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[FIX] passing of sparse in StableEmbedding

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Robin Schmidt 2021-11-15 17:27:02 +01:00
parent 037022e878
commit 67a1283501
1 changed files with 2 additions and 2 deletions
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4
bitsandbytes/nn/modules.py
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@ -15,8 +15,8 @@ from bitsandbytes.optim import GlobalOptimManager
class StableEmbedding(torch.nn.Embedding):
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
sparse: bool = True, _weight: Optional[Tensor] = None) -> None:
super(StableEmbedding, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, False, _weight)
sparse: bool = False, _weight: Optional[Tensor] = None) -> None:
super(StableEmbedding, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight)
self.norm = torch.nn.LayerNorm(embedding_dim)
GlobalOptimManager.get_instance().register_parameters(self.weight)
GlobalOptimManager.get_instance().override_config(self.weight, 'optim_bits', 32)