Merge pull request #4338 from IvyZX:docfix

PiperOrigin-RevId: 690684746

docs_nnx/api_reference/flax.nnx/nn/dtypes.rst

@@ -0,0 +1,8 @@
+Dtypes
+------------------------
+
+.. automodule:: flax.nnx.nn.dtypes
+.. currentmodule:: flax.nnx.nn.dtypes
+
+.. autofunction:: canonicalize_dtype
+.. autofunction:: promote_dtype

docs_nnx/api_reference/flax.nnx/nn/index.rst

@@ -9,8 +9,11 @@ See the `NNX page <https://flax.readthedocs.io/en/latest/nnx/index.html>`__ for
 
   activations
   attention
+  dtypes
   initializers
   linear
+  lora
   normalization
+  recurrent
   stochastic
 

docs_nnx/api_reference/flax.nnx/nn/lora.rst

@@ -0,0 +1,15 @@
+LoRA
+------------------------
+
+NNX LoRA classes.
+
+.. automodule:: flax.nnx
+.. currentmodule:: flax.nnx
+
+.. flax_module::
+  :module: flax.nnx
+  :class: LoRA
+
+.. flax_module::
+  :module: flax.nnx
+  :class: LoRALinear

docs_nnx/api_reference/flax.nnx/nn/recurrent.rst

@@ -0,0 +1,32 @@
+Recurrent
+------------------------
+
+.. automodule:: flax.nnx.nn.recurrent
+.. currentmodule:: flax.nnx.nn.recurrent
+
+.. flax_module::
+  :module: flax.nnx.nn.recurrent
+  :class: LSTMCell
+
+.. flax_module::
+  :module: flax.nnx.nn.recurrent
+  :class: OptimizedLSTMCell
+
+.. flax_module::
+  :module: flax.nnx.nn.recurrent
+  :class: SimpleCell
+
+.. flax_module::
+  :module: flax.nnx.nn.recurrent
+  :class: GRUCell
+
+.. flax_module::
+  :module: flax.nnx.nn.recurrent
+  :class: RNN
+
+.. flax_module::
+  :module: flax.nnx.nn.recurrent
+  :class: Bidirectional
+
+
+.. autofunction:: flip_sequences

docs_nnx/guides/haiku_to_flax.rst

@@ -82,7 +82,7 @@ There are two fundamental differences between Haiku and Flax ``Module`` objects:
 
 
 Variable creation
-======================
+=======================
 
 Next, let's discuss instantiating the model and initializing its parameters
 
@@ -122,7 +122,7 @@ If you want to access Flax model parameters in the stateless, dictionary-like fa
   assert model.block.linear.kernel.value.shape == (784, 256)
 
 Training step and compilation
-======================
+=======================
 
 
 Now, let's proceed to writing a training step and compiling it using `JAX just-in-time compilation <https://jax.readthedocs.io/en/latest/jit-compilation.html>`__. Below are certain differences between Haiku and Flax NNX approaches.
@@ -401,7 +401,7 @@ To call those custom methods:
 
 
 Transformations
-===============
+=======================
 
 Both Haiku and `Flax transformations <https://flax.readthedocs.io/en/latest/guides/jax_and_nnx_transforms.html>`__ provide their own set of transforms that wrap `JAX transforms <https://jax.readthedocs.io/en/latest/key-concepts.html#transformations>`__ in a way that they can be used with ``Module`` objects.
 
@@ -488,7 +488,7 @@ The only difference is that Flax ``nnx.scan`` allows you to specify which axis t
 
 
 Scan over layers
-================
+=======================
 
 Most Haiku transforms should look similar with Flax, since they all wraps their JAX counterparts, but the scan-over-layers use case is an exception.
 
@@ -636,7 +636,7 @@ Now inspect the variable pytree on both sides:
 
 
 Top-level Haiku functions vs top-level Flax modules
-================
+=======================
 
 In Haiku, it is possible to write the entire model as a single function by using
 the raw ``hk.{get,set}_{parameter,state}`` to define/access model parameters and

docs_nnx/index.rst

@@ -17,9 +17,9 @@ Python objects. Flax NNX is an evolution of the previous `Flax Linen <https://fl
 API, and it took years of experience to bring a simpler and more user-friendly API.
 
