taking upstream, squashing merge

Author: seashoo

.github/workflows/docs.yml

@@ -27,8 +27,6 @@ jobs:
       with:
         python-version: "3.13"
     - name: Install dependencies
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         uv pip install git+https://github.com/cornellius-gp/linear_operator.git
         uv pip install git+https://github.com/cornellius-gp/gpytorch.git

.github/workflows/nightly.yml

@@ -34,8 +34,6 @@ jobs:
       with:
         python-version: "3.13"
     - name: Install dependencies
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         uv pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
         uv pip install git+https://github.com/cornellius-gp/linear_operator.git
@@ -49,13 +47,9 @@ jobs:
         no_local_version=$(python -m setuptools_scm | cut -d "+" -f 1)
         echo "SETUPTOOLS_SCM_PRETEND_VERSION=${no_local_version}" >> $GITHUB_ENV
     - name: Build packages (wheel and source distribution)
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         python -m build --sdist --wheel
     - name: Verify packages
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         ./scripts/verify_py_packages.sh
     - name: Deploy to Test PyPI

.github/workflows/publish_website.yml

@@ -37,7 +37,7 @@ jobs:
     steps:
     - uses: actions/checkout@v4
       with:
-        ref: 'docusaurus-versions' # release branch
+        ref: 'gh-pages' # release branch
         fetch-depth: 0
     - name: Install uv
       uses: astral-sh/setup-uv@v5
@@ -57,8 +57,6 @@ jobs:
         uv pip install git+https://github.com/cornellius-gp/linear_operator.git
         uv pip install git+https://github.com/cornellius-gp/gpytorch.git
     - name: Install dependencies
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         uv pip install ."[dev, tutorials]"
     - if: ${{ inputs.new_version }}
@@ -92,7 +90,7 @@ jobs:
 
         git add versioned_docs/ versioned_sidebars/ versions.json
         git commit -m "Create version ${{ inputs.new_version }} of site in Docusaurus"
-        git push --force origin HEAD:docusaurus-versions
+        git push --force origin HEAD:gh-pages
     - name: Build website
       run: |
         bash scripts/build_docs.sh -b

.github/workflows/reusable_test.yml

@@ -38,8 +38,6 @@ jobs:
         uv pip install .[test]
     - name: Install dependencies with latest PyTorch & GPyTorch
       if: ${{ inputs.use_latest_pytorch_gpytorch }}
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         uv pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
         uv pip install git+https://github.com/cornellius-gp/linear_operator.git

.github/workflows/reusable_tutorials.yml

@@ -47,16 +47,13 @@ jobs:
     - if: ${{ inputs.use_stable_pytorch_gpytorch }}
       name: Install min required PyTorch, GPyTorch, and linear_operator
       run: |
-        python setup.py egg_info
-        req_txt="botorch.egg-info/requires.txt"
-        min_torch_version=$(grep '\btorch[>=]=' ${req_txt} | sed 's/[^0-9.]//g')
-        min_gpytorch_version=$(grep '\bgpytorch[>=]=' ${req_txt} | sed 's/[^0-9.]//g')
-        min_linear_operator_version=$(grep '\blinear_operator[>=]=' ${req_txt} | sed 's/[^0-9.]//g')
+        # Extract minimum versions from pyproject.toml
+        min_torch_version=$(grep '"torch>=' pyproject.toml | sed 's/.*"torch>=\([^"]*\)".*/\1/')
+        min_gpytorch_version=$(grep '"gpytorch>=' pyproject.toml | sed 's/.*"gpytorch>=\([^"]*\)".*/\1/')
+        min_linear_operator_version=$(grep '"linear_operator>=' pyproject.toml | sed 's/.*"linear_operator>=\([^"]*\)".*/\1/')
         uv pip install "numpy<2"  # Numpy >2.0 is not compatible with PyTorch <2.2.
         uv pip install "torch==${min_torch_version}" "gpytorch==${min_gpytorch_version}" "linear_operator==${min_linear_operator_version}" torchvision
     - name: Install BoTorch with tutorials dependencies
-      env:
-        ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         uv pip install .[tutorials]
     - if: ${{ inputs.smoke_test }}

