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gridwise_gemm_xdl_cshuffle_v3.hpp

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1 // SPDX-License-Identifier: MIT

2 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.

3 

4 #pragma once

5 

6 #include "ck/utility/common_header.hpp"

7 #include "ck/utility/env.hpp"

8 #include "ck/tensor_description/multi_index_transform_helper.hpp"

9 #include "ck/tensor_description/tensor_descriptor.hpp"

10 #include "ck/tensor_description/tensor_descriptor_helper.hpp"

11 #include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"

12 #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_selector.hpp"

13 #include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"

14 #include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"

15 #include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"

16 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

17 

18 namespace ck {

19 

20 // Currently we do not have a elegant way to put single lds buffer & double lds buffer pipe in same

21 // kernel function Blockers:

22 // 1. Two separted declaration of __shared__ pointer is the key to make sure data access operate on

23 // two lds chunks.

24 // 2. Occupied __shared__ won't release until whole shader end, a.k.a AB and C may not use same lds

25 // buffer when we declare __shared__ inside blkgemmpipe

26 template <typename GridwiseGemm,

27  bool HasMainKBlockLoop,

28  InMemoryDataOperationEnum CGlobalMemoryDataOperation,

29  index_t MinimumOccupancy = 1,

30  TailNumber TailNum = TailNumber::Full>

31 __global__ void

32 #if CK_USE_LAUNCH_BOUNDS

33  __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)

34 #endif

35  // __attribute__((amdgpu_waves_per_eu(1, 1)))

36  kernel_gemm_xdl_cshuffle_v3(typename GridwiseGemm::Argument karg)

37 {

38 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))

39  __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];

40 

41  auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);

42 

43  GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(

44  karg.p_a_grid + splitk_batch_offset.a_k_split_offset,

45  karg.p_b_grid + splitk_batch_offset.b_k_split_offset,

46  karg.p_c_grid + splitk_batch_offset.c_reduce_offset,

47  p_shared,

48  karg);

49 #else

50  ignore = karg;

51 #endif // end of if (defined(__gfx9__))

52 }

53 

54 template <typename GridwiseGemm,

55  bool HasMainKBlockLoop,

56  InMemoryDataOperationEnum CGlobalMemoryDataOperation,

57  index_t MinimumOccupancy = 1,

58  TailNumber TailNum = TailNumber::Full>

59 __global__ void

60 #if CK_USE_LAUNCH_BOUNDS

61  __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)

62 #endif

63  // __attribute__((amdgpu_waves_per_eu(1, 1)))

64  kernel_gemm_xdl_cshuffle_v3_2lds(typename GridwiseGemm::Argument karg)

65 {

66 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))

67  // Pass two lds pointer is the key to tell compiler that ds_read/write

68  // operate on different lds chunk at same time without order dependecy

69  __shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];

70  __shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];

71 

72  auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);

73 

74  GridwiseGemm::template Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(

75  karg.p_a_grid + splitk_batch_offset.a_k_split_offset,

76  karg.p_b_grid + splitk_batch_offset.b_k_split_offset,

77  karg.p_c_grid + splitk_batch_offset.c_reduce_offset,

78  p_shared_0,

79  p_shared_1,

80  karg);

81 #else

82  ignore = karg;

83 #endif // end of if (defined(__gfx9__))

84 }

85 

191 template <typename ALayout,

192  typename BLayout,

193  typename CLayout,

194  typename ADataType,

195  typename BDataType,

196  typename AccDataType,

197  typename CShuffleDataType,

198  typename CDataType,

199  typename AElementwiseOperation,

200  typename BElementwiseOperation,

201  typename CElementwiseOperation,

202  tensor_operation::device::GemmSpecialization GemmSpec,

203  index_t BlockSize,

204  index_t MPerBlock,

205  index_t NPerBlock,

206  index_t KPerBlock,

207  index_t AK1Value,

208  index_t BK1Value,

209  index_t MPerXdl,

210  index_t NPerXdl,

211  index_t MXdlPerWave,

212  index_t NXdlPerWave,

213  typename ABlockTransferThreadClusterLengths_AK0_M_AK1,

214  typename ABlockTransferThreadClusterArrangeOrder,

215  typename ABlockTransferSrcAccessOrder,

216  index_t ABlockTransferSrcVectorDim,

217  index_t ABlockTransferSrcScalarPerVector,

218  index_t ABlockTransferDstScalarPerVector_AK1,

219  bool AThreadTransferSrcResetCoordinateAfterRun,

220  index_t ABlockLdsExtraM,

221  typename BBlockTransferThreadClusterLengths_BK0_N_BK1,

222  typename BBlockTransferThreadClusterArrangeOrder,

223  typename BBlockTransferSrcAccessOrder,

224  index_t BBlockTransferSrcVectorDim,

225  index_t BBlockTransferSrcScalarPerVector,

226  index_t BBlockTransferDstScalarPerVector_BK1,

227  bool BThreadTransferSrcResetCoordinateAfterRun,

228  index_t BBlockLdsExtraN,

229  index_t CShuffleMXdlPerWavePerShuffle,

230  index_t CShuffleNXdlPerWavePerShuffle,

231  typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,

232  index_t CShuffleBlockTransferScalarPerVector_NPerBlock,

233  BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,

234  BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v4,

235  typename ComputeTypeA = CDataType,

236  typename ComputeTypeB = ComputeTypeA,

237  bool PermuteA = false,

238  bool PermuteB = false,

239  bool DoElementwiseBeforeCShuffle = false>

240 struct GridwiseGemm_xdl_cshuffle_v3

241 {

242  static constexpr auto I0 = Number<0>{};

243  static constexpr auto I1 = Number<1>{};

244  static constexpr auto I2 = Number<2>{};

245  static constexpr auto I3 = Number<3>{};

246  static constexpr auto I4 = Number<4>{};

247  static constexpr auto I5 = Number<5>{};

248  static constexpr auto I6 = Number<6>{};

249  static constexpr auto I7 = Number<7>{};

250 

251  // K1 should be Number<...>

252  static constexpr auto AK0Number = Number<KPerBlock / AK1Value>{};

253  static constexpr auto BK0Number = Number<KPerBlock / BK1Value>{};

254  static constexpr auto AK1Number = Number<AK1Value>{};

255  static constexpr auto BK1Number = Number<BK1Value>{};

256 

257  static constexpr auto lcm_AK1_BK1 = math::lcm(AK1Number, BK1Number);

258  static constexpr bool is_single_rate_mfma =

259  (((is_same<ComputeTypeA, half_t>::value || is_same<ComputeTypeA, bhalf_t>::value) &&

260  lcm_AK1_BK1 <= 4) ||

261  (is_same<ComputeTypeA, int8_t>::value && lcm_AK1_BK1 <= 8) ||

262  // gfx950 double rate mfma16x16 require at least 128 KPerBlock to consume

263  ((is_same<ComputeTypeA, f8_t>::value || is_same<ComputeTypeA, bf8_t>::value) &&

264  KPerBlock < 128 && MPerXdl == 16))

265  ? true

266  : false;

267  static constexpr auto is_scale_mfma = false;

268  static constexpr index_t KPack =

269  math::max(lcm_AK1_BK1,

270  MfmaSelector<ComputeTypeA,

271  MPerXdl,

272  NPerXdl,

273  ComputeTypeA,

274  is_single_rate_mfma,

275  is_scale_mfma>::selected_mfma.k_per_blk);

276 

277  using ThisThreadBlock = ThisThreadBlock<BlockSize>;

278 

279  static constexpr index_t APackedSize = []() {

280  if constexpr(is_same_v<remove_cvref_t<ADataType>, pk_i4_t>)

281  return 2;

282  else

283  return 1;

284  }();

285 

286  static constexpr index_t BPackedSize = []() {

287  if constexpr(is_same_v<remove_cvref_t<BDataType>, pk_i4_t>)

288  return 2;

289  else

290  return 1;

291  }();

292 

293  __host__ static auto CalculateGridSize(index_t M, index_t N, index_t KBatch)

294  {

295  return std::make_tuple(Block2CTileMap::CalculateGridSize(M, N), 1, KBatch);

296  }

297 

298  __host__ static auto CalculateMPadded(index_t M)

299  {

300  return math::integer_least_multiple(M, MPerBlock);

301  }

302 

303  __host__ static auto CalculateNPadded(index_t N)

304  {

305  return math::integer_least_multiple(N, NPerBlock);

306  }

307 

308  __host__ static auto CalculateKPadded(index_t K)

309  {

310  return math::integer_divide_ceil(K, KPerBlock) * KPerBlock;

311  }

312 

313  __host__ static auto CalculateAK0Padded(index_t K, index_t K_Batch = 1)

314  {

315  auto K_t = K_Batch * KPerBlock;

316  return (K + K_t - 1) / K_t * (KPerBlock / AK1Value);

317  }

318 

319  __host__ static auto CalculateBK0Padded(index_t K, index_t K_Batch = 1)

320  {

321  auto K_t = K_Batch * KPerBlock;

322  return (K + K_t - 1) / K_t * (KPerBlock / BK1Value);

323  }

324 

325  __host__ static auto CalculateKPadded(index_t K, index_t K_Batch = 1)

326  {

327  auto K_t = K_Batch * KPerBlock;

328  return (K + K_t - 1) / K_t * KPerBlock;

329  }

330 

331  __host__ static auto CalculateKRead(index_t K, index_t K_Batch = 1)

332  {

333  constexpr auto KReadVec = math::lcm(AK1Number, BK1Number);

334  auto K_t = K_Batch * KReadVec;

335  return (K + K_t - 1) / K_t * KReadVec;

336  }

337 

338  __host__ static auto CalculateMBlock(index_t M)

339  {

340  return math::integer_divide_ceil(M, MPerBlock);

341  }

342 

343  __host__ static auto CalculateNBlock(index_t N)

344  {

345  return math::integer_divide_ceil(N, NPerBlock);

346  }

347 

348  template <index_t MNXdlPerWave, index_t MNWaves, index_t MNPerXdl, typename TileDesc_K0_MN_K1>

349  __host__ __device__ static constexpr auto MakeGemmMmaTileDescriptor(const TileDesc_K0_MN_K1&)

350  {

351  constexpr index_t K0 = TileDesc_K0_MN_K1{}.GetLength(Number<0>{});

352  constexpr index_t K1 = TileDesc_K0_MN_K1{}.GetLength(Number<2>{});

353 

354  return transform_tensor_descriptor(

355  TileDesc_K0_MN_K1{},

356  make_tuple(make_merge_transform_v3_division_mod(make_tuple(Number<K0>{}, Number<K1>{})),

357  make_unmerge_transform(make_tuple(

358  Number<MNXdlPerWave>{}, Number<MNWaves>{}, Number<MNPerXdl>{}))),

359  make_tuple(Sequence<0, 2>{}, Sequence<1>{}),

360  make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}));

361  }

362 

363  __host__ __device__ static auto MakeAGridDescriptor_AK0_M_AK1(

364  index_t M, index_t MPad, index_t K, index_t KPad, index_t StrideA, index_t AK0)

365  {

366  const auto a_grid_desc_mraw_kraw = [&]() {

367  if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)

368  {

369  return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));

370  }

371  else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)

372  {

373  return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));

374  }

375  }();

376 

377  using GemmSpecialization = tensor_operation::device::GemmSpecialization;

378 

379  if constexpr(GemmSpec == GemmSpecialization::MKPadding ||

380  GemmSpec == GemmSpecialization::MNKPadding)

381  {

382  // pad both M and K

383  const auto a_grid_desc_m_k =

384  transform_tensor_descriptor(a_grid_desc_mraw_kraw,

385  make_tuple(make_right_pad_transform(M, MPad - M),

386  make_right_pad_transform(K, KPad - K)),

387  make_tuple(Sequence<0>{}, Sequence<1>{}),

388  make_tuple(Sequence<0>{}, Sequence<1>{}));

389 

390  const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(

391  a_grid_desc_m_k,

392  make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),

393  make_pass_through_transform(MPad)),

394  make_tuple(Sequence<1>{}, Sequence<0>{}),

395  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

396 

397  return a_grid_desc_ak0_m_ak1;

398  }

399  else if constexpr(GemmSpec == GemmSpecialization::MPadding ||

400  GemmSpec == GemmSpecialization::MNPadding)

401  {

402  // pad M, but not K

403  const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(

404  a_grid_desc_mraw_kraw,

405  make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),

406  make_right_pad_transform(M, MPad - M)),

407  make_tuple(Sequence<1>{}, Sequence<0>{}),

408  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

409 

410  return a_grid_desc_ak0_m_ak1;

