Create test for mismatch between C and python Psi implementation

Author: ricardoV94

pytensor/scalar/math.py

@@ -378,53 +378,42 @@ def L_op(self, inputs, outputs, grads):
 
     def c_support_code(self, **kwargs):
         return """
-            // For GPU support
-            #ifdef WITHIN_KERNEL
-            #define DEVICE WITHIN_KERNEL
-            #else
-            #define DEVICE
-            #endif
-
-            #ifndef ga_double
-            #define ga_double double
-            #endif
-
             #ifndef _PSIFUNCDEFINED
             #define _PSIFUNCDEFINED
-            DEVICE double _psi(ga_double x) {
+            double _psi(double x) {
 
-            /*taken from
-            Bernardo, J. M. (1976). Algorithm AS 103:
-            Psi (Digamma) Function. Applied Statistics. 25 (3), 315-317.
-            http://www.uv.es/~bernardo/1976AppStatist.pdf */
+                /*taken from
+                Bernardo, J. M. (1976). Algorithm AS 103:
+                Psi (Digamma) Function. Applied Statistics. 25 (3), 315-317.
+                http://www.uv.es/~bernardo/1976AppStatist.pdf */
 
-            ga_double y, R, psi_ = 0;
-            ga_double S  = 1.0e-5;
-            ga_double C = 8.5;
-            ga_double S3 = 8.333333333e-2;
-            ga_double S4 = 8.333333333e-3;
-            ga_double S5 = 3.968253968e-3;
-            ga_double D1 = -0.5772156649;
+                double y, R, psi_ = 0;
+                double S  = 1.0e-5;
+                double C = 8.5;
+                double S3 = 8.333333333e-2;
+                double S4 = 8.333333333e-3;
+                double S5 = 3.968253968e-3;
+                double D1 = -0.5772156649;
 
-            y = x;
+                y = x;
 
-            if (y <= 0.0)
-               return psi_;
+                if (y <= 0.0)
+                   return psi_;
 
-            if (y <= S)
-                return D1 - 1.0/y;
+                if (y <= S)
+                    return D1 - 1.0/y;
 
-            while (y < C) {
-                psi_ = psi_ - 1.0 / y;
-                y = y + 1;
-            }
+                while (y < C) {
+                    psi_ = psi_ - 1.0 / y;
+                    y = y + 1;
+                }
 
-            R = 1.0 / y;
-            psi_ = psi_ + log(y) - .5 * R ;
-            R= R*R;
-            psi_ = psi_ - R * (S3 - R * (S4 - R * S5));
+                R = 1.0 / y;
+                psi_ = psi_ + log(y) - .5 * R ;
+                R= R*R;
+                psi_ = psi_ - R * (S3 - R * (S4 - R * S5));
 
-            return psi_;
+                return psi_;
             }
             #endif
             """
@@ -433,8 +422,8 @@ def c_code(self, node, name, inp, out, sub):
         (x,) = inp
         (z,) = out
         if node.inputs[0].type in float_types:
-            return f"""{z} =
-                _psi({x});"""
+            dtype = "npy_" + node.outputs[0].dtype
+            return f"({dtype}){z} = _psi({x});"
         raise NotImplementedError("only floating point is implemented")
 
 

tests/scalar/test_math.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 import pytest
+import scipy
 import scipy.special as sp
 
 import pytensor.tensor as pt
@@ -19,6 +20,7 @@
     gammal,
     gammau,
     hyp2f1,
+    psi,
 )
 from tests.link.test_link import make_function
 
@@ -149,3 +151,35 @@ def test_scalarloop_grad_mixed_dtypes(op, scalar_loop_grads):
             (var.owner and isinstance(var.owner.op, ScalarLoop))
             for var in ancestors(grad)
         )
+
+
+@pytest.mark.parametrize(
+    "linker",
+    [
+        "py",
+        pytest.param(
+            "c",
+            marks=pytest.mark.xfail(
+                reason="C implementation does not support negative inputs"
+            ),
+        ),
+    ],
+)
+def test_psi(linker):
+    x = float64("x")
+    out = psi(x)
+
+    fn = function([x], out, mode=Mode(linker=linker, optimizer="fast_run"))
+    fn.dprint()
+
+    x_test = np.float64(0.5)
+    np.testing.assert_allclose(
+        fn(x_test),
+        scipy.special.psi(x_test),
+        strict=True,
+    )
+    np.testing.assert_allclose(
+        fn(-x_test),
+        scipy.special.psi(-x_test),
+        strict=True,
+    )