update notebooks to new-style typed PRNG keys

docs/notebooks/Common_Gotchas_in_JAX.ipynb

@@ -1006,7 +1006,7 @@
    "source": [
     "JAX instead implements an _explicit_ PRNG where entropy production and consumption are handled by explicitly passing and iterating PRNG state.  JAX uses a modern [Threefry counter-based PRNG](https://github.com/google/jax/blob/main/docs/jep/263-prng.md) that's __splittable__.  That is, its design allows us to __fork__ the PRNG state into new PRNGs for use with parallel stochastic generation.\n",
     "\n",
-    "The random state is described by two unsigned-int32s that we call a __key__:"
+    "The random state is described by a special array element that we call a __key__:"
    ]
   },
   {
@@ -1030,7 +1030,7 @@
    ],
    "source": [
     "from jax import random\n",
-    "key = random.PRNGKey(0)\n",
+    "key = random.key(0)\n",
     "key"
    ]
   },
@@ -2121,7 +2121,7 @@
     }
    ],
    "source": [
-    "x = random.uniform(random.PRNGKey(0), (1000,), dtype=jnp.float64)\n",
+    "x = random.uniform(random.key(0), (1000,), dtype=jnp.float64)\n",
     "x.dtype"
    ]
   },
@@ -2188,7 +2188,7 @@
    "source": [
     "import jax.numpy as jnp\n",
     "from jax import random\n",
-    "x = random.uniform(random.PRNGKey(0), (1000,), dtype=jnp.float64)\n",
+    "x = random.uniform(random.key(0), (1000,), dtype=jnp.float64)\n",
     "x.dtype # --> dtype('float64')"
    ]
   },

docs/notebooks/Common_Gotchas_in_JAX.md

@@ -463,14 +463,14 @@ The Mersenne Twister PRNG is also known to have a [number](https://cs.stackexcha
 
 JAX instead implements an _explicit_ PRNG where entropy production and consumption are handled by explicitly passing and iterating PRNG state.  JAX uses a modern [Threefry counter-based PRNG](https://github.com/google/jax/blob/main/docs/jep/263-prng.md) that's __splittable__.  That is, its design allows us to __fork__ the PRNG state into new PRNGs for use with parallel stochastic generation.
 
-The random state is described by two unsigned-int32s that we call a __key__:
+The random state is described by a special array element that we call a __key__:
 
 ```{code-cell} ipython3
 :id: yPHE7KTWgAWs
 :outputId: ae8af0ee-f19e-474e-81b6-45e894eb2fc3
 
 from jax import random
-key = random.PRNGKey(0)
+key = random.key(0)
 key
 ```
 
@@ -1071,7 +1071,7 @@ At the moment, JAX by default enforces single-precision numbers to mitigate the
 :id: CNNGtzM3NDkO
 :outputId: b422bb23-a784-44dc-f8c9-57f3b6c861b8
 
-x = random.uniform(random.PRNGKey(0), (1000,), dtype=jnp.float64)
+x = random.uniform(random.key(0), (1000,), dtype=jnp.float64)
 x.dtype
 ```
 
@@ -1117,7 +1117,7 @@ We can then confirm that `x64` mode is enabled:
 
 import jax.numpy as jnp
 from jax import random
-x = random.uniform(random.PRNGKey(0), (1000,), dtype=jnp.float64)
+x = random.uniform(random.key(0), (1000,), dtype=jnp.float64)
 x.dtype # --> dtype('float64')
 ```
 

