# MAX_PIXELS=602112 \ # CUDA_VISIBLE_DEVICES=6,7 \ # swift rollout \ # --model Qwen/Qwen2.5-VL-3B-Instruct \ # --vllm_data_parallel_size 2 \ # --vllm_max_model_len 10240 # DP size = world_size // (context_parallel_size * tensor_model_parallel_size * pipeline_model_parallel_size) # = 6 // (1 * 1 * 1) = 6 # NOTE: global_batch_size and micro_batch_size are completion-level # global_batch_size = micro_batch_size * DP size * gradient_accumulation_steps (96) # generation_batch_size = global_batch_size * steps_per_generation (96 * 4 = 384) # num_of_prompt_to_rollout = generation_batch_size / num_generations (384 / 8 = 48) # num_of_prompt_to_train = generation_batch_size / num_generations (96 / 8 = 12) CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 \ NPROC_PER_NODE=6 \ MAX_PIXELS=602112 \ MASTER_PORT=29600 \ megatron rlhf \ --rlhf_type grpo \ --model Qwen/Qwen2.5-VL-3B-Instruct \ --save_safetensors true \ --context_parallel_size 1 \ --tensor_model_parallel_size 1 \ --pipeline_model_parallel_size 1 \ --dataset AI-ModelScope/clevr_cogen_a_train#10000 \ --num_train_epochs 1 \ --global_batch_size 96 \ --micro_batch_size 4 \ --steps_per_generation 4 \ --num_generations 8 \ --external_plugins examples/train/grpo/plugin/plugin.py \ --reward_funcs external_r1v_acc format \ --use_vllm true \ --vllm_mode server \ --vllm_server_host 127.0.0.1 \ --vllm_server_port 8000 \ --max_length 8192 \ --max_completion_length 2048 \ --tuner_type full \ --lr 1e-6 \ --bf16 true \ --beta 0.001 \ --importance_sampling_level token \ --epsilon 0.2 \ --epsilon_high 0.2 \ --dynamic_sample false \ --overlong_filter true \ --loss_type grpo \ --sleep_level 2 \ --offload_model true \ --offload_bridge false \ --offload_optimizer true \ --logging_steps 1 \ --recompute_granularity selective \ --finetune \ --dataloader_num_workers 8 \ --dataset_num_proc 8 \ --no_save_optim \ --no_save_rng \ --attention_backend flash \ --temperature 1.0 \ --system examples/train/grpo/prompt.txt \ --padding_free true \ --log_completions true \ --report_to wandb \ --train_iters 100 \ --eval_steps 1000 \ --save_steps 1000