Merge pull request #16489 from wzzju/fix_slim_quant_bugs

Clean codes and fix some bugs.

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

@@ -26,6 +26,17 @@ __all__ = [
 ]
 
 
+def _init_var_node(var_node, value, scope, place):
+    assert isinstance(value,
+                      np.ndarray), 'The type of value should be numpy array.'
+    assert scope is not None, \
+    'The scope cannot be set None.'
+    assert place is not None, \
+    'The place cannot be set None.'
+    tensor = scope.var(var_node.name()).get_tensor()
+    tensor.set(value, place)
+
+
 class QuantizationTransformPass(object):
     def __init__(self,
                  scope=None,
@@ -88,14 +99,14 @@ class QuantizationTransformPass(object):
         assert activation_quantize_type != 'channel_wise_abs_max', "The activation quantization type does not support 'channel_wise_abs_max'."
         if activation_quantize_type not in quant_type:
             raise ValueError(
-                "Unknown activation_quantize_type : '%s'. It can only be ",
-                "'abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
-                str(activation_quantize_type))
+                "Unknown activation_quantize_type : '%s'. It can only be "
+                "'abs_max' or 'range_abs_max' or 'moving_average_abs_max'." %
+                (str(activation_quantize_type)))
         if weight_quantize_type not in quant_type:
             raise ValueError(
-                "Unknown weight_quantize_type: '%s'. It can only be ",
-                "'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
-                str(weight_quantize_type))
+                "Unknown weight_quantize_type: '%s'. It can only be "
+                "'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'."
+                % (str(weight_quantize_type)))
 
         self._activation_quantize_type = activation_quantize_type
         self._weight_quantize_type = weight_quantize_type
@@ -121,8 +132,6 @@ class QuantizationTransformPass(object):
         """
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
-        #sequential_execution = core.get_pass('sequential_execution_pass')
-        #sequential_execution.apply(graph.graph)
         self._is_test = graph.is_test()
         # marked the variable which has been dequantized.
         dequantized_vars = collections.OrderedDict()
@@ -203,9 +212,12 @@ class QuantizationTransformPass(object):
                     var_type=core.VarDesc.VarType.LOD_TENSOR,
                     shape=[1],
                     var_dtype=core.VarDesc.VarType.INT64)
-                self._init_var_node(
-                    global_step_in, np.zeros(
-                        [1], dtype='int64'))
+                _init_var_node(
+                    global_step_in,
+                    np.zeros(
+                        [1], dtype='int64'),
+                    self._scope,
+                    self._place)
                 global_step_out = graph.create_var_node_from_desc(
                     global_step_in.var())
                 # The attribute of `op_role` is needed by ParallelExecutor.
@@ -284,7 +296,12 @@ class QuantizationTransformPass(object):
             var_dtype=var_node.dtype())
         data_type = 'float64' if var_node.dtype(
         ) == core.VarDesc.VarType.FP64 else 'float32'
-        self._init_var_node(scale_in_node, np.array([0.001], dtype=data_type))
+        _init_var_node(
+            scale_in_node,
+            np.array(
+                [0.001], dtype=data_type),
+            self._scope,
+            self._place)
 
         scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
         inputs = {'X': var_node, 'InScale': scale_in_node}
@@ -299,9 +316,13 @@ class QuantizationTransformPass(object):
                 var_dtype=var_node.dtype())
             data_type = 'float64' if var_node.dtype(
             ) == core.VarDesc.VarType.FP64 else 'float32'
-            self._init_var_node(
-                scales_node, np.zeros(
-                    [self._window_size], dtype=data_type))
+            _init_var_node(
+                scales_node,
+                np.zeros(
+                    [self._window_size], dtype=data_type),
+                self._scope,
+                self._place)
+
             inputs['Iter'] = self._global_step
             outputs['OutScales'] = scales_node
         attrs = {
@@ -343,7 +364,12 @@ class QuantizationTransformPass(object):
             var_dtype=var_node.dtype())
         data_type = 'float64' if var_node.dtype(
         ) == core.VarDesc.VarType.FP64 else 'float32'
-        self._init_var_node(scale_in_node, np.array([0.001], dtype=data_type))
+        _init_var_node(
+            scale_in_node,
+            np.array(
+                [0.001], dtype=data_type),
+            self._scope,
+            self._place)
 
         scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
         ins = {'X': var_node, 'InScale': scale_in_node}
@@ -356,13 +382,23 @@ class QuantizationTransformPass(object):
                 shape=[1])
             data_type = 'float64' if var_node.dtype(
             ) == core.VarDesc.VarType.FP64 else 'float32'
-            self._init_var_node(scale_in_node, np.ones([1], dtype=data_type))
+            _init_var_node(
+                scale_in_node,
+                np.ones(
+                    [1], dtype=data_type),
+                self._scope,
+                self._place)
             accum_in_node = graph.create_persistable_node(
                 name=unique_name.generate('accum'),
                 var_type=core.VarDesc.VarType.LOD_TENSOR,
                 var_dtype=var_node.dtype(),
                 shape=[1])
-            self._init_var_node(accum_in_node, np.ones([1], dtype=data_type))
+            _init_var_node(
+                accum_in_node,
+                np.ones(
+                    [1], dtype=data_type),
+                self._scope,
+                self._place)
             state_out_node = graph.create_var_node_from_desc(state_in_node.var(
             ))
             accum_out_node = graph.create_var_node_from_desc(accum_in_node.var(
@@ -482,16 +518,6 @@ class QuantizationTransformPass(object):
         graph.link_to(dequant_op_node, dequant_var_node)
         return dequant_var_node
 
-    def _init_var_node(self, var_node, value):
-        assert isinstance(
-            value, np.ndarray), 'The type of value should be numpy array.'
-        assert self._scope is not None, \
-        'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
-        assert self._place is not None, \
-        'The place cannot be set None when activation_quantize_type equals to range_abs_max.'
-        tensor = self._scope.var(var_node.name()).get_tensor()
-        tensor.set(value, self._place)
-
     def _quantized_var_name(self, var_name):
         """
         Return quantized variable name for the input `var_name`.
@@ -594,8 +620,8 @@ class QuantizationFreezePass(object):
                                                     self._weight_bits)
                     self._restore_var(input_arg_name, quantized_param_v)
                 else:
-                    scale_v = self._to_node(op_node.outputs,
-                                            op_node.output('OutScale')[0])
+                    scale_v = graph._find_node_by_name(
+                        op_node.outputs, op_node.output('OutScale')[0])
                     self._var_scale_map[input_arg_name] = scale_v
 
         ops = graph.all_op_nodes()
@@ -627,8 +653,8 @@ class QuantizationFreezePass(object):
         return graph
 
     def _remove_fake_quant_and_dequant_op(self, graph, op_node):
-        k = self._to_node(op_node.outputs, op_node.output('Out')[0])
-        v = self._to_node(op_node.inputs, op_node.input('X')[0])
+        k = graph._find_node_by_name(op_node.outputs, op_node.output('Out')[0])
+        v = graph._find_node_by_name(op_node.inputs, op_node.input('X')[0])
         if v.node not in self._op_input_rename_map:
             self._op_input_rename_map[k.node] = v
         else:
@@ -663,8 +689,8 @@ class QuantizationFreezePass(object):
             raise ValueError("Only support one output, but op %s has"
                              " more than one output." % (op_node.name()))
 
-        output_var_node = self._to_node(op_node.outputs,
-                                        op_node.output_arg_names()[0])
+        output_var_node = graph._find_node_by_name(
+            op_node.outputs, op_node.output_arg_names()[0])
         weight_scale_node = graph.create_persistable_node(
             name=unique_name.generate('channel_scale'),
             var_type=core.VarDesc.VarType.LOD_TENSOR,
@@ -672,7 +698,9 @@ class QuantizationFreezePass(object):
             var_dtype=output_var_node.dtype())
         data_type = 'float64' if output_var_node.dtype(
         ) == core.VarDesc.VarType.FP64 else 'float32'
-        self._init_var_node(weight_scale_node, channel_scale.astype(data_type))
+        _init_var_node(weight_scale_node,
+                       channel_scale.astype(data_type), self._scope,
+                       self._place)
         dequant_var_node = graph.create_var_node(
             name=self._dequantized_var_name(output_var_node.name()),
             var_type=output_var_node.type(),
@@ -724,8 +752,8 @@ class QuantizationFreezePass(object):
             raise ValueError("Only support one output, but op %s has"
                              " more than one output." % (op_node.name()))
 
