Move pool2d to add_quant_dequant_pass, test=develop (#20586) (#20675)

* move pool2d to add_quant_dequant_pass, test=develop

Author: juncaipeng

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

@@ -26,7 +26,7 @@
     'AddQuantDequantPass'
 ]
 
-_quantizable_op_list = ['conv2d', 'depthwise_conv2d', 'mul', 'pool2d']
+_quantizable_op_list = ['conv2d', 'depthwise_conv2d', 'mul']
 
 _fake_quant_op_list = [
     'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
@@ -161,13 +161,11 @@ def apply(self, graph):
         persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
 
         def _quant_preprocess(op_node):
-            pool_skipped = op_node.op().has_attr("pooling_type") and \
-                    op_node.op().attr("pooling_type") == 'avg'
             user_skipped = isinstance(self._skip_pattern, str) and \
                            op_node.op().has_attr("op_namescope") and \
                            op_node.op().attr("op_namescope").find(self._skip_pattern) != -1
 
-            if pool_skipped or user_skipped:
+            if user_skipped:
                 op_node.op()._set_attr("skip_quant", True)
 
         def _transform_forward(graph, op):
@@ -1163,10 +1161,15 @@ def _scale_name(self, var_name):
 
 
 class AddQuantDequantPass(object):
-    def __init__(self, scope=None, place=None, moving_rate=0.9, quant_bits=8):
+    def __init__(self,
+                 scope=None,
+                 place=None,
+                 moving_rate=0.9,
+                 quant_bits=8,
+                 skip_pattern='skip_quant'):
         """
         This pass is used to add quant_dequant op for some ops, such as the
-        'elementwise_add' and 'average pool2d' op.
+        'elementwise_add' and 'pool2d' op.
         """
         self._scope = scope
         self._place = place
@@ -1175,11 +1178,12 @@ def __init__(self, scope=None, place=None, moving_rate=0.9, quant_bits=8):
         self._is_test = None
         self._target_ops = ["elementwise_add", "pool2d"]
         self._target_grad_ops = ['%s_grad' % (op) for op in self._target_ops]
+        self._skip_pattern = skip_pattern
 
     def apply(self, graph):
         """
         Add quant_dequant before some ops, such as the 'elementwise_add'
-        and 'average pool2d' op.
+        and 'pool2d' op.
         Args:
             graph(IrGraph): the target graph.
         """
@@ -1191,6 +1195,11 @@ def apply(self, graph):
 
         for op_node in ops:
             if op_node.name() in self._target_ops:
+                if isinstance(self._skip_pattern, str) and \
+                           op_node.op().has_attr("op_namescope") and \
+                           op_node.op().attr("op_namescope").find(self._skip_pattern) != -1:
+                    continue
+
                 in_nodes_all_not_persistable = True
                 for input_name in op_node.input_arg_names():
                     in_node = graph._find_node_by_name(op_node.inputs,
@@ -1201,10 +1210,6 @@ def apply(self, graph):
                 if not in_nodes_all_not_persistable:
                     continue
 
-                if op_node.op().has_attr("pooling_type") and \
-                    op_node.op().attr("pooling_type") == 'max':
-                    continue
-
                 input_names = op_node.input_arg_names()
                 for input_name in input_names:
                     in_node = graph._find_node_by_name(op_node.inputs,

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

@@ -42,7 +42,7 @@ def linear_fc(num):
     return loss
 
 
-def residual_block(num):
+def residual_block(num, quant_skip_pattern=None):
     def conv_bn_layer(input,
                       ch_out,
                       filter_size,
@@ -67,8 +67,14 @@ def conv_bn_layer(input,
         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')
-    pool = fluid.layers.pool2d(
-        input=hidden, pool_size=2, pool_type='avg', pool_stride=2)
+
+    if quant_skip_pattern:
+        with fluid.name_scope(quant_skip_pattern):
+            pool = fluid.layers.pool2d(
+                input=hidden, pool_size=2, pool_type='avg', pool_stride=2)
+    else:
+        pool = fluid.layers.pool2d(
+            input=hidden, pool_size=2, pool_type='avg', pool_stride=2)
     fc = fluid.layers.fc(input=pool, size=10)
     loss = fluid.layers.cross_entropy(input=fc, label=label)
     loss = fluid.layers.mean(loss)
@@ -134,7 +140,10 @@ def check_program(self, program):
                             arg_name.endswith('.quantized.dequantized'))
                         self.assertTrue(arg_name in quantized_ops)
 
