From 94793f9b9e288879786d96f62fdf85e669c5abc7 Mon Sep 17 00:00:00 2001
From: Cristian Garcia <cgarcia.e88@gmail.com>
Date: Fri, 15 Nov 2024 14:39:32 -0800
Subject: [PATCH] [nnx] add performance guide notebook

---
 .gitignore                                   |   3 +-
 docs_nnx/guides/images/performance-graph.png | Bin 0 -> 87827 bytes
 docs_nnx/guides/index.rst                    |   1 +
 docs_nnx/guides/performance.ipynb            | 151 +++++++++++++++++++
 docs_nnx/guides/performance.md               | 110 ++++++++++++++
 docs_nnx/nnx_basics.ipynb                    |  39 +----
 docs_nnx/nnx_basics.md                       |  37 +----
 pyproject.toml                               |   1 +
 uv.lock                                      | 108 ++++++++++---
 9 files changed, 365 insertions(+), 85 deletions(-)
 create mode 100644 docs_nnx/guides/images/performance-graph.png
 create mode 100644 docs_nnx/guides/performance.ipynb
 create mode 100644 docs_nnx/guides/performance.md

diff --git a/.gitignore b/.gitignore
index a1de4b1dda..0bc7f3cbe6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,8 +17,7 @@ flaxlib_src/build
 flaxlib_src/builddir
 flaxlib_src/dist
 flaxlib_src/subprojects
-target/
-flaxlib.cpython-*
+
 # used by direnv
 .envrc
 
diff --git a/docs_nnx/guides/images/performance-graph.png b/docs_nnx/guides/images/performance-graph.png
new file mode 100644
index 0000000000000000000000000000000000000000..34fb134e22dfee296ceb0472e66068cd0dc10573
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diff --git a/docs_nnx/guides/index.rst b/docs_nnx/guides/index.rst
index 5d917be3e3..58c9e80f12 100644
--- a/docs_nnx/guides/index.rst
+++ b/docs_nnx/guides/index.rst
@@ -8,6 +8,7 @@ Guides
    flax_gspmd
    filters_guide
    randomness
+   performance
    linen_to_nnx
    bridge_guide
    surgery
diff --git a/docs_nnx/guides/performance.ipynb b/docs_nnx/guides/performance.ipynb
new file mode 100644
index 0000000000..a745167114
--- /dev/null
+++ b/docs_nnx/guides/performance.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Performance Considerations\n",
+    "Currently `nnx.jit` traverses the object graph in pure Python, this is slow and adds overhead. To solve this in general we will be developing a Rust extension called `flaxlib` (see first steps in #4196) to speedup some of the traversal logic in `graph.py`, similar to how JAX solved the same issue with `jaxlib` for standard pytrees. However, there's two things to consider:\n",
+    "\n",
+    "* The overhead is only relevant for small models. See [Asynchronous dispatch](#asynchronous-dispatch).\n",
+    "* You can remove the overhead by using `jax.jit` + `nnx.split` / `nnx.merge` to stage out the traversal logic. See [Lowering the Python Overhead](#lowering-the-python-overhead).\n",
+    "\n",
+    "\n",
+    "## Asynchronous dispatch\n",
+    "In [benchmarks/nnx_simple_training.py](https://github.com/google/flax/blob/main/benchmarks/nnx_simple_training.py) we are increasing the layer width (features per layer) and measuring the total training time for the same model trained both with `nnx.jit` and `jax.jit`. As you can see in the graph below, after a certain model size the time spent in the traversal is completely absorbed by async dispatch. This happens when Python is able to finish the current for loop step, and reach the next `train_step` and JAX is still not done with the previous `train_step`. \n",
+    "\n",
+    "![performance-graph](images/performance-graph.png)\n",
+    "\n",
+    "This means that you only need to worry about the `nnx.jit` overhead for small models. If you are working with a small model, check out the next section to see how you can remove the overhead.\n",
+    "\n",
+    "## Lowering the Python Overhead\n",
+    "To remove the python overhead you can use regular `jax.jit` in combination with `nnx.split` and `nnx.merge` to stage out the traversal logic. To learn how to do this, lets first create this simple model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flax import nnx\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import optax\n",
+    "\n",
+    "class Model(nnx.Module):\n",
+    "  def __init__(self, din, dmid, dout, rngs: nnx.Rngs):\n",
