Principle:Norrrrrrr lyn WAInjectBench GPU Device Placement
| Knowledge Sources | |
|---|---|
| Domains | GPU_Computing, Deep_Learning |
| Last Updated | 2026-02-14 16:00 GMT |
Overview
A device management strategy that consolidates a model onto a single GPU, removes accelerate dispatch hooks, and aligns LoRA adapter devices with an OOM fallback to multi-GPU redispatching.
Description
When fine-tuning large models with LoRA, the model may have been loaded with HuggingFace's accelerate library which distributes layers across devices. For single-GPU training, all model components must be on the same device. The device placement step:
- Removes any accelerate hooks that intercept forward/backward passes
- Moves the entire model to the target GPU
- Aligns LoRA adapter module devices with their parent weight devices
- Clears the
hf_device_mapattribute - Falls back to automatic multi-GPU redispatching if single-GPU placement causes an OOM error
Usage
Use this after LoRA injection and before the training loop. It ensures all model parameters and buffers are on the correct device for gradient computation.
Theoretical Basis
# Device placement strategy with OOM fallback
try:
remove_accelerate_hooks(model)
model.to(target_device)
align_lora_devices(model)
except OOM:
redispatch_across_gpus(model)
The LoRA device alignment step is necessary because get_peft_model may create LoRA matrices on a different device than their base weight, causing device mismatch errors during the forward pass.