nemo-mbridge-perf-memory-tuning

Memory Tuning

Stable docs: @docs/parallelisms.md Card: @skills/nemo-mbridge-perf-memory-tuning/card.yaml

What It Is

GPU OOM failures during training often stem from memory fragmentation rather than raw capacity. PyTorch's default CUDA allocator can leave unusable gaps between allocations. The single most effective fix is:

export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

This tells PyTorch to use expandable (non-fixed-size) memory segments, which dramatically reduces fragmentation and often eliminates borderline OOM without any model or parallelism changes.

Beyond fragmentation, actual peak memory is determined by:

• Parameter + optimizer state memory — controlled by TP, PP, DP sharding (distributed optimizer, FSDP)
• Activation memory — controlled by activation recompute, sequence length, micro-batch size, and PEFT-specific retention of gathered inputs
• Temporary / workspace memory — CUDA kernels, NCCL buffers, CUDA graphs

For configuration planning, use the Bridge theoretical estimator before launching large jobs:

from megatron.bridge.training.utils.theoretical_memory_utils import estimate_training_memory

estimate = estimate_training_memory(cfg, num_microbatches=num_microbatches)

The estimator reports the most-loaded GPU shard and separates dense/embedding, routed MoE expert, and activation components. It does not include allocator fragmentation, CUDA/NCCL workspace, CUDA graph buffers, token imbalance, or dispatcher workspace, so validate final configs with runtime memory metrics.

Quick Decision

When a training run OOMs or is close to the memory limit:

1. Set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True first. This fixes fragmentation-induced OOM with zero performance cost. Most Slurm launch templates already include it.
2. For LoRA with sequence parallelism, enable input re-gather (LoRA(sequence_parallel_input_regather=True)). This avoids retaining the full gathered LoRA-A input in every eligible layer; it has no effect when SP is disabled.
3. Add selective activation recompute (recompute_modules=[core_attn]) if not already enabled. See @skills/nemo-mbridge-perf-activation-recompute/SKILL.md.
4. Avoid increasing TP as a memory fix — doubling TP dramatically increases NVLink all-reduce volume and often kills throughput (-28% on Llama3 70B).
5. Avoid increasing PP at the cost of DP — halving DP doubles gradient accumulation steps and hurts throughput (~6%).
6. Consider mlp recompute if still OOM. Saves ~3 GB but costs ~16% GPU utilization on large dense models (Llama3 70B).
7. CPU offloading is blocked when PP > 1.