fix: Add temporary upper bounds on NumPy and SciPy to pass tests

.github/workflows/docker.yml

@@ -101,8 +101,6 @@ jobs:
 
       - name: Run CLI API check
         run: |
-          printf "\npyhf\n"
-          docker run --rm pyhf/pyhf:sha-${GITHUB_SHA::8}
           printf "\npyhf --version\n"
           docker run --rm pyhf/pyhf:sha-${GITHUB_SHA::8} --version
           printf "\npyhf --help\n"

pyproject.toml

@@ -50,7 +50,8 @@ dependencies = [
     "jsonpatch>=1.15",
     "jsonschema>=4.15.0",  # for utils
     "pyyaml>=5.1",  # for parsing CLI equal-delimited options
-    "scipy>=1.5.2",  # requires numpy, which is required by pyhf and tensorflow
+    # c.f. https://github.com/scikit-hep/pyhf/issues/2593 for scipy v1.16.0 upper bound
+    "scipy>=1.5.2,<1.16.0",  # requires numpy, which is required by pyhf and tensorflow
     "tqdm>=4.56.0",  # for readxml
     "numpy",  # compatible versions controlled through scipy
 ]
@@ -70,7 +71,7 @@ Homepage = "https://github.com/scikit-hep/pyhf"
 shellcomplete = ["click_completion"]
 # TODO: 'tensorflow' supports all platform_machine for tensorflow v2.16.1+
 # but TensorFlow only supports python_version 3.8 up through tensorflow v2.13.1.
-# So until Python 3.8 support is dropped, split requirments on python_version
+# So until Python 3.8 support is dropped, split requirements on python_version
 # before and after 3.9.
 # NOTE: macos x86 support is deprecated from tensorflow v2.17.0 onwards.
 tensorflow = [
@@ -79,9 +80,14 @@ tensorflow = [
     "tensorflow-macos>=2.7.0; python_version < '3.9' and platform_machine == 'arm64' and platform_system == 'Darwin'",  # c.f. PR #2119, #2452
     "tensorflow-probability>=0.11.0; python_version < '3.9'",  # c.f. PR #1657, #2452
     # python >= 3.9
-    "tensorflow-probability[tf]>=0.24.0; python_version >= '3.9'"  # c.f. PR #2452
+    "tensorflow-probability[tf]>=0.24.0,<0.25.0; python_version >= '3.9' and platform_machine != 'arm64' and platform_system == 'Darwin'",  # c.f. TensorFlow v2.17.0
+    "tensorflow-probability[tf]>=0.24.0; python_version >= '3.9' and platform_machine == 'arm64' and platform_system == 'Darwin'",  # c.f. TensorFlow v2.17.0
+    "tensorflow-probability[tf]>=0.24.0; python_version >= '3.9' and platform_system != 'Darwin'"  # c.f. TensorFlow v2.17.0
+]
+torch = [
+    "torch>=1.10.0",  # c.f. PR #1657
+    "numpy<2.0"  # c.f. https://github.com/pytorch/pytorch/issues/157973
 ]
-torch = ["torch>=1.10.0"]  # c.f. PR #1657
 jax = [
     "jax>=0.4.1",  # c.f. PR #2079
     "jaxlib>=0.4.1",  # c.f. PR #2079
@@ -229,6 +235,11 @@ filterwarnings = [
     "ignore:Skipping device Apple Paravirtual device that does not support Metal 2.0:UserWarning",  # Can't fix given hardware/virtualized device
     'ignore:Type google._upb._message.[A-Z]+ uses PyType_Spec with a metaclass that has custom:DeprecationWarning',  # protobuf via tensorflow
     "ignore:jax.xla_computation is deprecated. Please use the AOT APIs:DeprecationWarning",  # jax v0.4.30
+    "ignore:'MultiCommand' is deprecated and will be removed in Click 9.0. Use 'Group' instead.:DeprecationWarning",  # Click
+    "ignore:Jupyter is migrating its paths to use standard platformdirs:DeprecationWarning",  # papermill
+    "ignore:datetime.datetime.utcnow\\(\\) is deprecated:DeprecationWarning",  # papermill
+    "ignore:In future, it will be an error for 'np.bool' scalars to be interpreted as an index:DeprecationWarning",  # PyTorch
+    "ignore:__array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments.:DeprecationWarning",  # PyTorch interacting with NumPy
 ]
 
