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Megatron-11b

Megatron-11b is a unidirectional language model with 11B parameters based on Megatron-LM. Following the original Megatron work, we trained the model using intra-layer model parallelism with each layer's parameters split across 8 GPUs.

Megatron-11b is trained on the same data and uses the same byte-pair encoding (BPE) as RoBERTa.

Pre-trained models

Model Description # params # filesize Download
megatron_11b megatron_11b unidirectional language model 11B 19Gb megatron_11b.tar.gz

Architecture:

Param Value
embed_dim 3072
ffn_dim 3072 * 6
layers 72
attention heads 32

Training details:

Param value
bsz 512
num_updates 300,000
peak_lr 1.5e-04
lr scheduler inverse_sqrt
clip norm 0.0

Example training command (model parallel)

Megatron-11b contains too many parameters to train on a single GPU. Following the original Megatron work, we adopt an intra-layer model parallel training approach in which each layer's parameters are split across multiple GPUs and activations and gradients are communicated during the forward/backward pass, respectively. We similarly split the loss computation using the vocab_parallel_cross_entropy criterion.

The following training command illustrates how to do model parallel training in fairseq. We assume that each machine (node) has 8 GPUs among which to split the model parameters (--model-parallel-size 8). If you have access to multiple nodes, you may combine this with data parallel training by increasing --distributed-world-size.

To train Megatron-11b on a single node:

fairseq-train <DATA_PATH> \
  --distributed-world-size 8  \
  --memory-efficient-fp16 \
  --num-workers 2 \
  --model-parallel-size 8 \
  --criterion vocab_parallel_cross_entropy \
  --task language_modeling \
  --sample-break-mode none \
  --tokens-per-sample 1024 \
  --arch transformer_lm_megatron_11b \
  --share-decoder-input-output-embed \
  --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 --clip-norm 0.0 \
  --lr-scheduler inverse_sqrt --lr 0.00015 \
  --warmup-updates 3000 --weight-decay 0.01 \
  --dropout 0.1 --attention-dropout 0.1 \
  --batch-size 2 \
  --max-update 300000;

Note: Above was tested on DGX-1 box, with 8xV100-32Gb GPUs.

Results

Wikitext103

Model Valid perplexity Test perplexity
megatron_11b 10.64 10.54

Evaluating megatron_11b on Wikitext-103

1. Downloading Megatron-11b

# WARNING: this file is 19GB
wget https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz
tar -xzvf megatron_11b.tar.gz

2. Download Wikitext-103

wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
unzip wikitext-103-raw-v1.zip

3. Detokenize test tokens

Megatron-11b uses a byte-level BPE that expects raw (untokenized) input. Since the wikitext-103 dataset comes tokenized, we apply a simple detokenization process to restore the untokenized test set:

python -m examples.megatron_11b.detok wikitext-103-raw/wiki.test.raw > wikitext-103-raw/wiki.test.detok

4. BPE encoding

wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'

python -m examples.roberta.multiprocessing_bpe_encoder \
    --encoder-json encoder.json \
    --vocab-bpe vocab.bpe \
    --inputs "wikitext-103-raw/wiki.test.detok" \
    --outputs "wikitext-103-raw/wiki.test.bpe" \
    --workers 60;

5. Fairseq binarize

fairseq-preprocess \
    --only-source \
    --testpref wikitext-103-raw/wiki.test.bpe \
    --srcdict megatron_11b/dict.txt \
    --destdir wikitext103-bin;

6. Evaluating perplexity.

We can now evaluate perplexity on the test set. Note that because we've modified the test set (via detokenization and BPE), the perplexity reported by fairseq-eval-lm needs to be renormalized.

Compute unnormalized perplexity:

DATA_PATH=wikitext103-bin/
fairseq-eval-lm \
  $DATA_PATH \
  --path megatron_11b/model.pt \
  --task language_modeling \
  --gen-subset test \
  --batch-size 8 \
  --criterion cross_entropy \
  --context-window 992 \
  --distributed-world-size 8 \
  --model-parallel-size 8;
# Expected PPL (unnormalized_ppl): [8.46]
# Note: the eval command needs to run on 8 GPUs for the released model

Renormalizing formula: 2 ^ ( log_2(unnormalized_PPL) * (270847 / 245566)). PPL After normalization: 10.54

To renormalize the perplexity, we must account for the change in token count after detokenizing and appling BPE. The formula for this is: 2 ^ ( log_2(unnormalized_PPL) * (new_token_cnt / orig_token_cnt))

For the wikitext-103 test set, the original token count is 245566 and the token count after detokenization and applying BPE is 270847.

The perplexity after renormalization is: 2 ^ ( log_2(8.46) * (270847 / 245566)) = 10.54