-        output_var_node = self._to_node(op_node.outputs,
-                                        op_node.output_arg_names()[0])
+        output_var_node = graph._find_node_by_name(
+            op_node.outputs, op_node.output_arg_names()[0])
         dequant_var_node = graph.create_var_node(
             name=self._dequantized_var_name(output_var_node.name()),
             var_type=output_var_node.type(),
@@ -746,24 +774,6 @@ class QuantizationFreezePass(object):
         self._op_output_rename_map[output_var_node.node] = dequant_var_node
         return dequant_var_node
 
-    def _init_var_node(self, var_node, value):
-        assert isinstance(
-            value, np.ndarray), 'The type of value should be numpy array.'
-        assert self._scope is not None, \
-        'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
-        assert self._place is not None, \
-        'The place cannot be set None when activation_quantize_type equals to range_abs_max.'
-        tensor = self._scope.var(var_node.name()).get_tensor()
-        tensor.set(value, self._place)
-
-    def _to_node(self, nodes, node_name):
-        target_node = None
-        for n in nodes:
-            if n.name() == node_name:
-                target_node = n
-        assert target_node is not None, "Cannot find the target node in the giving set."
-        return target_node
-
     def _load_var(self, name):
         return np.array(self._scope.find_var(name).get_tensor())
 

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

@@ -45,13 +45,14 @@ class QuantizationStrategy(Strategy):
                  activation_bits=8,
                  weight_bits=8,
                  activation_quantize_type='abs_max',
+                 weight_quantize_type='abs_max',
                  save_in_nodes=None,
                  save_out_nodes=None):
         """
         Args:
             start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
             end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
-            float_model_save_path(str): The path to save model with float weights. 
+            float_model_save_path(str): The path to save model with float weights.
                             None means it doesn't save float model. defalut: None.
             mobile_model_save_path(str): The path to save model for paddle-mobile execution.
                             None means it doesn't save mobile model. defalut: None.
@@ -66,9 +67,11 @@ class QuantizationStrategy(Strategy):
                 dynamically each step in both training and testing period. If use
                 'range_abs_max', a static quantization scale will be calculated
                 during training and used in inference.
-            save_in_nodes(list<str>): A list of variable names used to prune graph 
+            weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
+            The 'range_abs_max' usually is not used for weight, since weights are fixed once the model is well trained.
+            save_in_nodes(list<str>): A list of variable names used to prune graph
                                       for saving inference model.
-            save_out_nodes(list<str>): A list of variable names used to prune graph 
+            save_out_nodes(list<str>): A list of variable names used to prune graph
                                       for saving inference model.
 
         """
@@ -81,6 +84,7 @@ class QuantizationStrategy(Strategy):
         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.save_out_nodes = save_out_nodes
         self.save_in_nodes = save_in_nodes
 
@@ -100,7 +104,8 @@ class QuantizationStrategy(Strategy):
                 place=context.place,
                 weight_bits=self.weight_bits,
                 activation_bits=self.activation_bits,
-                activation_quantize_type=self.activation_quantize_type)
+                activation_quantize_type=self.activation_quantize_type,
+                weight_quantize_type=self.weight_quantize_type)
             transform_pass.apply(train_ir_graph)
             transform_pass.apply(test_ir_graph)
 
@@ -134,7 +139,8 @@ class QuantizationStrategy(Strategy):
                 scope=context.scope,
                 place=context.place,
                 weight_bits=self.weight_bits,
-                activation_bits=self.activation_bits)
+                activation_bits=self.activation_bits,
+                weight_quantize_type=self.weight_quantize_type)
             freeze_pass.apply(test_ir_graph)
 
             # for other strategies

python/paddle/fluid/contrib/slim/tests/quantization/compress.yaml

@@ -35,6 +35,8 @@ strategies:
         start_epoch: 0
         end_epoch: 0
         float_model_save_path: './output/float'
+        mobile_model_save_path: './output/mobile'
+        int8_model_save_path: './output/int8'
         weight_bits: 8
         activation_bits: 8
         weight_quantize_type: 'abs_max'

