| | import torch |
| | import numpy as np |
| | from jaxtyping import jaxtyped |
| | import typeguard |
| | import typing |
| | from functorch.dim import tree_map |
| | import torch |
| |
|
| |
|
| | @jaxtyped(typechecker=typeguard.typechecked) |
| | def torch_to_numpy(tensor: typing.Any) -> np.ndarray | float: |
| | """ |
| | Convert a torch tensor to a numpy array. |
| | """ |
| | if isinstance(tensor, torch.Tensor): |
| | return tensor.detach().cpu().numpy() |
| | else: |
| | return tensor |
| |
|
| |
|
| | @jaxtyped(typechecker=typeguard.typechecked) |
| | def torch_dict_to_numpy(d: dict) -> dict: |
| | return tree_map(torch_to_numpy, d) |
| |
|
| |
|
| | @jaxtyped(typechecker=typeguard.typechecked) |
| | def compute_grad_norm(model: torch.nn.Module, grads: None) -> float: |
| |
|
| | total_norm = 0 |
| | if grads is not None: |
| | for p in grads: |
| | param_norm = p.norm(2) |
| | total_norm += param_norm.item() ** 2 |
| | total_norm = total_norm ** (1.0 / 2) |
| | return total_norm |
| |
|
| | for p in model.parameters(): |
| | param_norm = p.grad.data.norm(2) |
| | total_norm += param_norm.item() ** 2 |
| | total_norm = total_norm ** (1.0 / 2) |
| | return total_norm |
| |
|
| |
|
| | def is_numpy(x: typing.Any) -> bool: |
| | return isinstance(x, np.ndarray) |
| |
|
