Performance Tuning Guide
==============================

Last updated: 07/17/2025.

Author: `Guangming Sheng <https://github.com/PeterSH6>`_, `Jiali Zheng <https://github.com/CurryRice233>`_

In this section, we will discuss how to tune the performance of all the stages in verl, including:

1. Rollout generation throughput.

2. Enable ``use_remove_padding=True`` for sequence packing (i.e., data packing and remove padding).

3. Batch size tuning for forward and backward computation

4. Enable ``use_dynamic_bsz=True`` for higher throughput.

5. Utilize Ulysses Sequence Parallel for Long Context Training

6. LigerKernel for SFT performance optimization

7. Forward prefetch in FSDP training backend

8. Memory optimization for entropy calculation from logits

Rollout Generation Tuning
--------------------------

verl currently supports two rollout backends: vLLM and TGI (with SGLang support coming soon). 

Below are key factors for tuning vLLM-based rollout. Before tuning, we recommend setting ``actor_rollout_ref.rollout.disable_log_stats=False`` so that rollout statistics are logged.

- Increase ``gpu_memory_utilization``.

  - For vLLM v0.7.0 and later, the vLLM instance will only use gpu_memory_utilization of the **total** memory.
  - For SGLang, it's the fraction of the free GPU memory used for **static** memory like model weights and KV cache. However, the remaining (1-gpu_memory_utilization) will also be used during inference.

  However, if model parameters and optimizer states are not offloaded, using too high a fraction can lead to OOM. 
  A value between 0.5 and 0.7 often strikes a good balance between high throughput and avoiding OOM.

  Note: since the definition of ``gpu_memory_utilization`` varies across inference engines, a value that works well for one engine may cause OOM for another.

- Adjust ``max_num_seqs`` or ``max_num_batched_tokens``.
  If the GPU cache utilization is relatively low in the log, increase ``max_num_seqs`` or ``max_num_batched_tokens`` 
  can enlarge the effective batch size in the decoding stage, allowing more concurrent requests per batch. 
  We recommend setting ``max_num_batched_tokens > 2048`` for higher throughput.

- Use a smaller ``tensor_parallel_size``. 
  When GPU resources allow, a smaller tensor parallel size spawns more vLLM replicas. 
  Data parallelism (DP) can yield higher throughput than tensor parallelism (TP), but also increases KVCache consumption. 
  Carefully balance the trade-off between more replicas and higher memory usage.
  Our experiment in Sec. 8.4 of `HybridFlow paper <https://arxiv.org/pdf/2409.19256v2>`_ evaluate this trade-off.

More tuning details such as dealing with Preemption and Chunked-prefill
can be found in `vLLM official tuning guide <https://docs.vllm.ai/en/latest/performance/optimization.html>`_ 

For optimal performance, we recommend using vLLM v0.8.3 or later. See https://github.com/volcengine/verl/blob/main/docs/README_vllm0.8.md for details.

Enable remove padding (sequence packing)
-----------------------------------------

Currently, for llama, mistral, gemma1 and qwen based models, users can enable `use_remove_padding=True` to utilize the 
sequence packing implementation provided by transformers library.

For other models, transformers library may also support it but we haven't tested it yet.
Users can add the desired model config to the  `test_transformer.py <https://github.com/volcengine/verl/blob/main/tests/models/test_transformer.py#L24>`_ file.
And test its functionality by running the following command:

.. code-block:: bash

  pytest -s tests/models/test_transformer.py

If the test passes, you can add your desired model into the model `registry.py <https://github.com/volcengine/verl/blob/main/verl/models/registry.py#L24>`_ file.
Then, you can enjoy the performance boost of sequence packing
and welcome to PR your tested model to verl!


Batch Size Tuning
-----------------

To achieve higher throughput in experience preparation (i.e., model fwd) and model update (i.e., actor/critic fwd/bwd), 
users may need to tune the ``*micro_batch_size_per_gpu`` for different computation.

