Make Transformers official

CHANGELOG.md

@@ -1,5 +1,14 @@
 # Changelog
 
+## [Unreleased](https://github.com/poets-ai/elegy/tree/HEAD)
+
+[Full Changelog](https://github.com/poets-ai/elegy/compare/0.7.0...HEAD)
+
+**Merged pull requests:**
+
+- More Docs: Expand documentation for the low-level API guides. [\#168](https://github.com/poets-ai/elegy/pull/168) ([cgarciae](https://github.com/cgarciae))
+- Rich Summary: uses rich to style the summary console output [\#167](https://github.com/poets-ai/elegy/pull/167) ([cgarciae](https://github.com/cgarciae))
+
 ## [0.7.0](https://github.com/poets-ai/elegy/tree/0.7.0) (2021-02-22)
 
 [Full Changelog](https://github.com/poets-ai/elegy/compare/0.6.0...0.7.0)
@@ -40,7 +49,6 @@
 **Merged pull requests:**
 
 - fix-maybe-initialize [\#155](https://github.com/poets-ai/elegy/pull/155) ([cgarciae](https://github.com/cgarciae))
-- Add simple Flax low-level API Model example to README.md [\#153](https://github.com/poets-ai/elegy/pull/153) ([sooheon](https://github.com/sooheon))
 
 ## [0.4.0](https://github.com/poets-ai/elegy/tree/0.4.0) (2021-02-01)
 
@@ -52,6 +60,7 @@
 
 **Merged pull requests:**
 
+- Add simple Flax low-level API Model example to README.md [\#153](https://github.com/poets-ai/elegy/pull/153) ([sooheon](https://github.com/sooheon))
 - Update Getting Started + README [\#152](https://github.com/poets-ai/elegy/pull/152) ([cgarciae](https://github.com/cgarciae))
 - Pretrained ResNet fix after \#139 [\#151](https://github.com/poets-ai/elegy/pull/151) ([alexander-g](https://github.com/alexander-g))
 - Dataset: better default batch\_fn and custom batch\_fn [\#148](https://github.com/poets-ai/elegy/pull/148) ([alexander-g](https://github.com/alexander-g))

docs/api/nn/MultiHeadAttention.md

@@ -0,0 +1,15 @@
+
+# elegy.nn.MultiHeadAttention
+
+::: elegy.nn.multi_head_attention.MultiHeadAttention
+    selection:
+        inherited_members: true
+        members:
+            - __init__
+            - call
+            - add_parameter
+            - get_parameters
+            - set_parameters
+            - reset
+            - init
+

docs/api/nn/Transformer.md

@@ -0,0 +1,15 @@
+
+# elegy.nn.Transformer
+
+::: elegy.nn.transformers.Transformer
+    selection:
+        inherited_members: true
+        members:
+            - __init__
+            - call
+            - add_parameter
+            - get_parameters
+            - set_parameters
+            - reset
+            - init
+

docs/api/nn/TransformerDecoder.md

@@ -0,0 +1,15 @@
+
+# elegy.nn.TransformerDecoder
+
+::: elegy.nn.transformers.TransformerDecoder
+    selection:
+        inherited_members: true
+        members:
+            - __init__
+            - call
+            - add_parameter
+            - get_parameters
+            - set_parameters
+            - reset
+            - init
+

docs/api/nn/TransformerDecoderLayer.md

@@ -0,0 +1,15 @@
+
+# elegy.nn.TransformerDecoderLayer
+
+::: elegy.nn.transformers.TransformerDecoderLayer
+    selection:
+        inherited_members: true
+        members:
+            - __init__
+            - call
+            - add_parameter
+            - get_parameters
+            - set_parameters
+            - reset
+            - init
+

docs/api/nn/TransformerEncoder.md

@@ -0,0 +1,15 @@
+
+# elegy.nn.TransformerEncoder
+
+::: elegy.nn.transformers.TransformerEncoder
+    selection:
+        inherited_members: true
+        members:
+            - __init__
+            - call
+            - add_parameter
+            - get_parameters
+            - set_parameters
+            - reset
+            - init
+