+    "    self.linear = nnx.Linear(din, dmid, rngs=rngs)\n",
+    "    self.bn = nnx.BatchNorm(dmid, rngs=rngs)\n",
+    "    self.dropout = nnx.Dropout(0.2, rngs=rngs)\n",
+    "    self.linear_out = nnx.Linear(dmid, dout, rngs=rngs)\n",
+    "\n",
+    "  def __call__(self, x):\n",
+    "    x = nnx.relu(self.dropout(self.bn(self.linear(x))))\n",
+    "    return self.linear_out(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets say we have this `train_step` function that is using `nnx.jit` and takes in a `model`, `optimizer`, and `metrics`, all of which are Flax NNX objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model(2, 64, 3, rngs=nnx.Rngs(0))  # eager initialization\n",
+    "optimizer = nnx.Optimizer(model, optax.adam(1e-3))  # reference sharing\n",
+    "metrics = nnx.MultiMetric(\n",
+    "  loss=nnx.metrics.Average('loss'),\n",
+    ")\n",
+    "\n",
+    "@nnx.jit  # <== currently slow\n",
+    "def train_step(model, optimizer, metrics, x, y):\n",
+    "  def loss_fn(model):\n",
+    "    y_pred = model(x)  # call methods directly\n",
+    "    return ((y_pred - y) ** 2).mean()\n",
+    "\n",
+    "  loss, grads = nnx.value_and_grad(loss_fn)(model)\n",
+    "  optimizer.update(grads)  # in-place updates\n",
+    "  metrics.update(loss=loss)\n",
+    "\n",
+    "  return loss\n",
+    "  \n",
+    "for _ in range(10):\n",
+    "  x, y = jnp.ones((32, 2)), jnp.zeros((32, 3))\n",
+    "  loss = train_step(model, optimizer, metrics, x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To speed it up, before starting the training loop we can use `nnx.split` over the all the Flax NNX objects that are inputs to `train_step` to create a `graphdef` and `state` pytrees that are fast to traverse. Next we change `train_step` so accept `graphdef` and `state` and use `nnx.merge` and `nnx.split` at the beginning and end of `train_step` to switch back and forth between the objects and their pytree representations. Even though `nnx.split` and `nnx.merge` are slow it doesn't matter because they will only run once during tracing. With this in place, we can change the `train_step` function to use `jax.jit` instead of `nnx.jit`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model(2, 64, 3, rngs=nnx.Rngs(0))  # eager initialization\n",
+    "optimizer = nnx.Optimizer(model, optax.adamw(1e-3))  # reference sharing\n",
+    "metrics = nnx.MultiMetric(\n",
+    "  loss=nnx.metrics.Average('loss'),\n",
+    ")\n",
+    "# split before training loop\n",
+    "graphdef, state = nnx.split((model, optimizer, metrics))\n",
+    "\n",
+    "@jax.jit  # regular JAX\n",
+    "def train_step(graphdef, state, x, y):\n",
+    "  # merge at the beginning of the function\n",
+    "  model, optimizer, metrics = nnx.merge(graphdef, state)\n",
+    "\n",
+    "  def loss_fn(model):\n",
+    "    y_pred = model(x)  # call methods directly\n",
+    "    return ((y_pred - y) ** 2).mean()\n",
+    "\n",
+    "  loss, grads = nnx.value_and_grad(loss_fn)(model)\n",
+    "  optimizer.update(grads)\n",
+    "  metrics.update(loss=loss)\n",
+    "\n",
+    "  # split at the end of the function\n",
+    "  _, state = nnx.split((model, optimizer, metrics))\n",
+    "\n",
+    "  # return new state\n",
+    "  return state, loss\n",
+    "\n",
+    "for _ in range(10):\n",
+    "  x, y = jnp.ones((32, 2)), jnp.zeros((32, 3))\n",
+    "  state, loss = train_step(graphdef, state, x, y)\n",
+    "\n",
+    "# update objects after training\n",
+    "nnx.update((model, optimizer, metrics), state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we only do this for `jit`, you can still use other transforms like `nnx.value_and_grad` shown in the example since their overhead is already absorbed by the outer `jit`. Also, after the training loop is done (or whenever need) `nnx.update` can be used to update Flax NNX objects like `model`, `optimizer`, and `metrics` to a new `state`."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md:myst"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs_nnx/guides/performance.md b/docs_nnx/guides/performance.md
new file mode 100644
index 0000000000..5d13b54b50
--- /dev/null
+++ b/docs_nnx/guides/performance.md
@@ -0,0 +1,110 @@
