Megatron-LM Backend
===================

Last updated: 06/24/2025.

We support Megatron Backend by implementing various workers for actor,
critic, reference, rollout and reward models. We also implement the
``3DHybridEngine`` using Megatron-LM and vLLM/SGLang in
`megatron_vllm.py <https://github.com/volcengine/verl/blob/main/verl/workers/sharding_manager/megatron_vllm.py>`_
and `megatron_sglang.py <https://github.com/volcengine/verl/blob/main/verl/workers/sharding_manager/megatron_sglang.py>`_.

**Pros**

- Support 5D parallelism (TP, EP, CP, DP, PP) and sequence parallelism
  for best scalablility and throughput.
- 3D HybridEngine can significantly reduce peak memory usage and reduce
  weight synchronize overhead between actor and rollout.

**Cons**

- Huggingface Models and Megatron checkpoints need tools for conversion.


Development Progress
--------------------


Note that [Deprecated] means that the feature is not supported in the latest
version of verl.
[To-Optimize] means that the feature is implemented but not optimized yet.
[WIP] means that the feature is working in progress.
[In-Release] means that the feature is ready and in review process,
coming at any time.


+---------------+-----------------------------------------------------------+
| [Deprecated]  | Megatron 3D Parallelism with custom models                |
+---------------+-----------------------------------------------------------+
| [Done]        | Megatron 0.11.0 ``GPTModel`` support                      |
+---------------+-----------------------------------------------------------+
| [Done]        | Megatron GRPO support                                     |
+---------------+-----------------------------------------------------------+
| [Done]        | Megatron with vLLM 0.8.2, with per-tensor weights loading |
+---------------+-----------------------------------------------------------+
| [Done]        | Megatron with Context Parallel                            |
+---------------+-----------------------------------------------------------+
| [Done]        | Qwen2MoE model support                                    |
+---------------+-----------------------------------------------------------+
| [To-Optimize] | Megatron dist Checkpoint                                  |
+---------------+-----------------------------------------------------------+
| [To-Optimize] | Huggingface and Megatron Checkpoint Converter             |
+---------------+-----------------------------------------------------------+
| [To-Optimize] | Efficient fused linear, entropy and cross entropy         |
+---------------+-----------------------------------------------------------+
| [Done]        | Megatron offload(param, grad, optimizer)                  |
+---------------+-----------------------------------------------------------+
| [Done]        | Megatron Profiler                                         |
+---------------+-----------------------------------------------------------+
| [In-Release]  | Megatron 0.12.0, TE 2.2 with vLLM 0.8.3 and Fused Attn    |
+---------------+-----------------------------------------------------------+
| [WIP]         | Moonlight/DeepSeek-V3 model support                       |
+---------------+-----------------------------------------------------------+
| [WIP]         | Expert Parallel support                                   |
+---------------+-----------------------------------------------------------+
| [WIP]         | Megatron support dynamic batch size                       |
+---------------+-----------------------------------------------------------+
| [To-Do]       | Performance tuning                                        |
+---------------+-----------------------------------------------------------+
| [MileStone]   | Runnable with DeepSeek-V3 671B post-training              |
+---------------+-----------------------------------------------------------+



Utils of Megatron Workers
-------------------------

MegatronWorker
^^^^^^^^^^^^^^

``MegatronWorker`` is the base class of different megatron worker
classes. In this class, ``get_megatron_global_info`` and
``get_megatron_rank_info`` function to retrieve the 3D parallel world
size and rank of each ``Worker`` running on specific GPU. These information
will be used in transfer protocol for Megatron Backend.

The following ``Worker`` class for different models will be utilized to
construct the ``WorkerGroup`` .

We implement various of APIs for each ``Worker`` class decorated by the
``@register(dispatch_mode=)`` . These APIs can be called by the ray
driver process. The data can be correctly collect and dispatch following
the ``dispatch_mode`` on each function. The supported dispatch_model
(i.e., transfer protocols) can be found in `decorator.py <https://github.com/volcengine/verl/blob/main/verl/single_controller/base/decorator.py>`_.

ActorRolloutRefWorker
^^^^^^^^^^^^^^^^^^^^^

This class is implemented for Actor/Rollout HybridEngine or for the
reference model to initialize their model and perform computation.

Actor/Rollout HybridEngine
''''''''''''''''''''''''''

1. HybridEngine, Actor and Rollout initialization API.

.. code:: python

   @register(dispatch_mode=Dispatch.ONE_TO_ALL)
   def init_model(self):

``ONE_TO_ALL``: when calling the ``init_model`` function from the driver
process, each worker (on a GPU) will execute the following model
initialization process.

