# HierarchicalModelAverager

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributed.algorithms.model_averaging.HierarchicalModelAverager.html

```python
class tensorplay.distributed.algorithms.model_averaging.HierarchicalModelAverager(period_group_size_dict=None, warmup_steps=0, process_group=None)
```

Runs hierarchical model averaging ([hierarchical SGD](https://arxiv.org/pdf/2010.12998.pdf)).

Process groups of different sizes are organized in a hierarchy, and they average parameters
by using different periods concurrently after the warm-up stage.
This is an extension of [PeriodicModelAverager](/docs/generated/tensorplay.distributed.algorithms.model_averaging.PeriodicModelAverager.html#tensorplay.distributed.algorithms.model_averaging.PeriodicModelAverager)
that supports [post-local SGD](https://arxiv.org/abs/1808.07217), which essentially only supports
a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
level is usually embedded in [post_localSGD_hook()](/docs/distributed.html#module-tensorplay.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook).
Similarly, the process groups within this class do not have such an intra-machine process
subgroup, which should be embedded by the post-local SGD communication hook instead.

Parameters:

- period_group_size_dict – An ordered dict mapping keys of model averaging period to process group size, used for initializing process groups of different sizes in a hierarchy to average parameters concurrently.

- warmup_steps ([int](https://docs.python.org/3/builtins/functions.html#int)) – The number of warm-up steps. During this stage, model averaging is skipped.

- process_group (ProcessGroup, optional) – The overall process group containing all the processes that runs model averaging.

> **Warning**
>
> The last group size in the dict must be the size of the provided process_group, which indicates model averaging at the highest level of the hierarchy. If process_group is not provided, then the last group size should be equal to the world size.

> **Warning**
>
> HierarchicalModelAverager is experimental and subject to change.

```python
average_parameters(params: Iterable)
```

Averages parameters or parameter groups of an optimizer.

Averaging only occurs if step is no less than warmup_steps
and it can be divided by a period in the keys of period_process_group_dict,
where step is increased by 1 at each iteration in the training loop.
If step can be divided by multiple periods in the keys of period_process_group_dict,
only the largest period is used, and the corresponding process group is used for averaging parameters.
:param params: The parameters of a model or parameter groups of an optimizer.