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160 lines
4.9 KiB
160 lines
4.9 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import os
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import shutil
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import unittest
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import tempfile
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import numpy as np
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from test_dist_base import TestDistBase, RUN_STEP
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class TestDistSaveLoadDense2x2(TestDistBase):
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def _setup_config(self):
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self._sync_mode = True
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self._enforce_place = "CPU"
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def check_with_place(self,
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model_file,
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delta=1e-3,
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check_error_log=False,
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need_envs={}):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"http_proxy": ""
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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model_dir = tempfile.mkdtemp()
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local_env = {}
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local_env["SAVE"] = "1"
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local_env["MODEL_DIR"] = model_dir
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local_env.update(required_envs)
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cluster_env = {}
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cluster_env["LOAD"] = "1"
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cluster_env["MODEL_DIR"] = model_dir
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cluster_env.update(required_envs)
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local_var = self._run_local(model_file, local_env, check_error_log)
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tr0_var, tr1_var = self._run_cluster(model_file, cluster_env,
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check_error_log)
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shutil.rmtree(model_dir)
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local_np = np.array(local_var)
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train0_np = np.array(tr0_var)
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train1_np = np.array(tr1_var)
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self.assertAlmostEqual(local_np.all(), train0_np.all(), delta=delta)
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self.assertAlmostEqual(local_np.all(), train1_np.all(), delta=delta)
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self.assertAlmostEqual(train0_np.all(), train1_np.all(), delta=delta)
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def test_dist(self):
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need_envs = {
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"IS_DISTRIBUTED": '0',
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"IS_SPARSE": '0',
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'IS_SELF_CONTAINED_LR': '1',
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'SAVE_MODE': 'LOCAL',
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}
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self.check_with_place(
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"dist_save_load.py",
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delta=0,
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check_error_log=False,
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need_envs=need_envs)
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class TestDistSaveLoadWithPServerStateDense2x2(TestDistBase):
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def _setup_config(self):
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self._sync_mode = True
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self._enforce_place = "CPU"
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def check_with_place(self,
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model_file,
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delta=1e-3,
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check_error_log=False,
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need_envs={}):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"http_proxy": ""
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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model_dir = tempfile.mkdtemp()
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save_env = {}
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save_env["SAVE_MODE"] = "DIST"
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save_env["SAVE"] = "1"
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save_env["MODEL_DIR"] = model_dir
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save_env.update(required_envs)
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tr0_var_1, tr1_var_1 = self._run_cluster(model_file, save_env,
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check_error_log)
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load_env = {}
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load_env["LOAD"] = "1"
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load_env["MODEL_DIR"] = model_dir
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load_env.update(required_envs)
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tr0_var_2, tr1_var_2 = self._run_cluster(model_file, load_env,
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check_error_log)
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shutil.rmtree(model_dir)
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train0_1_np = np.array(tr0_var_1)
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train1_1_np = np.array(tr1_var_1)
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train0_2_np = np.array(tr0_var_2)
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train1_2_np = np.array(tr1_var_2)
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self.assertAlmostEqual(
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train0_1_np.all(), train0_2_np.all(), delta=delta)
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self.assertAlmostEqual(
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train1_1_np.all(), train1_2_np.all(), delta=delta)
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def test_dist(self):
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need_envs = {
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"IS_DISTRIBUTED": '0',
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"IS_SPARSE": '0',
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'IS_SELF_CONTAINED_LR': '1',
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'SAVE_MODE': 'DIST',
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'OPTIMIZER': 'ADAM',
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'SKIP_STEPS': str(np.random.randint(2, 6))
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}
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self.check_with_place(
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"dist_save_load.py",
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delta=0,
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check_error_log=False,
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need_envs=need_envs)
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if __name__ == "__main__":
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unittest.main()
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