fix no_cvm in config_fleet (#22818)

* fix no cvm, when accessor is DownpourSparseValueAccessor, set no_cvm=True
* test=develop

python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py

@@ -420,6 +420,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
         if server._server.downpour_server_param.downpour_table_param[
                 0].accessor.accessor_class == "DownpourCtrAccessor":
             opt_info["dump_slot"] = True
+        elif server._server.downpour_server_param.downpour_table_param[
+                0].accessor.accessor_class == "DownpourSparseValueAccessor":
+            opt_info["no_cvm"] = True
         opt_info["adjust_ins_weight"] = strategy.get("adjust_ins_weight", {})
         opt_info["copy_table"] = strategy.get("copy_table", {})
         opt_info["loss_names"] = strategy.get("loss_names", [])

python/paddle/fluid/tests/unittests/test_fleet.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.
+"""Test fleet."""
+
+from __future__ import print_function
+import os
+import unittest
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+
+class TestFleet1(unittest.TestCase):
+    """
+    Test cases for fleet minimize.
+    """
+
+    def setUp(self):
+        """Set up, set envs."""
+        os.environ["PADDLE_TRAINERS_NUM"] = "2"
+        os.environ[
+            "PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"
+
+    def test_pslib_1(self):
+        """Test cases for pslib."""
+        import paddle.fluid as fluid
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
+        from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
+        try:
+            import netifaces
+        except:
+            print("warning: no netifaces, skip test_pslib_1")
+            return
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        role_maker = GeneralRoleMaker()
+        role_maker.generate_role()
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        fleet.init(role_maker)
+        train_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(train_program, startup_program):
+            show = fluid.layers.data(name="show", shape=[-1, 1], \
+                dtype="int64", lod_level=1, append_batch_size=False)
+            emb = fluid.layers.embedding(input=show, size=[1, 1], \
+                is_sparse=True, is_distributed=True, \
+                param_attr=fluid.ParamAttr(name="embedding"))
+            fc = fluid.layers.fc(input=emb, size=1, act=None)
+            label = fluid.layers.data(name="click", shape=[-1, 1], \
+                dtype="int64", lod_level=1, append_batch_size=False)
+            label_cast = fluid.layers.cast(label, dtype='float32')
+            cost = fluid.layers.log_loss(fc, label_cast)
+        try:
+            adam = fluid.optimizer.Adam(learning_rate=0.000005)
+            adam = fleet.distributed_optimizer(
+                adam,
+                strategy={
+                    "embedding": {
+                        "sparse_accessor_class": "DownpourSparseValueAccessor"
+                    }
+                })
+            adam.minimize([cost], [scope])
+            fleet.run_server()
+        except:
+            print("do not support pslib test, skip")
+            return
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_fleet_nocvm_1.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.
+"""Test fleet."""
+
+from __future__ import print_function
+import os
+import unittest
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+
+class TestFleet1(unittest.TestCase):
+    """
+    Test cases for fleet minimize.
+    """
+
+    def setUp(self):
+        """Set up, set envs."""
+        os.environ["PADDLE_TRAINERS_NUM"] = "2"
+        os.environ[
+            "PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"
+
+    def test_pslib_1(self):
+        """Test cases for pslib."""
+        import paddle.fluid as fluid
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
+        from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
+        try:
+            import netifaces
+        except:
+            print("warning: no netifaces, skip test_pslib_1")
+            return
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        role_maker = GeneralRoleMaker()
+        role_maker.generate_role()
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        fleet.init(role_maker)
+        train_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(train_program, startup_program):
+            show = fluid.layers.data(name="show", shape=[-1, 1], \
+                dtype="int64", lod_level=1, append_batch_size=False)
+            emb = fluid.layers.embedding(input=show, size=[1, 1], \
+                is_sparse=True, is_distributed=True, \
+                param_attr=fluid.ParamAttr(name="embedding"))
+            fc = fluid.layers.fc(input=emb, size=1, act=None)
+            label = fluid.layers.data(name="click", shape=[-1, 1], \
+                dtype="int64", lod_level=1, append_batch_size=False)
+            label_cast = fluid.layers.cast(label, dtype='float32')
+            cost = fluid.layers.log_loss(fc, label_cast)
+        try:
+            adam = fluid.optimizer.Adam(learning_rate=0.000005)
+            adam = fleet.distributed_optimizer(
+                adam,
+                strategy={
+                    "embedding": {
+                        "sparse_accessor_class": "DownpourCtrAccessor"
+                    }
+                })
+            adam.minimize([cost], [scope])
+            fleet.run_server()
+        except:
+            print("do not support pslib test, skip")
+            return
+
+
+if __name__ == "__main__":
+    unittest.main()