style nits in docs

docs/src/python/nn/layers.rst

@@ -9,7 +9,7 @@ Layers
    :toctree: _autosummary
    :template: nn-module-template.rst
 
-   Embedding
+   Sequential
    ReLU
    PReLU
    GELU
@@ -17,17 +17,19 @@ Layers
    Step
    SELU
    Mish
+   Embedding
    Linear
+   QuantizedLinear
    Conv1d
    Conv2d
    BatchNorm
    LayerNorm
    RMSNorm
    GroupNorm
-   RoPE
-   MultiHeadAttention
-   Sequential
-   QuantizedLinear
    Dropout
    Dropout2d
-
+   Transformer
+   MultiHeadAttention
+   ALiBi
+   RoPE
+   SinusoidalPositionalEncoding

python/mlx/nn/layers/__init__.py

@@ -14,6 +14,7 @@ from mlx.nn.layers.activations import (
     SiLU,
     Softplus,
     Step,
+    Tanh,
     celu,
     elu,
     gelu,
@@ -29,6 +30,7 @@ from mlx.nn.layers.activations import (
     silu,
     softplus,
     step,
+    tanh,
 )
 from mlx.nn.layers.base import Module
 from mlx.nn.layers.containers import Sequential
@@ -41,6 +43,7 @@ from mlx.nn.layers.positional_encoding import ALiBi, RoPE, SinusoidalPositionalE
 from mlx.nn.layers.quantized import QuantizedLinear
 from mlx.nn.layers.transformer import (
     MultiHeadAttention,
+    Transformer,
     TransformerEncoder,
     TransformerEncoderLayer,
 )

python/mlx/nn/layers/activations.py

@@ -179,12 +179,12 @@ def selu(x):
 
 
 def prelu(x: mx.array, alpha: mx.array) -> mx.array:
-    r"""Applies the element-wise function:
+    r"""Applies the element-wise parametric ReLU.
 
     .. math::
         \text{PReLU}(x) = \max(0,x) + a * \min(0,x)
 
-    Here :math:`a` is an array.
+    where :math:`a` is an array.
     """
     return mx.maximum(0, x) + alpha * mx.minimum(0, x)
 

python/mlx/nn/layers/positional_encoding.py

@@ -8,21 +8,22 @@ from mlx.nn.layers.base import Module
 
 
 class RoPE(Module):
-    """Implements the rotary positional encoding [1].
+    """Implements the rotary positional encoding.
 
     The traditional implementation rotates consecutive pairs of elements in the
     feature dimension while the default implementation rotates pairs with
     stride half the feature dimensions for efficiency.
 
-    [1]: https://arxiv.org/abs/2104.09864
+    For more details see `RoFormer: Enhanced Transformer with Rotary Position
+    Embedding <https://arxiv.org/abs/2104.09864>`_.
 
     Args:
         dims (int): The feature dimensions to be rotated. If the input feature
             is larger than dims then the rest is left unchanged.
         traditional (bool, optional): If set to True choose the traditional
-            implementation which is slightly less efficient. Default: ``False``
+            implementation which is slightly less efficient. Default: ``False``.
         base (float, optional): The base used to compute angular frequency for
-            each dimension in the positional encodings. Default: ``10000``
+            each dimension in the positional encodings. Default: ``10000``.
     """
 
     def __init__(self, dims: int, traditional: bool = False, base: float = 10000):
@@ -89,19 +90,23 @@ class RoPE(Module):
 
 
 class SinusoidalPositionalEncoding(Module):
-    """Implements sinusoidal positional encoding similar to [1].
+    r"""Implements sinusoidal positional encoding.
 
-    [1]: https://arxiv.org/abs/1706.03762
+    For more details see the paper `Attention Is All You Need
+    <https://arxiv.org/abs/1706.03762>`_.
 
