What does this PR do?

This PR provides a simple implementation of one step off async training with fsdp and vllm backend.

We conducted three different experiments with qwen2.5_3b model on 8 A100 GPUs:

1. baseline: all models are colocated
2. standalone rollout: rollout model runs on 4 GPUs and other models run on remaining 4GPUs
3. one step off: the same model placement as the second experiment, but with one step off async training

The pictures below demonstrate the results of these experiments:

In these experiments, baseline has the highest throughput, but we think it is just because we didn't find the best configure for one step off async training.

The exciting point is that our nccl based weights updating for rollout model has great performance. The latency is showed below:

At most of time, the latency is under 300ms, which is negligible for RLHF. Although it is only implemented with fsdp and vllm now, we think it is not complex to extend it to the other backend.

Checklist Before Starting

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• Format the PR title as [{modules}] {type}: {description} (This will be checked by the CI)
  • {modules} include fsdp, megatron, sglang, vllm, rollout, trainer, ci, training_utils, recipe, hardware, deployment, ray, worker, single_controller, misc, perf, model, algo, env, tool, ckpt, doc, data
  • If this PR involves multiple modules, separate them with , like [megatron, fsdp, doc]
  • {type} is in feat, fix, refactor, chore, test
  • If this PR breaks any API (CLI arguments, config, function signature, etc.), add [BREAKING] to the beginning of the title.
  • Example: [BREAKING][fsdp, megatron] feat: dynamic batching

Test

For changes that can not be tested by CI (e.g., algorithm implementation, new model support), validate by experiment(s) and show results like training curve plots, evaluation results, etc.

API and Usage Example

Demonstrate how the API changes if any, and provide usage example(s) if possible.

To use this feature, hybrid_engine option must be disabled to separate actor model and rollout model into difference GPU cluster. rollout.n_gpus option has been added to configure file to indicate how many GPUs rollout model would be occupied. The script below is an example to train qwen2.5_3b with 8 GPUs.

python3 -m recipe.async.async_main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=$HOME/data/gsm8k/train.parquet \
    data.val_files=$HOME/data/gsm8k/test.parquet \
    data.train_batch_size=1024 \
    data.max_prompt_length=512 \
    data.max_response_length=1024 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    data.shuffle=False \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \
    actor_rollout_ref.actor.optim.lr=3e-6 \
    actor_rollout_ref.hybrid_engine=False \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=40 \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=False \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
    actor_rollout_ref.rollout.n=5 \
    actor_rollout_ref.rollout.n_gpus=4 \
    actor_rollout_ref.rollout.load_format=safetensors \
    actor_rollout_ref.rollout.layered_summon=True \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.val_before_train=True \
    trainer.logger=['console','wandb'] \
    trainer.project_name='verl_grpo_example_gsm8k' \
    trainer.experiment_name='qwen2.5_3b_grpo_async_one_step_off' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=1 \
    trainer.save_freq=-1 \
    trainer.test_freq=-1 \
    trainer.total_epochs=15 $@

High-Level Design

Demonstrate the high-level design if this PR is complex.

Specific Changes

1. nccl based weights updating for rollout model.
2. one step off async trainer.

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