aws-samples · KeitaW · Dec 18, 2025 · Dec 18, 2025 · Dec 19, 2025
diff --git a/3.test_cases/pytorch/verl/.gitignore b/3.test_cases/pytorch/verl/.gitignore
@@ -0,0 +1 @@
+kustomization.yaml
diff --git a/3.test_cases/pytorch/verl/rlvr/Dockerfile b/3.test_cases/pytorch/verl/rlvr/Dockerfile
@@ -1,7 +1,8 @@
 # Dockerfile for VERL with EFA support
 # Using hiyouga/verl base image and adding EFA capabilities
-FROM hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.4-flashinfer0.2.2-cxx11abi0
-
+ARG TAG=vllm011.latest
+FROM verlai/verl:${TAG}
+ARG MEGATRON_LM_VERSION=core_v0.13.1
 # EFA configuration
 ARG OPEN_MPI_PATH=/opt/amazon/openmpi/
 ENV EFA_VERSION=1.43.3
@@ -115,10 +116,21 @@ RUN git clone https://github.com/volcengine/verl.git && \
     cd verl && \
     git checkout v0.6.1
 WORKDIR /workspace/verl
-
 # Install VERL in development mode
 RUN pip install -e .
 
+#####################
+# Install megatron-lm
+#####################
+RUN pip install -U setuptools
+RUN cd /workspace && git clone --depth 1 --branch ${MEGATRON_LM_VERSION} https://github.com/NVIDIA/Megatron-LM.git \
+    && cd Megatron-LM \
+    && pip install nltk  \
+    && pip install .
+# Check if speparate Apex is needed
+# https://verl.readthedocs.io/en/latest/start/install.html#install-from-docker-image
+
+
 # Set working directory
 WORKDIR /workspace
 

diff --git a/3.test_cases/pytorch/verl/rlvr/README.md b/3.test_cases/pytorch/verl/rlvr/README.md
@@ -70,9 +70,15 @@ Build a Docker image with verl, EFA networking support, and push to ECR:
 ./setup/build-push.sh
 ```
 
-Deploy the Ray cluster with head and worker pods configured for distributed training:
+Generate kustomization.yaml from your environment variables and deploy the Ray cluster:
 ```bash
-envsubst < setup/raycluster.yaml | kubectl apply -f -
+./setup/generate-kustomization.sh
+kubectl apply -k setup/
+```
+
+Alternatively, you can combine both steps:
+```bash
+./setup/generate-kustomization.sh && kubectl apply -k setup/
 ```
 
 > **Note**: Considerations before applying raycluster.yaml

diff --git a/3.test_cases/pytorch/verl/rlvr/recipe/run_qwen3-235b_megatron_96gb.sh b/3.test_cases/pytorch/verl/rlvr/recipe/run_qwen3-235b_megatron_96gb.sh
@@ -0,0 +1,182 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+## !!!!!!!important!!!!!!
+## set the following environment variables on all your nodes
+# env_vars:
+#   CUDA_DEVICE_MAX_CONNECTIONS: "1"
+#   NCCL_NVLS_ENABLE: "0"
+#   VLLM_USE_V1: 1
+# install mbridge=0.1.13 on all your node with the following command: 
+# pip3 install git+https://github.com/ISEEKYAN/mbridge
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+[ -f "${SCRIPT_DIR}/env.sh" ] && source "${SCRIPT_DIR}/env.sh"
+
+adv_estimator=grpo
+
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=True
+kl_loss_coef=0.001
+
+clip_ratio_low=0.2
+clip_ratio_high=0.28
+
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1204 * 8))
+enable_overlong_buffer=True
+overlong_buffer_len=$((1024 * 1))
+overlong_penalty_factor=1.0
+
+loss_agg_mode="token-mean"
+
+train_prompt_bsz=${TRAIN_BS:-32}
+n_resp_per_prompt=8
+train_prompt_mini_bsz=16
+
+# minimum nodes need for qwen3-235B-A22B
+NNODES=${NNODES:-4}
+# Paths
+
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+
+MODEL_PATH=$RAY_DATA_HOME/models/Qwen3-235B-A22B
+MODEL_PATH="${MODEL_PATH:-Qwen/Qwen3-VL-235B-A22B-Instruct}"
+
+TRAIN_FILE=/fsx/verl/data/geo3k/train.parquet
+TEST_FILE=/fsx/verl/data/geo3k/test.parquet
+
+# Algorithm
+temperature=1.0
+top_p=1.0
+top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
+val_top_p=0.7
+# Performance Related Parameter
+use_dynamic_bsz=True
+actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 10 / 10))
+infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1))
+offload=True
+OPTIM_OFFLOAD=${OPTIM_OFFLOAD:-True}
+gen_tp=8
+train_tp=${TP:-4}
+train_pp=${PP:-8}
+
+EP=${EP:-4}
+ETP=1
+CP=1
+optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.}
+last_layer=${LAST_LAYER:-10}
+
+project_name='verl-qwen3'
+exp_name="235B-${NNODES}-pp${train_pp}-tp${train_tp}-ep${EP}-actor-length${actor_ppo_max_token_len}"
+CKPTS_DIR=$RAY_DATA_HOME/ckpt/${project_name}/${exp_name}
+
+# TODO: support cuda graph for rollout by setting the following config
+    # actor_rollout_ref.rollout.cudagraph_capture_sizes=[1,2,4,8,16,32]
+    # actor_rollout_ref.rollout.enforce_eager=False
+
+python3 -m verl.trainer.main_ppo \
+    --config-path=config \
+    --config-name='ppo_megatron_trainer.yaml' \
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.prompt_key=prompt \
+    data.truncation='left' \
+    data.max_prompt_length=${max_prompt_length} \
+    data.max_response_length=${max_response_length} \
+    data.train_batch_size=${train_prompt_bsz} \
+    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.enforce_eager=True \
+    actor_rollout_ref.rollout.free_cache_engine=True \
+    algorithm.adv_estimator=${adv_estimator} \
+    algorithm.use_kl_in_reward=${use_kl_in_reward} \
+    algorithm.kl_ctrl.kl_coef=${kl_coef} \
+    actor_rollout_ref.model.use_fused_kernels=True \
+    actor_rollout_ref.actor.megatron.use_mbridge=True \
+    actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
+    actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
+    actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
+    actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
+    actor_rollout_ref.actor.clip_ratio_c=10.0 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \
+    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
+    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.model.path="${MODEL_PATH}" \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