 [tool.coverage.run]

src/pyhf/tensor/numpy_backend.py

@@ -11,13 +11,15 @@
 if TYPE_CHECKING:
     from numpy.typing import ArrayLike, DTypeLike, NBitBase, NDArray
 else:
+    ArrayLike = "ArrayLike"
     NBitBase = "NBitBase"
 
 from scipy import special
 from scipy.special import gammaln, xlogy
 from scipy.stats import norm, poisson
 
 from pyhf.typing import Literal, Shape
+from typing import cast
 
 T = TypeVar("T", bound=NBitBase)
 
@@ -26,27 +28,32 @@
 log = logging.getLogger(__name__)
 
 
-class _BasicPoisson:
+class _BasicPoisson(Generic[T]):
     def __init__(self, rate: Tensor[T]):
         self.rate = rate
 
     def sample(self, sample_shape: Shape) -> ArrayLike:
-        return poisson(self.rate).rvs(size=sample_shape + self.rate.shape)  # type: ignore[no-any-return]
+        return cast(
+            ArrayLike, poisson(self.rate).rvs(size=sample_shape + self.rate.shape)
+        )
 
-    def log_prob(self, value: NDArray[np.number[T]]) -> ArrayLike:
+    def log_prob(self, value: Tensor[T]) -> ArrayLike:
         tensorlib: numpy_backend[T] = numpy_backend()
         return tensorlib.poisson_logpdf(value, self.rate)
 
 
-class _BasicNormal:
+class _BasicNormal(Generic[T]):
     def __init__(self, loc: Tensor[T], scale: Tensor[T]):
         self.loc = loc
         self.scale = scale
 
     def sample(self, sample_shape: Shape) -> ArrayLike:
-        return norm(self.loc, self.scale).rvs(size=sample_shape + self.loc.shape)  # type: ignore[no-any-return]
+        return cast(
+            ArrayLike,
+            norm(self.loc, self.scale).rvs(size=sample_shape + self.loc.shape),
+        )
 
-    def log_prob(self, value: NDArray[np.number[T]]) -> ArrayLike:
+    def log_prob(self, value: Tensor[T]) -> ArrayLike:
         tensorlib: numpy_backend[T] = numpy_backend()
         return tensorlib.normal_logpdf(value, self.loc, self.scale)
 
@@ -125,7 +132,7 @@ def erf(self, tensor_in: Tensor[T]) -> ArrayLike:
         Returns:
             NumPy ndarray: The values of the error function at the given points.
         """
-        return special.erf(tensor_in)  # type: ignore[no-any-return]
+        return cast(ArrayLike, special.erf(tensor_in))
 
     def erfinv(self, tensor_in: Tensor[T]) -> ArrayLike:
         """
@@ -145,7 +152,7 @@ def erfinv(self, tensor_in: Tensor[T]) -> ArrayLike:
         Returns:
             NumPy ndarray: The values of the inverse of the error function at the given points.
         """
-        return special.erfinv(tensor_in)  # type: ignore[no-any-return]
+        return cast(ArrayLike, special.erfinv(tensor_in))
 
     def tile(self, tensor_in: Tensor[T], repeats: int | Sequence[int]) -> ArrayLike:
         """
@@ -207,7 +214,7 @@ def tolist(self, tensor_in: Tensor[T] | list[T]) -> list[T]:
             raise
 
