Merge pull request #15455 from wzzju/graph_quantization

Graph quantization pass. TODO(Add public API comments.)

paddle/fluid/framework/details/build_strategy.cc

@@ -24,6 +24,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/details/sequential_execution_pass.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 
 namespace paddle {
@@ -243,3 +244,4 @@ USE_PASS(sequential_execution_pass);
 USE_PASS(all_reduce_deps_pass);
 USE_PASS(modify_op_lock_and_record_event_pass);
 USE_PASS(lock_free_optimize_pass);
+USE_PASS(graph_to_program_pass);

paddle/fluid/framework/ir/pass.cc

@@ -28,10 +28,14 @@ std::unique_ptr<Graph> Pass::Apply(std::unique_ptr<Graph> graph) const {
     PADDLE_ENFORCE(graph->Has(attr), "Required graph atrribute %s not set.",
                    attr);
   }
+  auto* native_graph = graph.get();
   auto applied_graph = ApplyImpl(std::move(graph));
   // TODO(panyx0718): Add more verifications.
   PADDLE_ENFORCE(!HasCircle(*applied_graph),
                  "Illegal Pass. Generated graph shouldn't has cycle.");
+  PADDLE_ENFORCE(applied_graph.get() == native_graph,
+                 "Pass::Apply() cannot delete the passed graph and shouldn't "
+                 "return a new graph.(For the need of pybind11)");
   applied_ = true;
   return applied_graph;
 }

paddle/fluid/pybind/ir.cc

@@ -15,7 +15,9 @@
 #include "paddle/fluid/pybind/ir.h"
 #include <string>
 #include <unordered_map>
+#include <unordered_set>
 #include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/var_desc.h"
@@ -24,6 +26,7 @@
 namespace py = pybind11;
 using paddle::framework::ir::Graph;
 using paddle::framework::ir::Node;
+using paddle::framework::ir::GraphSafeRemoveNodes;
 using paddle::framework::OpDesc;
 using paddle::framework::ProgramDesc;
 using paddle::framework::VarDesc;
@@ -32,6 +35,7 @@ using pybind11::return_value_policy;
 namespace paddle {
 namespace pybind {
 void BindGraph(py::module *m) {
+  m->def("graph_safe_remove_nodes", GraphSafeRemoveNodes);
   py::class_<Graph, std::shared_ptr<Graph>>(
       *m, "Graph",
       "The graph is a Directed Acyclic Single Static Assignment Graph, see "
@@ -42,6 +46,8 @@ void BindGraph(py::module *m) {
       .def("get_float", &Graph::Get<float>)
       .def("get_double", &Graph::Get<double>)
       .def("get_string", &Graph::Get<std::string>)
+      .def("get_program", &Graph::Get<ProgramDesc>)
+      .def("get_marked_nodes", &Graph::Get<std::unordered_set<const Node *>>)
       .def("set", [](Graph &self, const std::string &attr_name,
                      int attr) { return self.Set(attr_name, new int(attr)); })
       .def("set",
@@ -57,6 +63,17 @@ void BindGraph(py::module *m) {
            [](Graph &self, const std::string &attr_name, double attr) {
              return self.Set(attr_name, new double(attr));
            })
+      .def("set",
+           [](Graph &self, const std::string &attr_name,
+              const ProgramDesc &attr) {
+             return self.Set(attr_name, new ProgramDesc(attr));
+           })
+      .def("set",
+           [](Graph &self, const std::string &attr_name,
+              const std::unordered_set<const Node *> &attr) {
+             return self.Set(attr_name,
+                             new std::unordered_set<const Node *>(attr));
+           })
       .def("erase", &Graph::Erase)
       .def("nodes", &Graph::Nodes, return_value_policy::reference)
       .def("create_var_node",
@@ -85,12 +102,52 @@ void BindNode(py::module *m) {
   py::class_<Node> node(*m, "Node");
   node.def("name", &Node::Name)
       .def("node_type", &Node::NodeType)
-      .def("var", &Node::Var)
-      .def("op", &Node::Op)
+      .def("var", &Node::Var, return_value_policy::reference)
+      .def("op", &Node::Op, return_value_policy::reference)
       .def("id", &Node::id)
       .def("is_op", &Node::IsOp)
       .def("is_var", &Node::IsVar)
       .def("is_ctrl_var", &Node::IsCtrlVar)
+      .def("inputs_remove",
+           [](Node &self, int node_id) {
+             for (auto it = self.inputs.begin(); it != self.inputs.end();
+                  it++) {
+               if ((*it)->id() == node_id) {
+                 self.inputs.erase(it);
+               }
+             }
+           })
+      .def("inputs_remove",
+           [](Node &self, Node &node) {
+             for (auto it = self.inputs.begin(); it != self.inputs.end();
+                  it++) {
+               if (*it == &node) {
+                 self.inputs.erase(it);
+               }
+             }
+           })
+      .def("inputs_append",
+           [](Node &self, Node &node) { self.inputs.push_back(&node); })
+      .def("outputs_remove",
+           [](Node &self, int node_id) {
+             for (auto it = self.outputs.begin(); it != self.outputs.end();
+                  it++) {
+               if ((*it)->id() == node_id) {
+                 self.outputs.erase(it);
+               }
+             }
+           })
+      .def("outputs_remove",
+           [](Node &self, Node &node) {
+             for (auto it = self.outputs.begin(); it != self.outputs.end();
+                  it++) {
+               if (*it == &node) {
+                 self.outputs.erase(it);
+               }
+             }
+           })
+      .def("outputs_append",
+           [](Node &self, Node &node) { self.outputs.push_back(&node); })
       .def_readwrite("inputs", &Node::inputs)
       .def_readwrite("outputs", &Node::outputs);
 

