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[megatron] feat: support gpt-oss #4323
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,251 @@ | ||
| #!/usr/bin/env bash | ||
| set -xeuo pipefail | ||
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| ################################################### document for gptoss ################################################### | ||
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| ####################### running environment: ####################### | ||
| # option 1: use a pre-built docker image dedicated for gptoss: `docker://iseekyan/verl:nemo.gptoss_vllm0.11.0`, which is | ||
| # built upon nemo's dedicated image, see Dockerfile at https://github.com/volcengine/verl/blob/main/docker/verl0.6-cu128-torch2.8.0-fa2.7.4/Dockerfile.vllm011.mcore_gpt-oss | ||
| # | ||
| # option 2: self build TE>=2.8 with CUDNN>=9.13.1, megatron with branch `core_dev_r0.15.0`, latest vllm or sglang | ||
| # you can modify the dockerfile to build the image, see Dockerfile at https://github.com/volcengine/verl/blob/main/docker/Dockerfile.stable.vllm or https://github.com/volcengine/verl/blob/main/docker/Dockerfile.stable.sglang | ||
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| ####################### before training: ####################### | ||
| # # install matched mbridge version | ||
| # pip uninstall -y mbridge && pip install git+https://github.com/ISEEKYAN/mbridge@gpt-oss | ||
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| # # convert gptoss to bf16 | ||
| cat > get_model.py << EOF | ||
| import torch | ||
| from transformers import AutoModelForCausalLM, AutoTokenizer, Mxfp4Config | ||
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| model_id = "openai/gpt-oss-20b" | ||
| output_dir = "$HOME/models/gpt-oss-20b-bf16" | ||
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| quantization_config = Mxfp4Config(dequantize=True) | ||
| model_kwargs = dict( | ||
| attn_implementation="eager", | ||
| torch_dtype=torch.bfloat16, | ||
| quantization_config=quantization_config, | ||
| use_cache=False, | ||
| device_map="auto", | ||
| ) | ||
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| model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs) | ||
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| # Patch config with custom attribute before saving | ||
| model.config.attn_implementation = "eager" | ||
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| model.save_pretrained(output_dir) | ||
| tokenizer = AutoTokenizer.from_pretrained(model_id) | ||
| tokenizer.save_pretrained(output_dir) | ||
| EOF | ||
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| python get_model.py | ||
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| ####################### specific training config: ####################### | ||
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| GPT_OSS_CONFIG=( | ||
| # only support mbridge for gptoss | ||
| actor_rollout_ref.actor.megatron.use_mbridge=True | ||
| # for now (latest TE=2.10), gptoss's optimized attn kernel is not supported for thd format, so we use bshd format here | ||
| # when bshd format is used, we need to pad the input_ids to the longest sequence length | ||
| # so we recommend to disable dynamic batch size and set micro batch size to 1 to avoid paddings | ||
| # but it is ok to try with micro_batch_size>1 | ||
| actor_rollout_ref.actor.megatron.use_remove_padding=False | ||
| ) | ||
| use_dynamic_bsz=False # recommended but not necessary | ||
|
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| ################################################### quick config ################################################### | ||
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| rollout_mode="sync" | ||
| rollout_name="vllm" # sglang or vllm | ||
| return_raw_chat="False" | ||
| if [ "$rollout_mode" = "async" ]; then | ||
| export VLLM_USE_V1=1 | ||
| return_raw_chat="True" | ||
| fi | ||
| dtype="bfloat16" # ["bfloat16", "float16"] | ||
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| project_name='DAPO' | ||
| exp_name='gptoss' | ||
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| adv_estimator=grpo | ||
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| use_kl_in_reward=False | ||
| kl_coef=0.0 | ||
| use_kl_loss=False | ||
| kl_loss_coef=0.0 | ||
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| clip_ratio_low=0.2 | ||
| clip_ratio_high=0.28 | ||
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| max_prompt_length=$((1024 * 2)) | ||
| max_response_length=$((1024 * 8)) | ||
| enable_overlong_buffer=True | ||
| overlong_buffer_len=$((1024 * 4)) | ||
| overlong_penalty_factor=1.0 | ||
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| loss_agg_mode="token-mean" | ||
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| train_prompt_bsz=32 | ||
| n_resp_per_prompt=16 | ||
| train_prompt_mini_bsz=32 | ||
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| # Ray | ||
| RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} | ||
| WORKING_DIR=${WORKING_DIR:-"${PWD}"} | ||
| RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/verl/trainer/runtime_env.yaml"} | ||
| NNODES=${NNODES:-1} | ||
| # Paths | ||
| RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} | ||
| MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/gpt-oss-20b"} | ||
| CKPTS_DIR=${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:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} | ||
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| # Algorithm | ||
| temperature=1.0 | ||
| top_p=1.0 | ||
| top_k=-1 # 0 for HF rollout, -1 for vLLM rollout | ||
| val_top_p=0.7 | ||
|
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| # Performance Related Parameter | ||
| actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) | ||
| infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) | ||
| offload=True | ||
| gen_tp=4 | ||
| train_tp=4 | ||
| EP=8 | ||
| ETP=1 | ||
| train_pp=1 | ||
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| ################################################### start of config ################################################### | ||
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| DATA=( | ||
| data.train_files="${TRAIN_FILE}" | ||
| data.val_files="${TEST_FILE}" | ||
| data.prompt_key=prompt | ||
| data.return_raw_chat=$return_raw_chat | ||
| data.truncation='left' | ||
| data.max_prompt_length=${max_prompt_length} | ||
| data.max_response_length=${max_response_length} | ||
| data.train_batch_size=${train_prompt_bsz} | ||
| ) | ||
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| REWARD_MODEL=( | ||
| +reward_model.reward_kwargs.overlong_buffer_cfg.enable=${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=${max_response_length} | ||
| reward_model.reward_manager=dapo | ||
| ) | ||
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| PERF_OPT=( | ||
| +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True | ||
| actor_rollout_ref.model.use_fused_kernels=False | ||
| +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform | ||
| +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full | ||
| +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 | ||
| actor_rollout_ref.actor.megatron.override_transformer_config.attention_backend=auto | ||
| +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 | ||
| +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True | ||
| +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True | ||
| +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True | ||
| ) | ||
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| ACTOR=( | ||
| actor_rollout_ref.actor.use_kl_loss=${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=${clip_ratio_high} | ||
| actor_rollout_ref.actor.clip_ratio_c=10.0 | ||
| actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 | ||
| actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} | ||
| actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} | ||
| actor_rollout_ref.actor.optim.lr=1e-6 | ||
| actor_rollout_ref.actor.optim.lr_warmup_steps=10 | ||
| actor_rollout_ref.actor.optim.weight_decay=0.1 | ||
| actor_rollout_ref.actor.optim.clip_grad=1.0 | ||
| actor_rollout_ref.actor.ppo_mini_batch_size=${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=${train_tp} | ||
| actor_rollout_ref.actor.megatron.expert_model_parallel_size=${EP} | ||
| actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ETP} | ||
| actor_rollout_ref.actor.entropy_coeff=0 | ||
| actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} | ||
| ) | ||
|
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| ROLLOUT=( | ||
| actor_rollout_ref.rollout.name=${rollout_name} | ||
| actor_rollout_ref.rollout.mode=${rollout_mode} | ||
| actor_rollout_ref.rollout.dtype=${dtype} | ||
| actor_rollout_ref.rollout.gpu_memory_utilization=0.70 | ||
| actor_rollout_ref.rollout.tensor_model_parallel_size=${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.rollout.val_kwargs.temperature=${temperature} | ||
| actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} | ||
| actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} | ||
| actor_rollout_ref.rollout.val_kwargs.do_sample=True | ||
| actor_rollout_ref.rollout.val_kwargs.n=1 | ||
| actor_rollout_ref.rollout.calculate_log_probs=True | ||
| actor_rollout_ref.rollout.n=${n_resp_per_prompt} | ||
| ) | ||
|
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| TRAINER=( | ||
| trainer.logger=['console','wandb'] | ||
| trainer.project_name="${project_name}" | ||
| trainer.experiment_name="${exp_name}" | ||
| trainer.n_gpus_per_node=8 | ||
| trainer.nnodes="${NNODES}" | ||
| trainer.val_before_train=False | ||
| trainer.test_freq=10 | ||
| trainer.save_freq=-1 | ||
| trainer.total_epochs=10 | ||
| trainer.default_local_dir="${CKPTS_DIR}" | ||
| trainer.resume_mode=auto | ||
| trainer.log_val_generations=10 | ||
| ) | ||
|
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| FORWARD_ONLY_SETS=( | ||
| 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.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} | ||
| actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} | ||
| 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} | ||
| ) | ||
|
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| MODEL=( | ||
| actor_rollout_ref.model.path="${MODEL_PATH}" | ||
| ) | ||
|
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| ALGORITHM=( | ||
| algorithm.adv_estimator=${adv_estimator} | ||
| algorithm.use_kl_in_reward=${use_kl_in_reward} | ||
| algorithm.kl_ctrl.kl_coef=${kl_coef} | ||
| ) | ||
| ################################################### start script ################################################### | ||
| ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ | ||
| -- python3 -m verl.trainer.main_ppo \ | ||
| --config-path=config \ | ||
| --config-name='ppo_megatron_trainer.yaml' \ | ||
| "${DATA[@]}" \ | ||
| "${ALGORITHM[@]}" \ | ||
| "${MODEL[@]}" \ | ||
| "${ROLLOUT[@]}" \ | ||
| "${ACTOR[@]}" \ | ||
| "${REWARD_MODEL[@]}" \ | ||
| "${PERF_OPT[@]}" \ | ||
| "${TRAINER[@]}" \ | ||
| "${GPT_OSS_CONFIG[@]}" \ | ||
| "${FORWARD_ONLY_SETS[@]}" \ | ||
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The default
MODEL_PATHis inconsistent with theoutput_dirin theget_model.pyscript. Theget_model.pyscript saves the model to"$HOME/models/gpt-oss-20b-bf16", butMODEL_PATHdefaults to"${RAY_DATA_HOME}/models/gpt-oss-20b", which resolves to"${HOME}/verl/models/gpt-oss-20b". This will cause the script to fail with a "model not found" error unlessMODEL_PATHis explicitly set. To ensure consistency, the defaultMODEL_PATHshould point to the correct directory.