Update PostQuantTraining zero size (#49868)

python/paddle/static/quantization/post_training_quantization.py

@@ -789,7 +789,7 @@ class PostTrainingQuantization:
         _logger.info("MSE searching stage ...")
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             var_tensor = var_tensor.flatten()
@@ -843,7 +843,7 @@ class PostTrainingQuantization:
         _logger.info("EMD searching stage ...")
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             var_tensor = var_tensor.flatten()
@@ -899,7 +899,7 @@ class PostTrainingQuantization:
 
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             abs_max_value = float(np.max(np.abs(var_tensor)))
@@ -940,7 +940,7 @@ class PostTrainingQuantization:
 
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             abs_max_value = float(np.max(np.abs(var_tensor)))
@@ -975,7 +975,7 @@ class PostTrainingQuantization:
 
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             min_value = float(np.min(var_tensor))
@@ -992,7 +992,7 @@ class PostTrainingQuantization:
     def _sample_histogram(self):
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if (not var_tensor.any()) or (
+            if (var_tensor.size == 0) or (
                 var_name not in self._sampling_act_histogram
             ):
                 self._zero_size_var_names.add(var_name)
@@ -1031,7 +1031,7 @@ class PostTrainingQuantization:
 
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             abs_max_value = float(np.max(np.abs(var_tensor)))
@@ -1094,7 +1094,7 @@ class PostTrainingQuantization:
         '''
         for var_name in self._quantized_act_var_name:
             var_tensor = utils.load_variable_data(self._scope, var_name)
-            if not var_tensor.any():
+            if var_tensor.size == 0:
                 self._zero_size_var_names.add(var_name)
                 continue
             var_tensor = np.abs(var_tensor)