paddle/fluid/pybind/protobuf.cc

@@ -228,7 +228,7 @@ void BindBlockDesc(pybind11::module *m) {
 
 void BindVarDsec(pybind11::module *m) {
   pybind11::class_<pd::VarDesc> var_desc(*m, "VarDesc", "");
-  var_desc
+  var_desc.def(pybind11::init<const std::string &>())
       .def("name", &pd::VarDesc::Name, pybind11::return_value_policy::reference)
       .def("set_name", &pd::VarDesc::SetName)
       .def("set_shape", &pd::VarDesc::SetShape)

paddle/fluid/pybind/pybind.cc

@@ -788,21 +788,33 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("disable_profiler", platform::DisableProfiler);
   m.def("is_profiler_enabled", platform::IsProfileEnabled);
   m.def("reset_profiler", platform::ResetProfiler);
+  m.def("get_pass", [](const py::bytes &binary_str) {
+    std::string pass_type(binary_str);
+    auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
+    return std::shared_ptr<framework::ir::Pass>(std::move(pass));
+  });
 
   py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
   pass.def(py::init())
+      .def("has", &ir::Pass::Has)
+      .def("set",
+           [](ir::Pass &self, const std::string &attr_name,
+              const ProgramDesc &attr) {
+             return self.Set(attr_name, new ProgramDesc(attr));
+           })
       .def(
-          "set_str",
+          "set",
           [](ir::Pass &self, const std::string &name, const std::string &attr) {
             self.Set<std::string>(name, new std::string(attr));
           })
-      .def("set_int", [](ir::Pass &self, const std::string &name,
-                         int val) { self.Set<const int>(name, new int(val)); })
+      .def("set", [](ir::Pass &self, const std::string &name,
+                     int val) { self.Set<const int>(name, new int(val)); })
+      .def("get_program", &ir::Pass::Get<ProgramDesc>)
       .def("type", &ir::Pass::Type)
       .def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
         std::unique_ptr<ir::Graph> origin_graph(graph.get());
         auto optim_graph = self.Apply(std::move(origin_graph));
-        graph.reset(optim_graph.release());
+        optim_graph.release();
       });
 
   py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(

python/paddle/fluid/contrib/slim/graph/graph.py

@@ -11,8 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
+from __future__ import print_function
+import os
+import subprocess
 from ....framework import Program
+from ....framework import Block
+from .... import core
 
 __all__ = ['Graph', 'ImitationGraph', 'IRGraph']
 

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

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from . import quantization_pass
+from .quantization_pass import *
+
+__all__ = quantization_pass.__all__