     def outer(self, tensor_in_1: Tensor[T], tensor_in_2: Tensor[T]) -> ArrayLike:
-        return np.outer(tensor_in_1, tensor_in_2)  # type: ignore[arg-type]
+        return cast(ArrayLike, np.outer(tensor_in_1, tensor_in_2))
 
     def gather(self, tensor: Tensor[T], indices: NDArray[np.integer[T]]) -> ArrayLike:
         return tensor[indices]
@@ -255,7 +262,7 @@ def sum(self, tensor_in: Tensor[T], axis: int | None = None) -> ArrayLike:
         return np.sum(tensor_in, axis=axis)
 
     def product(self, tensor_in: Tensor[T], axis: Shape | None = None) -> ArrayLike:
-        return np.prod(tensor_in, axis=axis)  # type: ignore[arg-type]
+        return cast(ArrayLike, np.prod(tensor_in, axis=axis))
 
     def abs(self, tensor: Tensor[T]) -> ArrayLike:
         return np.abs(tensor)
@@ -345,7 +352,7 @@ def percentile(
         .. versionadded:: 0.7.0
         """
         # see https://github.com/numpy/numpy/issues/22125
-        return np.percentile(tensor_in, q, axis=axis, interpolation=interpolation)  # type: ignore[call-overload,no-any-return]
+        return cast(ArrayLike, np.percentile(tensor_in, q, axis=axis, interpolation=interpolation))  # type: ignore[call-overload]
 
     def stack(self, sequence: Sequence[Tensor[T]], axis: int = 0) -> ArrayLike:
         return np.stack(sequence, axis=axis)
@@ -392,7 +399,7 @@ def simple_broadcast(self, *args: Sequence[Tensor[T]]) -> Sequence[Tensor[T]]:
         return np.broadcast_arrays(*args)
 
     def shape(self, tensor: Tensor[T]) -> Shape:
-        return tensor.shape
+        return cast(Shape, tensor.shape)
 
     def reshape(self, tensor: Tensor[T], newshape: Shape) -> ArrayLike:
         return np.reshape(tensor, newshape)
@@ -434,10 +441,10 @@ def einsum(self, subscripts: str, *operands: Sequence[Tensor[T]]) -> ArrayLike:
         Returns:
             tensor: the calculation based on the Einstein summation convention
         """
-        return np.einsum(subscripts, *operands)  # type: ignore[arg-type,no-any-return]
+        return cast(ArrayLike, np.einsum(subscripts, *operands))
 
     def poisson_logpdf(self, n: Tensor[T], lam: Tensor[T]) -> ArrayLike:
-        return xlogy(n, lam) - lam - gammaln(n + 1.0)  # type: ignore[no-any-return]
+        return cast(ArrayLike, xlogy(n, lam) - lam - gammaln(n + 1.0))
 
     def poisson(self, n: Tensor[T], lam: Tensor[T]) -> ArrayLike:
         r"""
@@ -481,7 +488,7 @@ def poisson(self, n: Tensor[T], lam: Tensor[T]) -> ArrayLike:
         """
         _n = np.asarray(n)
         _lam = np.asarray(lam)
-        return np.exp(xlogy(_n, _lam) - _lam - gammaln(_n + 1.0))  # type: ignore[no-any-return,operator]
+        return cast(ArrayLike, np.exp(xlogy(_n, _lam) - _lam - gammaln(_n + 1)))
 
     def normal_logpdf(self, x: Tensor[T], mu: Tensor[T], sigma: Tensor[T]) -> ArrayLike:
         # this is much faster than
@@ -491,7 +498,7 @@ def normal_logpdf(self, x: Tensor[T], mu: Tensor[T], sigma: Tensor[T]) -> ArrayL
         root2pi = np.sqrt(2 * np.pi)
         prefactor = -np.log(sigma * root2pi)
         summand = -np.square(np.divide((x - mu), (root2 * sigma)))
-        return prefactor + summand  # type: ignore[no-any-return]
+        return cast(ArrayLike, prefactor + summand)
 
