File size: 48,784 Bytes
3fd1a35 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 | #include "ling3/w4_linear.h"
#include "core_workers.h"
#include "ling3/quantization.h"
#include <algorithm>
#include <array>
#include <chrono>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <stdexcept>
#include <string>
#include <utility>
#if defined(__aarch64__)
#include <arm_neon.h>
#endif
#if LING3_WITH_RKNN
#include <rknn_api.h>
#include <rknn_matmul_api.h>
#endif
namespace ling3 {
namespace {
using Clock = std::chrono::steady_clock;
#if LING3_WITH_RKNN
double Milliseconds(Clock::time_point begin, Clock::time_point end) {
return std::chrono::duration<double, std::milli>(end - begin).count();
}
#endif
std::vector<std::pair<int, int>> SplitAligned(int value, int parts, int alignment) {
if (parts < 1 || value % alignment != 0 || value / alignment < parts) {
throw std::invalid_argument("cannot split tensor dimension into aligned ranges");
}
const int blocks = value / alignment;
const int base = blocks / parts;
const int extra = blocks % parts;
std::vector<std::pair<int, int>> ranges;
ranges.reserve(parts);
int offset = 0;
for (int index = 0; index < parts; ++index) {
const int size = (base + (index < extra ? 1 : 0)) * alignment;
ranges.emplace_back(offset, size);
offset += size;
}
return ranges;
}
#if LING3_WITH_RKNN
void CheckRknn(int status, const char * operation) {
if (status != RKNN_SUCC) {
throw std::runtime_error(
std::string(operation) + " failed with RKNN status " + std::to_string(status));
}
}
void PutNativeInt4(std::uint8_t * output, std::size_t index, std::int8_t value) {
const std::uint8_t nibble = static_cast<std::uint8_t>(value) & 0x0FU;
if ((index & 1U) == 0) {
output[index / 2] = static_cast<std::uint8_t>(nibble << 4U);
} else {
output[index / 2] = static_cast<std::uint8_t>(output[index / 2] | nibble);
}
}
#if defined(__aarch64__)
void PackActivation16(
int8x16_t values,
std::uint8_t * high_output,
std::uint8_t * low_output) {
const uint8x16_t mask = vdupq_n_u8(0x0F);
const uint8x16_t high = vandq_u8(
vreinterpretq_u8_s8(vshrq_n_s8(values, 4)), mask);
const uint8x16_t low = veorq_u8(
vandq_u8(vreinterpretq_u8_s8(values), mask), vdupq_n_u8(0x08));
const uint8x8_t high_even = vget_low_u8(vuzp1q_u8(high, high));
const uint8x8_t high_odd = vget_low_u8(vuzp2q_u8(high, high));
const uint8x8_t low_even = vget_low_u8(vuzp1q_u8(low, low));
const uint8x8_t low_odd = vget_low_u8(vuzp2q_u8(low, low));
vst1_u8(high_output, vorr_u8(vshl_n_u8(high_even, 4), high_odd));
vst1_u8(low_output, vorr_u8(vshl_n_u8(low_even, 4), low_odd));
}
#endif
void PackActivationRow(
const std::int8_t * input,
int count,
std::uint8_t * high_output,
std::uint8_t * low_output) {
if ((count & 1) != 0) throw std::invalid_argument("INT4 row width must be even");
int column = 0;
#if defined(__aarch64__)
for (; column + 16 <= count; column += 16) {
PackActivation16(
vld1q_s8(input + column),
high_output + column / 2,
low_output + column / 2);
}
#endif
for (; column < count; column += 2) {
auto nibble = [input, column](int lane, bool high) {
const int value = input[column + lane];
const int code = high
? (value < 0 ? -((-value + 15) / 16) : value / 16)
: ((value & 0x0F) ^ 0x08);
return static_cast<std::uint8_t>(code) & 0x0FU;
};
high_output[column / 2] = static_cast<std::uint8_t>(
(nibble(0, true) << 4U) | nibble(1, true));
low_output[column / 2] = static_cast<std::uint8_t>(
(nibble(0, false) << 4U) | nibble(1, false));
}
}
void PackSignedInt4Row(
const std::int8_t * input,
int count,
std::uint8_t * output) {
if ((count & 1) != 0) throw std::invalid_argument("INT4 row width must be even");
for (int column = 0; column < count; column += 2) {
output[column / 2] = static_cast<std::uint8_t>(
(static_cast<std::uint8_t>(input[column]) & 0x0FU) << 4U |
(static_cast<std::uint8_t>(input[column + 1]) & 0x0FU));
}
}
struct Segment {
int part = 0;
int offset_k = 0;
int size_k = 0;
int offset_n = 0;
int size_n = 0;
int core = 0;
rknn_matmul_ctx context = 0;
rknn_matmul_info info {};
rknn_matmul_io_attr attributes {};
rknn_tensor_mem * a = nullptr;
rknn_tensor_mem * b = nullptr;
rknn_tensor_mem * c = nullptr;
bool owns_a = true;
bool owns_b = true;
Segment() = default;
Segment(const Segment &) = delete;
Segment & operator=(const Segment &) = delete;
Segment(Segment && other) noexcept { *this = std::move(other); }
Segment & operator=(Segment && other) noexcept {
if (this == &other) return *this;
Release();
part = other.part;
offset_k = other.offset_k;
size_k = other.size_k;
offset_n = other.offset_n;
size_n = other.size_n;
core = other.core;
context = std::exchange(other.context, 0);
info = other.info;
attributes = other.attributes;
a = std::exchange(other.a, nullptr);
b = std::exchange(other.b, nullptr);
c = std::exchange(other.c, nullptr);
owns_a = std::exchange(other.owns_a, true);
owns_b = std::exchange(other.owns_b, true);
return *this;
}
~Segment() { Release(); }
void Release() noexcept {
if (context == 0) return;
if (a != nullptr && owns_a) rknn_destroy_mem(context, a);
if (b != nullptr && owns_b) rknn_destroy_mem(context, b);
if (c != nullptr) rknn_destroy_mem(context, c);
rknn_matmul_destroy(context);
context = 0;
a = nullptr;
b = nullptr;
c = nullptr;
owns_a = true;
owns_b = true;
}
};
struct BatchWorkspace {
int rows = 0;
bool w4a4 = false;
std::vector<Segment> segments;
std::vector<std::int8_t> quantized;
std::vector<std::int32_t> accumulator;
std::vector<float> scales;
};
// All batch shapes of one segment execute sequentially. Each keeps its own
// exact-sized FD views, backed by these maximum-sized, same-domain owners.
