mirror of https://github.com/hpcaitech/ColossalAI
[doc] explain suitable use case for each plugin
Pengtai Xu
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# Booster Plugins
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Author: [Hongxin Liu](https://github.com/ver217), [Baizhou Zhang](https://github.com/Fridge003)
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Author: [Hongxin Liu](https://github.com/ver217), [Baizhou Zhang](https://github.com/Fridge003), [Pengtai Xu](https://github.com/ppt0011)
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**Prerequisite:**
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- [Booster API](./booster_api.md)
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@ -11,16 +11,43 @@ As mentioned in [Booster API](./booster_api.md), we can use booster plugins to c
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We currently provide the following plugins:
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- [Low Level Zero Plugin](#low-level-zero-plugin): It wraps the `colossalai.zero.low_level.LowLevelZeroOptimizer` and can be used to train models with zero-dp. It only supports zero stage-1 and stage-2.
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- [Gemini Plugin](#gemini-plugin): It wraps the [Gemini](../features/zero_with_chunk.md) which implements Zero-3 with chunk-based and heterogeneous memory management.
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- [Torch DDP Plugin](#torch-ddp-plugin): It is a wrapper of `torch.nn.parallel.DistributedDataParallel` and can be used to train models with data parallelism.
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- [Torch FSDP Plugin](#torch-fsdp-plugin): It is a wrapper of `torch.distributed.fsdp.FullyShardedDataParallel` and can be used to train models with zero-dp.
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- [Low Level Zero Plugin](#low-level-zero-plugin): It wraps the `colossalai.zero.low_level.LowLevelZeroOptimizer` and can be used to train models with zero-dp. It only supports zero stage-1 and stage-2.
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- [Gemini Plugin](#gemini-plugin): It wraps the [Gemini](../features/zero_with_chunk.md) which implements Zero-3 with chunk-based and heterogeneous memory management.
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- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It provides a tidy interface that integrates the power of Shardformer, pipeline manager, mixied precision training, TorchDDP and Zero stage 1/2 feature. With this plugin, transformer models can be easily trained with any combination of tensor parallel, pipeline parallel and data parallel (DDP/Zero) efficiently, along with various kinds of optimization tools for acceleration and memory saving. Detailed information about supported parallel strategies and optimization tools is explained in the section below.
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More plugins are coming soon.
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## Choosing Your Plugin
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Generally only one plugin is used to train a model. Our recommended use case for each plugin is as follows.
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- [Torch DDP Plugin](#torch-ddp-plugin): It is suitable for models with less than 2 billion parameters.
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- [Torch FSDP Plugin](#torch-fsdp-plugin) / [Low Level Zero Plugin](#low-level-zero-plugin): It is suitable for models with less than 10 billion parameters.
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- [Gemini Plugin](#gemini-plugin): it is suitable for models with more than 10 billion parameters and is ideal for scenarios with high cross-node bandwidth and medium to small-scale clusters (below a thousand cards).
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- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It is suitable for models with more than 60 billion parameters, exceptionally long sequences, very large vocabularies, and is best suited for scenarios with low cross-node bandwidth and large-scale clusters (a thousand cards or more).
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## Plugins
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### Torch DDP Plugin
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More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
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{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
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### Torch FSDP Plugin
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> ⚠ This plugin is not available when torch version is lower than 1.12.0.
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> ⚠ This plugin does not support save/load sharded model checkpoint now.
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> ⚠ This plugin does not support optimizer that use multi params group.
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More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html).
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{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
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### Low Level Zero Plugin
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This plugin implements Zero-1 and Zero-2 (w/wo CPU offload), using `reduce` and `gather` to synchronize gradients and weights.
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@ -50,24 +77,6 @@ This plugin implements Zero-3 with chunk-based and heterogeneous memory manageme
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{{ autodoc:colossalai.booster.plugin.GeminiPlugin }}
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### Torch DDP Plugin
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More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
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{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
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### Torch FSDP Plugin
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> ⚠ This plugin is not available when torch version is lower than 1.12.0.
