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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

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* Update search_chunk.py

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* Update README.md

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@@ -32,11 +32,11 @@ and the first and second momentum estimates) are partitioned across the processe
3. **Shard Parameter**: The 16-bit model parameters are partitioned across the processes of a data parallel group.
4. **[Gemini](../advanced_tutorials/meet_gemini.md)**: Dynamic heterogeneous memory space manager for paramters, gradients and optimizer states.
4. **[Gemini](../advanced_tutorials/meet_gemini.md)**: Dynamic heterogeneous memory space manager for parameters, gradients and optimizer states.
Besides, this article will introduce the Zero Redundancy Optimizer with chunk-based memory management.
When using ZeRO, we distributed the model by sharding the parameters. The advantage of this method is that the memory of each node is load balanced. But this approach has two significiant disadvantages. First, during communication, a temporary memory buffer needs to be allocated and released afterwards, leading to the memory fragmentation problem. Secondly, using tensor as the granularity for communication will cause the network bandwidth underutilized. Generally, the longer the transmitted message length, the higher the bandwidth utilization.
When using ZeRO, we distributed the model by sharding the parameters. The advantage of this method is that the memory of each node is load balanced. But this approach has two significant disadvantages. First, during communication, a temporary memory buffer needs to be allocated and released afterwards, leading to the memory fragmentation problem. Secondly, using tensor as the granularity for communication will cause the network bandwidth underutilized. Generally, the longer the transmitted message length, the higher the bandwidth utilization.
Using the Chunk mechanism introduced in ColossalAI v0.1.8, we can improve the efficiency of ZeRO. We store a continuous set of parameters in initialization order into a Chunk (a chunk is a continuous memory space), and each Chunk has the same size. Organizing memory in chunks can lead to efficient use of network bandwidth between PCI-e and GPU-GPU, reduce the number of communications, and avoid potential memory fragmentation.