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import collections
+from .... import core
+from ....framework import IrGraph
+from ....framework import Program
+from ....framework import Variable
+from ....initializer import Constant
+from .... import unique_name
+
+__all__ = ['QuantizationTransformPass']
+
+
+class QuantizationTransformPass(object):
+    def __init__(self,
+                 scope=None,
+                 program_exe=None,
+                 weight_bits=8,
+                 activation_bits=8,
+                 activation_quantize_type='abs_max',
+                 weight_quantize_type='abs_max',
+                 window_size=10000):
+        """
+        Convert and rewrite the IrGraph according to weight and
+        activation quantization type.
+        Args:
+            weight_bits (int): quantization bit number for weights,
+                the bias is not quantized.
+            activation_bits (int): quantization bit number for activation.
+            activation_quantize_type (str): quantization type for activation,
+                now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
+                the quantization scale will be calculated 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.
+            weight_quantize_type (str): quantization type for weights,
+                support 'abs_max'. The 'range_abs_max' usually is not used for
+                weight, since weights are fixed once the model is well trained.
+            window_size (int): the window size for 'range_abs_max' quantization.
+        Examples:
+        .. code-block:: python
+            # The original graph will be rewrite.
+            import paddle.fluid as fluid
+            from paddle.fluid.contrib.slim.quantization \
+                import QuantizationTransformPass
+            from paddle.fluid.contrib.slim.graph import IrGraph
+            from paddle.fluid import core
+
+            graph = IrGraph(core.Graph(program.desc), for_test=False)
+            exe = fluid.Executor(fluid.CPUPlace())
+            transform_pass = QuantizationTransformPass(fluid.global_scope(),
+            exe)
+            transform_pass.apply(graph)
+        """
+        self._scope = scope
+        self._program_exe = program_exe
+        self._weight_bits = weight_bits
+        self._activation_bits = activation_bits
+
+        quant_type = ['abs_max', 'range_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'.", 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 'range_abs_max'.", str(weight_quantize_type))
+
+        self._activation_quantize_type = activation_quantize_type
+        self._weight_quantize_type = weight_quantize_type
+        self._window_size = window_size
+
+        self._need_initialized = collections.OrderedDict()
+        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+        self._quantizable_grad_ops = [
+            '%s_grad' % (op) for op in self._quantizable_ops
+        ]
+        self._fake_quant_op_types = [
+            'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
+        ]
+        self._fake_dequant_op_types = ['fake_dequantize_max_abs']
+        self._is_test = None
+        self._global_step = None
+
+    def apply(self, graph):
+        assert isinstance(graph,
+                          IrGraph), 'graph must be the instance of IrGraph.'
+        self._need_initialized.clear()
+        self._is_test = graph.is_test()
+        # marked the variable which has been dequantized.
+        dequantized_vars = collections.OrderedDict()
+        params = [p.name() for p in graph.all_parameters()]
+
+        def _transform_forward(graph, op):
+            for var_node in op.inputs:
+                if var_node.name() in dequantized_vars:
+                    dequant_var_node = dequantized_vars[var_node.name()]
+                else:
+                    quant_bits = self._weight_bits if var_node.name() in params \
+                    else self._activation_bits
+                    quant_type = self._weight_quantize_type if var_node.name() \
+                        in params else self._activation_quantize_type
+                    quant_var_node, scale_var_node = self._insert_quant_op(
