#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
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 HeteroskedasticSingleTaskGP
from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
from botorch.models.gp_regression_mixed import MixedSingleTaskGP
from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel
from botorch.models.model_list_gp_regression import ModelListGP
from botorch.models.transforms.input import InputTransform
from botorch.models.transforms.outcome import OutcomeTransform
from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
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.
Args:
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`.
Returns:
The requested sub-module.
Example:
>>> 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."
)
# TODO: Add support for outcome transforms.
if any(getattr(m, "outcome_transform", None) is not None for m in models):
raise UnsupportedError(
"Conversion of models with outcome transforms 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)
"""
was_training = model_list.training
model_list.train()
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].likelihood, FixedNoiseGaussianLikelihood):
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)
# add batched kernel, except if the model type is SingleTaskMultiFidelityGP,
# which does not have a `covar_module`
if not isinstance(models[0], SingleTaskMultiFidelityGP):
batch_length = len(models)
covar_module = _batched_kernel(models[0].covar_module, batch_length)
kwargs["covar_module"] = covar_module
# 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.train(mode=was_training)
def _batched_kernel(kernel, batch_length: int):
"""Adds a batch dimension of size `batch_length` to all non-scalar
Tensor parameters that govern the kernel function `kernel`.
NOTE: prior or constraint parameters are excluded from batching.
"""
# copy just in case there are non-tensor parameters that are passed by reference
kernel = deepcopy(kernel)
search_str = "raw_outputscale"
for key, attr in kernel.state_dict().items():
if isinstance(attr, Tensor) and (
attr.ndim > 0 or (search_str == key.rpartition(".")[-1])
):
attr = attr.unsqueeze(0).expand(batch_length, *attr.shape).clone()
set_attribute(kernel, key, torch.nn.Parameter(attr))
return kernel
# two helper functions for `batched_kernel`
# like `setattr` and `getattr` for object hierarchies
[docs]def set_attribute(obj, attr: str, val):
"""Like `setattr` but works with hierarchical attribute specification.
E.g. if obj=Zoo(), and attr="tiger.age", set_attribute(obj, attr, 3),
would set the Zoo's tiger's age to three.
"""
path_to_leaf, _, attr_name = attr.rpartition(".")
leaf = get_attribute(obj, path_to_leaf) if path_to_leaf else obj
setattr(leaf, attr_name, val)
[docs]def get_attribute(obj, attr: str):
"""Like `getattr` but works with hierarchical attribute specification.
E.g. if obj=Zoo(), and attr="tiger.age", get_attribute(obj, attr),
would return the Zoo's tiger's age.
"""
attr_names = attr.split(".")
while attr_names:
obj = getattr(obj, attr_names.pop(0))
return obj
[docs]def batched_to_model_list(batch_model: BatchedMultiOutputGPyTorchModel) -> ModelListGP:
"""Convert a BatchedMultiOutputGPyTorchModel to a ModelListGP.
Args:
batch_model: 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)
"""
was_training = batch_model.training
batch_model.train()
# TODO: Add support for HeteroskedasticSingleTaskGP.
if isinstance(batch_model, HeteroskedasticSingleTaskGP):
raise NotImplementedError(
"Conversion of HeteroskedasticSingleTaskGP is currently not supported."
)
if isinstance(batch_model, MixedSingleTaskGP):
raise NotImplementedError(
"Conversion of MixedSingleTaskGP is currently not supported."
)
input_transform = getattr(batch_model, "input_transform", None)
outcome_transform = getattr(batch_model, "outcome_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,
outcome_transform=outcome_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": batch_model.train_targets.select(input_bdims, i)
.clone()
.unsqueeze(-1),
}
if isinstance(batch_model.likelihood, FixedNoiseGaussianLikelihood):
noise_covar = batch_model.likelihood.noise_covar
kwargs["train_Yvar"] = (
noise_covar.noise.select(input_bdims, i).clone().unsqueeze(-1)
)
if isinstance(batch_model, SingleTaskMultiFidelityGP):
kwargs.update(batch_model._init_args)
# NOTE: Adding outcome transform to kwargs to avoid the multiple
# values for same kwarg issue with SingleTaskMultiFidelityGP.
if outcome_transform is not None:
octf = outcome_transform.subset_output(idcs=[i])
kwargs["outcome_transform"] = octf
# Update the outcome transform state dict entries.
sd = {
**sd,
**{"outcome_transform." + k: v for k, v in octf.state_dict().items()},
}
else:
kwargs["outcome_transform"] = None
model = batch_model.__class__(input_transform=input_transform, **kwargs)
model.load_state_dict(sd)
models.append(model)
return ModelListGP(*models).train(mode=was_training)
[docs]def batched_multi_output_to_single_output(
batch_mo_model: BatchedMultiOutputGPyTorchModel,
) -> BatchedMultiOutputGPyTorchModel:
"""Convert a model from batched multi-output to a batched single-output.