+---
+jupytext:
+  formats: ipynb,md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.13.8
+---
+
+# Performance Considerations
+Currently `nnx.jit` traverses the object graph in pure Python, this is slow and adds overhead. To solve this in general we will be developing a Rust extension called `flaxlib` (see first steps in #4196) to speedup some of the traversal logic in `graph.py`, similar to how JAX solved the same issue with `jaxlib` for standard pytrees. However, there's two things to consider:
+
+* The overhead is only relevant for small models. See [Asynchronous dispatch](#asynchronous-dispatch).
+* You can remove the overhead by using `jax.jit` + `nnx.split` / `nnx.merge` to stage out the traversal logic. See [Lowering the Python Overhead](#lowering-the-python-overhead).
+
+
+## Asynchronous dispatch
+In [benchmarks/nnx_simple_training.py](https://github.com/google/flax/blob/main/benchmarks/nnx_simple_training.py) we are increasing the layer width (features per layer) and measuring the total training time for the same model trained both with `nnx.jit` and `jax.jit`. As you can see in the graph below, after a certain model size the time spent in the traversal is completely absorbed by async dispatch. This happens when Python is able to finish the current for loop step, and reach the next `train_step` and JAX is still not done with the previous `train_step`. 
+
+![performance-graph](images/performance-graph.png)
+
+This means that you only need to worry about the `nnx.jit` overhead for small models. If you are working with a small model, check out the next section to see how you can remove the overhead.
+
+## Lowering the Python Overhead
+To remove the python overhead you can use regular `jax.jit` in combination with `nnx.split` and `nnx.merge` to stage out the traversal logic. To learn how to do this, lets first create this simple model:
+
+```{code-cell}
+from flax import nnx
+import jax
+import jax.numpy as jnp
+import optax
+
+class Model(nnx.Module):
+  def __init__(self, din, dmid, dout, rngs: nnx.Rngs):
+    self.linear = nnx.Linear(din, dmid, rngs=rngs)
+    self.bn = nnx.BatchNorm(dmid, rngs=rngs)
+    self.dropout = nnx.Dropout(0.2, rngs=rngs)
+    self.linear_out = nnx.Linear(dmid, dout, rngs=rngs)
+
+  def __call__(self, x):
+    x = nnx.relu(self.dropout(self.bn(self.linear(x))))
+    return self.linear_out(x)
+```
+
+Lets say we have this `train_step` function that is using `nnx.jit` and takes in a `model`, `optimizer`, and `metrics`, all of which are Flax NNX objects:
+
+```{code-cell}
+model = Model(2, 64, 3, rngs=nnx.Rngs(0))  # eager initialization
+optimizer = nnx.Optimizer(model, optax.adam(1e-3))  # reference sharing
+metrics = nnx.MultiMetric(
+  loss=nnx.metrics.Average('loss'),
+)
+
+@nnx.jit  # <== currently slow
+def train_step(model, optimizer, metrics, x, y):
+  def loss_fn(model):
+    y_pred = model(x)  # call methods directly
+    return ((y_pred - y) ** 2).mean()
+
+  loss, grads = nnx.value_and_grad(loss_fn)(model)
+  optimizer.update(grads)  # in-place updates
+  metrics.update(loss=loss)
+
+  return loss
+  
+for _ in range(10):
+  x, y = jnp.ones((32, 2)), jnp.zeros((32, 3))
+  loss = train_step(model, optimizer, metrics, x, y)
+```
+
+To speed it up, before starting the training loop we can use `nnx.split` over the all the Flax NNX objects that are inputs to `train_step` to create a `graphdef` and `state` pytrees that are fast to traverse. Next we change `train_step` so accept `graphdef` and `state` and use `nnx.merge` and `nnx.split` at the beginning and end of `train_step` to switch back and forth between the objects and their pytree representations. Even though `nnx.split` and `nnx.merge` are slow it doesn't matter because they will only run once during tracing. With this in place, we can change the `train_step` function to use `jax.jit` instead of `nnx.jit`:
+
+```{code-cell}
+model = Model(2, 64, 3, rngs=nnx.Rngs(0))  # eager initialization
+optimizer = nnx.Optimizer(model, optax.adamw(1e-3))  # reference sharing
+metrics = nnx.MultiMetric(
+  loss=nnx.metrics.Average('loss'),
+)
+# split before training loop
+graphdef, state = nnx.split((model, optimizer, metrics))
+
+@jax.jit  # regular JAX