docs/api/nn/TransformerEncoderLayer.md

@@ -0,0 +1,15 @@
+
+# elegy.nn.TransformerEncoderLayer
+
+::: elegy.nn.transformers.TransformerEncoderLayer
+    selection:
+        inherited_members: true
+        members:
+            - __init__
+            - call
+            - add_parameter
+            - get_parameters
+            - set_parameters
+            - reset
+            - init
+

docs/low-level-api/default-implementation.md

@@ -12,5 +12,6 @@ call_summary_step    call_pred_step   call_test_step                   call_trai
 ```
 This structure allows you to for example override `test_step` and still be able to use use `fit` since `train_step` (called by `fit`) will call your `test_step` via `grad_step`. It also means that if you implement `test_step` but not `pred_step` there is a high chance both `predict` and `summary` will not work.
 
-##### call_* methods
-The `call_<method>` method family are _entrypoints_ that usually just redirect to their inputs to `<method>`, you choose to override these if you need to perform some some computation only when method in question is the entry point. For example if you want to change the behavior of `evaluate` without affecting the behavior of `fit` while preserving most of the default implementation you can override `call_step_step` to do the corresponding adjustments and then call `test_step`. Since `train_step` does not depend on `call_step_step` then the change will  manifest during `evaluate` but not during `fit`.
+#### call_* methods
+The `call_<method>` method family are _entrypoints_ that usually just redirect to their inputs to `<method>`, you choose to override these if you need to perform some some computation only when method in question is the entry point i.e. when its not called by other methods in the bottom path. 
+For example if you want to change the behavior of `evaluate` without affecting the behavior of `fit` while preserving most of the default implementation you can override `call_step_step` to do the corresponding adjustments and then call `test_step`. Since `train_step` does not depend on `call_step_step` then the change will  manifest during `evaluate` but not during `fit`.

docs/low-level-api/pred_step.md → docs/low-level-api/methods/pred_step.md

@@ -68,4 +68,5 @@ Here we implement the same `LinearClassifier` from the [basics](./basics) sectio
 
 ### Default Implementation
 The default implementation of `pred_step` does the following:
+
 * Calls `api_module.init` or `api_module.apply` depending on state of `initializing`. `api_module` of type `GeneralizedModule` is a wrapper over the `module` object passed by the user to the `Model`s constructor.

docs/low-level-api/test_step.md → docs/low-level-api/methods/test_step.md

@@ -133,6 +133,7 @@ There are cases however where you might want to implement a forward pass inside
 
 ### Default Implementation
 The default implementation of `pred_step` does the following:
+
 * Call `pred_step` to get `y_pred`. 
 * Calls `api_loss.init` or `api_loss.apply` depending on state of `initializing`. `api_loss` of type `Losses` computes the aggregated batch loss from the loss functions passed by the user through the `loss` argument in the `Model`s constructor, and also computes a running mean of each loss individually which is passed for reporting to `logs`.
 * Calls `api_metrics.init` or `api_metrics.apply` depending on state of `initializing`. `api_metrics` of type `Metrics` calculates the metrics passed by the user through the `metrics` argument in the `Model`s constructor and passes their values to `logs` for reporting.

elegy/nn/__init__.py

@@ -1,19 +1,31 @@
 from .batch_normalization import BatchNormalization
 from .conv import Conv1D, Conv2D, Conv3D, ConvND
 from .dropout import Dropout
+from .embedding import Embedding, EmbedLookupStyle
 from .flatten import Flatten, Reshape
+from .layer_normalization import InstanceNormalization, LayerNormalization
 from .linear import Linear
-from .sequential_module import Sequential, sequential
-
-from .layer_normalization import LayerNormalization, InstanceNormalization
-from .embedding import Embedding, EmbedLookupStyle
-from .pool import MaxPool, AvgPool
 from .moving_averages import EMAParamsTree
-from . import transformers
+from .multi_head_attention import MultiHeadAttention
