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What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the architectures and pretraining objectives used across state-of-the-art models differ significantly, and there has been limited systematic comparison of these factors. In this work, we present a large-scale evaluation of modeling choices and their impact on zero-shot generalization. In particular, we focus on text-to-text models and experiment with three model architectures (causal/non-causal decoder-only and encoder-decoder), trained with two different pretraining objectives (autoregressive and masked language modeling), and evaluated with and without multitask prompted finetuning. We train models with over 5 billion parameters for more than 168 billion tokens, thereby increasing the likelihood that our conclusions will transfer to even larger scales. Our experiments show that causal decoder-only models trained on an autoregressive language modeling objective exhibit the strongest zero-shot generalization after purely self-supervised pretraining. However, models with non-causal visibility on their input trained with a masked language modeling objective followed by multitask finetuning perform the best among our experiments. We therefore consider the adaptation of pretrained models across architectures and objectives. Code and checkpoints are available at https://github.com/bigscience-workshop/architecture-objective.

Full paper is available at: https://arxiv.org/abs/2204.05832

Checkpoints

// TODO (@thomasw21): link to checkpoints.

checkpoints path
CD:FLM (168B) gs://bigscience-t5x/arch_objective_exps_v2/c_dec_c4_full_lm_bs2048/checkpoint_131072
ND:PLM (168B) gs://bigscience-t5x/arch_objective_exps_v2/nc_dec_c4_prefix_lm_bs2048/checkpoint_131072
ED:PLM (168B) gs://bigscience-t5x/arch_objective_exps_v2/enc_dec_c4_prefix_lm_bs2048/checkpoint_131072
CD:FLM (168B) + CD:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/cd_flm_131072-cd_mtf_141072/checkpoint_141072
ND:PLM (168B) + ND:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/nd_plm_131072-nd_mtf_141072/checkpoint_141072
ED:PLM (168B) + ED:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/ed_plm_131072-ed_mtf_141072/checkpoint_141072
CD:MLM (168B) + CD:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/cd_mlm_131072-cd_mtf_141072/checkpoint_141072
ND:MLM (168B) + ND:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/nd_mlm_131072-nd_mtf_141072/checkpoint_141072
ED:MLM (168B) + ED:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/ed_mlm_131072-ed_mtf_141072/checkpoint_141072
CD:FLM (219B) TODO @thomasw21
CD:FLM (219B) + ND:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/cd_flm_171008-cd_mtf_181008/checkpoint_181008
CD:FLM (219B) + ND:MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/cd_flm_171008-nd_mtf_181008/checkpoint_181008
CD:FLM (168B) + ND:MLM (51B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/cd_flm_131072-nd_mlm_171008/checkpoint_171008
CD:FLM (168B) + ND:MLM (51B) + MTF (13B) gs://bigscience-t5x/arch_objective_exps_v2/dropout_reruns/cd_flm_131072-nd_mlm_171008-nd_mtf_181008/checkpoint_181008

How to cite

@article{wang2022language,
  title={What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?},
  author={Wang, Thomas and Roberts, Adam and Hesslow, Daniel and Scao, Teven Le and Chung, Hyung Won and Beltagy, Iz and Launay, Julien and Raffel, Colin},
  journal={arXiv preprint arXiv:2204.05832},
  year={2022}
}

T5X

T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of sequence models (starting with language) at many scales.

It is essentially a new and improved implementation of the T5 codebase (based on Mesh TensorFlow) in JAX and Flax.

Installation

Note that all the commands in this document should be run in the commandline of the TPU VM instance unless otherwise stated.

  1. Follow the instructions to set up a Google Cloud Platform (GCP) account and enable the Cloud TPU API.

    Note: While T5X works with GPU as well, we haven't heavily tested the GPU usage.

  2. Create a Cloud TPU VM instance following this instruction. We recommend that you develop your workflow in a single v3-8 TPU (i.e., --accelerator-type=v3-8) and scale up to pod slices once the pipeline is ready. In this README, we focus on using a single v3-8 TPU. See here to learn more about TPU architectures.

  3. With Cloud TPU VMs, you ssh directly into the host machine of the TPU VM. You can install packages, run your code run, etc. in the host machine. Once the TPU instance is created, ssh into it with

    gcloud alpha compute tpus tpu-vm ssh ${TPU_NAME} --zone=${ZONE}

    where TPU_NAME and ZONE are the name and the zone used in step 2.

  4. Install T5X and the dependencies. JAX and Gin-config need to be installed from the source.

    git clone --branch=main https://github.com/google-research/t5x
cd t5x

python3 -m pip install -e . -f \
  https://storage.googleapis.com/jax-releases/libtpu_releases.html

  5. Create toogle Cloud Storage (GCS) bucket to store the dataset and model checkpoints. To create a GCS bucket, see these instructions.

