Numba dispatch of ScalarLoop

Author: ricardoV94

pytensor/link/numba/dispatch/scalar.py

@@ -16,6 +16,7 @@
     get_name_for_object,
     unique_name_generator,
 )
+from pytensor.scalar import ScalarLoop
 from pytensor.scalar.basic import (
     Add,
     Cast,
@@ -69,7 +70,7 @@ def numba_funcify_ScalarOp(op, node, **kwargs):
             scalar_func_numba = wrap_cython_function(
                 cython_func, output_dtype, input_dtypes
             )
-            has_pyx_skip_dispatch = scalar_func_numba.has_pyx_skip_dispatch
+            has_pyx_skip_dispatch = scalar_func_numba.has_pyx_skip_dispatch()
             input_inner_dtypes = scalar_func_numba.numpy_arg_dtypes()
             output_inner_dtype = scalar_func_numba.numpy_output_dtype()
 
@@ -331,3 +332,46 @@ def softplus(x):
         return numba_basic.direct_cast(value, out_dtype)
 
     return softplus
+
+
+@numba_funcify.register(ScalarLoop)
+def numba_funcify_ScalarLoop(op, node, **kwargs):
+    inner_fn = numba_basic.numba_njit(numba_funcify(op.fgraph))
+
+    if op.is_while:
+        n_update = len(op.outputs) - 1
+
+        @numba_basic.numba_njit
+        def while_loop(n_steps, *inputs):
+            carry, constant = inputs[:n_update], inputs[n_update:]
+
+            until = False
+            for i in range(n_steps):
+                outputs = inner_fn(*carry, *constant)
+                carry, until = outputs[:-1], outputs[-1]
+                if until:
+                    break
+
+            return *carry, until
+
+        return while_loop
+
+    else:
+        n_update = len(op.outputs)
+
+        @numba_basic.numba_njit
+        def for_loop(n_steps, *inputs):
+            carry, constant = inputs[:n_update], inputs[n_update:]
+
+            if n_steps < 0:
+                raise ValueError("ScalarLoop does not have a termination condition.")
+
+            for i in range(n_steps):
+                carry = inner_fn(*carry, *constant)
+
+            if n_update == 1:
+                return carry[0]
+            else:
+                return carry
+
+        return for_loop

tests/link/numba/test_elemwise.py

@@ -586,18 +586,43 @@ def test_elemwise_multiple_inplace_outs():
 
 
 def test_scalar_loop():
-    a = float64("a")
-    scalar_loop = pytensor.scalar.ScalarLoop([a], [a + a])
+    a_scalar = float64("a")
+    const_scalar = float64("const")
+    scalar_loop = pytensor.scalar.ScalarLoop(
+        init=[a_scalar],
+        update=[a_scalar + a_scalar + const_scalar],
+        constant=[const_scalar],
+    )
 
-    x = pt.tensor("x", shape=(3,))
-    elemwise_loop = Elemwise(scalar_loop)(3, x)
+    a = pt.tensor("a", shape=(3,))
+    const = pt.tensor("const", shape=(3,))
+    n_steps = 3
+    elemwise_loop = Elemwise(scalar_loop)(n_steps, a, const)
 
-    with pytest.warns(UserWarning, match="object mode"):
-        compare_numba_and_py(
-            [x],
-            [elemwise_loop],
-            (np.array([1, 2, 3], dtype="float64"),),
-        )
+    compare_numba_and_py(
+        [a, const],
+        [elemwise_loop],
+        [np.array([1, 2, 3], dtype="float64"), np.array([1, 1, 1], dtype="float64")],
+    )
+
+
+def test_gammainc_wrt_k_grad():
+    x = pt.vector("x", dtype="float64")
+    k = pt.vector("k", dtype="float64")
+
+    out = pt.gammainc(k, x)
+    grad_out = grad(out.sum(), k)
+
+    compare_numba_and_py(
+        [x, k],
+        [grad_out],
+        # These values of x and k trigger all the branches in the gradient of gammainc
+        [
+            np.array([0.0, 29.0, 31.0], dtype="float64"),
+            np.array([1.0, 13.0, 11.0], dtype="float64"),
+        ],
+        eval_obj_mode=False,
+    )
 
 
 class TestsBenchmark:

tests/link/numba/test_scalar.py

@@ -6,6 +6,7 @@
 import pytensor.scalar.math as psm
 import pytensor.tensor as pt
 from pytensor import config, function
+from pytensor.scalar import ScalarLoop
 from pytensor.scalar.basic import Composite
 from pytensor.tensor import tensor
 from pytensor.tensor.elemwise import Elemwise
@@ -184,3 +185,63 @@ def test_Softplus(dtype):
             strict=True,
             err_msg=f"Failed for value {value}",
         )
+
+
+class TestScalarLoop:
+    def test_scalar_for_loop_single_out(self):
+        n_steps = ps.int64("n_steps")
+        x0 = ps.float64("x0")
+        const = ps.float64("const")
+        x = x0 + const
+
+        op = ScalarLoop(init=[x0], constant=[const], update=[x])
+        x = op(n_steps, x0, const)
+
+        fn = function([n_steps, x0, const], [x], mode=numba_mode)
+
+        res_x = fn(n_steps=5, x0=0, const=1)
+        np.testing.assert_allclose(res_x, 5)
+
+        res_x = fn(n_steps=5, x0=0, const=2)
+        np.testing.assert_allclose(res_x, 10)
+
+        res_x = fn(n_steps=4, x0=3, const=-1)
+        np.testing.assert_allclose(res_x, -1)
+
+    def test_scalar_for_loop_multiple_outs(self):
+        n_steps = ps.int64("n_steps")
+        x0 = ps.float64("x0")
+        y0 = ps.int64("y0")
+        const = ps.float64("const")
+        x = x0 + const
+        y = y0 + 1
+
+        op = ScalarLoop(init=[x0, y0], constant=[const], update=[x, y])
+        x, y = op(n_steps, x0, y0, const)
+
+        fn = function([n_steps, x0, y0, const], [x, y], mode=numba_mode)
+
+        res_x, res_y = fn(n_steps=5, x0=0, y0=0, const=1)
+        np.testing.assert_allclose(res_x, 5)
+        np.testing.assert_allclose(res_y, 5)
+
+        res_x, res_y = fn(n_steps=5, x0=0, y0=0, const=2)
+        np.testing.assert_allclose(res_x, 10)
+        np.testing.assert_allclose(res_y, 5)
+
+        res_x, res_y = fn(n_steps=4, x0=3, y0=2, const=-1)
+        np.testing.assert_allclose(res_x, -1)
+        np.testing.assert_allclose(res_y, 6)
+
+    def test_scalar_while_loop(self):
+        n_steps = ps.int64("n_steps")
+        x0 = ps.float64("x0")
+        x = x0 + 1
+        until = x >= 10
+
+        op = ScalarLoop(init=[x0], update=[x], until=until)
+        fn = function([n_steps, x0], op(n_steps, x0), mode=numba_mode)
+        np.testing.assert_allclose(fn(n_steps=20, x0=0), [10, True])
+        np.testing.assert_allclose(fn(n_steps=20, x0=1), [10, True])
+        np.testing.assert_allclose(fn(n_steps=5, x0=1), [6, False])
+        np.testing.assert_allclose(fn(n_steps=0, x0=1), [1, False])