+    actor_rollout_ref.actor.optim.weight_decay=0.1 \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
+    actor_rollout_ref.actor.megatron.param_offload=${offload} \
+    actor_rollout_ref.actor.megatron.optimizer_offload=${OPTIM_OFFLOAD} \
+    actor_rollout_ref.actor.megatron.grad_offload=${offload} \
+    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \
+    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \
+    actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \
+    actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \
+    actor_rollout_ref.actor.megatron.context_parallel_size=${CP} \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.optim.clip_grad=1.0 \
+    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
+    actor_rollout_ref.rollout.enable_chunked_prefill=True \
+    actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
+    actor_rollout_ref.rollout.temperature=${temperature} \
+    actor_rollout_ref.rollout.top_p=${top_p} \
+    actor_rollout_ref.rollout.top_k=${top_k} \
+    actor_rollout_ref.nccl_timeout=1200 \
+    actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
+    actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
+    actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
+    actor_rollout_ref.rollout.val_kwargs.n=1 \
+    actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \
+    actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \
+    actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \
+    actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \
+    actor_rollout_ref.ref.megatron.context_parallel_size=${CP} \
+    actor_rollout_ref.ref.megatron.param_offload=${offload} \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_loss_in_pipeline_split=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_embedding_in_pipeline_split=True \
+    reward_model.reward_manager=dapo \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \
+    +reward_model.reward_kwargs.overlong_buffer_cfg.log=False \
+    +reward_model.reward_kwargs.max_resp_len=${max_response_length} \
+    trainer.logger=['console','wandb'] \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes="${NNODES}" \
+    trainer.val_before_train=False \
+    trainer.test_freq=10 \
+    trainer.save_freq=100 \
+    trainer.total_epochs=10 \
+    trainer.default_local_dir="${CKPTS_DIR}" \
+    trainer.resume_mode=auto \
+    trainer.log_val_generations=10
diff --git a/3.test_cases/pytorch/verl/rlvr/recipe/run_qwen3_vl-235b-megatron.sh b/3.test_cases/pytorch/verl/rlvr/recipe/run_qwen3_vl-235b-megatron.sh
@@ -0,0 +1,86 @@
+#laAdopted from https://github.com/volcengine/verl/blob/249c0831b793751f488d80bcfeafb8f7c544cd65/examples/grpo_trainer/run_qwen3_vl-235b-megatron.sh
+set -x
+ENGINE=${1:-vllm}
+export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping
+
+export VLLM_ALLREDUCE_USE_SYMM_MEM=0 # for vllm0.11.0 with TP
+
+
+HF_MODEL_PATH=${HF_MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-VL-235B-A22B-Instruct"}
+
+GEN_TP=${GEN_TP:-16}
+CP=${CP:-2}
+TP=${TP:-1}
+PP=${PP:-8}
+EP=${EP:-8}
+ETP=${ETP:-1}
+
+# Use env-provided data paths or default to Geometry3k under RAY_DATA_HOME
+train_path=${TRAIN_FILE:-${RAY_DATA_HOME}/data/geo3k/train.parquet}
+test_path=${TEST_FILE:-${RAY_DATA_HOME}/data/geo3k/test.parquet}
+
+python3 -m verl.trainer.main_ppo --config-path=config \
+    --config-name='ppo_megatron_trainer.yaml'\
+    algorithm.adv_estimator=grpo \
+    data.train_files="$train_path" \
+    data.val_files="$test_path" \
+    data.train_batch_size=512 \
+    data.max_prompt_length=1024 \
+    data.max_response_length=2048 \
+    data.filter_overlong_prompts=True \
+    data.truncation='error' \
+    actor_rollout_ref.model.path=$HF_MODEL_PATH \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=128 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \
+    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \
+    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \
+    actor_rollout_ref.actor.megatron.context_parallel_size=$CP \
+    actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \
+    actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.01 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=$GEN_TP \
+    actor_rollout_ref.actor.use_dynamic_bsz=True \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=4096 \
+    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \
+    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=4096 \
+    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \
+    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=4096 \
+    actor_rollout_ref.rollout.name=$ENGINE \
+    +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+    actor_rollout_ref.rollout.n=5 \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \
+    actor_rollout_ref.actor.megatron.use_mbridge=True \
+    actor_rollout_ref.actor.megatron.param_offload=True \
+    actor_rollout_ref.actor.megatron.optimizer_offload=True \
+    actor_rollout_ref.actor.megatron.grad_offload=True \
+    actor_rollout_ref.ref.megatron.param_offload=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \
+    +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_loss_in_pipeline_split=True \
+    +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_embedding_in_pipeline_split=True \
+    algorithm.use_kl_in_reward=False \
+    trainer.critic_warmup=0 \
+    trainer.logger='["console","wandb"]' \
+    trainer.project_name='verl_grpo_example_geo3k' \
+    trainer.experiment_name='qwen3_vl_235b_megatron' \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=4 \
+    trainer.save_freq=20 \
+    trainer.test_freq=5 \
+    trainer.total_epochs=15 $@