Collect output scale for quantized op and fused op (#23369)

* Collect output scale for quantized op and fused op
* Post_training_quantizaion sets batch_generator to support lod tensor

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

@@ -25,7 +25,9 @@ from ....log_helper import get_logger
 from .quantization_pass import QuantizationTransformPass
 from .quantization_pass import QuantizationFreezePass
 from .quantization_pass import AddQuantDequantPass
-from .quantization_pass import _op_real_in_out_name
+from .quantization_pass import _out_scale_op_list
+from .quantization_pass import _get_op_input_var_names
+from .quantization_pass import _get_op_output_var_names
 
 __all__ = ['PostTrainingQuantization', 'WeightQuantization']
 
@@ -68,14 +70,17 @@ class PostTrainingQuantization(object):
                  model_dir=None,
                  model_filename=None,
                  params_filename=None,
+                 batch_generator=None,
                  sample_generator=None,
                  batch_size=10,
                  batch_nums=None,
                  algo="KL",
                  quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
                  is_full_quantize=False,
-                 weight_bits=8,
                  activation_bits=8,
+                 weight_bits=8,
+                 activation_quantize_type='range_abs_max',
+                 weight_quantize_type='channel_wise_abs_max',
                  is_use_cache_file=False,
                  cache_dir="./temp_post_training"):
         '''
@@ -95,9 +100,14 @@ class PostTrainingQuantization(object):
                 When all parameters were saved in a single binary file, set it 
                 as the real filename. If parameters were saved in separate files, 
                 set it as 'None'. Default is 'None'.
-            sample_generator(Python Generator): The sample generator provides 
-                calibrate data for DataLoader, and it only returns a sample every 
-                time.
+            batch_generator(Python Generator): The batch generator provides 
+                calibrate data for DataLoader, and it returns a batch every
+                time. Note that, sample_generator and batch_generator, only one
+                should be set. Beisdes, batch_generator supports lod tensor.
+            sample_generator(Python Generator): The sample generator provides
+                calibrate data for DataLoader, and it only returns a sample every
+                time. Note that, sample_generator and batch_generator, only one
+                should be set. Beisdes, sample_generator dose not support lod tensor.
             batch_size(int, optional): The batch size of DataLoader. Default is 10.
             batch_nums(int, optional): If batch_nums is not None, the number of 
                 calibrate data is batch_size*batch_nums. If batch_nums is None, use 
@@ -114,8 +124,19 @@ class PostTrainingQuantization(object):
                 apply quantization to all supported quantizable op type. If set
                 is_full_quantized as False, only apply quantization to the op type 
                 according to the input quantizable_op_type.
-            weight_bits(int, optional): quantization bit number for weights.
             activation_bits(int): quantization bit number for activation.
+            weight_bits(int, optional): quantization bit number for weights.
+            activation_quantize_type(str): quantization type for activation,
+                now support 'range_abs_max', 'moving_average_abs_max' and 'abs_max'.
+                This param only specifies the fake ops in saving quantized model.
+                If it is 'range_abs_max' or 'moving_average_abs_max', we save the scale
+                obtained by post training quantization in fake ops. Note that, if it
+                is 'abs_max', the scale will not be saved in fake ops.
+            weight_quantize_type(str): quantization type for weights,
+                support 'abs_max' and 'channel_wise_abs_max'. This param only specifies
+                the fake ops in saving quantized model, and we save the scale obtained
+                by post training quantization in fake ops. Compared to 'abs_max',
+                the model accuracy is usually higher when it is 'channel_wise_abs_max'.
             is_use_cache_file(bool, optional): If set is_use_cache_file as False,
                 all temp data will be saved in memory. If set is_use_cache_file as True,
                 it will save temp data to disk. When the fp32 model is complex or
@@ -163,46 +184,67 @@ class PostTrainingQuantization(object):
             ptq.save_quantized_model(save_model_path)
         '''
 
