Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET

Last update: Dec 17, 2022

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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

Errors building DeepSpeed Ops: https://github.com/microsoft/DeepSpeed/issues/885

TODO

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

Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET

Related tags

Overview

Training COMET using seq2seq setting

Dependencies

Usage

1. Normal training without memory optimization:

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

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

4. Comparison of memory usage of different memory optimization methods

1. fp16

2. gradient_checkpointing

3. Deepspeed stage 2

4. Deepspeed stage 3

5. Automatic Evaluation Result on ATOMIC2020 data

Useful discussions regarding environment setups

TODO

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