fix bug in PostTrainingProgram for certain cases (#45616)

* fix bug in PostTrainingProgram for certain cases

python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py

@@ -26,6 +26,7 @@ except:
 from inspect import isgeneratorfunction
 from .... import io
 from .... import core
+from .... import reader
 from .... import framework
 from .... import unique_name
 from ....executor import global_scope, Executor
@@ -141,7 +142,6 @@ class PostTrainingQuantization(object):
                  is_use_cache_file=False,
                  skip_tensor_list=None,
                  same_scale_tensor_list=None,
-                 scale_trainable=False,
                  cache_dir=None,
                  scale_dict=None,
                  return_graph=False):
@@ -231,7 +231,6 @@ class PostTrainingQuantization(object):
                 `conv2d/depthwise_conv2d + bn`, the weights scale for all channel will
                 be different. In address this problem, fuse the pattern before
                 quantization. Default False.
-            scale_trainable(bool, optional): whether scale can be train.
             is_use_cache_file(bool, optional): This param is deprecated.
             cache_dir(str, optional): This param is deprecated.
         Returns:
@@ -296,7 +295,7 @@ class PostTrainingQuantization(object):
             batch_generator, data_loader]), "The sample_generator, batch_generator " \
             "and data_loader cannot be None in the same time."
         if data_loader is not None:
-            assert isinstance(data_loader, (io.DataLoader, type(isgeneratorfunction))), \
+            assert isinstance(data_loader, (io.DataLoader, type(isgeneratorfunction), reader.GeneratorLoader)), \
                 "data_loader only accepts `paddle.io.DataLoader` or Generator instance."
         assert batch_size > 0, "The batch_size should be greater than 0."
         assert algo in self._support_algo_type, \
@@ -366,9 +365,11 @@ class PostTrainingQuantization(object):
         self._quantized_threshold = {}
         self._same_scale_tensor_list = same_scale_tensor_list
         self._freeze_model = freeze_model
-        self._scale_trainable = scale_trainable
         self._scale_dict = scale_dict
         self._return_graph = return_graph
+        self.FLAG = False
+        if self._program is not None:
+            self.FLAG = True
 
     def quantize(self):
         '''
@@ -440,6 +441,7 @@ class PostTrainingQuantization(object):
             self._update_program()
 
         # save out_threshold for quantized ops.
+        if not self.FLAG:
             self._save_output_threshold()
 
         if any(op_type in self._quantizable_op_type
@@ -1001,8 +1003,7 @@ class PostTrainingQuantization(object):
                 activation_bits=self._activation_bits,
                 activation_quantize_type=self._activation_quantize_type,
                 weight_quantize_type=self._weight_quantize_type,
-                quantizable_op_type=major_quantizable_op_types,
-                is_test=not self._scale_trainable)
+                quantizable_op_type=major_quantizable_op_types)
         else:
             transform_pass = QuantizationTransformPassV2(
                 scope=self._scope,
@@ -1011,8 +1012,7 @@ class PostTrainingQuantization(object):
                 activation_bits=self._activation_bits,
                 activation_quantize_type=self._activation_quantize_type,
                 weight_quantize_type=self._weight_quantize_type,
-                quantizable_op_type=major_quantizable_op_types,
-                is_test=not self._scale_trainable)
+                quantizable_op_type=major_quantizable_op_types)
 
         for sub_graph in graph.all_sub_graphs():
             # Insert fake_quant/fake_dequantize op must in test graph, so
@@ -1029,15 +1029,13 @@ class PostTrainingQuantization(object):
             add_quant_dequant_pass = AddQuantDequantPass(
                 scope=self._scope,
                 place=self._place,
-                quantizable_op_type=minor_quantizable_op_types,
-                is_test=not self._scale_trainable)
+                quantizable_op_type=minor_quantizable_op_types)
         else:
             add_quant_dequant_pass = AddQuantDequantPassV2(
                 scope=self._scope,
                 place=self._place,
                 quantizable_op_type=minor_quantizable_op_types,
-                is_full_quantized=self._is_full_quantize,
-                is_test=not self._scale_trainable)
+                is_full_quantized=self._is_full_quantize)
 