411  }

412  else if constexpr(GemmSpec == GemmSpecialization::KPadding ||

413  GemmSpec == GemmSpecialization::NKPadding)

414  {

415  // pad K, but not M

416  const auto a_grid_desc_m_k = transform_tensor_descriptor(

417  a_grid_desc_mraw_kraw,

418  make_tuple(make_pass_through_transform(M), make_right_pad_transform(K, KPad - K)),

419  make_tuple(Sequence<0>{}, Sequence<1>{}),

420  make_tuple(Sequence<0>{}, Sequence<1>{}));

421 

422  const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(

423  a_grid_desc_m_k,

424  make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),

425  make_pass_through_transform(M)),

426  make_tuple(Sequence<1>{}, Sequence<0>{}),

427  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

428 

429  return a_grid_desc_ak0_m_ak1;

430  }

431  else

432  {

433  // not pad M or K

434  const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(

435  a_grid_desc_mraw_kraw,

436  make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),

437  make_pass_through_transform(M)),

438  make_tuple(Sequence<1>{}, Sequence<0>{}),

439  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

440 

441  return a_grid_desc_ak0_m_ak1;

442  }

443  }

444 

445  __host__ __device__ static auto MakeBGridDescriptor_BK0_N_BK1(

446  index_t K, index_t KPad, index_t N, index_t NPad, index_t StrideB, index_t BK0)

447  {

448  const auto b_grid_desc_nraw_kraw = [&]() {

449  if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)

450  {

451  return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(I1, StrideB));

452  }

453  else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)

454  {

455  return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(StrideB, I1));

456  }

457  }();

458 

459  using GemmSpecialization = tensor_operation::device::GemmSpecialization;

460 

461  static_assert(!(is_same_v<remove_cvref_t<ADataType>, pk_i4_t> &&

462  GemmSpec != GemmSpecialization::Default),

463  "pk_i4_t does not support padding");

464 

465  if constexpr(GemmSpec == GemmSpecialization::NKPadding ||

466  GemmSpec == GemmSpecialization::MNKPadding)

467  {

468  // pad both N and K

469  const auto b_grid_desc_n_k =

470  transform_tensor_descriptor(b_grid_desc_nraw_kraw,

471  make_tuple(make_right_pad_transform(N, NPad - N),

472  make_right_pad_transform(K, KPad - K)),

473  make_tuple(Sequence<0>{}, Sequence<1>{}),

474  make_tuple(Sequence<0>{}, Sequence<1>{}));

475 

476  const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(

477  b_grid_desc_n_k,

478  make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),

479  make_pass_through_transform(NPad)),

480  make_tuple(Sequence<1>{}, Sequence<0>{}),

481  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

482 

483  return b_grid_desc_bk0_n_bk1;

484  }

485  else if constexpr(GemmSpec == GemmSpecialization::NPadding ||

486  GemmSpec == GemmSpecialization::MNPadding)

487  {

488  // pad N, but not K

489  const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(

490  b_grid_desc_nraw_kraw,

491  make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),

492  make_right_pad_transform(N, NPad - N)),

493  make_tuple(Sequence<1>{}, Sequence<0>{}),

494  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

495 

496  return b_grid_desc_bk0_n_bk1;

497  }

498  else if constexpr(GemmSpec == GemmSpecialization::KPadding ||

499  GemmSpec == GemmSpecialization::MKPadding)

500  {

501  // pad K, but not N

502  const auto b_grid_desc_n_k = transform_tensor_descriptor(

503  b_grid_desc_nraw_kraw,

504  make_tuple(make_pass_through_transform(N), make_right_pad_transform(K, KPad - K)),

505  make_tuple(Sequence<0>{}, Sequence<1>{}),

506  make_tuple(Sequence<0>{}, Sequence<1>{}));

507 

508  const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(

509  b_grid_desc_n_k,

510  make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),

511  make_pass_through_transform(N)),

512  make_tuple(Sequence<1>{}, Sequence<0>{}),

513  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

514 

515  return b_grid_desc_bk0_n_bk1;

516  }

517  else

518  {

519  if constexpr(!PermuteB)

520  {

521  // not pad N or K

522  const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(

523  b_grid_desc_nraw_kraw,

524  make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),

525  make_pass_through_transform(N)),

526  make_tuple(Sequence<1>{}, Sequence<0>{}),

527  make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

528 

529  return b_grid_desc_bk0_n_bk1;

530  }

531  else

532  {

533  // Pre-shuffled Weight

534  // BGlobal[K / KPerBlock, N, KPerBlock / K1, K1] -> BTile[K / K1, N, K1]

535  constexpr index_t BK01 = KPerBlock / BK1Value;

536  const index_t BK0_ = StrideB / BK1Value;

537  const index_t BK00 = BK0_ / BK01;

538 

539  const auto b_grid_desc_bk00_n_bk01_bk1_permute =

540  make_naive_tensor_descriptor_packed(make_tuple(BK00, N, BK01, BK1Value));

541 

542  const auto b_grid_desc_bk0_n_bk1_permute = transform_tensor_descriptor(

543  b_grid_desc_bk00_n_bk01_bk1_permute,

544  make_tuple(make_merge_transform(make_tuple(BK00, BK01)),

545  make_pass_through_transform(make_tuple(N)),

546  make_pass_through_transform(BK1Value)),

547  make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}),

548  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

549 

550  return b_grid_desc_bk0_n_bk1_permute;

551  }

552  }

553  }

554 

555  template <typename ABlockDesc_AK0_M_AK1>

556  __host__ __device__ static constexpr auto

557  MakeAMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)

558  {

559  constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXdl);

560 

561  return MakeGemmMmaTileDescriptor<MXdlPerWave, MWaves, MPerXdl>(ABlockDesc_AK0_M_AK1{});

562  }

563 

564  template <typename BBlockDesc_BK0_N_BK1>

565  __host__ __device__ static constexpr auto

566  MakeBMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)

567  {

568  constexpr index_t NWaves = NPerBlock / (NXdlPerWave * NPerXdl);

569 

570  return MakeGemmMmaTileDescriptor<NXdlPerWave, NWaves, NPerXdl>(BBlockDesc_BK0_N_BK1{});

571  }

572 

573  __host__ __device__ static auto

574  MakeCGridDescriptor_M_N(index_t M, index_t MPad, index_t N, index_t NPad, index_t StrideC)

575  {

576  const auto c_grid_desc_mraw_nraw = [&]() {

577  if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)

578  {

579  return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));

580  }

581  else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)

582  {

583  return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));

584  }

585  }();

586 

587  // pad M and N

588  return transform_tensor_descriptor(c_grid_desc_mraw_nraw,

589  make_tuple(make_right_pad_transform(M, MPad - M),

590  make_right_pad_transform(N, NPad - N)),

591  make_tuple(Sequence<0>{}, Sequence<1>{}),

592  make_tuple(Sequence<0>{}, Sequence<1>{}));

593 #if 0

594  using GemmSpecialization = tensor_operation::device::GemmSpecialization;

595 

596  if constexpr(GemmSpec == GemmSpecialization::MNPadding ||

597  GemmSpec == GemmSpecialization::MNKPadding)

598  {

599  // pad M and N

600  return transform_tensor_descriptor(c_grid_desc_mraw_nraw,

601  make_tuple(make_right_pad_transform(M, MPad - M),

602  make_right_pad_transform(N, NPad - N)),

603  make_tuple(Sequence<0>{}, Sequence<1>{}),

604  make_tuple(Sequence<0>{}, Sequence<1>{}));

605  }

606  else if constexpr(GemmSpec == GemmSpecialization::MPadding ||

607  GemmSpec == GemmSpecialization::MKPadding)

608  {

609  // pad M, but not N

610  return transform_tensor_descriptor(

611  c_grid_desc_mraw_nraw,

612  make_tuple(make_right_pad_transform(M, MPad - M), make_pass_through_transform(N)),

613  make_tuple(Sequence<0>{}, Sequence<1>{}),

614  make_tuple(Sequence<0>{}, Sequence<1>{}));

615  }

616  else if constexpr(GemmSpec == GemmSpecialization::NPadding ||

617  GemmSpec == GemmSpecialization::NKPadding)

618  {

619  // pad N, but not M

620  return transform_tensor_descriptor(

621  c_grid_desc_mraw_nraw,

622  make_tuple(make_pass_through_transform(M), make_right_pad_transform(N, NPad - N)),

623  make_tuple(Sequence<0>{}, Sequence<1>{}),

624  make_tuple(Sequence<0>{}, Sequence<1>{}));

625  }

626  else

627  {

628  // not pad M or N

629  return c_grid_desc_mraw_nraw;

630  }

631 #endif

632  }

633 

634  struct Problem

635  {

636  __host__ Problem(index_t M_,

637  index_t N_,

638  index_t K_,

639  index_t StrideA_,

640  index_t StrideB_,

641  index_t StrideC_,

642  index_t KBatch_,

643  AElementwiseOperation a_element_op,

644  BElementwiseOperation b_element_op,

645  CElementwiseOperation c_element_op)

646  : M{M_},

647  N{N_},

648  K{K_},

649  StrideA{StrideA_},

650  StrideB{StrideB_},

651  StrideC{StrideC_},

652  KBatch{KBatch_},

653  MPadded{CalculateMPadded(M_)},

654  NPadded{CalculateNPadded(N_)},

655  KRead{CalculateKRead(K_, KBatch_)},

656  KPadded{CalculateKPadded(K_, KBatch_)},

657  AK0{CalculateAK0Padded(K_, KBatch_)},

658  BK0{CalculateBK0Padded(K_, KBatch_)},

659  MBlock{CalculateMBlock(M_)},

660  NBlock{CalculateNBlock(N_)},

661  a_element_op_{a_element_op},

662  b_element_op_{b_element_op},

663  c_element_op_{c_element_op}

664  {

665  }

666 

667  __host__ void Print() const

668  {

669  std::cout << "problem {"

670  << "M:" << M << ", "

671  << "N:" << N << ", "

672  << "K:" << K << ", "

673  << "SA:" << StrideA << ", "

674  << "SB:" << StrideB << ", "

675  << "SC:" << StrideC << ", "

676  << "MP:" << MPadded << ", "

677  << "NP:" << NPadded << ", "

678  << "KRead:" << KRead << ", "

679  << "KP:" << KPadded << ", "

680  << "AK0:" << AK0 << ", "

681  << "BK0:" << BK0 << ", "

682  << "MBlock: " << MBlock << ", "

683  << "NBlock: " << NBlock << "}" << std::endl;

684  }

685 

686  index_t M;

687  index_t N;

688  index_t K;

689  index_t StrideA;

690  index_t StrideB;

691  index_t StrideC;

692  index_t KBatch;

693  index_t MPadded;

694  index_t NPadded;

695  index_t KRead;

696  index_t KPadded;

697  index_t AK0;

698  index_t BK0;

699  index_t MBlock;

700  index_t NBlock;

701  AElementwiseOperation a_element_op_;

702  BElementwiseOperation b_element_op_;

703  CElementwiseOperation c_element_op_;

704  };

705 

706  // Argument

707  struct Argument : public tensor_operation::device::BaseArgument, public Problem

708  {

709  __host__ Argument(const ADataType* p_a_grid_,

710  const BDataType* p_b_grid_,

711  CDataType* p_c_grid_,

712  index_t M_,

713  index_t N_,

714  index_t K_,

715  index_t StrideA_,

716  index_t StrideB_,

717  index_t StrideC_,

718  index_t k_batch_,

719  bool is_reduce_ = false,

720  AElementwiseOperation a_element_op = AElementwiseOperation{},

721  BElementwiseOperation b_element_op = BElementwiseOperation{},

722  CElementwiseOperation c_element_op = CElementwiseOperation{})

723  : Problem{M_,

724  N_,

725  K_,

726  StrideA_,

727  StrideB_,

728  StrideC_,

729  k_batch_,

730  a_element_op,

731  b_element_op,

732  c_element_op},

733  p_a_grid{p_a_grid_},

734  p_b_grid{p_b_grid_},

735  p_c_grid{p_c_grid_},

736  is_reduce(is_reduce_)

737  {

738  }

739 

740  __host__ __device__ inline bool IsReduceAdd() const

741  {

742  return (Problem::KBatch > 1) && is_reduce;

743  }

744 

745  __host__ __device__ inline bool IsAtomicAdd() const

746  {

747  return (Problem::KBatch > 1) && (!is_reduce);

