Add Lookahead and ModelAverage Optimizer (#30004)

* test=develop, add model_average and lookahead

paddle/fluid/pybind/op_function_generator.cc

@@ -104,6 +104,9 @@ std::map<std::string, std::set<std::string>> op_passing_outs_map = {
     {"sgd", {"ParamOut"}},
     {"adam",
      {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut"}},
+    {"average_accumulates",
+     {"out_sum_1", "out_sum_2", "out_sum_3", "out_num_accumulates",
+      "out_old_num_accumulates", "out_num_updates"}},
     {"momentum", {"ParamOut", "VelocityOut"}},
     {"batch_norm", {"MeanOut", "VarianceOut"}},
     {"sync_batch_norm", {"MeanOut", "VarianceOut"}},

python/paddle/__init__.py

@@ -43,6 +43,7 @@ import paddle.optimizer
 import paddle.metric
 import paddle.device
 import paddle.regularizer
+import paddle.incubate
 
 # TODO: define alias in tensor and framework directory
 

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

@@ -0,0 +1,146 @@
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from paddle.fluid import core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+import paddle
+import paddle.nn as nn
+
+LOOKAHEAD_K = 5
+LOOKAHEAD_ALPHA = 0.2
+SGD_LR = 1.0
+
+
+class TestLookAhead(unittest.TestCase):
+    def test_lookahead_static(self):
+        paddle.enable_static()
+        place = fluid.CPUPlace()
+        shape = [2, 3, 8, 8]
+        exe = fluid.Executor(place)
+        train_program = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(train_program, startup):
+            with fluid.unique_name.guard():
+                data = fluid.data(name='X', shape=[None, 1], dtype='float32')
+                hidden = fluid.layers.fc(input=data, size=10)
+                loss = fluid.layers.mean(hidden)
+
+                optimizer = paddle.optimizer.SGD(learning_rate=SGD_LR)
+                lookahead = paddle.incubate.optimizer.LookAhead(
+                    optimizer, alpha=LOOKAHEAD_ALPHA, k=LOOKAHEAD_K)
+                lookahead.minimize(loss)
+
+        exe.run(startup)
+        slow_param = None
+        fast_param = None
+        for i in range(10):
+            if (i + 1) % LOOKAHEAD_K == 0:
+                slow_param = slow_param + LOOKAHEAD_ALPHA * (fast_param -
+                                                             slow_param)
+            x = np.random.random(size=(10, 1)).astype('float32')
+            latest_b, b_grad = exe.run(program=train_program,
+                                       feed={'X': x},
+                                       fetch_list=[
+                                           'fc_0.b_0',
+                                           'fc_0.b_0@GRAD',
+                                       ])
+            if i == 0:
+                slow_param = latest_b
+            if (i + 1) % LOOKAHEAD_K == 0:
+                self.assertAlmostEqual(
+                    slow_param.all(), latest_b.all(), delta=5e-3)
+            fast_param = latest_b - SGD_LR * b_grad
+
+    def test_look_ahead_dygraph(self):
+        BATCH_SIZE = 16
+        BATCH_NUM = 4
+        EPOCH_NUM = 4
+
+        IMAGE_SIZE = 784
+        CLASS_NUM = 10
+
+        # define a random dataset
+        class RandomDataset(paddle.io.Dataset):
+            def __init__(self, num_samples):
+                self.num_samples = num_samples
+
+            def __getitem__(self, idx):
+                image = np.random.random([IMAGE_SIZE]).astype('float32')
+                label = np.random.randint(0, CLASS_NUM - 1,
+                                          (1, )).astype('int64')
+                return image, label
+
+            def __len__(self):
+                return self.num_samples
+
+        class LinearNet(nn.Layer):
+            def __init__(self):
+                super(LinearNet, self).__init__()
+                self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
+                self.bias = self._linear.bias
+
+            @paddle.jit.to_static
+            def forward(self, x):
+                return self._linear(x)
+
+        def train(layer, loader, loss_fn, opt):
+            idx = 0
+            slow_param = None
+            fast_param = None
+            for epoch_id in range(EPOCH_NUM):
+                for batch_id, (image, label) in enumerate(loader()):
+                    idx += 1
+                    out = layer(image)
+                    loss = loss_fn(out, label)
+                    loss.backward()
+                    fast_param = layer.bias.numpy() - SGD_LR * layer.bias.grad
+                    opt.step()
+                    if idx == 1:
+                        slow_param = fast_param
+                    if idx % LOOKAHEAD_K == 0:
+                        slow_param = slow_param + LOOKAHEAD_ALPHA * (
+                            fast_param - slow_param)
