autocontrastive-gen 0.2.0

Auto-Contrastive Text Generation

Auto-Contrastive Generation

Code to experiment with multi-exit text generation, and to reproduce the Auto-Contrastive Decoding experiments from Gera et al. (2023).

Using this library you can:

1. Run inference on multi-exit generative language models, either using a specific model exit layer, or contrasting between model layers with Auto-Contrastive Decoding;
2. Train new multi-exit models, either for language modeling or for a specific task;
3. Try out new methods and algorithms for combining and contrasting the outputs of different model layers.

Table of contents

Quick start

Setting the Multi-Exit Configuration

Running language modeling benchmarks

Pre-trained model checkpoints

Reference

License

Quick start

1. Install the library with pip install autocontrastive-gen
2. Choose your desired multi-exit parameters - which model exit layer(s) do you want to use, and how?
3. Load a pre-trained multi-exit model and use it however you wish, within your own workflow

For instance, the following code will initialize the multi-exit GPT2 model we release to use Auto-Contrastive Decoding:

from autocontrastive_gen.modeling.configuration import MultiExitConfiguration
from autocontrastive_gen.modeling.auto_model import AutoMultiExitModel

# initialize a pre-trained multi-exit model to use auto-contrast between layer 24 and layer 12
multi_exit_config = MultiExitConfiguration(use_original_head=False, 
                                           contrast_layer_indices=(24, 12))
model = AutoMultiExitModel.from_pretrained("IBM/gpt2-medium-multiexit", multi_exit_config=multi_exit_config)

Then, the initialized model can be used as usual through the transformers library. For example, this code will run text generation using the initialized model:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("IBM/gpt2-medium-multiexit")
prompt = tokenizer("humpty dumpty sat on", return_tensors='pt')
generated_ids = model.generate(**prompt, max_new_tokens=15)
print(tokenizer.batch_decode(generated_ids))

Similarly, you can train the model with the transformers library just as you would do with any other model.

Note that the model behavior in inference and/or training depends on your choice of parameters when initializing the MultiExitConfiguration.

Setting the Multi-Exit Configuration

Model behavior is determined by the MultiExitConfiguration used to initialize it. Most of the the configuration parameters are related to inference-time text generation behavior, but some are relevant for model training as well. The following parameters can be set:

• lm_head_layer_indices: Tuple[int, ...]: the indices of model layers which are connected to language modeling exit heads. As this is a basic characteristic of the model, this parameter only needs to be set once for initial pre-training of these exit heads. Otherwise (e.g., when using one of the released models), there is no need to specify this parameter as it is read directly from the model's config file.
• freeze_parameters: bool: whether to freeze the language model parameters when training the model. You may wish to set this to True if training new exit layers for an existing (single-exit) pre-trained model checkpoint, but the default (False) is applicable for most use cases.
• use_original_head: bool: whether to use the original language modeling head of the pre-trained checkpoint for text generation. Setting this parameter to True turns off all special model behavior and ignores the additional exit heads, and thus also renders the parameters below irrelevant.
• output_layer_index: int: choose a single specific model exit for generation instead of the original (top layer) exit head.
• contrast_layer_indices: Tuple[Union[int, str], int]: use an auto-contrastive generation setting, contrasting between the two specified exit layers. To perform contrast with the original LM head, pass the string 'original'. For example: (24, 18) will perform contrast between the exits at layers 24 and 18, and ('original', 12) will perform contrast between the original LM head and the head at exit 12.
• contrast_function: Callable (Advanced): enables setting a custom contrast function, that gets logits of next-token predictions from two exit heads (i.e., those specified in contrast_layer_indices) and returns a modified set of predictions. By default, the calculate_contrasted_logits function is used, which performs the contrast calculation described in Gera et al. (2023).

Running language modeling benchmarks

For GPT-family models, it is possible to run benchmarks for a given multi-exit model with the Language Model Evaluation Harness library, for different generation settings.

This is done via python -m autocontrastive_gen.evaluation.lm_eval_harness.run_lm_eval, by specifying --model multi_exit_gpt and adding the desired multi-exit configuration settings to the --model_args runtime argument.

For example:

python -m autocontrastive_gen.evaluation.lm_eval_harness.run_lm_eval \
--tasks lambada_openai \
--model multi_exit_gpt \
--model_args pretrained=IBM/gpt2-medium-multiexit,use_original_head=False,contrast_layer_indices='original;12' \
--output_path my_output_path

For details on the benchmark tasks available and on additional runtime arguments of the evaluation script, refer to https://github.com/EleutherAI/lm-evaluation-harness#basic-usage.

Pre-trained model checkpoints

We release the following model checkpoints:

• ibm/gpt2-medium-multiexit - identical to the GPT-2 Medium pre-trained model checkpoint, but with 12 newly-trained linear exit heads.
• ibm/gpt-neo-125m-multiexit - identical to the GPT Neo 125M pre-trained model checkpoint, but with 6 newly-trained linear exit heads.

The new heads were trained on the English portion of the CC-100 dataset. For further details, refer to Appendix A of the paper.

Reference

Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim and Eyal Shnarch (2023). The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers. ACL 2023.

Please cite:

@inproceedings{gera2023autocontrastive,
  title={The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers},
  author={Gera, Ariel and Friedman, Roni and Arviv, Ofir and Gunasekara, Chulaka and Sznajder, Benjamin and Slonim, Noam and Shnarch, Eyal},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month={july},
  address={Toronto, Canada},
  year={2023}
}

License

This work is released under the Apache 2.0 license. The full text of the license can be found in LICENSE.