spaCy/website/docs/usage/layers-architectures.md

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Layers and Model Architectures	Power spaCy components with custom neural networks	Type Signatures type-sigs Swapping Architectures swap-architectures PyTorch & TensorFlow frameworks Thinc Models thinc Trainable Components components	/usage/projects

Example

from thinc.api import Model, chain

@spacy.registry.architectures.register("model.v1")
def build_model(width: int, classes: int) -> Model:
    tok2vec = build_tok2vec(width)
    output_layer = build_output_layer(width, classes)
    model = chain(tok2vec, output_layer)
    return model

A model architecture is a function that wires up a Thinc Model instance. It describes the neural network that is run internally as part of a component in a spaCy pipeline. To define the actual architecture, you can implement your logic in Thinc directly, or you can use Thinc as a thin wrapper around frameworks such as PyTorch, TensorFlow and MXNet. Each Model can also be used as a sublayer of a larger network, allowing you to freely combine implementations from different frameworks into one Thinc Model.

spaCy's built-in components require a Model instance to be passed to them via the config system. To change the model architecture of an existing component, you just need to update the config so that it refers to a different registered function. Once the component has been created from this config, you won't be able to change it anymore. The architecture is like a recipe for the network, and you can't change the recipe once the dish has already been prepared. You have to make a new one.

### config.cfg (excerpt)
[components.tagger]
factory = "tagger"

[components.tagger.model]
@architectures = "model.v1"
width = 512
classes = 16

Type signatures

Example

from typing import List
from thinc.api import Model, chain
from thinc.types import Floats2d
def chain_model(
    tok2vec: Model[List[Doc], List[Floats2d]], 
    layer1: Model[List[Floats2d], Floats2d], 
    layer2: Model[Floats2d, Floats2d]
) -> Model[List[Doc], Floats2d]:
    model = chain(tok2vec, layer1, layer2)
    return model

​ The Thinc Model class is a generic type that can specify its input and output types. Python uses a square-bracket notation for this, so the type Model[List, Dict] says that each batch of inputs to the model will be a list, and the outputs will be a dictionary. Both typing.List and typing.Dict are also generics, allowing you to be more specific about the data. For instance, you can write Model[List[Doc], Dict[str, float]] to specify that the model expects a list of Doc objects as input, and returns a dictionary mapping of strings to floats. Some of the most common types you'll see are: ​

Type	Description
List[Doc]	A batch of Doc objects. Most components expect their models to take this as input.
Floats2d	A two-dimensional numpy or cupy array of floats. Usually 32-bit.
Ints2d	A two-dimensional numpy or cupy array of integers. Common dtypes include uint64, int32 and int8.
List[Floats2d]	A list of two-dimensional arrays, generally with one array per Doc and one row per token.
Ragged	A container to handle variable-length sequence data in an unpadded contiguous array.
Padded	A container to handle variable-length sequence data in a padded contiguous array.

The model type signatures help you figure out which model architectures and components can fit together. For instance, the TextCategorizer class expects a model typed Model[List[Doc], Floats2d], because the model will predict one row of category probabilities per Doc. In contrast, the Tagger class expects a model typed Model[List[Doc], List[Floats2d]], because it needs to predict one row of probabilities per token.

There's no guarantee that two models with the same type signature can be used interchangeably. There are many other ways they could be incompatible. However, if the types don't match, they almost surely won't be compatible. This little bit of validation goes a long way, especially if you configure your editor or other tools to highlight these errors early. Thinc will also verify that your types match correctly when your config file is processed at the beginning of training.

If you're using a modern editor like Visual Studio Code, you can set up mypy with the custom Thinc plugin and get live feedback about mismatched types as you write code.

Swapping model architectures

Defining sublayers

​Model architecture functions often accept sublayers as arguments, so that you can try substituting a different layer into the network. Depending on how the architecture function is structured, you might be able to define your network structure entirely through the config system, using layers that have already been defined. ​The transformers documentation section shows a common example of swapping in a different sublayer.

In most neural network models for NLP, the most important parts of the network are what we refer to as the embed and encode steps. These steps together compute dense, context-sensitive representations of the tokens. Most of spaCy's default architectures accept a tok2vec embedding layer as an argument, so you can control this important part of the network separately. This makes it easy to switch between transformer, CNN, BiLSTM or other feature extraction approaches. And if you want to define your own solution, all you need to do is register a Model[List[Doc], List[Floats2d]] architecture function, and you'll be able to try it out in any of the spaCy components. ​

Wrapping PyTorch, TensorFlow and other frameworks

Thinc allows you to wrap models written in other machine learning frameworks like PyTorch, TensorFlow and MXNet using a unified Model API. As well as wrapping whole models, Thinc lets you call into an external framework for just part of your model: you can have a model where you use PyTorch just for the transformer layers, using "native" Thinc layers to do fiddly input and output transformations and add on task-specific "heads", as efficiency is less of a consideration for those parts of the network.

Implementing models in Thinc

Models for trainable components

def update(self, examples):
    docs = [ex.predicted for ex in examples]
    refs = [ex.reference for ex in examples]
    predictions, backprop = self.model.begin_update(docs)
    gradient = self.get_loss(predictions, refs)
    backprop(gradient)

def __call__(self, doc):
    predictions = self.model([doc])
    self.set_annotations(predictions)