748  }

749 

750  const ADataType* p_a_grid;

751  const BDataType* p_b_grid;

752  CDataType* p_c_grid;

753  bool is_reduce;

754  };

755 

756  struct SplitKBatchOffset

757  {

758 

759  __device__ SplitKBatchOffset(Argument& karg)

760  {

761  if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)

762  {

763  a_k_split_offset = blockIdx.z * karg.KRead / APackedSize;

764  }

765  else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)

766  {

767  a_k_split_offset = blockIdx.z * karg.KRead * karg.StrideA;

768  }

769 

770  if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)

771  {

772  b_k_split_offset = blockIdx.z * karg.KRead * karg.StrideB;

773  }

774  else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)

775  {

776  if constexpr(!PermuteB)

777  {

778  b_k_split_offset = blockIdx.z * karg.KRead / BPackedSize;

779  }

780  else

781  {

782  const int k0_offset = karg.KRead * karg.N;

783  b_k_split_offset = blockIdx.z * k0_offset / BPackedSize;

784  }

785  }

786 

787  if(blockIdx.z < static_cast<uint32_t>(karg.KBatch - 1))

788  {

789  karg.K = karg.KRead;

790  }

791  else

792  {

793  karg.K = karg.K - karg.KRead * (karg.KBatch - 1);

794  }

795 

796  if(karg.IsReduceAdd())

797  {

798  c_reduce_offset = blockIdx.z * karg.M * karg.N;

799  }

800  else

801  {

802  c_reduce_offset = 0;

803  }

804  }

805 

806  index_t a_k_split_offset;

807  index_t b_k_split_offset;

808  index_t c_reduce_offset;

809  };

810 

811  __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()

812  {

813  // A matrix in LDS memory, dst of blockwise copy

814  if constexpr(ABlockLdsExtraM || BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)

815  {

816  // bank conflict when writting the data into LDS, but don't worry, we have whole entire

817  // loop to hide it in v4. it may give you some benefit from less valu in compute address

818  return make_naive_tensor_descriptor(

819  make_tuple(AK0Number, Number<MPerBlock>{}, AK1Number),

820  make_tuple(Number<MPerBlock>{} * AK1Number, AK1Number, I1));

821  }

822  // xor tensor transformation request more unnecessary vgpr usage, would cause register spill

823  // in some cases.

824  else if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)

825  {

826  constexpr index_t LdsSize = 32 * 4 / KPerBlock / sizeof(ADataType) / APackedSize;

827  constexpr auto MLdsLayer = LdsSize < 1 ? 1 : LdsSize;

828  constexpr auto a_lds_block_desc = make_naive_tensor_descriptor(

829  make_tuple(

830  AK0Number * Number<MLdsLayer>{}, Number<MPerBlock / MLdsLayer>{}, AK1Number),

831  make_tuple(AK1Number, Number<KPerBlock * MLdsLayer>{}, I1));

832 

833  constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(

834  a_lds_block_desc,

835  make_tuple(make_xor_with_modulo_transform(make_tuple(

836  Number<MPerBlock / MLdsLayer>{}, Number<AK0Number * MLdsLayer>{})),

837  make_pass_through_transform(AK1Number)),

838  make_tuple(Sequence<1, 0>{}, Sequence<2>{}),

839  make_tuple(Sequence<1, 0>{}, Sequence<2>{}));

840 

841  constexpr auto a_lds_block_desc_ak0_mldslayer_m_ak1 = transform_tensor_descriptor(

842  a_lds_block_desc_permuted,

843  make_tuple(make_unmerge_transform(make_tuple(AK0Number, Number<MLdsLayer>{})),

844  make_pass_through_transform(Number<MPerBlock / MLdsLayer>{}),

845  make_pass_through_transform(AK1Number)),

846  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),

847  make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}));

848 

849  constexpr auto a_lds_block_desc_ak0_m_ak1 = transform_tensor_descriptor(

850  a_lds_block_desc_ak0_mldslayer_m_ak1,

851  make_tuple(make_pass_through_transform(AK0Number),

852  make_merge_transform_v3_division_mod(

853  make_tuple(Number<MPerBlock / MLdsLayer>{}, Number<MLdsLayer>{})),

854  make_pass_through_transform(AK1Number)),

855  make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),

856  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

857 

858  return a_lds_block_desc_ak0_m_ak1;

859  }

860  else // ColumnMajor A

861  {

862  // kfold and mpair dimension is not always required.

863  // more dimension in merge_transform increase the difficulty of generating immarg offset

864  // for compiler.

865  constexpr auto M0 = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);

866  constexpr auto M1 = MPerBlock / M0;

867 

868  constexpr auto KThreadWrite = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I0);

869  constexpr auto K0PerThreadWrite = AK0Number / KThreadWrite;

870  constexpr auto KThreadRead = 64 / MPerXdl;

871  constexpr auto K0PerThreadRead = AK0Number / KThreadRead;

872 

873  constexpr auto kfold = (AK1Number * M0 * sizeof(ADataType) > 128)

874  ? 1

875  : 128 / (AK1Number * M0 * sizeof(ADataType));

876  constexpr auto KThreadReadPerm =

877  (kfold * K0PerThreadWrite / K0PerThreadRead) > 1

878  ? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)

879  : KThreadRead;

880 

881  // 1<=mpair<=n0

882  constexpr auto mpair = (AK1Number * MPerXdl * sizeof(ADataType) > 128)

883  ? 1

884  : ((128 / (AK1Number * MPerXdl * sizeof(ADataType))) > M0

885  ? M0

886  : 128 / (AK1Number * MPerXdl * sizeof(ADataType)));

887 

888  constexpr auto a_lds_block_desc = make_naive_tensor_descriptor_packed(

889  make_tuple(Number<KThreadWrite / kfold / KThreadReadPerm>{},

890  Number<K0PerThreadWrite>{},

891  Number<KThreadReadPerm * M1>{},

892  Number<kfold * M0 / mpair>{},

893  Number<mpair>{},

894  AK1Number));

895 

896  constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(

897  a_lds_block_desc,

898  make_tuple(

899  make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),

900  make_pass_through_transform(Number<K0PerThreadWrite>{}),

901  make_xor_with_modulo_transform(

902  make_tuple(Number<KThreadReadPerm * M1>{}, Number<kfold * M0 / mpair>{})),

903  make_pass_through_transform(Number<mpair>{}),

904  make_pass_through_transform(AK1Number)),

905  make_tuple(

906  Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}),

907  make_tuple(

908  Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}));

909 

910  constexpr auto a_lds_block_desc_unmerged = transform_tensor_descriptor(

911  a_lds_block_desc_permuted,

912  make_tuple(

913  make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),

914  make_pass_through_transform(Number<K0PerThreadWrite>{}),

915  make_unmerge_transform(make_tuple(Number<KThreadReadPerm>{}, Number<M1>{})),

916  make_unmerge_transform(make_tuple(Number<kfold>{}, Number<M0 / mpair>{})),

917  make_pass_through_transform(Number<mpair>{}),

918  make_pass_through_transform(AK1Number)),

919  make_tuple(Sequence<0>{},

920  Sequence<1>{},

921  Sequence<2>{},

922  Sequence<3>{},

923  Sequence<4>{},

924  Sequence<5>{}),

925  make_tuple(Sequence<1>{},

926  Sequence<2>{},

927  Sequence<0, 3>{},

928  Sequence<4, 5>{},

929  Sequence<6>{},

930  Sequence<7>{}));

931 

932  constexpr auto a_lds_block_desc_ak0_m_ak1 = transform_tensor_descriptor(

933  a_lds_block_desc_unmerged,

934  make_tuple(make_merge_transform_v3_division_mod(

935  make_tuple(Number<KThreadReadPerm>{},

936  Number<KThreadWrite / kfold / KThreadReadPerm>{},

937  Number<kfold>{},

938  Number<K0PerThreadWrite>{})),

939  make_merge_transform_v3_division_mod(

940  make_tuple(Number<M0 / mpair>{}, Number<mpair>{}, Number<M1>{})),

941  make_pass_through_transform(AK1Number)),

942  make_tuple(Sequence<0, 1, 4, 2>{}, Sequence<5, 6, 3>{}, Sequence<7>{}),

943  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

944 

945  return a_lds_block_desc_ak0_m_ak1;

946  }

947  }

948 

949  __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()

950  {

951  // B matrix in LDS memory, dst of blockwise copy

952  if constexpr(BBlockLdsExtraN || BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)

953  {

954  // bank conflict when writting the data into LDS, but don't worry, we have whole entire

955  // loop to hide it in v4. it may give you some benefit from less valu in compute address

956  return make_naive_tensor_descriptor(

957  make_tuple(BK0Number, Number<NPerBlock>{}, BK1Number),

958  make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1Number, BK1Number, I1));

959  }

960  else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)

961  {

962  // NLdsLayer * K0 as logical Bank

963  constexpr index_t LdsSize = 32 * 4 / KPerBlock / sizeof(BDataType) / BPackedSize;

964  constexpr index_t NLdsLayer = LdsSize < 1 ? 1 : LdsSize;

965  constexpr auto b_lds_block_desc = make_naive_tensor_descriptor(

966  make_tuple(

967  BK0Number * Number<NLdsLayer>{}, Number<NPerBlock / NLdsLayer>{}, BK1Number),

968  make_tuple(BK1Number, Number<KPerBlock * NLdsLayer>{}, I1));

969 

970  constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(

971  b_lds_block_desc,

972  make_tuple(make_xor_with_modulo_transform(make_tuple(

973  Number<NPerBlock / NLdsLayer>{}, Number<BK0Number * NLdsLayer>{})),

974  make_pass_through_transform(BK1Number)),

975  make_tuple(Sequence<1, 0>{}, Sequence<2>{}),

976  make_tuple(Sequence<1, 0>{}, Sequence<2>{}));

977 

978  constexpr auto b_lds_block_desc_bk0_nldslayer_n_bk1 = transform_tensor_descriptor(

979  b_lds_block_desc_permuted,

980  make_tuple(make_unmerge_transform(make_tuple(BK0Number, Number<NLdsLayer>{})),

981  make_pass_through_transform(Number<NPerBlock / NLdsLayer>{}),

982  make_pass_through_transform(BK1Number)),

983  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),

984  make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}));

985 

986  constexpr auto b_lds_block_desc_bk0_n_bk1 = transform_tensor_descriptor(

987  b_lds_block_desc_bk0_nldslayer_n_bk1,

988  make_tuple(make_pass_through_transform(BK0Number),

989  make_merge_transform_v3_division_mod(

990  make_tuple(Number<NPerBlock / NLdsLayer>{}, Number<NLdsLayer>{})),

991  make_pass_through_transform(BK1Number)),

992  make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),

993  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

994 

995  return b_lds_block_desc_bk0_n_bk1;

996  }

997  else // RowMajor B

998  {

999  constexpr auto N0 = BBlockTransferThreadClusterLengths_BK0_N_BK1{}.At(I1);

1000  constexpr auto N1 = NPerBlock / N0;

1001 

1002  constexpr auto KThreadWrite = BBlockTransferThreadClusterLengths_BK0_N_BK1{}.At(I0);

1003  constexpr auto K0PerThreadWrite = BK0Number / KThreadWrite;

1004  constexpr auto KThreadRead = 64 / NPerXdl;

1005  constexpr auto K0PerThreadRead = BK0Number / KThreadRead;

1006 

1007  constexpr auto kfold = (BK1Number * N0 * sizeof(BDataType) > 128)

1008  ? 1

1009  : 128 / (BK1Number * N0 * sizeof(BDataType));

1010  constexpr auto KThreadReadPerm =

1011  (kfold * K0PerThreadWrite / K0PerThreadRead) > 1

1012  ? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)

1013  : KThreadRead;

1014 

1015  // 1<=npair<=n0

1016  constexpr auto npair = (BK1Number * NPerXdl * sizeof(BDataType) > 128)

1017  ? 1

1018  : ((128 / (BK1Number * NPerXdl * sizeof(BDataType))) > N0

1019  ? N0

1020  : 128 / (BK1Number * NPerXdl * sizeof(BDataType)));

