#

Attention Free Transformer (AFT) Experiment

This is an annotated PyTorch experiment to train a AFT model.

This is based on general training loop and configurations for auto-regressive NLP task.

14import torch
15
16from labml import experiment
17from labml.configs import option
18from labml_helpers.module import Module
19from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
20from labml_nn.transformers import TransformerConfigs, Encoder
21from labml_nn.transformers.utils import subsequent_mask

#

Simple autoregressive model

This consists of a token embedding layer, transformer encoder, and a final linear layer that gives token logits.

24class AutoregressiveTransformer(Module):

#

encoder is the transformer Encoder
src_embed is the token embedding module (with positional encodings)
generator is the final fully connected layer that gives the logits.

32    def __init__(self, encoder: Encoder, src_embed: Module, generator: Module):

#

39        super().__init__()
40        self.src_embed = src_embed
41        self.encoder = encoder
42        self.generator = generator

#

The mask will be initialized on the first call

45        self.mask = None

#

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

#

Create subsequent mask if mask is not initialized or if the size of the mask is different

50        if self.mask is None or self.mask.size(0) != len(x):

#

Subsequent mask, will mask out tokens from seeing future tokens

52            self.mask = subsequent_mask(len(x)).to(x.device)

#

Get the token embeddings with positional encodings

55        x = self.src_embed(x)

#

Transformer encoder

57        x = self.encoder(x, self.mask)

#

Get logits

59        x = self.generator(x)

#

Return results (second value is for state, since our trainer is used with RNNs also)

63        return x, None

#

Configurations

This inherits from NLPAutoRegressionConfigs

66class Configs(NLPAutoRegressionConfigs):

#

GPT model

75    model: AutoregressiveTransformer

#

Transformer

77    transformer: TransformerConfigs
78
79    local_window_size: int = 32

#

Transformer configurations

82@option(Configs.transformer, 'Transformer')
83def _transformer_configs(c: Configs):

#

We use our configurable transformer implementation

90    conf = TransformerConfigs()

#

Set the vocabulary sizes for embeddings and generating logits

92    conf.n_src_vocab = c.n_tokens
93    conf.n_tgt_vocab = c.n_tokens

#

Set the embedding size

95    conf.d_model = c.d_model

#

Replace self-attention with an AFT Local Module

97    from labml_nn.transformers.aft import AFTLocal
98    conf.encoder_attn = AFTLocal(c.d_model, c.seq_len, c.local_window_size)

#

101    return conf

#

Create an auto-regressive model

104@option(Configs.model)
105def _model(c: Configs):

#

109    m = AutoregressiveTransformer(c.transformer.encoder,
110                                  c.transformer.src_embed,
111                                  c.transformer.generator).to(c.device)
112
113    return m

#

116def main():

#

Create experiment

118    experiment.create(name="aft")

#

Create configs

120    conf = Configs()

#

Override configurations

122    experiment.configs(conf, {

#

Use character level tokenizer

124        'tokenizer': 'character',

#

Prompt separator is blank

126        'prompt_separator': '',

#

Starting prompt for sampling

128        'prompt': 'It is ',

#

Use Tiny Shakespeare dataset

130        'text': 'tiny_shakespeare',

#

Use a context size of $128$

133        'seq_len': 256,

#

Train for $32$ epochs

135        'epochs': 128,

#

Batch size $128$

137        'batch_size': 32,

#

Switch between training and validation for $10$ times per epoch

140        'inner_iterations': 10,

#

Embedding size

143        'd_model': 128,

#

FFN hidden dimension size

145        'transformer.ffn.d_ff': 256,

#

Optimizer

148        'optimizer.optimizer': 'Noam',
149        'optimizer.learning_rate': 1.,
150    })

#

Set models for saving and loading

153    experiment.add_pytorch_models({'model': conf.model})

#

Start the experiment

156    with experiment.start():

#

Run training

158        conf.run()

#

162if __name__ == '__main__':
163    main()