[doc] update hybrid parallelism doc

docs/source/en/features/1D_tensor_parallel.md

@@ -7,7 +7,7 @@ Author: Zhengda Bian, Yongbin Li
 - [Configure Parallelization](../basics/configure_parallelization.md)
 
 **Example Code**
-- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_1d.py)
+- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **Related Paper**
 - [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://deepakn94.github.io/assets/papers/megatron-sc21.pdf)
@@ -19,15 +19,15 @@ An efficient 1D tensor parallelism implementation was introduced by [Megatron-LM
 
 Let's take a linear layer as an example, which consists of a GEMM $Y = XA$. Given 2 processors, we split the columns of $A$ into $[A_1 ~ A_2]$, and calculate $Y_i = XA_i$ on each processor, which then forms $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. This is called a column-parallel fashion.
 
-When a second linear layer $Z=YB$ follows the column-parallel one, we split $B$ into 
-```math
+When a second linear layer $Z=YB$ follows the column-parallel one, we split $B$ into
+$$
 \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]
-```
+$$
 which is called a row-parallel fashion.
-To calculate 
-```math
+To calculate
+$$
 Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]
-```
+$$
 we first calculate $Y_iB_i$ on each processor, then use an all-reduce to aggregate the results as $Z=Y_1B_1+Y_2B_2$.
 
 We also need to note that in the backward pass, the column-parallel linear layer needs to aggregate the gradients of the input tensor $X$, because on each processor $i$ we only have $\dot{X_i}=\dot{Y_i}A_i^T$.

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

@@ -8,7 +8,7 @@ Author: Zhengda Bian, Yongbin Li
 - [1D Tensor Parallelism](./1D_tensor_parallel.md)
 
 **Example Code**
-- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2d.py)
+- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **Related Paper**
 - [An Efficient 2D Method for Training Super-Large Deep Learning Models](https://arxiv.org/pdf/2104.05343.pdf)
@@ -22,33 +22,33 @@ Let's still take a linear layer $Y = XA$ as an example.
 Given $P=q\times q$ processors (necessary condition), e.g. $q=2$, we split both the input $X$ and weight $A$ into
 
 $$
-\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right]
+\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right]
 \text{~and~}
-\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right].
+\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right].
 $$
 
 The calculation includes $q$ steps. When $t=1$, $X_{i0}$ is broadcasted in its row, and $A_{0j}$ is broadcasted in its column. So, we have
 
 $$
-\left[\begin{matrix} X_{10},A_{00} & X_{10},A_{01} \\ X_{00},A_{00} & X_{00},A_{01} \end{matrix} \right].
+\left[\begin{matrix} X_{00},A_{00} & X_{00},A_{01} \\ X_{10},A_{00} & X_{10},A_{01} \end{matrix} \right].
 $$
 
 Then we multiply $X_{i0}$ and $A_{0j}$ on each processor $(i, j)$ as
 
 $$
-\left[\begin{matrix} X_{10}A_{00} & X_{10}A_{01} \\ X_{00}A_{00} & X_{00}A_{01} \end{matrix} \right] (1).
+\left[\begin{matrix} X_{00}A_{00} & X_{00}A_{01} \\ X_{10}A_{00} & X_{10}A_{01} \end{matrix} \right] (1).
 $$
 
 Similarly, when $t=2$, $X_{i1}$ is broadcasted in its row, $A_{1j}$ is broadcasted in its column, and we multiply them as
 
 $$
-\left[\begin{matrix} X_{11}A_{10} & X_{11}A_{11} \\ X_{01}A_{10} & X_{01}A_{11} \end{matrix} \right] (2).
+\left[\begin{matrix} X_{01}A_{10} & X_{01}A_{11} \\ X_{11}A_{10} & X_{11}A_{11} \end{matrix} \right] (2).
 $$
 
 By adding $(1)$ and $(2)$ up, we have
 
 $$
-Y = XA = \left[\begin{matrix} X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \\ X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right].
+Y = XA = \left[\begin{matrix} X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \\ X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right].
 $$
 
