Please allow different batch sizes per gpu in ddp

Nobody has claimed this yet.

## 🚀 Feature

I propose adding instead of batch size a dictionary with batch size per GPU, for example {"cuda0":4, "cuda1", 6}

### Motivation

I have gv100 (32gb) and 3090 (24gb). using the current muti gpu strategies, i can only use 24gb of memory from the gv100

### Pitch

explained above

### Alternatives

Automatic batch size sizing would be really nice as well for multi gpu, with different batch on different gpus

### Additional context

______________________________________________________________________

#### If you enjoy Lightning, check out our other projects! ⚡

- [**Metrics**](https://github.com/Lightning-AI/metrics): Machine learning metrics for distributed, scalable PyTorch applications.

- [**Lite**](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_lite.html): enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

- [**Flash**](https://github.com/Lightning-AI/lightning-flash): The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

- [**Bolts**](https://github.com/Lightning-AI/lightning-bolts): Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

- [**Lightning Transformers**](https://github.com/Lightning-AI/lightning-transformers): Flexible interface for high-performance research using SOTA Transformers leveraging PyTorch Lightning, Transformers, and Hydra.

Start by tracing the DDP and multi-GPU batch-size configuration in the repository. Review how the current strategy determines batch size and how device names are exposed. Done means users can provide different batch sizes for individual GPUs, with coverage for the heterogeneous-GPU case described in the issue.

Written by the indexing model from the issue text.