Fix uniform distribution log_prob support

numpyro/distributions/censored.py

@@ -81,7 +81,7 @@ def __init__(
         base_dist: DistributionT,
         censored: ArrayLike = False,
         *,
-        validate_args: Optional[bool] = None,
+        validate_args: bool = False,
     ):
         # test if base_dist has an implemented cdf method
         if not hasattr(base_dist, "cdf"):
@@ -197,7 +197,7 @@ def __init__(
         base_dist: DistributionT,
         censored: ArrayLike = False,
         *,
-        validate_args: Optional[bool] = None,
+        validate_args: bool = False,
     ):
         # test if base_dist has an implemented cdf method
         if not hasattr(base_dist, "cdf"):
@@ -335,7 +335,7 @@ def __init__(
         left_censored: ArrayLike,
         right_censored: ArrayLike,
         *,
-        validate_args: Optional[bool] = None,
+        validate_args: bool = False,
     ):
         # Optionally test that cdf actually works (in validate_args mode)
         if validate_args:

numpyro/distributions/continuous.py

@@ -3577,7 +3577,7 @@ class Levy(Distribution):
     """
 
     arg_constraints = {
-        "loc": constraints.positive,
+        "loc": constraints.real,
         "scale": constraints.positive,
     }
 

numpyro/distributions/distribution.py

@@ -53,7 +53,7 @@
 
 from . import constraints
 
-_VALIDATION_ENABLED = False
+_VALIDATION_ENABLED = True
 
 
 def enable_validation(is_validate: bool = True) -> None:
@@ -1320,7 +1320,7 @@ class Unit(Distribution):
     arg_constraints = {"log_factor": constraints.real}
     support = constraints.real
 
-    def __init__(self, log_factor: ArrayLike, *, validate_args: Optional[bool] = None):
+    def __init__(self, log_factor: ArrayLike, *, validate_args: bool = False):
         batch_shape = jnp.shape(log_factor)
         event_shape = (0,)  # This satisfies .size == 0.
         self.log_factor = log_factor

test/contrib/test_module.py

@@ -620,7 +620,7 @@ def __call__(self, x, state):
             return x, state
 
     # Eager initialization of the Net module outside the model
-    net_module, eager_state = eqx.nn.make_with_state(Net)(key=random.PRNGKey(0))  # noqa: E1111
+    net_module, eager_state = eqx.nn.make_with_state(Net)(key=random.PRNGKey(0))
     x = dist.Normal(0, 1).expand([4, 3]).to_event(2).sample(random.PRNGKey(0))
 
     def model():

test/test_distributions.py

@@ -3693,11 +3693,11 @@ def test_vmap_validate_args():
 
 def test_explicit_validate_args():
     # Check validation passes for valid parameters.
-    d = dist.Normal(0, 1)
+    d = dist.Normal(0, 1, validate_args=False)
     d.validate_args()
 
     # Check validation fails for invalid parameters.
-    d = dist.Normal(0, -1)
+    d = dist.Normal(0, -1, validate_args=False)
     with pytest.raises(ValueError, match="got invalid scale parameter"):
         d.validate_args()
 
@@ -4766,3 +4766,16 @@ def log_prob_fn(params):
         f"All gradients for Beta({concentration1},{concentration0}) at x={value} "
         f"should be finite"
     )
+
+
+def test_uniform_log_prob_outside_support():
+    from numpyro.distributions.distribution import enable_validation
+
+    enable_validation()
+
+    d = dist.Uniform(0, 1)
+    with pytest.warns(
+        UserWarning,
+        match="Out-of-support values provided to log prob method. The value argument should be within the support.",
+    ):
+        d.log_prob(-0.5)

test/test_pickle.py

@@ -65,7 +65,7 @@ def bernoulli_model():
 
 
 def logistic_regression():
-    data = jnp.arange(10)
+    data = random.choice(random.PRNGKey(0), jnp.array([0, 1]), (10,))
     x = numpyro.sample("x", dist.Normal(0, 1))
     with numpyro.plate("N", 10, subsample_size=2):
         batch = numpyro.subsample(data, 0)

test/test_util.py

@@ -112,7 +112,7 @@ def test_format_shapes():
 
     def model_test():
         mean = numpyro.param("mean", jnp.zeros(len(data)))
-        scale = numpyro.sample("scale", dist.Normal(0, 1).expand([3]).to_event(1))
+        scale = numpyro.sample("scale", dist.LogNormal(0, 1).expand([3]).to_event(1))
         scale = scale.sum()
         with numpyro.plate("data", len(data), subsample_size=10) as ind:
             batch = data[ind]