// Parallel cores and K segments never share these buffers with each other.
struct BatchMemory {
rknn_matmul_ctx context = 0; // Borrowed from the live decode segment.
rknn_tensor_mem * a = nullptr;
rknn_tensor_mem * c = nullptr;
~BatchMemory() {
if (a) rknn_destroy_mem(context, a);
if (c) rknn_destroy_mem(context, c);
}
};
#endif
} // namespace
struct DynamicW4Linear::Impl {
W4LinearConfig config;
std::vector<float> scales;
std::vector<std::int32_t> correction;
std::vector<std::int8_t> quantized;
std::vector<std::int32_t> accumulator;
std::size_t resident_bytes = 0;
bool prefill_w4a4 = false;
#if LING3_WITH_RKNN
std::vector<Segment> segments;
// Reverse destruction order: batch views, backing memory, source contexts.
std::vector<std::unique_ptr<BatchMemory>> batch_memory;
std::array<std::unique_ptr<BatchWorkspace>, 8> batch_workspaces;
#endif
Impl(
W4LinearConfig value,
std::span<const std::byte> weights,
std::span<const float> weight_scales,
std::span<const std::int32_t> weight_correction)
: config(std::move(value)),
scales(weight_scales.begin(), weight_scales.end()),
correction(weight_correction.begin(), weight_correction.end()),
prefill_w4a4(std::getenv("LING3_PREFILL_W4A4") != nullptr) {
Validate(weights);
quantized.resize(config.k);
accumulator.resize(config.n);
#if LING3_WITH_RKNN
Initialize(weights);
#else
(void)weights;
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#endif
}
~Impl() = default;
void Validate(std::span<const std::byte> weights) const {
if (config.k < 1 || config.n < 1 || config.k_splits < 1 ||
config.k % 32 != 0 || config.n % 64 != 0) {
throw std::invalid_argument("W4 linear requires positive K/N, K%32=0 and N%64=0");
}
if (config.cores.empty() || config.cores.size() > 3) {
throw std::invalid_argument("W4 linear requires one to three NPU cores");
}
if (config.iommu_domain_id < 0 || config.iommu_domain_id > 15) {
throw std::invalid_argument("W4 IOMMU domain must be in [0, 15]");
}
auto sorted = config.cores;
std::sort(sorted.begin(), sorted.end());
if (sorted.front() < 0 || sorted.back() > 2 ||
std::adjacent_find(sorted.begin(), sorted.end()) != sorted.end()) {
throw std::invalid_argument("NPU cores must be unique values in [0, 2]");
}
SplitAligned(config.k, config.k_splits, 32);
SplitAligned(config.n, static_cast<int>(config.cores.size()), 64);
const auto expected = static_cast<std::size_t>(config.k) * config.n / 2;
if (weights.size() != expected || scales.size() != static_cast<std::size_t>(config.n) ||
correction.size() != static_cast<std::size_t>(config.n)) {
throw std::invalid_argument("W4 weight, scale, or correction size is inconsistent");
}
for (float scale : scales) {
if (!(scale > 0.0F) || !std::isfinite(scale)) {
throw std::invalid_argument("W4 weight scale must be finite and positive");
}
}
}
#if LING3_WITH_RKNN
void Initialize(std::span<const std::byte> weights) {
const auto k_ranges = SplitAligned(config.k, config.k_splits, 32);
const auto n_ranges = SplitAligned(config.n, static_cast<int>(config.cores.size()), 64);
segments.reserve(k_ranges.size() * n_ranges.size());
for (std::size_t part = 0; part < k_ranges.size(); ++part) {
for (std::size_t lane = 0; lane < n_ranges.size(); ++lane) {
Segment segment;
segment.part = static_cast<int>(part);
segment.offset_k = k_ranges[part].first;
segment.size_k = k_ranges[part].second;
segment.offset_n = n_ranges[lane].first;
segment.size_n = n_ranges[lane].second;
segment.core = config.cores[lane];
InitializeSegment(segment, weights);
resident_bytes += segment.attributes.B.size;
segments.push_back(std::move(segment));
}
}
}
void InitializeSegment(Segment & segment, std::span<const std::byte> weights) {
segment.info.M = 2;
segment.info.K = segment.size_k;
segment.info.N = segment.size_n;
segment.info.type = RKNN_INT4_MM_INT4_TO_INT16;
segment.info.B_layout = RKNN_MM_LAYOUT_NATIVE;
segment.info.B_quant_type = RKNN_QUANT_TYPE_PER_LAYER_SYM;
segment.info.AC_layout = RKNN_MM_LAYOUT_NATIVE;
segment.info.AC_quant_type = RKNN_QUANT_TYPE_PER_LAYER_SYM;
segment.info.iommu_domain_id = config.iommu_domain_id;
CheckRknn(
rknn_matmul_create(&segment.context, &segment.info, &segment.attributes),
"rknn_matmul_create W4");
CheckRknn(
rknn_matmul_set_core_mask(
segment.context, static_cast<rknn_core_mask>(1U << segment.core)),
"rknn_matmul_set_core_mask W4");
const auto expected_a = static_cast<std::size_t>(segment.size_k);