+        self._support_activation_quantize_type = [
+            'range_abs_max', 'moving_average_abs_max', 'abs_max'
+        ]
+        self._support_weight_quantize_type = ['abs_max', 'channel_wise_abs_max']
+        self._support_algo_type = ['KL', 'abs_max', 'min_max']
+        self._support_quantize_op_type = \
+            list(set(QuantizationTransformPass._supported_quantizable_op_type +
+                AddQuantDequantPass._supported_quantizable_op_type))
+
+        # Check inputs
         assert executor is not None, "The executor cannot be None."
         assert model_dir is not None, "The model_dir cannot be None."
-        assert sample_generator is not None, \
-            "The sample_generator cannot be None."
-        assert algo in ['KL', 'abs_max', 'min_max'], \
+        assert any([gen is not None] for gen in [sample_generator,
+            batch_generator]), "The sample_generator and batch_generator " \
+            "cannot be None in the same time."
+        assert batch_size > 0, "The batch_size should be greater than 0."
+        assert algo in self._support_algo_type, \
             "The algo should be KL, abs_max or min_max."
-
+        assert activation_quantize_type in self._support_activation_quantize_type, \
+            "The activation_quantize_type ({}) should in ({}).".format(
+            activation_quantize_type, self._support_activation_quantize_type)
+        assert weight_quantize_type in self._support_weight_quantize_type, \
+            "The weight_quantize_type ({}) shoud in ({}).".format(
+            weight_quantize_type, self._support_weight_quantize_type)
+
+        # Save input params
         self._executor = executor
         self._scope = global_scope() if scope == None else scope
         self._model_dir = model_dir
         self._model_filename = model_filename
         self._params_filename = params_filename
         self._sample_generator = sample_generator
+        self._batch_generator = batch_generator
         self._batch_size = batch_size
         self._batch_nums = batch_nums
         self._algo = algo
-        self._is_use_cache_file = is_use_cache_file
-        self._cache_dir = cache_dir
-        if self._is_use_cache_file and not os.path.exists(self._cache_dir):
-            os.mkdir(self._cache_dir)
-
-        supported_quantizable_op_type = \
-            QuantizationTransformPass._supported_quantizable_op_type + \
-            AddQuantDequantPass._supported_quantizable_op_type
+        self._activation_bits = activation_bits
+        self._weight_bits = weight_bits
+        self._activation_quantize_type = activation_quantize_type
+        self._weight_quantize_type = weight_quantize_type
+        self._is_full_quantize = is_full_quantize
         if is_full_quantize:
-            self._quantizable_op_type = supported_quantizable_op_type
+            self._quantizable_op_type = self._support_quantize_op_type
         else:
             self._quantizable_op_type = quantizable_op_type
             for op_type in self._quantizable_op_type:
-                assert op_type in supported_quantizable_op_type, \
+                assert op_type in self._support_quantize_op_type, \
                     op_type + " is not supported for quantization."
+        self._is_use_cache_file = is_use_cache_file
+        self._cache_dir = cache_dir
+        if self._is_use_cache_file and not os.path.exists(self._cache_dir):
+            os.mkdir(self._cache_dir)
 
+        # Define variables
         self._place = self._executor.place
         self._program = None
         self._feed_list = None
         self._fetch_list = None
         self._data_loader = None
 
-        self._op_real_in_out_name = _op_real_in_out_name
-        self._bit_length = 8
+        self._out_scale_op_list = _out_scale_op_list
         self._quantized_weight_var_name = set()
         self._quantized_act_var_name = set()
         self._sampling_data = {}
@@ -223,7 +265,7 @@ class PostTrainingQuantization(object):
             the program of quantized model.
         '''
         self._load_model_data()
-        self._collect_quantized_varnames()
+        self._collect_target_varnames()
         self._set_activation_persistable()
 
         batch_id = 0
@@ -257,17 +299,28 @@ class PostTrainingQuantization(object):
         self._save_output_threshold()
         return self._program
 
-    def save_quantized_model(self, save_model_path):
+    def save_quantized_model(self,
+                             save_model_path,
+                             model_filename=None,
+                             params_filename=None):
         '''
         Save the quantized model to the disk.
 