+from .pool import AvgPool, MaxPool
+from .sequential_module import Sequential, sequential
+from .transformers import (
+    Transformer,
+    TransformerDecoder,
+    TransformerDecoderLayer,
+    TransformerEncoder,
+    TransformerEncoderLayer,
+)
 
 __all__ = [
     "EMAParamsTree",
     "BatchNormalization",
+    "MultiHeadAttention",
+    "Transformer",
+    "TransformerDecoder",
+    "TransformerDecoderLayer",
+    "TransformerEncoder",
+    "TransformerEncoderLayer",
     "Conv1D",
     "Conv2D",
     "Conv3D",

elegy/nn/transformers.py

@@ -22,17 +22,13 @@ class TransformerEncoderLayer(Module):
     Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
     in a different way during application.
 
-    Args:
+    Arguments:
         head_size: the number of expected features in the input (required).
         num_heads: the number of heads in the multiheadattention models (required).
         output_size: the dimension of the feedforward network model (default=2048).
         dropout: the dropout value (default=0.1).
         activation: the activation function of intermediate layer, relu or gelu (default=relu).
 
-    Examples::
-        >>> # encoder_layer = nn.TransformerEncoderLayer(head_size=512, num_heads=8)
-        >>> # src = torch.rand(10, 32, 512)
-        >>> # out = encoder_layer(src)
     """
 
     def __init__(
@@ -47,7 +43,7 @@ def __init__(
         super().__init__(**kwargs)
         self.head_size = head_size
         self.num_heads = num_heads
-        self.output_size = output_size
+        self.output_size = output_size if output_size is not None else head_size
         self.dropout = dropout
         self.activation = activation
 
@@ -59,7 +55,7 @@ def call(
     ) -> np.ndarray:
         r"""Pass the input through the encoder layer.
 
-        Args:
+        Arguments:
             src: the sequence to the encoder layer (required).
             mask: the mask for the src sequence (optional).
             src_key_padding_mask: the mask for the src keys per batch (optional).
@@ -95,12 +91,12 @@ class TransformerEncoder(Module):
     r"""
     TransformerEncoder is a stack of N encoder layers
 
-    Args:
+    Arguments:
         encoder_layer: an instance of the TransformerEncoderLayer() class (required).
         num_layers: the number of sub-encoder-layers in the encoder (required).
         norm: the layer normalization component (optional).
 
-    Examples::
+    Examples:
         >>> import elegy
         >>> transformer_encoder = elegy.nn.transformers.TransformerEncoder(
         ...     lambda: elegy.nn.transformers.TransformerEncoderLayer(head_size=512, num_heads=8),
@@ -131,7 +127,7 @@ def call(
     ) -> np.ndarray:
         r"""Pass the input through the encoder layers in turn.
 
-        Args:
+        Arguments:
             src: the sequence to the encoder (required).
             mask: the mask for the src sequence (optional).
             src_key_padding_mask: the mask for the src keys per batch (optional).
@@ -158,32 +154,27 @@ class TransformerDecoderLayer(Module):
     Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
     in a different way during application.
 
-    Args:
+    Arguments:
         head_size: the number of expected features in the input (required).
         num_heads: the number of heads in the multiheadattention models (required).
         output_size: the dimension of the feedforward network model (default=2048).
         dropout: the dropout value (default=0.1).
         activation: the activation function of intermediate layer, relu or gelu (default=relu).
 
-    Examples::
-        >>> # decoder_layer = nn.TransformerDecoderLayer(head_size=512, num_heads=8)
-        >>> # memory = torch.rand(10, 32, 512)
-        >>> # tgt = torch.rand(20, 32, 512)
-        >>> # out = decoder_layer(tgt, memory)
     """
 
     def __init__(
         self,
         head_size: int,
         num_heads: int,
-        output_size: int = 2048,
+        output_size: tp.Optional[int] = None,
         dropout: float = 0.1,
         activation: tp.Callable[[np.ndarray], np.ndarray] = jax.nn.relu,
     ):
         super().__init__()
         self.head_size = head_size
         self.num_heads = num_heads
-        self.output_size = output_size
