README - Unilm — ContextQMD

NormFormer

This is the code for the "NormFormer: Improved Transformer Pretraining with Extra Normalization"

2021-10-19: Commands for CLM Experiments
Coming soon: Commands for MLM experiments

If you have any issues or questions please post a github issue and tag @sshleifer.

Data

To preprocess language modeling data, see here.
The replication commands below expect $DATA to be the path to the binarized data directory.
Note that NormFormer results in Table 2 use a much larger private dataset, and to get good results you should adapt the pre-processing instructions to your dataset and compare to a baseline on the same data, rather than Table 2.
The code uses FSDP, which requires pip install fairscale>=0.4.0.

Modify existing Command

To modify an existing fairseq-train command to use NormFormer, simply add the following flags:

bash

fairseq-train  ... \
    --scale-attn --scale-fc --scale-heads

you probably also want to increase your learning rate
if your model is small, you may want to add --scale-resids

Exact Training Commands

Note that NormFormer results in Table 2 use a much larger private dataset, and to get good results you should adapt the pre-processing instructions to your dataset. The full commands are functions defined here, so to run them you must source examples/normformer/train_lm.sh.
We default --distributed-world-size 8. You should adjust --update-freq and --batch-size and such that the effective batch size is (1024x1024x0.5) tokens for 125M and 355M, and (1024x1024) for 1.3B parameter and above. For small models, --update-freq=256/global_bs. For large models, --update-freq=512/global_bs, where global_bs = --batch-size * --distributed-world-size
The small models will all train on as few as 8 GPUs.

bash

train_125M --lr 6e-4  # GPT-3 Replicated
train_125M --lr 1e-3  # stronger high-lr baseline
train_125M --lr 3e-3 --scale-attn --scale-fc --scale-heads # No scale-resids
train_125M --lr 3e-3 --scale-attn --scale-fc --scale-heads --scale-resids  # Best command

bash

train_355M --lr 6e-4  # GPT-3 Replicated
train_355M --lr 1e-3  # stronger high-lr baseline
train_355M --lr 1e-3 --scale-attn --scale-fc --scale-heads # No scale-resids
train_355M --lr 1e-3 --scale-attn --scale-fc --scale-heads --scale-resids  # Slightly better

bash

train_1.3B --lr 2e-4  # GPT-3 Replicated
train_1.3B --lr 6e-4  # stronger high-lr baseline
train_1.3B --lr 6e-4 --scale-attn --scale-fc --scale-heads # NormFormer

bash

train_2.7B --lr 1.6e-4  # GPT-3 Replicated
train_2.7B --lr 1.6e-4 --activation-fn relu_squared # stronger Relu^2 baseline
train_2.7B --lr 6e-4 --activation-fn relu_squared --scale-attn --scale-fc --scale-heads # NormFormer 2.7B

Citation

bibtex

@misc{shleifer2021normformer,
      title={NormFormer: Improved Transformer Pretraining with Extra Normalization},
      author={Sam Shleifer and Jason Weston and Myle Ott},
      year={2021},
      eprint={2110.09456},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}