add lars to fleet meta optimizer (#25884)

paddle/fluid/framework/distributed_strategy.proto

@@ -40,6 +40,17 @@ message GradientMergeConfig {
   optional bool avg = 2 [ default = true ];
 }
 
+message LarsConfig {
+  optional float lars_coeff = 1 [ default = 0.001 ];
+  optional float lars_weight_decay = 2 [ default = 0.0005 ];
+}
+
+message LambConfig {
+  optional float beta1 = 1 [ default = 0.001 ];
+  optional float beta2 = 2 [ default = 0.999 ];
+  optional float epsilon = 3 [ default = 0.000001 ];
+}
+
 message BuildStrategy {
   optional bool enable_sequential_execution = 1 [ default = false ];
   optional bool fuse_elewise_add_act_ops = 2 [ default = false ];
@@ -102,7 +113,8 @@ message DistributedStrategy {
   optional GradientMergeConfig gradient_merge_configs = 104;
   optional PipelineConfig pipeline_configs = 106;
   optional AsyncConfig a_sync_configs = 107;
-
+  optional LarsConfig lars_configs = 108;
+  optional LambConfig lamb_configs = 109;
   optional BuildStrategy build_strategy = 201;
   optional ExecutionStrategy execution_strategy = 202;
 }

python/paddle/fleet/base/meta_optimizer_factory.py

@@ -17,6 +17,7 @@ from ..meta_optimizers import GradientMergeOptimizer
 from ..meta_optimizers import GraphExecutionOptimizer
 from ..meta_optimizers import PipelineOptimizer
 from ..meta_optimizers import LocalSGDOptimizer
+from ..meta_optimizers import LarsOptimizer
 
 __all__ = ["MetaOptimizerFactory"]
 
@@ -26,6 +27,7 @@ meta_optimizer_names = [
     "GraphExecutionOptimizer",
     "PipelineOptimizer",
     "LocalSGDOptimizer",
+    "LarsOptimizer",
 ]
 
 

python/paddle/fleet/meta_optimizers/__init__.py

@@ -16,10 +16,12 @@ from .gradient_merge_optimizer import GradientMergeOptimizer
 from .graph_execution_optimizer import GraphExecutionOptimizer
 from .pipeline_optimizer import PipelineOptimizer
 from .localsgd_optimizer import LocalSGDOptimizer
+from .lars_optimizer import LarsOptimizer
 
 __all__ = [
     'RecomputeOptimizer',
     'GradientMergeOptimizer',
     'PipelineOptimizer',
     'LocalSGDOptimizer',
+    'LarsOptimizer',
 ]

python/paddle/fleet/meta_optimizers/lars_optimizer.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
+
+from paddle.fluid.optimizer import Momentum, LarsMomentumOptimizer
+from .meta_optimizer_base import MetaOptimizerBase
+import logging
+
+__all__ = ["LarsOptimizer"]
+
+
+class LarsOptimizer(MetaOptimizerBase):
+    def __init__(self, optimizer):
+        super(LarsOptimizer, self).__init__(optimizer)
+        self.inner_opt = optimizer
+        self.lars_opt = None
+        # we do not allow meta optimizer to be inner optimizer currently
+        self.meta_optimizers_white_list = []
+
+    def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
+                        user_defined_strategy):
+        super(LarsOptimizer, self)._set_basic_info(
+            loss, role_maker, user_defined_optimizer, user_defined_strategy)
+
+        opt = self.inner_opt
+        if not isinstance(opt, Momentum):
+            return
+
+        configs = self.user_defined_strategy.lars_configs
+
+        self.lars_opt = LarsMomentumOptimizer(
+            learning_rate=opt._learning_rate,
+            momentum=opt._momentum,
+            lars_coeff=configs['lars_coeff'],
+            lars_weight_decay=configs['lars_weight_decay'],
+            parameter_list=opt._parameter_list,
+            regularization=opt.regularization,
+            grad_clip=opt._grad_clip,
+            name=opt._name)
+
+    def _can_apply(self):
+        if self.user_defined_strategy.lars:
+            if not isinstance(self.inner_opt, Momentum):
+                logging.warn(
+                    "lars need the inner optimizer to be Momentum optimizer.")
+                return False
+            return True
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.lars = False
+        dist_strategy.lars_configs = {
+            'lars_coeff': 0.001,
+            'lars_weight_decay': 0.0005,
+        }
+
+    def backward(self,
+                 loss,
+                 startup_program=None,
+                 parameter_list=None,
+                 no_grad_set=None,
+                 callbacks=None):
+        return self.lars_opt.backward(loss, startup_program, parameter_list,
+                                      no_grad_set, callbacks)
+
+    def minimize_impl(self,
+                      loss,
+                      startup_program=None,
+                      parameter_list=None,
+                      no_grad_set=None):
+        optimize_ops, params_grads = \
+            self.lars_opt.minimize(loss, startup_program,
+                                      parameter_list, no_grad_set)
+        return optimize_ops, params_grads

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -36,6 +36,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_fleet_graph_execution_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_pipeline_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_gradient_merge_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_localsgd_meta_optimizer)
+list(APPEND MIXED_DIST_TEST_OPS test_fleet_lars_meta_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_private_function)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_graph_executor)
 foreach(TEST_OP ${MIXED_DIST_TEST_OPS})
@@ -375,6 +376,7 @@ if(WITH_DISTRIBUTE)
     	   py_test_modules(test_fleet_private_function MODULES test_fleet_private_function ENVS ${dist_ENVS})
         if(NOT WIN32)
             py_test_modules(test_fleet_localsgd_meta_optimizer MODULES test_fleet_localsgd_meta_optimizer ENVS ${dist_ENVS})
+            py_test_modules(test_fleet_lars_meta_optimizer MODULES test_fleet_lars_meta_optimizer ENVS ${dist_ENVS})
         endif(NOT WIN32)
     endif(NOT APPLE)
     if(WITH_DGC)

python/paddle/fluid/tests/unittests/test_fleet_lars_meta_optimizer.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 unittest
+import paddle
+import os
+import paddle.fleet as fleet
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+
+class TestFleetLarsMetaOptimizer(unittest.TestCase):
+    def setUp(self):
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "2"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
+                       "127.0.0.1:36001,127.0.0.2:36001"
+
+    def net(self):
+        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        fleet.init(role)
+        input_x = paddle.fluid.layers.data(
+            name="x", shape=[32], dtype='float32')
+        input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
+
+        fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
+        fc_2 = paddle.fluid.layers.fc(input=fc_1, size=256, act='tanh')
+        prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
+        cost = paddle.fluid.layers.cross_entropy(
+            input=prediction, label=input_y)
+        avg_cost = paddle.fluid.layers.mean(x=cost)
+
+        strategy = paddle.fleet.DistributedStrategy()
+        strategy.lars = True
+        strategy.lars_configs = {
+            "lars_coeff": 0.001,
+            "lars_weight_decay": 0.0005,
+        }
+
+        return avg_cost, strategy
+
+    def test_lars_optimizer(self):
+        avg_cost, strategy = self.net()
+        optimizer = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+
+        ops = [op.type for op in avg_cost.block.ops]
+        self.assertIn('lars_momentum', ops)
+
+    def test_lars_not_apply_with_adam(self):
+        avg_cost, strategy = self.net()
+        optimizer = paddle.optimizer.Adam(learning_rate=0.01)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+
+        ops = [op.type for op in avg_cost.block.ops]
+        self.assertNotIn('lars_momentum', ops)
+
+
+if __name__ == "__main__":
+    unittest.main()