         Args:
-            save_model_path(str): The path to save the quantized model
+            save_model_path(str): The path to save the quantized model.
+            model_filename(str, optional): If the model_filename is None,
+                save the model to '__model__'. Otherwise, save the model
+                to the specified filename. Default: None.
+            params_filename(str, optional): If the params_filename is None,
+                save params to separted files. Otherwise, save all params
+                to the specified filename.
         Returns:
             None
         '''
         io.save_inference_model(
             dirname=save_model_path,
+            model_filename=model_filename,
+            params_filename=params_filename,
             feeded_var_names=self._feed_list,
             target_vars=self._fetch_list,
             executor=self._executor,
@@ -287,20 +340,31 @@ class PostTrainingQuantization(object):
             for var_name in self._feed_list]
         self._data_loader = io.DataLoader.from_generator(
             feed_list=feed_vars, capacity=3 * self._batch_size, iterable=True)
-        self._data_loader.set_sample_generator(
-            self._sample_generator,
-            batch_size=self._batch_size,
-            drop_last=True,
-            places=self._place)
-
-    def _collect_quantized_varnames(self):
+        if self._sample_generator is not None:
+            self._data_loader.set_sample_generator(
+                self._sample_generator,
+                batch_size=self._batch_size,
+                drop_last=True,
+                places=self._place)
+        elif self._batch_generator is not None:
+            self._data_loader.set_batch_generator(
+                self._batch_generator, places=self._place)
+
+    def _collect_target_varnames(self):
         '''
         Collect the variable names for sampling, and set activation
         variables to be persistable.
         '''
+        # TODO(juncaipeng), consider the name_scope of skip_quant
         _logger.info("Collect quantized variable names ...")
-        # TODO(juncaipeng), consider the name_scope of skip_quant and
-        # reduce the variables for sampling
+
+        def collect_var_name(var_name_list, persistable_var_names):
+            for var_name in var_name_list:
+                if var_name in persistable_var_names:
+                    self._quantized_weight_var_name.add(var_name)
+                else:
+                    self._quantized_act_var_name.add(var_name)
+
         persistable_var_names = []
         for var in self._program.list_vars():
             if var.persistable:
@@ -308,30 +372,22 @@ class PostTrainingQuantization(object):
 
         for op in self._program.global_block().ops:
             op_type = op.type
+            # For quantized ops, sample inputs and outputs
             if op_type in self._quantizable_op_type:
-                name_list = self._op_real_in_out_name[op_type]
-                for input_name in name_list[0]:
-                    for var_name in op.input(input_name):
-                        if var_name in persistable_var_names:
-                            self._quantized_weight_var_name.add(var_name)
-                        else:
-                            self._quantized_act_var_name.add(var_name)
-                for output_name in name_list[1]:
-                    for var_name in op.output(output_name):
-                        if var_name in persistable_var_names:
-                            self._quantized_weight_var_name.add(var_name)
-                        else:
-                            self._quantized_act_var_name.add(var_name)
+                collect_var_name(
+                    _get_op_input_var_names(op), persistable_var_names)
+                collect_var_name(
+                    _get_op_output_var_names(op), persistable_var_names)
+            # For other op, only sample output scale
+            elif op_type in self._out_scale_op_list:
+                collect_var_name(
+                    _get_op_output_var_names(op), persistable_var_names)
 
     def _set_activation_persistable(self):
         '''
         Set activation variables to be persistable, so can obtain 
         the tensor data in sample_data
         '''
-        persistable_var_names = []
-        for var in self._program.list_vars():
-            if var.persistable:
-                persistable_var_names.append(var.name)
         for var in self._program.list_vars():
             if var.name in self._quantized_act_var_name:
                 var.persistable = True
@@ -350,6 +406,7 @@ class PostTrainingQuantization(object):
         '''
         assert self._algo in ["abs_max", "min_max"], \
             "The algo should be abs_max or min_max to sample min max value."
+
         if self._algo == "abs_max":
             # Only calculate abs_max value for weight for once
             if self._quantized_var_abs_max == {}:
@@ -396,15 +453,13 @@ class PostTrainingQuantization(object):
             "The algo should be min_max to save input threshold."
         for op in self._program.global_block().ops:
             if op.type in self._quantizable_op_type:
-                input_name_list = self._op_real_in_out_name[op.type][0]
-                for input_name in input_name_list:
-                    for var_name in op.input(input_name):
-                        assert var_name in self._quantized_var_min
-                        assert var_name in self._quantized_var_max
-                        op._set_attr(var_name + ".min",
-                                     self._quantized_var_min[var_name])
-                        op._set_attr(var_name + ".max",
-                                     self._quantized_var_max[var_name])
+                for var_name in _get_op_input_var_names(op):
+                    assert var_name in self._quantized_var_min
+                    assert var_name in self._quantized_var_max
+                    op._set_attr(var_name + ".min",
+                                 self._quantized_var_min[var_name])
+                    op._set_attr(var_name + ".max",
+                                 self._quantized_var_max[var_name])
 