.github/workflows/test_stable.yml

@@ -34,14 +34,12 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        uv pip install setuptools  # Needed for next line on Python 3.13.
-        python setup.py egg_info
-        req_txt="botorch.egg-info/requires.txt"
-        min_torch_version=$(grep '\btorch[>=]=' ${req_txt} | sed 's/[^0-9.]//g')
+        # Extract minimum versions from pyproject.toml
+        min_torch_version=$(grep '"torch>=' pyproject.toml | sed 's/.*"torch>=\([^"]*\)".*/\1/')
         # The earliest PyTorch version on Python 3.13 available for all OS is 2.6.0.
         min_torch_version="$(if ${{ matrix.python-version == '3.13' }}; then echo "2.6.0"; else echo "${min_torch_version}"; fi)"
-        min_gpytorch_version=$(grep '\bgpytorch[>=]=' ${req_txt} | sed 's/[^0-9.]//g')
-        min_linear_operator_version=$(grep '\blinear_operator[>=]=' ${req_txt} | sed 's/[^0-9.]//g')
+        min_gpytorch_version=$(grep '"gpytorch>=' pyproject.toml | sed 's/.*"gpytorch>=\([^"]*\)".*/\1/')
+        min_linear_operator_version=$(grep '"linear_operator>=' pyproject.toml | sed 's/.*"linear_operator>=\([^"]*\)".*/\1/')
         uv pip install "numpy<2"  # Numpy >2.0 is not compatible with PyTorch <2.2.
         uv pip install "torch==${min_torch_version}" "gpytorch==${min_gpytorch_version}" "linear_operator==${min_linear_operator_version}"
         uv pip install .[test]

CHANGELOG.md

@@ -2,6 +2,62 @@
 
 The release log for BoTorch.
 
+## [0.15.1] -- Aug 12, 2025
+This is a compatibility release, coming only one week after 0.15.0.
+
+#### New features
+* Enable optimizing a sequence of acquisition functions in `optimize_acqf` (#2931).
+
+## [0.15.0] -- Aug 5, 2025
+
+#### New Features
+* NP Regression Model w/ LIG Acquisition (#2683).
+* Fully Bayesian Matern GP with dimension scaling prior (#2855).
+* Option for input warping in non-linear fully Bayesian GPs (#2858).
+* Support for `condition_on_observations` in `FullyBayesianMultiTaskGP` (#2871).
+* Improvements to `optimize_acqf_mixed_alternating`:
+  * Support categoricals in alternating optimization (#2866).
+  * Batch mixed optimization (#2895).
+  * Non-equidistant discrete dimensions for `optimize_acqf_mixed_alternating` (#2923).
+  * Update syntax for categoricals in `optimize_acqf_mixed_alternating` (#2942).
+  * Equality constraints for `optimize_acqf_mixed_alternating` (#2944).
+* Multi-output acquisition functions and related utilities:
+  * Multi-Output Acquisition Functions (#2935).
+  * Utility for greedily selecting an approximate hypervolume maximizing subset (#2936).
+  * Update optimize with NSGA-II (#2937).
+  * Add utility for running pymoo NSGA-II (#2868).
+* Batched L-BFGS-B for more efficient acquisition function optimization (#2870, #2892).
+* Pathwise Thompson sampling for ensemble models (#2877).
+* ROBOT tutorial notebook (#2883).
+* Add community notebooks to the botorch.org website (#2913).
+
+#### Bug Fixes
+* Fix model paths in prior fitted networks (#2843).
+* Fix a bug where input transforms were not applied in fully Bayesian models in train mode (#2859).
+* Fix local `Y` vs global `Y_Train` in `generate_batch` function in TURBO tutorial (#2862).
+* Fix CUDA support for `FullyBayesianMTGP` (#2875).
+* Fix edge case with NaNs in `is_non_dominated` (#2925).
+* Normalize for correct fidelity in `qLowerBoundMaxValueEntropy` (#2930).
+* Bug: Botorch_community `VBLLModel` posterior doesn't work with single value tensor (#2929).
+* Fix variance shape bug in Riemann posterior (#2939).
+* Fix input constructor for `LogProbabilityOfFeasibility` (#2945).
+* Fix `AugmentedRosenbrock` problem and expand testing for optimizers (#2950).
+
+#### Other Changes
+* Improved documentation for `optimize_acqf` (#2865).
+* Fully Bayesian Multi-Task GP cleanup (#2869).
+* `average_over_ensemble_models` decorator for acquisition functions (#2873).
+* Changes to I-BNN tutorial (#2889).
+* Allow batched fixed features in gen_candidates_scipy and gen_candidates_torch (#2893)
+* Refactor of `MultiTask` / `FullyBayesianMultiTaskGP` to use `ProductKernel` and `IndexKernel` (#2908).
+* Various changes to PFNs to improve Ax compatibility (#2915, #2940).
+* Eliminate expensive indexing in `separate_mtmvn` (#2920).
+* Added reset method to `StoppingCriterion` (#2927).
+* Simplify closure dispatch (#2947).
+* Add BaseTestProblem.is_minimization_problem property (#2949).
+* Simplify NdarrayOptimizationClosure (#2951).
+
+
 ## [0.14.0] -- May 6, 2025
 