 ## Efficiency

docs/source/en/features/2p5D_tensor_parallel.md

@@ -9,7 +9,7 @@ Author: Zhengda Bian, Yongbin Li
 - [2D Tensor Parallelism](./2D_tensor_parallel.md)
 
 **Example Code**
-- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2p5d.py)
+- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **Related Paper**
 - [2.5-dimensional distributed model training](https://arxiv.org/pdf/2105.14500.pdf)
@@ -23,29 +23,30 @@ Let's still take a linear layer $Y = XA$ as an example.
 Given $P=q \times q \times d$ processors (necessary condition), e.g. $q=d=2$, we split the input $X$ into $d\times q$ rows and $q$ columns as
 
 $$
-\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \\ X_{10} & X_{11} \\ X_{00} & X_{01}\end{matrix} \right],
+\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \\ X_{20} & X_{21} \\ X_{30} & X_{31}\end{matrix} \right],
 $$
+
 which can be reshaped into $d$ layers as
 
 $$
-\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \end{matrix} \right].
+\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{20} & X_{21} \\ X_{30} & X_{31} \end{matrix} \right].
 $$
 
 Also, the weight $A$ is split into
 
 $$
-\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right].
+\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right].
 $$
 
 For each layer of $X$, we use the SUMMA algorithm to multiply $X$ and $A$.
 Then, we have the output
 
 $$
-\left[\begin{matrix} Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \\ Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right]
+\left[\begin{matrix} Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \\ Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right]
 \text{~and~}
 $$
 $$
-\left[\begin{matrix} Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \\ Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \end{matrix} \right].
+\left[\begin{matrix} Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \\ Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \end{matrix} \right].
 $$
 
 ## Efficiency

docs/source/en/features/3D_tensor_parallel.md

@@ -9,7 +9,7 @@ Author: Zhengda Bian, Yongbin Li
 - [2D Tensor Parallelism](./2D_tensor_parallel.md)
 
 **Example Code**
-- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_3d.py)
+- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **Related Paper**
 - [Maximizing Parallelism in Distributed Training for Huge Neural Networks](https://arxiv.org/pdf/2105.14450.pdf)

docs/source/zh-Hans/features/1D_tensor_parallel.md

@@ -7,7 +7,7 @@
 - [并行配置](../basics/configure_parallelization.md)
 
 **示例代码**
-- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_1d.py)
+- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **相关论文**
 - [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://deepakn94.github.io/assets/papers/megatron-sc21.pdf)
@@ -20,15 +20,16 @@
 让我们以一个线性层为例，它包括一个 GEMM $Y = XA$。 给定2个处理器，我们把列 $A$ 划分为 $[A_1 ~ A_2]$, 并在每个处理器上计算 $Y_i = XA_i$ , 然后形成 $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. 这被称为列并行方式。
 
