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

* 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

revert to perceptron in line 73

* Update 2p5D_tensor_parallel.md

revert to perceptron in line 71

* Update 3D_tensor_parallel.md

revert to perceptron in line 80

* Update README.md

revert to resnet in line 42

* Update reorder_graph.py

revert to indice in line 7

* Update p2p.py

revert to megatron in line 94

* Update initialize.py

revert to torchrun in line 198

* Update routers.py

change to detailed in line 63

* Update routers.py

change to detailed in line 146

* Update README.md

revert  random number in line 402

colossalai/zero/wrapper.py

@@ -26,7 +26,7 @@ def zero_model_wrapper(model: nn.Module,
         zero_stage (int, optional): The stage of ZeRO DDP. You can find more information in ZeRO's paper.
             https://arxiv.org/abs/1910.02054
         gemini_config (dict, optional): The configuration dictionary of `GeminiDDP`. `GeminiDDP` is enabled
-            when the stage is set to 3. You can set the arguemnts of `GeminiDDP` in the gemini_config.
+            when the stage is set to 3. You can set the arguments of `GeminiDDP` in the gemini_config.
             Here is an example where we set the device of the model, the placement policy of Gemini, and the
             size of hidden dimension to help Gemini find out a unified chunk size.
 
@@ -78,7 +78,7 @@ def zero_optim_wrapper(model: nn.Module,
         max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do
             clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm.
         norm_type (float, optional): norm_type used for `clip_grad_norm`.
-        optim_config (dict, optinoal): The configuration used for the ZeRO optimizer.
+        optim_config (dict, optional): The configuration used for the ZeRO optimizer.
             Example:
 
                 >>> zero2_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True)