[BUG] DeepSpeed Cuda OOM on SwinUNETR from MONAI

Describe the bug
I'm trying to run training of SwinUNETR model on a multi-GPU node (4xV00 - 16GB VRAM) with effective batch size per GPU of 1 and sample size 96x96x96. However, even after many tweak in DS config I'm still getting CUDA OOM error.

To Reproduce
Steps to reproduce the behavior:

1. Clone 'MONAI SwinUNETR'
2. Use deepspeed init with following configuration:

{
    "train_micro_batch_size_per_gpu": 1,
    "steps_per_print": 1,
    "fp16": {
        "enabled": true    },
    "optimizer": {
        "type": "Adam",
        "params": {
            "lr": 0.001,
            "betas": [
                0.8,
                0.999            ],
            "eps": 1e-8,
            "weight_decay": 3e-7        }
    },
    "scheduler": {
        "type": "WarmupLR",
        "params": {
            "warmup_min_lr": 0,
            "warmup_max_lr": 0.001,
            "warmup_num_steps": 100        }
    },
    "wall_clock_breakdown": false,
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu"        },
        "offload_param": {
            "device": "cpu"        },
        "contiguous_gradients": true,
        "overlap_comm": false,
        "allgather_bucket_size": 5e5,
        "reduce_bucket_size": 5e5    },
    "zero_allow_untested_optimizer": false,
    "activation_checkpointing": {
        "partition_activations": true,
        "cpu_checkpointing": false,
        "contiguous_memory_optimization": false,
        "number_checkpoints": null,
        "synchronize_checkpoint_boundary": false,
        "profile": false    }
}

3. Get OOM error

Expected behavior
Training proceeds without OOM error

System info (please complete the following information):

• OS: Ubuntu 20.04
• GPU 4xV100 - 16GB VRAM
• Python version: 3.8

Launcher context
AML pipeline with PyTorch distribution:

distribution:
  type: pytorch

Docker context
mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.6-cudnn8-ubuntu20.04

Additional context

• Am I missing any further optimizations I can do?
• Is it possible to make train_batch_sizesmaller than #GPUs such that the GPUs can then share memory
• How to effectively enable model parallel in DeepSpeed it that is even possible