+                        self.assertAlmostEqual(
+                            np.mean(slow_param),
+                            np.mean(layer.bias.numpy()),
+                            delta=5e-3)
+                    opt.clear_grad()
+
+        layer = LinearNet()
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = paddle.optimizer.SGD(learning_rate=SGD_LR,
+                                         parameters=layer.parameters())
+        lookahead = paddle.incubate.optimizer.LookAhead(
+            optimizer, alpha=LOOKAHEAD_ALPHA, k=LOOKAHEAD_K)
+
+        # create data loader
+        dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
+        loader = paddle.io.DataLoader(
+            dataset,
+            batch_size=BATCH_SIZE,
+            shuffle=True,
+            drop_last=True,
+            num_workers=2)
+
+        train(layer, loader, loss_fn, lookahead)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -0,0 +1,209 @@
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from paddle.fluid import core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+import paddle
+import paddle.nn as nn
+
+
+class TestModelAverage(unittest.TestCase):
+    def test_model_average_static(self):
+        paddle.enable_static()
+        place = fluid.CPUPlace()
+        shape = [2, 3, 8, 8]
+        exe = fluid.Executor(place)
+        train_program = fluid.Program()
+        startup = fluid.Program()
+        test_program = fluid.Program()
+        with fluid.program_guard(train_program, startup):
+            with fluid.unique_name.guard():
+                data = fluid.data(name='X', shape=[None, 1], dtype='float32')
+                hidden = fluid.layers.fc(input=data, size=10)
+                loss = fluid.layers.mean(hidden)
+                test_program = train_program.clone()
+                optimizer = paddle.optimizer.Momentum(
+                    learning_rate=0.2, momentum=0.1)
+
+                optimizer.minimize(loss)
+                # build ModelAverage optimizer
+                model_average = paddle.incubate.optimizer.ModelAverage(
+                    0.15, min_average_window=2, max_average_window=10)
+
+        exe.run(startup)
+        for i in range(10):
+            x = np.random.random(size=(10, 1)).astype('float32')
+            latest_b, sum_1, sum_2, sum_3, num_accumulates, old_num_accumulates, num_updates = exe.run(
+                program=train_program,
+                feed={'X': x},
+                fetch_list=[
+                    'fc_0.b_0', 'fc_0.b_0_sum_1_0', 'fc_0.b_0_sum_2_0',
+                    'fc_0.b_0_sum_3_0', 'fc_0.b_0_num_accumulates_0',
+                    'fc_0.b_0_old_num_accumulates_0', 'fc_0.b_0_num_updates_0'
+                ])
+        self.assertTrue(
+            np.equal(
+                sum_1, np.zeros(
+                    shape=[10], dtype='float32')).all())
+        self.assertTrue(
+            np.equal(
+                sum_2, np.zeros(
+                    shape=[10], dtype='float32')).all())
+        self.assertTrue(
+            np.equal(
+                num_accumulates, np.array(
+                    [0], dtype='int64')).all())
+        self.assertTrue(
+            np.equal(
+                old_num_accumulates, np.array(
+                    [2], dtype='int64')).all())
+        self.assertTrue(
+            np.equal(
+                num_updates, np.array(
+                    [10], dtype='int64')).all())
+
+        average_b = (sum_1 + sum_2 + sum_3) / (
+            num_accumulates + old_num_accumulates)
+        # apply ModelAverage
+        with model_average.apply(exe):
+            x = np.random.random(size=(10, 1)).astype('float32')
+            outs, b = exe.run(program=test_program,
+                              feed={'X': x},
+                              fetch_list=[loss.name, 'fc_0.b_0'])
+            self.assertAlmostEqual(np.mean(average_b), np.mean(b))
+
+        x = np.random.random(size=(10, 1)).astype('float32')
+        outs, b = exe.run(program=test_program,
+                          feed={'X': x},
+                          fetch_list=[loss.name, 'fc_0.b_0'])
+        self.assertAlmostEqual(np.mean(latest_b), np.mean(b))
+
+    def test_model_average_dygraph(self):
+        BATCH_SIZE = 16
+        BATCH_NUM = 4
+        EPOCH_NUM = 4
+
+        IMAGE_SIZE = 784
+        CLASS_NUM = 10
+
+        # define a random dataset
+        class RandomDataset(paddle.io.Dataset):
+            def __init__(self, num_samples):
+                self.num_samples = num_samples
+
+            def __getitem__(self, idx):
+                image = np.random.random([IMAGE_SIZE]).astype('float32')
+                label = np.random.randint(0, CLASS_NUM - 1,
+                                          (1, )).astype('int64')
