#

Generate Text with GPT-NeoX

This shows how to generate text from GPT-NeoX with a single GPU.

This needs a GPU with more than 45GB memory.

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Imports

16from typing import List
17
18import torch
19from torch import nn
20
21from labml import monit
22from labml_nn.neox.model import LayerGenerator
23from labml_nn.neox.utils import get_tokens, print_tokens
24from labml_nn.neox.utils.cache import get_cache

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List of layers to load. This is used for testing. You can assign a subset of layers like {0, 1} so that it only loads the first to transformer layers.

29LAYERS = None

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Prompt to complete

32PROMPT = 'Einstein was born in the German Empire, but moved to Switzerland in 1895, forsaking his German'

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Predict the next token

model is the model
ids are the input token ids
device is the device of the model

35def infer(model: nn.Module, ids: List[int], device: torch.device):

#

44    with torch.no_grad():

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Get the tokens

46        x = torch.tensor(ids)[None, :].to(device)

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Eval model

48        x = model(x)

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Return predicted token

51    return x[0].max(dim=-1)[1].tolist()

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Generate text

54def generate():

#

Setup cache to cache intermediate key/value pairs for faster generation

60    cache = get_cache()
61    cache.set('use_cache', True)

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Device

64    device = torch.device('cuda:0')

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Load layers

67    layers = list(LayerGenerator(is_clone_layers=True,
68                                 filter_layers=LAYERS,
69                                 dtype=torch.float16,
70                                 device=device,
71                                 ).load())
72
73    model = nn.Sequential(*layers)

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Get token ids

76    ids = get_tokens(PROMPT)

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Run the model

79    cache.set('state_ids', (None, 1))
80    with monit.section('Infer'):
81        next_token = infer(model, ids, device)[-1]

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Append the predicted token

84    ids += [next_token]

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Predict 100 tokens

87    for i in range(1, 100):

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Set the state to use cached activations

89        cache.set('state_ids', (i, i + 1))

#

Get next token. Note that we only feed the last token to the model because we cache the key/value pairs of previous tokens.

92        with monit.section('Infer'):
93            next_token = infer(model, [next_token], device)[-1]

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Append the predicted token

95        ids += [next_token]

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Print

97        print_tokens(ids, [ids])

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101if __name__ == '__main__':
102    generate()