Update chex.assert_type to check concrete types instead of just asserting that the type is a floating/integer sub-type.

distrax/_src/distributions/deterministic_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions import deterministic
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -107,10 +108,11 @@ def test_sample_shape(self, loc, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(loc=jnp.zeros((), dtype=dtype))
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(loc=jnp.zeros((), dtype=dtype))
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/epsilon_greedy_test.py

@@ -22,6 +22,7 @@
 import chex
 from distrax._src.distributions import epsilon_greedy
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -51,11 +52,12 @@ def test_num_categories(self):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(
-        preferences=self.preferences, epsilon=self.epsilon, dtype=dtype)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(
+          preferences=self.preferences, epsilon=self.epsilon, dtype=dtype)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   def test_jittable(self):
     super()._test_jittable(

distrax/_src/distributions/gamma_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions import gamma
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -73,11 +74,12 @@ def test_sample_shape(self, distr_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(
-        concentration=jnp.ones((), dtype), rate=jnp.ones((), dtype))
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(
+          concentration=jnp.ones((), dtype), rate=jnp.ones((), dtype))
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/greedy_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions import greedy
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -48,10 +49,11 @@ def test_num_categories(self):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(preferences=self.preferences, dtype=dtype)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(preferences=self.preferences, dtype=dtype)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   def test_jittable(self):
     super()._test_jittable((np.array([0., 4., -1., 4.]),))

distrax/_src/distributions/gumbel_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions import gumbel
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -67,11 +68,12 @@ def test_sample_shape(self, distr_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(
-        loc=jnp.zeros((), dtype), scale=jnp.ones((), dtype))
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(
+          loc=jnp.zeros((), dtype), scale=jnp.ones((), dtype))
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/laplace_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions import laplace
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -65,11 +66,12 @@ def test_sample_shape(self, distr_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(
-        loc=jnp.zeros((), dtype), scale=jnp.ones((), dtype))
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(
+          loc=jnp.zeros((), dtype), scale=jnp.ones((), dtype))
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/log_stddev_normal_test.py

@@ -21,6 +21,7 @@
 from distrax._src.distributions import log_stddev_normal as lsn
 from distrax._src.distributions import normal
 import jax
+import jax.experimental
 import jax.numpy as jnp
 import mock
 import numpy as np
@@ -105,11 +106,12 @@ def test_sampling_batched_custom_dim(self):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = lsn.LogStddevNormal(
-        loc=jnp.zeros((), dtype), log_scale=jnp.zeros((), dtype))
-    samples = self.variant(dist.sample)(seed=jax.random.PRNGKey(0))
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = lsn.LogStddevNormal(
+          loc=jnp.zeros((), dtype), log_scale=jnp.zeros((), dtype))
+      samples = self.variant(dist.sample)(seed=jax.random.PRNGKey(0))
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   def test_kl_versus_normal(self):
     loc, scale = jnp.array([2.0]), jnp.array([2.0])

distrax/_src/distributions/logistic_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions import logistic
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -66,11 +67,12 @@ def test_sample_shape(self, distr_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(
-        loc=jnp.zeros((), dtype), scale=jnp.ones((), dtype))
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(
+          loc=jnp.zeros((), dtype), scale=jnp.ones((), dtype))
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/multinomial_test.py

@@ -22,6 +22,7 @@
 from distrax._src.utils import equivalence
 from distrax._src.utils import math
 import jax
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 from scipy import stats
@@ -405,12 +406,16 @@ def test_sample_and_log_prob(self, dist_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist_params = {
-        'logits': self.logits, 'dtype': dtype, 'total_count': self.total_count}
-    dist = self.distrax_cls(**dist_params)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist_params = {
+          'logits': self.logits,
+          'dtype': dtype,
+          'total_count': self.total_count,
+      }
+      dist = self.distrax_cls(**dist_params)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   def test_sample_extreme_probs(self):

