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mbell195 · Jul 29, 2023 · eae9369 · eae9369
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diff --git a/README.md b/README.md
diff --git a/docs/ScienceQA.md b/docs/ScienceQA.md
@@ -0,0 +1,129 @@
+### ScienceQA
+
+#### Prepare Data
+1. Please see ScienceQA [repo](https://github.com/lupantech/ScienceQA) for setting up the dataset.
+2. Generate ScienceQA dataset for LLaVA conversation-style format.
+
+```Shell
+python scripts/convert_sqa_to_llava \
+    convert_to_llava \
+    --base-dir /path/to/ScienceQA/data/scienceqa \
+    --split {train,val,minival,test,minitest}
+```
+
+#### Training
+**NOTE**: Due to that ScienceQA experiments were done earlier, the current checkpoints are trained *without* `<im_start>` and `<im_end>` tokens. Here we provide our training scripts for the current checkpoints.
+
+<details>
+<summary>1. Pretraining</summary>
+
+```Shell
+torchrun --nnodes=1 --nproc_per_node=8 --master_port=25001 \
+    llava/train/train_mem.py \
+    --model_name_or_path ./checkpoints/llama-vicuna-13b \
+    --data_path /path/to/cc3m_595k.json \
+    --image_folder /path/to/cc3m_595k \
+    --vision_tower openai/clip-vit-large-patch14 \
+    --tune_mm_mlp_adapter True \
+    --mm_vision_select_layer -2 \
+    --bf16 True \
+    --output_dir ./checkpoints/llava-13b-pretrain-no_im_start_end_token \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 2400 \
+    --save_total_limit 1 \
+    --learning_rate 2e-3 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 2048 \
+    --gradient_checkpointing True \
+    --lazy_preprocess True \
+    --report_to wandb
+```
+</details>
+
+<details>
+<summary>2. Finetuning</summary>
+
+You may download our pretrained `llava-13b-v0-pretrain-no_im_start_end_token.bin` [here](https://huggingface.co/liuhaotian/LLaVA-13b-pretrain-projector-v0/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption-no_im_token.bin).
+
+```Shell
+torchrun --nnodes=1 --nproc_per_node=8 --master_port=25001 \
+    llava/train/train_mem.py \
+    --model_name_or_path /path/to/llama-vicuna-13b \
+    --data_path /path/to/scienceqa/llava_train_QCM-LEPA.json \
+    --image_folder /path/to/scienceqa/images/train \
+    --vision_tower openai/clip-vit-large-patch14 \
+    --pretrain_mm_mlp_adapter ./checkpoints/llava-13b-pretrain-no_im_start_end_token/mm_projector.bin \
+    --mm_vision_select_layer -2 \
+    --bf16 True \
+    --output_dir ./checkpoints/llava-13b-pretrain-no_im_start_end_token-finetune_scienceqa \
+    --num_train_epochs 12 \
+    --per_device_train_batch_size 4 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 5000 \
+    --save_total_limit 3 \
+    --learning_rate 2e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --fsdp "full_shard auto_wrap" \
+    --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
+    --model_max_length 2048 \
+    --gradient_checkpointing True \
+    --lazy_preprocess True \
+    --report_to wandb
+```
+</details>
+
+#### Evaluation
+
+1. Download our pretrained LLaVA-13B (delta) weights for ScienceQA dataset [here](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0-science_qa).  Convert the delta weights to actual weights.
+
+```Shell
+python -m llava.model.apply_delta \
+    --base /path/to/llama-13b \
+    --target /path/to/LLaVA-13b-v0-science_qa \
+    --delta liuhaotian/LLaVA-13b-delta-v0-science_qa
+```
+
+2. [Option 1] Multiple-GPU inference
+You may evaluate this with multiple GPUs, and concatenate the generated jsonl files.  Please refer to our script for [batch evaluation](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_batch.sh) and [results gathering](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_gather.sh).
+
+3. [Option 2] Single-GPU inference
+
+(a) Generate LLaVA responses on ScienceQA dataset
+
+```Shell
+python -m llava.eval.model_vqa_science \
+    --model-path /path/to/LLaVA-13b-v0-science_qa \
+    --question-file /path/to/ScienceQA/data/scienceqa/llava_test.json \
+    --image-folder /path/to/ScienceQA/data/scienceqa/images/test \
+    --answers-file vqa/results/ScienceQA/test_llava-13b.jsonl \
+    --answer-prompter \
+    --conv-mode llava_v0
+```
+
+(b) Evaluate the generated responses
+
+```Shell
+python eval_science_qa.py \
+    --base-dir /path/to/ScienceQA/data/scienceqa \
+    --result-file vqa/results/ScienceQA/test_llava-13b.jsonl \
+    --output-file vqa/results/ScienceQA/test_llava-13b_output.json \
+    --output-result vqa/results/ScienceQA/test_llava-13b_result.json \
+```
+
+For reference, we attach our prediction file [`test_llava-13b_result.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_13b_v0.json) for comparison when reproducing our results, as well as for further analysis in detail.
diff --git a/images/demo_cli.gif b/images/demo_cli.gif
diff --git a/llava/model/builder.py b/llava/model/builder.py
@@ -29,6 +29,12 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
         kwargs['load_in_8bit'] = True
     elif load_4bit:
         kwargs['load_in_4bit'] = True
+        kwargs['quantization_config'] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4'
+        )
     else:
         kwargs['torch_dtype'] = torch.float16
 