     Args:
         dims (int): The dimensionality of the resulting positional embeddings.
-        min_freq (float): The minimum frequency expected (default: 0.0001)
+        min_freq (float, optional): The minimum frequency expected. Default:
-        max_freq (float): The maximum frequency expected (default: 1)
+            ``0.0001``.
-        scale (float): Scale the embeddings by that number (default: sqrt(dims//2))
+        max_freq (float, optional): The maximum frequency expected. Default:
-        cos_first (bool): If set to True embed using ``[cos(x); sin(x)]``
+            ``1``.
-            instead of the other way around (default: False)
+        scale (float, optional): A multiplicative scale for the embeddings.
-        full_turns (bool): If set to True multiply the frequencies
+            Default: ``sqrt(dims//2)``.
-            with ``2 pi`` (default: False)
+        cos_first (bool, optional): If ``True`` embed using ``[cos(x); sin(x)]``
+            instead of the reverse. Default: ``False``.
+        full_turns (bool, optional): If ``True`` multiply the frequencies with
+            :math:`2\pi`. Default: ``False``.
     """
 
     def __init__(

python/mlx/nn/layers/transformer.py

@@ -14,26 +14,34 @@ from mlx.nn.layers.normalization import LayerNorm
 class MultiHeadAttention(Module):
     """Implements the scaled dot product attention with multiple heads.
 
-    Given inputs for queries, keys and values the ``MultiHeadAttention`` produces
+    Given inputs for queries, keys and values the ``MultiHeadAttention``
-    new values by aggregating information from the input values according to
+    produces new values by aggregating information from the input values
-    the similarities of the input queries and keys.
+    according to the similarities of the input queries and keys.
 
-    All inputs as well as the output are linearly projected without biases.
+    All inputs as well as the output are linearly projected without biases by
+    default.
 
-    MultiHeadAttention also expects an additive attention mask that should be
+    ``MultiHeadAttention`` also takes an optional additive attention mask that
-    broadcastable with (batch, num_heads, # queries, # keys). The mask should
+    should be broadcastable with ``(batch, num_heads, # queries, # keys)``. The
-    have ``-inf`` or very negative numbers to the positions that should *not* be
+    mask should have ``-inf`` or very large negative numbers at the positions
-    attended to.
+    that should *not* be attended to.
 
     Args:
-        dims (int): The model dimensions. If no other dims are provided then
+        dims (int): The model dimensions. This is also the default
-            dims is used for queries, keys, values and the output.
+            value for the queries, keys, values, and the output.
-        num_heads (int): How many attention heads to use
+        num_heads (int): The number of attention heads to use.
-        query_input_dims (int, optional): The input dimensions of the queries (default: dims).
+        query_input_dims (int, optional): The input dimensions of the queries.
-        key_input_dims (int, optional): The input dimensions of the keys (default: dims).
+            Default: ``dims``.
-        value_input_dims (int, optional): The input dimensions of the values (default: key_input_dims).
+        key_input_dims (int, optional): The input dimensions of the keys.
-        value_dims (int, optional): The dimensions of the values after the projection (default: dims).
+            Default: ``dims``.
-        value_output_dims (int, optional): The dimensions the new values will be projected to (default: dims).
+        value_input_dims (int, optional): The input dimensions of the values.
+            Default: ``key_input_dims``.
+        value_dims (int, optional): The dimensions of the values after the
+            projection. Default: ``dims``.
+        value_output_dims (int, optional): The dimensions the new values will
+            be projected to. Default: ``dims``.
+        bias (bool, optional): Whether or not to use a bias in the projections.
+            Default: ``False``.
     """
 
     def __init__(
@@ -51,7 +59,8 @@ class MultiHeadAttention(Module):
 
         if (dims % num_heads) != 0:
             raise ValueError(
-                f"The input feature dimensions should be divisible by the number of heads ({dims} % {num_heads}) != 0"
+                "The input feature dimensions should be divisible by the "
+                f"number of heads ({dims} % {num_heads}) != 0"
             )
 
