Merge remote-tracking branch 'upstream' into update-test-time-action

Author: williambdean

.github/workflows/test.yml

@@ -54,7 +54,7 @@ jobs:
     if: ${{ needs.changes.outputs.changes == 'true' }}
     strategy:
       matrix:
-        python-version: ["3.10", "3.12"]
+        python-version: ["3.10", "3.13"]
     steps:
       - uses: actions/checkout@v4
         with:
@@ -75,15 +75,14 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.10", "3.12"]
+        python-version: ["3.10", "3.13"]
         numpy-version: ["~=1.26.0", ">=2.0"]
         fast-compile: [0, 1]
         float32: [0, 1]
         install-numba: [0]
         install-jax: [0]
         install-torch: [0]
         part:
-          - "--doctest-modules pytensor --ignore=pytensor/misc/check_duplicate_key.py --ignore=pytensor/link"
           - "tests --ignore=tests/tensor --ignore=tests/scan --ignore=tests/sparse"
           - "tests/scan"
           - "tests/sparse"
@@ -98,23 +97,24 @@ jobs:
             fast-compile: 1
           - python-version: "3.10"
             float32: 1
-          - python-version: "3.10"
-            part: "tests/tensor/test_math.py"
           - fast-compile: 1
             float32: 1
-          - part: "--doctest-modules pytensor --ignore=pytensor/misc/check_duplicate_key.py --ignore=pytensor/link"
-            float32: 1
-          - part: "--doctest-modules pytensor --ignore=pytensor/misc/check_duplicate_key.py --ignore=pytensor/link"
-            fast-compile: 1
           - numpy-version: "~=1.26.0"
             fast-compile: 1
           - numpy-version: "~=1.26.0"
             float32: 1
           - numpy-version: "~=1.26.0"
-            python-version: "3.12"
-          - numpy-version: "~=1.26.0"
-            part: "--doctest-modules pytensor --ignore=pytensor/misc/check_duplicate_key.py --ignore=pytensor/link"
+            python-version: "3.13"
         include:
+          - os: "ubuntu-latest"
+            part: "--doctest-modules pytensor --ignore=pytensor/misc/check_duplicate_key.py --ignore=pytensor/link"
+            python-version: "3.12"
+            numpy-version: ">=2.0"
+            fast-compile: 0
+            float32: 0
+            install-numba: 0
+            install-jax: 0
+            install-torch: 0
           - install-numba: 1
             os: "ubuntu-latest"
             python-version: "3.10"
@@ -124,7 +124,7 @@ jobs:
             part: "tests/link/numba"
           - install-numba: 1
             os: "ubuntu-latest"
-            python-version: "3.12"
+            python-version: "3.13"
             numpy-version: "~=2.1.0"
             fast-compile: 0
             float32: 0
@@ -138,7 +138,7 @@ jobs:
             part: "tests/link/jax"
           - install-jax: 1
             os: "ubuntu-latest"
-            python-version: "3.12"
+            python-version: "3.13"
             numpy-version: ">=2.0"
             fast-compile: 0
             float32: 0
@@ -151,23 +151,14 @@ jobs:
             float32: 0
             part: "tests/link/pytorch"
           - os: macos-15
-            python-version: "3.12"
+            python-version: "3.13"
             numpy-version: ">=2.0"
             fast-compile: 0
             float32: 0
             install-numba: 0
             install-jax: 0
             install-torch: 0
             part: "tests/tensor/test_blas.py tests/tensor/test_elemwise.py tests/tensor/test_math_scipy.py"
-          - os: "ubuntu-latest"
-            python-version: "3.10"
-            numpy-version: "~=1.26.0"
-            fast-compile: 0
-            float32: 0
-            install-numba: 0
-            install-jax: 0
-            install-torch: 0
-            part: "tests/tensor/test_math.py"
 
     steps:
       - uses: actions/checkout@v4
@@ -198,13 +189,13 @@ jobs:
         run: |
 
