diff --git a/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py b/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py
new file mode 100644
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+++ b/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py
@@ -0,0 +1,271 @@
+# 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 os
+import signal
+import time
+import unittest
+from multiprocessing import Process
+
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+from paddle.fluid.framework import Program, program_guard
+
+
+def run_pserver(pserver_id, use_cuda, sync_mode):
+ scope = fluid.core.Scope()
+ program = Program()
+ with fluid.scope_guard(scope):
+ with program_guard(program, startup_program=Program()):
+ # create table parameter in scope
+ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+ # create and initialize Param Variable
+ param = scope.var('table').get_tensor()
+
+ param_array = np.ones((5, 8)).astype("float32")
+ for i in range(len(param_array)):
+ param_array[i] *= param_array[i] * i + pserver_id * 10 + 1
+ param.set(param_array, place)
+
+ optimize_block = program._create_block(program.global_block().idx)
+ program.global_block().append_op(
+ type="listen_and_serv",
+ inputs={'X': []},
+ outputs={},
+ attrs={
+ "optimize_blocks": [optimize_block],
+ "endpoint": '127.0.0.1:0',
+ "Fanin": 1,
+ "sync_mode": True,
+ "grad_to_block_id": []
+ })
+
+ exe = fluid.Executor(place)
+ exe.run(program)
+
+
+class TestListenAndServOp(unittest.TestCase):
+ def setUp(self):
+ self.ps_timeout = 5
+
+ def _start_pserver(self, pserver_id, use_cuda, sync_mode, pserver_func):
+ p = Process(target=pserver_func, args=(pserver_id, use_cuda, sync_mode))
+ p.daemon = True
+ p.start()
+ return p
+
+ def _wait_ps_ready(self, pid):
+ start_left_time = self.ps_timeout
+ sleep_time = 0.5
+ while True:
+ assert start_left_time >= 0, "wait ps ready failed"
+ time.sleep(sleep_time)
+ try:
+ # the listen_and_serv_op would touch a file which contains the listen port
+ # on the /tmp directory until it was ready to process all the RPC call.
+ os.stat("/tmp/paddle.%d.port" % pid)
+ return
+ except os.error:
+ start_left_time -= sleep_time
+
+ def _get_pserver_port(self, pid):
+ with open("/tmp/paddle.%d.port" % pid, 'r') as f:
+ port = int(f.read().strip())
+ return port
+
+ def _run_nce_op_one_pserver(self, place, port):
+ scope = fluid.core.Scope()
+ program = Program()
+ with fluid.scope_guard(scope):
+ with program_guard(program, startup_program=Program()):
+ x = scope.var('X').get_tensor()
+ x_array = np.random.random((4, 8)).astype("float32") * 2
+ x.set(x_array, place)
+ # create and initialize Param Variable
+ param = scope.var('W').get_tensor()
+ param_array = np.zeros((5, 8)).astype("float32") * 2
+ param.set(param_array, place)
+
+ path_table = scope.var('PathTable').get_tensor()
+ path_table_array = np.array(
+ [(0, 2, -1, -1, -1), (0, 1, 2, -1, -1), (0, 1, 4, -1, -1),
+ (0, 2, -1, -1, -1)]).astype(
+ "int64"
+ ) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
+ path_table.set(path_table_array, place)
+
+ path_code = scope.var('PathCode').get_tensor()
+ path_code_array = np.array(
+ [(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
+ (0, 1, -1, -1, -1)]).astype("int64") #np.array to store
+ path_code.set(path_code_array, place)
+
+ label = scope.var('Label').get_tensor()
+ label_array = np.array([0, 1, 4, 5])
+ label.set(label_array, place)
+
+ bias = scope.var('Bias').get_tensor()
+ bias_array = np.random.random((5, 1)).astype("float32")
+ bias.set(bias_array, place)
+
+ out = scope.var('Out').get_tensor()
+
+ pre_out = scope.var('PreOut').get_tensor
+
+ w_out = scope.var('W_Out').get_tensor()
+ w_out.set(param_array, place)
+
+ emaps = ['127.0.0.1:' + str(port)]
+ table_names = ['table']
+ height_sections = [2]
+
