CUDA Out of Memory Error Fixes | Generated by AI

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Issue Analysis

The training script from nanoGPT is failing with a CUDA Out of Memory (OOM) error during the initial loss estimation (estimate_loss()) in the forward pass. This happens right after model initialization and compilation. The key problem is memory exhaustion when allocating a large buffer for the model’s output logits:

• Buffer details: A tensor of shape (65536, 50304) in bfloat16 (2 bytes per element), which is ~6.14 GiB. This corresponds to:
  • Batch size: 64
  • Sequence length (block_size): 1024
  • Total tokens: 64 × 1024 = 65,536
  • Vocab size: 50,304 (GPT-2 default)
• GPU status: Your GPU (likely an RTX 3060 or similar with 12 GB VRAM) has 11.69 GiB total capacity, but only 2.68 GiB free at the time of allocation. The process is already using ~7.04 GiB (6.78 GiB by PyTorch), leaving insufficient headroom after accounting for the model (~124M params × 2 bytes in bfloat16 ≈ 248 MB), optimizer states (~1-2 GB for AdamW), compilation caches, activations, and overhead.

This is common for GPT-2 sized models (124M params) on consumer GPUs when using large batch sizes or sequence lengths, especially with torch.compile enabled, which can temporarily inflate memory usage during graph capture and optimization.

Root Causes

1. High batch size (64): Combined with block_size=1024, this creates massive intermediate tensors (e.g., logits, attention outputs). The effective tokens per iteration (65,536) push VRAM limits.
2. Model compilation: torch.compile (enabled by default) uses Torch Inductor, which generates temporary CUDA kernels and buffers. The warning [0/0] Not enough SMs to use max_autotune_gemm mode suggests your GPU’s streaming multiprocessors (SMs) are limited for aggressive autotuning, potentially increasing fragmentation.
3. Data type and precision: Using bfloat16 (via torch.cuda.amp), but the deprecated GradScaler warning indicates potential inefficiencies. Other processes or prior runs may have fragmented VRAM.
4. Evaluation overhead: estimate_loss() runs forward passes on eval data (eval_iters=200, but batched), exacerbating the issue before training even starts.
5. Pre-existing memory use: ~7 GB already allocated suggests the model, optimizer, and dataset loader consumed space upfront. Non-PyTorch memory (224.90 MiB by the process) could include CUDA context or libraries.

Recommended Fixes

Start with the simplest changes in config/train_openwebtext.py (or override via command line). Rerun after each tweak to isolate what works. Goal: Reduce peak VRAM to ~8-9 GB while preserving training quality.

1. Reduce Batch Size (Primary Fix)

• Set batch_size = 4 (or even 1-2 initially) to drop the logits buffer to ~0.38 GiB (for batch=4).
• Compensate with gradient_accumulation_steps = 16 (effective batch=64, but lower peak memory).
• Why? Batch size scales linearly with memory for most tensors. This is the most effective for OOM without slowing training too much.
• Updated config snippet:

  batch_size = 4
  gradient_accumulation_steps = 16  # Adjust to match original effective batch
  

• Expected VRAM: ~4-6 GB total.

2. Disable or Optimize Compilation

• Add compile = False to skip torch.compile, avoiding Inductor overhead (~1-2 GB temporary spike).
• If keeping compile, add mode='reduce-overhead' for faster but less optimized kernels.
• Updated config:

  compile = False
  

• Alternative: Run with torch._dynamo.config.suppress_errors = True in the script for debugging, but fix the OOM first.

3. Reduce Sequence Length

• Set block_size = 512 (half the context) to cut tokens/iteration to ~32,768, halving logits memory (~3.07 GiB).
• Trade-off: Shorter context may slightly hurt model quality, but it’s recoverable with more training.
• Config:

  block_size = 512
  

4. Memory Management Tweaks

• Environment variable for fragmentation: As suggested in the error, set export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True before running. This allows PyTorch to use expandable memory segments on CUDA 12+ (reduces waste from reserved but unused blocks).
• Clear cache manually: Add torch.cuda.empty_cache() after model init in train.py (around line 100), but this is a band-aid.
• Use CPU offloading: For eval only, modify estimate_loss() to use smaller batches or CPU for non-critical parts, but this slows things down.
• Monitor VRAM: Run nvidia-smi -l 1 in another terminal to watch usage in real-time.

5. Other Config Adjustments

• Increase eval_interval = 1000 and reduce eval_iters = 50 to lighten eval load (less memory churn early on).
• If on a multi-GPU setup, enable DDP, but this seems single-GPU.
• For bfloat16 issues: Ensure your GPU supports it (Ampere+ like RTX 30-series). If not, force dtype = 'float16' in the script.
• Deprecate warning fix: In train.py line 196, change to scaler = torch.amp.GradScaler('cuda', enabled=(dtype == 'float16')).

6. Hardware/Setup Checks

• Free up VRAM: Kill other GPU processes (nvidia-smi to list PIDs, then kill -9 <PID>). Restart your terminal/session.
• Dataset: Ensure openwebtext_local is loaded efficiently; large datasets can preload into RAM/VRAM via DataLoader.
• PyTorch/CUDA version: Verify compatibility (e.g., PyTorch 2.4+ with CUDA 12). Reinstall if needed: pip install torch --index-url https://download.pytorch.org/whl/cu121.
• Fallback to CPU: For testing, add device = 'cpu' and compile = False, but expect very slow training (hours per iter vs. seconds on GPU).

Expected Outcome

With batch_size=4, gradient_accumulation_steps=16, and compile=False, you should clear the OOM and start training. Total runtime for max_iters=20,000 will be longer due to accumulation, but monitor loss via log_interval=100. If it still fails, drop batch_size further and scale up later.

If these don’t resolve it, share your GPU model (nvidia-smi), PyTorch version (torch.__version__), and updated config/log for more targeted advice.

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