+                        graph, var_node, quant_bits, quant_type)
+                    dequant_var_node = self._insert_dequant_op(
+                        graph, quant_var_node, scale_var_node, quant_bits)
+                    dequantized_vars[var_node.name()] = dequant_var_node
+                graph.update_input_link(var_node, dequant_var_node, op)
+
+        def _transform_backward(graph, op):
+            no_dequanted_input_vars = True
+            for var_node in op.inputs:
+                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)
+                    no_dequanted_input_vars = False
+            if no_dequanted_input_vars:
+                raise ValueError("There is no dequanted inputs for op %s." %
+                                 (op.name()))
+
+        if not self._is_test:
+            self._create_global_step(graph)
+        ops = graph.all_ops()
+        # The process of _transform_forward and _transform_backward is needed in two for loops.
+        # The loop for transforming the forward graph:
+        for op in ops:
+            if op.name() in self._quantizable_ops:
+                _transform_forward(graph, op)
+        # The loop for renaming the inputs of backward op.
+        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._program_exe is not None, \
+            'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.'
+            init_program = Program()
+            for var_desc, initializer in self._need_initialized.iteritems():
+                var = Variable(init_program.global_block())
+                var._set_desc(var_desc)
+                initializer(var, init_program.global_block())
+            self._program_exe.run(program=init_program, scope=self._scope)
+
+        return graph
+
+    def _create_global_step(self, graph):
+        if self._weight_quantize_type == 'range_abs_max' or \
+                self._activation_quantize_type == 'range_abs_max':
+            counter_name = '@STEP_COUNTER@'
+            for node in graph.all_vars():
+                if node.name() == counter_name:
+                    self._global_step = node
+            if self._global_step is None:
+                global_step_in = graph.create_param_node(
+                    name=counter_name,
+                    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)
+                global_step_out = graph.create_var_node_from_desc(
+                    global_step_in.var())
+                increment_op = graph.create_op_node(
+                    op_type='increment',
+                    attrs={'step': 1.0},
+                    inputs={'X': global_step_in},
+                    outputs={'Out': global_step_out})
+                graph.link_to(global_step_in, increment_op)
+                graph.link_to(increment_op, global_step_out)
+                self._global_step = global_step_out
+
+    def _insert_quant_op(self, graph, var_node, quant_bits, quant_type):
+        """
+        Insert fake_quantize_op in the graph.
+        """
+        if quant_type == 'abs_max':
+            return self._insert_quant_abs_max_op(graph, var_node, quant_bits)
+        elif quant_type == 'range_abs_max':
+            return self._insert_quant_range_abs_max_op(graph, var_node,
+                                                       quant_bits)
+
+    def _insert_quant_abs_max_op(self, graph, var_node, quant_bits):
+        """
+        Insert fake_quantize_abs_max op in the graph.
+        """
+        assert var_node.is_var(), '{} is not a var'.format(var_node.name())
+
+        quant_var_node = graph.create_var_node(
+            name=self._quantized_var_name(var_node.name()),
+            var_type=var_node.var().type(),
+            shape=var_node.var().shape(),
+            var_dtype=var_node.var().dtype())
+        scale_var_node = graph.create_var_node(
+            name=self._quantized_scale_name(var_node.name()),
+            var_type=var_node.var().type(),
+            shape=var_node.var().shape(),
+            var_dtype=var_node.var().dtype())
+        quant_op_node = graph.create_op_node(
+            op_type='fake_quantize_abs_max',
+            attrs={'bit_length': quant_bits},
