Support PyTensor deterministic operations as observations

pymc/data.py

@@ -30,14 +30,13 @@
 from pytensor.compile.sharedvalue import SharedVariable
 from pytensor.graph.basic import Variable
 from pytensor.raise_op import Assert
-from pytensor.scalar import Cast
-from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.random.basic import IntegersRV
 from pytensor.tensor.type import TensorType
 from pytensor.tensor.variable import TensorConstant, TensorVariable
 
 import pymc as pm
 
+from pymc.logprob.utils import rvs_in_graph
 from pymc.pytensorf import GeneratorOp, convert_data, smarttypeX
 from pymc.vartypes import isgenerator
 
@@ -158,13 +157,7 @@ def is_valid_observed(v) -> bool:
         return True
 
     return (
-        # The only PyTensor operation we allow on observed data is type casting
-        # Although we could allow for any graph that does not depend on other RVs
-        (
-            isinstance(v.owner.op, Elemwise)
-            and isinstance(v.owner.op.scalar_op, Cast)
-            and is_valid_observed(v.owner.inputs[0])
-        )
+        not rvs_in_graph(v)
         # Or Minibatch
         or (
             isinstance(v.owner.op, MinibatchOp)

pymc/pytensorf.py

@@ -177,6 +177,12 @@ def extract_obs_data(x: TensorVariable) -> np.ndarray:
             mask[mask_idx] = 1
             return np.ma.MaskedArray(array_data, mask)
 
+    from pymc.logprob.utils import rvs_in_graph
+
+    if not inputvars(x) and not rvs_in_graph(x):
+        cheap_eval_mode = Mode(linker="py", optimizer=None)
+        return x.eval(mode=cheap_eval_mode)
+
     raise TypeError(f"Data cannot be extracted from {x}")
 
 

tests/test_data.py

@@ -602,11 +602,11 @@ def test_allowed(self):
         mb = pm.Minibatch(pt.as_tensor(self.data).astype(int), batch_size=20)
         assert isinstance(mb.owner.op, MinibatchOp)
 
-        with pytest.raises(ValueError, match="not valid for Minibatch"):
-            pm.Minibatch(pt.as_tensor(self.data) * 2, batch_size=20)
+        mb = pm.Minibatch(pt.as_tensor(self.data) * 2, batch_size=20)
+        assert isinstance(mb.owner.op, MinibatchOp)
 
-        with pytest.raises(ValueError, match="not valid for Minibatch"):
-            pm.Minibatch(self.data, pt.as_tensor(self.data) * 2, batch_size=20)
+        for mb in pm.Minibatch(self.data, pt.as_tensor(self.data) * 2, batch_size=20):
+            assert isinstance(mb.owner.op, MinibatchOp)
 
     def test_assert(self):
         d1, d2 = pm.Minibatch(self.data, self.data[::2], batch_size=20)
@@ -623,3 +623,14 @@ def test_multiple_vars(self):
         [draw_mA, draw_mB] = pm.draw([mA, mB])
         assert draw_mA.shape == (10,)
         np.testing.assert_allclose(draw_mA, -draw_mB)
+
+
+def test_scaling_data_works_in_likelihood() -> None:
+    data = np.array([10, 11, 12, 13, 14, 15])
+
+    with pm.Model():
+        target = pm.Data("target", data)
+        scale = 12
+        scaled_target = target / scale
+        mu = pm.Normal("mu", mu=0, sigma=1)
+        pm.Normal("x", mu=mu, sigma=1, observed=scaled_target)

tests/test_pytensorf.py

@@ -199,6 +199,21 @@ def test_minibatch_variable(self):
         assert isinstance(res, np.ndarray)
         np.testing.assert_array_equal(res, y)
 
+    def test_pytensor_operations(self):
+        x = np.array([1, 2, 3])
+        target = 1 + 3 * pt.as_tensor_variable(x)
+
+        res = extract_obs_data(target)
+        assert isinstance(res, np.ndarray)
+        np.testing.assert_array_equal(res, np.array([4, 7, 10]))
+
+    def test_pytensor_operations_raises(self):
+        x = pt.scalar("x")
+        target = 1 + 3 * x
+
+        with pytest.raises(TypeError, match="Data cannot be extracted from"):
+            extract_obs_data(target)
+
 
 @pytest.mark.parametrize("input_dtype", ["int32", "int64", "float32", "float64"])
 def test_convert_data(input_dtype):