Merge branch 'main' of github.com:facebookresearch/bitsandbytes into 0.26.0

bitsandbytes/nn/modules.py

@@ -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)

bitsandbytes/optim/adam.py

@@ -2,7 +2,12 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
+
+import math
+import os
+
 import torch
+import torch.distributed as dist
 from bitsandbytes.optim.optimizer import Optimizer2State
 import bitsandbytes.functional as F
 
@@ -219,9 +224,9 @@ class AnalysisAdam(torch.optim.Optimizer):
                         if self.savedir != '' and state['step'] % 100 == 0:
                             if not os.path.exists(self.savedir): os.makedirs(self.savedir)
                             shapestr = '_'.join([str(dim) for dim in p_data_fp32.shape])
-                            pathe = join(self.savedir, f'{p_id}_{shapestr}_abserr.pkl')
-                            pathrele = join(self.savedir, f'{p_id}_{shapestr}_relerr.pkl')
-                            pathcounts = join(self.savedir, f'{p_id}_{shapestr}_counts.pkl')
+                            pathe = os.path.join(self.savedir, f'{p_id}_{shapestr}_abserr.pkl')
+                            pathrele = os.path.join(self.savedir, f'{p_id}_{shapestr}_relerr.pkl')
+                            pathcounts = os.path.join(self.savedir, f'{p_id}_{shapestr}_counts.pkl')
                             torch.save(e, pathe)
                             torch.save(rele, pathrele)
                             torch.save(counts, pathcounts)