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Showing content from https://alibaba.github.io/ROLL/docs/English/QuickStart/config_guide below:

Configuration Guide | ROLL

Configuration Guide Pipeline Config

Refer to RLVR Pipeline Start and Agentic Pipeline Start for more details about RLVR/Agentic pipeline configurations and reward settings.

rollout_batch_size: 64
prompt_length: 2048
response_length: 4096
num_return_sequences_in_group: 8

rollout_batch_size: The number of prompt samples to process in each inference batch.
num_return_sequences_in_group: The number of sequences to generate for each prompt. Notice that its value proportionally scales the actual training samples. In other words, the actual training global batch size is equivalent to num_return_sequences_in_group * rollout_batch_size.

Worker Config ActorTrain/ActorInfer/Critic/Reference

actor_train:
  model_args:
    dtype: bf16
    disable_gradient_checkpointing: False
    ...
  training_args:
    learning_rate: 1.0e-6
    weight_decay: 0
    per_device_train_batch_size: 1
    gradient_accumulation_steps: 32
    warmup_steps: 20
    ...
  data_args:
    template: native
    file_name: xxx/train.json
    prompt: instruction
  strategy_args:
    strategy_name: megatron_train  
    strategy_config:
      tensor_model_parallel_size: 1
      pipeline_model_parallel_size: 1
      expert_model_parallel_size: 1
  infer_batch_size: 4
  device_mapping: list(range(0,16))
actor_infer:
  model_args:
    ...
  generating_args:
    max_new_tokens: ${response_length}
    temperature: 0.99
    ...
  strategy_args:
    strategy_name: vllm  
    strategy_config:
      gpu_memory_utilization: 0.6
      block_size: 16
      max_model_len: 8000
  num_gpus_per_worker: 1
  device_mapping: list(range(0,16))
reference:
  model_args:
    ...
  strategy_args:
    strategy_name: megatron_infer
    strategy_config:
      tensor_model_parallel_size: 1
  device_mapping: list(range(0,16))

device_mapping: The list of device ids to use when training for the worker. Configure for any worker used gpu devices, including actor_train, actor_infer, critic and reference.
num_gpus_per_worker: The number of GPUs assigned per worker. Applicable to actor_infer only.
infer_batch_size: The batch size to used for inference or computing logprobs.

Model Arguments (model_args)

model_args.dtype: Set model dtype as fp32, bf16, or fp16, otherwise use config's torch_dtype
model_args.disable_gradient_checkpointing: Disable gradient checkpointing. Applicable only to actor_train when strategy_name is deepspeed_train

Data Arguments (data_args)

Configure data_args under actor_train.

data_args.template: The chat template used for constructing prompts during training and inference. Setting to native utilizes the default chat template tokenizer.apply_chat_template for prompt construction.
data_args.file_name: The file path for training data. Supported formats include JSON, JSONL, and CSV.
data_args.prompt: Which column in the file to use as prompt.
data_args.messages: Which column in the file to use as messages. (conflicts with prompt)

Generating Arguments (generating_args)

Configure generating_args under actor_infer.

generating_args.max_new_tokens: The maximum length of the generated text.
generating_args.temperature: The temperature to use for sampling.

Strategy Arguments (strategy_args)

strategy_args.strategy_name: The name of training/inference strategy. deepspeed_train/megatron_train for training, vllm/sglang/hf_infer for inference.
strategy_args.strategy_config: The config of training/inference strategy. Will be passed to strategy_name's constructor. E.g. strategy_config.tensor_model_parallel_size for megatron_train strategy and strategy_config.gpu_memory_utilization for vllm strategy.

Commonly used strategy configs are listed below:

Megatron Strategy Config

tensor_model_parallel_size: Degree of tensor model parallelism.
pipeline_model_parallel_size: Degree of pipeline model parallelism.
expert_model_parallel_size: Degree of expert model parallelism.
context_parallel_size: Degree of context parallelism.
virtual_pipeline_model_parallel_size: Num of virtual pipeline in a pipeline.
sequence_parallel: Enable sequence parallel optimization.
recompute_granularity: Checkpoint activations to allow for training with larger models, sequences, and batch sizes. It is supported at two granularities 1) full: whole transformer layer is recomputed, 2) selective: core attention part of the transformer layer is recomputed.
moe_layer_recompute: Memory optimization: checkpointing moe_layer to save activation memory.
moe_token_dispatcher_type: The type of token dispatcher to use. Options are 'allgather' and 'alltoall'.
moe_grouped_gemm: Enable grouped gemm for moe experts.
moe_shared_expert_overlap: Enable overlapping between shared expert computations and dispatcher communications.
overlap_grad_reduce: If true, overlap grad reduce-scatter with backward compute in distributed optimizer.

VLLM Strategy Config

gpu_memory_utilization: The fraction of GPU memory to be used for the model executor.
block_size: Token block size for contiguous chunks of tokens.
max_model_len: Model context length. If unspecified, will be automatically derived from the model config.
load_format: The format of the model weights to load. Since there will be a model update in the beginning, this value should can be set to dummy.

SGLang Strategy Config

mem_fraction_static: Fraction of the free GPU memory used for static memory like model weights and KV cache. Increase it if KV cache building fails. Decrease it if CUDA runs out of memory.
load_format: The format of the model weights to load. Since there will be a model update in the beginning, this value should can be set to dummy.

DeepSpeed Strategy Config

There are DeepSpeed configurations in ./examples/config/ that can be overridden in the default list for strategy configuration.

For example, to use the deepspeed_zero2 strategy, add the following to your config:

defaults:
  - ../config/envs@_here_
  - ../config/deepspeed_zero@_here_
  - ../config/deepspeed_zero2@_here_
  - ../config/deepspeed_zero3@_here_
  - ../config/deepspeed_zero3_cpuoffload@_here_
actor_train:
  strategy_args:
    strategy_name: deepspeed_train
    strategy_config: ${deepspeed_zero2}

Training Arguments (training_args)

Used for configuring training parameters such as learning_rate, weight_decay, warmup_steps, etc.

training_args.per_device_train_batch_size: The batch size to use when training.
training_args.gradient_accumulation_steps: The number of gradient accumulation steps.

In deepspeed training the global train batch size is per_device_train_batch_size * gradient_accumulation_steps * world_size (a.k.a length of device_mapping for actor_train/critic).

In megatron training the global train batch size is per_device_train_batch_size * gradient_accumulation_steps * world_size / tensor_model_parallel_size / pipeline_model_parallel_size / context_parallel_size (don't need to divide expert_model_parallel_size).

If you want to perform one optimization step in each rollout, set gradient_accumulation_steps to rollout_batch_size * num_return_sequences_in_group * tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size/ per_device_train_batch_size / world_size.

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