     def _sample_data(self, iter):
         '''
@@ -438,16 +493,21 @@ class PostTrainingQuantization(object):
         '''
         _logger.info("Calculate KL threshold ...")
         assert self._algo == "KL", "The algo should be KL to calculate kl threshold."
-        # apply channel_wise_abs_max quantization for weights
+
+        # Abs_max threshold for weights
         for var_name in self._quantized_weight_var_name:
-            data = self._sampling_data[var_name]
-            threshold_per_channel = []
-            for i in range(data.shape[0]):
-                abs_max_value = np.max(np.abs(data[i]))
-                threshold_per_channel.append(abs_max_value)
-            self._quantized_var_kl_threshold[var_name] = threshold_per_channel
-
-        # apply kl quantization for activation
+            weight_data = self._sampling_data[var_name]
+            weight_threshold = None
+            if self._weight_quantize_type == "abs_max":
+                weight_threshold = np.max(np.abs(weight_data))
+            elif self._weight_quantize_type == "channel_wise_abs_max":
+                weight_threshold = []
+                for i in range(weight_data.shape[0]):
+                    abs_max_value = np.max(np.abs(weight_data[i]))
+                    weight_threshold.append(abs_max_value)
+            self._quantized_var_kl_threshold[var_name] = weight_threshold
+
+        # KL threshold for activations
         if self._is_use_cache_file:
             for var_name in self._quantized_act_var_name:
                 sampling_data = []
@@ -484,10 +544,10 @@ class PostTrainingQuantization(object):
         transform_pass = QuantizationTransformPass(
             scope=self._scope,
             place=self._place,
-            weight_bits=self._bit_length,
-            activation_bits=self._bit_length,
-            activation_quantize_type='moving_average_abs_max',
-            weight_quantize_type='channel_wise_abs_max',
+            weight_bits=self._weight_bits,
+            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)
         transform_pass.apply(graph)
 
@@ -525,9 +585,9 @@ class PostTrainingQuantization(object):
         freeze_pass = QuantizationFreezePass(
             scope=self._scope,
             place=self._place,
-            weight_bits=self._bit_length,
-            activation_bits=self._bit_length,
-            weight_quantize_type='channel_wise_abs_max',
+            weight_bits=self._weight_bits,
+            activation_bits=self._activation_bits,
+            weight_quantize_type=self._weight_quantize_type,
             quantizable_op_type=major_quantizable_op_types)
         freeze_pass.apply(graph)
         self._program = graph.to_program()
@@ -536,30 +596,37 @@ class PostTrainingQuantization(object):
         '''
         Save output threshold to the quantized op.
         '''
+
+        def save_info(op_node, out_var_name, threshold_map, out_info_name,
+                      quantized_type):
+            assert out_var_name in threshold_map, \
+                "The output ({}) of {} node does not have threshold.".format(
+                out_var_name, op_node.type)
+            op_node._set_attr(out_info_name, threshold_map[var_name])
+            if op_node.type in self._quantizable_op_type:
+                op._set_attr("quantization_type", quantized_type)
+
+        def analysis_and_save_info(op_node, out_var_name):
+            if self._algo == "KL":
+                save_info(op_node, out_var_name,
+                          self._quantized_var_kl_threshold, "out_threshold",
+                          "post_kl")
+            elif self._algo == "abs_max":
+                save_info(op_node, out_var_name, self._quantized_var_abs_max,
+                          "out_threshold", "post_abs_max")
+            elif self._algo == "min_max":
+                save_info(op_node, out_var_name, self._quantized_var_min,
+                          "out_min", "post_min_max")
+                save_info(op_node, out_var_name, self._quantized_var_max,
+                          "out_max", "post_min_max")
+
         for op in self._program.global_block().ops:
-            if op.type in self._quantizable_op_type:
-                output_name_list = self._op_real_in_out_name[op.type][1]
-                for output_name in output_name_list:
-                    for var_name in op.output(output_name):
-                        if self._algo == "KL":
-                            assert var_name in self._quantized_var_kl_threshold
-                            op._set_attr(
-                                var_name + ".threshold",
-                                self._quantized_var_kl_threshold[var_name])
-                            op._set_attr("quantization_type", "post_kl")
-                        elif self._algo == "abs_max":
-                            assert var_name in self._quantized_var_abs_max
-                            op._set_attr(var_name + ".threshold",
-                                         self._quantized_var_abs_max[var_name])
-                            op._set_attr("quantization_type", "post_abs_max")
-                        elif self._algo == "min_max":
-                            assert var_name in self._quantized_var_min
-                            assert var_name in self._quantized_var_max
-                            op._set_attr(var_name + ".min",
-                                         self._quantized_var_min[var_name])
-                            op._set_attr(var_name + ".max",
-                                         self._quantized_var_max[var_name])
-                            op._set_attr("quantization_type", "post_min_max")
+            if op.type in (self._quantizable_op_type + self._out_scale_op_list):
+                out_var_names = _get_op_output_var_names(op)
+                assert len(out_var_names) == 1, "Post training " + \
+                    "quantization only support one output for " + op.type
+                for var_name in out_var_names:
+                    analysis_and_save_info(op, var_name)
 
