113 lines
3.7 KiB
113 lines
3.7 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Test fleet metric."""
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from __future__ import print_function
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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import os
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import unittest
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import paddle.distributed.fleet.metrics.metric as metric
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
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class TestFleetMetric(unittest.TestCase):
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"""Test cases for fleet metric."""
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def setUp(self):
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"""Set up, set envs."""
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class FakeFleet:
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"""Fake fleet only for test."""
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def __init__(self):
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"""Init."""
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self.gloo = fluid.core.Gloo()
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self.gloo.set_rank(0)
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self.gloo.set_size(1)
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self.gloo.set_prefix("123")
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self.gloo.set_iface("lo")
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self.gloo.set_hdfs_store("./tmp_test_metric", "", "")
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self.gloo.init()
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def _all_reduce(self, input, output, mode="sum"):
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"""All reduce using gloo."""
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input_list = [i for i in input]
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ans = self.gloo.all_reduce(input_list, mode)
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for i in range(len(ans)):
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output[i] = 1
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def _barrier_worker(self):
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"""Fake barrier worker, do nothing."""
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pass
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self.fleet = FakeFleet()
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fleet._role_maker = self.fleet
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def test_metric_1(self):
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"""Test cases for metrics."""
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train = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(train, startup):
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t = fluid.layers.create_global_var(
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shape=[1, 1],
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value=1,
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dtype='int64',
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persistable=True,
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force_cpu=True)
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t1 = fluid.layers.create_global_var(
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shape=[1, 1],
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value=1,
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dtype='int64',
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persistable=True,
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force_cpu=True)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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scope = fluid.Scope()
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with fluid.scope_guard(scope):
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exe.run(startup)
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metric.sum(t, scope)
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metric.max(t, scope)
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metric.min(t, scope)
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metric.auc(t, t1, scope)
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metric.mae(t1, 3, scope)
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metric.rmse(t1, 3, scope)
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metric.mse(t1, 3, scope)
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metric.acc(t, t1, scope)
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metric.sum(str(t.name), scope)
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metric.max(str(t.name), scope)
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metric.min(str(t.name), scope)
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metric.auc(str(t1.name), str(t.name), scope)
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metric.mae(str(t1.name), 3, scope)
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metric.rmse(str(t1.name), 3, scope)
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metric.mse(str(t1.name), 3, scope)
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metric.acc(str(t.name), str(t1.name), scope)
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arr = np.array([1, 2, 3, 4])
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metric.sum(arr)
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metric.max(arr)
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metric.min(arr)
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arr1 = np.array([[1, 2, 3, 4]])
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arr2 = np.array([[1, 2, 3, 4]])
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arr3 = np.array([1, 2, 3, 4])
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metric.auc(arr1, arr2)
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metric.mae(arr, 3)
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metric.rmse(arr, 3)
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metric.mse(arr, 3)
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metric.acc(arr, arr3)
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if __name__ == "__main__":
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unittest.main()
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