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

@@ -256,8 +256,6 @@ class TestQuantizationFreezePass(unittest.TestCase):
             place=place,
             activation_quantize_type=activation_quant_type,
             weight_quantize_type=weight_quant_type)
-        #transform_pass = QuantizationTransformPass(
-        #    scope=scope, place=place, activation_quantize_type=activation_quant_type)
         transform_pass.apply(main_graph)
         transform_pass.apply(test_graph)
         dev_name = '_gpu_' if use_cuda else '_cpu_'
@@ -315,7 +313,6 @@ class TestQuantizationFreezePass(unittest.TestCase):
         # Freeze graph for inference, but the weight of fc/conv is still float type.
         freeze_pass = QuantizationFreezePass(
             scope=scope, place=place, weight_quantize_type=weight_quant_type)
-        #freeze_pass = QuantizationFreezePass(scope=scope, place=place)
         freeze_pass.apply(test_graph)
         if not for_ci:
             marked_nodes = set()

python/paddle/fluid/framework.py

@@ -2347,40 +2347,6 @@ class IrGraph(object):
         """
         return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
 
-    def _find_var_node(self, key):
-        """
-        Get a variable node by the `key` from this graph. The key
-        can be a node name or a node id.
-
-        WARNS:
-            There are some nodes may have the same name. So, be
-            cautious about using this method when you find the
-            target var node by its name.
-
-        Args:
-            key(str|int): The str type denotes that the target variable node's name.
-            And the int type denotes that the target variable node's id.
-
-        Raises:
-            ValueError: If this graph doesn't have a variable with the giving name or id.
-
-        Returns:
-            IrVarNode: the variable node with the giving name or id.
-        """
-        target_var_node = None
-        var_nodes = self.all_var_nodes()
-        if isinstance(key, six.string_types):
-            for var_node in var_nodes:
-                if var_node.name() == key:
-                    target_var_node = var_node
-        elif isinstance(key, int):
-            for var_node in var_nodes:
-                if var_node.id() == key:
-                    target_var_node = var_node
-        if target_var_node is None:
-            raise ValueError("var_node %s not in this graph" % key)
-        return target_var_node
-
     def create_persistable_node(self, name, var_type, shape, var_dtype):
         """
         Create a persistable variable node in the graph. In IrGraph,
@@ -2525,14 +2491,6 @@ class IrGraph(object):
         core.graph_safe_remove_nodes(self.graph, original_nodes)
 
     def resolve_hazard(self):
-        def _to_node(nodes, node_name):
-            target_node = None
-            for n in nodes:
-                if n.name() == node_name:
-                    target_node = n
-            assert target_node is not None, "Cannot find the target node in the giving set."
-            return target_node
-
         ordered_nodes = core.topology_sort(self.graph)
         var_nodes = dict()
         for node in ordered_nodes:
@@ -2540,16 +2498,17 @@ class IrGraph(object):
                 for each_var_name in node.op().input_arg_names():
                     if each_var_name not in var_nodes:
                         var_nodes[each_var_name] = [
-                            _to_node(node.inputs, each_var_name)
+                            self._find_node_by_name(node.inputs, each_var_name)
                         ]
                 for each_var_name in node.op().output_arg_names():
                     if each_var_name not in var_nodes:
                         var_nodes[each_var_name] = [
-                            _to_node(node.outputs, each_var_name)
+                            self._find_node_by_name(node.outputs, each_var_name)
                         ]
                     else:
                         var_nodes[each_var_name].append(
-                            _to_node(node.outputs, each_var_name))
+                            self._find_node_by_name(node.outputs,
+                                                    each_var_name))
         self.graph.resolve_hazard(var_nodes)
 
     def has_circle(self):
@@ -2662,6 +2621,17 @@ class IrGraph(object):
         program = Program._construct_from_desc(desc)
         return program
 
+    def _find_node_by_name(self, nodes, node_name):
+        """
+        Find a node in the giving nodes set by the name.
+        """
+        target_node = None
+        for n in nodes:
+            if n.name() == node_name:
+                target_node = n
+        assert target_node is not None, "Cannot find the target node in the giving set."
+        return target_node
+
     def _update_desc_attr(self, desc, name, val):
         """
         Update the value of desc's attribute by attribute's name.