           if [[ $OS == "macos-15" ]]; then
-            micromamba install --yes -q "python~=${PYTHON_VERSION}=*_cpython" "numpy${NUMPY_VERSION}" scipy pip graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock libblas=*=*accelerate;
+            micromamba install --yes -q "python~=${PYTHON_VERSION}" "numpy${NUMPY_VERSION}" scipy pip graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock libblas=*=*accelerate;
           else
-            micromamba install --yes -q "python~=${PYTHON_VERSION}=*_cpython" mkl "numpy${NUMPY_VERSION}" scipy pip mkl-service graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock;
+            micromamba install --yes -q "python~=${PYTHON_VERSION}" mkl "numpy${NUMPY_VERSION}" scipy pip mkl-service graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock;
           fi
-          if [[ $INSTALL_NUMBA == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" "numba>=0.57"; fi
-          if [[ $INSTALL_JAX == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" jax jaxlib numpyro && pip install tensorflow-probability; fi
-          if [[ $INSTALL_TORCH == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" pytorch pytorch-cuda=12.1 "mkl<=2024.0" -c pytorch -c nvidia; fi
+          if [[ $INSTALL_NUMBA == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" "numba>=0.57"; fi
+          if [[ $INSTALL_JAX == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" jax jaxlib numpyro && pip install tensorflow-probability; fi
+          if [[ $INSTALL_TORCH == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" pytorch pytorch-cuda=12.1 "mkl<=2024.0" -c pytorch -c nvidia; fi
           pip install pytest-sphinx
           
           pip install -e ./
@@ -269,7 +260,7 @@ jobs:
       - name: Install dependencies
         shell: micromamba-shell {0}
         run: |
-          micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" mkl numpy scipy pip mkl-service cython pytest "numba>=0.57" jax jaxlib pytest-benchmark
+          micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" mkl numpy scipy pip mkl-service cython pytest "numba>=0.57" jax jaxlib pytest-benchmark
           pip install -e ./
           micromamba list && pip freeze
           python -c 'import pytensor; print(pytensor.config.__str__(print_doc=False))'
@@ -322,7 +313,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.13"
 
       - name: Install dependencies
         run: |

pyproject.toml

@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "pytensor"
 dynamic = ['version']
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<3.14"
 authors = [{ name = "pymc-devs", email = "[email protected]" }]
 description = "Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs."
 readme = "README.rst"
@@ -33,6 +33,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 
 keywords = [

pytensor/compile/function/pfunc.py

@@ -569,7 +569,7 @@ def construct_pfunc_ins_and_outs(
     if not fgraph:
         # Extend the outputs with the updates on input variables so they are
         # also cloned
-        additional_outputs = [i.update for i in inputs if i.update]
+        additional_outputs = [i.update for i in inputs if i.update is not None]
         if outputs is None:
             out_list = []
         else:
@@ -608,7 +608,7 @@ def construct_pfunc_ins_and_outs(
             new_i.variable = iv
 
             # If needed, replace the input's update by its cloned equivalent
-            if i.update:
+            if i.update is not None:
                 new_i.update = clone_d[i.update]
 
             new_inputs.append(new_i)

pytensor/compile/function/types.py

@@ -198,7 +198,7 @@ def std_fgraph(
     update_mapping = {}
     out_idx = len(output_specs)
     for idx, input_spec in enumerate(input_specs):
-        if input_spec.update:
+        if input_spec.update is not None:
             updates.append(input_spec.update)
             update_mapping[out_idx] = idx
             out_idx += 1
@@ -1195,7 +1195,7 @@ def insert_deepcopy(fgraph, wrapped_inputs, wrapped_outputs):
     updated_fgraph_inputs = {
         fgraph_i
         for i, fgraph_i in zip(wrapped_inputs, fgraph.inputs, strict=True)
-        if getattr(i, "update", False)
+        if getattr(i, "update", None) is not None
     }
 
     # We can't use fgraph.inputs as this don't include Constant Value.
@@ -1351,7 +1351,11 @@ def check_unused_inputs(inputs, outputs, on_unused_input):
             ancestors(
                 (
                     [o.variable for o in outputs]
-                    + [i.update for i in inputs if getattr(i, "update", False)]
+                    + [
+                        i.update
+                        for i in inputs
+                        if getattr(i, "update", None) is not None
+                    ]
                 ),
                 blockers=[i.variable for i in inputs],
             )

pytensor/compile/nanguardmode.py

@@ -36,7 +36,7 @@ def _is_numeric_value(arr, var):
         return False
     elif isinstance(arr, np.random.mtrand.RandomState | np.random.Generator):
         return False
-    elif var and isinstance(var.type, RandomType):
+    elif var is not None and isinstance(var.type, RandomType):
         return False
     elif isinstance(arr, slice):
         return False

pytensor/link/numba/dispatch/random.py

@@ -64,7 +64,6 @@ def numba_core_rv_funcify(op: Op, node: Apply) -> Callable:
 @numba_core_rv_funcify.register(ptr.LaplaceRV)
 @numba_core_rv_funcify.register(ptr.BinomialRV)
 @numba_core_rv_funcify.register(ptr.NegBinomialRV)
-@numba_core_rv_funcify.register(ptr.MultinomialRV)
 @numba_core_rv_funcify.register(ptr.PermutationRV)
 @numba_core_rv_funcify.register(ptr.IntegersRV)
 def numba_core_rv_default(op, node):
@@ -132,6 +131,15 @@ def random(rng, b, scale):
     return random
 
