#

Attention with Linear Biases (ALiBi) Experiment

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

14import torch
15from torch.utils.data import DataLoader
16
17from labml import experiment, tracker
18from labml.configs import option, calculate
19from labml_nn.helpers.datasets import SequentialUnBatchedDataset
20from labml_nn.transformers.alibi import AlibiMultiHeadAttention
21from labml_nn.experiments.nlp_autoregression import transpose_batch
22from labml_nn.transformers import TransformerConfigs
23from labml_nn.transformers.gpt import Configs as GPTConfigs

#

Configurations

We extend GPT configurations and change the attention mechanism.

26class Configs(GPTConfigs):

#

ALiBi based transformer (defined below)

34    transformer: TransformerConfigs = 'GPT_ALiBi'

#

Longer validation set

36    valid_seq_len: int = 128
37    valid_loader = 'shuffled_longer_valid_loader'

#

Log losses at the initial and final tokens

39    def other_metrics(self, output: torch.Tensor, target: torch.Tensor):

#

If there are more tokens that the training sequence length (during validation),

44        if self.seq_len < output.shape[0]:

#

Log the loss at training sequence length

46            tracker.add(f'loss.{self.seq_len - 1}.', self.loss_func(output[self.seq_len - 1], target[self.seq_len - 1]))

#

Log the loss at the first token

48            tracker.add(f'loss.0.', self.loss_func(output[0], target[0]))

#

Log the loss at the final token

50        tracker.add(f'loss.{int(output.shape[0]) - 1}.', self.loss_func(output[-1], target[-1]))

#

Create an ALiBi attention module

53def _alibi_mha(c: TransformerConfigs):

#

57    return AlibiMultiHeadAttention(c.n_heads, c.d_model, dropout_prob=c.dropout)

#

Set all attention mechanisms to ALiBi

61calculate(TransformerConfigs.encoder_attn, 'alibi_mha', _alibi_mha)
62calculate(TransformerConfigs.decoder_attn, 'alibi_mha', _alibi_mha)
63calculate(TransformerConfigs.decoder_mem_attn, 'alibi_mha', _alibi_mha)

#

Shuffled validation data loader with valid_seq_len sequence length

66@option(Configs.valid_loader)
67def shuffled_longer_valid_loader(c: Configs):

#

71    return DataLoader(SequentialUnBatchedDataset(text=c.text.valid,
72                                                 dataset=c.text,
73                                                 seq_len=c.valid_seq_len),
74                      batch_size=c.batch_size,
75                      collate_fn=transpose_batch,
76                      shuffle=True)

#

ALiBi based Transformer configurations

79@option(Configs.transformer, 'GPT_ALiBi')
80def _transformer_configs(c: Configs):

#

We use our configurable transformer implementation

87    conf = TransformerConfigs()

#

Set the vocabulary sizes for embeddings and generating logits

89    conf.n_src_vocab = c.n_tokens
90    conf.n_tgt_vocab = c.n_tokens

#

GPT uses GELU activation for position wise feedforward

92    conf.ffn.activation = 'GELU'

#

ALiBi doesn't use positional embeddings

95    conf.src_embed = 'no_pos'
96    conf.tgt_embed = 'no_pos'

#

Set all attention mechanisms to ALiBi

99    conf.encoder_attn = 'alibi_mha'
100    conf.decoder_attn = 'alibi_mha'
101    conf.decoder_mem_attn = 'alibi_mha'

#

104    return conf

#

107def main():

#

Create experiment

109    experiment.create(name="gpt_alibi")

#

Create configs

111    conf = Configs()

#

Override configurations

113    experiment.configs(conf, {

#

Use character level tokenizer

115        'tokenizer': 'character',

#

Prompt separator is blank

117        'prompt_separator': '',

#

Starting prompt for sampling

119        'prompt': 'It is ',

#

Use Tiny Shakespeare dataset

121        'text': 'tiny_shakespeare',

#

'text': 'tiny_shakespeare_no_split',

#

Use a context size of $128$

125        'seq_len': 64,

#

Use a context size of $128$

127        'valid_seq_len': 80,

#

Train for $32$ epochs

129        'epochs': 128,

#

Batch size $128$

131        'batch_size': 128,

#

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

134        'inner_iterations': 10,

#

Transformer configurations

137        'transformer.d_model': 128,
138        'transformer.ffn.d_ff': 512,
139        'transformer.n_heads': 8,
140        'transformer.n_layers': 4,
141        'transformer.dropout': 0.1,
142    })

#

Set models for saving and loading

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

#

Start the experiment

148    with experiment.start():

#

Run training

150        conf.run()

#

154if __name__ == '__main__':
155    main()