1021 

1022  constexpr auto b_lds_block_desc = make_naive_tensor_descriptor_packed(

1023  make_tuple(Number<KThreadWrite / kfold / KThreadReadPerm>{},

1024  Number<K0PerThreadWrite>{},

1025  Number<KThreadReadPerm * N1>{},

1026  Number<kfold * N0 / npair>{},

1027  Number<npair>{},

1028  BK1Number));

1029 

1030  constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(

1031  b_lds_block_desc,

1032  make_tuple(

1033  make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),

1034  make_pass_through_transform(Number<K0PerThreadWrite>{}),

1035  make_xor_with_modulo_transform(

1036  make_tuple(Number<KThreadReadPerm * N1>{}, Number<kfold * N0 / npair>{})),

1037  make_pass_through_transform(Number<npair>{}),

1038  make_pass_through_transform(BK1Number)),

1039  make_tuple(

1040  Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}),

1041  make_tuple(

1042  Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}));

1043 

1044  constexpr auto b_lds_block_desc_unmerged = transform_tensor_descriptor(

1045  b_lds_block_desc_permuted,

1046  make_tuple(

1047  make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),

1048  make_pass_through_transform(Number<K0PerThreadWrite>{}),

1049  make_unmerge_transform(make_tuple(Number<KThreadReadPerm>{}, Number<N1>{})),

1050  make_unmerge_transform(make_tuple(Number<kfold>{}, Number<N0 / npair>{})),

1051  make_pass_through_transform(Number<npair>{}),

1052  make_pass_through_transform(BK1Number)),

1053  make_tuple(Sequence<0>{},

1054  Sequence<1>{},

1055  Sequence<2>{},

1056  Sequence<3>{},

1057  Sequence<4>{},

1058  Sequence<5>{}),

1059  make_tuple(Sequence<1>{},

1060  Sequence<2>{},

1061  Sequence<0, 3>{},

1062  Sequence<4, 5>{},

1063  Sequence<6>{},

1064  Sequence<7>{}));

1065 

1066  constexpr auto b_lds_block_desc_bk0_n_bk1 = transform_tensor_descriptor(

1067  b_lds_block_desc_unmerged,

1068  make_tuple(make_merge_transform_v3_division_mod(

1069  make_tuple(Number<KThreadReadPerm>{},

1070  Number<KThreadWrite / kfold / KThreadReadPerm>{},

1071  Number<kfold>{},

1072  Number<K0PerThreadWrite>{})),

1073  make_merge_transform_v3_division_mod(

1074  make_tuple(Number<N0 / npair>{}, Number<npair>{}, Number<N1>{})),

1075  make_pass_through_transform(BK1Number)),

1076  make_tuple(Sequence<0, 1, 4, 2>{}, Sequence<5, 6, 3>{}, Sequence<7>{}),

1077  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

1078 

1079  return b_lds_block_desc_bk0_n_bk1;

1080  }

1081  }

1082 

1083  __device__ static constexpr auto GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()

1084  {

1085  constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);

1086  constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);

1087 

1088  constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =

1089  make_naive_tensor_descriptor_packed(

1090  make_tuple(I1,

1091  Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},

1092  I1,

1093  Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));

1094 

1095  return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;

1096  }

1097 

1098  using BlockwiseGemmPipe =

1099  remove_cvref_t<decltype(BlockGemmPipeline_Selector<

1100  BlkGemmPipelineVer,

1101  BlkGemmPipeSched,

1102  BlockSize,

1103  ADataType,

1104  BDataType,

1105  ComputeTypeA,

1106  AccDataType,

1107  decltype(GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()),

1108  decltype(GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()),

1109  decltype(MakeAMmaTileDescriptor_M0_M1_M2_K(

1110  GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1())),

1111  decltype(MakeBMmaTileDescriptor_N0_N1_N2_K(

1112  GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1())),

1113  ABlockTransferSrcScalarPerVector,

1114  BBlockTransferSrcScalarPerVector,

1115  MPerBlock,

1116  NPerBlock,

1117  KPerBlock,

1118  MPerXdl,

1119  NPerXdl,

1120  MXdlPerWave,

1121  NXdlPerWave,

1122  KPack>())>;

1123 

1124  __device__ static constexpr index_t GetSharedMemoryNumberOfByte()

1125  {

1126  // LDS allocation for A and B: be careful of alignment

1127  constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();

1128  constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();

1129 

1130  // lds max alignment

1131  constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);

1132 

1133  constexpr auto a_block_space_size_aligned = math::integer_least_multiple(

1134  a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);

1135 

1136  constexpr auto b_block_space_size_aligned = math::integer_least_multiple(

1137  b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);

1138 

1139  // LDS allocation for C shuffle in LDS

1140  constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =

1141  GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();

1142 

1143  constexpr auto c_block_size =

1144  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();

1145 

1146  return math::max((a_block_space_size_aligned * sizeof(ADataType) / APackedSize +

1147  b_block_space_size_aligned * sizeof(BDataType) / BPackedSize),

1148  c_block_size * sizeof(CShuffleDataType));

1149  }

1150 

1151  // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}

1152  __host__ static constexpr bool CheckValidity(const Argument& karg)

1153  {

1154  static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&

1155  (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,

1156  "Invalid tuning param!");

1157 

1158  if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||

1159  GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||

1160  GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||

1161  GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding) &&

1162  !(is_same<tensor_layout::gemm::RowMajor, ALayout>::value))

1163  {

1164  if(!(karg.M % MPerBlock == 0))

1165  {

1166  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1167  {

1168  std::cout << "Arg M value is not a multiple of MPerBlock! M: " << karg.M << " "

1169  << __FILE__ << ":" << __LINE__ << ", in function: " << __func__

1170  << std::endl;

1171  }

1172  return false;

1173  }

1174  }

1175 

1176  if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||

1177  GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||

1178  GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||

1179  GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding) &&

1180  (is_same<tensor_layout::gemm::RowMajor, BLayout>::value))

1181  {

1182  if(!(karg.N % NPerBlock == 0))

1183  {

1184  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1185  {

1186  std::cout << "Arg N value is not a multiple of NPerBlock! N: " << karg.N << " "

1187  << __FILE__ << ":" << __LINE__ << ", in function: " << __func__

1188  << std::endl;

1189  }

1190  return false;

1191  }

1192  }

1193 

1194  if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::KPadding ||

1195  GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||

1196  GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||

1197  GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))

1198  {

1199 

1200  auto K_t = karg.KBatch * KPerBlock;

1201  if(!(karg.K % K_t == 0))

1202  {

1203  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1204  {

1205  std::cout << "Arg K value is not a multiple of K_Batch * K0PerBlock * K1! K: "

1206  << karg.K << " " << __FILE__ << ":" << __LINE__

1207  << ", in function: " << __func__ << std::endl;

1208  }

1209  return false;

1210  }

1211  }

1212  else

1213  {

1214  constexpr auto KReadVec = math::lcm(AK1Number, BK1Number);

1215  auto K_t = karg.KBatch * KReadVec;

1216  auto KReadPadSplited = math::integer_divide_ceil(karg.K, K_t) * KReadVec;

1217  if((KReadPadSplited * (karg.KBatch - 1)) >= karg.K)

1218  {

1219  return false;

1220  }

1221  }

1222 

1223  if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)

1224  {

1225  if(karg.K % ABlockTransferSrcScalarPerVector != 0)

1226  {

1227  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1228  {

1229  std::cout << "Arg K (" << karg.K

1230  << ") value is not a multiple of ABlockTransferSrcScalarPerVector ("

1231  << ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"

1232  << __LINE__ << ", in function: " << __func__ << std::endl;

1233  }

1234  return false;

1235  }

1236  }

1237  else

1238  {

1239  if(karg.M % ABlockTransferSrcScalarPerVector != 0)

1240  {

1241  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1242  {

1243  std::cout << "Arg M (" << karg.M

1244  << ") value is not a multiple of ABlockTransferSrcScalarPerVector ("

1245  << ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"

1246  << __LINE__ << ", in function: " << __func__ << std::endl;

1247  }

1248  return false;

1249  }

1250  }

1251 

1252  if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)

1253  {

1254  if(karg.N % BBlockTransferSrcScalarPerVector != 0)

1255  {

1256  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1257  {

1258  std::cout << "Arg N (" << karg.N

1259  << ") value is not a multiple of BBlockTransferSrcScalarPerVector ("

1260  << BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"

1261  << __LINE__ << ", in function: " << __func__ << std::endl;

1262  }

1263  return false;

1264  }

1265  }

1266  else

1267  {

1268  if(karg.K % BBlockTransferSrcScalarPerVector != 0)

1269  {

1270  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1271  {

1272  std::cout << "Arg K (" << karg.K

1273  << ") value is not a multiple of BBlockTransferSrcScalarPerVector ("

1274  << BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"

1275  << __LINE__ << ", in function: " << __func__ << std::endl;

1276  }

1277  return false;

1278  }

1279  }

1280 

1281  if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)

1282  {

1283  if(karg.N % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)

1284  {

1285  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1286  {

1287  std::cout << "Arg N (" << karg.N

1288  << ") value is not a multiple of "

1289  "CShuffleBlockTransferScalarPerVector_NPerBlock ("

1290  << CShuffleBlockTransferScalarPerVector_NPerBlock << " )! "

1291  << __FILE__ << ":" << __LINE__ << ", in function: " << __func__

1292  << std::endl;

1293  }

1294  return false;

1295  }

1296  }

1297  else

1298  {

1299  if(karg.M % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)

1300  {

1301  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1302  {

1303  std::cout << "Arg M (" << karg.M

1304  << ") value is not a multiple of "

1305  "CShuffleBlockTransferScalarPerVector_NPerBlock ("

1306  << CShuffleBlockTransferScalarPerVector_NPerBlock << " )! "

1307  << __FILE__ << ":" << __LINE__ << ", in function: " << __func__

1308  << std::endl;

1309  }

1310  return false;

1311  }

1312  }

1313 

1314  if constexpr(!(is_same<remove_cvref_t<CDataType>, half_t>::value ||

1315  is_same<remove_cvref_t<CDataType>, float>::value ||

1316  is_same<remove_cvref_t<CDataType>, bhalf_t>::value ||

1317  is_same<remove_cvref_t<CDataType>, int32_t>::value))

1318  {

1319  if(!karg.IsReduceAdd())

1320  {

1321  if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))

1322  {

1323  std::cout << " KBatch: " << karg.KBatch << " > 1 is not support yet" << __FILE__

1324  << ":" << __LINE__ << ", in function: " << __func__ << std::endl;

1325  }

1326  if(karg.KBatch > 1)

1327  {

1328  return false;

1329  }

1330  }

1331  }

1332 

1333  // check gridwise gemm pipeline

1334  const auto num_k_loop = karg.AK0 / (KPerBlock / AK1Value);

1335 

1336  if constexpr(BlkGemmPipelineVer != BlockGemmPipelineVersion::v1)

1337  {

1338  if(num_k_loop <= BlockwiseGemmPipe::PrefetchStages)

1339  {

1340  return false;

1341  }

1342  }

1343 

1344  // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)

1345  return true;

1346  }

1347 

1348  __host__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)

1349  {

1350  const index_t num_loop = K / KPerBlock;

1351 

1352  return BlockwiseGemmPipe::BlockHasHotloop(num_loop);

1353  }

1354 

1355  __host__ static constexpr TailNumber CalculateKBlockLoopTailNum(index_t K)

1356  {

1357  const index_t num_loop = K / KPerBlock;

1358 

1359  return BlockwiseGemmPipe::BlockLoopTailNum(num_loop);

1360  }

1361 

1362  template <typename CGridDesc>

1363  __host__ __device__ static constexpr auto MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(

1364  const CGridDesc& c_grid_desc_m_n, index_t MBlock, index_t NBlock)

1365  {

1366  const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(

1367  c_grid_desc_m_n,

1368  make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),

1369  make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),

1370  make_tuple(Sequence<0>{}, Sequence<1>{}),

1371  make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));

1372 

1373  return c_grid_desc_mblock_mperblock_nblock_nperblock;

1374  }

1375 

1376  // return block_id to C matrix tile idx (m0, n0) mapping

1377  // if arch = gfx942

1378  using Block2CTileMap = BlockToCTileMap_Grouped_M00_N0_M01Adapt<8, MPerBlock, NPerBlock>;

1379  // using Block2CTileMap = BlockToCTileMap_3DGrid_KSplit<MPerBlock, NPerBlock>;

