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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)
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