[misc] Add dist optim to doc sidebar (#5806)

* add to sidebar

* fix chinese

docs/sidebars.json

@@ -56,6 +56,7 @@
         "features/pipeline_parallel",
         "features/nvme_offload",
         "features/lazy_init",
+        "features/distributed_optimizers",
         "features/cluster_utils"
       ]
     },

docs/source/en/features/distributed_optimizers.md

@@ -14,12 +14,6 @@ Apart from the widely adopted Adam and SGD, many modern optimizers require layer
 ## Optimizers
 Adafactor is a first-order Adam variant using Non-negative Matrix Factorization(NMF) to reduce memory footprint. CAME improves by introducting a confidence matrix to correct NMF. GaLore further reduces memory by projecting gradients into a low-rank space and 8-bit block-wise quantization. Lamb allows huge batch sizes without lossing accuracy via layer-wise adaptive update bounded by the inverse of its Lipschiz constant.
 
-## API Reference
-
-{{ autodoc:colossalai.nn.optimizer.distributed_adafactor.DistributedAdaFactor }}
-{{ autodoc:colossalai.nn.optimizer.distributed_lamb.DistributedLamb }}
-{{ autodoc:colossalai.nn.optimizer.distributed_galore.DistGaloreAwamW }}
-{{ autodoc:colossalai.nn.optimizer.distributed_came.DistributedCAME }}
 
 ## Hands-On Practice
 We now demonstrate how to use Distributed Adafactor with booster API combining Tensor Parallel and ZeRO 2 with 4 GPUs. **Note that even if you're not aware of distributed optimizers, the plugins automatically casts yours to the distributed version for convenience.**
@@ -140,3 +134,10 @@ optim = DistGaloreAwamW(
 </table>
 
 <!-- doc-test-command: colossalai run --nproc_per_node 4 distributed_optimizers.py  -->
+
+## API Reference
+
+{{ autodoc:colossalai.nn.optimizer.distributed_adafactor.DistributedAdaFactor }}
+{{ autodoc:colossalai.nn.optimizer.distributed_lamb.DistributedLamb }}
+{{ autodoc:colossalai.nn.optimizer.distributed_galore.DistGaloreAwamW }}
+{{ autodoc:colossalai.nn.optimizer.distributed_came.DistributedCAME }}

docs/source/zh-Hans/features/distributed_optimizers.md

@@ -13,12 +13,6 @@ Author: Wenxuan Tan, Junwen Duan, Renjie Mao
 ## 优化器
 Adafactor 是一种首次采用非负矩阵分解（NMF）的 Adam 变体，用于减少内存占用。CAME 通过引入一个置信度矩阵来改进 NMF 的效果。GaLore 通过将梯度投影到低秩空间，并使用 8 位块状量化进一步减少内存占用。Lamb 允许使用巨大的批量大小而不失准确性，通过按其 Lipschitz 常数的倒数界定的逐层自适应更新实现
 
-## API 参考
-
-{{ autodoc:colossalai.nn.optimizer.distributed_adafactor.DistributedAdaFactor }}
-{{ autodoc:colossalai.nn.optimizer.distributed_lamb.DistributedLamb }}
-{{ autodoc:colossalai.nn.optimizer.distributed_galore.DistGaloreAwamW }}
-{{ autodoc:colossalai.nn.optimizer.distributed_came.DistributedCAME }}
 
 ## 使用
 现在我们展示如何使用分布式 Adafactor 与 booster API 结合 Tensor Parallel 和 ZeRO 2。即使您不使用distributed optimizer，plugin 也会自动将optimizer转换为分布式版本以方便使用。
@@ -137,3 +131,10 @@ optim = DistGaloreAwamW(
 </table>
 
 <!-- doc-test-command: colossalai run --nproc_per_node 4 distributed_optimizers.py  -->
+
+## API 参考
+
+{{ autodoc:colossalai.nn.optimizer.distributed_adafactor.DistributedAdaFactor }}
+{{ autodoc:colossalai.nn.optimizer.distributed_lamb.DistributedLamb }}
+{{ autodoc:colossalai.nn.optimizer.distributed_galore.DistGaloreAwamW }}
+{{ autodoc:colossalai.nn.optimizer.distributed_came.DistributedCAME }}