Update references to JAX's GitHub repo

README.md

@@ -116,7 +116,7 @@ You can learn here how Trax works, how to create new models and how to train the
 
 The basic units flowing through Trax models are *tensors* - multi-dimensional arrays, sometimes also known as numpy arrays, due to the most widely used package for tensor operations -- `numpy`. You should take a look at the [numpy guide](https://numpy.org/doc/stable/user/quickstart.html) if you don't know how to operate on tensors: Trax also uses the numpy API for that.
 
-In Trax we want numpy operations to run very fast, making use of GPUs and TPUs to accelerate them. We also want to automatically compute gradients of functions on tensors. This is done in the `trax.fastmath` package thanks to its backends -- [JAX](https://github.com/google/jax) and [TensorFlow numpy](https://tensorflow.org/guide/tf_numpy).
+In Trax we want numpy operations to run very fast, making use of GPUs and TPUs to accelerate them. We also want to automatically compute gradients of functions on tensors. This is done in the `trax.fastmath` package thanks to its backends -- [JAX](https://github.com/jax-ml/jax) and [TensorFlow numpy](https://tensorflow.org/guide/tf_numpy).
 
 
 ```python

docs/source/notebooks/tf_numpy_and_keras.ipynb

@@ -26,7 +26,7 @@
         "\n",
         "In Trax, all computations rely on accelerated math operations happening in the `fastmath` module. This module can use different backends for acceleration. One of them is [TensorFlow NumPy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) which uses [TensorFlow 2](https://www.tensorflow.org/) to accelerate the computations.\n",
         "\n",
-        "The backend can be set using a call to `trax.fastmath.set_backend` as you'll see below. Currently available backends are `jax` (default), `tensorflow-numpy` and `numpy` (for debugging). The `tensorflow-numpy` backend uses [TensorFlow Numpy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) for executing `fastmath` functions on TensorFlow, while the `jax` backend calls [JAX](https://github.com/google/jax) which lowers to TensorFlow XLA.\n",
+        "The backend can be set using a call to `trax.fastmath.set_backend` as you'll see below. Currently available backends are `jax` (default), `tensorflow-numpy` and `numpy` (for debugging). The `tensorflow-numpy` backend uses [TensorFlow Numpy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) for executing `fastmath` functions on TensorFlow, while the `jax` backend calls [JAX](https://github.com/jax-ml/jax) which lowers to TensorFlow XLA.\n",
         "\n",
         "You may see that `tensorflow-numpy` and `jax` backends show different speed and memory characteristics. You may also see different error messages when debugging since it might expose you to the internals of the backends. However for the most part, users can choose a backend and not worry about the internal details of these backends.\n",
         "\n",

docs/source/notebooks/trax_intro.ipynb

@@ -228,7 +228,7 @@
         "\n",
         "The basic units flowing through Trax models are *tensors* - multi-dimensional arrays, sometimes also known as numpy arrays, due to the most widely used package for tensor operations -- `numpy`. You should take a look at the [numpy guide](https://numpy.org/doc/stable/user/quickstart.html) if you don't know how to operate on tensors: Trax also uses the numpy API for that.\n",
         "\n",
-        "In Trax we want numpy operations to run very fast, making use of GPUs and TPUs to accelerate them. We also want to automatically compute gradients of functions on tensors. This is done in the `trax.fastmath` package thanks to its backends -- [JAX](https://github.com/google/jax) and [TensorFlow numpy](https://tensorflow.org)."
+        "In Trax we want numpy operations to run very fast, making use of GPUs and TPUs to accelerate them. We also want to automatically compute gradients of functions on tensors. This is done in the `trax.fastmath` package thanks to its backends -- [JAX](https://github.com/jax-ml/jax) and [TensorFlow numpy](https://tensorflow.org)."