+def train_step(graphdef, state, x, y):
+  # merge at the beginning of the function
+  model, optimizer, metrics = nnx.merge(graphdef, state)
+
+  def loss_fn(model):
+    y_pred = model(x)  # call methods directly
+    return ((y_pred - y) ** 2).mean()
+
+  loss, grads = nnx.value_and_grad(loss_fn)(model)
+  optimizer.update(grads)
+  metrics.update(loss=loss)
+
+  # split at the end of the function
+  _, state = nnx.split((model, optimizer, metrics))
+
+  # return new state
+  return state, loss
+
+for _ in range(10):
+  x, y = jnp.ones((32, 2)), jnp.zeros((32, 3))
+  state, loss = train_step(graphdef, state, x, y)
+
+# update objects after training
+nnx.update((model, optimizer, metrics), state)
+```
+
+Notice that we only do this for `jit`, you can still use other transforms like `nnx.value_and_grad` shown in the example since their overhead is already absorbed by the outer `jit`. Also, after the training loop is done (or whenever need) `nnx.update` can be used to update Flax NNX objects like `model`, `optimizer`, and `metrics` to a new `state`.
diff --git a/docs_nnx/nnx_basics.ipynb b/docs_nnx/nnx_basics.ipynb
index d333db5dbc..30c2dcb499 100644
--- a/docs_nnx/nnx_basics.ipynb
+++ b/docs_nnx/nnx_basics.ipynb
@@ -8,27 +8,12 @@
     "\n",
     "Flax NNX is a new simplified API that is designed to make it easier to create, inspect, debug, and analyze neural networks in [JAX](https://jax.readthedocs.io/). It achieves this by adding first class support for Python reference semantics. This allows users to express their models using regular Python objects, which are modeled as PyGraphs (instead of pytrees), enabling reference sharing and mutability. Such API design should make PyTorch or Keras users feel at home.\n",
     "\n",
-    "In this guide you will learn about:\n",
-    "\n",
-    "- The Flax [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) system: An example of creating and initializing a custom `Linear` layer.\n",
-    "  - Stateful computation: An example of creating a Flax [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable) and updating its value (such as state updates needed during the forward pass).\n",
-    "  - Nested [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html)s: An MLP example with `Linear`, [`nnx.Dropout`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/stochastic.html#flax.nnx.Dropout), and [`nnx.BatchNorm`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/normalization.html#flax.nnx.BatchNorm) layers.\n",
-    "  - Model surgery: An example of replacing custom `Linear` layers inside a model with custom `LoraLinear` layers.\n",
-    "- Flax transformations: An example of using [`nnx.jit`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.jit) for automatic state management.\n",
-    "  - [`nnx.scan`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.scan) over layers.\n",
-    "- The Flax NNX Functional API: An example of a custom `StatefulLinear` layer with [`nnx.Param`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Param)s with fine-grained control over the state.\n",
-    "  - [`State`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/state.html#flax.nnx.State) and [`GraphDef`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/graph.html#flax.nnx.GraphDef).\n",
-    "  - [`split`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/graph.html#flax.nnx.split), [`merge`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/graph.html#flax.nnx.merge), and `update`\n",
-    "  - Fine-grained [`State`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/state.html#flax.nnx.State) control: An example of using [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable) type `Filter`s to split into multiple [`nnx.State`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/state.html#flax.nnx.State)s.\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "Install Flax with `pip` and import necessary dependencies:"
+    "To begin, install Flax with `pip` and import necessary dependencies:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "skip-execution"
@@ -36,7 +21,7 @@
    },
    "outputs": [],
    "source": [
-    "# ! pip install -U flax treescope"
