add pipeline optimizer for fleet (#25768)

* add pipeline optimizer

python/paddle/fleet/base/meta_optimizer_factory.py

@@ -15,6 +15,7 @@
 from ..meta_optimizers import RecomputeOptimizer
 from ..meta_optimizers import GradientMergeOptimizer
 from ..meta_optimizers import GraphExecutionOptimizer
+from ..meta_optimizers import PipelineOptimizer
 
 __all__ = ["MetaOptimizerFactory"]
 
@@ -22,6 +23,7 @@ meta_optimizer_names = [
     "RecomputeOptimizer",
     "GradientMergeOptimizer",
     "GraphExecutionOptimizer",
+    "PipelineOptimizer",
 ]
 
 

python/paddle/fleet/meta_optimizers/__init__.py

@@ -14,8 +14,10 @@
 from .recompute_optimizer import RecomputeOptimizer
 from .gradient_merge_optimizer import GradientMergeOptimizer
 from .graph_execution_optimizer import GraphExecutionOptimizer
+from .pipeline_optimizer import PipelineOptimizer
 
 __all__ = [
     'RecomputeOptimizer',
     'GradientMergeOptimizer',
+    'PipelineOptimizer',
 ]

python/paddle/fleet/meta_optimizers/pipeline_optimizer.py

+#   Copyright (c) 2019 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 PipelineOptimizer as PO
+from .meta_optimizer_base import MetaOptimizerBase
+
+__all__ = ["PipelineOptimizer"]
+
+
+class PipelineOptimizer(MetaOptimizerBase):
+    def __init__(self, optimizer):
+        super(PipelineOptimizer, self).__init__(optimizer)
+        self.inner_opt = optimizer
+        # 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(PipelineOptimizer, self)._set_basic_info(
+            loss, role_maker, user_defined_optimizer, user_defined_strategy)
+        num_microbatches = user_defined_strategy.pipeline_configs['micro_batch']
+        self.wrapped_opt = PO(self.inner_opt, num_microbatches=num_microbatches)
+
+    def _can_apply(self):
+        if self.user_defined_strategy.pipeline == True:
+            return True
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.pipeline = False
+        dist_strategy.pipeline_configs = {"micro_batch": 1}
+
+    def backward(self,
+                 loss,
+                 startup_program=None,
+                 parameter_list=None,
+                 no_grad_set=None,
+                 callbacks=None):
+        return self.wrapped_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, prog_list = \
+            self.wrapped_opt.minimize(loss, startup_program,
+                                      parameter_list, no_grad_set)
+        return optimize_ops, params_grads

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

@@ -32,6 +32,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_fleet_checkpoint)
 list(APPEND MIXED_DIST_TEST_OPS test_collective_optimizer)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_base)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_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_private_function)
 foreach(TEST_OP ${MIXED_DIST_TEST_OPS})
@@ -364,6 +365,7 @@ if(WITH_DISTRIBUTE)
     if(NOT APPLE)
     	   py_test_modules(test_fleet_base MODULES test_fleet_base ENVS ${dist_ENVS})
     	   py_test_modules(test_fleet_meta_optimizer MODULES test_fleet_meta_optimizer ENVS ${dist_ENVS})
+    	   py_test_modules(test_fleet_pipeline_meta_optimizer MODULES test_fleet_pipeline_meta_optimizer ENVS ${dist_ENVS})
            py_test_modules(test_fleet_gradient_merge_meta_optimizer MODULES test_fleet_gradient_merge_meta_optimizer ENVS ${dist_ENVS})
     	   py_test_modules(test_fleet_private_function MODULES test_fleet_private_function ENVS ${dist_ENVS})
     endif(NOT APPLE)

python/paddle/fluid/tests/unittests/test_fleet_pipeline_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
+
+
+class TestFleetMetaOptimizer(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+
+    def test_pipeline_optimizer(self):
+        import paddle.fleet as fleet
+        import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        fleet.init(role)
+        with paddle.fluid.device_guard("cpu"):
+            input_x = paddle.fluid.layers.data(
+                name="x", shape=[32], dtype='float32')
+            input_y = paddle.fluid.layers.data(
+                name="y", shape=[1], dtype='int64')
+            data_loader = paddle.fluid.io.DataLoader.from_generator(
+                feed_list=[input_x, input_y],
+                capacity=64,
+                use_double_buffer=True,
+                iterable=False)
+            fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
+
+        with paddle.fluid.device_guard("gpu:0"):
+            fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, 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.pipeline = True
+        strategy.pipeline_configs = {'micro_batch': 2}
+
+        optimizer = paddle.optimizer.SGD(learning_rate=0.01)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+
+
+if __name__ == "__main__":
+    unittest.main()