Update actions' versions

.github/workflows/lint.yml

@@ -22,9 +22,9 @@ jobs:
         lint-kind: [code, docs]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python 3.10
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
 

.github/workflows/publish.yml

@@ -12,9 +12,9 @@ jobs:
     environment: publish-pypi
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python 3.10
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
 

.github/workflows/tests.yml

@@ -25,9 +25,9 @@ jobs:
         jax-version: [jax-default, jax-latest]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python 3.9
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
@@ -51,7 +51,7 @@ jobs:
       image: docker://michalk8/cuda:12.2.2-cudnn8-devel-ubuntu22.04
       options: --gpus="device=2"
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Install dependencies
       run: |
@@ -84,9 +84,9 @@ jobs:
           os: macos-14
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 

tests/tools/gaussian_mixture/gaussian_mixture_pair_test.py

@@ -15,6 +15,7 @@
 
 import jax
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import numpy as np
 
 from ott.tools.gaussian_mixture import gaussian_mixture, gaussian_mixture_pair
@@ -170,7 +171,7 @@ def test_pytree_mapping(self, epsilon, tau, lock_gmm1):
     )
     expected_gmm1_loc = 2.0 * self.gmm1.loc if not lock_gmm1 else self.gmm1.loc
 
-    pair_x_2 = jax.tree_map(lambda x: 2.0 * x, pair)
+    pair_x_2 = jtu.tree_map(lambda x: 2.0 * x, pair)
     # gmm parameters should be doubled
     np.testing.assert_allclose(2.0 * pair.gmm0.loc, pair_x_2.gmm0.loc)
     np.testing.assert_allclose(expected_gmm1_loc, pair_x_2.gmm1.loc)

tests/tools/gaussian_mixture/gaussian_mixture_test.py

@@ -15,6 +15,7 @@
 
 import jax
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import numpy as np
 
 from ott.tools.gaussian_mixture import gaussian_mixture, linalg
@@ -163,7 +164,7 @@ def test_pytree_mapping(self, rng: jax.Array):
     gmm = gaussian_mixture.GaussianMixture.from_random(
         rng=rng, n_components=3, n_dimensions=2
     )
-    gmm_x_2 = jax.tree_map(lambda x: 2.0 * x, gmm)
+    gmm_x_2 = jtu.tree_map(lambda x: 2.0 * x, gmm)
     np.testing.assert_allclose(2.0 * gmm.loc, gmm_x_2.loc, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(
         2.0 * gmm.scale_params, gmm_x_2.scale_params, atol=1e-4, rtol=1e-4

tests/tools/gaussian_mixture/gaussian_test.py

@@ -15,6 +15,7 @@
 
 import jax
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import numpy as np
 
 from ott.tools.gaussian_mixture import gaussian, scale_tril
@@ -137,14 +138,14 @@ def test_transport(self, rng: jax.Array):
 
   def test_flatten_unflatten(self, rng: jax.Array):
     g = gaussian.Gaussian.from_random(rng, n_dimensions=3)
-    children, aux_data = jax.tree_util.tree_flatten(g)
-    g_new = jax.tree_util.tree_unflatten(aux_data, children)
+    children, aux_data = jtu.tree_flatten(g)
+    g_new = jtu.tree_unflatten(aux_data, children)
 
     assert g == g_new
 
   def test_pytree_mapping(self, rng: jax.Array):
     g = gaussian.Gaussian.from_random(rng, n_dimensions=3)
-    g_x_2 = jax.tree_map(lambda x: 2 * x, g)
+    g_x_2 = jtu.tree_map(lambda x: 2 * x, g)
 
     np.testing.assert_allclose(2.0 * g.loc, g_x_2.loc)
     np.testing.assert_allclose(2.0 * g.scale.params, g_x_2.scale.params)

tests/tools/gaussian_mixture/probabilities_test.py

@@ -15,6 +15,7 @@
 
 import jax
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import numpy as np
 
 from ott.tools.gaussian_mixture import probabilities
@@ -63,15 +64,15 @@ def test_sample(self, rng: jax.Array):
   def test_flatten_unflatten(self):
     probs = jnp.array([0.1, 0.2, 0.3, 0.4])
     pp = probabilities.Probabilities.from_probs(probs)
-    children, aux_data = jax.tree_util.tree_flatten(pp)
-    pp_new = jax.tree_util.tree_unflatten(aux_data, children)
+    children, aux_data = jtu.tree_flatten(pp)
+    pp_new = jtu.tree_unflatten(aux_data, children)
     np.testing.assert_array_equal(pp.params, pp_new.params)
     assert pp == pp_new
 
   def test_pytree_mapping(self):
     probs = jnp.array([0.1, 0.2, 0.3, 0.4])
     pp = probabilities.Probabilities.from_probs(probs)
-    pp_x_2 = jax.tree_map(lambda x: 2 * x, pp)
+    pp_x_2 = jtu.tree_map(lambda x: 2 * x, pp)
     np.testing.assert_allclose(
         2.0 * pp.params, pp_x_2.params, rtol=1e-6, atol=1e-6
     )

tests/tools/gaussian_mixture/scale_tril_test.py

@@ -15,6 +15,7 @@
 
 import jax
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import numpy as np
 
 from ott.math import matrix_square_root
@@ -102,12 +103,12 @@ def test_transport(self, rng: jax.Array):
 
   def test_flatten_unflatten(self, rng: jax.Array):
     scale = scale_tril.ScaleTriL.from_random(rng=rng, n_dimensions=3)
-    children, aux_data = jax.tree_util.tree_flatten(scale)
-    scale_new = jax.tree_util.tree_unflatten(aux_data, children)
+    children, aux_data = jtu.tree_flatten(scale)
+    scale_new = jtu.tree_unflatten(aux_data, children)
     np.testing.assert_array_equal(scale.params, scale_new.params)
     assert scale == scale_new
 
   def test_pytree_mapping(self, rng: jax.Array):
     scale = scale_tril.ScaleTriL.from_random(rng=rng, n_dimensions=3)
-    scale_x_2 = jax.tree_map(lambda x: 2 * x, scale)
+    scale_x_2 = jtu.tree_map(lambda x: 2 * x, scale)
     np.testing.assert_allclose(2.0 * scale.params, scale_x_2.params)