+                return image, label
+
+            def __len__(self):
+                return self.num_samples
+
+        class LinearNet(nn.Layer):
+            def __init__(self):
+                super(LinearNet, self).__init__()
+                self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
+                self.bias = self._linear.bias
+
+            @paddle.jit.to_static
+            def forward(self, x):
+                return self._linear(x)
+
+        def train(layer, loader, loss_fn, opt, model_average):
+            for epoch_id in range(EPOCH_NUM):
+                for batch_id, (image, label) in enumerate(loader()):
+                    out = layer(image)
+                    loss = loss_fn(out, label)
+                    loss.backward()
+                    opt.step()
+                    model_average.step()
+                    opt.clear_grad()
+                    model_average.clear_grad()
+                    # print("Train Epoch {} batch {}: loss = {}, bias = {}".format(
+                    #     epoch_id, batch_id, np.mean(loss.numpy()), layer.bias.numpy()))
+            sum_1 = model_average._get_accumulator('sum_1', layer.bias)
+            sum_2 = model_average._get_accumulator('sum_2', layer.bias)
+            sum_3 = model_average._get_accumulator('sum_3', layer.bias)
+            num_accumulates = model_average._get_accumulator('num_accumulates',
+                                                             layer.bias)
+            old_num_accumulates = model_average._get_accumulator(
+                'old_num_accumulates', layer.bias)
+            num_updates = model_average._get_accumulator('num_updates',
+                                                         layer.bias)
+
+            return ((sum_1 + sum_2 + sum_3) /
+                    (num_accumulates + old_num_accumulates)).numpy()
+
+        def evaluate(layer, loader, loss_fn, check_param):
+            for batch_id, (image, label) in enumerate(loader()):
+                out = layer(image)
+                loss = loss_fn(out, label)
+                loss.backward()
+                self.assertAlmostEqual(
+                    np.mean(layer.bias.numpy()),
+                    np.mean(check_param),
+                    delta=5e-3)
+                # print("Evaluate batch {}: loss = {}, bias = {}".format(
+                #     batch_id, np.mean(loss.numpy()), layer.bias.numpy()))
+
+            # create network
+
+        layer = LinearNet()
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = paddle.optimizer.Momentum(
+            learning_rate=0.2, momentum=0.1, parameters=layer.parameters())
+        # build ModelAverage optimizer
+        model_average = paddle.incubate.optimizer.ModelAverage(
+            0.15,
+            parameters=layer.parameters(),
+            min_average_window=2,
+            max_average_window=10)
+
+        # create data loader
+        dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
+        loader = paddle.io.DataLoader(
+            dataset,
+            batch_size=BATCH_SIZE,
+            shuffle=True,
+            drop_last=True,
+            num_workers=2)
+        eval_loader = paddle.io.DataLoader(
+            dataset,
+            batch_size=BATCH_SIZE,
+            shuffle=True,
+            drop_last=True,
+            num_workers=1)
+        # train
+        check_param = train(layer, loader, loss_fn, optimizer, model_average)
+        # print(check_param)
+        with model_average.apply(need_restore=False):
+            evaluate(layer, eval_loader, loss_fn, check_param)
+
+        check_param = (model_average._get_accumulator('restore',
+                                                      layer.bias)).numpy()
+        # print(check_param)
+        # print("\nEvaluate With Restored Paramters")
+        model_average.restore()
+        evaluate(layer, eval_loader, loss_fn, check_param)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/incubate/__init__.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from . import optimizer
+from ..fluid.contrib import reader
+
 __all__ = []
 __all__ += ["reader"]
-
+__all__ += optimizer.__all__
-from ..fluid.contrib import reader

python/paddle/incubate/optimizer/__init__.py

@@ -0,0 +1,18 @@
+#   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.
+
+from .lookahead import LookAhead
+from .modelaverage import ModelAverage
+
+__all__ = ['LookAhead', 'ModelAverage']

python/setup.py.in

@@ -143,6 +143,7 @@ packages=['paddle',
           'paddle.reader',
           'paddle.distributed',
           'paddle.incubate',
+          'paddle.incubate.optimizer',
           'paddle.distributed.fleet',
           'paddle.distributed.fleet.base',
           'paddle.distributed.fleet.meta_optimizers',