Replaces references to jax.numpy.DeviceArray with jax.Array.

PiperOrigin-RevId: 515678285

counterfactual_fairness/utils.py

@@ -16,6 +16,7 @@
 
 from typing import Optional, Union
 
+import jax
 from jax import random
 import jax.numpy as jnp
 import pandas as pd
@@ -42,8 +43,8 @@ def get_dataset(dataset: pd.DataFrame,
 
 
 def multinomial_mode(
-    distribution_or_probs: Union[tfd.Distribution, jnp.DeviceArray]
+    distribution_or_probs: Union[tfd.Distribution, jax.Array]
-    ) -> jnp.DeviceArray:
+    ) -> jax.Array:
   """Calculates the (one-hot) mode of a multinomial distribution.
 
   Args:
@@ -71,8 +72,8 @@ def multinomial_mode(
 
 
 def multinomial_class(
-    distribution_or_probs: Union[tfd.Distribution, jnp.DeviceArray]
+    distribution_or_probs: Union[tfd.Distribution, jax.Array]
-) -> jnp.DeviceArray:
+) -> jax.Array:
   """Computes the mode class of a multinomial distribution.
 
   Args:
@@ -90,7 +91,7 @@ def multinomial_class(
   return jnp.argmax(distribution_or_probs, axis=1)
 
 
-def multinomial_mode_ndarray(probs: jnp.DeviceArray) -> jnp.DeviceArray:
+def multinomial_mode_ndarray(probs: jax.Array) -> jax.Array:
   """Calculates the (one-hot) mode from an ndarray of class probabilities.
 
   Equivalent to `multinomial_mode` above, but implemented for numpy ndarrays
@@ -109,7 +110,7 @@ def multinomial_mode_ndarray(probs: jnp.DeviceArray) -> jnp.DeviceArray:
 
 
 def multinomial_accuracy(distribution_or_probs: tfd.Distribution,
-                         data: jnp.DeviceArray) -> jnp.DeviceArray:
+                         data: jax.Array) -> jax.Array:
   """Compute the accuracy, averaged over a batch of data.
 
   Args:
@@ -126,7 +127,7 @@ def multinomial_accuracy(distribution_or_probs: tfd.Distribution,
       jnp.sum(multinomial_mode(distribution_or_probs) * data, axis=1))
 
 
-def softmax_ndarray(logits: jnp.DeviceArray) -> jnp.DeviceArray:
+def softmax_ndarray(logits: jax.Array) -> jax.Array:
   """Softmax function, implemented for numpy ndarrays."""
   assert len(logits.shape) == 2
   # Normalise for better stability.
@@ -166,7 +167,7 @@ def get_samples(distribution, num_samples, seed=None):
 
 
 def mmd_loss(distribution: tfd.Distribution,
-             is_a: jnp.DeviceArray,
+             is_a: jax.Array,
              num_samples: int,
              rng: jnp.ndarray,
              num_random_features: int = 50,

counterfactual_fairness/variational.py

@@ -17,6 +17,7 @@
 from typing import Callable, Iterable, Optional
 
 import haiku as hk
+import jax
 import jax.numpy as jnp
 from tensorflow_probability.substrates import jax as tfp
 
@@ -31,8 +32,7 @@ class Variational(hk.Module):
 
   def __init__(self,
                common_layer_sizes: Iterable[int],
-               activation: Callable[[jnp.DeviceArray],
+               activation: Callable[[jax.Array], jax.Array] = jnp.tanh,
-                                    jnp.DeviceArray] = jnp.tanh,
                output_dim: int = 1,
                name: Optional[str] = None):
     """Initialises a `Variational` instance.

gated_linear_networks/examples/utils.py

@@ -18,6 +18,7 @@ from typing import Tuple
 
 import chex
 import haiku as hk
+import jax
 import jax.numpy as jnp
 import numpy as np
 from scipy.ndimage import interpolation
@@ -72,7 +73,7 @@ def load_deskewed_mnist(*a, **k):
 class MeanStdEstimator(hk.Module):
   """Online mean and standard deviation estimator using Welford's algorithm."""
 
-  def __call__(self, sample: jnp.DeviceArray) -> Tuple[Array, Array]:
+  def __call__(self, sample: jax.Array) -> Tuple[Array, Array]:
     if len(sample.shape) > 1:
       raise ValueError("sample must be a rank 0 or 1 DeviceArray.")
 

mmv/models/types.py

@@ -17,11 +17,11 @@
 
 from typing import Callable, Tuple, Union
 
-import jax.numpy as jnp
+import jax
 import numpy as np
 import optax
 
-TensorLike = Union[np.ndarray, jnp.DeviceArray]
+TensorLike = Union[np.ndarray, jax.Array]
 
 ActivationFn = Callable[[TensorLike], TensorLike]
 GatingFn = Callable[[TensorLike], TensorLike]

ogb_lsc/pcq/model.py

@@ -46,18 +46,18 @@ class RegressionLossConfig:
 
 
 def _sigmoid_cross_entropy(
-    logits: jnp.DeviceArray,
+    logits: jax.Array,
-    labels: jnp.DeviceArray,
+    labels: jax.Array,
-) -> jnp.DeviceArray:
+) -> jax.Array:
   log_p = jax.nn.log_sigmoid(logits)
   log_not_p = jax.nn.log_sigmoid(-logits)
   return -labels * log_p - (1. - labels) * log_not_p
 
 
 def _softmax_cross_entropy(
-    logits: jnp.DeviceArray,
+    logits: jax.Array,
-    targets: jnp.DeviceArray,
+    targets: jax.Array,
-) -> jnp.DeviceArray:
+) -> jax.Array:
   logits = jax.nn.log_softmax(logits)
   return -jnp.sum(targets * logits, axis=-1)