In verl, the core principle for setting batch sizes is:

- **Algorithmic metrics** (train batch size, PPO mini-batch size) are *global* (from a single-controller perspective), 
  normalized in each worker. See the `normalization code <https://github.com/volcengine/verl/blob/main/verl/workers/fsdp_workers.py#L120-L122>`_.

- **Performance-related parameters** (micro batch size, max token length for dynamic batch size) are *local* parameters that define the per-GPU data allocations. 
  See the `normalization code <https://github.com/volcengine/verl/blob/main/verl/workers/fsdp_workers.py#L127>`_.

.. note:: In your training script, please use ``*micro_batch_size_per_gpu`` instead of ``*micro_batch_size``. 
  So that you don't need to consider the normalization of the ``micro_batch_size`` and ``micro_batch_size`` will be deprecated.

Batch Size Tuning tips
""""""""""""""""""""""

Therefore, users may need to tune the ``*micro_batch_size_per_gpu`` to accelerate training. Here're some tips:

1. **Enable gradient checkpointing**: 
   Set ``actor_rollout_ref.model.enable_gradient_checkpointing=True`` and ``critic.model.enable_gradient_checkpointing=True``. 
   This often allows for larger micro-batch sizes and will be beneficial for large mini-batch training.

2. Increase the ``*micro_batch_size_per_gpu`` as much as possible till equals to normalized ``mini_batch_size``.

3. **Use larger forward-only parameters**: 
   Forward only parameter, such as ``actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu``, 
   ``actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu``, ``critic.forward_micro_batch_size_per_gpu`` could be larger (e.g., 2x) than training related micro batch sizes,
   such as ``actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu``, ``critic.ppo_micro_batch_size_per_gpu``.

4. **Allow larger micro-batch sizes for Critic and Reward models**:
   micro batch size of Critic and Reward model could be larger than Actor model. This is because the actor model has much larger vocab size in the final layer.

5. **Enable activation offloading**:
   Set ``actor_rollout_ref.model.enable_activation_offload=True`` and ``critic.model.enable_activation_offload=True``.
   This often works together with gradient checkpointing to get larger micro-batch sizes and it's only available in FSDP backend now.

Tuning for Dynamic Batch Size
-----------------------------

Dynamic batch size is a technique that allows the model to process similar number of tokens in a single forward pass (with different actual batch sizes).
This can significantly improve the training efficiency and reduce the memory usage.

To utilize this technique, users can set ``use_dynamic_bsz=True`` in actor, ref, critic and reward models.
With ``use_dynamic_bsz=True``, users don't need to tune ``*micro_batch_size_per_gpu``. 
Instead, users should tune the following parameters:

- ``actor_rollout_ref.actor.ppo_max_token_len_per_gpu``, ``critic.ppo_max_token_len_per_gpu``: 
  The maximum number of tokens to be processed in fwd and bwd of ``update_policy`` and ``update_critic``.

- ``actor_rollout_ref.ref.log_prob_max_token_len_per_gpu`` and ``actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu``: 
  The maximum number of tokens to be processed in a the fwd computation of ``compute_log_prob`` and ``compute_ref_log_prob``.

- ``critic.forward_micro_batch_size_per_gpu``, ``reward_model.forward_micro_batch_size_per_gpu``: 
  The maximum number of tokens to be processed in a the fwd computation of ``compute_values``, ``compute_rm_score``.

Dynamic Batch Size Tuning tips
""""""""""""""""""""""""""""""

Here're some tips to tune the above parameters:

1. **Increase** ``actor_rollout_ref.actor.ppo_max_token_len_per_gpu``  
   Make it at least 2 x (max_prompt_length + max_response_length). We set it to 3x in `run_qwen2-7b_rm_seq_balance.sh <https://github.com/volcengine/verl/blob/main/examples/ppo_trainer/run_qwen2-7b_rm_seq_balance.sh#L25>`_.
   Try to increase it to get higher throughput.

2. **Forward-only parameters can be larger**: 
   Similar to the non-dynamic-batch scenario, forward-only token limits can exceed those used in forward/backward operations.
 