       ]
     },
     {

trax/intro.ipynb

@@ -228,7 +228,7 @@
         "\n",
         "The basic units flowing through Trax models are *tensors* - multi-dimensional arrays, sometimes also known as numpy arrays, due to the most widely used package for tensor operations -- `numpy`. You should take a look at the [numpy guide](https://numpy.org/doc/stable/user/quickstart.html) if you don't know how to operate on tensors: Trax also uses the numpy API for that.\n",
         "\n",
-        "In Trax we want numpy operations to run very fast, making use of GPUs and TPUs to accelerate them. We also want to automatically compute gradients of functions on tensors. This is done in the `trax.fastmath` package thanks to its backends -- [JAX](https://github.com/google/jax) and [TensorFlow numpy](https://tensorflow.org)."
+        "In Trax we want numpy operations to run very fast, making use of GPUs and TPUs to accelerate them. We also want to automatically compute gradients of functions on tensors. This is done in the `trax.fastmath` package thanks to its backends -- [JAX](https://github.com/jax-ml/jax) and [TensorFlow numpy](https://tensorflow.org)."
       ]
     },
     {

trax/supervised/training_test.py

@@ -143,7 +143,7 @@ def test_loop_with_initialized_model(self):
 
   def test_train_save_restore_dense(self):
     """Saves and restores a checkpoint to check for equivalence."""
-    self.skipTest('Broken by https://github.com/google/jax/pull/11234')
+    self.skipTest('Broken by https://github.com/jax-ml/jax/pull/11234')
     train_data = data.Serial(lambda _: _very_simple_data(),
                              data.CountAndSkip('simple_data'))
     task = training.TrainTask(
@@ -327,7 +327,7 @@ def test_restores_step(self):
 
   def test_restores_memory_efficient_from_standard(self):
     """Training restores step from directory where it saved it."""
-    self.skipTest('Broken by https://github.com/google/jax/pull/11234')
+    self.skipTest('Broken by https://github.com/jax-ml/jax/pull/11234')
     model = tl.Serial(tl.Dense(4), tl.Dense(1))
     task_std = training.TrainTask(
         _very_simple_data(), tl.L2Loss(), optimizers.Adam(.0001))
@@ -345,7 +345,7 @@ def test_restores_memory_efficient_from_standard(self):
 
   def test_restores_from_smaller_model(self):
     """Training restores from a checkpoint created with smaller model."""
-    self.skipTest('Broken by https://github.com/google/jax/pull/11234')
+    self.skipTest('Broken by https://github.com/jax-ml/jax/pull/11234')
     model1 = tl.Serial(tl.Dense(1))
     task = training.TrainTask(
         _very_simple_data(), tl.L2Loss(), optimizers.Adam(.01))
@@ -374,7 +374,7 @@ def test_restore_fails_different_model(self):
 
   def test_restores_step_bfloat16(self):
     """Training restores step from directory where it saved it, w/ bfloat16."""
-    self.skipTest('Broken by https://github.com/google/jax/pull/11234')
+    self.skipTest('Broken by https://github.com/jax-ml/jax/pull/11234')
     model = tl.Serial(tl.Dense(1, use_bfloat16=True))
     # We'll also use Adafactor with bfloat16 to check restoring bfloat slots.
     opt = optimizers.Adafactor(.01, do_momentum=True, momentum_in_bfloat16=True)

trax/tf_numpy/jax_tests/lax_numpy_einsum_test.py

@@ -89,7 +89,7 @@ def test_two_operands_5(self):
     self._check(s, x, y)
 
   def test_two_operands_6(self):
-    # based on https://github.com/google/jax/issues/37#issuecomment-448572187
+    # based on https://github.com/jax-ml/jax/issues/37#issuecomment-448572187
     r = self.rng()
     x = r.randn(2, 1)
     y = r.randn(2, 3, 4)

trax/tf_numpy/jax_tests/lax_numpy_indexing_test.py

@@ -720,7 +720,7 @@ def testFloatIndexingError(self):
     with self.assertRaisesRegex(IndexError, error_regex):
       npe.jit(lambda idx: jnp.zeros((2, 2))[idx])((0, 0.))