diff --git a/llava/serve/cli.py b/llava/serve/cli.py
@@ -29,9 +29,11 @@ def main(args):
     disable_torch_init()
 
     model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
 
-    if "v1" in model_name.lower():
+    if 'llama-2' in model_name.lower():
+        conv_mode = "llava_llama_2"
+    elif "v1" in model_name.lower():
         conv_mode = "llava_v1"
     elif "mpt" in model_name.lower():
         conv_mode = "mpt"
@@ -107,10 +109,11 @@ def main(args):
     parser.add_argument("--model-base", type=str, default=None)
     parser.add_argument("--image-file", type=str, required=True)
     parser.add_argument("--num-gpus", type=int, default=1)
-    parser.add_argument("--device", type=str, choices=["cuda", "cpu"], default="cuda")
     parser.add_argument("--conv-mode", type=str, default=None)
     parser.add_argument("--temperature", type=float, default=0.2)
     parser.add_argument("--max-new-tokens", type=int, default=512)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
     parser.add_argument("--debug", action="store_true")
     args = parser.parse_args()
     main(args)
diff --git a/scripts/extract_mm_projector.py b/scripts/extract_mm_projector.py
diff --git a/scripts/finetune_full_schedule.sh b/scripts/finetune_full_schedule.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+
+# Uncomment and set the following variables correspondingly to run this script:
+
+################## VICUNA ##################
+# PROMPT_VERSION=v1
+# MODEL_VERSION="vicuna-v1-3-7b"
+################## VICUNA ##################
+
+################## LLaMA-2 ##################
+# PROMPT_VERSION="llava_llama_2"
+# MODEL_VERSION="llama-2-7b-chat"
+################## LLaMA-2 ##################
+
+deepspeed llava/train/train_mem.py \
+    --deepspeed /path/to/deepspeed.json \
+    --model_name_or_path ./checkpoints/$MODEL_VERSION \
+    --version $PROMPT_VERSION \
+    --data_path ./playground/data/llava_instruct_158k.json \
+    --image_folder /path/to/coco/train2017 \
+    --vision_tower openai/clip-vit-large-patch14 \
+    --pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --bf16 True \
+    --output_dir ./checkpoints/llava-$MODEL_VERSION-finetune \
+    --num_train_epochs 3 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 50000 \
+    --save_total_limit 1 \
+    --learning_rate 2e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 2048 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to wandb