Merge branch 'main' into hatchling

aesara/scalar/math.py

@@ -1481,3 +1481,180 @@ def c_code(self, *args, **kwargs):
 
 
 betainc_der = BetaIncDer(upgrade_to_float_no_complex, name="betainc_der")
+
+
+class Hyp2F1(ScalarOp):
+    """
+    Gaussian hypergeometric function ``2F1(a, b; c; z)``.
+    """
+
+    nin = 4
+    nfunc_spec = ("scipy.special.hyp2f1", 4, 1)
+
+    @staticmethod
+    def st_impl(a, b, c, z):
+        return scipy.special.hyp2f1(a, b, c, z)
+
+    def impl(self, a, b, c, z):
+        return Hyp2F1.st_impl(a, b, c, z)
+
+    def grad(self, inputs, grads):
+        a, b, c, z = inputs
+        (gz,) = grads
+        return [
+            gz * hyp2f1_der(a, b, c, z, wrt=0),
+            gz * hyp2f1_der(a, b, c, z, wrt=1),
+            gz * hyp2f1_der(a, b, c, z, wrt=2),
+            gz * ((a * b) / c) * hyp2f1(a + 1, b + 1, c + 1, z),
+        ]
+
+    def c_code(self, *args, **kwargs):
+        raise NotImplementedError()
+
+
+hyp2f1 = Hyp2F1(upgrade_to_float, name="hyp2f1")
+
+
+class Hyp2F1Der(ScalarOp):
+    """Derivatives of the Gaussian hypergeometric function :math:`2_F_1(a, b; c; z)`.
+
+    This is only implemented for one of the first three inputs.
+
+    Adapted from https://github.com/stan-dev/math/blob/develop/stan/math/prim/fun/grad_2F1.hpp
+
+    """
+
+    nin = 5
+
+    def impl(self, a, b, c, z, wrt):
+        def check_2f1_converges(a, b, c, z) -> bool:
+            num_terms = 0
+            is_polynomial = False
+
+            def is_nonpositive_integer(x):
+                return x <= 0 and x.is_integer()
+
+            if is_nonpositive_integer(a) and abs(a) >= num_terms:
+                is_polynomial = True
+                num_terms = int(np.floor(abs(a)))
+            if is_nonpositive_integer(b) and abs(b) >= num_terms:
+                is_polynomial = True
+                num_terms = int(np.floor(abs(b)))
+
+            is_undefined = is_nonpositive_integer(c) and abs(c) <= num_terms
+
+            return not is_undefined and (
+                is_polynomial or np.abs(z) < 1 or (np.abs(z) == 1 and c > (a + b))
+            )
+
+        def compute_grad_2f1(a, b, c, z, wrt):
+            r"""
+
+            Notes
+            -----
+            The algorithm can be derived by looking at the ratio of two successive terms in the series:
+
+            .. math::
+
+                \beta_{k+1} / \beta_{k} = A(k) / B(k) \\
+                \beta_{k+1} = A(k) / B(k) \beta_{k} \\
+                d[\beta_{k+1}] = d[A(k) / B(k)] \beta_{k} + A(k) / B(k) d[\beta_{k}]
+
+            via the product rule.
+
+            In the :math:`2_F_1`, :math:`A(k) / B(k)` corresponds to
+            :math:`(((a + k) (b + k) / ((c + k) (1 + k))) z` The partial
+            :math:`d[A(k)/B(k)]` with respect to the first three inputs can be
+            obtained from the ratio :math:`A(k)/B(k)`, by dropping the
+            respective term
+
+            .. math::
+
+                d/da[A(k) / B(k)] = A(k) / B(k) / (a + k) \\
+                d/db[A(k) / B(k)] = A(k) / B(k) / (b + k) \\
+                d/dc[A(k) / B(k)] = A(k) / B(k) (c + k)
+
+            The algorithm is implemented in the log scale, which adds the
+            complexity of working with absolute terms and tracking their signs.
+            """
+
+            wrt_a = wrt_b = False
+            if wrt == 0:
+                wrt_a = True
+            elif wrt == 1:
+                wrt_b = True
+            elif wrt != 2:
+                raise ValueError(f"wrt must be 0, 1, or 2; got {wrt}")
+
+            min_steps = 10  # https://github.com/stan-dev/math/issues/2857
+            max_steps = int(1e6)
+            precision = 1e-14
+
+            res = 0
+
+            if z == 0:
+                return res
+
+            log_g_old = -np.inf
+            log_t_old = 0.0
+            log_t_new = 0.0
+            sign_z = np.sign(z)
+            log_z = np.log(np.abs(z))
+
+            log_g_old_sign = 1
+            log_t_old_sign = 1
+            log_t_new_sign = 1
+            sign_zk = sign_z
+
+            for k in range(max_steps):
+                p = (a + k) * (b + k) / ((c + k) * (k + 1))
+                if p == 0:
+                    return res
+                log_t_new += np.log(np.abs(p)) + log_z
+                log_t_new_sign = np.sign(p) * log_t_new_sign
+
+                term = log_g_old_sign * log_t_old_sign * np.exp(log_g_old - log_t_old)
+                if wrt_a:
+                    term += np.reciprocal(a + k)
+                elif wrt_b:
+                    term += np.reciprocal(b + k)
+                else:
+                    term -= np.reciprocal(c + k)
+
+                log_g_old = log_t_new + np.log(np.abs(term))
+                log_g_old_sign = np.sign(term) * log_t_new_sign
+                g_current = log_g_old_sign * np.exp(log_g_old) * sign_zk
+                res += g_current
+
+                log_t_old = log_t_new
+                log_t_old_sign = log_t_new_sign
+                sign_zk *= sign_z
+
+                if k >= min_steps and np.abs(g_current) <= precision:
+                    return res
+
+            warnings.warn(
+                f"hyp2f1_der did not converge after {k} iterations",
+                RuntimeWarning,
+            )
+            return np.nan
+
+        # TODO: We could implement the Euler transform to expand supported domain, as Stan does
+        if not check_2f1_converges(a, b, c, z):
+            warnings.warn(
+                f"Hyp2F1 does not meet convergence conditions with given arguments a={a}, b={b}, c={c}, z={z}",
+                RuntimeWarning,
+            )
+            return np.nan
+
+        return compute_grad_2f1(a, b, c, z, wrt=wrt)
+
+    def __call__(self, a, b, c, z, wrt):
+        # This allows wrt to be a keyword argument
+        return super().__call__(a, b, c, z, wrt)
+
+    def c_code(self, *args, **kwargs):
+        raise NotImplementedError()
+
+
+hyp2f1_der = Hyp2F1Der(upgrade_to_float, name="hyp2f1_der")