const auto expected_b = static_cast<std::size_t>(segment.size_k) * segment.size_n / 2;
const auto expected_c = static_cast<std::size_t>(2) * segment.size_n * sizeof(std::int16_t);
if (segment.attributes.A.type != RKNN_TENSOR_INT4 ||
segment.attributes.B.type != RKNN_TENSOR_INT4 ||
segment.attributes.C.type != RKNN_TENSOR_INT16 ||
segment.attributes.A.size != expected_a ||
segment.attributes.B.size != expected_b ||
segment.attributes.C.size != expected_c ||
segment.attributes.A.n_dims != 3 || segment.attributes.B.n_dims != 4 ||
segment.attributes.C.n_dims != 3) {
throw std::runtime_error("RKNN returned unexpected W4 tensor attributes");
}
segment.a = rknn_create_mem2(
segment.context, segment.attributes.A.size, RKNN_FLAG_MEMORY_CACHEABLE);
segment.b = rknn_create_mem2(
segment.context, segment.attributes.B.size, RKNN_FLAG_MEMORY_CACHEABLE);
segment.c = rknn_create_mem2(
segment.context, segment.attributes.C.size, RKNN_FLAG_MEMORY_CACHEABLE);
if (segment.a == nullptr || segment.b == nullptr || segment.c == nullptr) {
throw std::runtime_error("rknn_create_mem2 failed for W4 linear");
}
std::memset(segment.b->virt_addr, 0, segment.attributes.B.size);
const int sub_n = static_cast<int>(segment.attributes.B.dims[2]);
const int sub_k = static_cast<int>(segment.attributes.B.dims[3]);
const int n_blocks = (segment.size_n + sub_n - 1) / sub_n;
const int k_blocks = (segment.size_k + sub_k - 1) / sub_k;
auto * native = static_cast<std::uint8_t *>(segment.b->virt_addr);
for (int n_block = 0; n_block < n_blocks; ++n_block) {
for (int k_block = 0; k_block < k_blocks; ++k_block) {
for (int inner_n = 0; inner_n < sub_n; ++inner_n) {
const int local_n = n_block * sub_n + inner_n;
for (int inner_k = 0; inner_k < sub_k; ++inner_k) {
const int local_k = k_block * sub_k + inner_k;
const auto code = local_k < segment.size_k && local_n < segment.size_n
? DecodeInt4LowFirst(
weights,
static_cast<std::size_t>(segment.offset_k + local_k) * config.n +
segment.offset_n + local_n)
: static_cast<std::int8_t>(0);
const auto output_index =
((static_cast<std::size_t>(n_block) * k_blocks + k_block) * sub_n +
inner_n) * sub_k + inner_k;
PutNativeInt4(native, output_index, code);
}
}
}
}
CheckRknn(
rknn_mem_sync(segment.context, segment.b, RKNN_MEMORY_SYNC_TO_DEVICE),
"sync W4 weight");
CheckRknn(
rknn_matmul_set_io_mem(segment.context, segment.a, &segment.attributes.A),
"bind W4 input");
CheckRknn(
rknn_matmul_set_io_mem(segment.context, segment.b, &segment.attributes.B),
"bind W4 weight");
CheckRknn(
rknn_matmul_set_io_mem(segment.context, segment.c, &segment.attributes.C),
"bind W4 output");
}
static int BatchRows(std::size_t rows) {
for (const int candidate : {1, 2, 4, 8, 16, 32, 64, 128}) {
if (rows <= static_cast<std::size_t>(candidate)) return candidate;
}
throw std::invalid_argument("W4 batch supports at most 128 rows");
}
static std::size_t BatchSlot(int rows) {
std::size_t slot = 0;
while ((1 << slot) < rows) ++slot;
return slot;
}
void InitializeBatchMemory() {
if (!batch_memory.empty()) return;
std::vector<std::unique_ptr<BatchMemory>> owners;
owners.reserve(segments.size());
const std::size_t matrix_rows = prefill_w4a4 ? 128 : 256;
for (const auto & segment : segments) {
auto memory = std::make_unique<BatchMemory>();
memory->context = segment.context;
memory->a = rknn_create_mem2(segment.context,
matrix_rows * segment.size_k / 2, RKNN_FLAG_MEMORY_CACHEABLE);
memory->c = rknn_create_mem2(segment.context,
matrix_rows * segment.size_n * sizeof(std::int16_t), RKNN_FLAG_MEMORY_CACHEABLE);
if (!memory->a || !memory->c)
throw std::runtime_error("rknn_create_mem2 failed for shared W4 batch memory");
owners.push_back(std::move(memory));
}
batch_memory = std::move(owners);
}
BatchWorkspace & InitializeBatch(std::size_t active_rows, bool indexed_input) {
const int rows = BatchRows(active_rows);
auto & stored = batch_workspaces[BatchSlot(rows)];
if (stored) {
if (!indexed_input && stored->quantized.empty())
stored->quantized.resize(static_cast<std::size_t>(rows) * config.k);
return *stored;
}
auto workspace = std::make_unique<BatchWorkspace>();
workspace->rows = rows;
workspace->w4a4 = prefill_w4a4 && rows >= 2;
if (!indexed_input)
workspace->quantized.resize(static_cast<std::size_t>(rows) * config.k);
// GatherBatch writes the final K part straight to the float output.
