File size: 18,424 Bytes
477da44 |
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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 |
/****************************************************************************
*
* 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;
typedef enum _rknn_matmul_quant_type
{
RKNN_QUANT_TYPE_PER_LAYER_SYM = 0,
RKNN_QUANT_TYPE_PER_LAYER_ASYM = 1,
RKNN_QUANT_TYPE_PER_CHANNEL_SYM = 2,
RKNN_QUANT_TYPE_PER_CHANNEL_ASYM = 3,
RKNN_QUANT_TYPE_PER_GROUP_SYM = 4,
RKNN_QUANT_TYPE_PER_GROUP_ASYM = 5,
} rknn_matmul_quant_type;
typedef struct _rknn_quant_params
{
char name[RKNN_MAX_NAME_LEN];
// matmul tensor scale
float* scale;
int32_t scale_len;
// matmul tensor zero point
int32_t* zp;
int32_t zp_len;
} rknn_quant_params;
typedef enum _rknn_matmul_type
{
RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 = 1,
RKNN_INT8_MM_INT8_TO_INT32 = 2,
RKNN_INT8_MM_INT8_TO_INT8 = 3,
RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 = 4,
RKNN_FLOAT16_MM_INT8_TO_FLOAT32 = 5,
RKNN_FLOAT16_MM_INT8_TO_FLOAT16 = 6,
RKNN_FLOAT16_MM_INT4_TO_FLOAT32 = 7,
RKNN_FLOAT16_MM_INT4_TO_FLOAT16 = 8,
RKNN_INT8_MM_INT8_TO_FLOAT32 = 9,
RKNN_INT4_MM_INT4_TO_INT16 = 10,
RKNN_INT8_MM_INT4_TO_INT32 = 11,
} 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_INT8_MM_INT8_TO_INT8:
return "RKNN_INT8_MM_INT8_TO_INT8";
case RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16:
return "RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16";
case RKNN_FLOAT16_MM_INT8_TO_FLOAT32:
return "RKNN_FLOAT16_MM_INT8_TO_FLOAT32";
case RKNN_FLOAT16_MM_INT8_TO_FLOAT16:
return "RKNN_FLOAT16_MM_INT8_TO_FLOAT16";
case RKNN_INT4_MM_INT4_TO_INT16:
return "RKNN_INT4_MM_INT4_TO_INT16";
case RKNN_FLOAT16_MM_INT4_TO_FLOAT32:
return "RKNN_FLOAT16_MM_INT4_TO_FLOAT32";
case RKNN_FLOAT16_MM_INT4_TO_FLOAT16:
return "RKNN_FLOAT16_MM_INT4_TO_FLOAT16";
case RKNN_INT8_MM_INT4_TO_INT32:
return "RKNN_INT8_MM_INT4_TO_INT32";
case RKNN_INT8_MM_INT8_TO_FLOAT32:
return "RKNN_INT8_MM_INT8_TO_FLOAT32";
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 dynamic shape struct
*/
typedef struct _rknn_matmul_shape
{
int32_t M;
int32_t K;
int32_t N;
} rknn_matmul_shape;
/*
the layout of matmul input/output tensor.
*/
typedef enum
{
RKNN_MM_LAYOUT_NORM = 0,
RKNN_MM_LAYOUT_NATIVE = 1,
RKNN_MM_LAYOUT_TP_NORM = 2,
} rknn_matmul_layout;
/*
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;
// RK3588/3576: 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;
// RK3588/3576: 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
int16_t B_layout;
// matmul quant type for B
// A and C only support per layer
// 0: per layer
// 1: per channel
// 2: per group
int16_t B_quant_type;
// matmul native layout for A and C
// 0: normal layout
// 1: native layout
int16_t AC_layout;
// matmul quant type for A and C, only support 0
int16_t AC_quant_type;
// iommu domain id, each domain has 4GB of space
int32_t iommu_domain_id;
// B_quant_type set 2, group size is enable
int16_t group_size;
// reserved field
int8_t reserved[34];
} 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_create_dynamic_shape
params:
rknn_matmul_ctx *ctx the handle of context.
