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@ -109,6 +109,14 @@ Self Attention的计算,这对于性能有明显的提升,提升大约30%。
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```bash
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accelerate launch --config_file configs/default_config.yaml pretrain_llama.py
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```
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某些情况下可能需要指定下列参数
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```
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--main_process_ip
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--main_process_port
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--num_processes
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--num_machines
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--machine_rank
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```
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我们使用[Wandb](https://wandb.ai/)进行训练的可视化,需要自行修改环境变量 WANDB_API_KEY 。
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其中我们使用了DeepSpeed stage1以减少显存占用。accelerate相关配置可见configs/default_config.yaml。
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10
README_en.md
10
README_en.md
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@ -99,6 +99,14 @@ We use the Accelerate library for multi-GPU parallel training. Launch training w
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```bash
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accelerate launch --config_file configs/default_config.yaml pretrain_llama.py
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```
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In some cases, it may be necessary to specify the following parameters.
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```
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--main_process_ip
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--main_process_port
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--num_processes
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--num_machines
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--machine_rank
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```
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We use [Wandb](https://wandb.ai/) for training visualization and you need to modify the environment variable WANDB_API_KEY.
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We use DeepSpeed stage 1 to reduce GPU memory usage. Accelerate-related configurations can be found in configs/default_config.yaml.
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@ -174,7 +182,7 @@ The following table summarizes the performance of current open-source models, al
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| Token | | 1T | 1T | 1.4T | 180B | 366B | 400B | 402B | 200B | 13.9B |
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| GPU Hour | | 82,432 | 135,168 | 1,022,362 | 809,472 | 1,082,990 | 43776 | 175680 | 47040 | 3072 |
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| speed token/s/gpu | 3290 | 3370 | 2055 | 380 | 61.8 | 93.9 | 105.7 | 635.6 | 1181 | 1257 |
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| 相关依赖 | xformers | xformers | | | measeq | Megatron-DeepSpeed | | | BMtrain | MindSpore |
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| Dependencies | xformers | xformers | | | measeq | Megatron-DeepSpeed | | | BMtrain | MindSpore |
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| speed token/s/gpu/B | 22701 | 22579 | 26715 | 24700 | 10815 | 16432 | 13741 | 12712 | 11810 | 16341 |
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## Future Plans
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