// Only split-K matrices need storage for preceding partial sums.
if (config.k_splits > 1)
workspace->accumulator.resize(static_cast<std::size_t>(rows) * config.n);
workspace->scales.resize(rows, 1.0F);
workspace->segments.reserve(segments.size());
InitializeBatchMemory();
for (std::size_t segment_index = 0; segment_index < segments.size(); ++segment_index) {
const auto & source = segments[segment_index];
Segment segment;
segment.part = source.part;
segment.offset_k = source.offset_k;
segment.size_k = source.size_k;
segment.offset_n = source.offset_n;
segment.size_n = source.size_n;
segment.core = source.core;
segment.info = source.info;
segment.info.M = workspace->w4a4 ? rows : 2 * rows;
CheckRknn(
rknn_matmul_create(&segment.context, &segment.info, &segment.attributes),
"rknn_matmul_create W4 batch");
CheckRknn(
rknn_matmul_set_core_mask(
segment.context, static_cast<rknn_core_mask>(1U << segment.core)),
"rknn_matmul_set_core_mask W4 batch");
const auto matrix_rows = workspace->w4a4 ? rows : 2 * rows;
const auto expected_a = static_cast<std::size_t>(matrix_rows) *
segment.size_k / 2;
const auto expected_c = static_cast<std::size_t>(matrix_rows) *
segment.size_n * sizeof(std::int16_t);
if (segment.attributes.A.type != RKNN_TENSOR_INT4 ||
segment.attributes.B.type != RKNN_TENSOR_INT4 ||
segment.attributes.C.type != RKNN_TENSOR_INT16 ||
segment.attributes.A.size != expected_a ||
segment.attributes.B.size != source.attributes.B.size ||
segment.attributes.C.size != expected_c) {
throw std::runtime_error("RKNN returned unexpected W4 batch attributes");
}
const auto & owner = *batch_memory[segment_index];
if (segment.attributes.A.size > owner.a->size || segment.attributes.C.size > owner.c->size)
throw std::runtime_error("W4 batch view exceeds its shared backing memory");
segment.a = rknn_create_mem_from_fd(segment.context, owner.a->fd,
owner.a->virt_addr, segment.attributes.A.size, 0);
segment.c = rknn_create_mem_from_fd(segment.context, owner.c->fd,
owner.c->virt_addr, segment.attributes.C.size, 0);
if (segment.a == nullptr || segment.c == nullptr) {
throw std::runtime_error("rknn_create_mem_from_fd failed for W4 batch view");
}
if (segment.a->size != segment.attributes.A.size || segment.c->size != segment.attributes.C.size)
throw std::runtime_error("W4 batch view changed the requested synchronization size");
segment.b = source.b;
segment.owns_b = false;
CheckRknn(
rknn_matmul_set_io_mem(segment.context, segment.a, &segment.attributes.A),
"bind W4 batch input");
CheckRknn(
rknn_matmul_set_io_mem(segment.context, segment.b, &segment.attributes.B),
"bind shared W4 batch weight");
CheckRknn(
rknn_matmul_set_io_mem(segment.context, segment.c, &segment.attributes.C),
"bind W4 batch output");
workspace->segments.push_back(std::move(segment));
}
stored = std::move(workspace);
return *stored;
}
void StageInput(std::span<const float> input, float & input_scale) {
input_scale = QuantizeSymmetricInt8(input, quantized).scale;
for (int part = 0; part < config.k_splits; ++part) {
const auto first = std::find_if(segments.begin(), segments.end(), [part](const Segment & item) {
return item.part == part;
});
if (first == segments.end()) throw std::runtime_error("W4 K split is missing");
const int sub_k = static_cast<int>(first->attributes.A.dims[2]);
if (sub_k < 1 || first->size_k % sub_k != 0) {
throw std::runtime_error("RKNN native W4 input has a partial K block");
}
auto * packed = static_cast<std::uint8_t *>(first->a->virt_addr);
for (int block = 0; block < first->size_k / sub_k; ++block) {
const auto high_index = static_cast<std::size_t>(block * 2) * sub_k;
const auto low_index = static_cast<std::size_t>(block * 2 + 1) * sub_k;
PackActivationRow(
quantized.data() + first->offset_k + block * sub_k,
sub_k,
packed + high_index / 2,
packed + low_index / 2);
}
for (auto & segment : segments) {
if (segment.part != part || &segment == &*first) continue;
if (segment.attributes.A.size != first->attributes.A.size) {
throw std::runtime_error("RKNN W4 contexts disagree on input size");
}
std::memcpy(segment.a->virt_addr, first->a->virt_addr, first->attributes.A.size);
}
}
}
void StageBatch(
BatchWorkspace & workspace,
std::span<const float> input,
std::size_t active_rows) {
std::fill_n(workspace.quantized.begin(), active_rows * config.k, 0);
