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[recipe] feat: Add Qwen3 30B MoE NPU recipe (#3189)
### What does this PR do? > Update recipe/dapo/run_dapo_qwen3_30b_npu.sh. ### Checklist Before Starting - [x] Search for similar PRs. Paste at least one query link here: https://github.com/volcengine/verl/pulls?q=fsdp+npu+30b+recipe - [x] 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. Critic/rewards/mean Comparison Chart, where the orange line represents ascend NPU, the pink line represents GPU. <img width="3182" height="1272" alt="image" src="https://github.com/user-attachments/assets/5c275127-6cb3-4bf9-ac89-0fa6abb668c0" /> ### API and Usage Example > Demonstrate how the API changes if any, and provide usage example(s) if possible. ```shell # Add code snippet or script demonstrating how to use this cd /path/to/verl bash recipe/dapo/run_dapo_qwen3_30b_base_npu_fsdp.sh ``` ### Design & Code Changes > Demonstrate the high-level design if this PR is complex, and list the specific changes. ### Checklist Before Submitting > [!IMPORTANT] > Please check all the following items before requesting a review, otherwise the reviewer might deprioritize this PR for review. - [x] Read the [Contribute Guide](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md). - [x] Apply [pre-commit checks](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md#code-linting-and-formatting): `pre-commit install && pre-commit run --all-files --show-diff-on-failure --color=always` - [x] Add / Update [the documentation](https://github.com/volcengine/verl/tree/main/docs). - [x] Add unit or end-to-end test(s) to [the CI workflow](https://github.com/volcengine/verl/tree/main/.github/workflows) to cover all the code. If not feasible, explain why: ... - [x] Once your PR is ready for CI, send a message in [the `ci-request` channel](https://verl-project.slack.com/archives/C091TCESWB1) in [the `verl` Slack workspace](https://join.slack.com/t/verl-project/shared_invite/zt-3855yhg8g-CTkqXu~hKojPCmo7k_yXTQ). (If not accessible, please try [the Feishu group (飞书群)](https://applink.larkoffice.com/client/chat/chatter/add_by_link?link_token=772jd4f1-cd91-441e-a820-498c6614126a).) Co-authored-by: Shangwei-Li <lishangwei2@huawei.com>
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@ -187,6 +187,8 @@ vllm & vllm-ascend
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+-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
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| DAPO | Qwen3-14B-base | 5.9% | pending | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
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+-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
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| DAPO | Qwen3-30B-base | 1.08% | pending | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
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+-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
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**表2** SFT类算法
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146
recipe/dapo/run_dapo_qwen3_moe_30b_base_npu_fsdp.sh
Normal file
146
recipe/dapo/run_dapo_qwen3_moe_30b_base_npu_fsdp.sh
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@ -0,0 +1,146 @@
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#!/usr/bin/env bash
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set -euxo pipefail
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project_name='DAPO'
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exp_name='DAPO-Qwen3-MOE-30B-FSDP-128rank-gbs512'
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NNODES=8
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NPUS_PER_NODE=16
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adv_estimator=grpo
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use_kl_in_reward=False
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kl_coef=0.0
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use_kl_loss=False
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kl_loss_coef=0.0
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clip_ratio_low=0.2
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clip_ratio_high=0.28
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max_prompt_length=$((1024 * 2))
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max_response_length=$((1024 * 20))
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enable_overlong_buffer=True
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overlong_buffer_len=$((1024 * 4))
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overlong_penalty_factor=1.0
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loss_agg_mode="token-mean"
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ppo_mini_batch_size=32
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enable_filter_groups=True
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filter_groups_metric=acc
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max_num_gen_batches=10
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train_prompt_bsz=512
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gen_prompt_bsz=$((train_prompt_bsz * 3))
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n_resp_per_prompt=16
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RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
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WORKING_DIR=${WORKING_DIR:-"${PWD}"}
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RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
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# Paths
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RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
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MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"}
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CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
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TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
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TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
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# Algorithm
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temperature=1.0
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top_p=1.0
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top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
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val_top_p=0.7
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# Performance Related Parameter
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sp_size=16 # For load-balance. For smaller cluster this can be set to as less as 2.
