# tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook.html

```python
tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook(state: PowerSGDState, bucket: GradBucket)
```

Implement simplified PowerSGD algorithm.

This DDP communication hook implements a simplified PowerSGD gradient compression
algorithm described in the [paper](https://arxiv.org/abs/1905.13727).
This variant does not compress the gradients layer by layer,
but instead compresses the flattened input tensor that batches all the gradients.
Therefore, it is faster than [powerSGD_hook()](/docs/generated/tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook.html#tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook),
but usually results in a much lower accuracy, unless matrix_approximation_rank is 1.

> **Warning**
>
> Increasing matrix_approximation_rank here may not necessarily increase the accuracy, because batching per-parameter tensors without column/row alignment can destroy low-rank structure.

Parameters:

- state ([PowerSGDState](/docs/generated/tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.html#tensorplay.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState)) – State information to configure the compression rate and support error feedback, warm start, etc.

- bucket (dist.GradBucket) – Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.

Returns:

Future handler of the communication, which updates the gradients in place.

Example::

```
>>> # xdoctest: +SKIP
>>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1)
>>> ddp_model.register_comm_hook(state, batched_powerSGD_hook)
```