std::fill_n(workspace.scales.begin(), active_rows, 1.0F);
for (std::size_t row = 0; row < active_rows; ++row) {
const auto row_input = input.subspan(
row * config.k, config.k);
auto row_quantized = std::span<std::int8_t>(workspace.quantized).subspan(
row * config.k, config.k);
workspace.scales[row] = workspace.w4a4
? QuantizeSymmetricInt4(row_input, row_quantized).scale
: QuantizeSymmetricInt8(row_input, row_quantized).scale;
}
PackBatch(workspace, active_rows, workspace.quantized, {});
}
void PackBatch(
BatchWorkspace & workspace,
std::size_t active_rows,
std::span<const std::int8_t> quantized,
std::span<const std::size_t> row_indices) {
const int packed_rows = workspace.w4a4 ? workspace.rows : 2 * workspace.rows;
for (int part = 0; part < config.k_splits; ++part) {
const auto first = std::find_if(
workspace.segments.begin(), workspace.segments.end(),
[part](const Segment & item) { return item.part == part; });
if (first == workspace.segments.end()) {
throw std::runtime_error("W4 batch K split is missing");
}
const int sub_k = static_cast<int>(first->attributes.A.dims[2]);
if (sub_k < 1 || first->size_k % sub_k != 0) {
throw std::runtime_error("RKNN native W4 batch input has a partial K block");
}
auto * packed = static_cast<std::uint8_t *>(first->a->virt_addr);
for (int block = 0; block < first->size_k / sub_k; ++block) {
for (int row = 0; row < workspace.rows; ++row) {
const auto element = static_cast<std::size_t>(
block * packed_rows + (workspace.w4a4 ? row : 2 * row)) * sub_k;
if (static_cast<std::size_t>(row) >= active_rows) {
std::memset(packed + element / 2, 0, sub_k / 2);
if (!workspace.w4a4) {
std::memset(packed + (element + sub_k) / 2, 0x88, sub_k / 2);
}
continue;
}
const std::size_t source_row = row_indices.empty() ? row : row_indices[row];
const auto * source = quantized.data() +
source_row * config.k + first->offset_k + block * sub_k;
if (workspace.w4a4) {
PackSignedInt4Row(source, sub_k, packed + element / 2);
} else {
PackActivationRow(
source, sub_k, packed + element / 2,
packed + (element + sub_k) / 2);
}
}
}
for (auto & segment : workspace.segments) {
if (segment.part != part || &segment == &*first) continue;
if (segment.attributes.A.size != first->attributes.A.size) {
throw std::runtime_error("RKNN W4 batch contexts disagree on input size");
}
std::memcpy(segment.a->virt_addr, first->a->virt_addr, first->attributes.A.size);
}
}
}
void SyncInputs() {
for (auto & segment : segments) {
CheckRknn(
rknn_mem_sync(segment.context, segment.a, RKNN_MEMORY_SYNC_TO_DEVICE),
"sync W4 input");
}
}
void SyncBatchInputs(BatchWorkspace & workspace) {
for (auto & segment : workspace.segments) {
CheckRknn(
rknn_mem_sync(segment.context, segment.a, RKNN_MEMORY_SYNC_TO_DEVICE),
"sync W4 batch input");
}
}
void BindBatchWeights(BatchWorkspace & workspace, const Impl & source) {
if (config.k != source.config.k || config.n != source.config.n ||
config.k_splits != source.config.k_splits ||
config.iommu_domain_id != source.config.iommu_domain_id ||
workspace.segments.size() != source.segments.size()) {
throw std::invalid_argument("W4 batch source has an incompatible shape");
}
for (std::size_t index = 0; index < workspace.segments.size(); ++index) {
auto & target = workspace.segments[index];
const auto & weight = source.segments[index];
if (target.attributes.B.size != weight.attributes.B.size || weight.b == nullptr) {
throw std::invalid_argument("W4 batch source has incompatible native weights");
}
if (target.b == weight.b) continue;
target.b = weight.b;
CheckRknn(
rknn_matmul_set_io_mem(target.context, target.b, &target.attributes.B),
"rebind W4 batch weight");
}
}
void RunWorkers(std::vector<Segment> & active_segments) {
if (config.cores.size() == 1) {
for (auto & segment : active_segments) {
CheckRknn(rknn_matmul_run(segment.context), "rknn_matmul_run W4");
}
return;
}
CoreWorkers::Instance().Run(config.cores, [&active_segments](int core) {
for (auto & segment : active_segments) {
if (segment.core == core) {
CheckRknn(rknn_matmul_run(segment.context), "rknn_matmul_run W4");
}
}
});
}
void RunWorkers() { RunWorkers(segments); }
void Gather(float input_scale, std::span<float> output) {
for (auto & segment : segments) {
CheckRknn(
rknn_mem_sync(segment.context, segment.c, RKNN_MEMORY_SYNC_FROM_DEVICE),
"sync W4 output");