+            inputs={'X': var_node},
+            outputs={'Out': quant_var_node,
+                     'OutScale': scale_var_node})
+        graph.link_to(var_node, quant_op_node)
+        graph.link_to(quant_op_node, quant_var_node)
+        graph.link_to(quant_op_node, scale_var_node)
+        return quant_var_node, scale_var_node
+
+    def _insert_quant_range_abs_max_op(self, graph, var_node, quant_bits):
+        """
+        Insert fake_quantize_range_abs_max on the graph.
+        """
+        assert var_node.is_var(), '{} is not a var'.format(var_node.name())
+
+        quant_var_node = graph.create_var_node(
+            name=self._quantized_var_name(var_node.name()),
+            var_type=var_node.var().type(),
+            shape=var_node.var().shape(),
+            var_dtype=var_node.var().dtype())
+
+        scale_in_node = graph.create_param_node(
+            name=self._quantized_scale_name(var_node.name()),
+            var_type=core.VarDesc.VarType.LOD_TENSOR,
+            shape=[1],
+            var_dtype=var_node.var().dtype())
+        self._need_initialized[scale_in_node.var()] = Constant(value=0.001)
+
+        scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
+        inputs = {'X': var_node, 'InScale': scale_in_node}
+        outputs = {'Out': quant_var_node, 'OutScale': scale_out_node}
+
+        if not self._is_test:
+            # The name of scales_var_node maybe 'scales_0', 'scales_1', etc.
+            scales_node = graph.create_param_node(
+                name=unique_name.generate('scales'),
+                var_type=core.VarDesc.VarType.LOD_TENSOR,
+                shape=[self._window_size],
+                var_dtype=var_node.var().dtype())
+            self._need_initialized[scales_node.var()] = Constant(value=0)
+            inputs['Iter'] = self._global_step
+            outputs['OutScales'] = scales_node
+        attrs = {
+            'window_size': self._window_size,
+            'bit_length': quant_bits,
+            'is_test': self._is_test
+        }
+        quant_op_node = graph.create_op_node(
+            op_type='fake_quantize_range_abs_max',
+            attrs=attrs,
+            inputs=inputs,
+            outputs=outputs)
+
+        graph.link_to(var_node, quant_op_node)
+        graph.link_to(scale_in_node, quant_op_node)
+        graph.link_to(quant_op_node, quant_var_node)
+        graph.link_to(quant_op_node, scale_out_node)
+
+        if not self._is_test:
+            graph.link_to(self._global_step, quant_op_node)
+            graph.link_to(quant_op_node, scales_node)
+
+        return quant_var_node, scale_out_node
+
+    def _insert_dequant_op(self, graph, var_node, scale_var_node, quant_bits):
+        """
+        Insert fake_dequantize_op in the graph.
+        """
+        assert var_node.is_var(), '{} is not a var'.format(var_node.name())
+
+        dequant_var_node = graph.create_var_node(
+            name=self._dequantized_var_name(var_node.name()),
+            var_type=var_node.var().type(),
+            shape=var_node.var().shape(),
+            var_dtype=var_node.var().dtype())
+        max_range = (1 << (quant_bits - 1)) - 1
+        dequant_op_node = graph.create_op_node(
+            op_type='fake_dequantize_max_abs',
+            attrs={'max_range': float(max_range)},
+            inputs={'X': var_node,
+                    'Scale': scale_var_node},
+            outputs={'Out': dequant_var_node})
+        graph.link_to(var_node, dequant_op_node)
+        graph.link_to(scale_var_node, dequant_op_node)
+        graph.link_to(dequant_op_node, dequant_var_node)
+        return dequant_var_node
+
+    def _quantized_var_name(self, var_name):
+        """
+        Return quantized variable name for the input `var_name`.
+        """
+        return "%s.quantized" % (var_name)
+
+    def _dequantized_var_name(self, var_name):
+        """
+        Return dequantized variable name for the input `var_name`.
+        """
+        return "%s.dequantized" % (var_name)
+
+    def _quantized_scale_name(self, var_name):
+        """
+        Return the scale name of quantized variable for the input `var_name`.
+        """
+        return "%s.scale" % (var_name)

