From 8ae9bb23ad9c61a92ab1a0ac6be65cd787c4fe5b Mon Sep 17 00:00:00 2001
From: dbaranchuk <dmitrybaranchuk@gmail.com>
Date: Tue, 23 Aug 2022 23:39:54 +0300
Subject: add memory efficient backward

---
 bitsandbytes/nn/modules.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

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

diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py
index b222f54..ef7fefc 100644
--- a/bitsandbytes/nn/modules.py
+++ b/bitsandbytes/nn/modules.py
@@ -148,10 +148,12 @@ class Int8Params(torch.nn.Parameter):
         has_fp16_weights=False,
         CB=None,
         SCB=None,
+        SCBt=None,
     ):
         cls.has_fp16_weights = has_fp16_weights
         cls.CB = None
         cls.SCB = None
+        cls.SCBt = None
         if data is None:
             data = torch.empty(0)
         return torch.Tensor._make_subclass(cls, data, requires_grad)
@@ -165,10 +167,10 @@ class Int8Params(torch.nn.Parameter):
             B = self.data.contiguous().half().cuda(device)
             CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
             del CBt
-            del SCBt
             self.data = CB
             setattr(self, "CB", CB)
             setattr(self, "SCB", SCB)
+            setattr(self, "SCBt", SCBt)
 
         return self
 
@@ -210,6 +212,7 @@ class Int8Params(torch.nn.Parameter):
             )
             new_param.CB = self.CB
             new_param.SCB = self.SCB
+            new_param.SCB = self.SCBt
 
             return new_param
 
@@ -240,8 +243,10 @@ class Linear8bitLt(nn.Linear):
     def init_8bit_state(self):
         self.state.CB = self.weight.CB
         self.state.SCB = self.weight.SCB
+        self.state.SCBt = self.weight.SCBt
         self.weight.CB = None
         self.weight.SCB = None
+        self.weight.SCBt = None
 
     def forward(self, x):
         self.state.is_training = self.training
@@ -255,11 +260,11 @@ class Linear8bitLt(nn.Linear):
 
         out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
 
-        if not self.state.has_fp16_weights and self.state.CB is not None:
+        # if not self.state.has_fp16_weights and self.state.CB is not None:
             # we converted 8-bit row major to turing/ampere format in the first inference pass
             # we no longer need the row-major weight
-            del self.state.CB
-            self.weight.data = self.state.CxB
+            # del self.state.CB
+            # self.weight.data = self.state.CxB
 
         return out
 
-- 
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