const auto * source = static_cast<const std::int16_t *>(segment.c->virt_addr);
const int sub_n = static_cast<int>(segment.attributes.C.dims[2]);
const int n_blocks = static_cast<int>(segment.attributes.C.dims[0]);
for (int block = 0; block < n_blocks; ++block) {
const int global_n = segment.offset_n + block * sub_n;
auto * destination = accumulator.data() + global_n;
const auto * high = source + static_cast<std::size_t>(block * 2) * sub_n;
const auto * low = source + static_cast<std::size_t>(block * 2 + 1) * sub_n;
const int columns = std::min(sub_n, segment.size_n - block * sub_n);
int column = 0;
#if defined(__aarch64__)
for (; column + 8 <= columns; column += 8) {
int32x4_t value0 = segment.part == 0
? vld1q_s32(correction.data() + global_n + column)
: vld1q_s32(destination + column);
int32x4_t value1 = segment.part == 0
? vld1q_s32(correction.data() + global_n + column + 4)
: vld1q_s32(destination + column + 4);
const int16x8_t high8 = vld1q_s16(high + column);
const int16x8_t low8 = vld1q_s16(low + column);
value0 = vmlal_n_s16(value0, vget_low_s16(high8), 16);
value1 = vmlal_n_s16(value1, vget_high_s16(high8), 16);
value0 = vaddw_s16(value0, vget_low_s16(low8));
value1 = vaddw_s16(value1, vget_high_s16(low8));
vst1q_s32(destination + column, value0);
vst1q_s32(destination + column + 4, value1);
}
#endif
for (; column < columns; ++column) {
const auto initial = segment.part == 0
? correction[global_n + column]
: destination[column];
destination[column] = initial + 16 * static_cast<std::int32_t>(high[column]) +
static_cast<std::int32_t>(low[column]);
}
}
}
DequantizePerChannel(accumulator, input_scale, scales, output);
}
void GatherBatch(
BatchWorkspace & workspace,
std::size_t active_rows,
const Impl & source_weights,
std::span<float> output) {
const int packed_rows = workspace.w4a4 ? workspace.rows : 2 * workspace.rows;
for (auto & segment : workspace.segments) {
CheckRknn(
rknn_mem_sync(segment.context, segment.c, RKNN_MEMORY_SYNC_FROM_DEVICE),
"sync W4 batch output");
}
// Expert runners already execute inside CoreWorkers. Only main-thread
// multi-core projections may dispatch the pool; each worker owns rows
// through all K parts, preserving accumulation and multiplication order.
const auto gather_rows = [&](std::size_t first, std::size_t last) {
for (auto & segment : workspace.segments) {
const bool final_part = segment.part == config.k_splits - 1;
const auto * source = static_cast<const std::int16_t *>(segment.c->virt_addr);
const int sub_n = static_cast<int>(segment.attributes.C.dims[2]);
const int n_blocks = static_cast<int>(segment.attributes.C.dims[0]);
for (int block = 0; block < n_blocks; ++block) {
const int global_n = segment.offset_n + block * sub_n;
const int columns = std::min(sub_n, segment.size_n - block * sub_n);
for (std::size_t row = first; row < last; ++row) {
// Avoid offset-pointer arithmetic on empty storage.
auto * destination = config.k_splits > 1
? workspace.accumulator.data() + row * config.n + global_n
: nullptr;
auto * result = output.data() + row * config.n + global_n;
const auto source_row = static_cast<std::size_t>(block) * packed_rows +
(workspace.w4a4 ? row : 2 * row);
const auto * high = source + source_row * sub_n;
const auto * low = workspace.w4a4 ? nullptr : high + sub_n;
int column = 0;
#if defined(__aarch64__)
for (; column + 8 <= columns; column += 8) {
int32x4_t value0 = segment.part == 0
? (workspace.w4a4
? vdupq_n_s32(0)
: vld1q_s32(
source_weights.correction.data() + global_n + column))
: vld1q_s32(destination + column);
int32x4_t value1 = segment.part == 0
? (workspace.w4a4
? vdupq_n_s32(0)
: vld1q_s32(
source_weights.correction.data() + global_n + column + 4))
: vld1q_s32(destination + column + 4);
const int16x8_t high8 = vld1q_s16(high + column);
if (workspace.w4a4) {
value0 = vaddw_s16(value0, vget_low_s16(high8));
value1 = vaddw_s16(value1, vget_high_s16(high8));
} else {
const int16x8_t low8 = vld1q_s16(low + column);
value0 = vmlal_n_s16(value0, vget_low_s16(high8), 16);
value1 = vmlal_n_s16(value1, vget_high_s16(high8), 16);
value0 = vaddw_s16(value0, vget_low_s16(low8));
value1 = vaddw_s16(value1, vget_high_s16(low8));
}
if (final_part) {
// Preserve DequantizePerChannel's multiplication order,
// but avoid writing and rereading the final accumulator.
