use torch constraints to check if covariance is positive definite during mean resizing. (#35693)

* use torch constraints to check for psd

* small nit

* Small change

* Small change for the ci

* nit

src/transformers/modeling_utils.py

@@ -37,6 +37,7 @@ import torch
 from huggingface_hub import split_torch_state_dict_into_shards
 from packaging import version
 from torch import Tensor, nn
+from torch.distributions import constraints
 from torch.nn import CrossEntropyLoss, Identity
 from torch.utils.checkpoint import checkpoint
 
@@ -2425,14 +2426,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         covariance = old_centered_embeddings.T @ old_centered_embeddings / old_num_tokens
 
         # Check if the covariance is positive definite.
-        eigenvalues = torch.linalg.eigvals(covariance)
-        is_covariance_psd = bool(
-            (covariance == covariance.T).all() and not torch.is_complex(eigenvalues) and (eigenvalues > 0).all()
-        )
+        epsilon = 1e-9
+        is_covariance_psd = constraints.positive_definite.check(epsilon * covariance).all()
         if is_covariance_psd:
             # If covariances is positive definite, a distribution can be created. and we can sample new weights from it.
             distribution = torch.distributions.multivariate_normal.MultivariateNormal(
-                mean_embeddings, covariance_matrix=1e-9 * covariance
+                mean_embeddings, covariance_matrix=epsilon * covariance
             )
             new_embeddings.weight.data[-1 * added_num_tokens :, :] = distribution.sample(
                 sample_shape=(added_num_tokens,)