Fix grad clip (#21784)
* fix grad clip， clip op belongs to Backward op when running in Parameter Server mode.release/1.7
Chengmo
5 years ago
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GitHub
parent
14aebc7a95
commit
5c33919328
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# Copyright (c) 2019 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 unittest
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import paddle.fluid as fluid
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
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from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig
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from test_dist_fleet_base import TestFleetBase
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from dist_simnet_bow import train_network
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class TestDistGeoClipByGlobalNormTranspiler(unittest.TestCase):
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def test_pserver(self):
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role = role_maker.UserDefinedRoleMaker(
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current_id=0,
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role=role_maker.Role.SERVER,
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worker_num=2,
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server_endpoints=["127.0.0.1:36011", "127.0.0.1:36012"])
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fleet.init(role)
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batch_size = 128
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is_sparse = True
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is_distribute = False
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strategy = DistributeTranspilerConfig()
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strategy.sync_mode = False
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strategy.geo_sgd_mode = True
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strategy.geo_sgd_need_push_nums = 5
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avg_cost, _, _ = train_network(batch_size, is_distribute, is_sparse)
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fluid.clip.set_gradient_clip(
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clip=fluid.clip.GradientClipByGlobalNorm(2.0))
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optimizer = fluid.optimizer.SGD(0.1)
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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pserver_startup_program = fleet.startup_program
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pserver_mian_program = fleet.main_program
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class TestDistGeoClipByGlobalNorm(TestFleetBase):
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def _setup_config(self):
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self._mode = "geo"
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self._reader = "dataset"
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self._geo_sgd_need_push_nums = 5
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self._grad_clip_mode = 3
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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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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": ""
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}
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required_envs.update(need_envs)
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_ctr.py", delta=1e-5, check_error_log=True)
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def _setup_config(self):
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self._sync_mode = False
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self._grad_clip_mode = 2
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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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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": ""
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}
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required_envs.update(need_envs)
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_ctr.py", delta=1e-5, check_error_log=True)
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class TestDistASyncClipByGlobalNorm(TestFleetBase):
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def _setup_config(self):
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self._mode = "async"
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self._reader = "dataset"
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self._grad_clip_mode = 3
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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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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": ""
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}
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required_envs.update(need_envs)
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_ctr.py", delta=1e-5, check_error_log=True)
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if __name__ == "__main__":
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unittest.main()
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