fix boft mixed precision

src/peft/tuners/boft/layer.py

@@ -27,6 +27,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.autograd import Function
+from torch.utils.cpp_extension import load
 
 from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
 
@@ -77,9 +78,6 @@ def get_fbd_cuda():
     if _FBD_CUDA is not None:
         return _FBD_CUDA
 
-    # This import initializes cuda context and should thus be local, see issue 1877
-    from torch.utils.cpp_extension import load
-
     curr_dir = os.path.dirname(__file__)
     # need ninja to build the extension
     try:
@@ -594,8 +592,8 @@ def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
         elif self.merged:
             result = self.base_layer(x, *args, **kwargs)
         else:
-            boft_rotation = torch.eye(self.in_features, device=x.device)
-            boft_scale = torch.ones((int(self.out_features), 1), device=x.device)
+            boft_rotation = torch.eye(self.in_features, device=x.device, dtype=previous_dtype) 
+            boft_scale = torch.ones((int(self.out_features), 1), device=x.device, dtype=previous_dtype) 
 
             for active_adapter in self.active_adapters:
                 if active_adapter not in self.boft_R.keys():
@@ -615,27 +613,32 @@ def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
                     orth_rotate_butterfly = orth_rotate_butterfly.squeeze(0)
                     block_diagonal_butterfly = torch.block_diag(*torch.unbind(orth_rotate_butterfly))
                     block_diagonal_butterfly = block_diagonal_butterfly.unsqueeze(0)
-
-                boft_P = self.boft_P.to(block_diagonal_butterfly.device)
+
+                boft_P = self.boft_P.to(block_diagonal_butterfly.device).to(previous_dtype)
+                block_diagonal_butterfly = block_diagonal_butterfly.to(previous_dtype)
                 butterfly_oft_mat_batch = torch.bmm(block_diagonal_butterfly, boft_P.permute(0, 2, 1))
                 butterfly_oft_mat_batch = torch.bmm(boft_P, butterfly_oft_mat_batch)
                 butterfly_oft_mat = butterfly_oft_mat_batch[0]
 
                 for i in range(1, butterfly_oft_mat_batch.shape[0]):
                     butterfly_oft_mat = butterfly_oft_mat_batch[i] @ butterfly_oft_mat
-
                 boft_rotation = butterfly_oft_mat @ boft_rotation
                 boft_scale = boft_s * boft_scale
+
 
             x = x.to(self.get_base_layer().weight.data.dtype)
 
             orig_weight = self.get_base_layer().weight.data
             orig_weight = torch.transpose(orig_weight, 0, 1)
+            boft_rotation = boft_rotation.to(previous_dtype)
+            orig_weight = orig_weight.to(previous_dtype)
             rotated_weight = torch.mm(boft_rotation, orig_weight)
             rotated_weight = torch.transpose(rotated_weight, 0, 1)
 
             scaled_rotated_weight = rotated_weight * boft_scale
-
+            scaled_rotated_weight = scaled_rotated_weight.to(previous_dtype)
+            if self.base_layer.bias is not None:
+                self.base_layer.bias = self.base_layer.bias.to(previous_dtype)
             result = F.linear(input=x, weight=scaled_rotated_weight, bias=self.base_layer.bias)
 
         result = result.to(previous_dtype)