Example: English to German translation

As a running example, we use the WMT14 En-De translation. The raw dataset is available in TensorFlow Datasets as "wmt_t2t_translate".

T5 casts the translation task such as the following

{'en': 'That is good.', 'de': 'Das ist gut.'}

to the form called "text-to-text":

{'inputs': 'translate English to German: That is good.', 'targets': 'Das ist gut.'}

This formulation allows many different classes of language tasks to be expressed in a uniform manner and a single encoder-decoder architecture can handle them without any task-specific parameters. For more detail, refer to the T5 paper (Raffel et al. 2019).

For a scalable data pipeline and an evaluation framework, we use SeqIO, which was factored out of the T5 library. A seqio.Task packages together the raw dataset, vocabulary, preprocessing such as tokenization and evaluation metrics such as BLEU and provides a tf.data instance.

The T5 library provides a number of seqio.Tasks that were used in the T5 paper. In this example, we use wmt_t2t_ende_v003.

Training

To run a training job, we use the t5x/train.py script.

# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.
MODEL_DIR="..."

# Data dir to save the processed dataset in "gs://data_dir" format.
TFDS_DATA_DIR="..."
T5X_DIR="..."  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_from_scratch.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \
  --tfds_data_dir=${TFDS_DATA_DIR}

The configuration for this training run is defined in the Gin file t5_1_1_base_wmt_from_scratch.gin. Gin-config is a library to handle configurations based on dependency injection. Among many benefits, Gin allows users to pass custom components such as a custom model to the T5X library without having to modify the core library. The custom components section shows how this is done.

While the core library is independent of Gin, it is central to the examples we provide. Therefore, we provide a short introduction to Gin in the context of T5X. All the configurations are written to a file "config.gin" in MODEL_DIR. This makes debugging as well as reproducing the experiment much easier.

In addition to the config.json, model-info.txt file summarizes the model parameters (shape, names of the axes, partitioning info) as well as the optimizer states.

TensorBoard

To monitor the training in TensorBoard, it is much easier (due to authentification issues) to launch the TensorBoard on your own machine and not in the TPU VM. So in the commandline where you ssh'ed into the TPU VM, launch the TensorBoard with the logdir pointing to the MODEL_DIR.

# NB: run this on your machine not TPU VM!
MODEL_DIR="..."  # Copy from the TPU VM.
tensorboard --logdir=${MODEL_DIR}

Or you can launch the TensorBoard inside a Colab. In a Colab cell, run

from google.colab import auth
auth.authenticate_user()

to authorize the Colab to access the GCS bucket and launch the TensorBoard.

%load_ext tensorboard
model_dir = "..."  # Copy from the TPU VM.
%tensorboard --logdir=model_dir

TODO(hwchung): Add tfds preparation instruction

Fine-tuning

We can leverage the benefits of self-supervised pre-training by initializing from one of our pre-trained models. Here we use the T5.1.1 Base checkpoint.

# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.
MODEL_DIR="..."

# Data dir to save the processed dataset in "gs://data_dir" format.
TFDS_DATA_DIR="..."
T5X_DIR="..."  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_finetune.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \
  --tfds_data_dir=${TFDS_DATA_DIR}

Note: when supplying a string, dict, list, tuple value, or a bash variable via a flag, you must put it in quotes. In the case of strings, it requires "triple quotes" ("'<string>'"). For example: --gin.utils.DatasetConfig.split="'validation'" or --gin.MODEL_DIR="'${MODEL_DIR}'".

Gin makes it easy to change a number of configurations. For example, you can change the partitioning.ModelBasedPjitPartitioner.num_partitions (overriding the value in t5_1_1_base_wmt_from_scratch.gin) to chanage the parallelism strategy and pass it as a commandline arg.

--gin.partitioning.ModelBasedPjitPartitioner.num_partitions=8

Evaluation

To run the offline (i.e. without training) evaluation, you can use t5x/eval.py script.

EVAL_OUTPUT_DIR="..."  # directory to write eval output
T5X_DIR="..."  # directory where the t5x is cloned, e.g., ${HOME}"/t5x".
TFDS_DATA_DIR="..."
CHECKPOINT_PATH="..."

python3 ${T5X_DIR}/t5x/eval.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_eval.gin" \
  --gin.CHECKPOINT_PATH="'${CHECKPOINT_PATH}'" \
  --gin.EVAL_OUTPUT_DIR="'${EVAL_OUTPUT_DIR}'" \
  --tfds_data_dir=${TFDS_DATA_DIR}

Inference

To run inference, you can use t5x/infer.py script. Here we use the same seqio.Task, but for inference we do not use the targets features other than logging them alongside the prediction in a JSON file.