+    "# ! pip install -U flax"
    ]
   },
   {
@@ -56,17 +41,9 @@
    "source": [
     "## The Flax NNX Module system\n",
     "\n",
-    "The main difference between the Flax[`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) and other `Module` systems in [Flax Linen](https://flax-linen.readthedocs.io/en/latest/api_reference/flax.linen/module.html) or [Haiku](https://dm-haiku.readthedocs.io/en/latest/notebooks/basics.html#Built-in-Haiku-nets-and-nested-modules) is that in NNX everything is **explicit**. This  means, among other things, that:\n",
-    "\n",
-    "1) The [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) itself holds the state (such as parameters) directly.\n",
-    "2) The [PRNG](https://jax.readthedocs.io/en/latest/random-numbers.html) state is threaded by the user.\n",
-    "3) All shape information must be provided on  initialization (no shape inference).\n",
+    "The main difference between the Flax[`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) and other `Module` systems in [Flax Linen](https://flax-linen.readthedocs.io/en/latest/api_reference/flax.linen/module.html) or [Haiku](https://dm-haiku.readthedocs.io/en/latest/notebooks/basics.html#Built-in-Haiku-nets-and-nested-modules) is that in NNX everything is **explicit**. This  means, among other things, that the [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) itself holds the state (such as parameters) directly, the [PRNG](https://jax.readthedocs.io/en/latest/random-numbers.html) state is threaded by the user, and all shape information must be provided on initialization (no shape inference).\n",
     "\n",
-    "Let's begin by creating a `Linear` [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html). The following code shows that:\n",
-    "\n",
-    "- Dynamic state is usually stored in [`nnx.Param`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Param)s, and static state  (all types not handled by NNX), such as integers or strings are stored directly.\n",
-    "- Attributes of type [`jax.Array`](https://jax.readthedocs.io/en/latest/_autosummary/jax.Array.html) and `numpy.ndarray` are also treated as dynamic states, although storing them inside [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable)s, such as `Param`, is preferred.\n",
-    "- The [`nnx.Rngs`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/rnglib.html#flax.nnx.Rngs) object can be used to get new unique keys based on a root PRNG key passed to the constructor."
+    "Let's begin by creating a `Linear` [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html). As shown next, dynamic state is usually stored in `nnx.Param`s, and static state (all types not handled by NNX) such as integers or strings are stored directly. Attributes of type `jax.Array` and `numpy.ndarray` are also treated as dynamic states, although storing them inside `nnx.Variable`s, such as `Param`, is preferred. Also the [`nnx.Rngs`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/rnglib.html#flax.nnx.Rngs) object can be used to get new unique keys based on a root PRNG key passed to the constructor."
    ]
   },
   {
@@ -90,9 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Also note that:\n",
-    "\n",
-    "- The inner values of [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable) can be accessed using the `value` property, but for convenience they implement all numeric operators and can be used directly in arithmetic expressions (as shown in the code above).\n",
+    "Also note that the inner values of `nnx.Variable`s can be accessed using the `value` property, but for convenience they implement all numeric operators and can be used directly in arithmetic expressions (as shown in the code above).\n",
     "\n",
     "To initialize a Flax [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html), you just call the constructor, and all the parameters of a `Module` are usually created eagerly. Since [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html)s hold their own state methods, you can call them directly without the need for a separate `apply` method.\n",
     "This can be very convenient for debugging, allowing you to directly inspect the entire structure of the model."
