wehub-resource-sync
149 lines
5.1 KiB
Python
149 lines
5.1 KiB
Python
# Copyright (c) 2021 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 numpy as np
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import paddle
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from paddle.distributed import fleet
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from paddle.distributed.fleet.base import topology as tp
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class TestNewGroupAPI:
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def __init__(self):
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paddle.distributed.init_parallel_env()
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topo = fleet.CommunicateTopology(
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["data", "sep", "model", "sharding", "pipe"], [2, 1, 1, 1, 1]
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)
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self.hcg = fleet.HybridCommunicateGroup(topo)
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d1 = np.array([1, 2, 3])
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d2 = np.array([2, 3, 4])
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self.tensor1 = paddle.to_tensor(d1)
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self.tensor2 = paddle.to_tensor(d2)
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def test_all(self):
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topo = self.hcg.topology()
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global_rank = self.hcg.get_data_parallel_rank()
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dp_rank = self.hcg.get_data_parallel_rank()
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dp_gp = self.hcg.get_data_parallel_group()
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dp_world_size = self.hcg.get_data_parallel_world_size()
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dp_src_rank = self.hcg.get_data_parallel_group_src_rank()
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np.testing.assert_array_equal(dp_world_size, 2)
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np.testing.assert_array_equal(dp_src_rank, 0)
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mp_rank = self.hcg.get_model_parallel_rank()
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mp_gp = self.hcg.get_model_parallel_group()
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mp_world_size = self.hcg.get_model_parallel_world_size()
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mp_src_rank = self.hcg.get_model_parallel_group_src_rank()
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np.testing.assert_array_equal(mp_world_size, 1)
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tmp = np.array([0, 0, 0])
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result = paddle.to_tensor(tmp)
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paddle.distributed.scatter(
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result,
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[self.tensor2, self.tensor1],
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src=dp_src_rank,
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group=dp_gp,
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sync_op=True,
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)
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if dp_rank == 0:
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np.testing.assert_array_equal(result, self.tensor2)
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elif dp_rank == 1:
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np.testing.assert_array_equal(result, self.tensor1)
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print("test scatter api ok")
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paddle.distributed.broadcast(result, src=1, group=dp_gp, sync_op=True)
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np.testing.assert_array_equal(result, self.tensor1)
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print("test broadcast api ok")
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paddle.distributed.reduce(
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result, dst=dp_src_rank, group=dp_gp, sync_op=True
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)
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if dp_rank == 0:
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np.testing.assert_array_equal(
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result, paddle.add(self.tensor1, self.tensor1)
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)
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elif dp_rank == 1:
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np.testing.assert_array_equal(result, self.tensor1)
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print("test reduce api ok")
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paddle.distributed.all_reduce(result, sync_op=True)
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np.testing.assert_array_equal(
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result,
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paddle.add(paddle.add(self.tensor1, self.tensor1), self.tensor1),
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)
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print("test all_reduce api ok")
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paddle.distributed.wait(result, dp_gp, use_calc_stream=True)
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paddle.distributed.wait(result, dp_gp, use_calc_stream=False)
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print("test wait api ok")
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result = []
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paddle.distributed.all_gather(
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result, self.tensor1, group=dp_gp, sync_op=True
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)
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np.testing.assert_array_equal(result[0], self.tensor1)
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np.testing.assert_array_equal(result[1], self.tensor1)
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print("test all_gather api ok")
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paddle.distributed.barrier(group=dp_gp)
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print("test barrier api ok")
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class TestHybridEPGroup:
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def __init__(self):
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paddle.distributed.init_parallel_env()
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group_names = [
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"moe_sharding",
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"sharding",
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"pipe",
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"sep",
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"data",
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"expert",
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"model",
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]
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dims = [1, 1, 1, 1, 1, 2, 2]
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self.hcg = tp.EPHybridCommunicateGroup(group_names, dims)
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def test_all(self):
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global_rank = paddle.distributed.get_rank()
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dp_rank = self.hcg.get_data_parallel_rank()
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assert dp_rank == 0
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assert self.hcg.get_expert_parallel_world_size() == 2
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assert self.hcg.get_moe_sharding_parallel_world_size() == 1
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assert self.hcg.get_model_parallel_world_size() == 2
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assert self.hcg.get_expert_parallel_rank() == global_rank
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assert self.hcg.get_moe_sharding_parallel_rank() == 0
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assert self.hcg.get_expert_parallel_group_src_rank() == 0
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assert (
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self.hcg.get_moe_sharding_parallel_group_src_rank() == global_rank
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)
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moe_sharding_group = self.hcg.get_moe_sharding_parallel_group()
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ep_group = self.hcg.get_expert_parallel_group()
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mp_group = self.hcg.get_model_parallel_group()
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assert moe_sharding_group.ranks == [global_rank]
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assert ep_group.ranks == [0, 1]
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assert mp_group.ranks == [0, 1]
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if __name__ == "__main__":
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gpt = TestNewGroupAPI()
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gpt.test_all()
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ep_test = TestHybridEPGroup()
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ep_test.test_all()
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