-    def linear_fc_quant(self, activation_quant_type, for_ci=True):
+    def linear_fc_quant(self,
+                        activation_quant_type,
+                        weight_quantize_type,
+                        for_ci=True):
         main = fluid.Program()
         startup = fluid.Program()
         with fluid.program_guard(main, startup):
@@ -146,7 +155,8 @@ def linear_fc_quant(self, activation_quant_type, for_ci=True):
         transform_pass = QuantizationTransformPass(
             scope=fluid.global_scope(),
             place=place,
-            activation_quantize_type=activation_quant_type)
+            activation_quantize_type=activation_quant_type,
+            weight_quantize_type=weight_quantize_type)
         transform_pass.apply(graph)
         if not for_ci:
             marked_nodes = set()
@@ -167,15 +177,19 @@ def linear_fc_quant(self, activation_quant_type, for_ci=True):
                            val_marked_nodes)
 
     def test_linear_fc_quant_abs_max(self):
-        self.linear_fc_quant('abs_max', for_ci=True)
+        self.linear_fc_quant('abs_max', 'abs_max', for_ci=True)
 
     def test_linear_fc_quant_range_abs_max(self):
-        self.linear_fc_quant('range_abs_max', for_ci=True)
+        self.linear_fc_quant('range_abs_max', 'abs_max', for_ci=True)
 
     def test_linear_fc_quant_moving_average_abs_max(self):
-        self.linear_fc_quant('moving_average_abs_max', for_ci=True)
+        self.linear_fc_quant(
+            'moving_average_abs_max', 'channel_wise_abs_max', for_ci=True)
 
-    def residual_block_quant(self, activation_quant_type, for_ci=True):
+    def residual_block_quant(self,
+                             activation_quant_type,
+                             weight_quantize_type,
+                             for_ci=True):
         main = fluid.Program()
         startup = fluid.Program()
         with fluid.program_guard(main, startup):
@@ -187,7 +201,8 @@ def residual_block_quant(self, activation_quant_type, for_ci=True):
         transform_pass = QuantizationTransformPass(
             scope=fluid.global_scope(),
             place=place,
-            activation_quantize_type=activation_quant_type)
+            activation_quantize_type=activation_quant_type,
+            weight_quantize_type=weight_quantize_type)
         transform_pass.apply(graph)
         if not for_ci:
             marked_nodes = set()
@@ -208,13 +223,14 @@ def residual_block_quant(self, activation_quant_type, for_ci=True):
                            val_marked_nodes)
 
     def test_residual_block_abs_max(self):
-        self.residual_block_quant('abs_max', for_ci=True)
+        self.residual_block_quant('abs_max', 'abs_max', for_ci=True)
 
     def test_residual_block_range_abs_max(self):
-        self.residual_block_quant('range_abs_max', for_ci=True)
+        self.residual_block_quant('range_abs_max', 'abs_max', for_ci=True)
 
     def test_residual_block_moving_average_abs_max(self):
-        self.residual_block_quant('moving_average_abs_max', for_ci=True)
+        self.residual_block_quant(
+            'moving_average_abs_max', 'channel_wise_abs_max', for_ci=True)
 
 
 class TestQuantizationFreezePass(unittest.TestCase):
@@ -494,11 +510,14 @@ def setUp(self):
         self._target_ops = {'elementwise_add', 'pool2d'}
         self._target_grad_ops = {'elementwise_add_grad', 'pool2d_grad'}
 
-    def check_graph(self, graph):
+    def check_graph(self, graph, skip_pattern=None):
         ops = graph.all_op_nodes()
-
         for op_node in ops:
             if op_node.name() in self._target_ops:
+                if skip_pattern and op_node.op().has_attr("op_namescope") and \
+                    op_node.op().attr("op_namescope").find(skip_pattern) != -1:
+                    continue
+
                 in_nodes_all_not_persistable = True
                 for input_name in op_node.input_arg_names():
                     in_node = graph._find_node_by_name(op_node.inputs,
@@ -508,20 +527,15 @@ def check_graph(self, graph):
                         not in_node.persistable())
                 if not in_nodes_all_not_persistable:
                     continue
-
-                if op_node.op().has_attr("pooling_type") and \
-                    op_node.op().attr("pooling_type") == 'max':
-                    continue
-
                 input_names = op_node.input_arg_names()
                 for input_name in input_names:
                     self.assertTrue(input_name.endswith('.quant_dequant'))
 
-    def residual_block_quant(self, for_ci=True):
+    def residual_block_quant(self, skip_pattern=None, for_ci=True):
         main = fluid.Program()
         startup = fluid.Program()
         with fluid.program_guard(main, startup):
-            loss = residual_block(1)
+            loss = residual_block(2, skip_pattern)
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
         place = fluid.CPUPlace()
@@ -535,7 +549,7 @@ def residual_block_quant(self, for_ci=True):
                 if op.name().find('quant') > -1:
                     marked_nodes.add(op)
             graph.draw('.', 'add_quant_dequant_graph', marked_nodes)
-        self.check_graph(graph)
+        self.check_graph(graph, skip_pattern)
         program = graph.to_program()
         val_graph = IrGraph(core.Graph(program.desc), for_test=False)
         if not for_ci:
@@ -546,7 +560,10 @@ def residual_block_quant(self, for_ci=True):
             val_graph.draw('.', 'val_add_quant_dequant_graph', val_marked_nodes)
 
     def test_residual_block(self):
-        self.residual_block_quant(for_ci=True)
+        self.residual_block_quant(skip_pattern=None, for_ci=True)
+
+    def test_residual_block_skip_pattern(self):
+        self.residual_block_quant(skip_pattern='skip_quant', for_ci=True)
 
 
 if __name__ == '__main__':