9.4 KiB
9.4 KiB
Example: Integration with FairSeq
Setup
# Install the repo as a package:
git clone https://github.com/microsoft/torchscale.git
cd torchscale
pip install -e .
pip install git+https://github.com/shumingma/fairseq.git@moe
pip install git+https://github.com/shumingma/infinibatch.git
pip install iopath
pip install numpy==1.23.0
Example: BERT Pretraining
Data Format
We use a streaming dataloader to read the data on-the-fly from the disk. It requires the data sharded into multiple small files (e.g. 10K lines per file), as well as a JSON file to contain some meta data and the paths to these files.
The overall data directory should be organized as follows:
Data/
├── json/
│ ├── train.json
│ └── valid.json
├── shard/
│ ├── train/
│ │ ├── 00000.txt
│ │ ├── 00001.txt
│ │ └── ...
│ └── valid/
│ ├── 00000.txt
│ ├── 00001.txt
│ └── ...
├── dict.txt
└── sentencepiece.bpe.model
We recommend that each sharded data files contains no more than 10K lines with one sentence per line, and two documents should be separated with an empty line.
Document 1 Line 1
Document 1 Line 2
Document 1 Line 3
Document 2 Line 1
Document 2 Line 2
...
Also, the JSON file should be in the format like this:
[
{
"source": [
"shard/train/00000.txt",
"shard/train/00001.txt",
...
],
"source_lang": "en",
"weight": 1.0
}
]
You can quickly get started with our processed vocabulary files: sentencepiece.bpe.model and dict.txt. Note that this vocabulary is English-only with 64K tokens. To train a new sentencepiece.bpe.model on your own data, please refer to the SentencePiece repo. With the sentecepiece model and the installed sentencepiece library, you can extract the dict.txt file from it by
spm_export_vocab --model=sentencepiece.bpe.model | sed 's/\t/ /g' | tail -n +4 > dict.txt
Dense Model
cd examples/fairseq/
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=8 train.py ${PATH_TO_DATA} \
--task pretraining \
--tokens-per-sample 512 \
--mask-prob 0.15 \
--span-length 3.0 \
--leave-unmasked-prob 0.0 \
--random-token-prob 0.0 \
--criterion masked_lm \
--arch mlm_base \
--share-encoder-input-output-embed \
--required-batch-size-multiple 8 \
--spm-model ${PATH_TO_DATA}/sentencepiece.bpe.model \
--dict-file ${PATH_TO_DATA}/dict.txt \
--optimizer adam \
--adam-betas '(0.9,0.98)' \
--adam-eps 1e-6 \
--clip-norm 2.0 \
--lr-scheduler polynomial_decay \
--lr 0.0005 \
--warmup-updates 10000 \
--total-num-update 125000 \
--max-update 125000 \
--max-sentences 32 \
--update-freq 1 \
--log-format simple \
--log-interval 100 \
--disable-validation \
--save-interval-updates 5000 \
--no-epoch-checkpoints \
--fp16 \
--fp16-init-scale 4 \
--fp16-scale-window 256 \
--min-loss-scale 0.0001 \
--seed 1 \
--save-dir ${PATH_TO_CKPT} \
--ddp-backend=no_c10d \
--distributed-no-spawn \
--reset-dataloader \
--batch-read-ahead 10000 \
--rel-pos-buckets 32 \
--max-rel-pos 128 \
--deepnorm
Sparse (MoE) Model
cd examples/fairseq/
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=8 train.py ${PATH_TO_DATA} \
--task pretraining \
--tokens-per-sample 512 \
--mask-prob 0.15 \
--span-length 3.0 \
--leave-unmasked-prob 0.0 \
--random-token-prob 0.0 \
--arch mlm_base \
--share-encoder-input-output-embed \
--required-batch-size-multiple 8 \
--spm-model ${PATH_TO_DATA}/sentencepiece.bpe.model \
--dict-file ${PATH_TO_DATA}/dict.txt \
--optimizer adam \
--adam-betas '(0.9,0.98)' \
--adam-eps 1e-6 \
--clip-norm 2.0 \
--lr-scheduler polynomial_decay \
--lr 0.0005 \
--warmup-updates 10000 \
--total-num-update 125000 \
--max-update 125000 \
--max-sentences 32 \
--update-freq 1 \
--log-format simple \
--log-interval 100 \
--disable-validation \
--save-interval-updates 5000 \
--no-epoch-checkpoints \
--fp16 \
--fp16-init-scale 4 \
--fp16-scale-window 256 \
--min-loss-scale 0.0001 \
--seed 1 \
--save-dir ${PATH_TO_CKPT} \
--ddp-backend=no_c10d \
--distributed-no-spawn \
--reset-dataloader \
--batch-read-ahead 10000 \
--rel-pos-buckets 32 \
--max-rel-pos 128 \
--deepnorm \
--moe-expert-count 64 --moe-freq 2 \
--moe-gating-use-fp32 --moe-second-expert-policy random --moe-normalize-gate-prob-before-dropping \
--moe-eval-capacity-token-fraction -1.0 \
--criterion masked_lm_moe_cross_entropy --moe-gate-loss-wt 0.01 --moe-gate-loss-combine-method sum \
--use-xmoe --pad-to-max-length
Example: GPT Pretraining
Data Format
We use the format as in the FairSeq's language modeling example.
