Merge pull request #16456 from wzzju/fix_quan_hang

Fix quantization hang bugs.

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

@@ -14,15 +14,10 @@
 
 import collections
 import numpy as np
-import six
 from ..... import compat as cpt
 from .... import core
-from .... import Executor
 from ....framework import IrGraph
 from ....framework import IrNode
-from ....framework import Program
-from ....initializer import Constant
-from ....initializer import NumpyArrayInitializer
 from .... import unique_name
 
 __all__ = [
@@ -107,7 +102,6 @@ class QuantizationTransformPass(object):
         self._window_size = window_size
         self._moving_rate = moving_rate
 
-        self._need_initialized = collections.OrderedDict()
         self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
         self._conv_ops = ['conv2d', 'depthwise_conv2d']
         self._quantizable_grad_ops = [
@@ -127,7 +121,8 @@ class QuantizationTransformPass(object):
         """
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
-        self._need_initialized.clear()
+        #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()
@@ -135,6 +130,8 @@ class QuantizationTransformPass(object):
 
         def _transform_forward(graph, op):
             for var_node in op.inputs:
+                if var_node.name() not in op.input_arg_names():
+                    continue
                 if var_node.name() in dequantized_vars:
                     dequant_var_node = dequantized_vars[var_node.name()]
                 else:
@@ -168,6 +165,8 @@ class QuantizationTransformPass(object):
         def _transform_backward(graph, op):
             no_dequanted_input_vars = True
             for var_node in op.inputs:
+                if var_node.name() not in op.input_arg_names():
+                    continue
                 if var_node.name() in dequantized_vars:
                     dequant_var_node = dequantized_vars[var_node.name()]
                     graph.update_input_link(var_node, dequant_var_node, op)
@@ -188,25 +187,7 @@ class QuantizationTransformPass(object):
         for op in ops:
             if op.name() in self._quantizable_grad_ops:
                 _transform_backward(graph, op)
-
-        if len(self._need_initialized) > 0:
-            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.'
-            init_program = Program()
-            for var_desc, initializer in six.iteritems(self._need_initialized):
-                var = init_program.global_block().create_var(
-                    name=var_desc.name(),
-                    shape=var_desc.shape(),
-                    dtype=var_desc.dtype(),
-                    type=var_desc.type(),
-                    lod_level=var_desc.lod_level(),
-                    persistable=var_desc.persistable())
-                initializer(var, init_program.global_block())
-            exe = Executor(self._place)
-            exe.run(program=init_program, scope=self._scope)
-
+        graph.resolve_hazard()
         return graph
 
     def _create_global_step(self, graph):
@@ -222,8 +203,9 @@ class QuantizationTransformPass(object):
                     var_type=core.VarDesc.VarType.LOD_TENSOR,
                     shape=[1],
                     var_dtype=core.VarDesc.VarType.INT64)
-                self._need_initialized[global_step_in.var()] = \
-                    Constant(value=0, force_cpu=True)
+                self._init_var_node(
+                    global_step_in, np.zeros(
+                        [1], dtype='int64'))
                 global_step_out = graph.create_var_node_from_desc(
                     global_step_in.var())
                 # The attribute of `op_role` is needed by ParallelExecutor.
@@ -300,7 +282,9 @@ class QuantizationTransformPass(object):
             var_type=core.VarDesc.VarType.LOD_TENSOR,
             shape=[1],
             var_dtype=var_node.dtype())
-        self._need_initialized[scale_in_node.var()] = Constant(value=0.001)
+        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))
 
         scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
         inputs = {'X': var_node, 'InScale': scale_in_node}
@@ -313,7 +297,11 @@ class QuantizationTransformPass(object):
                 var_type=core.VarDesc.VarType.LOD_TENSOR,
                 shape=[self._window_size],
                 var_dtype=var_node.dtype())
-            self._need_initialized[scales_node.var()] = Constant(value=0)
+            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))
             inputs['Iter'] = self._global_step
             outputs['OutScales'] = scales_node
         attrs = {
@@ -353,7 +341,9 @@ class QuantizationTransformPass(object):
             var_type=core.VarDesc.VarType.LOD_TENSOR,
             shape=[1],
             var_dtype=var_node.dtype())
-        self._need_initialized[scale_in_node.var()] = Constant(value=0.001)
+        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))
 
         scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
         ins = {'X': var_node, 'InScale': scale_in_node}
@@ -364,13 +354,15 @@ class QuantizationTransformPass(object):
                 var_type=core.VarDesc.VarType.LOD_TENSOR,
                 var_dtype=var_node.dtype(),
                 shape=[1])
-            self._need_initialized[state_in_node.var()] = Constant(value=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))
             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._need_initialized[accum_in_node.var()] = Constant(value=1)
+            self._init_var_node(accum_in_node, np.ones([1], dtype=data_type))
             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(
@@ -490,6 +482,16 @@ 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`.
@@ -592,7 +594,8 @@ class QuantizationFreezePass(object):
                                                     self._weight_bits)
                     self._restore_var(input_arg_name, quantized_param_v)
                 else:
-                    scale_v = graph.var_node(op_node.output('OutScale')[0])
+                    scale_v = self._to_node(op_node.outputs,
+                                            op_node.output('OutScale')[0])
                     self._var_scale_map[input_arg_name] = scale_v
 
         ops = graph.all_op_nodes()
@@ -613,32 +616,35 @@ class QuantizationFreezePass(object):
         for op_node in ops:
             # insert dequant_op after fc/conv, need to rename inputs of the followed ops
             for var_node in op_node.inputs:
-                name = var_node.name()
-                if name in self._op_output_rename_map:
-                    old_in = graph.var_node(name)
-                    new_in = self._op_output_rename_map[name]
+                if var_node.node in self._op_output_rename_map:
+                    old_in = var_node
+                    new_in = self._op_output_rename_map[var_node.node]
                     graph.update_input_link(old_in, new_in, op_node)
 
         # remove the unused var node in the graph
         self._remove_unused_var_nodes(graph)
+        graph.resolve_hazard()
         return graph
 
     def _remove_fake_quant_and_dequant_op(self, graph, op_node):
-        k = op_node.output('Out')[0]
-        v = op_node.input('X')[0]
-        if v not in self._op_input_rename_map:
-            self._op_input_rename_map[k] = v
+        k = self._to_node(op_node.outputs, op_node.output('Out')[0])
+        v = self._to_node(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:
-            self._op_input_rename_map[k] = self._op_input_rename_map[v]
+            self._op_input_rename_map[k.node] = self._op_input_rename_map[
+                v.node]
         graph.safe_remove_nodes(op_node)
 
     def _insert_post_channel_dequant_op(self, graph, op_node):
         persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
         for var_node in op_node.inputs:
             name = var_node.name()
-            if name in self._op_input_rename_map:
-                old_in = graph.var_node(name)
-                new_in = graph.var_node(self._op_input_rename_map[name])
+            if name not in op_node.input_arg_names():
+                continue
+            if var_node.node in self._op_input_rename_map:
+                old_in = var_node
+                new_in = self._op_input_rename_map[var_node.node]
                 new_in.clear_outputs()
                 graph.update_input_link(old_in, new_in, op_node)
             original_var_name = self._original_var_name(name)
@@ -653,28 +659,20 @@ class QuantizationFreezePass(object):
                 assert isinstance(scale_v, IrNode)
                 scale_var_node = self._var_scale_map[original_var_name]
 
-        if len(op_node.outputs) != 1:
+        if len(op_node.output_arg_names()) != 1:
             raise ValueError("Only support one output, but op %s has"
                              " more than one output." % (op_node.name()))
 
-        output_var_node = op_node.outputs[0]
+        output_var_node = self._to_node(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,
             shape=[channel_scale.shape[0]],
             var_dtype=output_var_node.dtype())
-        init_program = Program()
-        weight_scale_var = init_program.global_block().create_var(
-            name=weight_scale_node.name(),
-            shape=weight_scale_node.shape(),
-            dtype=weight_scale_node.dtype(),
-            type=weight_scale_node.type(),
-            lod_level=weight_scale_node.var().lod_level(),
-            persistable=weight_scale_node.persistable())
-        initializer = NumpyArrayInitializer(value=channel_scale)
-        initializer(weight_scale_var, init_program.global_block())
-        exe = Executor(self._place)
-        exe.run(program=init_program, scope=self._scope)
+        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))
         dequant_var_node = graph.create_var_node(
             name=self._dequantized_var_name(output_var_node.name()),
             var_type=output_var_node.type(),
@@ -695,16 +693,18 @@ class QuantizationFreezePass(object):
         graph.link_to(scale_var_node, dequant_op_node)
         graph.link_to(weight_scale_node, dequant_op_node)
         graph.link_to(dequant_op_node, dequant_var_node)
-        self._op_output_rename_map[output_var_node.name()] = dequant_var_node
+        self._op_output_rename_map[output_var_node.node] = dequant_var_node
         return dequant_var_node
 