aesara/tensor/inplace.py

@@ -392,6 +392,11 @@ def conj_inplace(a):
     """elementwise conjugate (inplace on `a`)"""
 
 
+@scalar_elemwise
+def hyp2f1_inplace(a, b, c, z):
+    """gaussian hypergeometric function"""
+
+
 pprint.assign(add_inplace, printing.OperatorPrinter("+=", -2, "either"))
 pprint.assign(mul_inplace, printing.OperatorPrinter("*=", -1, "either"))
 pprint.assign(sub_inplace, printing.OperatorPrinter("-=", -2, "left"))

aesara/tensor/math.py

@@ -1386,6 +1386,16 @@ def gammal(k, x):
     """Lower incomplete gamma function."""
 
 
+@scalar_elemwise
+def hyp2f1(a, b, c, z):
+    """Gaussian hypergeometric function."""
+
+
+@scalar_elemwise
+def hyp2f1_der(a, b, c, z):
+    """Derivatives for Gaussian hypergeometric function."""
+
+
 @scalar_elemwise
 def j0(x):
     """Bessel function of the first kind of order 0."""
@@ -3128,6 +3138,8 @@ def matmul(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None
     "power",
     "logaddexp",
     "logsumexp",
+    "hyp2f1",
+    "hyp2f1_der",
 ]
 
 DEPRECATED_NAMES = [

aesara/tensor/special.py

@@ -7,7 +7,7 @@
 from aesara.graph.basic import Apply
 from aesara.link.c.op import COp
 from aesara.tensor.basic import as_tensor_variable
-from aesara.tensor.math import neg, sum
+from aesara.tensor.math import gamma, neg, sum
 
 
 class SoftmaxGrad(COp):
@@ -768,7 +768,19 @@ def log_softmax(c, axis=UNSET_AXIS):
     return LogSoftmax(axis=axis)(c)
 
 
+def poch(z, m):
+    """Compute the Pochhammer/rising factorial."""
+    return gamma(z + m) / gamma(z)
+
+
+def factorial(n):
+    """Compute the factorial."""
+    return gamma(n + 1)
+
+
 __all__ = [
     "softmax",
     "log_softmax",
+    "poch",
+    "factorial",
 ]