Merge: Enable single point precision via env vars (#226)

Added
- env var for torch
- env var for numpy
- these are booleans that enforce usage of single precision if True,
False by default

Addressing suggestion from #223 

Notes:
- Did some very light testing via fulltest and setting the tox env vars
to use single precision there
- some tests expectedly fail due to numerical instability -> no action
required
- some tests fail because hypothesis generate incompatible floats -->
likely fixed by using `st.floats(...,width=32)` if single precision is
desired --> not done under the assumption we dont want tests enabled for
single precision, would require flexible checking everywhere
- some tests fail as constraints complain about combining Single and
Double precision --> likely because `rhs` and `coefficients` are defined
as python floats, hence 64 bit. This means using double precision with
env vars also implies providing explicit 32bit floats in such fields
(like `coefficients` or `rhs`), otherwise it wont work. This is not
something I'd fix but perhaps mention in the userguide

CHANGELOG.md

@@ -28,6 +28,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `torch`, `gpytorch` and `botorch` are lazy-loaded for improved startup time
 - If an exception is encountered during simulation, incomplete results are returned 
   with a warning instead of passing through the uncaught exception
+- Environment variables `BAYBE_NUMPY_USE_SINGLE_PRECISION` and
+  `BAYBE_TORCH_USE_SINGLE_PRECISION` to enforce single point precision usage
 
 ### Removed
 - `model_params` attribute from `Surrogate` base class, `GaussianProcessSurrogate` and

baybe/constraints/base.py

@@ -173,6 +173,7 @@ def to_botorch(
             if p in param_names
         ]
 
+        # TODO: Cast rhs to correct precision once BoTorch also supports single point.
         return (
             torch.tensor(param_indices),
             torch.tensor(self.coefficients, dtype=DTypeFloatTorch),

baybe/utils/numerical.py

@@ -1,11 +1,21 @@
 """Utilities for numeric operations."""
 
+import os
 from collections.abc import Sequence
 
 import numpy as np
 import numpy.typing as npt
 
-DTypeFloatNumpy = np.float64
+from baybe.utils.boolean import strtobool
+
+VARNAME_NUMPY_USE_SINGLE_PRECISION = "BAYBE_NUMPY_USE_SINGLE_PRECISION"
+"""Environment variable name for enforcing single precision in numpy."""
+
+DTypeFloatNumpy = (
+    np.float32
+    if strtobool(os.environ.get(VARNAME_NUMPY_USE_SINGLE_PRECISION, "False"))
+    else np.float64
+)
 """Floating point data type used for numpy arrays."""
 
 DTypeFloatONNX = np.float32

baybe/utils/torch.py

@@ -1,6 +1,18 @@
 """Torch utilities shipped as separate module for lazy-loading."""
 
+
+import os
+
 import torch
 
-DTypeFloatTorch = torch.float64
+from baybe.utils.boolean import strtobool
+
+VARNAME_TORCH_USE_SINGLE_PRECISION = "BAYBE_TORCH_USE_SINGLE_PRECISION"
+"""Environment variable name for enforcing single precision in torch."""
+
+DTypeFloatTorch = (
+    torch.float32
+    if strtobool(os.environ.get(VARNAME_TORCH_USE_SINGLE_PRECISION, "False"))
+    else torch.float64
+)
 """Floating point data type used for torch tensors."""