diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md index fa360a4b9213..55f1b4f53721 100644 --- a/docs/source/en/basics/booster_plugins.md +++ b/docs/source/en/basics/booster_plugins.md @@ -58,7 +58,11 @@ This plugin implements Zero-3 with chunk-based and heterogeneous memory manageme This plugin implements the combination of various parallel training strategies and optimization tools. The features of HybridParallelPlugin can be generally divided into four parts: -1. Shardformer: This plugin provides an entrance to Shardformer, which controls model sharding under tensor parallel and pipeline parallel setting. Shardformer also overloads the logic of model's forward/backward process to ensure the smooth working of tp/pp. Also, optimization tools including fused normalization, flash attention (xformers), JIT and sequence parallel are injected into the overloaded forward/backward method by Shardformer. More details can be found in chapter [Shardformer Doc](../features/shardformer.md). +1. Shardformer: This plugin provides an entrance to Shardformer, which controls model sharding under tensor parallel and pipeline parallel setting. Shardformer also overloads the logic of model's forward/backward process to ensure the smooth working of tp/pp. Also, optimization tools including fused normalization, flash attention (xformers), JIT and sequence parallel are injected into the overloaded forward/backward method by Shardformer. More details can be found in chapter [Shardformer Doc](../features/shardformer.md). The diagram below shows the features supported by shardformer together with hybrid parallel plugin. + +
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2. Mixed Precision Training: Support for fp16/bf16 mixed precision training. More details about its arguments configuration can be found in [Mixed Precision Training Doc](../features/mixed_precision_training_with_booster.md). diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md index 70352a7b9af3..c810d4ce40b0 100644 --- a/docs/source/zh-Hans/basics/booster_plugins.md +++ b/docs/source/zh-Hans/basics/booster_plugins.md @@ -55,7 +55,11 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累 这个插件实现了多种并行训练策略和优化工具的组合。Hybrid Parallel插件支持的功能大致可以被分为以下四个部分： -1. Shardformer: Shardformer负责在张量并行以及流水线并行下切分模型的逻辑，以及前向/后向方法的重载，这个插件为Shardformer功能提供了一个简单易用的接口。与此同时，Shardformer还负责将包括fused normalization, flash attention (xformers), JIT和序列并行在内的各类优化工具融入重载后的前向/后向方法。更多关于Shardformer的信息请参考 [Shardformer文档](../features/shardformer.md)。 +1. Shardformer: Shardformer负责在张量并行以及流水线并行下切分模型的逻辑，以及前向/后向方法的重载，这个插件为Shardformer功能提供了一个简单易用的接口。与此同时，Shardformer还负责将包括fused normalization, flash attention (xformers), JIT和序列并行在内的各类优化工具融入重载后的前向/后向方法。更多关于Shardformer的信息请参考 [Shardformer文档](../features/shardformer.md)。下图展示了Shardformer与Hybrid Parallel插件所支持的功能。 + +
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2. 混合精度训练：插件支持fp16/bf16的混合精度训练。更多关于混合精度训练的参数配置的详细信息请参考 [混合精度训练文档](../features/mixed_precision_training_with_booster.md)。