wehub-resource-sync
61 lines
1.8 KiB
Python
61 lines
1.8 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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from paddle.distributed import fleet
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def init_parallel_env(mode, global_batch_size, seed=1024):
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'''
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Args:
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mode:(str) DP1-MP1-PP1-SH1-O1
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'''
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def parse_mode(mode):
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assert "DP" == mode[:2]
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assert "-MP" in mode
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assert "-PP" in mode
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assert "-SH" in mode
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assert "-O" in mode
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modes = mode.split("-")
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DP = int(modes[0][2:])
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MP = int(modes[1][2:])
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PP = int(modes[2][2:])
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SH = int(modes[3][2:])
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Ostage = int(modes[4][1:])
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return DP, MP, PP, SH, Ostage
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DP, MP, PP, SH, Ostage = parse_mode(mode)
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strategy = fleet.DistributedStrategy()
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strategy.hybrid_configs = {
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"dp_degree": DP,
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"mp_degree": MP,
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"pp_degree": PP,
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"sharding_degree": SH,
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}
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accumulate_steps = 1
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if PP > 1:
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strategy.pipeline_configs = {
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"accumulate_steps": accumulate_steps,
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"micro_batch_size": global_batch_size // DP // accumulate_steps,
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}
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# set control in tensor parallel
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strategy.tensor_parallel_configs = {"tensor_init_seed": seed}
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fleet.init(is_collective=True, strategy=strategy)
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return fleet.get_hybrid_communicate_group()
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