 #### Highlights

README.md

@@ -56,8 +56,8 @@ Optimization simply use Ax.
 **Installation Requirements**
 - Python >= 3.10
 - PyTorch >= 2.0.1
-- gpytorch == 1.14
-- linear_operator == 0.6
+- gpytorch >= 1.14
+- linear_operator >= 0.6
 - pyro-ppl >= 1.8.4
 - scipy
 - multiple-dispatch
@@ -86,13 +86,11 @@ conda install botorch -c gpytorch -c conda-forge
 
 If you would like to try our bleeding edge features (and don't mind potentially
 running into the occasional bug here or there), you can install the latest
-development version directly from GitHub. If you want to also install the
-current `gpytorch` and `linear_operator` development versions, you will need
-to ensure that the `ALLOW_LATEST_GPYTORCH_LINOP` environment variable is set:
+development version directly from GitHub. You may also want to install the
+current `gpytorch` and `linear_operator` development versions:
 ```bash
 pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git
 pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
-export ALLOW_LATEST_GPYTORCH_LINOP=true
 pip install --upgrade git+https://github.com/pytorch/botorch.git
 ```
 
@@ -117,7 +115,6 @@ pip install -e .
 ```bash
 git clone https://github.com/pytorch/botorch.git
 cd botorch
-export ALLOW_BOTORCH_LATEST=true
 pip install -e ".[dev, tutorials]"
 ```
 
