update ut, test=develop

python/paddle/fluid/tests/unittests/test_nce_remote_table_op.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 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()