 .. note::
-   Flax Linen API is not going to be deprecated in the near future as most of Flax users still
-   rely on this API. However, new users are encouraged to use Flax NNX.
-   For existing Flax Linen users planning to move to Flax NNX, check out the `evolution guide <guides/linen_to_nnx.html>`_ and `Why Flax NNX <why.html>`_.
+   Flax Linen API is not going to be deprecated in the near future as most of Flax users still rely on this API. However, new users are encouraged to use Flax NNX. Check out `Why Flax NNX <why.html>`_ for a comparison between Flax NNX and Linen, and our reasoning to make the new API.
+
+   To move your Flax Linen codebase to Flax NNX, get familiarized with the API in `NNX Basics <https://flax.readthedocs.io/en/latest/nnx_basics.html>`_ and then start your move following the `evolution guide <guides/linen_to_nnx.html>`_.
 
 Features
 ^^^^^^^^^

flax/nnx/nn/recurrent.py

@@ -742,16 +742,17 @@ def flip_sequences(
     values for those sequences that were padded. This function keeps the padding
     at the end, while flipping the rest of the elements.
 
-    Example:
-    ```python
-    inputs = [[1, 0, 0],
-              [2, 3, 0]
-              [4, 5, 6]]
-    lengths = [1, 2, 3]
-    flip_sequences(inputs, lengths) = [[1, 0, 0],
-                                       [3, 2, 0],
-                                       [6, 5, 4]]
-    ```
+    Example::
+
+      >>> from flax.nnx.nn.recurrent import flip_sequences
+      >>> from jax import numpy as jnp
+      >>> inputs = jnp.array([[1, 0, 0], [2, 3, 0], [4, 5, 6]])
+      >>> lengths = jnp.array([1, 2, 3])
+      >>> flip_sequences(inputs, lengths, 1, False)
+      Array([[1, 0, 0],
+             [3, 2, 0],
+             [6, 5, 4]], dtype=int32)
+
 
     Args:
       inputs: An array of input IDs <int>[batch_size, seq_length].
@@ -810,27 +811,27 @@ def __call__(
 class Bidirectional(Module):
     """Processes the input in both directions and merges the results.
 
-    Example usage:
+    Example usage::
+
+      >>> from flax import nnx
+      >>> import jax
+      >>> import jax.numpy as jnp
 
-    ```python
-    import nnx
-    import jax
-    import jax.numpy as jnp
+      >>> # Define forward and backward RNNs
+      >>> forward_rnn = RNN(GRUCell(in_features=3, hidden_features=4, rngs=nnx.Rngs(0)))
+      >>> backward_rnn = RNN(GRUCell(in_features=3, hidden_features=4, rngs=nnx.Rngs(0)))
 
-    # Define forward and backward RNNs
-    forward_rnn = RNN(GRUCell(in_features=3, hidden_features=4, rngs=nnx.Rngs(0)))
-    backward_rnn = RNN(GRUCell(in_features=3, hidden_features=4, rngs=nnx.Rngs(0)))
+      >>> # Create Bidirectional layer
+      >>> layer = Bidirectional(forward_rnn=forward_rnn, backward_rnn=backward_rnn)
 
-    # Create Bidirectional layer
-    layer = Bidirectional(forward_rnn=forward_rnn, backward_rnn=backward_rnn)
+      >>> # Input data
+      >>> x = jnp.ones((2, 3, 3))
 
-    # Input data
-    x = jnp.ones((2, 3, 3))
+      >>> # Apply the layer
+      >>> out = layer(x)
+      >>> print(out.shape)
+      (2, 3, 8)
 
-    # Apply the layer
-    out = layer(x)
-    print(out.shape)
-    ```
     """
 
     forward_rnn: RNNBase