/**************************************************************************** * * Copyright (c) 2017 - 2018 by Rockchip Corp. All rights reserved. * * The material in this file is confidential and contains trade secrets * of Rockchip Corporation. This is proprietary information owned by * Rockchip Corporation. No part of this work may be disclosed, * reproduced, copied, transmitted, or used in any way for any purpose, * without the express written permission of Rockchip Corporation. * *****************************************************************************/ #ifndef _RKNN_MATMUL_API_H #define _RKNN_MATMUL_API_H #ifdef __cplusplus extern "C" { #endif #include "rknn_api.h" typedef rknn_context rknn_matmul_ctx; /* the process data type of matmul */ typedef enum _rknn_matmul_type { RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 = 1, RKNN_INT8_MM_INT8_TO_INT32 = 2, RKNN_INT4_MM_INT4_TO_INT16 = 10, } rknn_matmul_type; inline static const char* get_matmul_type_string(rknn_matmul_type type) { switch (type) { case RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32: return "RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32"; case RKNN_INT8_MM_INT8_TO_INT32: return "RKNN_INT8_MM_INT8_TO_INT32"; case RKNN_INT4_MM_INT4_TO_INT16: return "RKNN_INT4_MM_INT4_TO_INT16"; default: return "UNKNOW"; } } typedef struct _rknn_matmul_tensor_attr { char name[RKNN_MAX_NAME_LEN]; // indicate A(M, K) or B(K, N) or C(M, N) uint32_t n_dims; uint32_t dims[RKNN_MAX_DIMS]; // matmul tensor size uint32_t size; // matmul tensor data type // int8 : A, B // int32: C rknn_tensor_type type; } rknn_matmul_tensor_attr; typedef struct _rknn_matmul_io_attr { // indicate A(M, K) or B(K, N) or C(M, N) rknn_matmul_tensor_attr A; rknn_matmul_tensor_attr B; rknn_matmul_tensor_attr C; } rknn_matmul_io_attr; /* matmul information struct */ typedef struct rknn_matmul_info_t { int32_t M; int32_t K; // limit: RK3566/3568: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte; // RK3562: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte; // RK3588: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte, // int4 type must be aligned with 32byte; int32_t N; // limit: RK3566/3568: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; // RK3562: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; // RK3588: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte, // int4 type must be aligned with 64byte; // matmul data type // int4: int4(A) x int4(B) -> int16(C) // int8: int8(A) x int8(B) -> int32(C) // float16: float16(A) x float16(B) -> float32(C) rknn_matmul_type type; // matmul native layout for B // 0: normal layout // 1: native layout int32_t B_layout; // matmul native layout for A and C // 0: normal layout // 1: native layout int32_t AC_layout; } rknn_matmul_info; /* rknn_matmul_create params: rknn_matmul_ctx *ctx the handle of context. rknn_matmul_info *info the matmal information. rknn_matmul_io_attr *io_attr inputs/output attribute return: int error code */ int rknn_matmul_create(rknn_matmul_ctx* ctx, rknn_matmul_info* info, rknn_matmul_io_attr* io_attr); /* rknn_matmul_set_io_mem params: rknn_matmul_ctx ctx the handle of context. rknn_tensor_mem *mem the pointer of tensor memory information. rknn_matmul_tensor_attr *attr the attribute of input or output tensor buffer. return: int error code. formula: C = A * B, limit: K max: k <= 10240 K limit: RK3566/3568: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte; RK3562: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte; RK3588: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte, int4 type must be aligned with 32byte; N limit: RK3566/3568: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; RK3562: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; RK3588: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte, int4 type must be aligned with 64byte; A shape: M x K normal layout: (M, K) [M1K1, M1K2, ..., M1Kk, M2K1, M2K2, ..., M2Kk, ... MmK1, MmK2, ..., MmKk] for RK3566/3568： int8: native layout: (K / 8, M, 8) [K1M1, K2M1, ..., K8M1, K9M2, K10M2, ..., K16M2, ... K(k-7)Mm, K(k-6)Mm, ..., KkMm] float16: native layout: (K / 4, M, 4) [K1M1, K2M1, ..., K4M1, K9M2, K10M2, ..., K8M2, ... K(k-3)Mm, K(k-2)Mm, ..., KkMm] for RK3562： int8: native layout: (K / 16, M, 