# Copyright (c) 2020 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. """Test fleet metric.""" import numpy as np import paddle import paddle.fluid as fluid import os import unittest import numpy as np import paddle.distributed.fleet.metrics.metric as metric import paddle.distributed.fleet as fleet from paddle.distributed.fleet.base.util_factory import UtilBase paddle.enable_static() class TestFleetMetric(unittest.TestCase): """Test cases for fleet metric.""" def setUp(self): """Set up, set envs.""" class FakeUtil(UtilBase): def __init__(self, fake_fleet): super(FakeUtil, self).__init__() self.fleet = fake_fleet def all_reduce(self, input, mode="sum", comm_world="worker"): input = np.array(input) input_shape = input.shape input_list = input.reshape(-1).tolist() self.fleet._barrier(comm_world) ans = self.fleet._all_reduce(input_list, mode) output = np.array(ans).reshape(input_shape) return output class FakeFleet: """Fake fleet only for test.""" def __init__(self): """Init.""" self.gloo = fluid.core.Gloo() self.gloo.set_rank(0) self.gloo.set_size(1) self.gloo.set_prefix("123") self.gloo.set_iface("lo") self.gloo.set_hdfs_store("./tmp_test_metric", "", "") self.gloo.init() def _all_reduce(self, input, mode="sum"): """All reduce using gloo.""" ans = self.gloo.all_reduce(input, mode) return ans def _barrier(self, comm_world="worker"): """Fake barrier, do nothing.""" pass self.util = FakeUtil(FakeFleet()) fleet.util = self.util def test_metric_1(self): """Test cases for metrics.""" train = fluid.Program() startup = fluid.Program() with fluid.program_guard(train, startup): t = fluid.layers.create_global_var(shape=[1, 1], value=1, dtype='int64', persistable=True, force_cpu=True) t1 = fluid.layers.create_global_var(shape=[1, 1], value=1, dtype='int64', persistable=True, force_cpu=True) place = fluid.CPUPlace() exe = fluid.Executor(place) scope = fluid.Scope() with fluid.scope_guard(scope): exe.run(startup) metric.sum(t, scope, self.util) metric.max(t, scope, self.util) metric.min(t, scope, self.util) metric.auc(t, t1, scope, self.util) metric.mae(t, t1, scope, self.util) metric.rmse(t, t1, scope, self.util) metric.mse(t, t1, scope, self.util) metric.acc(t, t1, scope, self.util) metric.sum(str(t.name)) metric.max(str(t.name)) metric.min(str(t.name)) metric.auc(str(t1.name), str(t.name)) metric.mae(str(t1.name), str(t.name)) metric.rmse(str(t1.name), str(t.name)) metric.mse(str(t1.name), str(t.name)) metric.acc(str(t.name), str(t1.name)) arr = np.array([1, 2, 3, 4]) metric.sum(arr, util=self.util) metric.max(arr, util=self.util) metric.min(arr, util=self.util) arr1 = np.array([[1, 2, 3, 4]]) arr2 = np.array([[1, 2, 3, 4]]) arr3 = np.array([1, 2, 3, 4]) metric.auc(arr1, arr2, util=self.util) metric.mae(arr, arr3, util=self.util) metric.rmse(arr, arr3, util=self.util) metric.mse(arr, arr3, util=self.util) metric.acc(arr, arr3, util=self.util) if __name__ == "__main__": unittest.main()