     def _insert_post_dequant_op(self, graph, op_node):
         persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
         for var_node in op_node.inputs:
             name = var_node.name()
-            if name in self._op_input_rename_map:
-                old_in = graph.var_node(name)
-                new_in = graph.var_node(self._op_input_rename_map[name])
+            if name not in op_node.input_arg_names():
+                continue
+            if var_node.node in self._op_input_rename_map:
+                old_in = var_node
+                new_in = self._op_input_rename_map[var_node.node]
                 new_in.clear_outputs()
                 graph.update_input_link(old_in, new_in, op_node)
             original_var_name = self._original_var_name(name)
@@ -720,11 +720,12 @@ class QuantizationFreezePass(object):
                 assert isinstance(scale_v, IrNode)
                 scale_var_node = self._var_scale_map[original_var_name]
 
-        if len(op_node.outputs) != 1:
+        if len(op_node.output_arg_names()) != 1:
             raise ValueError("Only support one output, but op %s has"
                              " more than one output." % (op_node.name()))
 
-        output_var_node = op_node.outputs[0]
+        output_var_node = self._to_node(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(),
@@ -742,9 +743,27 @@ class QuantizationFreezePass(object):
         graph.link_to(output_var_node, dequant_op_node)
         graph.link_to(scale_var_node, dequant_op_node)
         graph.link_to(dequant_op_node, dequant_var_node)
-        self._op_output_rename_map[output_var_node.name()] = dequant_var_node
+        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())
 
@@ -848,6 +867,7 @@ class ConvertToInt8Pass(object):
 
         # remove the unused var node in the graph
         self._remove_unused_var_nodes(graph)
+        graph.resolve_hazard()
         return graph
 
     def _convert_to_int8(self, graph, var_node):
@@ -930,5 +950,5 @@ class TransformForMobilePass(object):
                 for output_node in op_node.outputs:
                     graph.link_to(dequant_node, output_node)
                 graph.safe_remove_nodes(op_node)
-
+        graph.resolve_hazard()
         return graph

python/paddle/fluid/framework.py

@@ -2052,6 +2052,28 @@ class IrOpNode(IrNode):
         else:
             desc._set_attr(name, val)
 
+    def input_arg_names(self):
+        """
+        Return input arguments' names of this op node.
+
+        Returns:
+            list(str): input arguments' names of this op node.
+        """
+        assert self.node.op() is not None, \
+            "The node operator description cannot be None."
+        return self.node.op().input_arg_names()
+
+    def output_arg_names(self):
+        """
+        Return output arguments' names of this op node.
+
+        Returns:
+            list(str): output arguments' names of this op node.
+        """
+        assert self.node.op() is not None, \
+            "The node operator description cannot be None."
+        return self.node.op().output_arg_names()
+
     @property
     def inputs(self):
         """
@@ -2142,31 +2164,38 @@ class IrGraph(object):
         """
         return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
 
-    def var_node(self, name):
+    def _find_var_node(self, key):
         """
-        Get a variable node by name from the graph.
+        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:
-            name(str): the name of the variable node.
+            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: The If input's type is not str, or this graph
-            doesn't have a variable with the giving name.
+            ValueError: If this graph doesn't have a variable with the giving name or id.
 
         Returns:
-            IrVarNode: the variable node with the giving name.
+            IrVarNode: the variable node with the giving name or id.
         """
-        if not isinstance(name, six.string_types):
-            raise TypeError(
-                "var require string as parameter, but get %s instead." %
-                (type(name)))
         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() == name:
+                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" % name)
+            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):
@@ -2312,6 +2341,34 @@ class IrGraph(object):
         original_nodes = {n.node for n in remove_nodes}
         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:
+            if node.is_op() and node.op() is not None:
+                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)
+                        ]
+                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)
+                        ]
+                    else:
+                        var_nodes[each_var_name].append(
+                            _to_node(node.outputs, each_var_name))
+        self.graph.resolve_hazard(var_nodes)
+
     def has_circle(self):
         """
         Check if the graph has a circle.