[nfc] fix some typo with colossalai/ docs/ etc. (#4920)

colossalai/inference/README.md

@@ -94,7 +94,7 @@ For various models, experiments were conducted using multiple batch sizes under
 
 ### Single GPU Performance:
 
-Currently the stats below are calculated based on A100 (single GPU), and we calculate token latency based on average values of context-forward and decoding forward process, which means we combine both of processes to calculate token generation times. We are actively developing new features and methods to furthur optimize the performance of LLM models. Please stay tuned.
+Currently the stats below are calculated based on A100 (single GPU), and we calculate token latency based on average values of context-forward and decoding forward process, which means we combine both of processes to calculate token generation times. We are actively developing new features and methods to further optimize the performance of LLM models. Please stay tuned.
 
 #### Llama
 

colossalai/shardformer/README.md

@@ -77,7 +77,7 @@ Following are the description `ShardConfig`'s arguments:
 
 - `enable_sequence_parallelism`:  Whether to turn on sequence parallelism, which partitions non-tensor-parallel regions along the sequence dimension. Defaults to False.
 
-- `enable_sequence_overlap`: Whether to turn on sequence overlap, wheich overlap the computation and communication in sequence parallelism. It can only be used when `enable_sequence_parallelism` is True. Defaults to False.
+- `enable_sequence_overlap`: Whether to turn on sequence overlap, which overlap the computation and communication in sequence parallelism. It can only be used when `enable_sequence_parallelism` is True. Defaults to False.
 
 -  `enable_all_optimization`: Whether to turn on all optimization tools including `fused normalizaion`, `flash attention`, `JIT fused operators`, `sequence parallelism` and `sequence overlap`. Defaults to False.
 

docs/source/en/basics/booster_plugins.md

@@ -15,7 +15,7 @@ We currently provide the following plugins:
 - [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.
 - [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.
 - [Gemini Plugin](#gemini-plugin): It wraps the [Gemini](../features/zero_with_chunk.md) which implements Zero-3 with chunk-based and heterogeneous memory management.
-- [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.
+- [Hybrid Parallel 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.
 
 More plugins are coming soon.