VkCooperativeVectorMatrixLayoutNV

Possible values for VkCooperativeVectorMatrixLayoutNV include:

typedef enum VkCooperativeVectorMatrixLayoutNV {
    VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_ROW_MAJOR_NV = 0,
    VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_COLUMN_MAJOR_NV = 1,
    VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_INFERENCING_OPTIMAL_NV = 2,
    VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_TRAINING_OPTIMAL_NV = 3,
} VkCooperativeVectorMatrixLayoutNV;

• VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_ROW_MAJOR_NV corresponds to SPIR-V RowMajorNV layout.
• VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_COLUMN_MAJOR_NV corresponds to SPIR-V ColumnMajorNV layout.
• VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_INFERENCING_OPTIMAL_NV corresponds to SPIR-V InferencingOptimalNV layout.
• VK_COOPERATIVE_VECTOR_MATRIX_LAYOUT_TRAINING_OPTIMAL_NV corresponds to SPIR-V TrainingOptimalNV layout.

All enum values match the corresponding SPIR-V value.

Row-major layout has elements of each row stored consecutively in memory, with a controllable stride from the start of one row to the start of the next row. Column-major layout has elements of each column stored consecutively in memory, with a controllable stride from the start of one column to the start of the next column. Inferencing-optimal and Training-optimal layouts are implementation-dependent, and the application can convert a matrix to those layouts using vkConvertCooperativeVectorMatrixNV or vkCmdConvertCooperativeVectorMatrixNV. Training-optimal layout with VK_COMPONENT_TYPE_FLOAT16_KHR or VK_COMPONENT_TYPE_FLOAT32_KHR type has the additional guarantee that the application can reinterpret the data as an array of elements and perform element-wise operations on the data, and finite values in any padding elements do not affect the result of a matrix-vector multiply (inf/NaN values may still cause NaN values in the result).