Add L2 unit test (#14792)

* add l2 unit test
test=develop

* code refine
test=develop

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

@@ -15,7 +15,12 @@
 from __future__ import print_function
 
 import unittest
-
+from functools import partial
+import contextlib
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
 import paddle.fluid.framework as framework
 import paddle.fluid.optimizer as optimizer
 import paddle.fluid.regularizer as regularizer
@@ -97,5 +102,134 @@ class TestL1DecayRegularizer(unittest.TestCase):
         self.assertEqual(block.ops[-3].type, 'sign')
 
 
+def bow_net(data,
+            label,
+            dict_dim,
+            is_sparse=False,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2):
+    """
+    BOW net
+    This model is from https://github.com/PaddlePaddle/models:
+    fluid/PaddleNLP/text_classification/nets.py
+    """
+    emb = fluid.layers.embedding(
+        input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
+    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
+    bow_tanh = fluid.layers.tanh(bow)
+    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
+    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
+    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    return avg_cost
+
+
+class TestRegularizer(unittest.TestCase):
+    def setUp(self):
+        self.word_dict = paddle.dataset.imdb.word_dict()
+        reader = paddle.batch(
+            paddle.dataset.imdb.train(self.word_dict), batch_size=8)()
+        self.train_data = [next(reader) for _ in range(5)]
+
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        return places
+
+    @contextlib.contextmanager
+    def scope_prog_guard(self, main_prog, startup_prog):
+        scope = fluid.core.Scope()
+        with fluid.unique_name.guard():
+            with fluid.scope_guard(scope):
+                with fluid.program_guard(main_prog, startup_prog):
+                    yield
+
+    def run_program(self, place, feed_list):
+        exe = fluid.Executor(place)
+        feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
+        exe.run(fluid.default_startup_program())
+
+        main_prog = fluid.default_main_program()
+        param_list = [var.name for var in main_prog.block(0).all_parameters()]
+
+        param_sum = []
+        for data in self.train_data:
+            out = exe.run(main_prog,
+                          feed=feeder.feed(data),
+                          fetch_list=param_list)
+            p_sum = 0
+            for v in out:
+                p_sum += np.sum(np.abs(v))
+            param_sum.append(p_sum)
+        return param_sum
+
+    def check_l2decay_regularizer(self, place, model):
+        main_prog = fluid.framework.Program()
+        startup_prog = fluid.framework.Program()
+        startup_prog.random_seed = 1
+        with self.scope_prog_guard(
+                main_prog=main_prog, startup_prog=startup_prog):
+            data = fluid.layers.data(
+                name="words", shape=[1], dtype="int64", lod_level=1)
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+            avg_cost = model(data, label, len(self.word_dict))
+
+            optimizer = fluid.optimizer.Adagrad(
+                learning_rate=0.1,
+                regularization=fluid.regularizer.L2Decay(1.0))
+            optimizer.minimize(avg_cost)
+            param_sum = self.run_program(place, [data, label])
+        return param_sum
+
+    def check_l2decay(self, place, model):
+        main_prog = fluid.framework.Program()
+        startup_prog = fluid.framework.Program()
+        startup_prog.random_seed = 1
+        with self.scope_prog_guard(
+                main_prog=main_prog, startup_prog=startup_prog):
+            data = fluid.layers.data(
+                name="words", shape=[1], dtype="int64", lod_level=1)
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+            avg_cost_l2 = model(data, label, len(self.word_dict))
+
+            param_list = fluid.default_main_program().block(0).all_parameters()
+            para_sum = []
+            for para in param_list:
+                para_mul = fluid.layers.square(x=para)
+                para_sum.append(fluid.layers.reduce_sum(input=para_mul))
+            avg_cost_l2 += fluid.layers.sums(para_sum) * .5
+
+            optimizer = fluid.optimizer.Adagrad(learning_rate=0.1)
+            optimizer.minimize(avg_cost_l2)
+            param_sum = self.run_program(place, [data, label])
+        return param_sum
+
+    def test_l2(self):
+        for place in self.get_places():
+            dense_sparse_p_sum = []
+            for sparse in [True, False]:
+                model = partial(bow_net, is_sparse=sparse)
+                framework_l2 = self.check_l2decay_regularizer(place, model)
+                l2 = self.check_l2decay(place, model)
+                assert len(l2) == len(framework_l2)
+                for i in range(len(l2)):
+                    assert np.isclose(a=framework_l2[i], b=l2[i], rtol=5e-5)
+                dense_sparse_p_sum.append(framework_l2)
+
+            assert len(dense_sparse_p_sum[0]) == len(dense_sparse_p_sum[1])
+            for i in range(len(dense_sparse_p_sum[0])):
+                assert np.isclose(
+                    a=dense_sparse_p_sum[0][i],
+                    b=dense_sparse_p_sum[1][i],
+                    rtol=5e-5)
+
+
 if __name__ == '__main__':
     unittest.main()