wehub-resource-sync
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
# Copyright (c) 2022 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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import unittest
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.distributed.fleet.base.strategy_group import (
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DPGroup,
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MPGroup,
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PPGroup,
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ShardingGroup,
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StrategyGroupBase,
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)
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def _check_using_all_reduce(group):
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data = paddle.to_tensor([1, 2, 3])
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result = paddle.to_tensor([2, 4, 6])
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dist.all_reduce(data, group=group)
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np.testing.assert_array_equal(data, result)
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def _check_using_send(group, dst):
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data = paddle.to_tensor([1, 2, 3])
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dist.send(data, dst=dst, group=group)
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def _check_using_recv(group, src):
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result = paddle.to_tensor([1, 2, 3])
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data = paddle.to_tensor([0, 0, 0])
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dist.recv(data, src=src, group=group)
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np.testing.assert_array_equal(data, result)
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class TestStrategyGroupAPI(unittest.TestCase):
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def setUp(self):
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self._num_of_ranks = 2
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self._list_of_rank = [[0, 1]]
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self._list_of_ranks = [[0, 1], [0, 1]]
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dist.init_parallel_env()
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self._global_rank = dist.get_rank()
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self._peer_rank = 0 if self._global_rank == 1 else 1
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def test_strategy_group_base(self):
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strategy_group = StrategyGroupBase(self._list_of_rank)
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self.assertEqual(strategy_group.world_size, self._num_of_ranks)
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self.assertEqual(strategy_group.group.nranks, self._num_of_ranks)
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_check_using_all_reduce(strategy_group.group)
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def test_data_parallel_group(self):
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dp_group = DPGroup(self._list_of_rank)
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self.assertEqual(dp_group.world_size, self._num_of_ranks)
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self.assertEqual(dp_group.group.nranks, self._num_of_ranks)
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_check_using_all_reduce(dp_group.group)
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def test_model_parallel_group(self):
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mp_group = MPGroup(self._list_of_rank)
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self.assertEqual(mp_group.world_size, self._num_of_ranks)
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self.assertEqual(mp_group.group.nranks, self._num_of_ranks)
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_check_using_all_reduce(mp_group.group)
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def test_sharding_parallel_group(self):
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sharding_group = ShardingGroup(self._list_of_rank)
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self.assertEqual(sharding_group.world_size, self._num_of_ranks)
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self.assertEqual(sharding_group.group.nranks, self._num_of_ranks)
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_check_using_all_reduce(sharding_group.group)
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def test_pipeline_parallel_group(self):
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pp_group = PPGroup(self._list_of_rank)
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(
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send_next_group,
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send_prev_group,
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recv_next_group,
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recv_prev_group,
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) = pp_group.p2p_groups
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if self._global_rank == 0:
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self.assertEqual(pp_group.rank_of_next_stage, 1)
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self.assertEqual(pp_group.rank_of_prev_stage, 1)
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_check_using_send(send_next_group, self._peer_rank)
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_check_using_send(send_prev_group, self._peer_rank)
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_check_using_recv(recv_prev_group, self._peer_rank)
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_check_using_recv(recv_next_group, self._peer_rank)
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else:
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self.assertEqual(pp_group.rank_of_next_stage, 0)
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self.assertEqual(pp_group.rank_of_prev_stage, 0)
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_check_using_recv(recv_prev_group, self._peer_rank)
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_check_using_recv(recv_next_group, self._peer_rank)
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_check_using_send(send_next_group, self._peer_rank)
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_check_using_send(send_prev_group, self._peer_rank)
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if __name__ == '__main__':
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unittest.main()
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