change typecast behavior

bitsandbytes/autograd/_functions.py

@@ -230,16 +230,14 @@ class MatMul8bitLt(torch.autograd.Function):
             state.outlier_pool = GlobalOutlierPooler.get_instance()
 
         # Cast A to fp16
-        A_dtype = A.dtype
-        if A_dtype != torch.float16:
-            warnings.warn(f"MatMul8bitLt: input matrix will be converted from {A_dtype} to float16")
-            A = A.to(torch.float16)
+        if A.dtype != torch.float16:
+            warnings.warn(f"MatMul8bitLt: input matrix will be cast from {A.dtype} to float16")
 
         # 1. Quantize A
         if len(A.shape) == 3:
             A = A.view(-1, A.shape[-1]).contiguous()
         CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
-            A, threshold=state.threshold
+            A.to(torch.float16), threshold=state.threshold
         )
 
         if state.threshold > 0.0 and coo_tensorA is not None:
@@ -316,10 +314,10 @@ 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)
+            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)
+            output = output.to(A.dtype).add_(bias)
 
         # 4. Mixed-precision decomposition matmul
         if coo_tensorA is not None and subA is not None: