---
title: make_layout
description: "创建Layout，规定矩阵内存布局。"
url: https://www.hikunpeng.com/document/detail/zh/kunpenghpcs/hpckit/devg/KunpengHPCKit_developer_107.html
sourcePath: /source/zh/kunpenghpcs/hpckit/devg/KunpengHPCKit_developer_107.html
indexId: ed72ab35891bb44a4aca0349b1a6ab3498081af382cab4fa5f82d239b37936aa67
---
# make_layout

创建Layout，规定矩阵内存布局。

#### 接口定义

template <typename Shape, typename Stride>

Layout<Shape,Stride> make_layout(Shape shape, Stride stride);


#### 模板参数


**表1 模板参数定义**

| 参数名 | 类型 | 描述 |
| --- | --- | --- |
| Shape | typename | 矩阵形状类型。 |
| Stride | typename | 矩阵元素跨度类型。 |


#### 参数


**表2 参数定义**

| 参数名 | 类型 | 描述 | 输入/输出 |
| --- | --- | --- | --- |
| shape | Shape | 矩阵形状。 | 输入 |
| stride | Stride | 矩阵元素跨度。 | 输入 |


#### 返回值

返回Layout<Shape,Stride>对象


#### 示例

```
#include "stdlib.h"
#include "kupl_mma.h"
using namespace kupl::tensor;
int main()
{
constexpr int MATRIX_M  = 32;
constexpr int MATRIX_N  = 16;
constexpr int MATRIX_K = 512;
double *data_a = (double *)malloc(sizeof(double) * MATRIX_M * MATRIX_K);
double *data_b = (double *)malloc(sizeof(double) * MATRIX_K * MATRIX_N);
double *data_c = (double *)malloc(sizeof(double) * MATRIX_M * MATRIX_N);
auto shape_a = make_shape(Int<32>{}, Int<512>{});
auto shape_b = make_shape(Int<512>{}, Int<16>{});
auto shape_c = make_shape(Int<32>{}, Int<16>{});
auto stride_a = make_stride(Int<1>{}, Int<32>{});
auto stride_b = make_stride(Int<16>{}, Int<1>{});
auto stride_c = make_stride(Int<16>{}, Int<1>{});
auto layout_a =
make_layout
(shape_a, stride_a);
auto layout_b =
make_layout
(shape_b, stride_b);
auto layout_c =
make_layout
(shape_c, stride_c);
auto mma_atom_shape = make_shape(Int<1>{}, Int<1>{}, Int<1>{});
auto tiled_mma = make_tiled_mma(Ops<KP36_32x16x512_F64F64F64>{}, mma_atom_shape);
auto store_atom_shape = make_shape(Int<1>{}, Int<1>{});
auto tiled_store = make_tiled_store(Ops<KP36_32x16_F64_STORE>{}, store_atom_shape);
auto tensor_a = make_tensor(data_a, layout_a);
auto tensor_b = make_tensor(data_b, layout_b);
auto tensor_c = make_tensor(data_c, layout_c);
mma(tiled_mma, tensor_c, tensor_a, tensor_b, tensor_c);
store(tiled_store, tensor_c);
free(data_a);
free(data_b);
free(data_c);
return 0;
}
```

上述示例演示了基于32*16*512_F64F64F64矩阵形状的mma流程，其中通过make_layout创建矩阵布局。