3. **Use larger limits for Critic and Reward models**:
   Critic and Reward parameters can be set at least 2× the Actor’s limits. For instance, we set them to 4× here:  
   `run_qwen2-7b_rm_seq_balance.sh <https://github.com/volcengine/verl/blob/main/examples/ppo_trainer/run_qwen2-7b_rm_seq_balance.sh#L40>`_
   
.. :math:`\text{critic.ppo_max_token_len_per_gpu}  = 2 \times  \text{actor.ppo_max_token_len_per_gpu})`.

Ulysses Sequence Parallel for Long Context Training
----------------------------------------------------

To utilize this technique, users can set ``ulysses_sequence_parallel_size>1`` in actor, ref, critic and reward models.

We support different model utilize different ulysses_sequence_parallel_size sizes.

To train long sequence (>32k), users may need to decrease the ``*micro_batch_size_per_gpu`` and ``*max_token_len_per_gpu`` to avoid OOM.

LigerKernel for SFT
----------------------

LigerKernel is a high-performance kernel for Supervised Fine-Tuning (SFT) that can improve training efficiency. To enable LigerKernel in your SFT training:

1. Install liger-kernel via ``pip3 install liger-kernel``. In your SFT configuration file (e.g., ``verl/trainer/config/sft_trainer.yaml``), set the ``use_liger`` parameter:

   .. code-block:: yaml

      model:
        use_liger: True  # Enable LigerKernel for SFT

2. The default value is ``False``. Enable it only when you want to use LigerKernel's optimizations.

3. LigerKernel is particularly useful for improving training performance in SFT scenarios.

Forward prefetch in FSDP training backend
----------------------

During the training phase, users can enable forward prefetching in FSDP by setting ``fsdp_config.forward_prefetch=True``. For example, ``actor_rollout_ref.actor.fsdp_config.forward_prefetch=True``. This configuration prefetches the next forward-pass all-gather operation before completing the current forward computation, overlapping communication with computation and improving efficiency. For further details, refer to the `FSDP forward_prefetch <https://docs.pytorch.org/docs/stable/fsdp.html#module-torch.distributed.fsdp>`_ documentation.

.. note::
    Backward prefetch is unsupported because the ``BACKWARD_POST`` policy may prefetch incorrectly in nested-module cases. For details, see the `FSDP documentation <https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md?plain=1#L70>`_

Migrating to FSDP2
----------------------

FSDP2 offers notable improvements over FSDP1. According to `PyTorch TorchTitan benchmarks <https://arxiv.org/abs/2410.06511v1>`_:

- 7% lower GPU memory usage on average
- 1.5% throughput improvement with BF16 training
- Better composability with DTensor and per-parameter sharding

**Enabling FSDP2 in VERL:**

   .. code-block:: python

    # Enable FSDP2 in actor configuration
    actor_rollout_ref.actor.strategy="fsdp2"

.. note:: 
   FSDP2 requires PyTorch 2.1+ and is recommended for models with transformer architecture.

Memory optimization for entropy calculation from logits
----------------------

The ``logits`` tensor (typically of shape ``[bsz*seq_len, voc]``) can consume significant memory. When using ``compute_entropy_from_logits``, memory usage reaches approximately ``[bsz*seq_len, voc] × (4 bytes (float32) + 2 bytes (autocast for softmax+logsumexp) + 1 byte (softmax output))``.

To reduce this memory peak, enable chunked computation by setting:
``actor_rollout_ref.ref.entropy_from_logits_with_chunking = True``
This processes the tensor in chunks of shape ``[chunk_size, voc]`` (e.g., 2048) rather than the full sequence length, exclusively during the model's forward pass.

Additionally, during training, standard gradient checkpointing (``enable_gradient_checkpointing=True``) does not apply to entropy calculations. To reduce memory peaks in this context, set:
``actor_rollout_ref.actor.entropy_checkpointing = True``
This enables entropy recomputation specifically for the entropy calculation, lowering memory usage during training.