     def _get_kl_scaling_factor(self, activation_blob, num_quantized_bins=255):
         '''

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

@@ -18,12 +18,13 @@ from ..... import compat as cpt
 from .... import core
 from ....framework import IrGraph
 from ....framework import IrNode
+from ....framework import Operator
 from .... import unique_name
 
 __all__ = [
     'QuantizationTransformPass', 'QuantizationFreezePass', 'ConvertToInt8Pass',
-    'TransformForMobilePass', 'ScaleForTrainingPass', 'ScaleForInferencePass',
-    'AddQuantDequantPass'
+    'TransformForMobilePass', 'OutScaleForTrainingPass',
+    'OutScaleForInferencePass', 'AddQuantDequantPass'
 ]
 
 _fake_quant_op_list = [
@@ -40,9 +41,9 @@ _fake_quant_dequant_op_list = [
 ]
 
 _out_scale_op_list = [
-    "mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid", "depthwise_conv2d",
-    "batch_norm", "concat", "tanh", "pad", "elementwise_add", "elementwise_mul",
-    "dropout", "split", "prelu", "conv2d_transpose", "leaky_relu"
+    "conv2d", "depthwise_conv2d", "mul", "matmul", "relu", "leaky_relu",
+    "relu6", "sigmoid", "tanh", "prelu", "swish", "softmax", "batch_norm",
+    "elementwise_add", "pool2d", "reshape2", "transpose2"
 ]
 
 # list op real input and output names, to avoid processing input such as AxisTensor.
@@ -67,6 +68,7 @@ _op_real_in_out_name = {
     "not_equal": [["X", "Y"], ["Out"]],
     "reshape": [["X"], ["Out"]],
     "reshape2": [["X"], ["Out"]],
+    "transpose2": [["X"], ["Out"]],
     "bilinear_interp": [["X"], ["Out"]],
     "nearest_interp": [["X"], ["Out"]],
     "trilinear_interp": [["X"], ["Out"]],
@@ -76,11 +78,49 @@ _op_real_in_out_name = {
     "relu": [["X"], ["Out"]],
     "relu6": [["X"], ["Out"]],
     "leaky_relu": [["X"], ["Out"]],
+    "prelu": [["X"], ["Out"]],
     "tanh": [["X"], ["Out"]],
     "swish": [["X"], ["Out"]],
+    "dropout": [["X"], ["Out"]],
+    "batch_norm": [["X"], ["Y"]],
+    "sigmoid": [["X"], ["Y"]],
 }
 