1380 

1381  template <typename AGridDesc_AK0_M_K1,

1382  typename BGridDesc_BK0_N_K1,

1383  typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,

1384  bool HasMainKBlockLoop,

1385  InMemoryDataOperationEnum CGlobalMemoryDataOperation,

1386  TailNumber TailNum = TailNumber::Odd>

1387  __device__ static void Run(const ADataType* p_a_grid,

1388  const BDataType* p_b_grid,

1389  CDataType* p_c_grid,

1390  void* p_shared,

1391  const Problem& problem,

1392  const AGridDesc_AK0_M_K1& a_grid_desc_ak0_m_ak1,

1393  const BGridDesc_BK0_N_K1& b_grid_desc_bk0_n_bk1,

1394  const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&

1395  c_grid_desc_mblock_mperblock_nblock_nperblock)

1396  {

1397  const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(

1398  p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());

1399  const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(

1400  p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());

1401  auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(

1402  p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

1403 

1404  // divide block work by [M, N]

1405  const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N, 4};

1406 

1407  const auto block_work_idx =

1408  block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));

1409 

1410  if(!block_2_ctile_map.ValidCTileIndex(

1411  block_work_idx,

1412  make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),

1413  c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))

1414  {

1415  return;

1416  }

1417 

1418  const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);

1419  const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);

1420 

1421  // HACK: this force m/n_block_data_idx_on_grid into SGPR

1422  const index_t m_block_data_idx_on_grid =

1423  __builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);

1424 

1425  const index_t n_block_data_idx_on_grid =

1426  __builtin_amdgcn_readfirstlane(block_n_id * NPerBlock);

1427 

1428  // lds max alignment

1429  constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);

1430 

1431  // A matrix in LDS memory, dst of blockwise copy

1432  constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();

1433 

1434  // B matrix in LDS memory, dst of blockwise copy

1435  constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();

1436 

1437  // A matrix blockwise copy

1438  auto a_blockwise_copy =

1439  ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,

1440  AElementwiseOperation,

1441  ck::tensor_operation::element_wise::PassThrough,

1442  InMemoryDataOperationEnum::Set,

1443  Sequence<AK0Number, MPerBlock, AK1Number>,

1444  ABlockTransferThreadClusterLengths_AK0_M_AK1,

1445  ABlockTransferThreadClusterArrangeOrder,

1446  ADataType,

1447  ADataType,

1448  decltype(a_grid_desc_ak0_m_ak1),

1449  decltype(a_block_desc_ak0_m_ak1),

1450  ABlockTransferSrcAccessOrder,

1451  Sequence<0, 1, 2>,

1452  ABlockTransferSrcVectorDim,

1453  2,

1454  ABlockTransferSrcScalarPerVector,

1455  ABlockTransferDstScalarPerVector_AK1,

1456  1,

1457  1,

1458  AThreadTransferSrcResetCoordinateAfterRun,

1459  true,

1460  BlockwiseGemmPipe::GlobalBufferNum>(

1461  a_grid_desc_ak0_m_ak1,

1462  make_multi_index(0, m_block_data_idx_on_grid, 0),

1463  problem.a_element_op_,

1464  a_block_desc_ak0_m_ak1,

1465  make_multi_index(0, 0, 0),

1466  ck::tensor_operation::element_wise::PassThrough{});

1467 

1468  // B matrix blockwise copy

1469  auto b_blockwise_copy =

1470  ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,

1471  BElementwiseOperation,

1472  ck::tensor_operation::element_wise::PassThrough,

1473  InMemoryDataOperationEnum::Set,

1474  Sequence<BK0Number, NPerBlock, BK1Number>,

1475  BBlockTransferThreadClusterLengths_BK0_N_BK1,

1476  BBlockTransferThreadClusterArrangeOrder,

1477  BDataType,

1478  BDataType,

1479  decltype(b_grid_desc_bk0_n_bk1),

1480  decltype(b_block_desc_bk0_n_bk1),

1481  BBlockTransferSrcAccessOrder,

1482  Sequence<0, 1, 2>,

1483  BBlockTransferSrcVectorDim,

1484  2,

1485  BBlockTransferSrcScalarPerVector,

1486  BBlockTransferDstScalarPerVector_BK1,

1487  1,

1488  1,

1489  BThreadTransferSrcResetCoordinateAfterRun,

1490  true,

1491  BlockwiseGemmPipe::GlobalBufferNum>(

1492  b_grid_desc_bk0_n_bk1,

1493  make_multi_index(0, n_block_data_idx_on_grid, 0),

1494  problem.b_element_op_,

1495  b_block_desc_bk0_n_bk1,

1496  make_multi_index(0, 0, 0),

1497  ck::tensor_operation::element_wise::PassThrough{});

1498 

1499  // LDS allocation for A and B: be careful of alignment

1500  constexpr auto a_block_space_size_aligned = math::integer_least_multiple(

1501  a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);

1502 

1503  // Cast after lds

1504  auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1505  static_cast<ADataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());

1506 

1507  auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1508  reinterpret_cast<BDataType*>(static_cast<char*>(p_shared) + a_block_space_size_aligned *

1509  sizeof(ADataType) /

1510  APackedSize),

1511  b_block_desc_bk0_n_bk1.GetElementSpaceSize());

1512 

1513  constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);

1514  constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1Number, 0, 0);

1515 

1516  // Blockwise GEMM pipeline

1517  static_assert(std::is_default_constructible_v<BlockwiseGemmPipe>);

1518  auto blockwise_gemm_pipeline = BlockwiseGemmPipe{};

1519  auto c_thread_buf = blockwise_gemm_pipeline.GetCThreadBuffer();

1520 

1521  const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(

1522  (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /

1523  KPerBlock);

1524 

1525  blockwise_gemm_pipeline.template Run<HasMainKBlockLoop, TailNum>(a_grid_desc_ak0_m_ak1,

1526  a_block_desc_ak0_m_ak1,

1527  a_blockwise_copy,

1528  a_grid_buf,

1529  a_block_buf,

1530  a_block_slice_copy_step,

1531  b_grid_desc_bk0_n_bk1,

1532  b_block_desc_bk0_n_bk1,

1533  b_blockwise_copy,

1534  b_grid_buf,

1535  b_block_buf,

1536  b_block_slice_copy_step,

1537  c_thread_buf,

1538  num_k_block_main_loop);

1539 

1540  // shuffle C and write out

1541  {

1542  static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&

1543  NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,

1544  "wrong!");

1545 

1546  constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);

1547  constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);

1548 

1549  // TODO: hacky, fix it!

1550  constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =

1551  blockwise_gemm_pipeline.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

1552 

1553  // TODO: hacky, fix it!

1554  // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths

1555  constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =

1556  blockwise_gemm_pipeline.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

1557 

1558  constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);

1559  constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);

1560  constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);

1561  constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);

1562  constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);

1563  constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);

1564  constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);

1565  constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);

1566 

1567  constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =

1568  GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();

1569 

1570  auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1571  static_cast<CShuffleDataType*>(p_shared),

1572  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

1573 

1574  constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(

1575  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,

1576  make_tuple(

1577  make_freeze_transform(I0),

1578  make_unmerge_transform(make_tuple(

1579  Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle

1580  M1, // M1 = MWave

1581  M2, // M2 * M3 * M4 = MPerXdl

1582  M3,

1583  M4)),

1584  make_freeze_transform(I0),

1585  make_unmerge_transform(make_tuple(

1586  Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle

1587  N1, // N1 = NWave

1588  N2))), // N2 = NPerXdl

1589  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),

1590  make_tuple(

1591  Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));

1592 

1593  // calculate origin of thread output tensor on global memory

1594  // blockwise GEMM c matrix starting index

1595  const auto c_thread_mtx_on_block =

1596  blockwise_gemm_pipeline.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);

1597 

1598  const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];

1599  const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];

1600 

1601  const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =

1602  make_single_stage_tensor_adaptor(

1603  make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),

1604  make_tuple(Sequence<0, 1, 2, 3, 4>{}),

1605  make_tuple(Sequence<0>{}));

1606 

1607  const auto m_thread_data_on_block_idx =

1608  m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(

1609  make_multi_index(m_thread_data_on_block));

1610 

1611  const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =

1612  make_single_stage_tensor_adaptor(

1613  make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),

1614  make_tuple(Sequence<0, 1, 2>{}),

1615  make_tuple(Sequence<0>{}));

1616 

1617  const auto n_thread_data_on_block_idx =

1618  n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(

1619  make_multi_index(n_thread_data_on_block));

1620 

1621  tensor_operation::element_wise::PassThrough pass_through{};

1622  const auto& vpgr_to_lds_element_op = [&] {

1623  if constexpr(DoElementwiseBeforeCShuffle)

1624  {

1625  return problem.c_element_op_;

1626  }

1627  else

1628  {

1629  return pass_through;

1630  }

1631  };

1632  const auto& lds_to_global_element_op = [&] {

1633  if constexpr(!DoElementwiseBeforeCShuffle)

1634  {

1635  return problem.c_element_op_;

1636  }

1637  else

1638  {

1639  return pass_through;

1640  }

1641  };

1642 

1643  // shuffle: threadwise copy C from VGPR to LDS

1644  auto c_thread_copy_vgpr_to_lds = ThreadwiseTensorSliceTransfer_v1r3<

1645  AccDataType,

1646  CShuffleDataType,

1647  decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),

1648  decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),

1649  conditional_t<DoElementwiseBeforeCShuffle,

1650  CElementwiseOperation,

1651  tensor_operation::element_wise::PassThrough>,

1652  Sequence<CShuffleMXdlPerWavePerShuffle,

1653  CShuffleNXdlPerWavePerShuffle,

1654  I1,

1655  I1,

1656  M2,

1657  I1,

1658  M4,

1659  I1>,

1660  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,

1661  7,

1662  1,

1663  InMemoryDataOperationEnum::Set,

1664  1,

1665  true>{c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,

1666  make_multi_index(0,

1667  0,

1668  m_thread_data_on_block_idx[I1],

1669  n_thread_data_on_block_idx[I1],

1670  m_thread_data_on_block_idx[I2],

1671  m_thread_data_on_block_idx[I3],

1672  m_thread_data_on_block_idx[I4],

1673  n_thread_data_on_block_idx[I2]),

1674  vpgr_to_lds_element_op()};

1675 

1676  // shuffle: blockwise copy C from LDS to global

1677  auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<

1678  ThisThreadBlock, // ThreadGroup

1679  conditional_t<!DoElementwiseBeforeCShuffle,

1680  CElementwiseOperation,

1681  tensor_operation::element_wise::PassThrough>,

1682  CGlobalMemoryDataOperation, // DstInMemOp,

1683  Sequence<1,

1684  CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,

1685  1,

1686  CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,

1687  CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,

1688  Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,

1689  CShuffleDataType, // typename SrcData,

1690  CDataType, // typename DstData,

1691  decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),

1692  decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),

1693  Sequence<0, 1, 2, 3>, // typename DimAccessOrder,

1694  3, // index_t VectorDim,

1695  CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,

1696  true, // bool ThreadTransferSrcResetCoordinateAfterRun,

1697  false> // bool ThreadTransferDstResetCoordinateAfterRun>

1698  {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,

1699  make_multi_index(0, 0, 0, 0),

1700  c_grid_desc_mblock_mperblock_nblock_nperblock,

1701  make_multi_index(block_m_id, 0, block_n_id, 0),

1702  lds_to_global_element_op()};

1703 

1704  // space filling curve for threadwise C in VGPR

1705  constexpr auto sfc_c_vgpr =

1706  SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,

1707  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,

1708  Sequence<CShuffleMXdlPerWavePerShuffle,

1709  CShuffleNXdlPerWavePerShuffle,

1710  1,

1711  1,

1712  M2,

1713  1,

1714  M4,

1715  1>>{};

1716 

1717  // space filling curve for shuffled blockwise C in global mem

1718  constexpr auto sfc_c_global =

1719  SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,

1720  Sequence<0, 2, 1, 3>,

1721  Sequence<1,

1722  CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,

1723  1,

1724  CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};

1725 

1726  constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();

1727 

1728  static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");

1729 

1730  static_for<0, num_access, 1>{}([&](auto access_id) {

1731  // make sure it's safe to write to LDS

1732  block_sync_lds();

1733 

1734  // each thread write its data from VGPR to LDS

1735  c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,

1736  sfc_c_vgpr.GetIndexTupleOfNumber(access_id),

1737  c_thread_buf,

1738  c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,

1739  c_shuffle_block_buf);

1740 

1741  // make sure it's safe to read from LDS

1742  block_sync_lds();

1743 

1744  // each block copy its data from LDS to global

1745  c_shuffle_block_copy_lds_to_global.Run(

1746  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,

1747  c_shuffle_block_buf,

1748  c_grid_desc_mblock_mperblock_nblock_nperblock,

1749  c_grid_buf);

