Warn that in future arguments after the initial (prediction, ground_truth) positional arguments will become keyword-only in optax losses.

optax/losses/_classification.py

@@ -14,6 +14,7 @@
 # ==============================================================================
 """Classification losses."""
 
+import functools
 from typing import Optional
 
 import chex
@@ -138,8 +139,11 @@ def softmax_cross_entropy_with_integer_labels(
   return log_normalizers - label_logits
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=2)
 def poly_loss_cross_entropy(
-    logits: chex.Array, labels: chex.Array, epsilon: float = 2.0
+    logits: chex.Array,
+    labels: chex.Array,
+    epsilon: float = 2.0
 ) -> chex.Array:
   r"""Computes PolyLoss between logits and labels.
 
@@ -236,7 +240,8 @@ def kl_divergence_with_log_targets(
 
 
 def convex_kl_divergence(
-    log_predictions: chex.Array, targets: chex.Array
+    log_predictions: chex.Array,
+    targets: chex.Array
 ) -> chex.Array:
   """Computes a convex version of the Kullback-Leibler divergence loss.
 
@@ -262,6 +267,7 @@ def convex_kl_divergence(
   )
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=4)
 def ctc_loss_with_forward_probs(
     logits: chex.Array,
     logit_paddings: chex.Array,
@@ -392,6 +398,7 @@ def loop_body(prev, x):
   return per_seq_loss, logalpha_phi, logalpha_emit
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=4)
 def ctc_loss(
     logits: chex.Array,
     logit_paddings: chex.Array,
@@ -432,6 +439,7 @@ def ctc_loss(
   return per_seq_loss
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=2)
 def sigmoid_focal_loss(
     logits: chex.Array,
     labels: chex.Array,

optax/losses/_classification_test.py

@@ -144,7 +144,7 @@ def setUp(self):
   def test_scalar(self, eps, expected):
     np.testing.assert_allclose(
         self.variant(_classification.poly_loss_cross_entropy)(
-            self.logits, self.labels, eps
+            self.logits, self.labels, epsilon=eps
         ),
         expected,
         atol=1e-4,
@@ -163,7 +163,7 @@ def test_scalar(self, eps, expected):
   def test_batched(self, eps, expected):
     np.testing.assert_allclose(
         self.variant(_classification.poly_loss_cross_entropy)(
-            self.batched_logits, self.batched_labels, eps
+            self.batched_logits, self.batched_labels, epsilon=eps
         ),
         expected,
         atol=1e-4,

optax/losses/_regression.py

@@ -14,6 +14,7 @@
 # ==============================================================================
 """Regression losses."""
 
+import functools
 from typing import Optional
 
 import chex
@@ -76,6 +77,7 @@ def l2_loss(
   return 0.5 * squared_error(predictions, targets)
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=2)
 def huber_loss(
     predictions: chex.Array,
     targets: Optional[chex.Array] = None,
@@ -135,6 +137,7 @@ def log_cosh(
   return jnp.logaddexp(errors, -errors) - jnp.log(2.0).astype(errors.dtype)
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=2)
 def cosine_similarity(
     predictions: chex.Array,
     targets: chex.Array,
@@ -170,6 +173,7 @@ def cosine_similarity(
   return jnp.sum(unit_targets * unit_predictions, axis=-1)
 
 
+@functools.partial(chex.warn_only_n_pos_args_in_future, n=2)
 def cosine_distance(
     predictions: chex.Array,
     targets: chex.Array,
@@ -193,4 +197,4 @@ def cosine_distance(
   """
   chex.assert_type([predictions, targets], float)
   # cosine distance = 1 - cosine similarity.
-  return 1. - cosine_similarity(predictions, targets, epsilon)
+  return 1. - cosine_similarity(predictions, targets, epsilon=epsilon)

pyproject.toml

@@ -31,7 +31,7 @@ classifiers = [
 ]
 dependencies = [
   "absl-py>=0.7.1",
-  "chex>=0.1.7",
+  "chex>=0.1.86",
   "jax>=0.1.55",
   "jaxlib>=0.1.37",
   "numpy>=1.18.0",