diff options
Diffstat (limited to 'bitsandbytes/nn')
| -rw-r--r-- | bitsandbytes/nn/__init__.py | 2 | ||||
| -rw-r--r-- | bitsandbytes/nn/modules.py | 33 |
2 files changed, 32 insertions, 3 deletions
diff --git a/bitsandbytes/nn/__init__.py b/bitsandbytes/nn/__init__.py index 177540f..27ad6ca 100644 --- a/bitsandbytes/nn/__init__.py +++ b/bitsandbytes/nn/__init__.py @@ -2,4 +2,4 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .modules import StableEmbedding +from .modules import StableEmbedding, Embedding diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py index ce2f3a4..dc0a171 100644 --- a/bitsandbytes/nn/modules.py +++ b/bitsandbytes/nn/modules.py @@ -18,8 +18,7 @@ class StableEmbedding(torch.nn.Embedding): 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) + GlobalOptimManager.get_instance().register_module_override(self, 'weight', {'optim_bits': 32}) def reset_parameters(self) -> None: torch.nn.init.xavier_uniform_(self.weight) @@ -42,3 +41,33 @@ class StableEmbedding(torch.nn.Embedding): self.norm_type, self.scale_grad_by_freq, self.sparse) return self.norm(emb) + + +class Embedding(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 = False, _weight: Optional[Tensor] = None) -> None: + super(Embedding, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight) + GlobalOptimManager.get_instance().register_module_override(self, 'weight', {'optim_bits': 32}) + + def reset_parameters(self) -> None: + torch.nn.init.xavier_uniform_(self.weight) + self._fill_padding_idx_with_zero() + + ''' !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding + to make the Layer compatible with Pytorch < 1.9. + This means that if this changes in future PyTorch releases this need to change too + which is cumbersome. However, with this we can ensure compatibility with previous + PyTorch releases. + ''' + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def forward(self, input: Tensor) -> Tensor: + emb = F.embedding( + input, self.weight, self.padding_idx, self.max_norm, + self.norm_type, self.scale_grad_by_freq, self.sparse) + + return emb |