+ # create and run sgd operator
+ hsigmoid_op = Operator(
+ "hierarchical_sigmoid",
+ X='X',
+ W='W',
+ PathTable='PathTable',
+ PathCode='PathCode',
+ Label='Label',
+ Bias='Bias',
+ Out='Out',
+ PreOut='PreOut',
+ W_Out='W_Out',
+ remote_prefetch=True,
+ epmap=emaps,
+ table_names=table_names,
+ height_sections=height_sections)
+
+ hsigmoid_op.run(scope, place)
+
+ # get and compare result
+ result_array = np.array(w_out)
+ self.assertEqual(list(result_array.shape), [5, 8])
+ correct = None
+ for i in range(5):
+ if i != 3:
+ correct = np.full((1, 8), i + 1).astype("float32")
+ self.assertTrue((result_array[i] == correct).all())
+ else:
+ correct = np.full((1, 8), 0).astype("float32")
+ self.assertTrue((result_array[i] == correct).all())
+
+ def _run_nce_op_two_pserver(self, place, port0, port1):
+ scope = fluid.core.Scope()
+ program = Program()
+ with fluid.scope_guard(scope):
+ with program_guard(program, startup_program=Program()):
+ x = scope.var('X').get_tensor()
+ x_array = np.random.random((4, 8)).astype("float32") * 2
+ x.set(x_array, place)
+ # create and initialize Param Variable
+ param = scope.var('W').get_tensor()
+ param_array = np.zeros((5, 8)).astype("float32") * 2
+ param.set(param_array, place)
+
+ path_table = scope.var('PathTable').get_tensor()
+ path_table_array = np.array(
+ [(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
+ (0, 2, -1, -1, -1)]).astype(
+ "int64"
+ ) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
+ path_table.set(path_table_array, place)
+
+ path_code = scope.var('PathCode').get_tensor()
+ path_code_array = np.array(
+ [(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
+ (0, 1, -1, -1, -1)]).astype("int64") #np.array to store
+ path_code.set(path_code_array, place)
+
+ label = scope.var('Label').get_tensor()
+ label_array = np.array([0, 1, 4, 5])
+ label.set(label_array, place)
+
+ bias = scope.var('Bias').get_tensor()
+ bias_array = np.random.random((5, 1)).astype("float32")
+ bias.set(bias_array, place)
+
+ out = scope.var('Out').get_tensor()
+
+ pre_out = scope.var('PreOut').get_tensor
+
+ w_out = scope.var('W_Out').get_tensor()
+ w_out.set(param_array, place)
+
+ emaps = ['127.0.0.1:' + str(port0), '127.0.0.1:' + str(port1)]
+ table_names = ['table', 'table']
+ height_sections = [2, 3]
+
+ # create and run sgd operator
+ hsigmoid_op = Operator(
+ "hierarchical_sigmoid",
+ X='X',
+ W='W',
+ PathTable='PathTable',
+ PathCode='PathCode',
+ Label='Label',
+ Bias='Bias',
+ Out='Out',
+ PreOut='PreOut',
+ W_Out='W_Out',
+ remote_prefetch=True,
+ epmap=emaps,
+ table_names=table_names,
+ height_sections=height_sections)
+ hsigmoid_op.run(scope, place)
+
+ # get and compare result
+ result_array = np.array(w_out)
+ self.assertEqual(list(result_array.shape), [5, 8])
+ correct = None
+ for i in range(5):
+ if i < 2:
+ correct = np.full((1, 8), i + 1).astype("float32")
+ self.assertTrue((result_array[i] == correct).all())
+ else:
+ correct = np.full((1, 8), i + 9).astype("float32")
+ self.assertTrue((result_array[i] == correct).all())
+
+ def test_nce_op_remote(self):
+ os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
+ # run pserver on CPU in sync mode
+ p0 = self._start_pserver(0, False, True, run_pserver)
+ self._wait_ps_ready(p0.pid)
+ port0 = self._get_pserver_port(p0.pid)
+
+ p1 = self._start_pserver(1, False, True, run_pserver)
+ self._wait_ps_ready(p1.pid)
+ port1 = self._get_pserver_port(p1.pid)
+
+ places = [core.CPUPlace()]
+ if core.is_compiled_with_cuda():
+ places.append(core.CUDAPlace(0))
+
+ for place in places:
+ self._run_nce_op_one_pserver(place, port0)
+ self._run_nce_op_two_pserver(place, port0, port1)
+
+ # raise SIGTERM to pserver
+ os.kill(p0.pid, signal.SIGINT)
+ p0.join()
+ os.kill(p1.pid, signal.SIGINT)
+ p1.join()
+
+
+if __name__ == '__main__':
+ unittest.main()