update pytensor version, make xfail more elaborate

Author: ferrine

requirements.txt

@@ -4,6 +4,6 @@ cloudpickle
 fastprogress>=0.2.0
 numpy>=1.15.0
 pandas>=0.24.0
-pytensor>=2.18.1,<2.19
+pytensor>=2.19.0,<2.20
 scipy>=1.4.1
 typing-extensions>=3.7.4

tests/sampling/test_jax.py

@@ -85,16 +85,12 @@ def test_jax_PosDefMatrix():
  pytest.param(1),
  pytest.param(
  2,
- marks=pytest.mark.skipif(
- len(jax.devices()) < 2, reason="not enough devices"
- ),
+ marks=pytest.mark.skipif(len(jax.devices()) < 2, reason="not enough devices"),
  ),
  ],
 )
 @pytest.mark.parametrize("postprocessing_vectorize", ["scan", "vmap"])
-def test_transform_samples(
- sampler, postprocessing_backend, chains, postprocessing_vectorize
-):
+def test_transform_samples(sampler, postprocessing_backend, chains, postprocessing_vectorize):
  pytensor.config.on_opt_error = "raise"
  np.random.seed(13244)
 
@@ -241,9 +237,7 @@ def test_replace_shared_variables():
  x = pytensor.shared(5, name="shared_x")
 
  new_x = _replace_shared_variables([x])
- shared_variables = [
- var for var in graph_inputs(new_x) if isinstance(var, SharedVariable)
- ]
+ shared_variables = [var for var in graph_inputs(new_x) if isinstance(var, SharedVariable)]
  assert not shared_variables
 
  x.default_update = x + 1
@@ -333,30 +327,23 @@ def test_idata_kwargs(
 
  posterior = idata.get("posterior")
  assert posterior is not None
- x_dim_expected = idata_kwargs.get(
- "dims", model_test_idata_kwargs.named_vars_to_dims
- )["x"][0]
+ x_dim_expected = idata_kwargs.get("dims", model_test_idata_kwargs.named_vars_to_dims)["x"][0]
  assert x_dim_expected is not None
  assert posterior["x"].dims[-1] == x_dim_expected
 
- x_coords_expected = idata_kwargs.get("coords", model_test_idata_kwargs.coords)[
- x_dim_expected
- ]
+ x_coords_expected = idata_kwargs.get("coords", model_test_idata_kwargs.coords)[x_dim_expected]
  assert x_coords_expected is not None
  assert list(x_coords_expected) == list(posterior["x"].coords[x_dim_expected].values)
 
  assert posterior["z"].dims[2] == "z_coord"
  assert np.all(
- posterior["z"].coords["z_coord"].values
- == np.array(["apple", "banana", "orange"])
+ posterior["z"].coords["z_coord"].values == np.array(["apple", "banana", "orange"])
  )
 
 
 def test_get_batched_jittered_initial_points():
  with pm.Model() as model:
- x = pm.MvNormal(
- "x", mu=np.zeros(3), cov=np.eye(3), shape=(2, 3), initval=np.zeros((2, 3))
- )
+ x = pm.MvNormal("x", mu=np.zeros(3), cov=np.eye(3), shape=(2, 3), initval=np.zeros((2, 3)))
 
  # No jitter
  ips = _get_batched_jittered_initial_points(
@@ -365,17 +352,13 @@ def test_get_batched_jittered_initial_points():
  assert np.all(ips[0] == 0)
 
  # Single chain
- ips = _get_batched_jittered_initial_points(
- model=model, chains=1, random_seed=1, initvals=None
- )
+ ips = _get_batched_jittered_initial_points(model=model, chains=1, random_seed=1, initvals=None)
 
  assert ips[0].shape == (2, 3)
  assert np.all(ips[0] != 0)
 
  # Multiple chains
- ips = _get_batched_jittered_initial_points(
- model=model, chains=2, random_seed=1, initvals=None
- )
+ ips = _get_batched_jittered_initial_points(model=model, chains=2, random_seed=1, initvals=None)
 
  assert ips[0].shape == (2, 2, 3)
  assert np.all(ips[0][0] != ips[0][1])
@@ -395,9 +378,7 @@ def test_get_batched_jittered_initial_points():
  pytest.param(1),
  pytest.param(
  2,
- marks=pytest.mark.skipif(
- len(jax.devices()) < 2, reason="not enough devices"
- ),
+ marks=pytest.mark.skipif(len(jax.devices()) < 2, reason="not enough devices"),
  ),
  ],
 )
@@ -421,12 +402,8 @@ def test_seeding(chains, random_seed, sampler):
  assert all_equal
 
  if chains > 1:
- assert np.all(
- result1.posterior["x"].sel(chain=0) != result1.posterior["x"].sel(chain=1)
- )
- assert np.all(
- result2.posterior["x"].sel(chain=0) != result2.posterior["x"].sel(chain=1)
- )
+ assert np.all(result1.posterior["x"].sel(chain=0) != result1.posterior["x"].sel(chain=1))
+ assert np.all(result2.posterior["x"].sel(chain=0) != result2.posterior["x"].sel(chain=1))
 
 
 @mock.patch("numpyro.infer.MCMC")
@@ -541,7 +518,21 @@ def test_vi_sampling_jax(method):
  pm.fit(10, method=method, fn_kwargs=dict(mode="JAX"))
 
 
-@pytest.mark.xfail(reason="Due to https://github.com/pymc-devs/pytensor/issues/595")
+@pytest.mark.xfail(
+ reason="""
+During equilibrium rewriter this error happens. Probably one of the routines in SVGD is problematic.
+
+TypeError: The broadcast pattern of the output of scan
+(Matrix(float64, shape=(?, 1))) is inconsistent with the one provided in `output_info`
+(Vector(float64, shape=(?,))). The output on axis 0 is `True`, but it is `False` on axis
+1 in `output_info`. This can happen if one of the dimension is fixed to 1 in the input,
+while it is still variable in the output, or vice-verca. You have to make them consistent,
+e.g. using pytensor.tensor.{unbroadcast, specify_broadcastable}.
+
+Instead of fixing this error it makes sense to rework the internals of the variational to utilize
+pytensor vectorize instead of scan.
+"""
+)
 def test_vi_sampling_jax_svgd():
  with pm.Model():
  x = pm.Normal("x")