experiment.py

#

1import torch
2import torch.nn as nn
3from labml import experiment
4from labml.configs import option
5from labml.utils.pytorch import get_modules
6from labml_helpers.module import Module
7
8from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
9from labml_nn.hypernetworks.hyper_lstm import HyperLSTM
10from labml_nn.lstm import LSTM

#

Auto regressive model

13class AutoregressiveModel(Module):

#

18    def __init__(self, n_vocab: int, d_model: int, rnn_model: Module):
19        super().__init__()

#

Token embedding module

21        self.src_embed = nn.Embedding(n_vocab, d_model)
22        self.lstm = rnn_model
23        self.generator = nn.Linear(d_model, n_vocab)

#

25    def forward(self, x: torch.Tensor):
26        x = self.src_embed(x)

#

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

28        res, state = self.lstm(x)

#

Generate logits of the next token

30        return self.generator(res), state

#

Configurations

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

33class Configs(NLPAutoRegressionConfigs):

#

40    model: AutoregressiveModel
41    rnn_model: Module
42
43    d_model: int = 512
44    n_rhn: int = 16
45    n_z: int = 16

#

Initialize the auto-regressive model

48@option(Configs.model)
49def autoregressive_model(c: Configs):

#

53    m = AutoregressiveModel(c.n_tokens, c.d_model, c.rnn_model)
54    return m.to(c.device)

#

57@option(Configs.rnn_model)
58def hyper_lstm(c: Configs):
59    return HyperLSTM(c.d_model, c.d_model, c.n_rhn, c.n_z, 1)
60
61
62@option(Configs.rnn_model)
63def lstm(c: Configs):
64    return LSTM(c.d_model, c.d_model, 1)
65
66
67def main():

#

Create experiment

69    experiment.create(name="hyper_lstm", comment='')

#

Create configs

71    conf = Configs()

#

Load configurations

73    experiment.configs(conf,

#

A dictionary of configurations to override

75                       {'tokenizer': 'character',
76                        'text': 'tiny_shakespeare',
77                        'optimizer.learning_rate': 2.5e-4,
78                        'optimizer.optimizer': 'Adam',
79                        'prompt': 'It is',
80                        'prompt_separator': '',
81
82                        'rnn_model': 'hyper_lstm',
83
84                        'train_loader': 'shuffled_train_loader',
85                        'valid_loader': 'shuffled_valid_loader',
86
87                        'seq_len': 512,
88                        'epochs': 128,
89                        'batch_size': 2,
90                        'inner_iterations': 25})

#

Set models for saving and loading

93    experiment.add_pytorch_models(get_modules(conf))

#

Start the experiment

96    with experiment.start():

#

TrainValidConfigs.run

98        conf.run()
99
100
101if __name__ == '__main__':
102    main()