const float32x4_t activation = vdupq_n_f32(workspace.scales[row]);
vst1q_f32(result + column, vmulq_f32(
vmulq_f32(vcvtq_f32_s32(value0), activation),
vld1q_f32(source_weights.scales.data() + global_n + column)));
vst1q_f32(result + column + 4, vmulq_f32(
vmulq_f32(vcvtq_f32_s32(value1), activation),
vld1q_f32(source_weights.scales.data() + global_n + column + 4)));
} else {
vst1q_s32(destination + column, value0);
vst1q_s32(destination + column + 4, value1);
}
}
#endif
for (; column < columns; ++column) {
std::int32_t value;
if (workspace.w4a4) {
const auto initial = segment.part == 0 ? 0 : destination[column];
value =
initial + static_cast<std::int32_t>(high[column]);
} else {
const auto initial = segment.part == 0
? source_weights.correction[global_n + column]
: destination[column];
value = initial +
16 * static_cast<std::int32_t>(high[column]) +
static_cast<std::int32_t>(low[column]);
}
if (final_part) {
result[column] = static_cast<float>(value) * workspace.scales[row] *
source_weights.scales[global_n + column];
} else {
destination[column] = value;
}
}
}
}
}
};
if (config.cores.size() > 1 && active_rows * config.n >= 65536 &&
std::getenv("LING3_DISABLE_PARALLEL_GATHER") == nullptr) {
constexpr std::array<int, 4> workers {0, 1, 2, 3};
CoreWorkers::Instance().Run(workers, [&](int worker) {
gather_rows(active_rows * worker / 4, active_rows * (worker + 1) / 4);
});
} else {
gather_rows(0, active_rows);
}
}
#endif
W4RunTimings Run(std::span<const float> input, std::span<float> output) {
if (input.size() != static_cast<std::size_t>(config.k) ||
output.size() != static_cast<std::size_t>(config.n)) {
throw std::invalid_argument("W4 input or output has the wrong size");
}
#if !LING3_WITH_RKNN
(void)input;
(void)output;
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
W4RunTimings timings;
const auto begin = Clock::now();
float input_scale = 1.0F;
StageInput(input, input_scale);
const auto staged = Clock::now();
SyncInputs();
const auto synced = Clock::now();
RunWorkers();
const auto executed = Clock::now();
Gather(input_scale, output);
const auto end = Clock::now();
timings.quantize_pack_ms = Milliseconds(begin, staged);
timings.input_sync_ms = Milliseconds(staged, synced);
timings.npu_ms = Milliseconds(synced, executed);
timings.gather_ms = Milliseconds(executed, end);
timings.total_ms = Milliseconds(begin, end);
timings.input_scale = input_scale;
return timings;
#endif
}
W4RunTimings RunBatch(
std::span<const float> input,
std::size_t rows,
const Impl & source_weights,
std::span<float> output,
std::span<const std::int8_t> quantized = {},
std::span<const float> input_scales = {},
std::span<const std::size_t> row_indices = {}) {
const bool indexed = !row_indices.empty();
if (rows < 1 || rows > 128 || (!indexed && input.size() != rows * config.k) ||
output.size() != rows * config.n) {
throw std::invalid_argument("W4 batch input, rows, or output has the wrong size");
}
if (indexed) {
if (row_indices.size() != rows || input_scales.empty() ||
quantized.size() / config.k != input_scales.size() ||
quantized.size() % config.k != 0) {
throw std::invalid_argument("W4 indexed activation table has the wrong size");
}
if (prefill_w4a4 && rows >= 2) {
throw std::invalid_argument("indexed INT8 activations require W4A8 batch mode");
}
for (const auto row : row_indices) {
if (row >= input_scales.size() || !(input_scales[row] > 0.0F) ||
!std::isfinite(input_scales[row])) {
throw std::invalid_argument("W4 indexed activation row or scale is invalid");
}
}
}
#if !LING3_WITH_RKNN
(void)input;
(void)rows;
(void)source_weights;
(void)output;
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
auto & workspace = InitializeBatch(rows, indexed);
BindBatchWeights(workspace, source_weights);
W4RunTimings timings;
const auto begin = Clock::now();
if (indexed) {
for (std::size_t row = 0; row < rows; ++row) {
workspace.scales[row] = input_scales[row_indices[row]];
}
PackBatch(workspace, rows, quantized, row_indices);
} else {
StageBatch(workspace, input, rows);
}
const auto staged = Clock::now();
SyncBatchInputs(workspace);
const auto synced = Clock::now();
RunWorkers(workspace.segments);
const auto executed = Clock::now();
GatherBatch(workspace, rows, source_weights, output);
const auto end = Clock::now();
timings.quantize_pack_ms = Milliseconds(begin, staged);
timings.input_sync_ms = Milliseconds(staged, synced);
timings.npu_ms = Milliseconds(synced, executed);
timings.gather_ms = Milliseconds(executed, end);
timings.total_ms = Milliseconds(begin, end);
return timings;
#endif
}
W4RunTimings RunBatch32(std::span<const float> input, std::span<float> output) {
return RunBatch(input, 32, *this, output);
}
void PrepareBatch(std::size_t rows, bool indexed_input) {
if (rows < 1 || rows > 128) {
throw std::invalid_argument("W4 batch rows must be in [1, 128]");
}
#if !LING3_WITH_RKNN
(void)rows;
(void)indexed_input;
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
if (indexed_input && prefill_w4a4 && rows >= 2)
throw std::invalid_argument("indexed INT8 activations require W4A8 batch mode");
auto & workspace = InitializeBatch(rows, indexed_input);
BindBatchWeights(workspace, *this);
#endif
}
float PrepareInput(std::span<const float> input) {
