Add quantization transform pass and UT.

paddle/fluid/pybind/ir.cc

@@ -148,8 +148,8 @@ void BindNode(py::module *m) {
            })
       .def("outputs_append",
            [](Node &self, Node &node) { self.outputs.push_back(&node); })
-      .def_readwrite("inputs", &Node::inputs)
-      .def_readwrite("outputs", &Node::outputs);
+      .def_readonly("inputs", &Node::inputs)
+      .def_readonly("outputs", &Node::outputs);
 
   py::enum_<Node::Type>(node, "Type")
       .value("Operation", Node::Type::kOperation)

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

@@ -26,10 +26,20 @@ class PyGraph(object):
     PyGraph uses core.Graph as the delegation to accomplish the manipulation.
     """
 
-    def __init__(self, graph):
+    def __init__(self, graph, for_test=False):
+        """
+        Construct the PyGraph 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()
@@ -103,7 +113,7 @@ class PyGraph(object):
             remove_nodes = set(remove_nodes)
         core.graph_safe_remove_nodes(self.graph, remove_nodes)
 
-    def draw_graph(self, save_path, name, marked_nodes=None):
+    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 \
@@ -126,6 +136,8 @@ class PyGraph(object):
             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')
@@ -137,8 +149,8 @@ class PyGraph(object):
         convert_pass = core.get_pass('graph_to_program_pass')
         convert_pass.set_program('program', Program().desc)
         convert_pass.apply(self.graph)
-        program = Program()
-        program.desc = convert_pass.get_program('program')
+        desc = convert_pass.get_program('program')
+        program = Program.construct_from_desc(desc)
         return program
 
 

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

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-from . import quantization_performer
-from .quantization_performer import *
+from . import quantization_pass
+from .quantization_pass import *
 
-__all__ = quantization_performer.__all__
+__all__ = quantization_pass.__all__

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

@@ -15,22 +15,26 @@
 import collections
 import numpy as np
 from .... import core
+from ....framework import Program
+from ....framework import Variable
 from ....initializer import Constant
 from .... import unique_name
 from ..graph import PyGraph
 
-__all__ = ['QuantizationPerformer']
+__all__ = ['QuantizationTransformPass']
 
 
-class QuantizationPerformer(object):
+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
+        Convert and rewrite the PyGraph according to weight and
         activation quantization type.
         Args:
             weight_bits (int): quantization bit number for weights,
@@ -48,15 +52,21 @@ class QuantizationPerformer(object):
             window_size (int): the window size for 'range_abs_max' quantization.
         Examples:
         .. code-block:: python
-            # the original graph will be rewrite, if you don't want to
-            # change it, please clone at first.
-            # graph = graph.clone()
-            from paddle.fluid.contrib.slim import *
-            from paddle.fluid.contrib.quantize import *
-            graph = IRGraph(program)
-            performer = QuantizationPerformer()
-            performer.quantize_transform(graph)
+            # 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 PyGraph
+            from paddle.fluid import core
+
+            graph = PyGraph(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
 
@@ -74,7 +84,7 @@ class QuantizationPerformer(object):
         self.weight_quantize_type = weight_quantize_type
         self.window_size = window_size
 
-        self.need_inited_outer = collections.OrderedDict()
+        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
@@ -86,11 +96,11 @@ class QuantizationPerformer(object):
         self.is_test = None
         self.global_step = None
 
-    def quantize_transform(self, graph, is_test):
-        self.need_inited_outer.clear()
-        self.is_test = is_test
+    def apply(self, graph):
         assert isinstance(graph,
                           PyGraph), 'graph must be the instance of PyGraph.'
+        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()]
@@ -138,7 +148,19 @@ class QuantizationPerformer(object):
             if op.name() in self.quantizable_grad_ops:
                 _transform_backward(graph, op)
 
-        return self.need_inited_outer
+        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.construct_from_desc(init_program.global_block(),
+                                                   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 \
@@ -153,7 +175,7 @@ class QuantizationPerformer(object):
                     var_type=core.VarDesc.VarType.LOD_TENSOR,
                     shape=[1],
                     var_dtype=core.VarDesc.VarType.INT64)
-                self.need_inited_outer[global_step_in.var()] = \
+                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())
@@ -220,7 +242,7 @@ class QuantizationPerformer(object):
             var_type=core.VarDesc.VarType.LOD_TENSOR,
             shape=[1],
             var_dtype=var_node.var().dtype())
-        self.need_inited_outer[scale_in_node.var()] = Constant(value=0.001)
+        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}
@@ -233,7 +255,7 @@ class QuantizationPerformer(object):
                 var_type=core.VarDesc.VarType.LOD_TENSOR,
                 shape=[self.window_size],
                 var_dtype=var_node.var().dtype())
-            self.need_inited_outer[scales_node.var()] = Constant(value=0)
+            self.need_initialized[scales_node.var()] = Constant(value=0)
             inputs['Iter'] = self.global_step
             outputs['OutScales'] = scales_node
         attrs = {

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

@@ -15,11 +15,10 @@
 import unittest
 import random
 import numpy as np
-import paddle
 import paddle.fluid as fluid
 import six
 from paddle.fluid.framework import Program
-from paddle.fluid.contrib.slim.quantization import QuantizationPerformer
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
 from paddle.fluid.contrib.slim.graph import PyGraph
 from paddle.fluid import core
 
@@ -66,22 +65,39 @@ def residual_block(num):
     return loss
 
 
-class TestQuantizationPerformer(unittest.TestCase):
+class TestQuantizationTransformPass(unittest.TestCase):
     def setUp(self):
-        # since quant_op and dequant_op is not ready, use cos and sin for test
-        self.weight_quant_op_type = 'fake_quantize_abs_max'
-        self.dequant_op_type = 'fake_dequantize_max_abs'
         self.quantizable_op_and_inputs = {
             'conv2d': ['Input', 'Filter'],
             'depthwise_conv2d': ['Input', 'Filter'],
             'mul': ['X', 'Y']
         }
-        self.quantizable_op_grad_and_inputs = {
+        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()
@@ -89,14 +105,26 @@ class TestQuantizationPerformer(unittest.TestCase):
             loss = linear_fc(3)
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
-        graph = PyGraph(core.Graph(main.desc))
-        performer = QuantizationPerformer(activation_quantize_type=quant_type)
-        performer.quantize_transform(graph, False)
+        exe = fluid.Executor(fluid.CPUPlace())
+        graph = PyGraph(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_graph('.', 'quantize_fc_' + quant_type, marked_nodes)
+        graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
+        program = graph.to_program()
+        self.check_program(transform_pass, program)
+        val_graph = PyGraph(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'
@@ -113,14 +141,26 @@ class TestQuantizationPerformer(unittest.TestCase):
             loss = residual_block(2)
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
-        graph = PyGraph(core.Graph(main.desc))
-        performer = QuantizationPerformer(activation_quantize_type=quant_type)
-        performer.quantize_transform(graph, False)
+        exe = fluid.Executor(fluid.CPUPlace())
+        graph = PyGraph(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_graph('.', 'quantize_residual_' + quant_type, marked_nodes)
+        graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
+        program = graph.to_program()
+        self.check_program(transform_pass, program)
+        val_graph = PyGraph(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'

python/paddle/fluid/framework.py

@@ -378,6 +378,27 @@ class Variable(object):
             self._ivar.desc = self.desc
             self._ivar.stop_gradient = stop_gradient
 
+    @staticmethod
+    def construct_from_desc(block, desc):
+        """
+        Construct a Variable from variable desc.
+        Args:
+            desc(core.VarDesc): The  variable desc for constructing.
+
+        Returns:
+            Variable: A variable.
+        """
+        v = Variable(
+            block=block,
+            type=desc.type(),
+            name=desc.name(),
+            shape=desc.shape(),
+            dtype=desc.dtype(),
+            lod_level=desc.lod_level(),
+            persistable=desc.persistable())
+        v.desc = desc
+        return v
+
     def _numpy(self):
         tensor = self._ivar.value().get_tensor()
         return np.array(tensor)
@@ -1925,6 +1946,25 @@ class Program(object):
         p._sync_with_cpp()
         return p
 
+    @staticmethod
+    def construct_from_desc(desc):
+        """
+        Construct a program from program desc.
+
+        Notes: All information about parameters will be lost.
+
+        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):
         """