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#!/bin/bash

./move.sh

cd src/r1-v

export DEBUG_MODE="true"
export LOG_PATH="./vllm_run.txt"


QWEN_PATH='Qwen/Qwen2.5-VL-3B-Instruct'

DATA_FILE="pool_multiple_choice_chunk_01"
HF_DATASET="./Video-R1-data/${DATA_FILE}.json"
OUTPUT_DIR="./log/3B-LLMEval/${DATA_FILE}"
if [ ! -d "$OUTPUT_DIR" ]; then
 mkdir -p "$OUTPUT_DIR"
fi
RUN_NAME="3B-LLMEval-${DATA_FILE}"
DS_CONFIG="local_scripts/zero3.json"  

# Set temporal to choose between T-GRPO and GRPO, and len_control to enable or disable the length control reward.
# NOTE: you are expected to use X + 1 cards for X training proc and 1 vLLM proc 
# e.g., the visible devices should be 0,1,2,3,4 for 5 cards, and  --nproc_per_node="4"

CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" torchrun \
    --nproc_per_node="8" \
    --nnodes="1" \
    --node_rank="0" \
    --master_addr="127.0.0.1" \
    --master_port="12345" \
    src/open_r1/grpo-cot-LLMEval.py \
    --use_vllm false \
    --output_dir ${OUTPUT_DIR} \
    --model_name_or_path ${QWEN_PATH} \
    --dataset_name ${HF_DATASET} \
    --max_prompt_length 16384 \
    --max_completion_length 1400 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 8 \
    --learning_rate 1e-6 \
    --lr_scheduler_type "cosine" \
    --weight_decay 0.01 \
    --logging_steps 1 \
    --bf16 true \
    --gradient_checkpointing true \
    --attn_implementation flash_attention_2 \
    --min_pixels 3136 \
    --max_pixels 501760 \
    --num_train_epochs 1 \
    --run_name ${RUN_NAME} \
    --save_steps 50 \
    --save_only_model false \
    --temporal true \
    --len_control true \
    --report_to wandb \
    --beta 0.04 \
    --max_grad_norm 5 \
    --temperature 1.0 \
    --num_generations 8 \
    --vllm_device "cuda:6" \
    --vllm_gpu_memory_utilization 0.7 \
    --deepspeed ${DS_CONFIG} \
    2>&1 | tee "${OUTPUT_DIR}/training_log.txt"


python /cq_1/share_1603164/user/zongxia/workspace/gpu_burn.py

	#!/bin/bash

	./move.sh

	cd src/r1-v

	export DEBUG_MODE="true"
	export LOG_PATH="./vllm_run.txt"


	QWEN_PATH='Qwen/Qwen2.5-VL-3B-Instruct'

	DATA_FILE="pool_multiple_choice_chunk_01"
	HF_DATASET="./Video-R1-data/${DATA_FILE}.json"
	OUTPUT_DIR="./log/3B-LLMEval/${DATA_FILE}"
	if [ ! -d "$OUTPUT_DIR" ]; then
	mkdir -p "$OUTPUT_DIR"
	fi
	RUN_NAME="3B-LLMEval-${DATA_FILE}"
	DS_CONFIG="local_scripts/zero3.json"

	# Set temporal to choose between T-GRPO and GRPO, and len_control to enable or disable the length control reward.
	# NOTE: you are expected to use X + 1 cards for X training proc and 1 vLLM proc
	# e.g., the visible devices should be 0,1,2,3,4 for 5 cards, and --nproc_per_node="4"

	CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" torchrun \
	--nproc_per_node="8" \
	--nnodes="1" \
	--node_rank="0" \
	--master_addr="127.0.0.1" \
	--master_port="12345" \
	src/open_r1/grpo-cot-LLMEval.py \
	--use_vllm false \
	--output_dir ${OUTPUT_DIR} \
	--model_name_or_path ${QWEN_PATH} \
	--dataset_name ${HF_DATASET} \
	--max_prompt_length 16384 \
	--max_completion_length 1400 \
	--per_device_train_batch_size 1 \
	--gradient_accumulation_steps 8 \
	--learning_rate 1e-6 \
	--lr_scheduler_type "cosine" \
	--weight_decay 0.01 \
	--logging_steps 1 \
	--bf16 true \
	--gradient_checkpointing true \
	--attn_implementation flash_attention_2 \
	--min_pixels 3136 \
	--max_pixels 501760 \
	--num_train_epochs 1 \
	--run_name ${RUN_NAME} \
	--save_steps 50 \
	--save_only_model false \
	--temporal true \
	--len_control true \
	--report_to wandb \
	--beta 0.04 \
	--max_grad_norm 5 \
	--temperature 1.0 \
	--num_generations 8 \
	--vllm_device "cuda:6" \
	--vllm_gpu_memory_utilization 0.7 \
	--deepspeed ${DS_CONFIG} \
	2>&1 \| tee "${OUTPUT_DIR}/training_log.txt"


	python /cq_1/share_1603164/user/zongxia/workspace/gpu_burn.py