Dense Model
cd examples/fairseq/
python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 train.py \
${PATH_TO_DATA} \
--num-workers 2 \
--activation-fn gelu \
--share-decoder-input-output-embed \
--validate-interval-updates 1000 \
--save-interval-updates 1000 \
--no-epoch-checkpoints \
--memory-efficient-fp16 \
--fp16-init-scale 4 \
--arch lm_base \
--task language_modeling \
--sample-break-mode none \
--tokens-per-sample 128 \
--optimizer adam --adam-betas "(0.9, 0.98)" \
--adam-eps 1e-08 \
--clip-norm 0.0 \
--lr 5e-4 \
--lr-scheduler polynomial_decay \
--warmup-updates 750 \
--dropout 0.1 \
--attention-dropout 0.1 \
--weight-decay 0.01 \
--batch-size 4 \
--update-freq 1 \
--required-batch-size-multiple 1 \
--total-num-update 50000 \
--max-update 50000 \
--seed 1 \
--ddp-backend=c10d
Sparse (MoE) Model
cd examples/fairseq/
python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 train.py \
${PATH_TO_DATA} \
--num-workers 2 \
--activation-fn gelu \
--share-decoder-input-output-embed \
--validate-interval-updates 1000 \
--save-interval-updates 1000 \
--no-epoch-checkpoints \
--memory-efficient-fp16 \
--fp16-init-scale 4 \
--arch lm_base \
--task language_modeling \
--sample-break-mode none \
--tokens-per-sample 128 \
--optimizer adam --adam-betas "(0.9, 0.98)" \
--adam-eps 1e-08 \
--clip-norm 0.0 \
--lr 5e-4 \
--lr-scheduler polynomial_decay \
--warmup-updates 750 \
--dropout 0.1 \
--attention-dropout 0.1 \
--weight-decay 0.01 \
--batch-size 4 \
--update-freq 1 \
--required-batch-size-multiple 1 \
--total-num-update 50000 \
--max-update 50000 \
--seed 1 \
--ddp-backend=no_c10d \
--moe-expert-count 2 --moe-freq 2 \
--moe-gating-use-fp32 --moe-second-expert-policy random --moe-normalize-gate-prob-before-dropping \
--moe-eval-capacity-token-fraction -1.0 \
--criterion moe_cross_entropy --moe-gate-loss-wt 0.01 --moe-gate-loss-combine-method sum \
--use-xmoe
Example: Machine Translation
Data Format
We follow the FairSeq's neural machine translation example to preprocess the data.
Dense Model
cd examples/fairseq/
python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 train.py \
${PATH_TO_DATA} \
--arch mt_base --share-decoder-input-output-embed \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
--lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
--dropout 0.3 --weight-decay 0.0001 \
--max-tokens 4096 --fp16
Sparse (MoE) Model
cd examples/fairseq/
python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 train.py \
${PATH_TO_DATA} \
--arch mt_base --share-decoder-input-output-embed \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
--lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
--dropout 0.3 --weight-decay 0.0001 \
--moe-expert-count 2 --moe-freq 2 \
--moe-gating-use-fp32 --moe-second-expert-policy random --moe-normalize-gate-prob-before-dropping \
--moe-eval-capacity-token-fraction -1.0 \
--criterion moe_cross_entropy --moe-gate-loss-wt 0.01 --moe-gate-loss-combine-method sum \
--use-xmoe \
--max-tokens 4096 --fp16