 
+@numba_core_rv_funcify.register(ptr.InvGammaRV)
+def numba_core_InvGammaRV(op, node):
+    @numba_basic.numba_njit
+    def random(rng, shape, scale):
+        return 1 / rng.gamma(shape, 1 / scale)
+
+    return random
+
+
 @numba_core_rv_funcify.register(ptr.CategoricalRV)
 def core_CategoricalRV(op, node):
     @numba_basic.numba_njit
@@ -142,6 +150,29 @@ def random_fn(rng, p):
     return random_fn
 
 
+@numba_core_rv_funcify.register(ptr.MultinomialRV)
+def core_MultinomialRV(op, node):
+    dtype = op.dtype
+
+    @numba_basic.numba_njit
+    def random_fn(rng, n, p):
+        n_cat = p.shape[0]
+        draws = np.zeros(n_cat, dtype=dtype)
+        remaining_p = np.float64(1.0)
+        remaining_n = n
+        for i in range(n_cat - 1):
+            draws[i] = rng.binomial(remaining_n, p[i] / remaining_p)
+            remaining_n -= draws[i]
+            if remaining_n <= 0:
+                break
+            remaining_p -= p[i]
+        if remaining_n > 0:
+            draws[n_cat - 1] = remaining_n
+        return draws
+
+    return random_fn
+
+
 @numba_core_rv_funcify.register(ptr.MvNormalRV)
 def core_MvNormalRV(op, node):
     method = op.method

pytensor/scalar/basic.py

@@ -823,6 +823,37 @@ def get_scalar_type(dtype, cache: dict[str, ScalarType] = {}) -> ScalarType:
 
 
 class _scalar_py_operators:
+    # These can't work because Python requires native output types
+    def __bool__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python boolean. "
+            "Call `.astype(bool)` for the symbolic equivalent."
+        )
+
+    def __index__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python integer. "
+            "Call `.astype(int)` for the symbolic equivalent."
+        )
+
+    def __int__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python integer. "
+            "Call `.astype(int)` for the symbolic equivalent."
+        )
+
+    def __float__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python float. "
+            "Call `.astype(float)` for the symbolic equivalent."
+        )
+
+    def __complex__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python complex number. "
+            "Call `.astype(complex)` for the symbolic equivalent."
+        )
+
     # So that we can simplify checking code when we have a mixture of ScalarType
     # variables and Tensor variables
     ndim = 0
@@ -843,11 +874,6 @@ def __abs__(self):
     def __neg__(self):
         return neg(self)
 
-    # CASTS
-    # def __int__(self): return AsInt(self).out
-    # def __float__(self): return AsDouble(self).out
-    # def __complex__(self): return AsComplex(self).out
-
     # BITWISE
     def __invert__(self):
         return invert(self)

pytensor/scalar/loop.py

@@ -60,12 +60,12 @@ def __init__(
             constant = []
         if not len(init) == len(update):
             raise ValueError("An update must be given for each init variable")
-        if until:
+        if until is not None:
             inputs, outputs = clone([*init, *constant], [*update, until])
         else:
             inputs, outputs = clone([*init, *constant], update)
 
-        self.is_while = bool(until)
+        self.is_while = until is not None
         self.inputs, self.outputs = self._cleanup_graph(inputs, outputs)
         self._validate_updates(self.inputs, self.outputs)
 

pytensor/scalar/math.py

@@ -856,7 +856,7 @@ def inner_loop_a(sum_a, delta, xpow, k_minus_one_minus_n, fac, dfac, x):
             dfac = k_minus_one_minus_n * dfac + fac
             fac *= k_minus_one_minus_n
             delta = dfac / xpow
-            return (sum_a, delta, xpow, k_minus_one_minus_n, fac, dfac), ()
+            return (sum_a, delta, xpow, k_minus_one_minus_n, fac, dfac), None
 
         init = [sum_a0, delta, xpow, k_minus_one_minus_n, fac, dfac]
         constant = [x]

pytensor/scan/basic.py

@@ -979,7 +979,7 @@ def wrap_into_list(x):
         # user-specified within the inner-function (e.g. by returning an update
         # `dict`) or the `SharedVariable.default_update`s of a shared variable
         # created in the inner-function.
-        if input.update and (is_local or input.variable in updates):
+        if input.update is not None and (is_local or input.variable in updates):
             # We need to remove the `default_update`s on the shared
             # variables created within the context of the loop function
             # (e.g. via use of `RandomStream`); otherwise, they'll get