 
+def _get_op_input_var_names(op):
+    """ """
+    assert isinstance(op, (IrNode, Operator)), \
+        "The input op should be IrNode or Operator."
+    var_names = []
+    op_name = op.name() if isinstance(op, IrNode) \
+        else op.type
+    name_list = _op_real_in_out_name[op_name][0]
+    for name in name_list:
+        var_name = op.input(name)
+        if isinstance(var_name, list):
+            var_names.extend(var_name)
+        else:
+            var_names.append(var_name)
+    return var_names
+
+
+def _get_op_output_var_names(op):
+    """ """
+    assert isinstance(op, (IrNode, Operator)), \
+        "The input op should be IrNode or Operator."
+    var_names = []
+    op_name = op.name() if isinstance(op, IrNode) \
+        else op.type
+    name_list = _op_real_in_out_name[op_name][1]
+    for name in name_list:
+        var_name = op.output(name)
+        if isinstance(var_name, list):
+            var_names.extend(var_name)
+        else:
+            var_names.append(var_name)
+    return var_names
+
+
 def _init_var_node(var_node, value, scope, place):
     assert isinstance(value,
                       np.ndarray), 'The type of value should be numpy array.'
@@ -97,17 +137,18 @@ def _is_input_all_not_persistable(graph, op_node):
     Analyse the real inputs of the op node are all not persistable.
     '''
     is_input_all_not_persistable = True
-    op_node_name = op_node.name()
-    input_name_list = _op_real_in_out_name[op_node_name][0]
-    for input_name in input_name_list:
-        for arg_name in op_node.input(input_name):
-            in_node = graph._find_node_by_name(op_node.inputs, arg_name)
-            is_input_all_not_persistable = (is_input_all_not_persistable and \
-                (not in_node.persistable()))
+    for var_name in _get_op_input_var_names(op_node):
+        in_node = graph._find_node_by_name(op_node.inputs, var_name)
+        is_input_all_not_persistable = (is_input_all_not_persistable and \
+            (not in_node.persistable()))
     return is_input_all_not_persistable
 
 
 class QuantizationTransformPass(object):
+    """
+    Quantize the ops that have weights. Add quant and dequant ops for the quantized
+    ops's inputs.
+    """
     _supported_quantizable_op_type = [
         'conv2d', 'depthwise_conv2d', 'mul', 'matmul'
     ]
@@ -124,8 +165,7 @@ class QuantizationTransformPass(object):
                  skip_pattern=['skip_quant'],
                  quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul']):
         """
-        Convert and rewrite the IrGraph according to weight and
-        activation quantization type.
+        Constructor.
 
         Args:
             scope(fluid.Scope): When activation use 'range_abs_max' as the quantize
@@ -1088,7 +1128,7 @@ class TransformForMobilePass(object):
         return graph
 
 
-class ScaleForTrainingPass(object):
+class OutScaleForTrainingPass(object):
     def __init__(self, scope=None, place=None, moving_rate=0.9):
         """
         This pass is used for calculating output scales of some operators.
@@ -1195,7 +1235,7 @@ class ScaleForTrainingPass(object):
         return "%s@scale" % (var_name)
 
 
-class ScaleForInferencePass(object):
+class OutScaleForInferencePass(object):
     def __init__(self, scope=None):
         """
         This pass is used for setting output scales of some operators.
@@ -1226,7 +1266,7 @@ class ScaleForInferencePass(object):
                 scale_name = self._scale_name(op_node.output_arg_names()[0])
                 scale_v = np.array(
                     self._scope.find_var(scale_name).get_tensor())[0]
-                op_node.op()._set_attr("out_scale", float(scale_v))
+                op_node.op()._set_attr("out_threshold", float(scale_v))
         graph.resolve_hazard()
         return graph
 
@@ -1238,6 +1278,10 @@ class ScaleForInferencePass(object):
 
 
 class AddQuantDequantPass(object):
+    """
+    Quantize the ops that do not have weights, and add quant_dequant op for the 
+    quantized ops's inputs.
+    """
     _supported_quantizable_op_type = [
         "pool2d", "elementwise_add", "concat", "softmax", "argmax", "transpose",
         "equal", "gather", "greater_equal", "greater_than", "less_equal",
@@ -1259,9 +1303,7 @@ class AddQuantDequantPass(object):
                  quantizable_op_type=["elementwise_add", "pool2d"],
                  is_full_quantized=False):
         """
-        This pass add quant_dequant op for some ops, of which all the inputs must be 
-        not persistable.
-        The input scales can be obtained from the quant_dequant op.
+        Constructor.
 
         Args:
             scope(fluid.Scope): The scope is used to initialize these new parameters.
@@ -1338,10 +1380,7 @@ class AddQuantDequantPass(object):
                 op_node.op()._set_attr("quantization_type",
                                        "qat_without_weight")
                 op_node.op()._set_attr("activation_bits", self._quant_bits)
-                input_name_list = _op_real_in_out_name[op_node.name()][0]
-                arg_names = []
-                for input_name in input_name_list:
-                    arg_names.extend(op_node.input(input_name))
+                arg_names = _get_op_input_var_names(op_node)
                 for arg_name in arg_names:
                     in_node = graph._find_node_by_name(op_node.inputs, arg_name)
                     if arg_name in dequantized_vars_map:

python/paddle/fluid/contrib/slim/tests/CMakeLists.txt

@@ -107,15 +107,14 @@ function(save_qat_model_test target qat_model_dir fp32_model_save_path int8_mode
 		 --quantized_ops ${quantized_ops})
 endfunction()
 