+        self.output_size = output_size if output_size is not None else head_size
         self.dropout = dropout
         self.activation = activation
 
@@ -198,7 +189,7 @@ def call(
     ) -> np.ndarray:
         r"""Pass the inputs (and mask) through the decoder layer.
 
-        Args:
+        Arguments:
             tgt: the sequence to the decoder layer (required).
             memory: the sequence from the last layer of the encoder (required).
             tgt_mask: the mask for the tgt sequence (optional).
@@ -237,17 +228,11 @@ def call(
 class TransformerDecoder(Module):
     r"""TransformerDecoder is a stack of N decoder layers
 
-    Args:
+    Arguments:
         decoder_layer: an instance of the TransformerDecoderLayer() class (required).
         num_layers: the number of sub-decoder-layers in the decoder (required).
         norm: the layer normalization component (optional).
 
-    Examples::
-        >>> # decoder_layer = nn.TransformerDecoderLayer(head_size=512, num_heads=8)
-        >>> # transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
-        >>> # memory = torch.rand(10, 32, 512)
-        >>> # tgt = torch.rand(20, 32, 512)
-        >>> # out = transformer_decoder(tgt, memory)
     """
 
     def __init__(
@@ -272,7 +257,7 @@ def call(
     ) -> np.ndarray:
         r"""Pass the inputs (and mask) through the decoder layer in turn.
 
-        Args:
+        Arguments:
             tgt: the sequence to the decoder (required).
             memory: the sequence from the last layer of the encoder (required).
             tgt_mask: the mask for the tgt sequence (optional).
@@ -309,7 +294,7 @@ class Transformer(Module):
     Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805)
     model with corresponding parameters.
 
-    Args:
+    Arguments:
         head_size: the number of expected features in the encoder/decoder inputs (default=512).
         num_heads: the number of heads in the multiheadattention models (default=8).
         num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
@@ -320,14 +305,23 @@ class Transformer(Module):
         custom_encoder: custom encoder (default=None).
         custom_decoder: custom decoder (default=None).
 
-    Examples::
-        >>> # transformer_model = nn.Transformer(num_heads=16, num_encoder_layers=12)
-        >>> # src = torch.rand((10, 32, 512))
-        >>> # tgt = torch.rand((20, 32, 512))
-        >>> # out = transformer_model(src, tgt)
+    Examples:
+        >>> import elegy
+        >>> import numpy as np
+
+        >>> transformer_model = elegy.nn.Transformer(
+        ...     head_size=64,
+        ...     num_heads=4,
+        ...     num_encoder_layers=2,
+        ...     num_decoder_layers=2,
+        ... )
+
+        >>> src = np.random.uniform(size=(5, 32, 64))
+        >>> tgt = np.random.uniform(size=(5, 32, 64))
+
+        >>> _, params, collections = transformer_model.init(rng=elegy.RNGSeq(42))(src, tgt)
+        >>> out, params, collections = transformer_model.apply(params, collections, rng=elegy.RNGSeq(420))(src, tgt)
 
-    Note: A full example to apply nn.Transformer module for the word language model is available in
-    https://github.com/pytorch/examples/tree/master/word_language_model
     """
 
     def __init__(
@@ -336,7 +330,7 @@ def __init__(
         num_heads: int = 8,
         num_encoder_layers: int = 6,
         num_decoder_layers: int = 6,
-        output_size: int = 2048,
+        output_size: tp.Optional[int] = None,
         dropout: float = 0.1,
         activation: tp.Callable[[np.ndarray], np.ndarray] = jax.nn.relu,
         custom_encoder: tp.Optional[tp.Any] = None,
@@ -367,7 +361,7 @@ def call(
     ) -> np.ndarray:
         r"""Take in and process masked source/target sequences.
 
-        Args:
+        Arguments:
             src: the sequence to the encoder (required).
             tgt: the sequence to the decoder (required).
             src_mask: the additive mask for the src sequence (optional).
@@ -406,8 +400,6 @@ def call(
             where S is the source sequence length, T is the target sequence length, N is the
             batch size, E is the feature number
 
-        Examples:
-            >>> # output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
         """
 
         if src.shape[0] != tgt.shape[0]: