diff --git a/mindspore/compression/quant/qat.py b/mindspore/compression/quant/qat.py
index a4baa4344c..57d2f1b9ff 100644
--- a/mindspore/compression/quant/qat.py
+++ b/mindspore/compression/quant/qat.py
@@ -56,7 +56,7 @@ def create_quant_config(quant_observer=(nn.FakeQuantWithMinMaxObserver, nn.FakeQ
             Default: (QuantDtype.INT8, QuantDtype.INT8)
         per_channel (Union[bool, list, tuple]):  Quantization granularity based on layer or on channel. If `True`
             then base on per channel otherwise base on per layer. The first element represents weights
-            and second element represents data flow. Default: (False, False)
+            and second element represents data flow, and second element must be `False` now. Default: (False, False)
         symmetric (Union[bool, list, tuple]): Whether the quantization algorithm is symmetric or not. If `True` then
             base on symmetric otherwise base on asymmetric. The first element represents weights and second
             element represents data flow. Default: (False, False)
@@ -66,6 +66,8 @@ def create_quant_config(quant_observer=(nn.FakeQuantWithMinMaxObserver, nn.FakeQ
     Returns:
         QuantConfig, Contains the observer type of weight and activation.
     """
+    if per_channel[-1]:
+        raise ValueError("Arg 'per_channel' second element must be 'False'.")
     weight_observer = quant_observer[0].partial_init(quant_delay=quant_delay[0], quant_dtype=quant_dtype[0],
                                                      per_channel=per_channel[0], symmetric=symmetric[0],
                                                      narrow_range=narrow_range[0])
@@ -130,7 +132,7 @@ class QuantizationAwareTraining(Quantizer):
             Default: (QuantDtype.INT8, QuantDtype.INT8)
         per_channel (Union[bool, list, tuple]):  Quantization granularity based on layer or on channel. If `True`
             then base on per channel otherwise base on per layer. The first element represents weights
-            and second element represents data flow. Default: (False, False)
+            and second element represents data flow, and second element must be `False` now. Default: (False, False)
         symmetric (Union[bool, list, tuple]): Whether the quantization algorithm is symmetric or not. If `True` then
             base on symmetric otherwise base on asymmetric. The first element represents weights and second
             element represents data flow. Default: (False, False)