     # def normal_logpdf(self, x, mu, sigma):
     #     return norm.logpdf(x, loc=mu, scale=sigma)
@@ -522,7 +529,7 @@ def normal(self, x: Tensor[T], mu: Tensor[T], sigma: Tensor[T]) -> ArrayLike:
         Returns:
             NumPy float: Value of Normal(x|mu, sigma)
         """
-        return norm.pdf(x, loc=mu, scale=sigma)  # type: ignore[no-any-return]
+        return cast(ArrayLike, norm.pdf(x, loc=mu, scale=sigma))
 
     def normal_cdf(
         self, x: Tensor[T], mu: float | Tensor[T] = 0, sigma: float | Tensor[T] = 1
@@ -548,9 +555,9 @@ def normal_cdf(
         Returns:
             NumPy float: The CDF
         """
-        return norm.cdf(x, loc=mu, scale=sigma)  # type: ignore[no-any-return]
+        return cast(ArrayLike, norm.cdf(x, loc=mu, scale=sigma))
 
-    def poisson_dist(self, rate: Tensor[T]) -> _BasicPoisson:
+    def poisson_dist(self, rate: Tensor[T]) -> _BasicPoisson[T]:
         r"""
         The Poisson distribution with rate parameter :code:`rate`.
 
@@ -571,7 +578,7 @@ def poisson_dist(self, rate: Tensor[T]) -> _BasicPoisson:
         """
         return _BasicPoisson(rate)
 
-    def normal_dist(self, mu: Tensor[T], sigma: Tensor[T]) -> _BasicNormal:
+    def normal_dist(self, mu: Tensor[T], sigma: Tensor[T]) -> _BasicNormal[T]:
         r"""
         The Normal distribution with mean :code:`mu` and standard deviation :code:`sigma`.
 

tests/conftest.py

@@ -1,4 +1,5 @@
 import json
+import os
 import pathlib
 import shutil
 import sys
@@ -18,6 +19,12 @@ def pytest_addoption(parser):
         choices=["tensorflow", "pytorch", "jax", "minuit"],
         help="list of backends to disable in tests",
     )
+    parser.addoption(
+        "--enable-cuda",
+        action="store_true",
+        default=False,
+        help="Allow CUDA enabled backends to run CUDA accelerated code on GPUs",
+    )
 
 
 # Factory as fixture pattern
@@ -167,3 +174,14 @@ def datadir(tmp_path, request):
         shutil.copytree(test_dir, tmp_path, dirs_exist_ok=True)
 
     return tmp_path
+
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_cuda_environment(request):
+    """
+    Automatically force CUDA enabled backends to run in CPU mode unless
+    --enable-cuda is passed.
+    """
+    if not request.config.getoption("--enable-cuda"):
+        # Ensure testing on CPU and not GPU
+        os.environ["CUDA_VISIBLE_DEVICES"] = ""

tests/test_calculator.py

@@ -85,6 +85,7 @@
             fitted_pars.free_fit_to_data
         )
         # lower tolerance for amd64 and arm64 to agree
+        # FIXME: SciPy v1.16.0 gives a different result from SciPy v1.15.3
         assert pytest.approx(
             [7.6470499e-05, 1.4997178], rel=1e-3
         ) == pyhf.tensorlib.tolist(fitted_pars.free_fit_to_asimov)

tests/test_scripts.py

@@ -666,7 +666,12 @@ def test_missing_contrib_download(caplog):
 
         from pyhf.contrib.cli import download
 
-        runner = CliRunner(mix_stderr=False)
+        # mix_stderr removed in Click v8.2.0.
+        # Can simplify once pyhf is Python 3.10+.
+        try:
+            runner = CliRunner(mix_stderr=False)
+        except TypeError:
+            runner = CliRunner()
         result = runner.invoke(
             download,
             [