INFER_OUTPUT_DIR="..."  # directory to write infer output
T5X_DIR="..."  # directory where the t5x is cloned, e.g., ${HOME}"/t5x".
TFDS_DATA_DIR="..."
CHECKPOINT_PATH="..."

python3 ${T5X_DIR}/t5x/infer.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_infer.gin" \
  --gin.CHECKPOINT_PATH="'${CHECKPOINT_PATH}'" \
  --gin.INFER_OUTPUT_DIR="'${INFER_OUTPUT_DIR}'" \
  --tfds_data_dir=${TFDS_DATA_DIR}

Custom components

The translation example uses the encoder-decoder model that T5X provides as well as the dataset from the T5 library. This section shows how you can use your own dataset and a model and pass via Gin.

Example: custom dataset in a user directory

For this example, we have the following directory structure with ${HOME}/dir1/user_dir representing a user directory with custom components.

${HOME}
└── dir1
    └── user_dir
        ├── t5_1_1_base_de_en.gin
        └── tasks.py

As an example, let's define a new dataset. Here we use the same Translation dataset but we define the translation task in the opposite direction, i.e., German to English intead of English to German. We define this task in tasks.py

# ${HOME}/dir1/user_dir/tasks.py

import functools
import seqio
import tensorflow_datasets as tfds
from t5.evaluation import metrics
from t5.data import preprocessors

vocabulary = seqio.SentencePieceVocabulary(
    'gs://t5-data/vocabs/cc_all.32000/sentencepiece.model', extra_ids=100)
output_features = {
    'inputs': seqio.Feature(vocabulary=vocabulary),
    'targets': seqio.Feature(vocabulary=vocabulary)
}

seqio.TaskRegistry.add(
    'wmt_t2t_de_en_v003',
    source=seqio.TfdsDataSource(tfds_name='wmt_t2t_translate/de-en:1.0.0'),
    preprocessors=[
        functools.partial(
            preprocessors.translate,
            source_language='de', target_language='en'),
        seqio.preprocessors.tokenize,
        seqio.CacheDatasetPlaceholder(),
        seqio.preprocessors.append_eos_after_trim,
    ],
    metric_fns=[metrics.bleu],
    output_features=output_features)

In the Gin file, most of the settings are equivalent to those used in the En->De example. So we include the Gin file from that example. To use "wmt_t2t_de_en_v003" task we just defined, we need to import the task module "tasks.py". Note that we use a relative path defined with respect to the user directory. This will be specified as a flag.

# ${HOME}/dir1/user_dir/t5_1_1_base_de_en.gin
from __gin__ import dynamic_registration
import tasks  # This imports the task defined in dir1/user_dir/tasks.py.

include "t5x-tmp/t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_from_scratch.gin"
MIXTURE_OR_TASK_NAME = "wmt_t2t_de_en_v003"

Finally, we launch training passing the user directory as a flag gin_search_paths such that the Gin file and python modules can be specified with relative paths.

PROJECT_DIR=${HOME}"/dir1/user_dir"
T5X_DIR="..."  # directory where the t5x is cloned.
TFDS_DATA_DIR="..."
MODEL_DIR="..."
export PYTHONPATH=${PROJECT_DIR}

python3 ${T5X_DIR}/t5x/train.py \
  --gin_search_paths=${PROJECT_DIR} \
  --gin_file="t5_1_1_base_de_en.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \
  --tfds_data_dir=${TFDS_DATA_DIR}

Released Checkpoints

We release the checkpoints for the T5.1.1 models in a native T5X format.

These are converted from the public Mesh TensorFlow checkpoints .

Compatibility with the Mesh TensorFlow checkpoints

The Mesh TensorFlow checkpoints trained using the T5 library can be directly loaded into T5X. For example, we can rerun the fine-tuning example initializing from the MTF checkpoint by changing the INIT_CHECKPOINT Gin macro.

# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.
MODEL_DIR="..."

# Data dir to save the processed dataset in "gs://data_dir" format.
TFDS_DATA_DIR="..."
T5X_DIR="..."  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/wmt19_ende_from_scratch.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \
  --gin.MIXTURE_OR_TASK_NAME="'wmt_t2t_ende_v003'" \
  --gin.INIT_CHECKPOINT="'gs://t5-data/pretrained_models/t5.1.1.base/model.ckpt-1000000'" \
  --tfds_data_dir=${TFDS_DATA_DIR}

Note that restoring directly from the Mesh TensorFlow checkpoints can be inefficient if heavy model parallelism is used for large models. This is because each host loads the entire copy of the model first and then keep only the relevant slices dictated by the model parallelism specification. If you have Mesh TensorFlow checkpoints that you run often, we recommend converting the checkpoints to T5X native format using Checkpointer.convert_from_tf_checkpoint.

TODO(hwchung): Add a conversion script.

Note

This is not an officially supported Google product

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