!20337 warnning clean for quant

Merge pull request !20337 from zhang_sss/warnning_clean_2

mindspore/compression/export/quant_export.py

@@ -203,6 +203,7 @@ class ExportToQuantInferNetwork:
         network = Validator.check_isinstance('network', network, (nn.Cell,))
         self.data_type = mstype.int8
         self.network = copy.deepcopy(network)
+        self.network_bk = copy.deepcopy(network)
         self.get_inputs_table(inputs)
         self.mean = mean
         self.std_dev = std_dev
@@ -217,7 +218,6 @@ class ExportToQuantInferNetwork:
 
     def run(self):
         """Start to convert."""
-        self.network_bk = copy.deepcopy(self.network)
         self.network.update_cell_prefix()
         network = self.network
         if isinstance(network, _AddFakeQuantInput):
@@ -255,6 +255,19 @@ class ExportToQuantInferNetwork:
             block = QuantBlock(op_core, weight, quant_op, dequant_op, scale_deq, bias, activation)
         return block
 
+    def _get_input_quant_param(self, minq_name, np_type, param_dict):
+        """get input quant parameter for quant block"""
+        fake_quant_a_in_prefix = minq_name[:-5]
+        cells = self.network_bk.cells_and_names()
+        for cell in cells:
+            if cell[0].endswith(fake_quant_a_in_prefix):
+                fake_quant_a_in = cell[1]
+                break
+        scale_a_in, zp_a_in, param_dict["input_maxq"], param_dict["input_minq"] = \
+            quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_a_in, np_type)
+        param_dict["input_narrow_range"] = fake_quant_a_in.narrow_range
+        return scale_a_in, zp_a_in
+
     def __get_quant_param(self, cell_core, fake_quant_a_out):
         """get parameter for quant block"""
         w_minq_name = cell_core.fake_quant_weight.minq.name
@@ -291,15 +304,7 @@ class ExportToQuantInferNetwork:
                 scale_a_in, zp_a_in, param_dict["input_maxq"], param_dict["input_minq"] = \
                     (1 / self.std_dev), round(self.mean), 'None', 'None'
             else:
-                fake_quant_a_in_prefix = minq_name[:-5]
-                cells = self.network_bk.cells_and_names()
-                for cell in cells:
-                    if cell[0].endswith(fake_quant_a_in_prefix):
-                        fake_quant_a_in = cell[1]
-                        break
-                scale_a_in, zp_a_in, param_dict["input_maxq"], param_dict["input_minq"] = \
-                    quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_a_in, np_type)
-                param_dict["input_narrow_range"] = fake_quant_a_in.narrow_range
+                scale_a_in, zp_a_in = self._get_input_quant_param(minq_name, np_type, param_dict)
         else:
             # skip quant layer
             scale_a_in, zp_a_in = 1.0, 0.0

mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad.py

@@ -177,6 +177,7 @@ def fake_learned_scale_quant_perlayer_grad_d_param(input_x, alpha, quant_max,
     quant_max_data = tvm.placeholder(quant_max_shape, name="quant_max_data", dtype=quant_max_dtype)
     return dout_data, input_data, alpha_data, quant_max_data
 
+
 @util.check_input_type(dict, dict, dict, dict, dict, dict, bool, str)
 def fake_learned_scale_quant_perlayer_grad_d(dout, input_x, alpha, quant_max, dx, dalpha, neg_trunc,
                                              kernel_name="fake_learned_scale_quant_perlayer_grad_d"):