@@ -355,6 +330,8 @@
     "1. The updates to each of the [`nnx.BatchNorm`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/normalization.html#flax.nnx.BatchNorm) and [`nnx.Dropout`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/stochastic.html#flax.nnx.Dropout) layer's state is automatically propagated from within `loss_fn` to `train_step` all the way to the `model` reference outside.\n",
     "2. The `optimizer` holds a mutable reference to the `model` - this relationship is preserved inside the `train_step` function making it possible to update the model's parameters using the optimizer alone.\n",
     "\n",
+    "> **Note**<br> `nnx.jit` has performance overhead for small models, check the [Performance Considerations](https://flax.readthedocs.io/en/latest/guides/performance.html) guide for more information.\n",
+    "\n",
     "### Scan over layers\n",
     "\n",
     "The next example uses Flax [`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.\n",
diff --git a/docs_nnx/nnx_basics.md b/docs_nnx/nnx_basics.md
index e836ef53d1..fbf9be0a26 100644
--- a/docs_nnx/nnx_basics.md
+++ b/docs_nnx/nnx_basics.md
@@ -12,27 +12,12 @@ jupytext:
 
 Flax NNX is a new simplified API that is designed to make it easier to create, inspect, debug, and analyze neural networks in [JAX](https://jax.readthedocs.io/). It achieves this by adding first class support for Python reference semantics. This allows users to express their models using regular Python objects, which are modeled as PyGraphs (instead of pytrees), enabling reference sharing and mutability. Such API design should make PyTorch or Keras users feel at home.
 
-In this guide you will learn about:
-
-- The Flax [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) system: An example of creating and initializing a custom `Linear` layer.
-  - Stateful computation: An example of creating a Flax [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable) and updating its value (such as state updates needed during the forward pass).
-  - Nested [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html)s: An MLP example with `Linear`, [`nnx.Dropout`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/stochastic.html#flax.nnx.Dropout), and [`nnx.BatchNorm`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/normalization.html#flax.nnx.BatchNorm) layers.
-  - Model surgery: An example of replacing custom `Linear` layers inside a model with custom `LoraLinear` layers.
-- Flax transformations: An example of using [`nnx.jit`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.jit) for automatic state management.
-  - [`nnx.scan`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/transforms.html#flax.nnx.scan) over layers.
-- The Flax NNX Functional API: An example of a custom `StatefulLinear` layer with [`nnx.Param`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Param)s with fine-grained control over the state.
-  - [`State`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/state.html#flax.nnx.State) and [`GraphDef`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/graph.html#flax.nnx.GraphDef).
-  - [`split`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/graph.html#flax.nnx.split), [`merge`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/graph.html#flax.nnx.merge), and `update`
-  - Fine-grained [`State`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/state.html#flax.nnx.State) control: An example of using [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable) type `Filter`s to split into multiple [`nnx.State`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/state.html#flax.nnx.State)s.
-
-## Setup
-
-Install Flax with `pip` and import necessary dependencies:
+To begin, install Flax with `pip` and import necessary dependencies:
 
 ```{code-cell} ipython3
 :tags: [skip-execution]
 
-# ! pip install -U flax treescope
+# ! pip install -U flax
 ```
 
 ```{code-cell} ipython3
@@ -43,17 +28,9 @@ import jax.numpy as jnp
 
 ## The Flax NNX Module system
 
-The main difference between the Flax[`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) and other `Module` systems in [Flax Linen](https://flax-linen.readthedocs.io/en/latest/api_reference/flax.linen/module.html) or [Haiku](https://dm-haiku.readthedocs.io/en/latest/notebooks/basics.html#Built-in-Haiku-nets-and-nested-modules) is that in NNX everything is **explicit**. This  means, among other things, that:
-
-1) The [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) itself holds the state (such as parameters) directly.
