From 4d6174bc6336fb6fba712f1d2c903de1de677747 Mon Sep 17 00:00:00 2001
From: dbaranchuk <dmitrybaranchuk@gmail.com>
Date: Thu, 25 Aug 2022 19:09:23 +0300
Subject: memory efficient fp16 backward

---
 bitsandbytes/autograd/_functions.py | 40 +++++--------------------------------
 1 file changed, 5 insertions(+), 35 deletions(-)

(limited to 'bitsandbytes/autograd')

diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
index 7cf4999..52e56d0 100644
--- a/bitsandbytes/autograd/_functions.py
+++ b/bitsandbytes/autograd/_functions.py
@@ -196,7 +196,6 @@ class MatmulLtState:
 
         self.CxBt = None
         self.SBt = None
-        self.CBt = None
 
 
 class MatMul8bitLt(torch.autograd.Function):
@@ -327,15 +326,12 @@ class MatMul8bitLt(torch.autograd.Function):
         #clone_func = torch.clone
         return clone_func(output.view(output_shape))
 
-    @staticmethod
     def backward(ctx, grad_output):
         if ctx.is_empty:
             bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
             return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
         req_gradA, req_gradB, req_gradBias = ctx.req_grads
-        CAt, subA = ctx.tensors
-        SCAt, idx = ctx.tensor_states
-        formatB = ctx.formatB
+        assert not req_gradB, "TODO: support weight updates as well"
         state = ctx.state
 
         if len(grad_output.shape) == 3:
@@ -345,37 +341,11 @@ class MatMul8bitLt(torch.autograd.Function):
 
         grad_A = grad_B = grad_bias = None
 
-        Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output)
-        if req_gradB:
-            CxAt, SAt = F.transform(CAt, formatB, transpose=True)
-            C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
-            gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
-            grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
-            if state.threshold > 0.0 and subA is not None:
-                grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
-
         if req_gradA:
-            C32grad, Sgrad = F.transform(Cgrad, "col32")
-            if state.CxBt is None:
-                if state.has_fp16_weights:
-                    CBt = state.CBt
-                else:
-                    # Restore CBt from CB
-                    assert state.CBt is None, "CBt should not be stored in state"
-                    CB = state.CB.half()
-                    SCB = state.SCB.unsqueeze(1).half()
-                    SCBt = state.SCBt.unsqueeze(1).half()
-                    Bt = (CB * SCB).t().contiguous()
-                    CBt = (Bt / SCBt).t().to(torch.int8)
-
-                # intentionally, do not store CxBt in state  
-                CxBt, SBt = F.transform(
-                    CBt, to_order=formatB, transpose=True
-                )
-            else:
-                CxBt = state.CxBt
-            gradA32, SgradA32 = F.igemmlt(C32grad, CxBt, Sgrad, SBt)
-            grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape)
+            CB = state.CB.half()
+            SCB = state.SCB.unsqueeze(1).half()
+            B = (CB * SCB) / 127.0
+            grad_A = torch.mm(grad_output, B).view(ctx.grad_shape)
 
         if req_gradBias:
             grad_bias = grad_output.sum(0)
-- 
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