{"record":{"id":"d4f76a73e9372482","repo":"invoke-ai/InvokeAI","slug":"failed-to-configure-the-pytorch-cuda-memory-alloca","errorCode":null,"errorMessage":"Failed to configure the PyTorch CUDA memory allocator. Expected backend: '{expected_backend}', but got '{allocator_backend}'. Verify that 1) the pytorch_cuda_alloc_conf is set correctly, and 2) that torch is not imported before calling configure_torch_cuda_allocator().","messagePattern":"Failed to configure the PyTorch CUDA memory allocator\\. Expected backend: '(.+?)', but got '(.+?)'\\. Verify that 1\\) the pytorch_cuda_alloc_conf is set correctly, and 2\\) that torch is not imported before calling configure_torch_cuda_allocator\\(\\)\\.","errorType":"exception","errorClass":"RuntimeError","httpStatus":null,"severity":"error","filePath":"invokeai/app/util/torch_cuda_allocator.py","lineNumber":46,"sourceCode":"            return\n\n    # Configure the PyTorch CUDA memory allocator.\n    # NOTE: It is important that this happens before torch is imported.\n    os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = pytorch_cuda_alloc_conf\n\n    import torch\n\n    # Relevant docs: https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf\n    if not torch.cuda.is_available():\n        raise RuntimeError(\n            \"Attempted to configure the PyTorch CUDA memory allocator, but no CUDA devices are available.\"\n        )\n\n    # Verify that the torch allocator was properly configured.\n    allocator_backend = torch.cuda.get_allocator_backend()\n    expected_backend = \"cudaMallocAsync\" if \"cudaMallocAsync\" in pytorch_cuda_alloc_conf else \"native\"\n    if allocator_backend != expected_backend:\n        raise RuntimeError(\n            f\"Failed to configure the PyTorch CUDA memory allocator. Expected backend: '{expected_backend}', but got \"\n            f\"'{allocator_backend}'. Verify that 1) the pytorch_cuda_alloc_conf is set correctly, and 2) that torch is \"\n            \"not imported before calling configure_torch_cuda_allocator().\"\n        )\n\n    logger.info(f\"PyTorch CUDA memory allocator: {torch.cuda.get_allocator_backend()}\")\n","sourceCodeStart":28,"sourceCodeEnd":53,"githubUrl":"https://github.com/invoke-ai/InvokeAI/blob/0b6a024f2ff6a86bfb953dcdb9cc504ef7397a06/invokeai/app/util/torch_cuda_allocator.py#L28-L53","documentation":"After configuring, the function reads torch.cuda.get_allocator_backend() and compares it to the expected backend ('cudaMallocAsync' when that backend is requested, otherwise 'native'). A mismatch means the allocator was not applied as requested, so it raises with a diagnostic naming both backends.","triggerScenarios":"Setting a PYTORCH_CUDA_ALLOC_CONF whose backend the installed torch version doesn't support; torch imported earlier so the env var was ignored while cudaMallocAsync was still the effective backend (or vice versa); typos in the conf string like 'backend:cudaMallocAsync' misspellings; older torch versions lacking cudaMallocAsync.","commonSituations":"Upgrading/downgrading PyTorch and the configured backend no longer exists; a stale cached import meant config never took effect; copying an alloc-conf snippet from docs incompatible with the installed torch.","solutions":["Confirm torch is not imported before configure_torch_cuda_allocator() (fix import order)","Check the conf string for typos and that the backend is supported by the installed torch version (print torch.__version__)","Simplify the conf to just the backend directive first, then add options back one at a time","Align the configured backend with what the torch build supports, or upgrade torch"],"exampleFix":"// before\nconfigure_torch_cuda_allocator(\"backend:cudaMallocAsync,max_split_size_mb:512\")  # torch 1.12 lacks cudaMallocAsync\n\n// after\nconfigure_torch_cuda_allocator(\"backend:native\")  # or upgrade torch to >=2.0 for cudaMallocAsync","handlingStrategy":"try-catch","validationCode":"import torch\nbackend = torch.cuda.get_allocator_backend()\nwanted = \"cudaMallocAsync\" if \"cudaMallocAsync\" in conf else \"native\"\nassert backend == wanted, f\"backend {backend} != {wanted}\"","typeGuard":"def allocator_matches(expected: str) -> bool:\n    import torch\n    return torch.cuda.get_allocator_backend() == expected","tryCatchPattern":"try:\n    configure_torch_cuda_allocator(conf)\nexcept RuntimeError as e:\n    if \"Failed to configure the PyTorch CUDA memory allocator\" in str(e):\n        logger.warning(\"allocator backend mismatch: %s\", e)\n    else:\n        raise","preventionTips":["Keep torch import strictly after configuration","Match the conf backend to the installed torch version's capabilities","Test the conf string in a scratch process after torch upgrades","Start with a minimal conf (backend only) and add options incrementally"],"tags":["pytorch","cuda","allocator","configuration"],"backgroundTag":"cuda-allocator-mismatch","analyzedSha":"0b6a024f2ff6a86bfb953dcdb9cc504ef7397a06","analyzedAt":"2026-08-29T04:46:49.967Z","schemaVersion":2},"datasetVersion":"2026-08-29T07:17:48.351Z"}