#

Train Fast Weights Transformer

This trains a fast weights transformer model for auto-regression.

Here’s a Colab notebook for training a fast weights transformer on Tiny Shakespeare dataset.

16import torch
17from torch import nn
18
19from labml import experiment
20from labml.configs import option
21from labml.utils.pytorch import get_modules
22from labml_helpers.module import Module
23from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs

#

Auto regressive model

26class AutoregressiveModel(Module):

#

31    def __init__(self, n_vocab: int, d_model: int, transformer: Module):
32        super().__init__()

#

Token embedding module

34        self.src_embed = nn.Embedding(n_vocab, d_model)
35        self.transformer = transformer
36        self.generator = nn.Linear(d_model, n_vocab)

#

38    def forward(self, x: torch.Tensor):

#

Embed the tokens

40        x = self.src_embed(x)

#

Run it through the the transformer

42        res = self.transformer(x)

#

Generate logits of the next token

44        return self.generator(res), None

#

Configurations

The default configs can and will be over-ridden when we start the experiment

47class Configs(NLPAutoRegressionConfigs):

#

54    model: AutoregressiveModel
55
56    d_model: int = 512
57    nu: int = 1
58    heads: int = 8
59    dropout: float = 0.0
60    d_ff: int = 2048
61    n_layers: int = 6

#

Create fast weights transformer.

64@option(Configs.model)
65def fast_weights_transformer(c: Configs):

#

69    from labml_nn.transformers.fast_weights import FastWeightsAttentionTransformer, \
70        FastWeightsAttentionTransformerLayer, FastWeightsAttention, FeedForward
71
72    from labml_nn.transformers.fast_weights import DPFP
73    return AutoregressiveModel(
74        c.n_tokens, c.d_model,
75        FastWeightsAttentionTransformer(
76            FastWeightsAttentionTransformerLayer(d_model=c.d_model,
77                                                 attn=FastWeightsAttention(c.heads, c.d_model, c.dropout, DPFP(nu=c.nu)),
78                                                 feed_forward=FeedForward(c.d_model, c.d_ff, c.dropout),
79                                                 dropout_prob=c.dropout),
80            c.n_layers)).to(c.device)

#

83def main():

#

Create experiment

85    experiment.create(name="fast_weights_transformer")

#

Create configs

87    conf = Configs()

#

Load configurations

89    experiment.configs(conf,

#

A dictionary of configurations to override

91                       {'tokenizer': 'character',
92                        'text': 'tiny_shakespeare',
93                        'optimizer.learning_rate': 1.0,
94                        'optimizer.optimizer': 'Noam',
95                        'prompt': 'It is',
96                        'prompt_separator': '',
97
98                        'train_loader': 'shuffled_train_loader',
99                        'valid_loader': 'shuffled_valid_loader',
100
101                        'seq_len': 128,
102                        'epochs': 128,
103                        'batch_size': 16,
104                        'inner_iterations': 25})

#

Set models for saving and loading

107    experiment.add_pytorch_models(get_modules(conf))

#

Start the experiment

110    with experiment.start():

#

Run the training loop

112        conf.run()
113
114
115if __name__ == '__main__':
116    main()