Note: the underlying GPyTorch GP does not change. The GPyTorch GP's batch_shape
(referred to as `_aug_batch_shape`) is still `_input_batch_shape x num_outputs`.
The only things that change are the attributes of the
BatchedMultiOutputGPyTorchModel that are responsible the internal accounting of
the number of outputs: namely, num_outputs, _input_batch_shape, and
_aug_batch_shape.
Initially for the batched MO models these are: `num_outputs = m`,
`_input_batch_shape = train_X.batch_shape`, and
`_aug_batch_shape = train_X.batch_shape + torch.Size([num_outputs])`.
In the new SO model, these are: `num_outputs = 1`,
`_input_batch_shape = train_X.batch_shape + torch.Size([num_outputs])`,
and `_aug_batch_shape = train_X.batch_shape + torch.Size([num_outputs])`.
This is a (hopefully) temporary measure until multi-output MVNs with
independent outputs have better support in GPyTorch (see
https://github.com/cornellius-gp/gpytorch/pull/1083).
Args:
batched_mo_model: The BatchedMultiOutputGPyTorchModel
Returns:
The model converted into a batch single-output model.
Example:
>>> train_X = torch.rand(5, 2)
>>> train_Y = torch.rand(5, 2)
>>> batch_mo_gp = SingleTaskGP(train_X, train_Y)
>>> batch_so_gp = batched_multioutput_to_single_output(batch_gp)
"""
was_training = batch_mo_model.training
batch_mo_model.train()
# TODO: Add support for HeteroskedasticSingleTaskGP.
if isinstance(batch_mo_model, HeteroskedasticSingleTaskGP):
raise NotImplementedError(
"Conversion of HeteroskedasticSingleTaskGP currently not supported."
)
elif not isinstance(batch_mo_model, BatchedMultiOutputGPyTorchModel):
raise UnsupportedError("Only BatchedMultiOutputGPyTorchModels are supported.")
# TODO: Add support for custom likelihoods.
elif getattr(batch_mo_model, "_is_custom_likelihood", False):
raise NotImplementedError(
"Conversion of models with custom likelihoods is currently unsupported."
)
input_transform = getattr(batch_mo_model, "input_transform", None)
batch_sd = batch_mo_model.state_dict()
# TODO: add support for outcome transforms.
if hasattr(batch_mo_model, "outcome_transform"):
raise NotImplementedError(
"Converting batched multi-output models with outcome transforms "
"is not currently supported."
)
kwargs = {
"train_X": batch_mo_model.train_inputs[0].clone(),
"train_Y": batch_mo_model.train_targets.clone().unsqueeze(-1),
}
if isinstance(batch_mo_model.likelihood, FixedNoiseGaussianLikelihood):
noise_covar = batch_mo_model.likelihood.noise_covar
kwargs["train_Yvar"] = noise_covar.noise.clone().unsqueeze(-1)
if isinstance(batch_mo_model, SingleTaskMultiFidelityGP):
kwargs.update(batch_mo_model._init_args)
single_outcome_model = batch_mo_model.__class__(
input_transform=input_transform, **kwargs
)
single_outcome_model.load_state_dict(batch_sd)
return single_outcome_model.train(mode=was_training)
def _get_adjusted_batch_keys(
batch_state_dict: Dict[str, Tensor],
input_transform: Optional[InputTransform],
outcome_transform: Optional[OutcomeTransform] = None,
) -> 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.
outcome_transform: The outcome 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 params/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 transform buffers, so add them to non-adjusted set and remove
# them from tensors.
for transform, transform_type in [
(input_transform, "input_transform."),
(outcome_transform, "outcome_transform."),
]:
if transform is not None:
transform_keys = {
transform_type + n for n, p in transform.state_dict().items()
}
adjusted_batch_keys = adjusted_batch_keys - 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
