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[doc] Fix typo under colossalai and doc(#3618)

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* Fixed several spelling errors under colossalai

* Fix the spelling error in colossalai and docs directory

* Cautious Changed the spelling error under the example folder

* Update runtime_preparation_pass.py

revert autograft to autograd

* Update search_chunk.py

utile to until

* Update check_installation.py

change misteach to mismatch in line 91

* Update 1D_tensor_parallel.md

revert to perceptron

* Update 2D_tensor_parallel.md

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* Update 2p5D_tensor_parallel.md

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* Update 3D_tensor_parallel.md

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* Update README.md

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* Update reorder_graph.py

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* Update p2p.py

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* Update initialize.py

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* Update routers.py

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* Update routers.py

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* Update README.md

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digger-yu
2023-04-26 11:38:43 +08:00
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docs/source/en/advanced_tutorials/meet_gemini.md
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@@ -44,7 +44,7 @@ In some solutions, the [Zero-offload](https://arxiv.org/abs/2101.06840) adopted
</figure>
Colossal-AI designed Gemini, just like two-stars, which manages the memory space of CPU and GPU efficiently. It can make the tensor dynamically distributed in the storage space of CPU-GPU during training, so that the model training can break through the memory wall of GPU. The memory manager consists of two parts: **MemStatsCollector (MSC)** and **StatefuleTensorMgr (STM)**.
Colossal-AI designed Gemini, just like two-stars, which manages the memory space of CPU and GPU efficiently. It can make the tensor dynamically distributed in the storage space of CPU-GPU during training, so that the model training can break through the memory wall of GPU. The memory manager consists of two parts: **MemStatsCollector (MSC)** and **StatefulTensorMgr (STM)**.
We take advantage of the iterative characteristics of the deep learning network training process. We divide iterations into two stages: warmup and non-warmup. One or several iterative steps at the beginning belong to the warmup stage, and the other iterative steps belong to the non-warmup stage. In the warmup stage, we collect information for the MSC, while in the non-warmup stage, STM gets the information collected by the MSC to move the tensor, so as to minimize the CPU-GPU data movement volume.