-# Disable the unittest temporary
-list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
 if(WIN32)
-    list(REMOVE_ITEM TEST_OPS test_light_nas)
+	list(REMOVE_ITEM TEST_OPS test_light_nas)
+	list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
     list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
     list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
 endif()
 
-# Disable unittest for random error
+# Disable unittest for random error temporary
 list(REMOVE_ITEM TEST_OPS test_quantization_scale_pass)
 
 if(LINUX AND WITH_MKLDNN)

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py

@@ -140,9 +140,9 @@ class TestPostTrainingQuantization(unittest.TestCase):
 
         self.batch_size = 1 if os.environ.get('DATASET') == 'full' else 50
         self.sample_iterations = 50 if os.environ.get(
-            'DATASET') == 'full' else 1
+            'DATASET') == 'full' else 2
         self.infer_iterations = 50000 if os.environ.get(
-            'DATASET') == 'full' else 1
+            'DATASET') == 'full' else 2
 
         self.timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
         self.int8_model = os.path.join(os.getcwd(),
@@ -287,11 +287,12 @@ class TestPostTrainingQuantization(unittest.TestCase):
         (int8_throughput, int8_latency, int8_acc1) = self.run_program(
             self.int8_model, batch_size, infer_iterations)
 
+        print("---Post training quantization of {} method---".format(algo))
         print(
-            "FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
+            "FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}.".
             format(model, batch_size, fp32_throughput, fp32_latency, fp32_acc1))
         print(
-            "INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
+            "INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}.\n".
             format(model, batch_size, int8_throughput, int8_latency, int8_acc1))
         sys.stdout.flush()
 
@@ -308,7 +309,10 @@ class TestPostTrainingKLForMobilenetv1(TestPostTrainingQuantization):
         ]
         data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
         quantizable_op_type = [
-            "conv2d", "depthwise_conv2d", "mul", "pool2d", "elementwise_add"
+            "conv2d",
+            "depthwise_conv2d",
+            "mul",
+            "pool2d",
         ]
         is_full_quantize = False
         is_use_cache_file = False
@@ -326,10 +330,12 @@ class TestPostTrainingAbsMaxForMobilenetv1(TestPostTrainingQuantization):
         ]
         data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
         quantizable_op_type = [
-            "conv2d", "depthwise_conv2d", "mul", "pool2d", "elementwise_add"
+            "conv2d",
+            "mul",
         ]
         is_full_quantize = False
         is_use_cache_file = False
+        # The accuracy diff of post-traing quantization (abs_max) maybe bigger
         diff_threshold = 0.05
         self.run_test(model, algo, data_urls, data_md5s, quantizable_op_type,
                       is_full_quantize, is_use_cache_file, diff_threshold)

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

@@ -22,8 +22,8 @@ import paddle
 from paddle.fluid.framework import IrGraph
 from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
 from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
-from paddle.fluid.contrib.slim.quantization import ScaleForTrainingPass
-from paddle.fluid.contrib.slim.quantization import ScaleForInferencePass
+from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass
+from paddle.fluid.contrib.slim.quantization import OutScaleForInferencePass
 from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
 from paddle.fluid import core
 
@@ -112,7 +112,7 @@ class TestQuantizationScalePass(unittest.TestCase):
         add_quant_dequant_pass.apply(main_graph)
         add_quant_dequant_pass.apply(test_graph)
 
-        scale_training_pass = ScaleForTrainingPass(scope=scope, place=place)
+        scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
         scale_training_pass.apply(main_graph)
 
         dev_name = '_gpu' if use_cuda else '_cpu'
@@ -151,7 +151,7 @@ class TestQuantizationScalePass(unittest.TestCase):
                 if not for_ci:
                     print('{}: {}'.format('loss' + dev_name, loss_v))
 
-        scale_inference_pass = ScaleForInferencePass(scope=scope)
+        scale_inference_pass = OutScaleForInferencePass(scope=scope)
         scale_inference_pass.apply(test_graph)
 
         # Freeze graph for inference, but the weight of fc/conv is still float type.