docs/notebooks/Distributed_arrays_and_automatic_parallelization.ipynb

@@ -131,7 +131,7 @@
    ],
    "source": [
     "# Create an array of random values:\n",
-    "x = jax.random.normal(jax.random.PRNGKey(0), (8192, 8192))\n",
+    "x = jax.random.normal(jax.random.key(0), (8192, 8192))\n",
     "# and use jax.device_put to distribute it across devices:\n",
     "y = jax.device_put(x, sharding.reshape(4, 2))\n",
     "jax.debug.visualize_array_sharding(y)"
@@ -272,7 +272,7 @@
    "outputs": [],
    "source": [
     "import jax\n",
-    "x = jax.random.normal(jax.random.PRNGKey(0), (8192, 8192))"
+    "x = jax.random.normal(jax.random.key(0), (8192, 8192))"
    ]
   },
   {
@@ -1513,7 +1513,7 @@
    },
    "outputs": [],
    "source": [
-    "x = jax.random.normal(jax.random.PRNGKey(0), (8192, 8192))\n",
+    "x = jax.random.normal(jax.random.key(0), (8192, 8192))\n",
     "x = jax.device_put(x, sharding.reshape(4, 2))"
    ]
   },
@@ -1738,7 +1738,7 @@
     "layer_sizes = [784, 8192, 8192, 8192, 10]\n",
     "batch_size = 8192\n",
     "\n",
-    "params, batch = init_model(jax.random.PRNGKey(0), layer_sizes, batch_size)"
+    "params, batch = init_model(jax.random.key(0), layer_sizes, batch_size)"
    ]
   },
   {
@@ -2184,7 +2184,7 @@
     "  numbers = jax.random.uniform(key, x.shape)\n",
     "  return x + numbers\n",
     "\n",
-    "key = jax.random.PRNGKey(42)\n",
+    "key = jax.random.key(42)\n",
     "x_sharding = jax.sharding.PositionalSharding(jax.devices())\n",
     "x = jax.device_put(jnp.arange(24), x_sharding)"
    ]

docs/notebooks/Distributed_arrays_and_automatic_parallelization.md

@@ -81,7 +81,7 @@ sharding = PositionalSharding(mesh_utils.create_device_mesh((8,)))
 :outputId: 3b518df8-5c29-4848-acc3-e41df939f30b
 
 # Create an array of random values:
-x = jax.random.normal(jax.random.PRNGKey(0), (8192, 8192))
+x = jax.random.normal(jax.random.key(0), (8192, 8192))
 # and use jax.device_put to distribute it across devices:
 y = jax.device_put(x, sharding.reshape(4, 2))
 jax.debug.visualize_array_sharding(y)
@@ -144,7 +144,7 @@ For example, here's a value with a single-device `Sharding`:
 :id: VmoX4SUp3vGJ
 
 import jax
-x = jax.random.normal(jax.random.PRNGKey(0), (8192, 8192))
+x = jax.random.normal(jax.random.key(0), (8192, 8192))
 ```
 
 ```{code-cell}
@@ -609,7 +609,7 @@ sharding = PositionalSharding(mesh_utils.create_device_mesh((8,)))
 ```{code-cell}
 :id: Q1wuDp-L3vGT
 
-x = jax.random.normal(jax.random.PRNGKey(0), (8192, 8192))
+x = jax.random.normal(jax.random.key(0), (8192, 8192))
 x = jax.device_put(x, sharding.reshape(4, 2))
 ```
 
@@ -720,7 +720,7 @@ def init_model(key, layer_sizes, batch_size):
 layer_sizes = [784, 8192, 8192, 8192, 10]
 batch_size = 8192
 
-params, batch = init_model(jax.random.PRNGKey(0), layer_sizes, batch_size)
+params, batch = init_model(jax.random.key(0), layer_sizes, batch_size)
 ```
 
 +++ {"id": "sJv_h0AS2drh"}
@@ -902,7 +902,7 @@ def f(key, x):
   numbers = jax.random.uniform(key, x.shape)
   return x + numbers
 
-key = jax.random.PRNGKey(42)
+key = jax.random.key(42)
 x_sharding = jax.sharding.PositionalSharding(jax.devices())
 x = jax.device_put(jnp.arange(24), x_sharding)
 ```

docs/notebooks/Neural_Network_and_Data_Loading.ipynb

@@ -84,7 +84,7 @@
     "num_epochs = 8\n",
     "batch_size = 128\n",
     "n_targets = 10\n",
-    "params = init_network_params(layer_sizes, random.PRNGKey(0))"
+    "params = init_network_params(layer_sizes, random.key(0))"
    ]
   },
   {
@@ -150,7 +150,7 @@
    ],
    "source": [
     "# This works on single examples\n",
-    "random_flattened_image = random.normal(random.PRNGKey(1), (28 * 28,))\n",
+    "random_flattened_image = random.normal(random.key(1), (28 * 28,))\n",
     "preds = predict(params, random_flattened_image)\n",
     "print(preds.shape)"
    ]
@@ -173,7 +173,7 @@
    ],
    "source": [
     "# Doesn't work with a batch\n",
-    "random_flattened_images = random.normal(random.PRNGKey(1), (10, 28 * 28))\n",
+    "random_flattened_images = random.normal(random.key(1), (10, 28 * 28))\n",
     "try:\n",
     "  preds = predict(params, random_flattened_images)\n",
     "except TypeError:\n",