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use_dynamic_bsz=True
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actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / 2))
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infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / 2))
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offload=True
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recompute=True
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max_num_seqs=128
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gen_tp=2
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gen_world_size=$((NNODES * NPUS_PER_NODE)) # nnodes* npus_in_per_node
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ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \
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-- python3 -m recipe.dapo.main_dapo \
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data.train_files="${TRAIN_FILE}" \
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data.val_files="${TEST_FILE}" \
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data.prompt_key=prompt \
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data.truncation='left' \
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data.max_prompt_length=${max_prompt_length} \
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data.max_response_length=${max_response_length} \
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data.gen_batch_size=${gen_prompt_bsz} \
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data.train_batch_size=${train_prompt_bsz} \
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actor_rollout_ref.rollout.name=vllm \
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actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
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actor_rollout_ref.rollout.max_num_seqs=${max_num_seqs} \
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actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
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algorithm.adv_estimator=${adv_estimator} \
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algorithm.use_kl_in_reward=${use_kl_in_reward} \
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algorithm.kl_ctrl.kl_coef=${kl_coef} \
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actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
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actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
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actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
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actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
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actor_rollout_ref.actor.clip_ratio_c=10.0 \
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algorithm.filter_groups.enable=${enable_filter_groups} \
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algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \
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algorithm.filter_groups.metric=${filter_groups_metric} \
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actor_rollout_ref.model.use_remove_padding=True \
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actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
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actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
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actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
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actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
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actor_rollout_ref.model.path="${MODEL_PATH}" \
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+actor_rollout_ref.model.override_config.attention_dropout=0. \
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+actor_rollout_ref.model.override_config.embd_pdrop=0. \
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+actor_rollout_ref.model.override_config.resid_pdrop=0. \
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actor_rollout_ref.model.enable_gradient_checkpointing=${recompute} \
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actor_rollout_ref.actor.optim.lr=1e-6 \
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actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
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actor_rollout_ref.actor.optim.weight_decay=0.1 \
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actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} \
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actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \
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actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \
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actor_rollout_ref.actor.fsdp_config.forward_prefetch=False \
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actor_rollout_ref.actor.entropy_coeff=0 \
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actor_rollout_ref.actor.grad_clip=1.0 \
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actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
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actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
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actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
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+actor_rollout_ref.rollout.rollout_world_size=${gen_world_size} \
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actor_rollout_ref.rollout.enable_chunked_prefill=True \
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actor_rollout_ref.rollout.temperature=${temperature} \
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actor_rollout_ref.rollout.top_p=${top_p} \
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actor_rollout_ref.rollout.top_k=${top_k} \
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actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
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actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
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actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
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actor_rollout_ref.rollout.val_kwargs.do_sample=True \
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actor_rollout_ref.rollout.val_kwargs.n=1 \
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actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \
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actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \
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actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
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actor_rollout_ref.ref.fsdp_config.forward_prefetch=False \
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actor_rollout_ref.rollout.enforce_eager=False \
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actor_rollout_ref.rollout.free_cache_engine=True \
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reward_model.reward_manager=dapo \
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reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
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reward_model.overlong_buffer.len=${overlong_buffer_len} \
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reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
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trainer.logger=['console','wandb'] \
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trainer.project_name="${project_name}" \
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trainer.experiment_name="${exp_name}" \
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trainer.n_gpus_per_node="${NPUS_PER_NODE}" \
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trainer.nnodes="${NNODES}" \
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trainer.val_before_train=False \
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trainer.test_freq=5 \
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trainer.save_freq=-1 \
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trainer.total_epochs=1 \
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trainer.device="npu" \
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actor_rollout_ref.actor.use_torch_compile=False \
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actor_rollout_ref.ref.use_torch_compile=False
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