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Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language Modeling (EMNLP 2024)

[Paper][Model Checkpoints][Data][Training]

Requirements

conda create -p ~/sharedscratch/conda_envs/athnlp2024-cu12 python=3.10

# Install dependencies
poetry install

# Install flash attention / mamba

poe install-flash-attn
poe install-mamba-ssm
poe install-causal-conv1d

Model Checkpoints 🤗

Pretrained Checkpoints

Model
Pythia-VL-1B
Mamba-VL-790M
Pythia-VL-1.4B
Mamba-VL-1.4B
Pythia-VL-2.8B
Mamba-VL-2.8B

Instruction-tuned Checkpoints

Model	COCO	NoCaps	VQAv2	GQA	V7W (test-T)	VSR	POPE	RefCOCO (testA)	RefCOCO (testB)	RefCOCO+ (testA)	RefCOCO+ (testB)	RefCOCOg	V7W (test-P)	TextCaps	TextVQA	AI2D

Pythia-VL-1B	132.89	97.61	72.26	53.79	81.96	72.43	86.77	76.00	62.48	45.36	47.44	67.58	83.78	92.73	35.22	77.62
Mamba-VL-790M	133.81	99.00	71.67	54.95	81.82	75.39	86.77	67.84	56.35	57.97	41.43	59.16	74.01	94.30	40.72	79.27
Pythia-VL-1.4B	134.06	100.72	73.57	57.05	83.06	77.72	86.40	82.43	68.39	72.35	55.16	72.56	86.13	94.60	37.54	79.27
Mamba-VL-1.4B	134.76	100.56	74.46	58.44	83.78	80.18	85.32	76.60	63.48	68.40	52.11	68.82	80.18	98.68	41.30	80.86
Pythia-VL-2.8B	134.97	101.27	75.08	59.76	84.34	80.86	86.87	85.39	70.82	75.39	58.62	76.24	86.61	99.74	39.14	81.57
Mamba-VL-2.8B	135.53	102.00	76.08	60.41	85.31	81.45	87.33	79.29	64.97	71.64	53.94	71.27	82.50	100.47	42.14	83.71

Data

Instructions for specific datasets

GRIT

GRIT is downloaded using img2dataset. Note that some of the urls may not be available by the time of the downloading

./scripts/download_grit storage/datasets/grit_url_folder storage/datasets/grit

To avoid training on the whole data, filter out grit by the noun_phrases (see appendix in the paper for full details)

python prepare_grit_dataset.py \
	--cache_dir /path/to/downloaded/grit \
	--output_folder /path/to/downsampled/grit \
	--downsample_images \
	--check_overlap

OCRVQA

We also filter out examples from OCRVQA (see appendix in the paper for details)

python filter_out_ocrvqa_images.py \
	--cache_dir /path/to/downloaded/ocrvqa \
	--output_json /path/to/filtered/ocrvqa/examples \

Prepare pretraining dataset

python prepare_dataset.py \
	--dataset_subset llava_pretrain \
	--root_dataset_path storage/datasets \
	--cache_dir storage/datasets/vl_mamba \

Prepare instruction tuning dataset

python prepare_dataset.py \
	--dataset_subset instruction_tuning \
	--root_dataset_path storage/datasets \
	--cache_dir storage/datasets/vl_mamba \

Prepare a single dataset

python prepare_dataset.py \
	--dataset_subset coco \
	--root_dataset_path storage/datasets \
	--cache_dir storage/datasets/vl_mamba \

see DatasetNames in src/vl_mamba/datamodels/datamodels.py for the names of different datasets

Training

Pretraining

Pythia

./scripts/pretrain_pythia.sh

Mamba

./scripts/pretrain_mamba.sh

Instruction-tuning

Pythia

./scripts/finetune_pythia.sh path/to/pretrained/pythia/model /path/to/dataset/cache /path/to/root/dataset/path /output/model/directory wandb_run_name

Mamba

./scripts/finetune_mamba.sh path/to/pretrained/mamba/model /path/to/dataset/cache /path/to/root/dataset/path /output/model/directory wandb_run_name

Training logs

All the logs regarding pretraining / finetuning can be found on wandb Note that some of the runs were resumed from a previous checkpoint.