         query_input_dims = query_input_dims or dims
@@ -171,9 +180,7 @@ class TransformerEncoder(Module):
     def __call__(self, x, mask):
         for l in self.layers:
             x = l(x, mask)
-        x = self.ln(x)
+        return self.ln(x)
-
-        return x
 
 
 class TransformerDecoderLayer(Module):
@@ -261,32 +268,45 @@ class TransformerDecoder(Module):
     def __call__(self, x, memory, x_mask, memory_mask):
         for l in self.layers:
             x = l(x, memory, x_mask, memory_mask)
-        x = self.ln(x)
+        return self.ln(x)
-
-        return x
 
 
 class Transformer(Module):
     """
-    Implements a standard Transformer model based on the paper "Attention Is All You Need".
+    Implements a standard Transformer model.
 
-    The Transformer model consists of an encoder and a decoder. The encoder processes
+    The implementation is based on `Attention Is All You Need
-    the input sequence and the decoder generates the output sequence. The interaction
+    <https://arxiv.org/abs/1706.03762>`_.
-    between encoder and decoder happens through the attention mechanism.
+
+    The Transformer model contains an encoder and a decoder. The encoder
+    processes the input sequence and the decoder generates the output sequence.
+    The interaction between encoder and decoder happens through the attention
+    mechanism.
 
     Args:
-        dims (int): The number of expected features in the encoder/decoder inputs  (default: 512)
+        dims (int, optional): The number of expected features in the
-        num_heads (int): The number of heads in the multi-head attention models (default: 8)
+            encoder/decoder inputs. Default: ``512``.
-        num_encoder_layers (int): The number of sub-encoder-layers in the Transformer encoder (default: 6)
+        num_heads (int, optional): The number of attention heads. Default:
-        num_decoder_layers (int): The number of sub-decoder-layers in the Transformer decoder (default: 6)
+            ``8``.
-        mlp_dims (Optional[int]): The dimensionality of the feedforward network model in each Transformer layer,
+        num_encoder_layers (int, optional): The number of encoder layers in the
-                                  Defaults to 4*dims if not provided (default: None)
+            Transformer encoder. Default: ``6``.
-        dropout (float): The dropout value for Transformer encoder/decoder (default: 0.0)
+        num_decoder_layers (int, optional): The number of decoder layers in the
-        activation (Callable[[Any], Any]): the activation function of encoder/decoder intermediate layer (default: relu)
+            Transformer decoder. Default: ``6``.
-        custom_encoder (Optional[Any]): A custom encoder to replace the standard Transformer encoder (default: None)
+        mlp_dims (int, optional): The hidden dimension of the MLP block in each
-        custom_decoder (Optional[Any]): A custom decoder to replace the standard Transformer decoder (default: None)
+            Transformer layer. Defaults to ``4*dims`` if not provided. Default:
-        norm_first (bool): if ``True``, encoder and decoder layers will perform LayerNorms before
+            ``None``.
-            other attention and feedforward operations, otherwise after (default: False)
+        dropout (float, optional): The dropout value for the Transformer
+            encoder and decoder. Dropout is used after each attention layer and
+            the activation in the MLP layer. Default: ``0.0``.
+        activation (function, optional): the activation function for the MLP
+            hidden layer. Default: :func:`mlx.nn.relu`.
+        custom_encoder (nn.Module, optional): A custom encoder to replace the
+            standard Transformer encoder. Default: ``None``.
+        custom_decoder (nn.Module, optional): A custom decoder to replace the
+            standard Transformer decoder. Default: ``None``.
+        norm_first (bool, optional): if ``True``, encoder and decoder layers
+            will perform layer normalization before attention and MLP
+            operations, otherwise after. Default: ``False``.
     """
 
     def __init__(
@@ -331,6 +351,4 @@ class Transformer(Module):
 
     def __call__(self, src, tgt, src_mask, tgt_mask, memory_mask):
         memory = self.encoder(src, src_mask)
-        output = self.decoder(tgt, memory, tgt_mask, memory_mask)
+        return self.decoder(tgt, memory, tgt_mask, memory_mask)
-
-        return output