1750 

1751  if constexpr(access_id < num_access - 1)

1752  {

1753  constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);

1754 

1755  // move on C

1756  c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(

1757  c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);

1758  }

1759  });

1760  }

1761  }

1762 

1763  template <bool HasMainKBlockLoop,

1764  InMemoryDataOperationEnum CGlobalMemoryDataOperation,

1765  TailNumber TailNum = TailNumber::Odd>

1766  __device__ static void Run(const ADataType* p_a_grid,

1767  const BDataType* p_b_grid,

1768  CDataType* p_c_grid,

1769  void* p_shared,

1770  const Problem& problem)

1771  {

1772  const auto a_grid_desc_ak0_m_ak1 = MakeAGridDescriptor_AK0_M_AK1(

1773  problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);

1774  const auto b_grid_desc_bk0_n_bk1 = MakeBGridDescriptor_BK0_N_BK1(

1775  problem.K, problem.KPadded, problem.N, problem.NPadded, problem.StrideB, problem.BK0);

1776  const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(

1777  problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);

1778  const auto c_grid_desc_mblock_mperblock_nblock_nperblock =

1779  MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(

1780  c_grid_desc_m_n, problem.MBlock, problem.NBlock);

1781 

1782  Run<decltype(a_grid_desc_ak0_m_ak1),

1783  decltype(b_grid_desc_bk0_n_bk1),

1784  decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),

1785  HasMainKBlockLoop,

1786  CGlobalMemoryDataOperation,

1787  TailNum>(p_a_grid,

1788  p_b_grid,

1789  p_c_grid,

1790  p_shared,

1791  problem,

1792  a_grid_desc_ak0_m_ak1,

1793  b_grid_desc_bk0_n_bk1,

1794  c_grid_desc_mblock_mperblock_nblock_nperblock);

1795  }

1796 

1797  template <typename AGridDesc_AK0_M_K1,

1798  typename BGridDesc_BK0_N_K1,

1799  typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,

1800  bool HasMainKBlockLoop,

1801  InMemoryDataOperationEnum CGlobalMemoryDataOperation,

1802  TailNumber TailNum = TailNumber::Odd>

1803  __device__ static void Run_2Lds(const ADataType* p_a_grid,

1804  const BDataType* p_b_grid,

1805  CDataType* p_c_grid,

1806  void* p_shared_0,

1807  void* p_shared_1,

1808  const Problem& problem,

1809  const AGridDesc_AK0_M_K1& a_grid_desc_ak0_m_ak1,

1810  const BGridDesc_BK0_N_K1& b_grid_desc_bk0_n_bk1,

1811  const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&

1812  c_grid_desc_mblock_mperblock_nblock_nperblock)

1813  {

1814  const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(

1815  p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());

1816  const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(

1817  p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());

1818  auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(

1819  p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

1820 

1821  // divide block work by [M, N]

1822  const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N, 4};

1823 

1824  const auto block_work_idx =

1825  block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));

1826 

1827  if(!block_2_ctile_map.ValidCTileIndex(

1828  block_work_idx,

1829  make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),

1830  c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))

1831  {

1832  return;

1833  }

1834 

1835  const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);

1836  const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);

1837 

1838  // HACK: this force m/n_block_data_idx_on_grid into SGPR

1839  const index_t m_block_data_idx_on_grid =

1840  __builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);

1841 

1842  const index_t n_block_data_idx_on_grid =

1843  __builtin_amdgcn_readfirstlane(block_n_id * NPerBlock);

1844 

1845  // lds max alignment

1846  constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);

1847 

1848  // A matrix in LDS memory, dst of blockwise copy

1849  constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();

1850 

1851  // B matrix in LDS memory, dst of blockwise copy

1852  constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();

1853 

1854  // A matrix blockwise copy

1855  auto a_blockwise_copy =

1856  ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,

1857  AElementwiseOperation,

1858  ck::tensor_operation::element_wise::PassThrough,

1859  InMemoryDataOperationEnum::Set,

1860  Sequence<AK0Number, MPerBlock, AK1Number>,

1861  ABlockTransferThreadClusterLengths_AK0_M_AK1,

1862  ABlockTransferThreadClusterArrangeOrder,

1863  ADataType,

1864  ADataType,

1865  decltype(a_grid_desc_ak0_m_ak1),

1866  decltype(a_block_desc_ak0_m_ak1),

1867  ABlockTransferSrcAccessOrder,

1868  Sequence<0, 1, 2>,

1869  ABlockTransferSrcVectorDim,

1870  2,

1871  ABlockTransferSrcScalarPerVector,

1872  ABlockTransferDstScalarPerVector_AK1,

1873  1,

1874  1,

1875  AThreadTransferSrcResetCoordinateAfterRun,

1876  true,

1877  BlockwiseGemmPipe::GlobalBufferNum>(

1878  a_grid_desc_ak0_m_ak1,

1879  make_multi_index(0, m_block_data_idx_on_grid, 0),

1880  problem.a_element_op_,

1881  a_block_desc_ak0_m_ak1,

1882  make_multi_index(0, 0, 0),

1883  ck::tensor_operation::element_wise::PassThrough{});

1884 

1885  // B matrix blockwise copy

1886  auto b_blockwise_copy =

1887  ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,

1888  BElementwiseOperation,

1889  ck::tensor_operation::element_wise::PassThrough,

1890  InMemoryDataOperationEnum::Set,

1891  Sequence<BK0Number, NPerBlock, BK1Number>,

1892  BBlockTransferThreadClusterLengths_BK0_N_BK1,

1893  BBlockTransferThreadClusterArrangeOrder,

1894  BDataType,

1895  BDataType,

1896  decltype(b_grid_desc_bk0_n_bk1),

1897  decltype(b_block_desc_bk0_n_bk1),

1898  BBlockTransferSrcAccessOrder,

1899  Sequence<0, 1, 2>,

1900  BBlockTransferSrcVectorDim,

1901  2,

1902  BBlockTransferSrcScalarPerVector,

1903  BBlockTransferDstScalarPerVector_BK1,

1904  1,

1905  1,

1906  BThreadTransferSrcResetCoordinateAfterRun,

1907  true,

1908  BlockwiseGemmPipe::GlobalBufferNum>(

1909  b_grid_desc_bk0_n_bk1,

1910  make_multi_index(0, n_block_data_idx_on_grid, 0),

1911  problem.b_element_op_,

1912  b_block_desc_bk0_n_bk1,

1913  make_multi_index(0, 0, 0),

1914  ck::tensor_operation::element_wise::PassThrough{});

1915 

1916  // LDS allocation for A and B: be careful of alignment

1917  constexpr auto a_block_space_size_aligned = math::integer_least_multiple(

1918  a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);

1919 

1920  auto a_block_buf_ping = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1921  static_cast<ADataType*>(p_shared_0), a_block_desc_ak0_m_ak1.GetElementSpaceSize());

1922 

1923  auto b_block_buf_ping = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1924  bit_cast<BDataType*>(static_cast<char*>(p_shared_0) +

1925  a_block_space_size_aligned * sizeof(ADataType)),

1926  b_block_desc_bk0_n_bk1.GetElementSpaceSize());

1927 

1928  auto a_block_buf_pong = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1929  static_cast<ADataType*>(p_shared_1), a_block_desc_ak0_m_ak1.GetElementSpaceSize());

1930 

1931  auto b_block_buf_pong = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1932  bit_cast<BDataType*>(bit_cast<char*>(p_shared_1) +

1933  a_block_space_size_aligned * sizeof(ADataType)),

1934  b_block_desc_bk0_n_bk1.GetElementSpaceSize());

1935 

1936  auto a_block_bufs = make_tuple(a_block_buf_ping, a_block_buf_pong);

1937  auto b_block_bufs = make_tuple(b_block_buf_ping, b_block_buf_pong);

1938 

1939  constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);

1940  constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1Number, 0, 0);

1941 

1942  // Blockwise GEMM pipeline

1943  static_assert(std::is_default_constructible_v<BlockwiseGemmPipe>);

1944  auto blockwise_gemm_pipeline = BlockwiseGemmPipe{};

1945  auto c_thread_buf = blockwise_gemm_pipeline.GetCThreadBuffer();

1946 

1947  const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(

1948  (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /

1949  KPerBlock);

1950 

1951  blockwise_gemm_pipeline.template Run<HasMainKBlockLoop, TailNum>(a_grid_desc_ak0_m_ak1,

1952  a_block_desc_ak0_m_ak1,

1953  a_blockwise_copy,

1954  a_grid_buf,

1955  a_block_bufs,

1956  a_block_slice_copy_step,

1957  b_grid_desc_bk0_n_bk1,

1958  b_block_desc_bk0_n_bk1,

1959  b_blockwise_copy,

1960  b_grid_buf,

1961  b_block_bufs,

1962  b_block_slice_copy_step,

1963  c_thread_buf,

1964  num_k_block_main_loop);

1965 

1966  // shuffle C and write out

1967  {

1968  static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&

1969  NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,

1970  "wrong!");

1971 

1972  constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);

1973  constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);

1974 

1975  // TODO: hacky, fix it!

1976  constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =

1977  blockwise_gemm_pipeline.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

1978 

1979  // TODO: hacky, fix it!

1980  // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths

1981  constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =

1982  blockwise_gemm_pipeline.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

1983 

1984  constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);

1985  constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);

1986  constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);

1987  constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);

1988  constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);

1989  constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);

1990  constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);

1991  constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);

1992 

1993  constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =

1994  GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();

1995 

1996  auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(

1997  static_cast<CShuffleDataType*>(p_shared_0),

1998  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

1999 

2000  constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(

2001  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,

2002  make_tuple(

2003  make_freeze_transform(I0),

2004  make_unmerge_transform(make_tuple(

2005  Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle

2006  M1, // M1 = MWave

2007  M2, // M2 * M3 * M4 = MPerXdl

2008  M3,

2009  M4)),

2010  make_freeze_transform(I0),

2011  make_unmerge_transform(make_tuple(

2012  Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle

2013  N1, // N1 = NWave

2014  N2))), // N2 = NPerXdl

2015  make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),

2016  make_tuple(

2017  Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));

2018 

2019  // calculate origin of thread output tensor on global memory

2020  // blockwise GEMM c matrix starting index

2021  const auto c_thread_mtx_on_block =

2022  blockwise_gemm_pipeline.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);

2023 

2024  const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];

2025  const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];

2026 

2027  const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =

2028  make_single_stage_tensor_adaptor(

2029  make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),

2030  make_tuple(Sequence<0, 1, 2, 3, 4>{}),

2031  make_tuple(Sequence<0>{}));

2032 

2033  const auto m_thread_data_on_block_idx =

2034  m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(

2035  make_multi_index(m_thread_data_on_block));

2036 

2037  const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =

2038  make_single_stage_tensor_adaptor(

2039  make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),

2040  make_tuple(Sequence<0, 1, 2>{}),

2041  make_tuple(Sequence<0>{}));

2042 

2043  const auto n_thread_data_on_block_idx =

2044  n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(

2045  make_multi_index(n_thread_data_on_block));

2046 

2047  // shuffle: threadwise copy C from VGPR to LDS

2048  auto c_thread_copy_vgpr_to_lds =

2049  ThreadwiseTensorSliceTransfer_v1r3<AccDataType,

2050  CShuffleDataType,

2051  decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),

2052  decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),

2053  ck::tensor_operation::element_wise::PassThrough,

2054  Sequence<CShuffleMXdlPerWavePerShuffle,

2055  CShuffleNXdlPerWavePerShuffle,

2056  I1,

2057  I1,

2058  M2,

2059  I1,

2060  M4,

2061  I1>,

2062  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,

2063  7,

2064  1,

2065  InMemoryDataOperationEnum::Set,

2066  1,

2067  true>{

2068  c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,

2069  make_multi_index(0,

2070  0,

2071  m_thread_data_on_block_idx[I1],

2072  n_thread_data_on_block_idx[I1],

2073  m_thread_data_on_block_idx[I2],

2074  m_thread_data_on_block_idx[I3],

2075  m_thread_data_on_block_idx[I4],

2076  n_thread_data_on_block_idx[I2]),

2077  ck::tensor_operation::element_wise::PassThrough{}};

