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Merged
mattdangerw merged 2 commits into from
Jan 19, 2023
Merged

Handle trainable and name in the backbone base class #680

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Handle trainable and name in the backbone base class
mattdangerw Jan 18, 2023
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Add comments explaining get_config/from_config
mattdangerw Jan 19, 2023
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32 changes: 17 additions & 15 deletions keras_nlp/models/albert/albert_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -253,21 +253,23 @@ def call(x, padding_mask):
self.cls_token_index = cls_token_index

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"num_groups": self.num_groups,
"num_inner_repetitions": self.num_inner_repetitions,
"embedding_dim": self.embedding_dim,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"num_segments": self.num_segments,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"num_groups": self.num_groups,
"num_inner_repetitions": self.num_inner_repetitions,
"embedding_dim": self.embedding_dim,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"num_segments": self.num_segments,
}
)
return config

@classproperty
def presets(cls):
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10 changes: 10 additions & 0 deletions keras_nlp/models/backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,18 @@ class Backbone(keras.Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)

def get_config(self):
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@jbischof jbischof Jan 18, 2023

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Q: Couldn't you technically chain all the way up to keras.Model for this?

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@mattdangerw mattdangerw Jan 18, 2023 •
edited
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We cannot because of a pain point with functional subclassed models.

By default get_config on a functional model will return a huge nested description of all layers, that can only be used with from_config for the function keras model class itself. There's no way to use the standard functional model get_config and from_config and still get back a BertBackbone, you would just get back a vanilla functional model (with no special properties, methods, docstring etc).

So essentially for functional subclasses, you have to "break the chain" of get_config and from_config chaining to super, which is unusual. Everywhere else in Keras you want to do this! But at least we are able to hide this pain point from our downstream model implementation now. And potentially we could prioritize some work in core Keras at some point to improve the "functional subclass" experience and remove this weird gotcha.

I can leave a (much shorter than this), comment explaining this on the backbone class.

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@jbischof jbischof Jan 19, 2023

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Thanks for the explanation! Lifelong learner 🤓

# Don't chain to super here. The default `get_config()` for functional
# models is nested and cannot be passed to our Backbone constructors.
return {
"name": self.name,
"trainable": self.trainable,
}

@classmethod
def from_config(cls, config):
# The default `from_config()` for functional models will return a
# vanilla `keras.Model`. We override it to get a subclass instance back.
return cls(**config)

@classproperty
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26 changes: 14 additions & 12 deletions keras_nlp/models/bert/bert_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -203,18 +203,20 @@ def __init__(
self.cls_token_index = cls_token_index

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"max_sequence_length": self.max_sequence_length,
"num_segments": self.num_segments,
"dropout": self.dropout,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"max_sequence_length": self.max_sequence_length,
"num_segments": self.num_segments,
"dropout": self.dropout,
}
)
return config

@classproperty
def presets(cls):
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26 changes: 14 additions & 12 deletions keras_nlp/models/deberta_v3/deberta_v3_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -185,18 +185,20 @@ def __init__(
self.start_token_index = 0

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"bucket_size": self.bucket_size,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"bucket_size": self.bucket_size,
}
)
return config

@classproperty
def presets(cls):
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24 changes: 13 additions & 11 deletions keras_nlp/models/distil_bert/distil_bert_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,17 +166,19 @@ def __init__(
self.cls_token_index = 0

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
}
)
return config

@classproperty
def presets(cls):
Expand Down
24 changes: 13 additions & 11 deletions keras_nlp/models/f_net/f_net_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,17 +202,19 @@ def __init__(
self.cls_token_index = cls_token_index

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"num_segments": self.num_segments,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"num_segments": self.num_segments,
}
)
return config

@classproperty
def presets(cls):
Expand Down
24 changes: 13 additions & 11 deletions keras_nlp/models/gpt2/gpt2_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,17 +173,19 @@ def __init__(
self.max_sequence_length = max_sequence_length

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
}
)
return config

@classproperty
def presets(cls):
Expand Down
24 changes: 13 additions & 11 deletions keras_nlp/models/roberta/roberta_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,17 +160,19 @@ def __init__(
self.start_token_index = 0

def get_config(self):
return {
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
"name": self.name,
"trainable": self.trainable,
}
config = super().get_config()
config.update(
{
"vocabulary_size": self.vocabulary_size,
"num_layers": self.num_layers,
"num_heads": self.num_heads,
"hidden_dim": self.hidden_dim,
"intermediate_dim": self.intermediate_dim,
"dropout": self.dropout,
"max_sequence_length": self.max_sequence_length,
}
)
return config

@classproperty
def presets(cls):
Expand Down
4 changes: 4 additions & 0 deletions keras_nlp/models/task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,8 @@ def preprocessor(self):
return self._preprocessor

def get_config(self):
# Don't chain to super here. The default `get_config()` for functional
# models is nested and cannot be passed to our Task constructors.
return {
"backbone": keras.layers.serialize(self.backbone),
"preprocessor": keras.layers.serialize(self.preprocessor),
Expand All @@ -49,6 +51,8 @@ def get_config(self):

@classmethod
def from_config(cls, config):
# The default `from_config()` for functional models will return a
# vanilla `keras.Model`. We override it to get a subclass instance back.
if "backbone" in config and isinstance(config["backbone"], dict):
config["backbone"] = keras.layers.deserialize(config["backbone"])
if "preprocessor" in config and isinstance(
Expand Down