distrax/_src/distributions/mvn_diag_plus_low_rank_test.py

@@ -22,6 +22,7 @@
 from distrax._src.utils import equivalence
 
 import jax
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 from tensorflow_probability.substrates import jax as tfp
@@ -180,13 +181,14 @@ def test_sample_shape(self, sample_shape, loc_shape, scale_diag_shape,
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist_params = {
-        'loc': np.array([0., 0.], dtype),
-        'scale_diag': np.array([1., 1.], dtype)}
-    dist = MultivariateNormalDiagPlusLowRank(**dist_params)
-    samples = self.variant(dist.sample)(seed=jax.random.PRNGKey(0))
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist_params = {
+          'loc': np.array([0., 0.], dtype),
+          'scale_diag': np.array([1., 1.], dtype)}
+      dist = MultivariateNormalDiagPlusLowRank(**dist_params)
+      samples = self.variant(dist.sample)(seed=jax.random.PRNGKey(0))
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/mvn_diag_test.py

@@ -22,6 +22,7 @@
 from distrax._src.distributions import normal
 from distrax._src.utils import equivalence
 import jax
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -214,13 +215,14 @@ def test_sample_and_log_prob(self, distr_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist_params = {
-        'loc': np.array([0., 0.], dtype),
-        'scale_diag': np.array([1., 1.], dtype)}
-    dist = self.distrax_cls(**dist_params)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist_params = {
+          'loc': np.array([0., 0.], dtype),
+          'scale_diag': np.array([1., 1.], dtype)}
+      dist = self.distrax_cls(**dist_params)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/mvn_full_covariance_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions.mvn_full_covariance import MultivariateNormalFullCovariance
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -106,13 +107,14 @@ def test_sample_shape(self, sample_shape, loc_shape, covariance_matrix_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist_params = {
-        'loc': np.array([0., 0.], dtype),
-        'covariance_matrix': np.array([[1., 0.], [0., 1.]], dtype)}
-    dist = self.distrax_cls(**dist_params)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist_params = {
+          'loc': np.array([0., 0.], dtype),
+          'covariance_matrix': np.array([[1., 0.], [0., 1.]], dtype)}
+      dist = self.distrax_cls(**dist_params)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/mvn_tri_test.py

@@ -20,6 +20,7 @@
 import chex
 from distrax._src.distributions.mvn_tri import MultivariateNormalTri
 from distrax._src.utils import equivalence
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 
@@ -114,13 +115,14 @@ def test_sample_shape(self, sample_shape, loc_shape, scale_tri_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist_params = {
-        'loc': np.array([0., 0.], dtype),
-        'scale_tri': np.array([[1., 0.], [0., 1.]], dtype)}
-    dist = self.distrax_cls(**dist_params)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist_params = {
+          'loc': np.array([0., 0.], dtype),
+          'scale_tri': np.array([[1., 0.], [0., 1.]], dtype)}
+      dist = self.distrax_cls(**dist_params)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/one_hot_categorical_test.py

@@ -23,6 +23,7 @@
 from distrax._src.utils import equivalence
 from distrax._src.utils import math
 import jax
+import jax.experimental
 import jax.numpy as jnp
 import numpy as np
 import scipy
@@ -178,11 +179,12 @@ def test_sample_and_log_prob(self, distr_params, sample_shape):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist_params = {'logits': self.logits, 'dtype': dtype}
-    dist = self.distrax_cls(**dist_params)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist_params = {'logits': self.logits, 'dtype': dtype}
+      dist = self.distrax_cls(**dist_params)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   @chex.all_variants
   @parameterized.named_parameters(

distrax/_src/distributions/softmax_test.py

@@ -71,11 +71,12 @@ def test_parameters(self):
       ('float32', jnp.float32),
       ('float64', jnp.float64))
   def test_sample_dtype(self, dtype):
-    dist = self.distrax_cls(
-        logits=self.logits, temperature=self.temperature, dtype=dtype)
-    samples = self.variant(dist.sample)(seed=self.key)
-    self.assertEqual(samples.dtype, dist.dtype)
-    chex.assert_type(samples, dtype)
+    with jax.experimental.enable_x64(dtype.dtype.itemsize == 8):
+      dist = self.distrax_cls(
+          logits=self.logits, temperature=self.temperature, dtype=dtype)
+      samples = self.variant(dist.sample)(seed=self.key)
+      self.assertEqual(samples.dtype, dist.dtype)
+      chex.assert_type(samples, dtype)
 
   def test_jittable(self):
     super()._test_jittable((np.array([2., 4., 1., 3.]),))