Fixing Mixed Precision Underutilization for Speed Gains

Issue

Despite using float16 or bfloat16, training was not significantly faster.
Profiling revealed:

• Tensor Cores underutilized
• Inconsistent performance across training steps
• Suboptimal memory bandwidth usage

Solution

We fixed AMP underuse by enforcing correct mixed-precision behavior:

1. Enable Autocast + GradScaler

• Wrapped forward/backward in torch.cuda.amp.autocast()
• Used GradScaler to handle loss scaling and overflow detection
• Verified mixed precision was being correctly applied at all layers

2. Model & Layer Compliance

• Ensured all model components supported float16 (e.g., no hard-coded float32)
• Disabled layers that defaulted to float32 manually

🚀 Outcome

• Training speed increased by 1.3× to 2× on Ampere/Hopper GPUs
• Lower VRAM usage with bfloat16 on A100s
• Stable training with no accuracy drop

💡 Takeaway

Mixed precision only helps if configured correctly.
Always validate that your model is truly using AMP and that Tensor Cores are active.