2078 

2079  // shuffle: blockwise copy C from LDS to global

2080  auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<

2081  ThisThreadBlock, // ThreadGroup

2082  CElementwiseOperation, // ElementwiseOperation,

2083  CGlobalMemoryDataOperation, // DstInMemOp,

2084  Sequence<1,

2085  CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,

2086  1,

2087  CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,

2088  CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,

2089  Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,

2090  CShuffleDataType, // typename SrcData,

2091  CDataType, // typename DstData,

2092  decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),

2093  decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),

2094  Sequence<0, 1, 2, 3>, // typename DimAccessOrder,

2095  3, // index_t VectorDim,

2096  CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,

2097  true, // bool ThreadTransferSrcResetCoordinateAfterRun,

2098  false> // bool ThreadTransferDstResetCoordinateAfterRun>

2099  {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,

2100  make_multi_index(0, 0, 0, 0),

2101  c_grid_desc_mblock_mperblock_nblock_nperblock,

2102  make_multi_index(block_m_id, 0, block_n_id, 0),

2103  problem.c_element_op_};

2104 

2105  // space filling curve for threadwise C in VGPR

2106  constexpr auto sfc_c_vgpr =

2107  SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,

2108  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,

2109  Sequence<CShuffleMXdlPerWavePerShuffle,

2110  CShuffleNXdlPerWavePerShuffle,

2111  1,

2112  1,

2113  M2,

2114  1,

2115  M4,

2116  1>>{};

2117 

2118  // space filling curve for shuffled blockwise C in global mem

2119  constexpr auto sfc_c_global =

2120  SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,

2121  Sequence<0, 2, 1, 3>,

2122  Sequence<1,

2123  CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,

2124  1,

2125  CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};

2126 

2127  constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();

2128 

2129  static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");

2130 

2131  static_for<0, num_access, 1>{}([&](auto access_id) {

2132  // make sure it's safe to write to LDS

2133  block_sync_lds();

2134 

2135  // each thread write its data from VGPR to LDS

2136  c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,

2137  sfc_c_vgpr.GetIndexTupleOfNumber(access_id),

2138  c_thread_buf,

2139  c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,

2140  c_shuffle_block_buf);

2141 

2142  // make sure it's safe to read from LDS

2143  block_sync_lds();

2144 

2145  // each block copy its data from LDS to global

2146  c_shuffle_block_copy_lds_to_global.Run(

2147  c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,

2148  c_shuffle_block_buf,

2149  c_grid_desc_mblock_mperblock_nblock_nperblock,

2150  c_grid_buf);

2151 

2152  if constexpr(access_id < num_access - 1)

2153  {

2154  constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);

2155 

2156  // move on C

2157  c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(

2158  c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);

2159  }

2160  });

2161  }

2162  }

2163 

2164  template <bool HasMainKBlockLoop,

2165  InMemoryDataOperationEnum CGlobalMemoryDataOperation,

2166  TailNumber TailNum = TailNumber::Odd>

2167  __device__ static void Run_2Lds(const ADataType* p_a_grid,

2168  const BDataType* p_b_grid,

2169  CDataType* p_c_grid,

2170  void* p_shared_0,

2171  void* p_shared_1,

2172  const Problem& problem)

2173  {

2174  const auto a_grid_desc_ak0_m_ak1 = MakeAGridDescriptor_AK0_M_AK1(

2175  problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);

2176  const auto b_grid_desc_bk0_n_bk1 = MakeBGridDescriptor_BK0_N_BK1(

2177  problem.K, problem.KPadded, problem.N, problem.NPadded, problem.StrideB, problem.BK0);

2178  const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(

2179  problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);

2180 

2181  const auto c_grid_desc_mblock_mperblock_nblock_nperblock =

2182  MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(

2183  c_grid_desc_m_n, problem.MBlock, problem.NBlock);

2184 

2185  Run_2Lds<decltype(a_grid_desc_ak0_m_ak1),

2186  decltype(b_grid_desc_bk0_n_bk1),

2187  decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),

2188  HasMainKBlockLoop,

2189  CGlobalMemoryDataOperation,

2190  TailNum>(p_a_grid,

2191  p_b_grid,

2192  p_c_grid,

2193  p_shared_0,

2194  p_shared_1,

2195  problem,

2196  a_grid_desc_ak0_m_ak1,

2197  b_grid_desc_bk0_n_bk1,

2198  c_grid_desc_mblock_mperblock_nblock_nperblock);

2199  }

2200 };

2201 

2202 } // namespace ck

block_to_ctile_map.hpp

blockwise_gemm_pipeline_xdlops_selector.hpp

CK_MAX_THREAD_PER_BLOCK

#define CK_MAX_THREAD_PER_BLOCK

Definition: ck.hpp:29

common_header.hpp

element_wise_operation.hpp

multi_index_transform_helper.hpp

ck::math::lcm

Y __host__ constexpr __device__ auto lcm(X x, Y y)

Definition: math.hpp:198

ck::math::integer_least_multiple

__host__ constexpr __device__ auto integer_least_multiple(X x, Y y)

Definition: math.hpp:78

ck::math::integer_divide_ceil

__host__ constexpr __device__ auto integer_divide_ceil(X x, Y y)

Definition: math.hpp:72

ck::math::max

__host__ constexpr __device__ T max(T x)

Definition: math.hpp:84

ck::tensor_operation::device::GemmSpecialization

GemmSpecialization

Definition: gemm_specialization.hpp:11

ck::tensor_operation::device::GemmSpecialization::MKPadding

@ MKPadding

ck::tensor_operation::device::GemmSpecialization::KPadding

@ KPadding

ck::tensor_operation::device::GemmSpecialization::NPadding

@ NPadding

ck::tensor_operation::device::GemmSpecialization::MPadding

@ MPadding

ck::tensor_operation::device::GemmSpecialization::MNKPadding

@ MNKPadding

ck::tensor_operation::device::GemmSpecialization::MNPadding

@ MNPadding

ck::tensor_operation::device::GemmSpecialization::NKPadding

@ NKPadding

ck_tile::int32_t

int32_t int32_t

Definition: integer.hpp:10

ck

Definition: ck.hpp:269

ck::make_multi_index

__host__ constexpr __device__ auto make_multi_index(Xs &&... xs)

Definition: array_multi_index.hpp:15

ck::make_naive_tensor_descriptor

__host__ constexpr __device__ auto make_naive_tensor_descriptor(const Tuple< Lengths... > &lengths, const Tuple< Strides... > &strides)

Definition: tensor_descriptor_helper.hpp:49

ck::InMemoryDataOperationEnum

InMemoryDataOperationEnum

Definition: ck.hpp:278

ck::InMemoryDataOperationEnum::Set

@ Set

ck::make_naive_tensor_descriptor_packed

__host__ constexpr __device__ auto make_naive_tensor_descriptor_packed(const Tuple< Lengths... > &lengths)

Definition: tensor_descriptor_helper.hpp:101

ck::make_merge_transform

__host__ constexpr __device__ auto make_merge_transform(const LowLengths &low_lengths)

Definition: multi_index_transform_helper.hpp:55

ck::BlockGemmPipelineVersion

BlockGemmPipelineVersion

Definition: blkgemmpipe_scheduler.hpp:12

ck::BlockGemmPipelineVersion::v4

@ v4

ck::BlockGemmPipelineVersion::v1

@ v1

ck::make_merge_transform_v3_division_mod

__host__ constexpr __device__ auto make_merge_transform_v3_division_mod(const LowLengths &low_lengths)

Definition: multi_index_transform_helper.hpp:84

ck::TailNumber

TailNumber

Definition: blkgemmpipe_scheduler.hpp:31

ck::TailNumber::Odd

@ Odd

ck::TailNumber::Full

@ Full

ck::BlockGemmPipeline_Selector

constexpr auto BlockGemmPipeline_Selector()

Definition: blockwise_gemm_pipeline_wmma_selector.hpp:31

ck::half_t

_Float16 half_t

Definition: data_type.hpp:30

ck::make_single_stage_tensor_adaptor

__host__ constexpr __device__ auto make_single_stage_tensor_adaptor(const Transforms &transforms, LowerDimensionOldTopIdss, UpperDimensionNewTopIdss)

Definition: tensor_adaptor.hpp:429

ck::bhalf_t

ushort bhalf_t

Definition: data_type.hpp:29

ck::kernel_gemm_xdl_cshuffle_v3_2lds

__global__ void kernel_gemm_xdl_cshuffle_v3_2lds(typename GridwiseGemm::Argument karg)

Definition: gridwise_gemm_xdl_cshuffle_streamk_v3.hpp:59

ck::make_freeze_transform

__host__ constexpr __device__ auto make_freeze_transform(const LowerIndex &low_idx)

Definition: multi_index_transform_helper.hpp:98

ck::ignore

constexpr detail::ignore_t ignore

Definition: ignore.hpp:20

ck::get_block_1d_id

__device__ index_t get_block_1d_id()

Definition: get_id.hpp:25

ck::conditional_t

typename conditional< predicate, X, Y >::type conditional_t

Definition: functional.hpp:115

ck::make_xor_with_modulo_transform

__host__ constexpr __device__ auto make_xor_with_modulo_transform(const LowLengths &low_lengths)

Definition: multi_index_transform_helper.hpp:132

ck::EnvIsEnabled

bool EnvIsEnabled(EnvVar)

Definition: env.hpp:139

ck::make_pass_through_transform

__host__ constexpr __device__ auto make_pass_through_transform(const LowLength &low_length)

Definition: multi_index_transform_helper.hpp:12

ck::is_same_v

constexpr bool is_same_v

Definition: type.hpp:283

ck::BlockGemmPipelineScheduler

BlockGemmPipelineScheduler

Definition: blkgemmpipe_scheduler.hpp:25

ck::BlockGemmPipelineScheduler::Intrawave

@ Intrawave

ck::make_tuple

__host__ constexpr __device__ auto make_tuple(Xs &&... xs)

Definition: tuple.hpp:211

ck::remove_cvref_t

remove_cv_t< remove_reference_t< T > > remove_cvref_t

Definition: type.hpp:297

ck::kernel_gemm_xdl_cshuffle_v3

__global__ void kernel_gemm_xdl_cshuffle_v3(typename GridwiseGemm::Argument karg)

Definition: gridwise_gemm_xdl_cshuffle_streamk_v3.hpp:38

ck::make_unmerge_transform

__host__ constexpr __device__ auto make_unmerge_transform(const UpLengths &up_lengths, integral_constant< bool, Use24BitIntegerCalculation >=integral_constant< bool, false >{})

Definition: multi_index_transform_helper.hpp:90

ck::index_t

int32_t index_t

Definition: ck.hpp:300

ck::transform_tensor_descriptor

__host__ constexpr __device__ auto transform_tensor_descriptor(const OldTensorDescriptor &old_tensor_desc, const NewTransforms &new_transforms, NewLowerDimensionOldVisibleIdss, NewUpperDimensionNewVisibleIdss)

Definition: tensor_descriptor.hpp:319

ck::make_right_pad_transform

__host__ constexpr __device__ auto make_right_pad_transform(const LowLength &low_length, const RightPadLength &right_pad, integral_constant< bool, SkipIsValidCheck >=integral_constant< bool, false >{})

Definition: multi_index_transform_helper.hpp:37

ck::block_sync_lds

__device__ void block_sync_lds()

Definition: synchronization.hpp:10

ck::BlockToCTileMap_Grouped_M00_N0_M01Adapt

Definition: block_to_ctile_map.hpp:270

ck::BlockToCTileMap_Grouped_M00_N0_M01Adapt::CalculateGridSize

__host__ static constexpr __device__ index_t CalculateGridSize(index_t M, index_t N)

Definition: block_to_ctile_map.hpp:282

ck::GridwiseGemm_xdl_cshuffle_v3::Argument

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:708

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::b_element_op

const BElementwiseOperation b_element_op

Definition: gridwise_gemm_xdl_cshuffle_v3_b_scale.hpp:649

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::Argument

__host__ Argument(const ADataType *p_a_grid_, const BDataType *p_b_grid_, CDataType *p_c_grid_, index_t M_, index_t N_, index_t K_, index_t StrideA_, index_t StrideB_, index_t StrideC_, index_t k_batch_, bool is_reduce_=false, AElementwiseOperation a_element_op=AElementwiseOperation{}, BElementwiseOperation b_element_op=BElementwiseOperation{}, CElementwiseOperation c_element_op=CElementwiseOperation{})

