bug fix in fake quant ops

mindspore/ops/operations/_quant_ops.py

@@ -15,6 +15,7 @@
 
 """Operators for quantization."""
 
+import mindspore.context as context
 from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ..primitive import PrimitiveWithInfer, prim_attr_register
@@ -82,6 +83,8 @@ class FakeQuantPerLayer(PrimitiveWithInfer):
                  narrow_range=False,
                  training=True):
         """init FakeQuantPerLayer OP"""
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import fake_quant_perlayer
         if num_bits not in self.support_quant_bit:
             raise ValueError(
                 f"For '{self.name}' attr \'num_bits\' is not support.")
@@ -143,6 +146,8 @@ class FakeQuantPerLayerGrad(PrimitiveWithInfer):
                  quant_delay=0,
                  symmetric=False,
                  narrow_range=False):
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import fake_quant_perlayer_grad
         if num_bits not in self.support_quant_bit:
             raise ValueError(
                 f"For '{self.name}' attr \'num_bits\' is not support.")
@@ -222,6 +227,8 @@ class FakeQuantPerChannel(PrimitiveWithInfer):
                  training=True,
                  channel_axis=1):
         """init FakeQuantPerChannel OP"""
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import fake_quant_perchannel
         if num_bits not in self.support_quant_bit:
             raise ValueError(
                 f"For '{self.name}' Attr \'num_bits\' is not support.")
@@ -286,6 +293,8 @@ class FakeQuantPerChannelGrad(PrimitiveWithInfer):
                  narrow_range=False,
                  channel_axis=1):
         """init FakeQuantPerChannelGrad Fill"""
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import fake_quant_perchannel_grad
         if num_bits not in self.support_quant_bit:
             raise ValueError(
                 f"For '{self.name}' attr \'num_bits\' is not support.")
@@ -454,6 +463,8 @@ class CorrectionMul(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, channel_axis=0):
         """init correction mul layer"""
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import correction_mul
         self.channel_axis = channel_axis
         self.init_prim_io_names(inputs=['x', 'batch_std', 'running_std'],
                                 outputs=['out'])
@@ -486,6 +497,8 @@ class CorrectionMulGrad(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, channel_axis=0):
         """init correction mul layer"""
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import correction_mul_grad
         self.channel_axis = channel_axis
         self.init_prim_io_names(inputs=['dout', 'x', 'gamma', 'running_std'],
                                 outputs=['dx', 'd_gamma'])
@@ -847,9 +860,8 @@ class FakeQuantMinMaxPerLayerUpdate(PrimitiveWithInfer):
     def __init__(self, num_bits=8, ema=False, ema_decay=0.999, symmetric=False, narrow_range=False,
                  training=True):
         """init FakeQuantMinMaxPerLayerUpdate OP"""
-        from mindspore.ops._op_impl._custom_op import correction_mul, correction_mul_grad
-        from mindspore.ops._op_impl._custom_op import fake_quant_with_min_max, fake_quant_with_min_max_grad
-        from mindspore.ops._op_impl._custom_op import fake_quant_with_min_max_update
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import fake_quant_minmax_perlayer_update
         if num_bits not in self.support_quant_bit:
             raise ValueError(
                 f"For '{self.name}' attr \'num_bits\' is not support.")
@@ -922,6 +934,8 @@ class FakeQuantMinMaxPerChannelUpdate(PrimitiveWithInfer):
     def __init__(self, num_bits=8, ema=False, ema_decay=0.999, symmetric=False, narrow_range=False,
                  training=True, channel_axis=1):
         """init FakeQuantPerChannelUpdate OP for Ascend"""
+        if context.get_context('device_target') == "Ascend":
+            from mindspore.ops._op_impl._custom_op import fake_quant_minmax_perchannel_update
         if num_bits not in self.support_quant_bit:
             raise ValueError(
                 f"For '{self.name}' attr \'num_bits\' is not support.")