DistributedDataParallel non-floating point dtype parameter with requires_grad=False · Issue #32018 · pytorch/pytorch · GitHub

🐛 Bug Using DistributedDataParallel on a model that has at-least one non-floating point dtype parameter with requires_grad=False with a WORLD_SIZE <= nGPUs/2 on the machine results in an error "Only Tensors of floating point dtype can re

modules/data_parallel.py at master · RobertCsordas/modules · GitHub

Wrong gradients when using DistributedDataParallel and autograd

DistributedDataParallel does not get the same grad at all. · Issue

If a module passed to DistributedDataParallel has no parameter

Cannot update part of the parameters in DistributedDataParallel

Distributed Data Parallel and Its Pytorch Example

Pytorch 並列 DataParallel/DistributedDataParallelについて - 適当な

Rethinking PyTorch Fully Sharded Data Parallel (FSDP) from First

Rethinking PyTorch Fully Sharded Data Parallel (FSDP) from First

Don't understand why only Tensors of floating point dtype can

torch.distributed.barrier Bug with pytorch 2.0 and Backend=NCCL

Rethinking PyTorch Fully Sharded Data Parallel (FSDP) from First

Issue for DataParallel · Issue #8637 · pytorch/pytorch · GitHub

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