| 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) |
|
|
