Isolated CUDASetup logging; all tests green.

2 files changed, 1 insertions, 45 deletions

diff --git a/bitsandbytes/nn/__init__.py b/bitsandbytes/nn/__init__.py
index 98d4aa0..edc595a 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 Int8Params, Linear8bit, Linear8bitLt, StableEmbedding
+from .modules import Int8Params, Linear8bitLt, StableEmbedding
diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py
index 9250fec..4f82cdc 100644
--- a/bitsandbytes/nn/modules.py
+++ b/bitsandbytes/nn/modules.py
@@ -271,47 +271,3 @@ class Linear8bitLt(nn.Linear):
                 del self.state.CxB
 
         return out
-
-
-class Linear8bit(nn.Linear):
-    def __init__(
-        self,
-        input_features,
-        output_features,
-        bias=True,
-        quant_type="vector",
-        index=None,
-        args=None,
-        sparse_decomp=False,
-    ):
-        super(Linear8bit, self).__init__(input_features, output_features, bias)
-        self.quant_type = quant_type
-        self.index = index
-        self.args = args
-        self.iter = 0
-
-    def forward(self, x):
-        self.iter += 1
-        if self.iter % self.args.clip_freq == 0:
-            with torch.no_grad():
-                maxval, maxidx = torch.topk(
-                    torch.abs(self.weight.flatten()), k=self.args.clip_idx
-                )
-                if not dist.is_initialized() or dist.get_rank() == 0:
-                    print("clip", maxval[-1].item())
-                self.weight.clip_(-maxval[-1], maxval[-1])
-
-        if self.args is not None:
-            out = bnb.nn.functional.sparse_decomposed_linear8bit(
-                x,
-                self.weight,
-                self.bias,
-                qval=self.args.sparse_decomp_val,
-                quant_type=self.args.quant_type,
-            )
-        else:
-            out = bnb.nn.functional.linear8bit(
-                x, self.weight, self.bias, quant_type=self.args.quant_type
-            )
-
-        return out