 当第二个线性层 $Z=YB$ 跟随上述列并行层的时候, 我们把 $B$ 划分为
-```math
+$$
 \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]
 ```
 这就是所谓的行并行方式.
+$$
 
 为了计算
-```math
+$$
 Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]
-```
+$$
 我们首先在每个处理器上计算 $Y_iB_i$ 然后使用一个all-reduce操作将结果汇总为 $Z=Y_1B_1+Y_2B_2$。
 
 我们还需要注意，在后向计算中，列并行线性层需要聚合输入张量 $X$, 因为在每个处理器 $i$ 上，我们只有 $\dot{X_i}=\dot{Y_i}A_i^T$，因此，我们在各处理器之间进行all-reduce，得到 $\dot{X}=\dot{Y}A^T=\dot{Y_1}A_1^T+\dot{Y_2}A_2^T$。

docs/source/zh-Hans/features/2D_tensor_parallel.md

@@ -8,7 +8,7 @@
 - [1D 张量并行](./1D_tensor_parallel.md)
 
 **示例代码**
-- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2d.py)
+- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **相关论文**
 - [An Efficient 2D Method for Training Super-Large Deep Learning Models](https://arxiv.org/pdf/2104.05343.pdf)
@@ -22,33 +22,33 @@
 给定 $P=q\times q$ 个处理器（必要条件）, 如 $q=2$, 我们把输入 $X$ 和权重A $A$ 都划分为
 
 $$
-\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right]
+\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right]
 \text{~and~}
-\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right]。
+\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right].
 $$
 
 该计算包括 $q$ 步。 当 $t=1$ 时, $X_{i0}$ 在其行中被广播, 而 $A_{0j}$ 在其列中被广播。因此，我们有
 
 $$
-\left[\begin{matrix} X_{10},A_{00} & X_{10},A_{01} \\ X_{00},A_{00} & X_{00},A_{01} \end{matrix} \right]。
+\left[\begin{matrix} X_{00},A_{00} & X_{00},A_{01} \\ X_{10},A_{00} & X_{10},A_{01} \end{matrix} \right].
 $$
 
 然后我们在每个处理器 $(i, j)$ 上将 $X_{i0}$ 和 $A_{0j}$ 相乘为
 
 $$
-\left[\begin{matrix} X_{10}A_{00} & X_{10}A_{01} \\ X_{00}A_{00} & X_{00}A_{01} \end{matrix} \right] (1)。
+\left[\begin{matrix} X_{00}A_{00} & X_{00}A_{01} \\ X_{10}A_{00} & X_{10}A_{01} \end{matrix} \right] (1).
 $$
 
 同样，当 $t=2$ 时, $X_{i1}$ 在其行中被广播, $A_{1j}$ 在其列中被广播, 我们将它们相乘为
 
 $$
-\left[\begin{matrix} X_{11}A_{10} & X_{11}A_{11} \\ X_{01}A_{10} & X_{01}A_{11} \end{matrix} \right] (2)。
+\left[\begin{matrix} X_{01}A_{10} & X_{01}A_{11} \\ X_{11}A_{10} & X_{11}A_{11} \end{matrix} \right] (2).
 $$
 
 通过将 $(1)$ 和 $(2)$ 相加，我们有
 
 $$
-Y = XA = \left[\begin{matrix} X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \\ X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right]。
+Y = XA = \left[\begin{matrix} X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \\ X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right].
 $$
 
 ## 效率

docs/source/zh-Hans/features/2p5D_tensor_parallel.md

@@ -9,7 +9,7 @@
 - [2D 张量并行](./2D_tensor_parallel.md)
 
 **示例代码**
-- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2p5d.py)
+- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **相关论文**
 - [2.5-dimensional distributed model training](https://arxiv.org/pdf/2105.14500.pdf)
@@ -22,29 +22,29 @@
 给定 $P=q \times q \times d$ 个处理器（必要条件）, 如 $q=d=2$, 我们把输入 $X$ 划分为 $d\times q$ 行和 $q$ 列
 
 $$
-\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \\ X_{10} & X_{11} \\ X_{00} & X_{01}\end{matrix} \right],
+\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \\ X_{20} & X_{21} \\ X_{30} & X_{31}\end{matrix} \right],
 $$
 它可以被重塑为 $d$ 层
 
 $$
-\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \end{matrix} \right].
+\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{20} & X_{21} \\ X_{30} & X_{31} \end{matrix} \right].
 $$
 
 另外，权重 $A$ 被分割为
 
 $$
-\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right].
+\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right].
 $$
 
 对于 $X$ 相关的每一层, 我们使用SUMMA算法将 $X$ 与 $A$ 相乘。
 然后，我们得到输出
 
 $$
-\left[\begin{matrix} Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \\ Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right]
+\left[\begin{matrix} Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \\ Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right]
 \text{~and~}
 $$
 $$
-\left[\begin{matrix} Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \\ Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \end{matrix} \right].
+\left[\begin{matrix} Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \\ Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \end{matrix} \right].
 $$
 
 ## 效率

docs/source/zh-Hans/features/3D_tensor_parallel.md

@@ -9,7 +9,7 @@
 - [2D 张量并行](./2D_tensor_parallel.md)
 
 **示例代码**
-- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_3d.py)
+- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)
 
 **相关论文**
 - [Maximizing Parallelism in Distributed Training for Huge Neural Networks](https://arxiv.org/pdf/2105.14450.pdf)