Question about using Autotuner with ZeRO and tensor parallelism

I’m reading through the Autotuner code and found this function:

https://github.com/microsoft/DeepSpeed/blob/f743feca033515fdded50a98093da5a48eb41e74/deepspeed/autotuning/autotuner.py#L278-L302

It computes

total_gpus = self.exp_num_nodes * self.exp_num_gpus

based on the autotuning config. If ZeRO is enabled, then based on which stages are enabled, optimizer_mem, gradients_mem, and/or params_mem get sharded across the GPUs. But then if self.mp_size() (for tensor parallelism, right?) is greater than 1, then the total memory usage is divided again by the amount of tensor parallelism. So if ZeRO and tensor parallelism are both enabled, this is double-dipping, right? With N GPUs, we can’t get the per-GPU memory usage any smaller than 1/N. I’m not sure if

1. there’s a bug here
2. if the value of the num_gpus flag supposed to be reduced by the amount of tensor parallelism, or
3. if I’m not understanding this correctly.

Start in deepspeed/autotuning/autotuner.py at lines 278-302 and trace the ZeRO memory calculations alongside self.mp_size() for tensor parallelism. Compare the resulting per-GPU memory estimate when both features are enabled; done means determining whether memory is divided twice and identifying the intended handling of num_gpus.