{"record":{"id":"e51bb81d39038f41","repo":"huggingface/pytorch-image-models","slug":"invalid-name-value","errorCode":null,"errorMessage":"Invalid {name}: {value}","messagePattern":"Invalid (.+?): (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"timm/optim/_helpers.py","lineNumber":76,"sourceCode":"        state_steps: Sequence[Tensor],\n        supports_xla: bool = True,\n) -> None:\n    capturable_supported_devices = _get_capturable_supported_devices(supports_xla=supports_xla)\n    assert all(\n        p.device.type == step.device.type and p.device.type in capturable_supported_devices\n        for p, step in zip(params, state_steps)\n    ), f\"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}.\"\n\n\ndef _validate_scalar(name: str, value, min_value: float = 0.0, max_value: Optional[float] = None) -> None:\n    if torch.is_tensor(value):\n        if value.numel() != 1:\n            raise ValueError(f\"{name} must be a scalar or scalar tensor.\")\n        value_float = float(value.detach().cpu())\n    else:\n        value_float = float(value)\n    if value_float < min_value or (max_value is not None and value_float >= max_value):\n        raise ValueError(f\"Invalid {name}: {value}\")\n\n\ndef _add_scaled_(param: Tensor, update: Tensor, scale) -> None:\n    if torch.is_tensor(scale):\n        param.add_(update * scale)\n    else:\n        param.add_(update, alpha=scale)\n\n\ndef _addcdiv_scaled_(param: Tensor, tensor1: Tensor, tensor2: Tensor, scale) -> None:\n    if torch.is_tensor(scale):\n        param.add_(tensor1 / tensor2 * scale)\n    else:\n        param.addcdiv_(tensor1, tensor2, value=scale)\n","sourceCodeStart":58,"sourceCodeEnd":91,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/optim/_helpers.py#L58-L91","documentation":"The range check in _validate_scalar: values must satisfy value >= min_value (default 0) and, when max_value is given, value < max_value. Violations (negative lr/eps/weight decay, or betas >= 1) raise 'Invalid {name}'.","triggerScenarios":"AdamW(lr=-0.1), eps=0, or betas=(1.2, 0.999) with timm optimizers; weight_decay=-1e-4 via create_optimizer_v2 kwargs.","commonSituations":"Sweep configs where a search produces out-of-range values; reading betas/eps from env vars or YAML without validation; sign errors on weight decay.","solutions":["Clamp/validate hyper-parameters to their valid ranges before constructing the optimizer (lr>0, 0<=beta<1, eps>0)","Fix the sign or magnitude in the config source (YAML/CLI) that produced the bad value"],"exampleFix":"# before\nopt = timm.optim.AdamW(params, lr=-1e-3, betas=(1.2, 0.99))\n# after\nopt = timm.optim.AdamW(params, lr=1e-3, betas=(0.9, 0.99))","handlingStrategy":"validation","validationCode":"assert lr > 0 and 0.0 <= beta1 < 1.0 and 0.0 <= beta2 < 1.0 and eps > 0 and weight_decay >= 0","typeGuard":"def valid_hp(name: str, v, lo=0.0, hi=None) -> bool:\n    v = v.item() if torch.is_tensor(v) else v\n    return v >= lo and (hi is None or v < hi)","tryCatchPattern":"try:\n    opt = timm.optim.create_optimizer_v2(model, opt='adamw', lr=lr, **extra)\nexcept ValueError as e:\n    if 'Invalid' in str(e):\n        lr = max(lr, 1e-8)\n        opt = timm.optim.create_optimizer_v2(model, opt='adamw', lr=lr, **extra)\n    else:\n        raise","preventionTips":["Validate hyper-parameter ranges in config loaders before training","Clamp sweep results to valid ranges","Watch sign errors on weight_decay"],"tags":["timm","optimizer","hyperparameter","range-check"],"backgroundTag":"hyperparameter-out-of-range","analyzedSha":"9a5261e31b3b5128526eb2658333b4c0a54464ae","analyzedAt":"2026-08-27T02:34:25.417Z","schemaVersion":2},"datasetVersion":"2026-08-27T03:17:27.898Z"}