make_tensor
创建Tensor对象,包含源数据及内存布局。
接口定义
template<typename dtype, typename Layout>
Tensor<dtype, Layout> make_tensor(dtype *ptr, Layout layout);
模板参数
参数名 |
类型 |
描述 |
---|---|---|
dtype |
typename |
精度类型 |
Layout |
typename |
布局类型 |
参数
参数名 |
类型 |
描述 |
输入/输出 |
---|---|---|---|
ptr |
dtype * |
矩阵源数据指针 |
输入 |
layout |
Layout |
矩阵内存布局 |
输入 |
返回值
- 返回Tensor<dtype, Layout>对象
示例
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | #include "stdlib.h" #include "kupl.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<MMA_32x16x512_F64F64F64>{}, mma_atom_shape); auto store_atom_shape = make_shape(Int<1>{}, Int<1>{}); auto tile_store = make_tiled_store(Ops<STORE_32x16_F64>{}, 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); tensor_tiled_mma(tiled_mma, tensor_c, tensor_a, tensor_b, tensor_c); tensor_tiled_store(tile_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_tensor创建矩阵对象tensor,作为后续mma/store接口参数。
父主题: 矩阵编程接口函数