[megatron, sglang, rollout, doc] feat: Add Validation StateMachine to async-rl, async ref_logp and support nccl-sync

[ziqi-wlb]

What does this PR do?

1. Add validation StateMachine
2. Support async ref_logp: remove all offloads(param/grad/optimizer..)
3. Change doc for async-rl

Performance:

1. async-ref-logp: Compared with the previous async-rl the performance is further improved by 20% (170s -> 140s, after tuning engines tp, 140s -> 112s)
2. nccl-sync: The end-to-end's performance is 50% higher than Verl's hybrid-engine. The (actor-gather/broadcast/engine-load)overlap+bucket-fused nccl-sync optimization reduces parameter synchronization time by 60%. (dots2.0 300B + nGPUs=512)

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API and Usage Example

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

# Async RL Configuration
+actor_rollout_ref.async_pipeline=True \
 
# Resource Management
+trainer.sperated_node_ratios=[0.5,0.5] \
# means: each task group uses 0.5 of total nodes
# means: train/logp/ref_logp use 0.5 ngpus, generate use 0.5 ngpus

# Performance Tuning, enable async-param-update
+actor_rollout_ref.rollout.enable_dual_buffer=True \
# support: async-cpu or sync-nccl
+actor_rollout_ref.rollout.enable_param_async=False \
# The sender granularity of the actor training node during parameter update
+actor_rollout_ref.rollout.param_update_preduce_bucket_size_mb=512 \
# The receiver granularity of the rollout inference node is too large, which will cause GPU-OOM
+actor_rollout_ref.rollout.param_update_consume_bucket_size_mb=128 \
 
# The granularity of offpolicy, 2 means that generate is faster than the train node to execute 2 steps, that is, one-step-offpolicy
+trainer.generate_ahead_steps=2 \

Task Group Configuration Examples

Example 1: Complete Separation

+trainer.sperated_node_tasks=[logp,ref_logp,actor-train,generate] \
+trainer.sperated_node_ratios=[0.25,0.25,0.25,0.25] \

Explanation: Each task gets 25% of total nodes

• logp: 25% nodes
• ref_logp: 25% nodes
• actor-train: 25% nodes
• generate: 25% nodes

Example 2: Hybrid Mode (logp + actor-train grouped)

+trainer.sperated_node_tasks=[[logp,actor-train],ref_logp,generate] \
+trainer.sperated_node_ratios=[0.5,0.25,0.25] \

Explanation:

• First group [logp,actor-train]: 50% nodes (shared)
• ref_logp: 25% nodes
• generate: 25% nodes

Example 3: Hybrid Mode (logp + actor-train + ref_logp grouped)

+trainer.sperated_node_tasks=[[logp,actor-train,ref_logp],generate] \
+trainer.sperated_node_ratios=[0.5,0.5] \

Explanation:

• First group [logp,actor-train,ref_logp]: 50% nodes (shared)
• generate: 50% nodes

Script of red-moe

  python3 -m verl.trainer.main_ppo --config-path=$ROOT_PATH/run_verl --config-name='redmoe_megatron' \
	++hydra.run.dir=outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}-${env:RANK,0} \
	algorithm.adv_estimator=grpo \
	data.train_files="$TRAIN_DATA_PATH" \
	data.val_files="$TEST_DATA_PATH" \
	data.train_batch_size=128 \
	data.max_prompt_length=$max_prompt_length \
	data.max_response_length=$max_response_length \
	data.filter_overlong_prompts=True \
	data.filter_overlong_prompts_workers=32 \
	data.truncation='error' \
	actor_rollout_ref.hybrid_engine=False \
	actor_rollout_ref.model.path=$MODEL_PATH \
	actor_rollout_ref.model.trust_remote_code=True \
	actor_rollout_ref.actor.optim.lr=1e-6 \
	actor_rollout_ref.actor.load_weight=True \
	actor_rollout_ref.actor.ppo_mini_batch_size=128 \
	actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \
	actor_rollout_ref.actor.megatron.param_offload=False \
	actor_rollout_ref.actor.megatron.grad_offload=False \
	actor_rollout_ref.actor.megatron.optimizer_offload=False \
	actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=1 \
	actor_rollout_ref.actor.megatron.tensor_model_parallel_size=1 \
	actor_rollout_ref.actor.megatron.expert_model_parallel_size=8 \
  actor_rollout_ref.ref.megatron.param_offload=False \
  actor_rollout_ref.ref.megatron.grad_offload=False \
  actor_rollout_ref.ref.megatron.optimizer_offload=False \
  actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \
  actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=1 \
  actor_rollout_ref.ref.megatron.tensor_model_parallel_size=1 \
  actor_rollout_ref.ref.megatron.expert_model_parallel_size=1 \
	actor_rollout_ref.actor.use_kl_loss=True \
  actor_rollout_ref.rollout.n=16 \
  +actor_rollout_ref.rollout.enable_dual_buffer=True \
  +actor_rollout_ref.rollout.param_update_preduce_bucket_size_mb=256 \
  +actor_rollout_ref.rollout.param_update_consume_bucket_size_mb=128 \
	actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
	actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \
	actor_rollout_ref.rollout.name=sglang \
	actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
	actor_rollout_ref.rollout.free_cache_engine=False \
	actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
	algorithm.use_kl_in_reward=False \
  +trainer.async_pipeline=True \
  +trainer.sperated_node_tasks=[[logp,actor-train],ref_logp,generate] \
  +trainer.sperated_node_ratios=[0.5,0.25,0.25] \
  +trainer.generate_ahead_steps=1 \
	trainer.val_only=False \
	trainer.critic_warmup=0 \
  trainer.resume_mode=disable \
	trainer.logger=['console','tensorboard'] \
	trainer.project_name="verl_async_rl_redmoe16b" \
	trainer.experiment_name=$EXP_NAME \
	trainer.n_gpus_per_node=8 \
	trainer.nnodes=${WORLD_SIZE} \
	trainer.save_freq=200 \
	trainer.test_freq=64 \
	trainer.total_epochs=100 2>&1 | tee log_async_rl_n16_${RANK}.txt $@