16) [K1M1, K2M1, ..., K16M1, K17M2, K18M2, ..., K32M2, ... K(k-15)Mm, K(k-14)Mm, ..., KkMm] float16: native layout: (K / 8, M, 8) [K1M1, K2M1, ..., K8M1, K9M2, K10M2, ..., K16M2, ... K(k-7)Mm, K(k-6)Mm, ..., KkMm] for RK3588： int4: native layout: (K / 32, M, 32) [K1M1, K2M1, ..., K32M1, K33M2, K10M2, ..., K64M2, ... K(k-31)Mm, K(k-30)Mm, ..., KkMm] int8: native layout: (K / 16, M, 16) [K1M1, K2M1, ..., K16M1, K17M2, K18M2, ..., K32M2, ... K(k-15)Mm, K(k-14)Mm, ..., KkMm] float16: native layout: (K / 8, M, 8) [K1M1, K2M1, ..., K8M1, K9M2, K10M2, ..., K16M2, ... K(k-7)Mm, K(k-6)Mm, ..., KkMm] B shape: K x N normal layout: (K, N) [K1N1, K1N2, ..., K1Nn, K2N1, K2N2, ..., K2Nn, ... KkN1, KkN2, ..., KkNn] for RK3566/3568： int8: native layout: (N / 16, K / 32, 16, 32) [K1N1, K2N1, ..., K32N1, K1N2, K2N2, ..., K32N2, ... K1N16, K2N16, ..., K32N16, K33N1, K34N1, ..., K64N1, K33N2, K34N2, ..., K64N2, ... K(k-31)N16, K(k-30)N16, ..., KkN16, K1N17, K2N17, ..., K32N17, K1N18, K2N18, ..., K32N18, ... K(k-31)Nn, K(k-30)Nn, ..., KkNn] float16: native layout: (N / 8, K / 16, 8, 16) [K1N1, K2N1, ..., K16N1, K1N2, K2N2, ..., K16N2, ... K1N8, K2N8, ..., K16N8, K17N1, K18N1, ..., K32N1, K17N2, K18N2, ..., K32N2, ... K(k-15)N8, K(k-30)N8, ..., KkN8, K1N9, K2N9, ..., K16N9, K1N10, K2N10, ..., K16N10, ... K(k-15)Nn, K(k-14)Nn, ..., KkNn] for RK3562： int8: native layout: (N / 16, K / 32, 16, 32) [K1N1, K2N1, ..., K32N1, K1N2, K2N2, ..., K32N2, ... K1N16, K2N16, ..., K32N16, K33N1, K34N1, ..., K64N1, K33N2, K34N2, ..., K64N2, ... K(k-31)N16, K(k-30)N16, ..., KkN16, K1N17, K2N17, ..., K32N17, K1N18, K2N18, ..., K32N18, ... K(k-31)Nn, K(k-30)Nn, ..., KkNn] float16: native layout: (N / 8, K / 32, 8, 32) [K1N1, K2N1, ..., K32N1, K1N2, K2N2, ..., K32N2, ... K1N8, K2N8, ..., K32N8, K33N1, K34N1, ..., K64N1, K33N2, K34N2, ..., K64N2, ... K(k-31)N8, K(k-30)N8, ..., KkN8, K1N9, K2N9, ..., K16N9, K1N10, K2N10, ..., K16N10, ... K(k-31)Nn, K(k-30)Nn, ..., KkNn] for RK3588： int4: native layout: (N / 64, K / 32, 64, 32) [K1N1, K2N1, ..., K32N1, K1N2, K2N2, ..., K32N2, ... K1N64, K2N64, ..., K32N64, K33N1, K34N1, ..., K64N1, K33N2, K34N2, ..., K64N2, ... K(k-31)N64, K(k-30)N64, ..., KkN64, K1N65, K2N65, ..., K32N65, K1N66, K2N66, ..., K32N66, ... K(k-31)Nn, K(k-30)Nn, ..., KkNn] int8: native layout: (N / 32, K / 32, 32, 32) [K1N1, K2N1, ..., K32N1, K1N2, K2N2, ..., K32N2, ... K1N32, K2N32, ..., K32N32, K33N1, K34N1, ..., K64N1, K33N2, K34N2, ..., K64N2, ... K(k-31)N32, K(k-30)N32, ..., KkN32, K1N33, K2N33, ..., K32N33, K1N34, K2N34, ..., K32N34, ... K(k-31)Nn, K(k-30)Nn, ..., KkNn] float16: native layout: (N / 16, K / 32, 16, 32) [K1N1, K2N1, ..., K32N1, K1N2, K2N2, ..., K32N2, ... K1N16, K2N16, ..., K32N16, K33N1, K34N1, ..., K64N1, K33N2, K34N2, ..., K64N2, ... K(k-31)N16, K(k-30)N16, ..., KkN16, K1N17, K2N17, ..., K32N17, K1N18, K2N18, ..., K32N18, ... K(k-31)Nn, K(k-30)Nn, ..., KkNn] C shape: M x N normal layout: (M, N) [M1N1, M1N2, ..., M1Nn, M2N1, M2N2, ..., M2Nn, ... MmN1, MmN2, ..., MmNn] native layout: (N / 4, M, 4) [N1M1, N2M1, ..., N4M1, N5M2, N6M2, ..., N8M2, ... N(n-3)Mm, N(n-2)Mm, ..., NnMm] for RK3588： int4: native layout: (N / 8, M, 8) [N1M1, N2M1, ..., N8M1, N9M2, N10M2, ..., N16M2, ... N(n-7)Mm, N(n-6)Mm, ..., NnMm] */ int rknn_matmul_set_io_mem(rknn_matmul_ctx ctx, rknn_tensor_mem* mem, rknn_matmul_tensor_attr* attr); /* rknn_matmul_set_core_mask set rknn core mask.(only support RK3588 in current) RKNN_NPU_CORE_AUTO: auto mode, default value RKNN_NPU_CORE_0: core 0 mode RKNN_NPU_CORE_1: core 1 mode RKNN_NPU_CORE_2: core 2 mode RKNN_NPU_CORE_0_1: combine core 0/1 mode RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode input: rknn_matmul_ctx context the handle of context. rknn_core_mask core_mask the core mask. return: int error code. */ int rknn_matmul_set_core_mask(rknn_matmul_ctx context, rknn_core_mask core_mask); /* rknn_matmul_run run the matmul in blocking mode params: rknn_matmul_ctx ctx the handle of context. return: int error code. */ int rknn_matmul_run(rknn_matmul_ctx ctx); /* rknn_matmul_destroy destroy the matmul context params: rknn_matmul_ctx ctx the handle of context. return: int error code. */ int rknn_matmul_destroy(rknn_matmul_ctx ctx); #ifdef __cplusplus } // extern "C" #endif #endif // _RKNN_MATMUL_API_H