 
-  def testIndexOutOfBounds(self):  # https://github.com/google/jax/issues/2245
+  def testIndexOutOfBounds(self):  # https://github.com/jax-ml/jax/issues/2245
     array = jnp.ones(5)
     self.assertAllClose(array, array[:10], check_dtypes=True)
 

trax/tf_numpy/jax_tests/lax_numpy_test.py

@@ -2051,14 +2051,14 @@ def testAstype(self):
   # TODO(mattjj): test other ndarray-like method overrides
 
   def testOnpMean(self):
-    # from https://github.com/google/jax/issues/125
+    # from https://github.com/jax-ml/jax/issues/125
     x = lnp.add(lnp.eye(3, dtype=lnp.float_), 0.)
     ans = onp.mean(x)
     self.assertAllClose(ans, onp.array(1./3), check_dtypes=False)
 
   @jtu.disable
   def testArangeOnFloats(self):
-    # from https://github.com/google/jax/issues/145
+    # from https://github.com/jax-ml/jax/issues/145
     expected = onp.arange(0.0, 1.0, 0.1, dtype=lnp.float_)
     ans = lnp.arange(0.0, 1.0, 0.1)
     self.assertAllClose(expected, ans, check_dtypes=True)
@@ -2407,7 +2407,7 @@ def testSymmetrizeDtypePromotion(self):
 
   @jtu.disable
   def testIssue347(self):
-    # https://github.com/google/jax/issues/347
+    # https://github.com/jax-ml/jax/issues/347
     def test_fail(x):
       x = lnp.sqrt(lnp.sum(x ** 2, axis=1))
       ones = lnp.ones_like(x)
@@ -2419,7 +2419,7 @@ def test_fail(x):
     assert not onp.any(onp.isnan(result))
 
   def testIssue453(self):
-    # https://github.com/google/jax/issues/453
+    # https://github.com/jax-ml/jax/issues/453
     a = onp.arange(6) + 1
     ans = lnp.reshape(a, (3, 2), order='F')
     expected = onp.reshape(a, (3, 2), order='F')
@@ -2432,7 +2432,7 @@ def testIssue453(self):
                             (bool, lnp.bool_), (complex, lnp.complex_)]
       for op in ["atleast_1d", "atleast_2d", "atleast_3d"]))
   def testAtLeastNdLiterals(self, pytype, dtype, op):
-    # Fixes: https://github.com/google/jax/issues/634
+    # Fixes: https://github.com/jax-ml/jax/issues/634
     onp_fun = lambda arg: getattr(onp, op)(arg).astype(dtype)
     lnp_fun = lambda arg: getattr(lnp, op)(arg)
     args_maker = lambda: [pytype(2)]
@@ -2550,7 +2550,7 @@ def testMathSpecialFloatValues(self, op, dtype):
                           rtol=tol)
 
   def testIssue883(self):
-    # from https://github.com/google/jax/issues/883
+    # from https://github.com/jax-ml/jax/issues/883
 
     @partial(npe.jit, static_argnums=(1,))
     def f(x, v):
@@ -2907,7 +2907,7 @@ def testDisableNumpyRankPromotionBroadcasting(self):
       FLAGS.jax_numpy_rank_promotion = prev_flag
 
   def testStackArrayArgument(self):
-    # tests https://github.com/google/jax/issues/1271
+    # tests https://github.com/jax-ml/jax/issues/1271
     @npe.jit
     def foo(x):
       return lnp.stack(x)
@@ -3120,7 +3120,7 @@ def testOpGradSpecialValue(self, op, special_value, order):
 
   @jtu.disable
   def testTakeAlongAxisIssue1521(self):
-    # https://github.com/google/jax/issues/1521
+    # https://github.com/jax-ml/jax/issues/1521
     idx = lnp.repeat(lnp.arange(3), 10).reshape((30, 1))
 
     def f(x):

trax/tf_numpy/jax_tests/test_util.py

@@ -316,7 +316,7 @@ def test_method_wrapper(self, *args, **kwargs):
     return test_method_wrapper
   return skip
 
