# tensorplay.distributed.algorithms.ddp_comm_hooks.quantization_hooks.quantization_perchannel_hook

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributed.algorithms.ddp_comm_hooks.quantization_hooks.quantization_perchannel_hook.html

```python
tensorplay.distributed.algorithms.ddp_comm_hooks.quantization_hooks.quantization_perchannel_hook(process_group, bucket: GradBucket, bucket_size=512)
```

Apply quantize_per_channel logic to DDP using allgather protocol.

Compared to per-tensor, the main motivation of per-channel is
for considerably large tensors such as a tensor that contains 6 million
elements quantizing per a bucket size of 512 (or 128) elements may significantly
increase the resolution.

It first splits GradBucket tensor into multiple chunks (channels) of bucket_size
elements. Then, workers allgather the scales and zero points of their own
GradBucket prior to the quantization. After all workers have that information,
the first then callback called quantize_and_allgather quantizes worker’s
own gradient tensor, and uses allgather to communicate these across all workers.
The final then callback called dequantize_and_aggregate, dequantizes, flattens, and
aggregates each quantized gradient tensor locally and returns the mean.

> **Warning**
>
> This is experimental, and uses allgather protocol which is considerably slower than allreduce protocol. It works only with flattened grads.