forked from mindspore-Ecosystem/mindspore
!6508 add split allreduce testcase for bert_thor ut
Merge pull request !6508 from wangshuangling/master
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59bde6921f
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@ -107,6 +107,32 @@ def create_bert_dataset(device_num=1, rank=0, do_shuffle="true", data_dir=None,
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logger.info("repeat count: {}".format(ds.get_repeat_count()))
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return ds
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def _set_bert_all_reduce_split():
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"""set bert all_reduce fusion split, support num_hidden_layers is 12 and 24."""
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from mindspore.parallel._auto_parallel_context import auto_parallel_context
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if bert_net_cfg.num_hidden_layers == 12:
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if bert_net_cfg.use_relative_positions:
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auto_parallel_context().set_all_reduce_fusion_split_indices([29, 58, 87, 116, 145, 174, 203, 217],
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"hccl_world_groupsum1")
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auto_parallel_context().set_all_reduce_fusion_split_indices([29, 58, 87, 116, 145, 174, 203, 217],
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"hccl_world_groupsum3")
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else:
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auto_parallel_context().set_all_reduce_fusion_split_indices([28, 55, 82, 109, 136, 163, 190, 205],
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"hccl_world_groupsum1")
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auto_parallel_context().set_all_reduce_fusion_split_indices([28, 55, 82, 109, 136, 163, 190, 205],
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"hccl_world_groupsum3")
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elif bert_net_cfg.num_hidden_layers == 24:
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if bert_net_cfg.use_relative_positions:
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auto_parallel_context().set_all_reduce_fusion_split_indices([30, 90, 150, 210, 270, 330, 390, 421],
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"hccl_world_groupsum1")
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auto_parallel_context().set_all_reduce_fusion_split_indices([30, 90, 150, 210, 270, 330, 390, 421],
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"hccl_world_groupsum3")
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else:
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auto_parallel_context().set_all_reduce_fusion_split_indices([38, 77], "hccl_world_groupsum1")
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auto_parallel_context().set_all_reduce_fusion_split_indices([38, 77], "hccl_world_groupsum3")
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def train_process_bert_thor(q, device_id, epoch_size, device_num):
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os.system("mkdir " + str(device_id))
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os.chdir(str(device_id))
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@ -120,10 +146,11 @@ def train_process_bert_thor(q, device_id, epoch_size, device_num):
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D.init()
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rank = device_id % device_num
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context.reset_auto_parallel_context()
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_set_bert_all_reduce_split()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
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device_num=device_num)
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bert_net_cfg.num_hidden_layers = 2
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bert_net_cfg.num_hidden_layers = 4
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ds = create_bert_dataset(device_num=device_num, rank=rank, do_shuffle=False, data_dir=DATASET_PATH, schema_dir=None)
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net_with_loss = BertNetworkWithLoss(bert_net_cfg, True)
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@ -200,8 +227,8 @@ def test_bert_thor_mlperf_8p():
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os.system("rm -rf " + str(i))
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print("End training...")
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assert mean_cost < 51
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assert mean_loss < 8.5
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assert mean_cost < 64.2
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assert mean_loss < 7.9
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if __name__ == '__main__':
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test_bert_thor_mlperf_8p()
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