From df86625a9399d16d6fb2e3bab6bb7bcc729f3b7d Mon Sep 17 00:00:00 2001
From: Tim Dettmers <tim.dettmers@gmail.com>
Date: Mon, 24 Oct 2022 11:54:25 -0700
Subject: Isolated CUDASetup logging; all tests green.

---
 bitsandbytes/nn/modules.py | 44 --------------------------------------------
 1 file changed, 44 deletions(-)

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

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
-- 
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