docs/notebooks/Neural_Network_and_Data_Loading.md

@@ -71,7 +71,7 @@ step_size = 0.01
 num_epochs = 8
 batch_size = 128
 n_targets = 10
-params = init_network_params(layer_sizes, random.PRNGKey(0))
+params = init_network_params(layer_sizes, random.key(0))
 ```
 
 +++ {"id": "BtoNk_yxWtIw"}
@@ -109,7 +109,7 @@ Let's check that our prediction function only works on single images.
 :outputId: 9d3b29e8-fab3-4ecb-9f63-bc8c092f9006
 
 # This works on single examples
-random_flattened_image = random.normal(random.PRNGKey(1), (28 * 28,))
+random_flattened_image = random.normal(random.key(1), (28 * 28,))
 preds = predict(params, random_flattened_image)
 print(preds.shape)
 ```
@@ -119,7 +119,7 @@ print(preds.shape)
 :outputId: d5d20211-b6da-44e9-f71e-946f2a9d0fc4
 
 # Doesn't work with a batch
-random_flattened_images = random.normal(random.PRNGKey(1), (10, 28 * 28))
+random_flattened_images = random.normal(random.key(1), (10, 28 * 28))
 try:
   preds = predict(params, random_flattened_images)
 except TypeError:

docs/notebooks/Writing_custom_interpreters_in_Jax.ipynb

@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "x = random.normal(random.PRNGKey(0), (5000, 5000))\n",
+    "x = random.normal(random.key(0), (5000, 5000))\n",
     "def f(w, b, x):\n",
     "  return jnp.tanh(jnp.dot(x, w) + b)\n",
     "fast_f = jit(f)"

docs/notebooks/Writing_custom_interpreters_in_Jax.md

@@ -48,7 +48,7 @@ JAX provides a NumPy-like API for numerical computing which can be used as is, b
 ```{code-cell} ipython3
 :id: HmlMcICOcSXR
 
-x = random.normal(random.PRNGKey(0), (5000, 5000))
+x = random.normal(random.key(0), (5000, 5000))
 def f(w, b, x):
   return jnp.tanh(jnp.dot(x, w) + b)
 fast_f = jit(f)

docs/notebooks/autodiff_cookbook.ipynb

@@ -27,7 +27,7 @@
     "from jax import grad, jit, vmap\n",
     "from jax import random\n",
     "\n",
-    "key = random.PRNGKey(0)"
+    "key = random.key(0)"
    ]
   },
   {
@@ -1055,7 +1055,7 @@
     "    outs, = vmap(vjp_fun)(M)\n",
     "    return outs\n",
     "\n",
-    "key = random.PRNGKey(0)\n",
+    "key = random.key(0)\n",
     "num_covecs = 128\n",
     "U = random.normal(key, (num_covecs,) + y.shape)\n",
     "\n",
@@ -1306,7 +1306,7 @@
    "outputs": [],
    "source": [
     "def check(seed):\n",
-    "  key = random.PRNGKey(seed)\n",
+    "  key = random.key(seed)\n",
     "\n",
     "  # random coeffs for u and v\n",
     "  key, subkey = random.split(key)\n",
@@ -1399,7 +1399,7 @@
    "outputs": [],
    "source": [
     "def check(seed):\n",
-    "  key = random.PRNGKey(seed)\n",
+    "  key = random.key(seed)\n",
     "\n",
     "  # random coeffs for u and v\n",
     "  key, subkey = random.split(key)\n",

docs/notebooks/autodiff_cookbook.md

@@ -29,7 +29,7 @@ import jax.numpy as jnp
 from jax import grad, jit, vmap
 from jax import random
 
-key = random.PRNGKey(0)
+key = random.key(0)
 ```
 
 +++ {"id": "YxnjtAGN6vu2"}
@@ -614,7 +614,7 @@ def vmap_mjp(f, x, M):
     outs, = vmap(vjp_fun)(M)
     return outs
 
-key = random.PRNGKey(0)
+key = random.key(0)
 num_covecs = 128
 U = random.normal(key, (num_covecs,) + y.shape)
 
@@ -770,7 +770,7 @@ Here's a check:
 :id: BGZV__zupIMS
 
 def check(seed):
-  key = random.PRNGKey(seed)
+  key = random.key(seed)
 