-# TODO(phawkins): workaround for bug https://github.com/google/jax/issues/432
+# TODO(phawkins): workaround for bug https://github.com/jax-ml/jax/issues/432
 # Delete this code after the minimum jaxlib version is 0.1.46 or greater.
 skip_on_mac_linalg_bug = partial(
   unittest.skipIf,

trax/tf_numpy/jax_tests/vmap_test.py

@@ -28,7 +28,7 @@
 class VmapTest(tf.test.TestCase, parameterized.TestCase):
 
   def test_vmap_in_axes_list(self):
-    # https://github.com/google/jax/issues/2367
+    # https://github.com/jax-ml/jax/issues/2367
     dictionary = {'a': 5., 'b': tf_np.ones(2)}
     x = tf_np.zeros(3)
     y = tf_np.arange(3.)
@@ -41,7 +41,7 @@ def f(dct, x, y):
     self.assertAllClose(out1, out2)
 
   def test_vmap_in_axes_tree_prefix_error(self):
-    # https://github.com/google/jax/issues/795
+    # https://github.com/jax-ml/jax/issues/795
     self.assertRaisesRegex(
         ValueError,
         'vmap in_axes specification must be a tree prefix of the corresponding '
@@ -63,14 +63,14 @@ def test_vmap_out_axes_leaf_types(self):
               tf_np.array([1., 2.]))
 
   def test_vmap_unbatched_object_passthrough_issue_183(self):
-    # https://github.com/google/jax/issues/183
+    # https://github.com/jax-ml/jax/issues/183
     fun = lambda f, x: f(x)
     vfun = extensions.vmap(fun, (None, 0))
     ans = vfun(lambda x: x + 1, tf_np.arange(3))
     self.assertAllClose(ans, np.arange(1, 4))
 
   def test_vmap_mismatched_axis_sizes_error_message_issue_705(self):
-    # https://github.com/google/jax/issues/705
+    # https://github.com/jax-ml/jax/issues/705
     with self.assertRaisesRegex(
         ValueError, 'vmap must have at least one non-None value in in_axes'):
       # If the output is mapped, there must be a non-None in_axes

trax/tf_numpy_and_keras.ipynb

@@ -26,7 +26,7 @@
         "\n",
         "In Trax, all computations rely on accelerated math operations happening in the `fastmath` module. This module can use different backends for acceleration. One of them is [TensorFlow NumPy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) which uses [TensorFlow 2](https://www.tensorflow.org/) to accelerate the computations.\n",
         "\n",
-        "The backend can be set using a call to `trax.fastmath.set_backend` as you'll see below. Currently available backends are `jax` (default), `tensorflow-numpy` and `numpy` (for debugging). The `tensorflow-numpy` backend uses [TensorFlow Numpy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) for executing `fastmath` functions on TensorFlow, while the `jax` backend calls [JAX](https://github.com/google/jax) which lowers to TensorFlow XLA.\n",
+        "The backend can be set using a call to `trax.fastmath.set_backend` as you'll see below. Currently available backends are `jax` (default), `tensorflow-numpy` and `numpy` (for debugging). The `tensorflow-numpy` backend uses [TensorFlow Numpy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) for executing `fastmath` functions on TensorFlow, while the `jax` backend calls [JAX](https://github.com/jax-ml/jax) which lowers to TensorFlow XLA.\n",
         "\n",
         "You may see that `tensorflow-numpy` and `jax` backends show different speed and memory characteristics. You may also see different error messages when debugging since it might expose you to the internals of the backends. However for the most part, users can choose a backend and not worry about the internal details of these backends.\n",
         "\n",