         for sub_graph in graph.all_sub_graphs():
             sub_graph._for_test = True
@@ -1055,11 +1053,11 @@ class PostTrainingQuantization(object):
                     max_scale = None
                     tmp_tensor_list = []
                     for tensor_name in tensor_list:
-                        if tensor_name not in scale_dict.keys():
-                            continue
                         if '#' in tensor_name:
                             real_tensor_name, opera, scalar = tensor_name.split(
                                 '#')
+                            if real_tensor_name not in scale_dict.keys():
+                                continue
                             if opera == '*':
                                 scale_dict[real_tensor_name] = float(
                                     scale_dict[real_tensor_name]) * float(
@@ -1072,16 +1070,18 @@ class PostTrainingQuantization(object):
                                 real_tensor_name] if max_scale is None else max(
                                     max_scale, scale_dict[real_tensor_name])
                         else:
+                            if tensor_name not in scale_dict.keys():
+                                continue
                             max_scale = scale_dict[
                                 tensor_name] if max_scale is None else max(
                                     max_scale, scale_dict[tensor_name])
 
                     for tensor_name in tensor_list:
-                        if tensor_name not in scale_dict.keys():
-                            continue
                         if '#' in tensor_name:
                             real_tensor_name, opera, scalar = tensor_name.split(
                                 '#')
+                            if real_tensor_name not in scale_dict.keys():
+                                continue
                             if opera == '*':
                                 scale_dict[
                                     real_tensor_name] = max_scale / float(
@@ -1091,6 +1091,8 @@ class PostTrainingQuantization(object):
                                     real_tensor_name] = max_scale * float(
                                         scalar)
                         else:
+                            if tensor_name not in scale_dict.keys():
+                                continue
                             scale_dict[tensor_name] = max_scale
             self._scale_dict = scale_dict
 
@@ -1265,7 +1267,6 @@ class PostTrainingQuantizationProgram(PostTrainingQuantization):
                  is_use_cache_file=False,
                  skip_tensor_list=None,
                  same_scale_tensor_list=None,
-                 scale_trainable=False,
                  cache_dir=None,
                  scale_dict=None,
                  return_graph=True):
@@ -1276,9 +1277,12 @@ class PostTrainingQuantizationProgram(PostTrainingQuantization):
                          activation_bits, weight_bits, activation_quantize_type,
                          weight_quantize_type, onnx_format, freeze_model,
                          optimize_model, is_use_cache_file, skip_tensor_list,
-                         same_scale_tensor_list, scale_trainable, cache_dir,
-                         scale_dict, return_graph)
+                         same_scale_tensor_list, cache_dir, scale_dict,
+                         return_graph)
+        self.FLAG = False
         self._program = program
+        if self._program is not None:
+            self.FLAG = True
         assert feed_list is not None, \
             "Feed list should not be None."
         assert fetch_list is not None, \

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -1470,9 +1470,10 @@ class OutScaleForTrainingPass(object):
                     data_type = 'float64' if in_node.dtype() \
                         == core.VarDesc.VarType.FP64 else 'float32'
                     try:
-                        scale_node = graph._find_node_by_name(
+                        graph._find_node_by_name(
                             graph.all_var_nodes(),
                             self._scale_name(in_node.name()))
+                        continue
                     except:
                         scale_node = graph.create_persistable_node(
                             name=self._scale_name(in_node.name()),

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_program_resnet50.py

@@ -186,7 +186,11 @@ class TestPostTrainingQuantizationProgram(TestPostTrainingQuantization):
         ], ['batch_norm_27.tmp_2', 'batch_norm_26.tmp_2'],
                                   [
                                       'test_scale_name_not_in_scale_dict1',
-                                      'test_scale_name_not_in_scale_dict1'
+                                      'test_scale_name_not_in_scale_dict2'
+                                  ],
+                                  [
+                                      'test_scale_name_not_in_scale_dict1#/#1',
+                                      'test_scale_name_not_in_scale_dict2#/#1'
                                   ]]
         ptq = PostTrainingQuantizationProgram(
             executor=exe,