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:709

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::p_b_grid

const BDataType * p_b_grid

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:751

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::p_c_grid

CDataType * p_c_grid

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:752

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::IsReduceAdd

__host__ __device__ bool IsReduceAdd() const

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:740

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::a_element_op

const AElementwiseOperation a_element_op

Definition: gridwise_gemm_xdl_cshuffle_v3_b_scale.hpp:648

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::IsAtomicAdd

__host__ __device__ bool IsAtomicAdd() const

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:745

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::p_a_grid

const ADataType * p_a_grid

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:750

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::c_element_op

const CElementwiseOperation c_element_op

Definition: gridwise_gemm_xdl_cshuffle_v3_b_scale.hpp:650

ck::GridwiseGemm_xdl_cshuffle_v3::Argument::is_reduce

bool is_reduce

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:753

ck::GridwiseGemm_xdl_cshuffle_v3::Problem

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:635

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::N

index_t N

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:687

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::NPadded

index_t NPadded

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:694

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::KBatch

index_t KBatch

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:692

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::StrideA

index_t StrideA

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:689

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::Problem

__host__ Problem(index_t M_, index_t N_, index_t K_, index_t StrideA_, index_t StrideB_, index_t StrideC_, index_t KBatch_, AElementwiseOperation a_element_op, BElementwiseOperation b_element_op, CElementwiseOperation c_element_op)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:636

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::c_element_op_

CElementwiseOperation c_element_op_

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:703

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::BK0

index_t BK0

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:698

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::M

index_t M

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:686

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::NBlock

index_t NBlock

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:700

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::MPadded

index_t MPadded

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:693

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::K

index_t K

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:688

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::StrideB

index_t StrideB

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:690

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::KPadded

index_t KPadded

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:696

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::StrideC

index_t StrideC

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:691

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::MBlock

index_t MBlock

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:699

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::b_element_op_

BElementwiseOperation b_element_op_

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:702

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::AK0

index_t AK0

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:697

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::KRead

index_t KRead

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:695

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::a_element_op_

AElementwiseOperation a_element_op_

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:701

ck::GridwiseGemm_xdl_cshuffle_v3::Problem::Print

__host__ void Print() const

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:667

ck::GridwiseGemm_xdl_cshuffle_v3::SplitKBatchOffset

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:757

ck::GridwiseGemm_xdl_cshuffle_v3::SplitKBatchOffset::a_k_split_offset

index_t a_k_split_offset

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:806

ck::GridwiseGemm_xdl_cshuffle_v3::SplitKBatchOffset::b_k_split_offset

index_t b_k_split_offset

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:807

ck::GridwiseGemm_xdl_cshuffle_v3::SplitKBatchOffset::SplitKBatchOffset

__device__ SplitKBatchOffset(Argument &karg)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:759

ck::GridwiseGemm_xdl_cshuffle_v3::SplitKBatchOffset::c_reduce_offset

index_t c_reduce_offset

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:808

ck::GridwiseGemm_xdl_cshuffle_v3

"Universal" GEMM kernel with SplitK support.

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:241

ck::GridwiseGemm_xdl_cshuffle_v3::MakeBMmaTileDescriptor_N0_N1_N2_K

__host__ static constexpr __device__ auto MakeBMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1 &)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:566

ck::GridwiseGemm_xdl_cshuffle_v3::MakeBGridDescriptor_BK0_N_BK1

__host__ static __device__ auto MakeBGridDescriptor_BK0_N_BK1(index_t K, index_t KPad, index_t N, index_t NPad, index_t StrideB, index_t BK0)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:445

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateKRead

static __host__ auto CalculateKRead(index_t K, index_t K_Batch=1)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:331

ck::GridwiseGemm_xdl_cshuffle_v3::is_scale_mfma

static constexpr auto is_scale_mfma

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:267

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateKPadded

static __host__ auto CalculateKPadded(index_t K)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:308

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateMPadded

static __host__ auto CalculateMPadded(index_t M)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:298

ck::GridwiseGemm_xdl_cshuffle_v3::BK1Number

static constexpr auto BK1Number

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:255

ck::GridwiseGemm_xdl_cshuffle_v3::APackedSize

static constexpr index_t APackedSize

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:279

ck::GridwiseGemm_xdl_cshuffle_v3::is_single_rate_mfma

static constexpr bool is_single_rate_mfma

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:258

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateKBlockLoopTailNum

static constexpr __host__ TailNumber CalculateKBlockLoopTailNum(index_t K)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1355

ck::GridwiseGemm_xdl_cshuffle_v3::ThisThreadBlock

ThisThreadBlock< BlockSize > ThisThreadBlock

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:277

ck::GridwiseGemm_xdl_cshuffle_v3::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock

__host__ static constexpr __device__ auto MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const CGridDesc &c_grid_desc_m_n, index_t MBlock, index_t NBlock)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1363

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateAK0Padded

static __host__ auto CalculateAK0Padded(index_t K, index_t K_Batch=1)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:313

ck::GridwiseGemm_xdl_cshuffle_v3::I2

static constexpr auto I2

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:244

ck::GridwiseGemm_xdl_cshuffle_v3::KPack

static constexpr index_t KPack

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:268

ck::GridwiseGemm_xdl_cshuffle_v3::lcm_AK1_BK1

static constexpr auto lcm_AK1_BK1

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:257

ck::GridwiseGemm_xdl_cshuffle_v3::MakeGemmMmaTileDescriptor

__host__ static constexpr __device__ auto MakeGemmMmaTileDescriptor(const TileDesc_K0_MN_K1 &)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:349

ck::GridwiseGemm_xdl_cshuffle_v3::I7

static constexpr auto I7

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:249

ck::GridwiseGemm_xdl_cshuffle_v3::BlockwiseGemmPipe

remove_cvref_t< decltype(BlockGemmPipeline_Selector< BlkGemmPipelineVer, BlkGemmPipeSched, BlockSize, ADataType, BDataType, ComputeTypeA, AccDataType, decltype(GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()), decltype(GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()), decltype(MakeAMmaTileDescriptor_M0_M1_M2_K(GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1())), decltype(MakeBMmaTileDescriptor_N0_N1_N2_K(GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1())), ABlockTransferSrcScalarPerVector, BBlockTransferSrcScalarPerVector, MPerBlock, NPerBlock, KPerBlock, MPerXdl, NPerXdl, MXdlPerWave, NXdlPerWave, KPack >())> BlockwiseGemmPipe

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1122

ck::GridwiseGemm_xdl_cshuffle_v3::I5

static constexpr auto I5

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:247

ck::GridwiseGemm_xdl_cshuffle_v3::AK1Number

static constexpr auto AK1Number

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:254

ck::GridwiseGemm_xdl_cshuffle_v3::CheckValidity

static constexpr __host__ bool CheckValidity(const Argument &karg)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1152

ck::GridwiseGemm_xdl_cshuffle_v3::Run_2Lds

static __device__ void Run_2Lds(const ADataType *p_a_grid, const BDataType *p_b_grid, CDataType *p_c_grid, void *p_shared_0, void *p_shared_1, const Problem &problem, const AGridDesc_AK0_M_K1 &a_grid_desc_ak0_m_ak1, const BGridDesc_BK0_N_K1 &b_grid_desc_bk0_n_bk1, const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock &c_grid_desc_mblock_mperblock_nblock_nperblock)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1803

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateGridSize

static __host__ auto CalculateGridSize(index_t M, index_t N, index_t KBatch)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:293

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateMBlock

static __host__ auto CalculateMBlock(index_t M)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:338

ck::GridwiseGemm_xdl_cshuffle_v3::Run_2Lds

static __device__ void Run_2Lds(const ADataType *p_a_grid, const BDataType *p_b_grid, CDataType *p_c_grid, void *p_shared_0, void *p_shared_1, const Problem &problem)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:2167

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateNPadded

static __host__ auto CalculateNPadded(index_t N)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:303

ck::GridwiseGemm_xdl_cshuffle_v3::MakeAMmaTileDescriptor_M0_M1_M2_K

__host__ static constexpr __device__ auto MakeAMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1 &)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:557

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateBK0Padded

static __host__ auto CalculateBK0Padded(index_t K, index_t K_Batch=1)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:319

ck::GridwiseGemm_xdl_cshuffle_v3::GetSharedMemoryNumberOfByte

static constexpr __device__ index_t GetSharedMemoryNumberOfByte()

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1124

ck::GridwiseGemm_xdl_cshuffle_v3::GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1

static constexpr __device__ auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:949

ck::GridwiseGemm_xdl_cshuffle_v3::BPackedSize

static constexpr index_t BPackedSize

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:286

ck::GridwiseGemm_xdl_cshuffle_v3::Run

static __device__ void Run(const ADataType *p_a_grid, const BDataType *p_b_grid, CDataType *p_c_grid, void *p_shared, const Problem &problem, const AGridDesc_AK0_M_K1 &a_grid_desc_ak0_m_ak1, const BGridDesc_BK0_N_K1 &b_grid_desc_bk0_n_bk1, const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock &c_grid_desc_mblock_mperblock_nblock_nperblock)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1387

ck::GridwiseGemm_xdl_cshuffle_v3::I6

static constexpr auto I6

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:248

ck::GridwiseGemm_xdl_cshuffle_v3::GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1

static constexpr __device__ auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:811

ck::GridwiseGemm_xdl_cshuffle_v3::I1

static constexpr auto I1

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:243

ck::GridwiseGemm_xdl_cshuffle_v3::I0

static constexpr auto I0

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:242

ck::GridwiseGemm_xdl_cshuffle_v3::I3

static constexpr auto I3

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:245

ck::GridwiseGemm_xdl_cshuffle_v3::Run

static __device__ void Run(const ADataType *p_a_grid, const BDataType *p_b_grid, CDataType *p_c_grid, void *p_shared, const Problem &problem)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1766

ck::GridwiseGemm_xdl_cshuffle_v3::I4

static constexpr auto I4

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:246

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateKPadded

static __host__ auto CalculateKPadded(index_t K, index_t K_Batch=1)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:325

ck::GridwiseGemm_xdl_cshuffle_v3::MakeAGridDescriptor_AK0_M_AK1

__host__ static __device__ auto MakeAGridDescriptor_AK0_M_AK1(index_t M, index_t MPad, index_t K, index_t KPad, index_t StrideA, index_t AK0)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:363

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateNBlock

static __host__ auto CalculateNBlock(index_t N)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:343

ck::GridwiseGemm_xdl_cshuffle_v3::BK0Number

static constexpr auto BK0Number

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:253

ck::GridwiseGemm_xdl_cshuffle_v3::GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock

static constexpr __device__ auto GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1083

ck::GridwiseGemm_xdl_cshuffle_v3::AK0Number

static constexpr auto AK0Number

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:252

ck::GridwiseGemm_xdl_cshuffle_v3::CalculateHasMainKBlockLoop

static constexpr __host__ bool CalculateHasMainKBlockLoop(index_t K)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:1348

ck::GridwiseGemm_xdl_cshuffle_v3::MakeCGridDescriptor_M_N

__host__ static __device__ auto MakeCGridDescriptor_M_N(index_t M, index_t MPad, index_t N, index_t NPad, index_t StrideC)

Definition: gridwise_gemm_xdl_cshuffle_v3.hpp:574

ck::MfmaSelector

Definition: xdlops_gemm.hpp:942

ck::Sequence

Definition: sequence.hpp:43

ck::SpaceFillingCurve

Definition: tensor_space_filling_curve.hpp:20

ck::ThreadGroupTensorSliceTransfer_v4r1

Blockwise data transfer.

Definition: thread_group_tensor_slice_transfer_v4r1.hpp:46

ck::ThreadGroupTensorSliceTransfer_v6r1

Definition: thread_group_tensor_slice_transfer_v6r1.hpp:34

ck::ThreadwiseTensorSliceTransfer_v1r3

Definition: threadwise_tensor_slice_transfer.hpp:39

ck::integral_constant

Definition: integral_constant.hpp:20

ck::is_same

Definition: type.hpp:177

ck::pk_i4_t

Definition: data_type.hpp:186

ck::static_for

Definition: functional2.hpp:33

ck::tensor_operation::device::BaseArgument

Definition: device_base.hpp:51

ck::tensor_operation::element_wise::PassThrough

Definition: unary_element_wise_operation.hpp:308

tensor_descriptor.hpp

tensor_descriptor_helper.hpp

thread_group_tensor_slice_transfer_v4r1.hpp

thread_group_tensor_slice_transfer_v6r1.hpp

threadwise_tensor_slice_transfer.hpp

env.hpp

CK_ENV

#define CK_ENV(name)

Definition: env.hpp:128