP
    if any(isinstance(m, HeteroskedasticSingleTaskGP) for m in models):
        raise NotImplementedError(
            "Conversion of HeteroskedasticSingleTaskGP is currently unsupported."

    # TODO: Add support for custom likelihoods
    if any(getattr(m, "_is_custom_likelihood", False) for m in models):
        raise NotImplementedError(
            "Conversion of models with custom likelihoods is currently unsupported."

    # check that each model is single-output
    if not all(m._num_outputs == 1 for m in models):
        raise UnsupportedError("All models must be single-output.")

    # check that training inputs are the same
    if not all(
        torch.equal(ti, tj)
        for m in models[1:]
        for ti, tj in zip(models[0].train_inputs, m.train_inputs)
        raise UnsupportedError("training inputs must agree for all sub-models.")

    # check that there are no batched input transforms
    default_size = torch.Size([])
    for m in models:
        if hasattr(m, "input_transform"):
            if (
                m.input_transform is not None
                and len(getattr(m.input_transform, "batch_shape", default_size)) != 0
                raise UnsupportedError("Batched input_transforms are not supported.")

    # check that all models have the same input transforms
    if any(hasattr(m, "input_transform") for m in models):
        if not all(
            m.input_transform.equals(models[0].input_transform) for m in models[1:]
            raise UnsupportedError("All models must have the same input_transforms.")

[docs]def model_list_to_batched(model_list: ModelListGP) -> BatchedMultiOutputGPyTorchModel: """Convert a ModelListGP to a BatchedMultiOutputGPyTorchModel. Args: model_list: The `ModelListGP` to be converted to the appropriate `BatchedMultiOutputGPyTorchModel`. All sub-models must be of the same type and have the shape (batch shape and number of training inputs). Returns: The model converted into a `BatchedMultiOutputGPyTorchModel`. Example: >>> list_gp = ModelListGP(gp1, gp2) >>> batch_gp = model_list_to_batched(list_gp) """ models = model_list.models _check_compatibility(models) # if the list has only one model, we can just return a copy of that if len(models) == 1: return deepcopy(models[0]) # construct inputs train_X = deepcopy(models[0].train_inputs[0]) train_Y = torch.stack([m.train_targets.clone() for m in models], dim=-1) kwargs = {"train_X": train_X, "train_Y": train_Y} if isinstance(models[0], FixedNoiseGP): kwargs["train_Yvar"] = torch.stack( [m.likelihood.noise_covar.noise.clone() for m in models], dim=-1 ) if isinstance(models[0], SingleTaskMultiFidelityGP): init_args = models[0]._init_args if not all( v == m._init_args[k] for m in models[1:] for k, v in init_args.items() ): raise UnsupportedError("All models must have the same fidelity parameters.") kwargs.update(init_args) # construct the batched GP model input_transform = getattr(models[0], "input_transform", None) batch_gp = models[0].__class__(input_transform=input_transform, **kwargs) adjusted_batch_keys, non_adjusted_batch_keys = _get_adjusted_batch_keys( batch_state_dict=batch_gp.state_dict(), input_transform=input_transform ) input_batch_dims = len(models[0]._input_batch_shape) # ensure scalars agree (TODO: Allow different priors for different outputs) for n in non_adjusted_batch_keys: v0 = _get_module(models[0], n) if not all(torch.equal(_get_module(m, n), v0) for m in models[1:]): raise UnsupportedError("All scalars must have the same value.") # ensure dimensions of all tensors agree for n in adjusted_batch_keys: shape0 = _get_module(models[0], n).shape if not all(_get_module(m, n).shape == shape0 for m in models[1:]): raise UnsupportedError("All tensors must have the same shape.") # now construct the batched state dict non_adjusted_batch_state_dict = { s: p.clone() for s, p in models[0].state_dict().items() if s in non_adjusted_batch_keys } adjusted_batch_state_dict = { t: ( torch.stack( [m.state_dict()[t].clone() for m in models], dim=input_batch_dims ) if "active_dims" not in t else models[0].state_dict()[t].clone() ) for t in adjusted_batch_keys } batch_state_dict = {**non_adjusted_batch_state_dict, **adjusted_batch_state_dict} # load the state dict into the new model batch_gp.load_state_dict(batch_state_dict) return batch_gp
[docs]def batched_to_model_list(batch_model: BatchedMultiOutputGPyTorchModel) -> ModelListGP: """Convert a BatchedMultiOutputGPyTorchModel to a ModelListGP. Args: model_list: The `BatchedMultiOutputGPyTorchModel` to be converted to a `ModelListGP`. Returns: The model converted into a `ModelListGP`. Example: >>> train_X = torch.rand(5, 2) >>> train_Y = torch.rand(5, 2) >>> batch_gp = SingleTaskGP(train_X, train_Y) >>> list_gp = batched_to_model_list(batch_gp) """ # TODO: Add support for HeteroskedasticSingleTaskGP if isinstance(batch_model, HeteroskedasticSingleTaskGP): raise NotImplementedError( "Conversion of HeteroskedasticSingleTaskGP currently not supported." ) input_transform = getattr(batch_model, "input_transform", None) batch_sd = batch_model.state_dict() adjusted_batch_keys, non_adjusted_batch_keys = _get_adjusted_batch_keys( batch_state_dict=batch_sd, input_transform=input_transform ) input_bdims = len(batch_model._input_batch_shape) models = [] for i in range(batch_model._num_outputs): non_adjusted_batch_sd = { s: batch_sd[s].clone() for s in non_adjusted_batch_keys } adjusted_batch_sd = { t: ( batch_sd[t].select(input_bdims, i).clone() if "active_dims" not in t else batch_sd[t].clone() ) for t in adjusted_batch_keys } sd = {**non_adjusted_batch_sd, **adjusted_batch_sd} kwargs = { "train_X": batch_model.train_inputs[0].select(input_bdims, i).clone(), "train_Y":, i) .clone() .unsqueeze(-1), } if isinstance(batch_model, FixedNoiseGP): noise_covar = batch_model.likelihood.noise_covar kwargs["train_Yvar"] = (, i).clone().unsqueeze(-1) ) if isinstance(batch_model, SingleTaskMultiFidelityGP): kwargs.update(batch_model._init_args) model = batch_model.__class__(input_transform=input_transform, **kwargs) model.load_state_dict(sd) models.append(model) return ModelListGP(*models)
def _get_adjusted_batch_keys( batch_state_dict: Dict[str, Tensor], input_transform: Optional[InputTransform] ) -> Tuple[Set[str], Set[str]]: r"""Group the keys based on whether the value requires batch shape changes. Args: batch_state_dict: The state dict of the batch model input_transform: The input transform Returns: A two-element tuple containing: - The keys of the parameters/buffers that require a batch shape adjustment - The keys of the parameters/buffers that do not require a batch shape adjustment """ # these are the names of the parameters/buffers that need their batch shape adjusted adjusted_batch_keys = {n for n, p in batch_state_dict.items() if len(p.shape) > 0} # don't modify input transform buffers, so add them to non-adjusted set and remove # them from tensors if input_transform is not None: input_transform_keys = { "input_transform." + n for n, p in input_transform.state_dict().items() } adjusted_batch_keys = adjusted_batch_keys - input_transform_keys # these are the names of the parameters/buffers that don't need their # batch shape adjusted non_adjusted_batch_keys = set(batch_state_dict) - adjusted_batch_keys return adjusted_batch_keys, non_adjusted_batch_keys