if (input.size() != static_cast<std::size_t>(config.k)) {
throw std::invalid_argument("W4 input has the wrong size");
}
#if !LING3_WITH_RKNN
(void)input;
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
float input_scale = 1.0F;
StageInput(input, input_scale);
SyncInputs();
return input_scale;
#endif
}
W4RunTimings RunPrepared(float input_scale, std::span<float> output) {
if (output.size() != static_cast<std::size_t>(config.n) ||
!(input_scale > 0.0F) || !std::isfinite(input_scale)) {
throw std::invalid_argument("prepared W4 scale or output has the wrong value");
}
#if !LING3_WITH_RKNN
(void)input_scale;
(void)output;
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
W4RunTimings timings;
const auto begin = Clock::now();
RunWorkers();
const auto executed = Clock::now();
Gather(input_scale, output);
const auto end = Clock::now();
timings.npu_ms = Milliseconds(begin, executed);
timings.gather_ms = Milliseconds(executed, end);
timings.total_ms = Milliseconds(begin, end);
timings.input_scale = input_scale;
return timings;
#endif
}
void ShareInputFrom(Impl & owner) {
if (this == &owner) return;
if (config.k != owner.config.k || config.k_splits != owner.config.k_splits) {
throw std::invalid_argument("shared W4 inputs require matching K partitions");
}
#if !LING3_WITH_RKNN
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
if (segments.size() != owner.segments.size()) {
throw std::invalid_argument("shared W4 inputs require matching K partitions");
}
for (std::size_t index = 0; index < segments.size(); ++index) {
auto & target = segments[index];
auto & source = owner.segments[index];
if (!source.owns_a || source.a == nullptr || target.a == nullptr ||
source.attributes.A.size != target.attributes.A.size ||
source.offset_k != target.offset_k || source.size_k != target.size_k) {
throw std::runtime_error("shared W4 input memory is incompatible");
}
if (target.owns_a) {
CheckRknn(
rknn_destroy_mem(target.context, target.a),
"destroy private W4 input before sharing");
}
target.a = source.a;
target.owns_a = false;
CheckRknn(
rknn_matmul_set_io_mem(target.context, target.a, &target.attributes.A),
"bind shared W4 input");
}
#endif
}
void SetSingleCore(int core) {
if (config.cores.size() != 1 || core < 0 || core > 2) {
throw std::invalid_argument("SetSingleCore requires one core in [0, 2]");
}
if (config.cores[0] == core) return;
#if !LING3_WITH_RKNN
throw std::runtime_error("DynamicW4Linear requires a build with RKNN support");
#else
for (auto & segment : segments) {
CheckRknn(
rknn_matmul_set_core_mask(
segment.context, static_cast<rknn_core_mask>(1U << core)),
"rknn_matmul_set_core_mask W4 rebind");
segment.core = core;
}
for (auto & workspace : batch_workspaces) {
if (!workspace) continue;
for (auto & segment : workspace->segments) {
CheckRknn(
rknn_matmul_set_core_mask(
segment.context, static_cast<rknn_core_mask>(1U << core)),
"rknn_matmul_set_core_mask W4 batch rebind");
segment.core = core;
}
}
config.cores[0] = core;
#endif
}
};
DynamicW4Linear::DynamicW4Linear(
W4LinearConfig config,
std::span<const std::byte> packed_weights,
std::span<const float> weight_scales,
std::span<const std::int32_t> correction)
: impl_(std::make_unique<Impl>(
std::move(config), packed_weights, weight_scales, correction)) {}
DynamicW4Linear::~DynamicW4Linear() = default;
DynamicW4Linear::DynamicW4Linear(DynamicW4Linear &&) noexcept = default;
DynamicW4Linear & DynamicW4Linear::operator=(DynamicW4Linear &&) noexcept = default;
W4RunTimings DynamicW4Linear::Run(std::span<const float> input, std::span<float> output) {
return impl_->Run(input, output);
}
W4RunTimings DynamicW4Linear::RunBatch(
std::span<const float> input,
std::size_t rows,
std::span<float> output) {
return impl_->RunBatch(input, rows, *impl_, output);
}
W4RunTimings DynamicW4Linear::RunBatchWithWeights(
std::span<const float> input,
std::size_t rows,
const DynamicW4Linear & weights,
std::span<float> output) {
return impl_->RunBatch(input, rows, *weights.impl_, output);
}
W4RunTimings DynamicW4Linear::RunBatch32(
std::span<const float> input, std::span<float> output) {
return impl_->RunBatch32(input, output);
}
W4RunTimings DynamicW4Linear::RunBatchQuantizedRows(
std::span<const std::int8_t> input,
std::span<const float> input_scales,
std::span<const std::size_t> row_indices,
const DynamicW4Linear & weights,
std::span<float> output) {
return impl_->RunBatch({}, row_indices.size(), *weights.impl_, output,
input, input_scales, row_indices);
}
void DynamicW4Linear::PrepareBatch(std::size_t rows, bool indexed_input) {
impl_->PrepareBatch(rows, indexed_input);
}
float DynamicW4Linear::PrepareInput(std::span<const float> input) {
return impl_->PrepareInput(input);
}
W4RunTimings DynamicW4Linear::RunPrepared(float input_scale, std::span<float> output) {
return impl_->RunPrepared(input_scale, output);
}
void DynamicW4Linear::ShareInputFrom(DynamicW4Linear & owner) {
impl_->ShareInputFrom(*owner.impl_);
}
void DynamicW4Linear::SetSingleCore(int core) { impl_->SetSingleCore(core); }
const W4LinearConfig & DynamicW4Linear::config() const noexcept { return impl_->config; }
std::size_t DynamicW4Linear::resident_weight_bytes() const noexcept { return impl_->resident_bytes; }
} // namespace ling3
|