[nnx] use jax-like transforms API in nnx_basics

docs/nnx/nnx_basics.ipynb

@@ -365,17 +365,21 @@
     "  using the optimizer alone.\n",
     "\n",
     "#### Scan over layers\n",
-    "Next lets take a look at a different example using `nnx.vmap` to create an\n",
-    "`MLP` stack and `nnx.scan` to iteratively apply each layer in the stack to the\n",
-    "input (scan over layers). \n",
+    "Next lets take a look at a different example, which uses\n",
+    "[nnx.vmap](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.vmap)\n",
+    "to create a stack of multiple MLP layers and\n",
+    "[nnx.scan](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.scan)\n",
+    "to iteratively apply each layer of the stack to the input.\n",
     "\n",
     "Notice the following:\n",
-    "1. The `create_model` function creates a (single) `MLP` object that is lifted by\n",
-    "  `nnx.vmap` to have an additional dimension of size `axis_size`.\n",
-    "2. The `forward` function indexes the `MLP` object's state to get a different set of\n",
-    "  parameters at each step.\n",
-    "3. `nnx.scan` automatically propagates the state updates for the `BatchNorm` and \n",
-    "`Dropout` layers from within `forward` to the `model` reference outside."
+    "1. The `create_model` function takes in a key and returns an `MLP` object, since we create 5 keys\n",
+    "  and use `nnx.vmap` over `create_model` a stack of 5 `MLP` objects is created.\n",
+    "2. We use `nnx.scan` to iteratively apply each `MLP` in the stack to the input `x`.\n",
+    "3. The `nnx.scan` API (consciously) deviates from `jax.lax.scan` and instead mimicks `vmap` which is\n",
+    "  more expressive. `nnx.scan` allows specifying multiple inputs, the scan axes of each input/output,\n",
+    "  and the position of the carry.\n",
+    "4. State updates for the `BatchNorm` and `Dropout` layers are automatically propagated \n",
+    "  by `nnx.scan`."
    ]
   },
   {
@@ -404,15 +408,14 @@
     }
    ],
    "source": [
-    "from functools import partial\n",
+    "@nnx.vmap(in_axes=0, out_axes=0)\n",
+    "def create_model(key: jax.Array):\n",
+    "  return MLP(10, 32, 10, rngs=nnx.Rngs(key))\n",
     "\n",
-    "@partial(nnx.vmap, axis_size=5)\n",
-    "def create_model(rngs: nnx.Rngs):\n",
-    "  return MLP(10, 32, 10, rngs=rngs)\n",
+    "keys = jax.random.split(jax.random.key(0), 5)\n",
+    "model = create_model(keys)\n",
     "\n",
-    "model = create_model(nnx.Rngs(0))\n",
-    "\n",
-    "@nnx.scan\n",
+    "@nnx.scan(in_axes=(nnx.Carry, 0), out_axes=0)\n",
     "def forward(x, model: MLP):\n",
     "  x = model(x)\n",
     "  return x, None\n",

docs/nnx/nnx_basics.md

@@ -219,28 +219,31 @@ Theres a couple of things happening in this example that are worth mentioning:
   using the optimizer alone.
 
 #### Scan over layers
-Next lets take a look at a different example using `nnx.vmap` to create an
-`MLP` stack and `nnx.scan` to iteratively apply each layer in the stack to the
-input (scan over layers). 
+Next lets take a look at a different example, which uses
+[nnx.vmap](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.vmap)
+to create a stack of multiple MLP layers and
+[nnx.scan](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.scan)
+to iteratively apply each layer of the stack to the input.
 
 Notice the following:
-1. The `create_model` function creates a (single) `MLP` object that is lifted by
-  `nnx.vmap` to have an additional dimension of size `axis_size`.
-2. The `forward` function indexes the `MLP` object's state to get a different set of
-  parameters at each step.
-3. `nnx.scan` automatically propagates the state updates for the `BatchNorm` and 
-`Dropout` layers from within `forward` to the `model` reference outside.
+1. The `create_model` function takes in a key and returns an `MLP` object, since we create 5 keys
+  and use `nnx.vmap` over `create_model` a stack of 5 `MLP` objects is created.
+2. We use `nnx.scan` to iteratively apply each `MLP` in the stack to the input `x`.
+3. The `nnx.scan` API (consciously) deviates from `jax.lax.scan` and instead mimicks `vmap` which is
+  more expressive. `nnx.scan` allows specifying multiple inputs, the scan axes of each input/output,
+  and the position of the carry.
+4. State updates for the `BatchNorm` and `Dropout` layers are automatically propagated 
+  by `nnx.scan`.
 
 ```{code-cell} ipython3
-from functools import partial
+@nnx.vmap(in_axes=0, out_axes=0)
+def create_model(key: jax.Array):
+  return MLP(10, 32, 10, rngs=nnx.Rngs(key))
 
-@partial(nnx.vmap, axis_size=5)
-def create_model(rngs: nnx.Rngs):
-  return MLP(10, 32, 10, rngs=rngs)
+keys = jax.random.split(jax.random.key(0), 5)
+model = create_model(keys)
 
-model = create_model(nnx.Rngs(0))
-
-@nnx.scan
+@nnx.scan(in_axes=(nnx.Carry, 0), out_axes=0)
 def forward(x, model: MLP):
   x = model(x)
   return x, None