   # random coeffs for u and v
   key, subkey = random.split(key)
@@ -833,7 +833,7 @@ Here's a check of the VJP rules:
 :id: 4J7edvIBttcU
 
 def check(seed):
-  key = random.PRNGKey(seed)
+  key = random.key(seed)
 
   # random coeffs for u and v
   key, subkey = random.split(key)

docs/notebooks/convolutions.ipynb

@@ -60,7 +60,7 @@
     "import jax.numpy as jnp\n",
     "import numpy as np\n",
     "\n",
-    "key = random.PRNGKey(1701)\n",
+    "key = random.key(1701)\n",
     "\n",
     "x = jnp.linspace(0, 10, 500)\n",
     "y = jnp.sin(x) + 0.2 * random.normal(key, shape=(500,))\n",
@@ -130,7 +130,7 @@
     "ax[0].set_title('original')\n",
     "\n",
     "# Create a noisy version by adding random Gaussian noise\n",
-    "key = random.PRNGKey(1701)\n",
+    "key = random.key(1701)\n",
     "noisy_image = image + 50 * random.normal(key, image.shape)\n",
     "ax[1].imshow(noisy_image, cmap='binary_r')\n",
     "ax[1].set_title('noisy')\n",

docs/notebooks/convolutions.md

@@ -43,7 +43,7 @@ from jax import random
 import jax.numpy as jnp
 import numpy as np
 
-key = random.PRNGKey(1701)
+key = random.key(1701)
 
 x = jnp.linspace(0, 10, 500)
 y = jnp.sin(x) + 0.2 * random.normal(key, shape=(500,))
@@ -84,7 +84,7 @@ ax[0].imshow(image, cmap='binary_r')
 ax[0].set_title('original')
 
 # Create a noisy version by adding random Gaussian noise
-key = random.PRNGKey(1701)
+key = random.key(1701)
 noisy_image = image + 50 * random.normal(key, image.shape)
 ax[1].imshow(noisy_image, cmap='binary_r')
 ax[1].set_title('noisy')

docs/notebooks/neural_network_with_tfds_data.ipynb

@@ -97,7 +97,7 @@
     "num_epochs = 10\n",
     "batch_size = 128\n",
     "n_targets = 10\n",
-    "params = init_network_params(layer_sizes, random.PRNGKey(0))"
+    "params = init_network_params(layer_sizes, random.key(0))"
    ]
   },
   {
@@ -163,7 +163,7 @@
    ],
    "source": [
     "# This works on single examples\n",
-    "random_flattened_image = random.normal(random.PRNGKey(1), (28 * 28,))\n",
+    "random_flattened_image = random.normal(random.key(1), (28 * 28,))\n",
     "preds = predict(params, random_flattened_image)\n",
     "print(preds.shape)"
    ]
@@ -186,7 +186,7 @@
    ],
    "source": [
     "# Doesn't work with a batch\n",
-    "random_flattened_images = random.normal(random.PRNGKey(1), (10, 28 * 28))\n",
+    "random_flattened_images = random.normal(random.key(1), (10, 28 * 28))\n",
     "try:\n",
     "  preds = predict(params, random_flattened_images)\n",
     "except TypeError:\n",

docs/notebooks/neural_network_with_tfds_data.md

@@ -79,7 +79,7 @@ step_size = 0.01
 num_epochs = 10
 batch_size = 128
 n_targets = 10
-params = init_network_params(layer_sizes, random.PRNGKey(0))
+params = init_network_params(layer_sizes, random.key(0))
 ```
 
 +++ {"id": "BtoNk_yxWtIw"}
@@ -117,7 +117,7 @@ Let's check that our prediction function only works on single images.
 :outputId: ce9d86ed-a830-4832-e04d-10d1abb1fb8a
 
 # This works on single examples
-random_flattened_image = random.normal(random.PRNGKey(1), (28 * 28,))
+random_flattened_image = random.normal(random.key(1), (28 * 28,))
 preds = predict(params, random_flattened_image)
 print(preds.shape)
 ```
@@ -127,7 +127,7 @@ print(preds.shape)
 :outputId: f43bbc9d-bc8f-4168-ee7b-79ee9d33f245
 
 # Doesn't work with a batch
-random_flattened_images = random.normal(random.PRNGKey(1), (10, 28 * 28))
+random_flattened_images = random.normal(random.key(1), (10, 28 * 28))
 try:
   preds = predict(params, random_flattened_images)
 except TypeError: