support user defined quantization func and preprocess (#24720)

* add user defined func test=develop

* update test=develop

* update test=develop

* fix name conflicts test=develop

* add unittest test=develop

* change 2018 to 2020 test=develop

* add comment test=develop

* add comment for function test=develop

* fix details test=develop

* fix details test=develop

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

+#   copyright (c) 2020 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 os
+import unittest
+import random
+import numpy as np
+import six
+import paddle.fluid as fluid
+import paddle
+from paddle.fluid.framework import IrGraph
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
+from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass
+from paddle.fluid.contrib.slim.quantization import OutScaleForInferencePass
+from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
+from paddle.fluid import core
+from paddle.fluid.layer_helper import LayerHelper
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+os.environ["CPU_NUM"] = "1"
+
+
+def residual_block(img, label, num=1):
+    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)
+
+    hidden = img
+    for _ in six.moves.xrange(num):
+        conv = conv_bn_layer(hidden, 20, 3, 1, 1, act=None, bias_attr=True)
+        short = conv_bn_layer(hidden, 20, 1, 1, 0, act=None)
+        hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
+    fc = fluid.layers.fc(input=hidden, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=fc, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
+def pact(x, name=None):
+    helper = LayerHelper("pact", **locals())
+    dtype = 'float32'
+    init_thres = 20
+    u_param_attr = fluid.ParamAttr(
+        name=x.name + '_pact',
+        initializer=fluid.initializer.ConstantInitializer(value=init_thres),
+        regularizer=fluid.regularizer.L2Decay(0.0001),
+        learning_rate=1)
+    u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
+    x = fluid.layers.elementwise_sub(
+        x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
+    x = fluid.layers.elementwise_add(
+        x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)))
+
+    return x
+
+
+class TestUserDefinedQuantization(unittest.TestCase):
+    def quantization_scale(self,
+                           use_cuda,
+                           seed,
+                           activation_quant_type,
+                           weight_quant_type='abs_max',
+                           for_ci=False,
+                           act_preprocess_func=None,
+                           weight_preprocess_func=None,
+                           act_quantize_func=None,
+                           weight_quantize_func=None):
+        def build_program(main, startup, is_test):
+            main.random_seed = seed
+            startup.random_seed = seed
+            with fluid.unique_name.guard():
+                with fluid.program_guard(main, startup):
+                    img = fluid.layers.data(
+                        name='image', shape=[1, 28, 28], dtype='float32')
+                    img.stop_gradient = False
+                    label = fluid.layers.data(
+                        name='label', shape=[1], dtype='int64')
+                    loss = residual_block(img, label, 1)
+                    if not is_test:
+                        opt = fluid.optimizer.SGD(learning_rate=0.0001)
+                        opt.minimize(loss)
+            return [img, label], loss
+
+        def get_optimizer():
+            return fluid.optimizer.MomentumOptimizer(0.0001, 0.9)
+
+        random.seed(0)
+        np.random.seed(0)
+
+        main = fluid.Program()
+        startup = fluid.Program()
+        test_program = fluid.Program()
+        feeds, loss = build_program(main, startup, False)
+        build_program(test_program, startup, True)
+        test_program = test_program.clone(for_test=True)
+        main_graph = IrGraph(core.Graph(main.desc), for_test=False)
+        test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
+
+        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        scope = fluid.Scope()
+        with fluid.scope_guard(scope):
+            exe.run(startup)
+        train_transform_pass = QuantizationTransformPass(
+            scope=scope,
+            place=place,
+            activation_quantize_type=activation_quant_type,
+            weight_quantize_type=weight_quant_type,
+            act_preprocess_func=act_preprocess_func,
+            weight_preprocess_func=weight_preprocess_func,
+            act_quantize_func=act_quantize_func,
+            weight_quantize_func=weight_quantize_func,
+            optimizer_func=get_optimizer,
+            executor=exe)
+        train_transform_pass.apply(main_graph)
+        test_transform_pass = QuantizationTransformPass(
+            scope=scope,
+            place=place,
+            activation_quantize_type=activation_quant_type,
+            weight_quantize_type=weight_quant_type,
+            act_preprocess_func=act_preprocess_func,
+            weight_preprocess_func=weight_preprocess_func,
+            act_quantize_func=act_quantize_func,
+            weight_quantize_func=weight_quantize_func,
+            optimizer_func=get_optimizer,
