Add OS-independent #4652 regression tests

pymc3/tests/test_ode.py

@@ -11,6 +11,7 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+import sys
 
 import aesara
 import numpy as np
@@ -168,6 +169,9 @@ def ode_func_5(y, t, p):
         np.testing.assert_array_equal(np.ravel(model5_sens_ic), model5._sens_ic)
 
 
+@pytest.mark.xfail(
+    condition=sys.platform == "win32", reason="https://github.com/pymc-devs/aesara/issues/390"
+)
 def test_logp_scalar_ode():
     """Test the computation of the log probability for these models"""
 

pymc3/tests/test_shape_handling.py

@@ -12,6 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+import aesara
 import numpy as np
 import pytest
 
@@ -219,3 +220,28 @@ def test_sample_generate_values(fixture_model, fixture_sizes):
         prior = pm.sample_prior_predictive(samples=fixture_sizes)
         for rv in RVs:
             assert prior[rv.name].shape == size + tuple(rv.distribution.shape)
+
+
+@pytest.mark.xfail(reason="https://github.com/pymc-devs/aesara/issues/390")
+def test_size32_doesnt_break_broadcasting():
+    size32 = at.constant([1, 10], dtype="int32")
+    rv = pm.Normal.dist(0, 1, size=size32)
+    assert rv.broadcastable == (True, False)
+
+
+def test_observed_with_column_vector():
+    with pm.Model() as model:
+        # The `observed` is a broadcastable column vector
+        obs = at.as_tensor_variable(np.ones((3, 1), dtype=aesara.config.floatX))
+        assert obs.broadcastable == (False, True)
+
+        # Both shapes describe broadcastable volumn vectors
+        size64 = at.constant([3, 1], dtype="int64")
+        # But the second shape is upcasted from an int32 vector
+        cast64 = at.cast(at.constant([3, 1], dtype="int32"), dtype="int64")
+
+        pm.Normal("x_size64", mu=0, sd=1, size=size64, observed=obs)
+        model.logp()
+
+        pm.Normal("x_cast64", mu=0, sd=1, size=cast64, observed=obs)
+        model.logp()