Faster PyTorch Training by Reducing Peak Memory (combining backward pass + optimizer step)

Training large models often encounters GPU out-of-memory errors and performance degradation because parameters, optimizer states, activations, gradients, communication buffers, and allocator behavior can create damaging peak-memory spikes. The described Lightning Fabric feature, `fsdp_overlap_step_with_backward`, addresses this by applying optimizer updates during backpropagation as each parameter’s gradient becomes available, immediately releasing gradient memory instead of retaining all gradients until the backward pass ends; it requires one optimizer per parameter and can reduce peak memory by roughly 20–40% or more. In experiments with FSDP-trained Lit-LLaMA models, the approach also reduced allocator pressure and GPU stalls, delivering substantially faster and more consistent training, including a reported 7× throughput gain on 20% fewer GPUs for a 7B model and a 3.4× speed improvement for a 13B model. The discussion also identifies fused optimizer operations such as AdamW’s default `foreach=True`, excessive FSDP all-gathers, and PyTorch’s reserved-versus-allocated memory behavior as additional sources of memory pressure, with settings such as disabling `foreach` and enabling `limit_all_gathers=True` helping in the evaluated workload.

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