un-fuse bias

bitsandbytes/autograd/_functions.py

@@ -314,10 +314,13 @@ class MatMul8bitLt(torch.autograd.Function):
         out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
         # we apply the fused bias here
 
-        fused_bias = bias if bias.dtype == torch.float16 else None
-        output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=fused_bias)
-        if fused_bias is None and bias is not None:
-            output.add_(bias.to(output.dtype))
+        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)
+
+        else:  # apply bias separately
+            output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
+            output = output.to(A_dtype).add_(bias)
 
         # 4. Mixed-precision decomposition matmul
         if coo_tensorA is not None and subA is not None:
@@ -338,8 +341,6 @@ class MatMul8bitLt(torch.autograd.Function):
             ctx.tensor_states = (None, None)
             ctx.save_for_backward(None, None)
 
-        # Cast fp16 output back to A.dtype
-        output = output.to(A_dtype)
 
         clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
         return clone_func(output.view(output_shape))