-2) The [PRNG](https://jax.readthedocs.io/en/latest/random-numbers.html) state is threaded by the user.
-3) All shape information must be provided on  initialization (no shape inference).
+The main difference between the Flax[`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) and other `Module` systems in [Flax Linen](https://flax-linen.readthedocs.io/en/latest/api_reference/flax.linen/module.html) or [Haiku](https://dm-haiku.readthedocs.io/en/latest/notebooks/basics.html#Built-in-Haiku-nets-and-nested-modules) is that in NNX everything is **explicit**. This  means, among other things, that the [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html) itself holds the state (such as parameters) directly, the [PRNG](https://jax.readthedocs.io/en/latest/random-numbers.html) state is threaded by the user, and all shape information must be provided on initialization (no shape inference).
 
-Let's begin by creating a `Linear` [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html). The following code shows that:
-
-- Dynamic state is usually stored in [`nnx.Param`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Param)s, and static state  (all types not handled by NNX), such as integers or strings are stored directly.
-- Attributes of type [`jax.Array`](https://jax.readthedocs.io/en/latest/_autosummary/jax.Array.html) and `numpy.ndarray` are also treated as dynamic states, although storing them inside [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable)s, such as `Param`, is preferred.
-- The [`nnx.Rngs`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/rnglib.html#flax.nnx.Rngs) object can be used to get new unique keys based on a root PRNG key passed to the constructor.
+Let's begin by creating a `Linear` [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html). As shown next, dynamic state is usually stored in `nnx.Param`s, and static state (all types not handled by NNX) such as integers or strings are stored directly. Attributes of type `jax.Array` and `numpy.ndarray` are also treated as dynamic states, although storing them inside `nnx.Variable`s, such as `Param`, is preferred. Also the [`nnx.Rngs`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/rnglib.html#flax.nnx.Rngs) object can be used to get new unique keys based on a root PRNG key passed to the constructor.
 
 ```{code-cell} ipython3
 class Linear(nnx.Module):
@@ -67,9 +44,7 @@ class Linear(nnx.Module):
     return x @ self.w + self.b
 ```
 
-Also note that:
-
-- The inner values of [`nnx.Variable`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/variables.html#flax.nnx.Variable) can be accessed using the `value` property, but for convenience they implement all numeric operators and can be used directly in arithmetic expressions (as shown in the code above).
+Also note that the inner values of `nnx.Variable`s can be accessed using the `value` property, but for convenience they implement all numeric operators and can be used directly in arithmetic expressions (as shown in the code above).
 
 To initialize a Flax [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html), you just call the constructor, and all the parameters of a `Module` are usually created eagerly. Since [`nnx.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/module.html)s hold their own state methods, you can call them directly without the need for a separate `apply` method.
 This can be very convenient for debugging, allowing you to directly inspect the entire structure of the model.
@@ -209,6 +184,8 @@ There are two things happening in this example that are worth mentioning:
 1. The updates to each of the [`nnx.BatchNorm`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/normalization.html#flax.nnx.BatchNorm) and [`nnx.Dropout`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/nn/stochastic.html#flax.nnx.Dropout) layer's state is automatically propagated from within `loss_fn` to `train_step` all the way to the `model` reference outside.
 2. The `optimizer` holds a mutable reference to the `model` - this relationship is preserved inside the `train_step` function making it possible to update the model's parameters using the optimizer alone.
 
+> **Note**<br> `nnx.jit` has performance overhead for small models, check the [Performance Considerations](https://flax.readthedocs.io/en/latest/guides/performance.html) guide for more information.
