fix dp (#33297)

python/paddle/distributed/fleet/meta_optimizers/raw_program_optimizer.py

@@ -113,7 +113,8 @@ class RawProgramOptimizer(MetaOptimizerBase):
 
         optimize_ops, params_grads = self.inner_opt.minimize(
             loss, startup_program, parameter_list, no_grad_set)
-
+        if self.nranks == 1:
+            return optimize_ops, params_grads
         self._init_process_group()
 
         self.main_program = program

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

@@ -104,6 +104,7 @@ if(((NOT WITH_ROCM) AND (NOT WITH_GPU)) OR WIN32)
     LIST(REMOVE_ITEM TEST_OPS test_collective_sendrecv_api)
     LIST(REMOVE_ITEM TEST_OPS test_collective_wait)
     LIST(REMOVE_ITEM TEST_OPS test_memcpy_op)
+    LIST(REMOVE_ITEM TEST_OPS test_raw_program_optimizer)
 endif()
 
 if(WIN32)

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

+#   Copyright (c) 2018 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 paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.distributed.fleet as fleet
+import numpy as np
+import os
+
+
+class TestRawProgramOptimizer(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+
+    def mlp(self, input_x, input_y, hid_dim=128, label_dim=2):
+        fc_1 = paddle.static.nn.fc(x=input_x, size=hid_dim, activation='tanh')
+        fc_2 = paddle.static.nn.fc(x=fc_1, size=hid_dim, activation='tanh')
+        prediction = paddle.static.nn.fc(x=[fc_2],
+                                         size=label_dim,
+                                         activation='softmax')
+        cost = paddle.nn.functional.cross_entropy(
+            input=prediction, label=input_y)
+        avg_cost = paddle.mean(x=cost)
+        return avg_cost
+
+    def gen_data(self):
+        return {
+            "x": np.random.random(size=(128, 32)).astype('float32'),
+            "y": np.random.randint(
+                2, size=(128, 1)).astype('int64')
+        }
+
+    def test_single_gpu(self):
+        paddle.enable_static()
+        fleet.init(is_collective=True)
+        sharding_program = paddle.static.Program()
+        sharding_startup_program = paddle.static.Program()
+        strategy = fleet.DistributedStrategy()
+        strategy.without_graph_optimization = True
+        with fluid.program_guard(sharding_program, sharding_startup_program):
+            with fluid.unique_name.guard():
+                input_x = paddle.static.data(
+                    name="x", shape=[None, 32], dtype='float32')
+                input_y = paddle.static.data(
+                    name="y", shape=[None, 1], dtype='int64')
+                cost = self.mlp(input_x=input_x, input_y=input_y)
+                output_name = cost.name
+                optimizer = fleet.distributed_optimizer(fluid.optimizer.Adam(),
+                                                        strategy)
+                optimizer.minimize(cost)
+
+        trainer_id = fleet.worker_index()
+        exe = paddle.static.Executor(paddle.CUDAPlace(trainer_id))
+        rank = fleet.worker_index()
+        exe.run(sharding_startup_program)
+        exe.run(program=sharding_program, feed=self.gen_data())
+
+
+if __name__ == "__main__":
+    unittest.main()