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---
name: mixed-precision
description: Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
metadata:
category: tooling
trigger-keywords: "training,gpu,memory,speed,precision,fp16,bf16"
applicable-stages: "10,12"
priority: "5"
version: "1.0"
author: researchclaw
references: "Micikevicius et al., Mixed Precision Training, ICLR 2018"
code-template: |
scaler = torch.cuda.amp.GradScaler()
for batch in dataloader:
optimizer.zero_grad()
with torch.cuda.amp.autocast():
output = model(batch)
loss = criterion(output, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
---
## Mixed Precision Training Best Practice
Use torch.cuda.amp for automatic mixed precision:
- Wrap forward pass in torch.cuda.amp.autocast()
- Use GradScaler for loss scaling
- BF16 preferred over FP16 on Ampere+ GPUs (RTX 3xxx, A100, RTX 4xxx)
- Watch for NaN gradients — reduce learning rate if needed
- Do NOT use amp with custom CUDA kernels unless tested
