un-fuse bias

bitsandbytes/autograd/_functions.py

@@ -316,15 +316,14 @@ class MatMul8bitLt(torch.autograd.Function):
 
         if bias is None or bias.dtype == torch.float16:
             output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
-            output = output.to(A_dtype)
-
+            delayed_bias = None
         else:  # apply bias separately
             output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
-            output = output.to(A_dtype).add_(bias)
+            delayed_bias = bias
 
         # 4. Mixed-precision decomposition matmul
         if coo_tensorA is not None and subA is not None:
-            output += torch.matmul(subA, state.subB)
+            output.addmm_(subA, state.subB)
 
         # 5. Save state
         ctx.state = state
@@ -341,6 +340,9 @@ class MatMul8bitLt(torch.autograd.Function):
             ctx.tensor_states = (None, None)
             ctx.save_for_backward(None, None)
 
+        output = output.to(A_dtype)
+        if delayed_bias is not None:
+            output.add_(delayed_bias)
 
         clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
         return clone_func(output.view(output_shape))