From 7439924891496025edf60c9da6a782f362a50c70 Mon Sep 17 00:00:00 2001
From: Tim Dettmers <tim.dettmers@gmail.com>
Date: Tue, 5 Oct 2021 19:16:20 -0700
Subject: Initial commit

---
 bitsandbytes/nn/modules.py | 44 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 bitsandbytes/nn/modules.py

(limited to 'bitsandbytes/nn/modules.py')

diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py
new file mode 100644
index 0000000..bf0945c
--- /dev/null
+++ b/bitsandbytes/nn/modules.py
@@ -0,0 +1,44 @@
+# Copyright (c) Facebook, Inc. and its affiliates. 
+#   
+# This source code is licensed under the MIT license found in the 
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from typing import Optional
+
+from torch import Tensor
+from torch.nn.parameter import Parameter
+import torch.nn.functional as F
+
+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)
+        self.norm = torch.nn.LayerNorm(embedding_dim)
+        GlobalOptimManager.get_instance().register_parameters(self.weight)
+        GlobalOptimManager.get_instance().override_config(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 self.norm(emb)
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