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> ⚠ This plugin does not support save/load sharded model checkpoint now.
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> ⚠ This plugin does not support optimizer that use multi params group.
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More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html).
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{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
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### Hybrid Parallel Plugin
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@ -87,5 +96,4 @@ This plugin implements the combination of various parallel training strategies a
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{{ autodoc:colossalai.booster.plugin.HybridParallelPlugin }}
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<!-- doc-test-command: echo -->
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@ -11,16 +11,41 @@
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我们现在提供以下插件:
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- [Low Level Zero 插件](#low-level-zero-插件): 它包装了 `colossalai.zero.low_level.LowLevelZeroOptimizer`，可用于使用 Zero-dp 训练模型。它仅支持 Zero 阶段1和阶段2。
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- [Gemini 插件](#gemini-插件): 它包装了 [Gemini](../features/zero_with_chunk.md)，Gemini 实现了基于Chunk内存管理和异构内存管理的 Zero-3。
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- [Torch DDP 插件](#torch-ddp-插件): 它包装了 `torch.nn.parallel.DistributedDataParallel` 并且可用于使用数据并行训练模型。
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- [Torch FSDP 插件](#torch-fsdp-插件): 它包装了 `torch.distributed.fsdp.FullyShardedDataParallel` 并且可用于使用 Zero-dp 训练模型。
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- [Low Level Zero 插件](#low-level-zero-插件): 它包装了 `colossalai.zero.low_level.LowLevelZeroOptimizer`，可用于使用 Zero-dp 训练模型。它仅支持 Zero 阶段1和阶段2。
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- [Gemini 插件](#gemini-插件): 它包装了 [Gemini](../features/zero_with_chunk.md)，Gemini 实现了基于Chunk内存管理和异构内存管理的 Zero-3。
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- [Hybrid Pararllel 插件](#hybrid-parallel-插件): 它为Shardformer，流水线管理器，混合精度运算，TorchDDP以及Zero-1/Zero-2功能提供了一个统一且简洁的接口。使用该插件可以简单高效地实现transformer模型在张量并行，流水线并行以及数据并行（DDP, Zero）间任意组合并行训练策略，同时支持多种训练速度和内存的优化工具。有关这些训练策略和优化工具的具体信息将在下一章中阐述。
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更多插件即将推出。
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## 插件选择
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- [Torch DDP 插件](#torch-ddp-插件): 适用于参数少于 20 亿的模型。
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- [Torch FSDP 插件](#torch-fsdp-插件) / [Low Level Zero 插件](#low-level-zero-插件): 适用于参数少于 100 亿的模型。
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- [Gemini 插件](#gemini-插件): 适合参数超过 100 亿的模型，且跨节点带宽高、中小规模集群（千卡以下）的场景。
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- [Hybrid Pararllel 插件](#hybrid-parallel-插件): 适合参数超过 600 亿的模型、超长序列、超大词表等特殊模型，且跨节点带宽低、大规模集群（千卡以上）的场景。
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## 插件
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### Torch DDP 插件
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更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
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{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
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### Torch FSDP 插件
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> ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。
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> ⚠ 该插件现在还不支持保存/加载分片的模型 checkpoint。
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> ⚠ 该插件现在还不支持使用了multi params group的optimizer。
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更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html).
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{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
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### Low Level Zero 插件
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该插件实现了 Zero-1 和 Zero-2（使用/不使用 CPU 卸载），使用`reduce`和`gather`来同步梯度和权重。
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@ -50,26 +75,6 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累
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{{ autodoc:colossalai.booster.plugin.GeminiPlugin }}
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### Torch DDP 插件
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更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
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{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
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### Torch FSDP 插件
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> ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。
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> ⚠ 该插件现在还不支持保存/加载分片的模型 checkpoint。
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> ⚠ 该插件现在还不支持使用了multi params group的optimizer。
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更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html).
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{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
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### Hybrid Parallel 插件
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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：
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