mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-19 12:58:07 +00:00
51c2afc1fc
* updated verl ex * updated script * removed curriculum verl and updated * updatied linting errors * renamed * updated config
204 lines
6.9 KiB
YAML
204 lines
6.9 KiB
YAML
defaults:
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- ppo_trainer
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- _self_
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reasoning_gym:
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dataset_size: 20000
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developer_prompt: DeepSeekZero
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datasets:
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ab:
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weight: 1
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data:
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tokenizer: null
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train_files: null
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val_files: null
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prompt_key: prompt
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max_prompt_length: 512
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max_response_length: 512
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train_batch_size: 16
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val_batch_size: 1
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actor_rollout_ref:
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hybrid_engine: True
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model:
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path: Qwen/Qwen2.5-Math-1.5B
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external_lib: null
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override_config: { }
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enable_gradient_checkpointing: True
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use_remove_padding: False
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actor:
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loss_agg_mode: "token-mean"
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strategy: fsdp # This is for backward-compatibility
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ppo_mini_batch_size: 16
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ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
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ppo_micro_batch_size_per_gpu: 4
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use_dynamic_bsz: False
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ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length}
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grad_clip: 1.0
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clip_ratio: 0.2 # default value if clip_ratio_low and clip_ratio_high are not specified
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clip_ratio_low: 0.2
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clip_ratio_high: 0.2
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clip_ratio_c: 3.0 # lower bound of the value for Dual-clip PPO from https://arxiv.org/pdf/1912.09729
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entropy_coeff: 0
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use_kl_loss: False # True for GRPO
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use_torch_compile: True # False to disable torch compile
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kl_loss_coef: 0.001 # for grpo
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kl_loss_type: low_var_kl # for grpo
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ppo_epochs: 1
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shuffle: False
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ulysses_sequence_parallel_size: 1 # sp size
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optim:
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lr: 1e-6
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lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime
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min_lr_ratio: null # only useful for warmup with cosine
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warmup_style: constant # select from constant/cosine
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total_training_steps: -1 # must be override by program
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fsdp_config:
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wrap_policy:
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# transformer_layer_cls_to_wrap: None
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min_num_params: 0
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param_offload: False
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optimizer_offload: False
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fsdp_size: -1
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checkpoint:
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contents: ['model', 'optimizer', 'extra']
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ref:
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fsdp_config:
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param_offload: False
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wrap_policy:
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# transformer_layer_cls_to_wrap: None
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min_num_params: 0
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log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
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log_prob_micro_batch_size_per_gpu: 4
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log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
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log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}
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ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size
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rollout:
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name: vllm
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mode: sync
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temperature: 1.0
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max_model_len: 2048
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top_k: -1 # 0 for hf rollout, -1 for vllm rollout
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top_p: 1
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prompt_length: ${data.max_prompt_length} # not use for opensource
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response_length: ${data.max_response_length}
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# for vllm rollout
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dtype: bfloat16 # should align with FSDP
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gpu_memory_utilization: 0.5
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ignore_eos: False
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enforce_eager: True
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free_cache_engine: True
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load_format: dummy_dtensor
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tensor_model_parallel_size: 1
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max_num_batched_tokens: 8192
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max_num_seqs: 1024
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log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
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log_prob_micro_batch_size_per_gpu: 4
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log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
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log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}
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disable_log_stats: True
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enable_chunked_prefill: True # could get higher throughput
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# for hf rollout
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do_sample: True
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use_fire_sampling: False
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# number of responses (i.e. num sample times)
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n: 16 # > 1 for grpo
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val_kwargs:
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do_sample: True
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multi_turn:
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enable: False # set to True for multi-turn tool interaction tasks; should set rollout.name to sglang as well
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max_turns: null # null for no limit (default max_length // 3)
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tool_config_path: null # null for no tool
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format: chatml
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critic:
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strategy: fsdp
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optim:
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lr: 1e-5
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lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime
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min_lr_ratio: null # only useful for warmup with cosine
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warmup_style: constant # select from constant/cosine
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total_training_steps: -1 # must be override by program
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model:
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path: ~/models/deepseek-llm-7b-chat
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tokenizer_path: ${actor_rollout_ref.model.path}
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override_config: { }
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external_lib: ${actor_rollout_ref.model.external_lib}
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enable_gradient_checkpointing: True
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use_remove_padding: False
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fsdp_config:
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param_offload: False
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optimizer_offload: False
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wrap_policy:
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# transformer_layer_cls_to_wrap: None
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min_num_params: 0
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fsdp_size: -1
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ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size}
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ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
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ppo_micro_batch_size_per_gpu: null
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forward_micro_batch_size: ${critic.ppo_micro_batch_size}
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forward_micro_batch_size_per_gpu: ${critic.ppo_micro_batch_size_per_gpu}
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use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
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ppo_max_token_len_per_gpu: 32768 # (${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}) * 2
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forward_max_token_len_per_gpu: ${critic.ppo_max_token_len_per_gpu}
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ulysses_sequence_parallel_size: 1 # sp size
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ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs}
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shuffle: ${actor_rollout_ref.actor.shuffle}
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grad_clip: 1.0
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cliprange_value: 0.5
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reward_model:
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enable: False
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strategy: fsdp
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model:
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input_tokenizer: ${actor_rollout_ref.model.path} # set this to null if the chat template is identical
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path: ~/models/FsfairX-LLaMA3-RM-v0.1
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external_lib: ${actor_rollout_ref.model.external_lib}
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use_remove_padding: False
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fsdp_config:
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min_num_params: 0
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param_offload: False
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fsdp_size: -1
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micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu
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micro_batch_size_per_gpu: null # set a number
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max_length: null
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ulysses_sequence_parallel_size: 1 # sp size
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use_dynamic_bsz: ${critic.use_dynamic_bsz}
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forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
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launch_reward_fn_async: False
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algorithm:
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use_kl_in_reward: False
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gamma: 1.0
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lam: 1.0
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adv_estimator: grpo
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kl_penalty: kl # how to estimate kl divergence
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kl_ctrl:
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type: fixed
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kl_coef: 0.001
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use_pf_ppo: False
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pf_ppo:
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reweight_method: pow # ["pow", "max_min", "max_random"]
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weight_pow: 2.0
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trainer:
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balance_batch: True
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total_epochs: 30
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total_training_steps: null
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project_name: verl_examples
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experiment_name: chain_sum
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logger: [ 'console', 'wandb' ]
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val_generations_to_log_to_wandb: 0
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nnodes: 1
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n_gpus_per_node: 1
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save_freq: -1
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# auto: find the last ckpt to resume. If can't find, start from scratch
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resume_mode: auto # or auto or resume_path if
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resume_from_path: False
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test_freq: -1
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critic_warmup: 0
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default_hdfs_dir: null
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remove_previous_ckpt_in_save: False
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del_local_ckpt_after_load: False
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default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
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