The initialization details of HybridEngine, Actor and Rollout are
highlighted below:

1. ``MegatronPPOActor`` implements the simple PPO computation logics
   when the model is built with Megatron, including compute log prob,
   model update.
2. ``vLLMRollout`` support generation with vLLM. We modify the vLLM
   Engine and make it executed under SPMD to fit into our
   ``WorkerGroup`` design.
3. ``MegatronVLLMShardingManager`` a context manager to perform actual
   resharding between actor and rollout.

See `source code <https://github.com/volcengine/verl/blob/main/verl/workers/megatron_workers.py#L63>`_ for more information.

.. code:: python

   # build actor model
   self.actor = MegatronPPOActor(config=self.config.actor,
                                 model_config=self.actor_model_config,
                                 megatron_config=megatron_config,
                                 actor_module=self.actor_module,
                                 actor_optimizer=self.actor_optimizer,
                                 actor_optimizer_config=self.actor_optim_config)

   # build rollout
   # rollout initialization
   rollout = vLLMRollout(actor_module=params,
                        config=self.config.rollout,
                        tokenizer=self.tokenizer,
                        model_hf_config=self.actor_model_config,
                        train_tp=mpu.get_tensor_model_parallel_world_size())
   # perform weight resharding between actor and rollout
   sharding_manager = MegatronVLLMShardingManager(module=self.hybrid_engine,
                                                  inference_engine=rollout.inference_engine,
                                                  model_config=self.actor_model_config,
                                                  layer_name_mapping=layer_name_mapping)
   ...

1. Generate sequence and recompute log prob

.. code:: python

   @register(dispatch_mode=Dispatch.MEGATRON_PP_AS_DP_PROTO)
   def generate_sequences(self, prompts: DataProto):

- ``Dispatch.MEGATRON_PP_AS_DP_PROTO``: The PP dimension of the actor
  model will be regarded as DP dimension. Then the driver process will
  dispatch and collect the data according to this reorganization. This
  is because, in HybridEngine, the actor weight, which usually applied
  larger 3D parallel sizes, will be gathered along the PP dimension and
  TP dimension. Therefore, the corresponding data should be dispatched
  and collected through the 3D parallel group of the rollout model,
  rather than the actor model. However, the world_size and rank
  information can only be retrieved from ``get_megatron_global_info`` and
  ``get_megatron_rank_info``, which records the 3D information for the
  actor model. Moreover, the data resharding inside TP dimension will be
  processed within the HybridEngine.

- In this function, the rollout model will perform auto-regressive
  generation and the actor model will recompute the old log prob for the
  generated response.

3. Update actor model

.. code:: python

   @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
   def update_actor(self, data: DataProto):

- ``Dispatch.MEGATRON_COMPUTE_PROTO``: User passes the data partitioned
  by DP dimension. The data is dispatched to all tp/pp ranks within the
  same dp group, and ultimately only collects output data from tp=0 and
  the last pp.
- Update the actor model weight using PPO & entropy loss.


..note:: 

   Currently, training Tensor Parallel Size can be different from inference
   Tensor Parallel Size.


ReferenceModel
''''''''''''''

1. Reference model initialization

The reference model is initialized using the same function as the actor
model without initializing the HybridEngine and Optimizer. Then the
actor model is also wrapped by the ``MegatronPPOActor``.

2. Compute reference log prob

.. code:: python

   @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
   def compute_ref_log_prob(self, data: DataProto):

- In this function, the reference model will call the compute log prob
  function in ``MegatronPPOActor`` to compute the reference log prob.

CriticWorker and RewardWorker
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

1. Model initialization

Quite similar to reference model. The CriticWorker will perform
additional initialization for the Optimizer.

2. Compute Values for CriticWorker

.. code:: python

   @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
   def compute_values(self, data: DataProto):

3. Update Critic

.. code:: python

   @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
   def update_critic(self, data: DataProto):

4. Compute Reward

.. code:: python

   @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
   def compute_rm_score(self, data: DataProto):


Utils of Train Optimization
---------------------------

Offload
^^^^^^^
When resources are tight, the offload method can lower GPU memory 
usage, helping training and inference frameworks work well under verl. 
It moves parameters, gradients, and optimizers to CPU memory and only 
loads them back to the GPU when needed.

If you want to use the offload, you can add the following parameters 
for the actor and ref separately. 

.. code:: python

   # For the actor
   actor_rollout_ref.actor.megatron.param_offload=True \
   actor_rollout_ref.actor.megatron.grad_offload=True \
   actor_rollout_ref.actor.megatron.optimizer_offload=True \
   # For the ref w/o grad and optimizer
   actor_rollout_ref.ref.megatron.param_offload=True \


For the critic, you can include these parameters.

.. code:: python

   # For the critic
   critic.megatron.param_offload=True \
   critic.megatron.grad_offload=True \
   critic.megatron.optimizer_offload=True \


Related MCore Document
----------------------

There is also a detailed document of using MCore to train different
kinds of models, please refer to `MCore Document <https://github.com/volcengine/verl/blob/main/verl/models/mcore/readme.md>`_.
