liang_chaoming@huawei.com
48 lines
1.6 KiB
YAML
48 lines
1.6 KiB
YAML
tensorflow_config:
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#中文数据用 bert_config_large_cn.json 英文用bert_config_large_en.json
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bert_config_file: bert_config_large_cn.json
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#数据集句子长度是256时 设置为 256,40，句子长度是128时设置为128,20
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max_seq_length: 128
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max_predictions_per_seq: 20
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# 最佳性能train_batch_size为96，如果超显存，可调小至32
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train_batch_size: 96
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learning_rate: 3.125e-5
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num_warmup_steps: 100
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num_train_steps: 1000
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optimizer_type: adam
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manual_fp16: True
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use_fp16_cls: True
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input_files_dir: /home/data/bert_nv/dataset/cn-wiki-128/
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eval_files_dir: /home/data/bert_nv/dataset/cn-wiki-128/
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do_train: True
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do_eval: False
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num_accumulation_steps: 1
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iterations_per_loop: 100
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npu_bert_loss_scale: 0
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distributed: True
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graph_memory_max_size: 27917287424
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variable_memory_max_size: 5368709120
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npu_bert_tail_optimize: True
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save_checkpoints_steps: 1000
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npu_bert_clip_by_global_norm: False
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# docker 镜像名称:版本号
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docker_image: c75:b031
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# 仅多机执行需要配置: ip1:卡数量1,ip2:卡数量2
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mpirun_ip: 90.90.140.199:8,90.90.140.229:8
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# 指定 device id, 多个 id 使用空格分隔, 数量需与 rank_size 相同
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device_group_1p: 0
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device_group_2p: 1 4
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device_group_4p: 0 1 2 3
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#profiling 配置
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PROFILING_MODE: false
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AICPU_PROFILING_MODE: false
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PROFILING_OPTIONS: training_trace
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FP_POINT: bert/embeddings/GatherV2
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BP_POINT: gradients/bert/embeddings/IdentityN_1_grad/UnsortedSegmentSum
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