#

Gated Linear Units and Variants

This trains a simple transformer model for auto-regression. We try different variants for the position-wise feedforward network. The reusable & configurable are defined in configs.py .

16import torch
17from labml import experiment
18from labml.configs import option
19from labml.utils.pytorch import get_modules
20from labml_helpers.module import Module
21
22from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
23from labml_nn.transformers import Encoder, Generator, TransformerConfigs
24from labml_nn.transformers.utils import subsequent_mask

#

Auto regressive model

27class AutoregressiveModel(Module):

#

32    def __init__(self, src_embed: Module, encoder: Encoder, generator: Generator):
33        super().__init__()

#

Token embedding module

35        self.src_embed = src_embed

#

Transformer based encoder

37        self.encoder = encoder

#

Next token generation layer; this give logits of the the next token

40        self.generator = generator

#

This will be initialized on the first call

42        self.src_mask = None

#

44    def forward(self, src: torch.Tensor):

#

Create subsequent mask, so that the transformer can only pay attention to past tokens.

46        if self.src_mask is None or self.src_mask.size(0) != len(src):
47            self.src_mask = subsequent_mask(len(src)).to(src.device)

#

Embed the tokens (src ) and run it through the the transformer

49        res = self.encoder(self.src_embed(src), self.src_mask)

#

Generate logits of the next token

51        return self.generator(res), None

#

Configurations

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

54class Configs(NLPAutoRegressionConfigs):

#

61    transformer: TransformerConfigs
62    model: AutoregressiveModel

#

Initialize the auto-regressive model

65@option(Configs.model)
66def autoregressive_model(c: Configs):

#

70    m = AutoregressiveModel(c.transformer.src_embed, c.transformer.encoder, c.transformer.generator)
71    return m.to(c.device)

#

Initialize the configurable transformer encoder for our autoregressive model.

74@option(Configs.transformer)
75def transformer_c(c: Configs):

#

79    tc = TransformerConfigs()
80    tc.n_src_vocab = c.n_tokens
81    tc.n_tgt_vocab = c.n_tokens
82
83    return tc

#

86def main():

#

Create experiment

88    experiment.create(name="glu_variants")

#

Create configs

90    conf = Configs()

#

Load configurations

92    experiment.configs(conf,

#

A dictionary of configurations to override

94                       {'tokenizer': 'character',
95                        'prompt_separator': '',
96                        'prompt': 'It is ',
97                        'text': 'tiny_shakespeare',
98
99                        'optimizer.optimizer': 'Noam',
100                        'optimizer.learning_rate': 1.,
101                        'optimizer.d_model': 256,
102
103                        'seq_len': 1024,
104                        'epochs': 128,
105                        'batch_size': 6,
106                        'inner_iterations': 10,

#

GLU Variant, one of GLU, Bilinear, ReGLU, GEGLU, SwiGLU

These are defined in the configurable FFN implementation

112                        'transformer.ffn.glu_variant': 'Bilinear',

#

Transformer configurations

115                        'transformer.d_model': 256,
116                        'transformer.ffn.d_ff': 1024,
117                        'transformer.n_heads': 8,
118                        'transformer.n_layers': 6})

#

This is needed to initialize models

121    conf.n_tokens = conf.text.n_tokens

#

Set models for saving and loading

124    experiment.add_pytorch_models(get_modules(conf))

#

Start the experiment

127    with experiment.start():

#

TrainValidConfigs.run

129        conf.run()
130
131
132if __name__ == '__main__':
133    main()