rknn_matmul_info *info the matmal information.
int shape_num the supported shape number of matmul.
rknn_matmul_shape dynamic_shapes[] the supported M,K,N shape struct array.
rknn_matmul_io_attr *io_attr the array of inputs and output attribute
return:
int error code
*/
/*
原来的info.M, K, N无效
*/
int rknn_matmul_create_dynamic_shape(rknn_matmul_ctx* ctx, rknn_matmul_info* info, int shape_num,
rknn_matmul_shape dynamic_shapes[], rknn_matmul_io_attr io_attrs[]);
/* 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;
RK3588/3576: 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;
RK3588/3576: 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 RK3588/3576：
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：
when K > 8192, the B data will be split into T segments.
int T = std::ceil(K / 8192);
For example: normal layout -> native layout
K = 20488, N = 4096, T = 3, the data will be split into 3 segments.
subN = rknn_matmul_io_attr.B.dims[2];
subK = rknn_matmul_io_attr.B.dims[3];
(8196, 4096) (4096 / subN, 8196 / subK, subN, subK)
(K, N) = (20488, 4096) -> (8196, 4096) -> (4096 / subN, 8196 / subK, subN, subK)
normal layout (4096, 4096) (4096 / subN, 4096 / subK, subN, subK)
T normal layout T native layout
It is recommended to use the rknn_B_normal_layout_to_native_layout interface for direct data conversion.
for RK3576：
when K > 4096, the B data will be split into T segments.
int T = std::ceil(K / 4096);
For example: normal layout -> native layout
K = 10240, N = 2048, T = 3, the data will be split into 3 segments.
subN = rknn_matmul_io_attr.B.dims[2];
subK = rknn_matmul_io_attr.B.dims[3];
(4096, 2048) (2048 / subN, 4096 / subK, subN, subK)
(K, N) = (10240, 2048) -> (4096, 2048) -> (2048 / subN, 4096 / subK, subN, subK)
normal layout (2048, 2048) (2048 / subN, 2048 / subK, subN, subK)
T normal layout T native layout
It is recommended to use the rknn_B_normal_layout_to_native_layout interface for direct data conversion.
for RK3588/3576：
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_set_quant_params
set quant params.(only support matmul type RKNN_INT8_MM_INT8_TO_INT8, RKNN_INT8_MM_INT8_TO_INT32)
input:
rknn_matmul_ctx context the handle of context.
rknn_quant_params params quant params.
return:
int error code.
*/
int rknn_matmul_set_quant_params(rknn_matmul_ctx context, rknn_quant_params* params);
/* rknn_matmul_get_quant_params
get per channel quant params.(only support matmul type RKNN_INT8_MM_INT8_TO_INT32)
input:
rknn_matmul_ctx context the handle of context.
rknn_quant_params params quant params.
float scale get scale for user.
return:
int error code.
*/
int rknn_matmul_get_quant_params(rknn_matmul_ctx ctx, rknn_quant_params* params, float* scale);
/* rknn_matmul_set_dynamic_shape
set the matmul input/output shape. matmul will run under current input shape after rknn_matmul_set_dynamic_shape,
only support M dynamicly now.
input:
rknn_matmul_ctx ctx the handle of context.
rknn_matmul_shape* shape the M,K,N shape of matmul currently
return:
int error code.
*/
int rknn_matmul_set_dynamic_shape(rknn_matmul_ctx ctx, rknn_matmul_shape* shape);
/* 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);
/* rknn_B_normal_layout_to_native_layout
change the B normal layout buffer to native layout buffer
params:
void* B_input B normal layout buffer.
void* B_output B native layout buffer.
int K K
int N N
rknn_matmul_info info matmul info
return:
int error code.
*/
int rknn_B_normal_layout_to_native_layout(void* B_input, void* B_output, int K, int N, rknn_matmul_info* info);
#ifdef __cplusplus
} // extern "C"
#endif
#endif // _RKNN_MATMUL_API_H |