+            executor=exe)
+
+        test_transform_pass.apply(test_graph)
+
+        add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
+        add_quant_dequant_pass.apply(main_graph)
+        add_quant_dequant_pass.apply(test_graph)
+
+        scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
+        scale_training_pass.apply(main_graph)
+
+        dev_name = '_gpu' if use_cuda else '_cpu'
+
+        build_strategy = fluid.BuildStrategy()
+        build_strategy.memory_optimize = False
+        build_strategy.enable_inplace = False
+        build_strategy.fuse_all_reduce_ops = False
+        binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
+            loss_name=loss.name, build_strategy=build_strategy)
+        iters = 5
+        batch_size = 8
+
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.mnist.train(), buf_size=500),
+            batch_size=batch_size)
+        feeder = fluid.DataFeeder(feed_list=feeds, place=place)
+        with fluid.scope_guard(scope):
+            for _ in range(iters):
+                data = next(train_reader())
+                loss_v = exe.run(binary,
+                                 feed=feeder.feed(data),
+                                 fetch_list=[loss])
+
+    def test_act_preprocess_cuda(self):
+        if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
+                self.quantization_scale(
+                    True,
+                    seed=1,
+                    activation_quant_type='moving_average_abs_max',
+                    weight_quant_type='channel_wise_abs_max',
+                    for_ci=True,
+                    act_preprocess_func=pact)
+
+    def test_act_preprocess_cpu(self):
+        with fluid.unique_name.guard():
+            self.quantization_scale(
+                False,
+                seed=2,
+                activation_quant_type='moving_average_abs_max',
+                weight_quant_type='channel_wise_abs_max',
+                for_ci=True,
+                act_preprocess_func=pact)
+
+    def test_weight_preprocess_cuda(self):
+        if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
+                self.quantization_scale(
+                    True,
+                    seed=1,
+                    activation_quant_type='moving_average_abs_max',
+                    weight_quant_type='channel_wise_abs_max',
+                    for_ci=True,
+                    weight_preprocess_func=pact)
+
+    def test_weight_preprocess_cpu(self):
+        with fluid.unique_name.guard():
+            self.quantization_scale(
+                False,
+                seed=2,
+                activation_quant_type='moving_average_abs_max',
+                weight_quant_type='channel_wise_abs_max',
+                for_ci=True,
+                weight_preprocess_func=pact)
+
+    def test_act_quantize_cuda(self):
+        if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
+                self.quantization_scale(
+                    True,
+                    seed=1,
+                    activation_quant_type='moving_average_abs_max',
+                    weight_quant_type='channel_wise_abs_max',
+                    for_ci=True,
+                    act_quantize_func=pact)
+
+    def test_act_quantize_cpu(self):
+        with fluid.unique_name.guard():
+            self.quantization_scale(
+                False,
+                seed=2,
+                activation_quant_type='moving_average_abs_max',
+                weight_quant_type='channel_wise_abs_max',
+                for_ci=True,
+                act_quantize_func=pact)
+
+    def test_weight_quantize_cuda(self):
+        if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
+                self.quantization_scale(
+                    True,
+                    seed=1,
+                    activation_quant_type='moving_average_abs_max',
+                    weight_quant_type='channel_wise_abs_max',
+                    for_ci=True,
+                    weight_quantize_func=pact)
+
+    def test_weight_quantize_cpu(self):
+        with fluid.unique_name.guard():
+            self.quantization_scale(
+                False,
+                seed=2,
+                activation_quant_type='moving_average_abs_max',
+                weight_quant_type='channel_wise_abs_max',
+                for_ci=True,
+                weight_quantize_func=pact)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/framework.py

@@ -3341,8 +3341,6 @@ class IrOpNode(IrNode):
         """
         assert self.node.op() is not None, \
             "The node operator description can not be None."
-        print("op: {}, old: {}, new: {}\n".format(self.node.op().type(
-        ), old_output_name, new_output_name))
         self.node.op()._rename_output(old_output_name, new_output_name)
 
     def input(self, name):
@@ -3566,6 +3564,12 @@ class IrGraph(object):
         var_desc.set_dtype(var_dtype)
         return IrVarNode(self.graph.create_var_node(var_desc))
 
+    def create_control_dep_var(self):
+        """
+        create a control var
+        """
+        return IrVarNode(self.graph.create_control_dep_var())
+
     def create_var_node_from_desc(self, var_desc):
         """
         Create a variable node by using an existing VarDesc in the graph.