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

+#   copyright (c) 2018 paddlepaddle authors. all rights reserved.
+#
+# licensed under the apache license, version 2.0 (the "license");
+# you may not use this file except in compliance with the license.
+# you may obtain a copy of the license at
+#
+#     http://www.apache.org/licenses/license-2.0
+#
+# unless required by applicable law or agreed to in writing, software
+# distributed under the license is distributed on an "as is" basis,
+# without warranties or conditions of any kind, either express or implied.
+# see the license for the specific language governing permissions and
+# limitations under the license.
+
+import unittest
+import random
+import numpy as np
+import paddle.fluid as fluid
+import six
+from paddle.fluid.framework import Program
+from paddle.fluid.framework import IrGraph
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid import core
+
+
+def linear_fc(num):
+    data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    hidden = data
+    for _ in six.moves.xrange(num):
+        hidden = fluid.layers.fc(hidden, size=128, act='relu')
+    loss = fluid.layers.cross_entropy(input=hidden, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
+def residual_block(num):
+    def conv_bn_layer(input,
+                      ch_out,
+                      filter_size,
+                      stride,
+                      padding,
+                      act='relu',
+                      bias_attr=False):
+        tmp = fluid.layers.conv2d(
+            input=input,
+            filter_size=filter_size,
+            num_filters=ch_out,
+            stride=stride,
+            padding=padding,
+            act=None,
+            bias_attr=bias_attr)
+        return fluid.layers.batch_norm(input=tmp, act=act)
+
+    data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    hidden = data
+    for _ in six.moves.xrange(num):
+        conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
+        short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
+        hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
+    fc = fluid.layers.fc(input=hidden, size=10)
+    loss = fluid.layers.cross_entropy(input=fc, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
+class TestQuantizationTransformPass(unittest.TestCase):
+    def setUp(self):
+        self.quantizable_op_and_inputs = {
+            'conv2d': ['Input', 'Filter'],
+            'depthwise_conv2d': ['Input', 'Filter'],
+            'mul': ['X', 'Y']
+        }
+        self.quantizable_grad_op_inputs = {
+            'conv2d_grad': ['Input', 'Filter'],
+            'depthwise_conv2d_grad': ['Input', 'Filter'],
+            'mul_grad': ['X', 'Y']
+        }
+
+    def check_program(self, transform_pass, program):
+        quantized_ops = set()
+        for block in program.blocks:
+            for op in block.ops:
+                # check forward
+                if op.type in self.quantizable_op_and_inputs:
+                    for arg_name in op.input_arg_names:
+                        self.assertTrue(
+                            arg_name.endswith('.quantized.dequantized'))
+                        quantized_ops.add(arg_name)
+
+            for op in block.ops:
+                # check backward
+                if op.type in self.quantizable_grad_op_inputs:
+                    for pname in self.quantizable_grad_op_inputs[op.type]:
+                        arg_name = op.input(pname)[0]
+                        self.assertTrue(
+                            arg_name.endswith('.quantized.dequantized'))
+                        self.assertTrue(arg_name in quantized_ops)
+
+    def linear_fc_quant(self, quant_type):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            loss = linear_fc(3)
+            opt = fluid.optimizer.Adam(learning_rate=0.001)
+            opt.minimize(loss)
+        exe = fluid.Executor(fluid.CPUPlace())
+        graph = IrGraph(core.Graph(main.desc), for_test=False)
+        transform_pass = QuantizationTransformPass(
+            scope=fluid.global_scope(),
+            program_exe=exe,
+            activation_quantize_type=quant_type)
+        transform_pass.apply(graph)
+        marked_nodes = set()
+        for op in graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
+        program = graph.to_program()
+        self.check_program(transform_pass, program)
+        val_graph = IrGraph(core.Graph(program.desc), for_test=False)
+        val_marked_nodes = set()
+        for op in val_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                val_marked_nodes.add(op)
+        val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes)
+
+    def test_linear_fc_quant_abs_max(self):
+        self.act_quant_op_type = 'fake_quantize_abs_max'
+        self.linear_fc_quant('abs_max')
+
+    def test_linear_fc_quant_range_abs_max(self):
+        self.act_quant_op_type = 'fake_quantize_range_abs_max'
+        self.linear_fc_quant('range_abs_max')
+
+    def residual_block_quant(self, quant_type):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            loss = residual_block(2)
+            opt = fluid.optimizer.Adam(learning_rate=0.001)
+            opt.minimize(loss)
+        exe = fluid.Executor(fluid.CPUPlace())
+        graph = IrGraph(core.Graph(main.desc), for_test=False)
+        transform_pass = QuantizationTransformPass(
+            scope=fluid.global_scope(),
+            program_exe=exe,
+            activation_quantize_type=quant_type)
+        transform_pass.apply(graph)
+        marked_nodes = set()
+        for op in graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
+        program = graph.to_program()
+        self.check_program(transform_pass, program)
+        val_graph = IrGraph(core.Graph(program.desc), for_test=False)
+        val_marked_nodes = set()
+        for op in val_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                val_marked_nodes.add(op)
+        val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
+
+    def test_residual_block_abs_max(self):
+        self.act_quant_op_type = 'fake_quantize_abs_max'
+        self.residual_block_quant('abs_max')
+
+    def test_residual_block_range_abs_max(self):
+        self.act_quant_op_type = 'fake_quantize_range_abs_max'
+        self.residual_block_quant('range_abs_max')
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/framework.py

