# Distributed PPO Training on Stage 3
## Detach Experience Makers and Trainers
We can completely separate the trainers and makers.
- The experience maker performs inference, produces experience, and remotely delivers it to the trainer (1).
- The trainer consumes experience to train models, and periodically transmits new model parameters to the maker (2.1, 2.2).
- Using an experience buffer to overlap transmission and computing.
In this manner, each node will work continuously without model idle time, and different optimization strategies can be applied for inference and training to meet the needs of speed or storage. It is also helpful for scalability.
`DetachedPPOTrainer` and `ExperienceMakerHolder` are Ray Actors (distinguished from Actor Model), representing Trainer and Experience Maker on the graph above, respectively.
[More about Ray Core](https://docs.ray.io/en/latest/ray-core/walkthrough.html)
## Usage
See examples at `ColossalAI/application/Chat/examples/ray`
### Setup Makers
- define makers' environment variables :
```python
env_info_makers = [{
'local_rank': '0',
'rank': str(rank),
'world_size': str(num_makers),
'master_port': maker_port,
'master_addr': master_addr
} for rank in range(num_makers)]
```
- define maker models :
```python
def model_fn():
actor = get_actor_from_args(...)
critic = get_critic_from_args(...)
reward_model = get_reward_model_from_args(...)
initial_model = get_actor_from_args(...)
return actor, critic, reward_model, initial_model
```
- set experience_holder_refs :
```python
experience_holder_refs = [
ExperienceMakerHolder.options(
name=f"maker_{i}",
num_gpus=1,
max_concurrency=2
).remote(
detached_trainer_name_list=[f"trainer_{x}" for x in target_trainers(...)],
model_fn=model_fn,
...)
for i, env_info_maker in enumerate(env_info_makers)
]
```
The names in the `detached_trainer_name_list` refer to the target trainers that the maker should send experience to.
We set a trainer's name the same as a maker, by `.options(name="str")`. See below.
### Setup Trainers
- define trainers' environment variables :
```python
env_info_trainers = [{
'local_rank': '0',
'rank': str(rank),
'world_size': str(num_trainers),
'master_port': trainer_port,
'master_addr': master_addr
} for rank in range(num_trainers)]
```
- define trainer models :
```python
def trainer_model_fn():
actor = get_actor_from_args(...)
critic = get_critic_from_args(...)
return actor, critic
```
- set trainer_refs :
```python
trainer_refs = [
DetachedPPOTrainer.options(
name=f"trainer{i}",
num_gpus=1,
max_concurrency=2
).remote(
experience_maker_holder_name_list=[f"maker{x}" for x in target_makers(...)],
model_fn = trainer_model_fn(),
...)
for i, env_info_trainer in enumerate(env_info_trainers)
]
```
The names in `experience_maker_holder_name_list` refer to the target makers that the trainer should send updated models to.
By setting `detached_trainer_name_list` and `experience_maker_holder_name_list`, we can customize the transmission graph.
### Launch Jobs
- define data_loader :
```python
def data_loader_fn():
return = torch.utils.data.DataLoader(dataset=dataset)
```
- launch makers :
```python
wait_tasks = []
for experience_holder_ref in experience_holder_refs:
wait_tasks.append(
experience_holder_ref.workingloop.remote(data_loader_fn(),
num_steps=experience_steps))
```
- launch trainers :
```python
for trainer_ref in trainer_refs:
wait_tasks.append(trainer_ref.fit.remote(total_steps, update_steps, train_epochs))
```
- wait for done :
```python
ray.get(wait_tasks)
```
## Flexible Structure
We can deploy different strategies to makers and trainers. Here are some notions.
### 2 Makers 1 Trainer
### 2 Makers 2 Trainer
### Maker Inference Quantization
### Tensor Parallel
## TODO
- [ ] Support LoRA
- [ ] Support TP & PP