[doc] add shardformer support matrix/update tensor parallel documents (#4728)

* add compatibility matrix for shardformer doc

* update tp doc

docs/source/en/features/2D_tensor_parallel.md

@@ -60,83 +60,9 @@ Given $P=q\times q$ processors, we present the theoretical computation and memor
 
 ## Usage
 
-To enable 2D tensor parallelism for our model, e.g. on 4 GPUs, we need to configure the parallelism setting as below.
-```python
-CONFIG = dict(parallel=dict(
-    data=1,
-    pipeline=1,
-    tensor=dict(size=4, mode='2d'),
-))
-```
-Then Colossal-AI will automatically apply 2D parallelism to all the layers from `colossalai.nn`.
+Currently the newest version of ColossalAI doesn't support 2D tensor parallelism, but this feature will be integrated into `Shardformer` in future releases.
+For more details about ideas and usages of `Shardformer`, please refer to [Shardformer Doc](./shardformer.md).
 
-Let's define a model that consists of a two-layer multi-layer perceptron (MLP) as below.
-```python
-import colossalai
-import colossalai.nn as col_nn
-import torch
-from colossalai.utils import print_rank_0
+For users of older version of ColossalAI, please refer to [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md).
 
-class MLP(torch.nn.Module):
-    def __init__(self, dim: int = 256):
-        super().__init__()
-        intermediate_dim = dim * 4
-        self.dense_1 = col_nn.Linear(dim, intermediate_dim)
-        print_rank_0(f'Weight of the first linear layer: {self.dense_1.weight.shape}')
-        self.activation = torch.nn.GELU()
-        self.dense_2 = col_nn.Linear(intermediate_dim, dim)
-        print_rank_0(f'Weight of the second linear layer: {self.dense_2.weight.shape}')
-        self.dropout = col_nn.Dropout(0.1)
-
-    def forward(self, x):
-        x = self.dense_1(x)
-        print_rank_0(f'Output of the first linear layer: {x.shape}')
-        x = self.activation(x)
-        x = self.dense_2(x)
-        print_rank_0(f'Output of the second linear layer: {x.shape}')
-        x = self.dropout(x)
-        return x
-```
-Launch Colossal-AI on 4 GPUs and build the model
-```python
-parser = colossalai.get_default_parser()
-colossalai.launch(config=CONFIG,
-                  rank=args.rank,
-                  world_size=args.world_size,
-                  local_rank=args.local_rank,
-                  host=args.host,
-                  port=args.port)
-
-m = MLP()
-```
-We will see the shapes of partitioned parameters(e.g. weights) in the MLP model.
-```shell
-Weight of the first linear layer: torch.Size([128, 512])
-Weight of the second linear layer: torch.Size([512, 128])
-```
-The complete weight of the first linear layer is supposed to have the shape `[256, 1024]`. After the partitioning of 2D parallelism, it becomes `[128, 512]` on each GPU.
-Similarly, the second layer partitions the weight `[1024, 256]` into `[512, 128]`.
-
-We can run the model with some random inputs.
-```python
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.utils import get_current_device
-
-x = torch.randn((16, 256), device=get_current_device())
-# partition input
-torch.distributed.broadcast(x, src=0)
-x = torch.chunk(x, 2, dim=0)[gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)]
-x = torch.chunk(x, 2, dim=-1)[gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)]
-print_rank_0(f'Input: {x.shape}')
-
-x = m(x)
-```
-Then we can see the shapes of activation results.
-```shell
-Input: torch.Size([8, 128])
-Output of the first linear layer: torch.Size([8, 512])
-Output of the second linear layer: torch.Size([8, 128])
-```
-The activation tensors in 2D parallelism are all split in both row and column.
-E.g. the output of the first linear layer has the shape `[8, 512]`, while the second layer has the output of `[8, 128]`.
+<!-- doc-test-command: echo  -->