@@ -23,6 +23,7 @@ import traceback
 import six
 
 import numpy as np
+import subprocess
 
 from .. import compat as cpt
 from .proto import framework_pb2
@@ -1512,6 +1513,154 @@ class Block(object):
         return ret_var
 
 
+class IrGraph(object):
+    """
+    IrGraph uses core.Graph as the delegation to accomplish the manipulation.
+    """
+
+    def __init__(self, graph, for_test=False):
+        """
+        Construct the IrGraph using core.Graph.
+        Args:
+            graph(core.Graph): C++ Graph.
+            for_test(bool): True for the test graph and false for the train graph.
+        """
+        assert isinstance(
+            graph, core.Graph), 'graph must be the instance of core.Graph.'
+        self.graph = graph
+        self._for_test = for_test
+
+    def is_test(self):
+        return self._for_test
+
+    def all_parameters(self):
+        param_nodes = set()
+        for node in self.graph.nodes():
+            if node.is_var() and node.var() is not None and node.var(
+            ).persistable():
+                param_nodes.add(node)
+        return param_nodes
+
+    def all_vars(self):
+        return {node for node in self.graph.nodes() if node.is_var()}
+
+    def all_ops(self):
+        return {node for node in self.graph.nodes() if node.is_op()}
+
+    def create_param_node(self, name, var_type, shape, var_dtype):
+        var_desc = core.VarDesc(name)
+        var_desc.set_type(var_type)
+        var_desc.set_shape(shape)
+        var_desc.set_dtype(var_dtype)
+        var_desc.set_persistable(True)
+        return self.graph.create_var_node(var_desc)
+
+    def create_var_node(self, name, var_type, shape, var_dtype):
+        var_desc = core.VarDesc(name)
+        var_desc.set_type(var_type)
+        var_desc.set_shape(shape)
+        var_desc.set_dtype(var_dtype)
+        return self.graph.create_var_node(var_desc)
+
+    def create_var_node_from_desc(self, var_desc):
+        return self.graph.create_var_node(var_desc)
+
+    def create_op_node(self, op_type, attrs, inputs, outputs):
+        op_desc = core.OpDesc()
+        op_desc.set_type(op_type)
+        for attr, value in attrs.iteritems():
+            self._update_desc_attr(op_desc, attr, value)
+        for input_name, var_nodes in inputs.iteritems():
+            if not isinstance(var_nodes, list):
+                var_nodes = [var_nodes]
+            op_desc.set_input(input_name,
+                              [var_node.name() for var_node in var_nodes])
+        for output_name, var_nodes in outputs.iteritems():
+            if not isinstance(var_nodes, list):
+                var_nodes = [var_nodes]
+            op_desc.set_output(output_name,
+                               [var_node.name() for var_node in var_nodes])
+        return self.graph.create_op_node(op_desc)
+
+    def create_op_node_from_desc(self, op_desc):
+        return self.graph.create_op_node(op_desc)
+
+    def update_input_link(self, old_input_node, new_input_node, op_node):
+        assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
+            op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
+        old_input_node.outputs_remove(op_node)
+        op_node.inputs_remove(old_input_node)
+        new_input_node.outputs_append(op_node)
+        op_node.inputs_append(new_input_node)
+        op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
+
+    def link_to(self, node_in, node_out):
+        assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
+            'Th two arguments must be in the graph nodes.'
+        node_in.outputs_append(node_out)
+        node_out.inputs_append(node_in)
+
+    def safe_remove_nodes(self, remove_nodes):
+        if not isinstance(remove_nodes, set):
+            remove_nodes = set(remove_nodes)
+        core.graph_safe_remove_nodes(self.graph, remove_nodes)
+
+    def draw(self, save_path, name, marked_nodes=None):
+        def _convert_to_pdf(dot_file_path):
+            pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
+            exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
+                            + ' -o ' + pdf_save_path, shell=True)
+            if exited_code != 0:
+                print('The dot command is needed for creating pdf files.')
+                print('The {} is saved as the dot filetype.'.format(
+                    dot_file_path))
+
+        remove_ctr_vars = set()
+        ops_num = 0
+        for node in self.graph.nodes():
+            if node.is_ctrl_var():
+                remove_ctr_vars.add(node)
+            elif node.is_op():
+                ops_num += 1
+        print('Total ops num = {}.'.format(ops_num))
+        self.safe_remove_nodes(remove_ctr_vars)
+        if marked_nodes is not None:
+            if not isinstance(marked_nodes, set):
+                marked_nodes = set(marked_nodes)
+            marked_nodes = marked_nodes - remove_ctr_vars
+            if self.graph.has('__graphviz__marked_node__'):
+                self.graph.erase('__graphviz__marked_node__')
+            self.graph.set('__graphviz__marked_node__', marked_nodes)
+        viz_dot_path = os.path.join(save_path, name) + '.dot'
+        viz_pass = core.get_pass('graph_viz_pass')
+        viz_pass.set('graph_viz_path', viz_dot_path)
+        viz_pass.apply(self.graph)
+        _convert_to_pdf(viz_dot_path)
+
+    def to_program(self):
+        convert_pass = core.get_pass('graph_to_program_pass')
+        convert_pass.set('program', Program().desc)
+        convert_pass.apply(self.graph)
+        desc = convert_pass.get_program('program')
+        program = Program._construct_from_desc(desc)
+        return program
+
+    def _update_desc_attr(self, desc, name, val):
+        """
+        Update the value of desc's attribute by attribute's name.
+        """
+        if isinstance(val, Block):
+            desc.set_block_attr(name, val.desc)
+        elif isinstance(val, list) and val and all(
+                isinstance(v, Block) for v in val):
+            desc.set_blocks_attr(name, [v.desc for v in val])
+        elif isinstance(val, core.BlockDesc) or \
+                isinstance(val, core.ProgramDesc):
+            desc.set_serialized_attr(name, val.serialize_to_string())
+        else:
+            desc._set_attr(name, val)
+
+
 class Program(object):
     """
     Python Program. Beneath it is a ProgramDesc, which is used for
@@ -1936,6 +2085,23 @@ class Program(object):
         p._sync_with_cpp()
         return p
 