+
 ### Scan over layers
 
 The next example uses Flax [`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.
diff --git a/pyproject.toml b/pyproject.toml
index 6c67a21cc2..658b2f15d5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ dependencies = [
     "rich>=11.1",
     "typing_extensions>=4.2",
     "PyYAML>=5.4.1",
+    "treescope>=0.1.2",
 ]
 classifiers = [
     "Development Status :: 3 - Alpha",
diff --git a/uv.lock b/uv.lock
index b4a0aaa65d..0d68a86b72 100644
--- a/uv.lock
+++ b/uv.lock
@@ -773,7 +773,7 @@ wheels = [
 
 [[package]]
 name = "flax"
-version = "0.10.0"
+version = "0.10.1"
 source = { editable = "." }
 dependencies = [
     { name = "jax" },
@@ -784,6 +784,7 @@ dependencies = [
     { name = "pyyaml" },
     { name = "rich" },
     { name = "tensorstore" },
+    { name = "treescope" },
     { name = "typing-extensions" },
 ]
 
@@ -890,6 +891,7 @@ requires-dist = [
     { name = "tensorflow-text", marker = "platform_system != 'Darwin' and extra == 'testing'", specifier = ">=2.11.0" },
     { name = "tensorstore" },
     { name = "torch", marker = "extra == 'testing'" },
+    { name = "treescope", specifier = ">=0.1.2" },
     { name = "treescope", marker = "python_full_version >= '3.10' and extra == 'testing'", specifier = ">=0.1.1" },
     { name = "typing-extensions", specifier = ">=4.2" },
 ]
@@ -2261,7 +2263,7 @@ wheels = [
 
 [[package]]
 name = "orbax-checkpoint"
-version = "0.9.1"
+version = "0.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
@@ -2275,12 +2277,13 @@ dependencies = [
     { name = "protobuf", version = "3.20.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "protobuf", version = "4.25.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pyyaml" },
+    { name = "simplejson" },
     { name = "tensorstore" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/48/ce/4a3386e9e4bc95ab638c2779e259a231ffc2ae1fc2b67a68f4d6d8a794f2/orbax_checkpoint-0.9.1.tar.gz", hash = "sha256:edf76d8fc482a9a0296645522f6d13ee09c499af0ef2f9369899cbfee31c7f88", size = 212247 }
+sdist = { url = "https://files.pythonhosted.org/packages/07/24/f13f75810a00873f779625b4fff9419d09f95a56bedb01453ac2b4990ce8/orbax_checkpoint-0.10.1.tar.gz", hash = "sha256:aaf44f5a10ced74badc7fcaf8a2396e9047a20a61487ad5e8514e539d7992cd8", size = 230081 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8a/41/19de59b4a8581ad364970b2429481838cea8d190f50bc8f672ca329b64e0/orbax_checkpoint-0.9.1-py3-none-any.whl", hash = "sha256:d33e23e63b7ffcf66de3fb5daa3d11bf24a34e66d133eb4a037788f22155857e", size = 296514 },
+    { url = "https://files.pythonhosted.org/packages/b3/67/a175072cd7e5a215b12f39f4d9d891881a6220d75e30ae6480d05647bdf4/orbax_checkpoint-0.10.1-py3-none-any.whl", hash = "sha256:b4d7ae295d89a329c39109f945ff690d47c1db04eac644fa5316b2f42b5fa9e5", size = 328311 },
 ]
 
 [[package]]
@@ -3100,6 +3103,67 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b0/12/c657047c11a47e1c3e51bdc26bd6f2661a268fd0384bd8ed56b227530486/simple_parsing-0.1.5-py3-none-any.whl", hash = "sha256:46f35ed7002f9bb25dca3a49eac491cc78d2140e4adcbe156225ae643c2874ea", size = 113568 },
 ]
 
+[[package]]
+name = "simplejson"
+version = "3.19.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3d/29/085111f19717f865eceaf0d4397bf3e76b08d60428b076b64e2a1903706d/simplejson-3.19.3.tar.gz", hash = "sha256:8e086896c36210ab6050f2f9f095a5f1e03c83fa0e7f296d6cba425411364680", size = 85237 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/39/24/260ad03435ce8ef2436031951134659c7161776ec3a78094b35b9375ceea/simplejson-3.19.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:50d8b742d74c449c4dcac570d08ce0f21f6a149d2d9cf7652dbf2ba9a1bc729a", size = 93660 },
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