Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET

Related tags

Text Data & NLPcomet-deepspeed
Overview

Training COMET using seq2seq setting

Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET. The codes are modified from run_summarization.py in the official example codes for transformers version 4.16.0.dev0.

The ./deepspeed/ folder is copied from https://github.com/huggingface/transformers/tree/master/tests/deepspeed .

The training data of ATOMIC2020 can be downloaded at https://allenai.org/data/atomic-2020. You need to convert the .tsv file to .csv to be compatible with the dataloader in transformers.

Dependencies

python

torch==1.7.1
cudatoolkit=11.0
transformers==4.15.0
deepspeed==0.5.10

others

GCC/G++ 5.2.0 (to complie deepspeed ops)

Usage

1. Normal training without memory optimization:

CUDA_VISIBLE_DEVICES=0 python models/comet_seq2seq.py \
    --model_name_or_path t5-small \
    --do_train \
    --train_file /path/to/train.csv \
    --source_prefix "" \
    --output_dir data/models/t5-small \
    --overwrite_output_dir \
    --gradient_accumulation_steps=4 \
    --per_device_train_batch_size=8 \
    --per_device_eval_batch_size=4 \
    --max_source_length 16 \
    --max_target_length 18 \
    --text_column head_event --summary_column tail_event \
    --save_strategy epoch \
    --num_train_epochs 3 \
    --learning_rate 1e-5

2. Train with gradient_checkpointing=True. Smaller memory usage, meanwhile lower training speed.

CUDA_VISIBLE_DEVICES=0 python models/comet_seq2seq.py \
    --model_name_or_path t5-small \
    --do_train \
    --train_file /path/to/train.csv \
    --source_prefix "" \
    --output_dir data/models/t5-small \
    --overwrite_output_dir \
    --gradient_accumulation_steps=4 \
    --per_device_train_batch_size=8 \
    --per_device_eval_batch_size=4 \
    --max_source_length 16 \
    --max_target_length 18 \
    --text_column head_event --summary_column tail_event \
    --save_strategy epoch \
    --num_train_epochs 3 \
    --learning_rate 1e-5 \
    --gradient_checkpointing

3. Train with DeepSpeed (Either zero-stage2 or zero-stage3)

# google/t5-3B training, on 2080Ti (11GB)
deepspeed --include localhost:0,1 --master_port 30000 models/comet_seq2seq.py \
    --deepspeed deepspeed/ds_config_zero2.json \
    --model_name_or_path google/t5-xl-lm-adapt \
    --do_train \
    --train_file data/kg/atomic2020_data-feb2021/train.csv \
    --source_prefix "" \
    --output_dir data/models/comet/t5_xl_s2_bs32_fp16 \
    --overwrite_output_dir \
    --gradient_accumulation_steps=1 \
    --per_device_train_batch_size=16 \
    --max_source_length 16 \
    --max_target_length 18 \
    --text_column head_event --summary_column tail_event \
    --save_strategy epoch \
    --num_train_epochs 3 \
    --learning_rate 1e-5 \
    --fp16

4. Comparison of memory usage of different memory optimization methods

Compare the memory usage on NVIDIA RTX A6000 (48685MB memory) and Nvidia GeForce 3090 (24268MB memory).

1. fp16

T5-3B: effects of fp16. A 20% reduce of memory size.

Device fp16 Batch Size x Grad-Accum x Num-GPU Memory Usage Time to Train a Batch
vanilla A6000 False 8x4x1 47.5k M 1.5s/32ex
vanilla A6000 True 8x4x1 31k M 1.0s/32ex
vanilla 3090 False 1x32x1 ❌ -
vanilla 3090 True 1x32x1 ❌ -

2. gradient_checkpointing

T5-3B: Effects of gradient_checkpointing.

Device fp16 Batch Size x Grad-Accum x Num-GPU Memory Usage Time to Train a Batch
vanilla A6000 False 8x4x1 47k M 1.5s/32ex
vanilla A6000 True 8x4x1 31k M 1.0s/32ex
grad-ckpt A6000 False 8x4x1 46.4k M 1.3s/32ex
grad-ckpt A6000 True 8x4x1 23.9k M 1.1/32ex
vanilla 3090 True 1x32x1 ❌ -
grad-ckpt 3090 True 1x32x1 23.8k M 15s/32ex

3. Deepspeed stage 2

T5-3B: Effects of deepspeed.

Device fp16 Batch Size x Grad-Accum x Num-GPU Memory Usage Time to Train a Batch
vanilla 3090 True 1x32x1 ❌ -
grad-ckpt 3090 True 1x32x1 23k M 13.5s/32ex
stage2 3090 True 32x1x1 20.3k M 7.5s/32ex
stage2 3090 True 16x1x2 20.3k M 6.36s/32ex
stage2 3090 True 32x1x2 20.3k M 3.75s/32ex

4. Deepspeed stage 3

stage3 will lead to smaller usage of memory but way smaller training speed.

5. Automatic Evaluation Result on ATOMIC2020 data

BLEU-1 BLEU-2 BLEU-3 BLEU-4 METEOR ROUGE-L CIDEr
T5-3B (no deepspeed), lr1e-5, epoch 3 0.346 0.184 0.12 0.084 0.19 0.422 0.646
T5-3B (no deepspeed), lr1e-5, epoch 2 0.348 0.185 0.121 0.085 0.19 0.424 0.651
T5-3B (no deepspeed), lr1e-5, epoch 1 0.343 0.177 0.113 0.079 0.186 0.416 0.629
T5-3B (ds_stage2, fp16) epoch 3 0.340 0.182 0.118 0.083 0.189 0.418 0.637
T5-3B (ds_stage2, fp16) epoch 2 0.337 0.177 0.114 0.078 0.189 0.419 0.633
T5-3B (ds_stage2, fp16) epoch 1 0.335 0.174 0.112 0.076 0.186 0.415 0.632

Useful discussions regarding environment setups

TODO

DeepSpeed without Trainer(): https://huggingface.co/docs/transformers/main_classes/deepspeed#deepspeed-non-trainer-integration

Owner
tqfang
Ph.D. at HKUST, interested in commonsense in NLP
tqfang
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