fix autoreparam because dims are no longer static

pymc_experimental/model/transforms/autoreparam.py

@@ -1,3 +1,4 @@
+import logging
 from dataclasses import dataclass
 from functools import singledispatch
 from typing import Dict, List, Optional, Sequence, Tuple, Union
@@ -8,7 +9,6 @@
 import pytensor.tensor as pt
 import scipy.special
 from pymc.distributions import SymbolicRandomVariable
-from pymc.exceptions import NotConstantValueError
 from pymc.logprob.transforms import Transform
 from pymc.model.fgraph import (
     ModelDeterministic,
@@ -19,10 +19,12 @@
     model_from_fgraph,
     model_named,
 )
-from pymc.pytensorf import constant_fold, toposort_replace
+from pymc.pytensorf import toposort_replace
 from pytensor.graph.basic import Apply, Variable
 from pytensor.tensor.random.op import RandomVariable
 
+_log = logging.getLogger("pmx")
+
 
 @dataclass
 class VIP:
@@ -174,15 +176,19 @@ def vip_reparam_node(
 ) -> Tuple[ModelDeterministic, ModelNamed]:
     if not isinstance(node.op, RandomVariable | SymbolicRandomVariable):
         raise TypeError("Op should be RandomVariable type")
-    rv = node.default_output()
-    try:
-        [rv_shape] = constant_fold([rv.shape])
-    except NotConstantValueError:
-        raise ValueError("Size should be static for autoreparametrization.")
+    # FIXME: This is wrong when size is None
+    _, size, *_ = node.inputs
+    eval_size = size.eval(mode="FAST_COMPILE")
+    if eval_size is not None:
+        rv_shape = tuple(eval_size)
+    else:
+        rv_shape = ()
+    lam_name = f"{name}::lam_logit__"
+    _log.debug(f"Creating {lam_name} with shape of {rv_shape}")
     logit_lam_ = pytensor.shared(
         np.zeros(rv_shape),
         shape=rv_shape,
-        name=f"{name}::lam_logit__",
+        name=lam_name,
     )
     logit_lam = model_named(logit_lam_, *dims)
     lam = pt.sigmoid(logit_lam)

tests/model/transforms/test_autoreparam.py

@@ -7,20 +7,21 @@
 
 @pytest.fixture
 def model_c():
-    with pm.Model() as mod:
+    # TODO: Restructure tests so they check one dist at a time
+    with pm.Model(coords=dict(a=range(5))) as mod:
         m = pm.Normal("m")
         s = pm.LogNormal("s")
-        pm.Normal("g", m, s, shape=5)
+        pm.Normal("g", m, s, dims="a")
         pm.Exponential("e", scale=s, shape=7)
     return mod
 
 
 @pytest.fixture
 def model_nc():
-    with pm.Model() as mod:
+    with pm.Model(coords=dict(a=range(5))) as mod:
         m = pm.Normal("m")
         s = pm.LogNormal("s")
-        pm.Deterministic("g", pm.Normal("z", shape=5) * s + m)
+        pm.Deterministic("g", pm.Normal("z", dims="a") * s + m)
         pm.Deterministic("e", pm.Exponential("z_e", 1, shape=7) * s)
     return mod
 
@@ -102,3 +103,29 @@ def test_set_truncate(model_c: pm.Model):
     vip.truncate_lambda(g=0.2)
     np.testing.assert_allclose(vip.get_lambda()["g"], 1)
     np.testing.assert_allclose(vip.get_lambda()["m"], 0.9)
+
+
+@pytest.mark.xfail(reason="FIX shape computation for lambda")
+def test_lambda_shape():
+    with pm.Model(coords=dict(a=[1, 2])) as model:
+        b1 = pm.Normal("b1", dims="a")
+        b2 = pm.Normal("b2", shape=2)
+        b3 = pm.Normal("b3", size=2)
+        b4 = pm.Normal("b4", np.asarray([1, 2]))
+    model_v, vip = vip_reparametrize(model, ["b1", "b2", "b3", "b4"])
+    lams = vip.get_lambda()
+    for v in ["b1", "b2", "b3", "b4"]:
+        assert lams[v].shape == (2,), v
+
+
+@pytest.mark.xfail(reason="FIX shape computation for lambda")
+def test_lambda_shape_transformed_1d():
+    with pm.Model(coords=dict(a=[1, 2])) as model:
+        b1 = pm.Exponential("b1", 1, dims="a")
+        b2 = pm.Exponential("b2", 1, shape=2)
+        b3 = pm.Exponential("b3", 1, size=2)
+        b4 = pm.Exponential("b4", np.asarray([1, 2]))
+    model_v, vip = vip_reparametrize(model, ["b1", "b2", "b3", "b4"])
+    lams = vip.get_lambda()
+    for v in ["b1", "b2", "b3", "b4"]:
+        assert lams[v].shape == (2,), v