@@ -136,7 +133,7 @@ For more details see our [Documentation](https://botorch.org/docs/introduction)
   ```python
   import torch
   from botorch.models import SingleTaskGP
-  from botorch.models.transforms import Normalize, Standardize
+  from botorch.models.transforms import Normalize
   from botorch.fit import fit_gpytorch_mll
   from gpytorch.mlls import ExactMarginalLogLikelihood
 
@@ -150,7 +147,6 @@ For more details see our [Documentation](https://botorch.org/docs/introduction)
       train_X=train_X,
       train_Y=Y,
       input_transform=Normalize(d=2),
-      outcome_transform=Standardize(m=1),
   )
   mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
   fit_gpytorch_mll(mll)

botorch/acquisition/analytic.py

@@ -37,7 +37,6 @@
 from botorch.utils.safe_math import log1mexp, logmeanexp
 from botorch.utils.transforms import (
     average_over_ensemble_models,
-    convert_to_target_pre_hook,
     t_batch_mode_transform,
 )
 from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
@@ -100,7 +99,7 @@ def _mean_and_sigma(
             posterior. Removes the last two dimensions if they have size one. Only
             returns a single tensor of means if compute_sigma is True.
         """
-        self.to(device=X.device)  # ensures buffers / parameters are on the same device
+        self.to(X)  # ensures buffers / parameters are on the same device and dtype
         posterior = self.model.posterior(
             X=X, posterior_transform=self.posterior_transform
         )
@@ -584,7 +583,6 @@ def __init__(
         self.objective_index = objective_index
         self.register_buffer("best_f", torch.as_tensor(best_f))
         ConstrainedAnalyticAcquisitionFunctionMixin.__init__(self, constraints)
-        self.register_forward_pre_hook(convert_to_target_pre_hook)
 
     @t_batch_mode_transform(expected_q=1)
     @average_over_ensemble_models
@@ -638,9 +636,7 @@ class LogProbabilityOfFeasibility(
     _log: bool = True
 
     def __init__(
-        self,
-        model: Model,
-        constraints: dict[int, tuple[float | None, float | None]],
+        self, model: Model, constraints: dict[int, tuple[float | None, float | None]]
     ) -> None:
         r"""Analytic Log Probability of Feasibility.
 
@@ -654,7 +650,6 @@ def __init__(
         AcquisitionFunction.__init__(self, model=model)
         self.posterior_transform = None
         ConstrainedAnalyticAcquisitionFunctionMixin.__init__(self, constraints)
-        self.register_forward_pre_hook(convert_to_target_pre_hook)
 
     @t_batch_mode_transform(expected_q=1)
     @average_over_ensemble_models
@@ -730,7 +725,6 @@ def __init__(
         self.objective_index = objective_index
         self.register_buffer("best_f", torch.as_tensor(best_f))
         ConstrainedAnalyticAcquisitionFunctionMixin.__init__(self, constraints)
-        self.register_forward_pre_hook(convert_to_target_pre_hook)
 
     @t_batch_mode_transform(expected_q=1)
     @average_over_ensemble_models

botorch/acquisition/cost_aware.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-import warnings
 from abc import ABC, abstractmethod
 from collections.abc import Callable
 
@@ -21,7 +20,6 @@
     IdentityMCObjective,
     MCAcquisitionObjective,
 )
-from botorch.exceptions.warnings import CostAwareWarning
 from botorch.models.deterministic import DeterministicModel
 from botorch.models.gpytorch import GPyTorchModel
 from botorch.sampling.base import MCSampler
@@ -112,7 +110,7 @@ def __init__(
         cost_model: DeterministicModel | GPyTorchModel,
         use_mean: bool = True,
         cost_objective: MCAcquisitionObjective | None = None,
-        min_cost: float = 1e-2,
+        log: bool = False,
     ) -> None:
         r"""Cost-aware utility that weights increase in utility by inverse cost.
         For negative increases in utility, the utility is instead scaled by the
@@ -130,7 +128,8 @@ def __init__(
                 un-transform predictions/samples of a cost model fit on the
                 log-transformed cost (often done to ensure non-negativity). If the
                 cost model is multi-output, then by default this will sum the cost
-                across outputs.
+                across outputs. NOTE: ``cost_objective`` must output
+                strictly positive values; forward will raise a ``ValueError`` otherwise.
             min_cost: A value used to clamp the cost samples so that they are not
                 too close to zero, which may cause numerical issues.
         Returns:
@@ -147,7 +146,7 @@ def __init__(
         self.cost_model = cost_model
         self.cost_objective: MCAcquisitionObjective = cost_objective
         self._use_mean = use_mean
-        self._min_cost = min_cost
+        self._log = log
 
     def forward(
         self,
@@ -202,18 +201,21 @@ def forward(
             cost = none_throws(sampler)(cost_posterior)
         cost = self.cost_objective(cost)
 
-        # Ensure non-negativity of the cost
-        if torch.any(cost < -1e-7):
-            warnings.warn(
-                "Encountered negative cost values in InverseCostWeightedUtility",
-                CostAwareWarning,
-                stacklevel=2,
+        # Ensure that costs are positive
+        if not torch.all(cost > 0.0):
+            raise ValueError(
+                "Costs must be strictly positive. Consider clamping cost_objective."
             )
-        # clamp (away from zero) and sum cost across elements of the q-batch -
-        # this will be of shape `num_fantasies x batch_shape` or `batch_shape`
-        cost = cost.clamp_min(self._min_cost).sum(dim=-1)
+
+        # sum costs along q-batch
+        cost = cost.sum(dim=-1)
 
         # compute and return the ratio on the sample level - If `use_mean=True`
         # this operation involves broadcasting the cost across fantasies.
-        # We multiply by the cost if the deltas are <= 0, see discussion #2914
-        return torch.where(deltas > 0, deltas / cost, deltas * cost)
+        if self._log:
+            # if _log is True then input deltas are in log space
+            # so original deltas cannot be <= 0
+            return deltas - torch.log(cost)
+        else:
+            # We multiply by the cost if the deltas are <= 0, see discussion #2914
+            return torch.where(deltas > 0, deltas / cost, deltas * cost)