From b00b090149a23dc04e7d98afae15691790481894 Mon Sep 17 00:00:00 2001 From: Lingfeng Wang <62014693+wlf-darkmatter@users.noreply.github.com> Date: Sat, 13 Sep 2025 19:08:23 +0800 Subject: [PATCH] [megatron,recipe] feat: support Qwen3-30B (MoE) DAPO training on ASCEND NPU (#3203) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### What does this PR do? Fix of megatron config, and example shell of Qwen3-30B-Dapo with megatron. ### Checklist Before Starting - [x] Search for similar PRs. Paste at least one query link here: ... - [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 critic/reward/mean: dapo_30b_megatron response_length/mean: image ### 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: ... - [ ] 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).) --- recipe/dapo/config/dapo_megatron_trainer.yaml | 28 +++ ...> run_dapo_qwen3_moe_30b_base_fsdp_npu.sh} | 0 .../run_dapo_qwen3_moe_30b_megatron_npu.sh | 169 ++++++++++++++++++ .../_generated_ppo_megatron_trainer.yaml | 1 + verl/trainer/config/ppo_megatron_trainer.yaml | 3 + verl/utils/reward_score/__init__.py | 2 +- 6 files changed, 202 insertions(+), 1 deletion(-) create mode 100644 recipe/dapo/config/dapo_megatron_trainer.yaml rename recipe/dapo/{run_dapo_qwen3_moe_30b_base_npu_fsdp.sh => run_dapo_qwen3_moe_30b_base_fsdp_npu.sh} (100%) create mode 100644 recipe/dapo/run_dapo_qwen3_moe_30b_megatron_npu.sh diff --git a/recipe/dapo/config/dapo_megatron_trainer.yaml b/recipe/dapo/config/dapo_megatron_trainer.yaml new file mode 100644 index 000000000..b846eaeb7 --- /dev/null +++ b/recipe/dapo/config/dapo_megatron_trainer.yaml @@ -0,0 +1,28 @@ +hydra: + searchpath: + - file://verl/trainer/config + +defaults: + - ppo_megatron_trainer + - _self_ + +data: + gen_batch_size: ${data.train_batch_size} + +reward_model: + reward_manager: dapo + overlong_buffer: + enable: False # We try to avoid forgetting to set enable + len: 0 + penalty_factor: 0.0 + log: False + +algorithm: + filter_groups: + _target_: verl.trainer.config.FilterGroupsConfig + enable: False # We try to avoid forgetting to set enable + metric: null # acc / score / seq_reward / seq_final_reward / ... + max_num_gen_batches: 0 # Non-positive values mean no upper limit + +trainer: + project_name: verl-dapo diff --git a/recipe/dapo/run_dapo_qwen3_moe_30b_base_npu_fsdp.sh b/recipe/dapo/run_dapo_qwen3_moe_30b_base_fsdp_npu.sh similarity index 100% rename from recipe/dapo/run_dapo_qwen3_moe_30b_base_npu_fsdp.sh rename to recipe/dapo/run_dapo_qwen3_moe_30b_base_fsdp_npu.sh diff --git a/recipe/dapo/run_dapo_qwen3_moe_30b_megatron_npu.sh b/recipe/dapo/run_dapo_qwen3_moe_30b_megatron_npu.sh new file mode 100644 index 000000000..81d5b1505 --- /dev/null +++ b/recipe/dapo/run_dapo_qwen3_moe_30b_megatron_npu.sh @@ -0,0 +1,169 @@ +#!/bin/bash + +project_name='DAPO' +exp_name='DAPO-Qwen3-30B-megatron' + +adv_estimator=grpo + +use_kl_in_reward=False +kl_coef=0.0 +use_kl_loss=False +kl_loss_coef=0.0 + +clip_ratio_low=0.2 +clip_ratio_high=0.28 + +max_prompt_length=$((1024 * 2)) +max_response_length=$((1024 * 20)) +enable_overlong_buffer=True +overlong_buffer_len=$((1024 * 4)) +overlong_penalty_factor=1.0 + +loss_agg_mode="token-mean" + +enable_filter_groups=True +filter_groups_metric=acc +max_num_gen_batches=10 +train_prompt_bsz=16 +gen_prompt_bsz=$((train_prompt_bsz * 2)) +n_resp_per_prompt=16 +train_prompt_mini_bsz=2 + +# Ray +RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} +WORKING_DIR=${WORKING_DIR:-"${PWD}"} +RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} +NNODES=${NNODES:-1} +# Paths +RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} +MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B"} +# MCORE_MODEL_PATH points to the converted checkpoint. +# To avoid loading these weights, set actor_rollout_ref.actor.megatron.use_dist_checkpointing=False. +MCORE_MODEL_PATH=${MCORE_MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-dist_ckpt"} +CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} +TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} +TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} + +# Algorithm +temperature=1.0 +top_p=1.0 +top_k=-1 # 0 for HF rollout, -1 for vLLM rollout + +# Performance Related Parameter +sp_size=8 +use_dynamic_bsz=True +actor_ppo_max_token_len=$(((max_prompt_length + max_response_length))) +infer_ppo_max_token_len=$(((max_prompt_length + max_response_length))) +offload=True + +max_num_batched_tokens=$((max_prompt_length + max_response_length)) + +# Megatron backen +train_tp=4 +train_ep=2 +train_pp=2 +train_cp=1 + +ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ + --address "${RAY_ADDRESS}" \ + -- python3 -m recipe.dapo.main_dapo \ + --config-name="dapo_megatron_trainer" \ + data.filter_overlong_prompts=False \ + data.train_files="${TRAIN_FILE}" \ + data.val_files="${TEST_FILE}" \ + data.shuffle=False \ + data.prompt_key=prompt \ + data.truncation='left' \ + data.max_prompt_length=${max_prompt_length} \ + data.max_response_length=${max_response_length} \ + data.gen_batch_size=${gen_prompt_bsz} \ + data.train_batch_size=${train_prompt_bsz} \ + algorithm.adv_estimator=${adv_estimator} \ + algorithm.use_kl_in_reward=${use_kl_in_reward} \ + algorithm.kl_ctrl.kl_coef=${kl_coef} \ + actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ + actor_rollout_ref.rollout.name=vllm \ + 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_epochs=1 \ + algorithm.filter_groups.enable=${enable_filter_groups} \ + algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \ + algorithm.filter_groups.metric=${filter_groups_metric} \ + actor_rollout_ref.model.use_remove_padding=True \ + actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ + actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ + actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ + actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ + 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.model.override_config.attention_dropout=0. \ + +actor_rollout_ref.model.override_config.embd_pdrop=0. \ + +actor_rollout_ref.model.override_config.resid_pdrop=0. \ + actor_rollout_ref.actor.optim.lr=1e-6 \ + +actor_rollout_ref.critic.optim.lr=5e-8 \ + actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ + 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=${offload} \ + actor_rollout_ref.actor.megatron.grad_offload=${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=${train_ep} \ + actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} \ + actor_rollout_ref.actor.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} \ + actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \ + actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ + actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ + actor_rollout_ref.ref.megatron.expert_model_parallel_size=${train_ep} \ + actor_rollout_ref.ref.megatron.context_parallel_size=${train_cp} \ + actor_rollout_ref.ref.megatron.param_offload=${offload} \ + actor_rollout_ref.ref.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} \ + actor_rollout_ref.actor.entropy_coeff=0 \ + actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ + actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ + actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ + actor_rollout_ref.rollout.enable_chunked_prefill=True \ + actor_rollout_ref.rollout.enable_prefix_caching=False \ + actor_rollout_ref.rollout.max_num_batched_tokens=${max_num_batched_tokens} \ + actor_rollout_ref.rollout.max_model_len=$((max_prompt_length + max_response_length)) \ + actor_rollout_ref.rollout.temperature=${temperature} \ + actor_rollout_ref.rollout.top_p=${top_p} \ + actor_rollout_ref.rollout.top_k=${top_k} \ + actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ + actor_rollout_ref.rollout.val_kwargs.top_p=${top_p} \ + actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ + actor_rollout_ref.rollout.val_kwargs.do_sample=True \ + actor_rollout_ref.rollout.val_kwargs.n=1 \ + actor_rollout_ref.rollout.enforce_eager=True \ + actor_rollout_ref.rollout.free_cache_engine=True \ + actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \ + reward_model.reward_manager=dapo \ + reward_model.overlong_buffer.enable=${enable_overlong_buffer} \ + reward_model.overlong_buffer.len=${overlong_buffer_len} \ + reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \ + trainer.logger=['console'] \ + trainer.project_name="${project_name}" \ + trainer.experiment_name="${exp_name}" \ + trainer.n_gpus_per_node=16 \ + trainer.nnodes="${NNODES}" \ + trainer.val_before_train=False \ + trainer.test_freq=-1 \ + trainer.save_freq=-1 \ + trainer.total_epochs=1 \ + trainer.default_local_dir="${CKPTS_DIR}" \ + trainer.device="npu" \ + actor_rollout_ref.nccl_timeout=14400 \ + actor_rollout_ref.actor.use_torch_compile=False \ + actor_rollout_ref.ref.use_torch_compile=False \ + +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True \ + +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 + diff --git a/verl/trainer/config/_generated_ppo_megatron_trainer.yaml b/verl/trainer/config/_generated_ppo_megatron_trainer.yaml index dba8ee89d..4c81aaf2c 100644 --- a/verl/trainer/config/_generated_ppo_megatron_trainer.yaml +++ b/verl/trainer/config/_generated_ppo_megatron_trainer.yaml @@ -264,6 +264,7 @@ actor_rollout_ref: freeze_moe_router: false use_fused_kernels: false trust_remote_code: false + use_remove_padding: false data: tokenizer: null use_shm: false diff --git a/verl/trainer/config/ppo_megatron_trainer.yaml b/verl/trainer/config/ppo_megatron_trainer.yaml index a9bfb2fba..478582ea3 100644 --- a/verl/trainer/config/ppo_megatron_trainer.yaml +++ b/verl/trainer/config/ppo_megatron_trainer.yaml @@ -43,6 +43,9 @@ actor_rollout_ref: trust_remote_code: False + # Whether to remove padding tokens in inputs during training + use_remove_padding: false + rollout: # may get higher throughput when set to True. When activated, Please increase max_num_batched_tokens or decrease max_model_len. enable_chunked_prefill: False diff --git a/verl/utils/reward_score/__init__.py b/verl/utils/reward_score/__init__.py index 44b68aba7..6a3c3cb0f 100644 --- a/verl/utils/reward_score/__init__.py +++ b/verl/utils/reward_score/__init__.py @@ -55,7 +55,7 @@ def default_compute_score( # from . import math_verify # res = math_verify.compute_score(solution_str, ground_truth) - elif data_source == "math_dapo" or data_source.startswith("aime"): + elif data_source in ["math_dapo", "math", "math_dapo_reasoning"] or data_source.startswith("aime"): from . import math_dapo res = math_dapo.compute_score(solution_str, ground_truth)