[Bug] Pytorch CUDA 13 'torch._C._CudaDeviceProperties' object has no attribute 'total_mem'

[alroman2]

Using pytorch for cuda 13 causes attribute error for methods that check the device property memory during train_model.py.

python src/bot_mmorpg/scripts/train_model.py --data data/raw --model efficientnet_lstm
[Warning] Could not import models_pytorch: attempted relative import with no known parent package
Traceback (most recent call last):
  File "G:\Git\bot\BOT-MMORPG-AI\src\bot_mmorpg\scripts\train_model.py", line 717, in <module>
    sys.exit(main())
             ^^^^^^
  File "G:\Git\bot\BOT-MMORPG-AI\src\bot_mmorpg\scripts\train_model.py", line 589, in main
    total_mem_gb = torch.cuda.get_device_properties(0).total_mem / 1e9
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'torch._C._CudaDeviceProperties' object has no attribute 'total_mem'. Did you mean: 'total_memory'?

Impact:

1. Training completely fails due to torch device property call differring for cuda 13 pytorch.

Steps to reproduce:

1. Install pytorch from --index-url https://download.pytorch.org/whl/cu130
2. Attempt to run python src/bot_mmorpg/scripts/train_model.py

Proposed Fixes:

1. consider get_device_properties as a nullable operation and safely unwrap the result. If we are logging provide an error string and log the issue.
2. If we require the total memory such as when auto batch size is being calculated -> provide a safe default value if we unwrap a null value

def _log_gpu_memory(prefix: str = "") -> None:
    """Print current GPU memory usage (no-op on CPU)."""
    if not PYTORCH_AVAILABLE or not torch.cuda.is_available():
        return
    alloc = torch.cuda.memory_allocated() / 1e9
    reserved = torch.cuda.memory_reserved() / 1e9
    total = torch.cuda.get_device_properties(0).total_mem / 1e9 <- unsafe a will crash program on cuda 13
    print(
        f"  [GPU] {prefix}Allocated: {alloc:.2f}GB | Reserved: {reserved:.2f}GB | Total: {total:.2f}GB"
    )

  # Auto-detect safe batch-size for GPU VRAM (fixes #27 — CUDA OOM on 8GB cards)
    batch_size = args.batch_size
    if device.type == "cuda" and not args.cpu:
        total_mem_gb = torch.cuda.get_device_properties(0).total_mem / 1e9  <- unsafe a will crash program on cuda 13
        if total_mem_gb <= 6 and batch_size > 4:
            old_bs = batch_size
            batch_size = 4
            use_amp = True
            use_grad_ckpt = True
            print(
                f"[Auto] GPU has {total_mem_gb:.1f}GB VRAM — "
                f"reducing batch_size {old_bs} -> {batch_size}, "
                f"enabling AMP (fp16) and gradient checkpointing"
            )

[ruslanmv]

Thank you for reporting this and for providing the detailed traceback, reproduction steps, and suggested fix.

You’re right — this appears to be caused by a PyTorch CUDA 13 API difference where total_mem is no longer available and should be replaced with total_memory. We’ll update the GPU memory detection logic to handle this safely, including a fallback so training does not crash if device properties cannot be read.

This should improve both GPU memory logging and automatic batch-size detection. Thanks again for catching this and for the clear explanation.

[ruslanmv]

Thanks for the precise diagnosis and the suggested fix — that's exactly what landed.

torch._C._CudaDeviceProperties was renamed total_mem → total_memory in the CUDA 13 PyTorch wheels (>= torch 2.10), so any call to torch.cuda.get_device_properties(0).total_mem crashes on those builds.

Fix on branch claude/fix-open-issues-7LDKz (src/bot_mmorpg/scripts/train_model.py):

• New _safe_total_vram_gb() helper tries total_memory first, then falls back to total_mem for older builds, and returns None for anything else (ROCm, oddball forks, etc.) — never raises.
• _log_gpu_memory prints Total: unknown instead of crashing when the helper returns None.
• The auto-batch-size path drops into the conservative 6GB tier (batch_size=4, AMP, gradient checkpointing) when VRAM can't be read, instead of dying with AttributeError before the first epoch.

This means a CUDA 13 install will now train successfully and a non-CUDA-13 install behaves identically to before.

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