+    @staticmethod
+    def _construct_from_desc(desc):
+        """
+        Construct a program from program desc.
+
+        Args:
+            desc(core.ProgramDesc): The program desc for constructing.
+
+        Returns:
+            Program: A program.
+        """
+        p = Program()
+        p.desc = desc
+        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
+        p._sync_with_cpp()
+        return p
+
     @property
     def random_seed(self):
         """

python/paddle/fluid/tests/unittests/test_dist_base.py

@@ -123,7 +123,7 @@ class TestDistRunnerBase(object):
             pass_builder = build_stra._finalize_strategy_and_create_passes()
             mypass = pass_builder.insert_pass(
                 len(pass_builder.all_passes()) - 2, "multi_batch_merge_pass")
-            mypass.set_int("num_repeats", args.batch_merge_repeat)
+            mypass.set("num_repeats", args.batch_merge_repeat)
 
         if args.update_method == "nccl2":
             build_stra.num_trainers = len(args.endpoints.split(","))

python/paddle/fluid/tests/unittests/test_pass_builder.py

@@ -111,7 +111,7 @@ class TestPassBuilder(unittest.TestCase):
 
         pass_builder.remove_pass(len(pass_builder.all_passes()) - 1)
         self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
-        viz_pass.set_str("graph_viz_path", "/tmp/test_viz_pass")
+        viz_pass.set("graph_viz_path", "/tmp/test_viz_pass")
 
         self.check_network_convergence(
             use_cuda=core.is_compiled_with_cuda(),

python/setup.py.in

@@ -113,6 +113,7 @@ packages=['paddle',
           'paddle.fluid.contrib.slim.core',
           'paddle.fluid.contrib.slim.graph',
           'paddle.fluid.contrib.slim.prune',
+          'paddle.fluid.contrib.slim.quantization',
           'paddle.fluid.contrib.utils',
           'paddle.fluid.transpiler',
           'paddle.fluid.transpiler.details']