From e196fa367bc6087f08bfce44bdc194ed426c69cf Mon Sep 17 00:00:00 2001 From: tangwei12 Date: Mon, 17 Dec 2018 10:52:05 +0800 Subject: [PATCH] update ut, test=develop --- .../unittests/test_nce_remote_table_op.py | 271 ++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py 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 index 00000000000..f08b270d89b --- /dev/null +++ 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() -- GitLab