wehub-resource-sync
150 lines
5.2 KiB
Python
150 lines
5.2 KiB
Python
# Copyright (c) 2023 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 random
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import unittest
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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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def set_random_seed(seed, dp_id, rank_id):
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"""Set random seed for reproducibility."""
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random.seed(seed)
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np.random.seed(seed + dp_id)
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paddle.seed(seed + dp_id)
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batch_size = 4
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micro_batch_size = 2
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class TestDistDPTraining(unittest.TestCase):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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self.model_parallel_size = 1
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self.data_parallel_size = 2
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self.pipeline_parallel_size = 1
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strategy.hybrid_configs = {
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"dp_degree": self.data_parallel_size,
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"mp_degree": self.model_parallel_size,
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"pp_degree": self.pipeline_parallel_size,
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}
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strategy.pipeline_configs = {
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"accumulate_steps": batch_size // micro_batch_size,
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"micro_batch_size": micro_batch_size,
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}
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fleet.init(is_collective=True, strategy=strategy)
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def build_optimizer(self, model):
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scheduler = paddle.optimizer.lr.PiecewiseDecay(
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boundaries=[2], values=[0.001, 0.002], verbose=True
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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=scheduler, parameters=model.parameters()
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)
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return scheduler, optimizer
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def test_communication_perf(self):
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test_comm_types = [
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"allreduce",
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"reduce",
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"broadcast",
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"allgather",
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"reduce_scatter",
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]
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# test comm type in test_comm(list), scan package from 1M to 1G
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for comm_type in test_comm_types:
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fleet.collective_perf(comm_type, round=1)
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# context: {comm_type:[size, time]}
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# only test allreduce for package(1024B) and time threshold(0.00000001s),
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# and test allgather for package(8192B) and time threshold(2s),
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fleet.collective_perf("allreduce", 30, size_and_time={1024: 0.00000001})
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fleet.collective_perf("reduce", 30, size_and_time={1024: 0.00000001})
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fleet.collective_perf("broadcast", 30, size_and_time={1024: 0.00000001})
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fleet.collective_perf("allgather", 30, size_and_time={8192: 2})
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# test allreduce for specific size and time.
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fleet.collective_perf(
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"allreduce",
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round=50,
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size_and_time={1024: 0.00000001, 4096: 0.01, 8192: 2},
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)
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class TestDistMPTraining(unittest.TestCase):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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self.model_parallel_size = 2
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self.data_parallel_size = 1
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self.pipeline_parallel_size = 1
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strategy.hybrid_configs = {
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"dp_degree": self.data_parallel_size,
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"mp_degree": self.model_parallel_size,
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"pp_degree": self.pipeline_parallel_size,
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}
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strategy.pipeline_configs = {
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"accumulate_steps": batch_size // micro_batch_size,
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"micro_batch_size": micro_batch_size,
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}
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fleet.init(is_collective=True, strategy=strategy)
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from paddle.distributed.fleet.base.topology import (
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CommunicateTopology,
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HybridCommunicateGroup,
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)
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topo = CommunicateTopology(
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hybrid_group_names=["data", "pipe", "sharding", "sep", "model"],
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dims=[1, 1, 1, 1, 2],
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)
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self.hcg = HybridCommunicateGroup(topo)
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def build_optimizer(self, model):
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scheduler = paddle.optimizer.lr.PiecewiseDecay(
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boundaries=[2], values=[0.001, 0.002], verbose=True
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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=scheduler, parameters=model.parameters()
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)
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return scheduler, optimizer
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def test_communication_perf(self):
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# test comm type in test_comm(list), scan package from 1M to 1G
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comm_types = ["allreduce", "allgather", "reduce_scatter"]
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for comm_type in comm_types:
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fleet.collective_perf(
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comm_type=comm_type,
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round=1,
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)
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# context: {comm_type:[size, time]}
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# only test reduce for package(1024B) and time threshold(1s),
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# and test allgather for package(8192B) and time threshold(0.00000002s)
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fleet.collective_perf("reduce", 30, size_and_time={1024: 1})
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fleet.collective_perf("allgather", 30, size_and_time={8192: 0.00000002})
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fleet.collective_perf(
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"reduce_scatter", 30, size_and_time={8192: 0.00000002}
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)
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# test allgather for specific size and time.
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fleet.collective_perf(
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"allgather",
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round=50,
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size_and_time={1024: 1, 4096: 0.01, 8192: 0.00000002},
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)
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if __name__ == "__main__":
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unittest.main()
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