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Commit 7572a691 authored by coderfeli's avatar coderfeli
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merge develop

parents 7796fc73 6b6fcd37
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include/ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -57,6 +59,18 @@ struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
using BLayout = typename Base::BLayout;
using CLayout = typename Base::CLayout;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
using P_ = GemmPipeline;
return concat('_', "gemm_batched", gemm_prec_str<ADataType, BDataType>,
concat('x', P_::kMPerBlock, P_::kNPerBlock, P_::kKPerBlock),
concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
// clang-format on
}
struct BatchedGemmKernelArgs : GemmKernelArgs
{
index_t batch_stride_A;
......@@ -67,9 +81,10 @@ struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
using KernelArgs = BatchedGemmKernelArgs;
__host__ static constexpr auto GridSize(index_t M, index_t N, index_t batch_count)
__host__ static constexpr auto
GridSize(index_t M, index_t N, index_t KBatch, index_t batch_count)
{
return TilePartitioner::GridSize(M, N, batch_count);
return dim3(TilePartitioner::GridSize(M, N), batch_count, KBatch);
}
__host__ static constexpr auto BlockSize() { return dim3(Base::KernelBlockSize); }
......@@ -85,7 +100,8 @@ struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
hostArgs.K,
hostArgs.stride_A,
hostArgs.stride_B,
hostArgs.stride_C},
hostArgs.stride_C,
hostArgs.k_batch},
hostArgs.batch_stride_A,
hostArgs.batch_stride_B,
hostArgs.batch_stride_C,
......@@ -99,23 +115,42 @@ struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
CK_TILE_DEVICE void operator()(BatchedGemmKernelArgs kargs) const
{
const auto [i_m, i_n] = TilePartitioner{}();
const auto i_batch = __builtin_amdgcn_readfirstlane(blockIdx.z);
const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockIdx.x);
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
const auto i_batch = __builtin_amdgcn_readfirstlane(blockIdx.y);
const auto i_splitk = __builtin_amdgcn_readfirstlane(blockIdx.z);
const typename Base::SplitKBatchOffset splitk_batch_offset(kargs, i_splitk);
// options
const auto batch_stride_A = __builtin_amdgcn_readfirstlane(kargs.batch_stride_A);
const auto batch_offset_A = __builtin_amdgcn_readfirstlane(i_batch * batch_stride_A);
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr) + batch_offset_A;
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr) + batch_offset_A +
splitk_batch_offset.a_k_split_offset;
const auto batch_stride_B = __builtin_amdgcn_readfirstlane(kargs.batch_stride_B);
const auto batch_offset_B = __builtin_amdgcn_readfirstlane(i_batch * batch_stride_B);
const BDataType* b_ptr = static_cast<const BDataType*>(kargs.b_ptr) + batch_offset_B;
const BDataType* b_ptr = static_cast<const BDataType*>(kargs.b_ptr) + batch_offset_B +
splitk_batch_offset.b_k_split_offset;
const auto batch_stride_C = __builtin_amdgcn_readfirstlane(kargs.batch_stride_C);
const auto batch_offset_C = __builtin_amdgcn_readfirstlane(i_batch * batch_stride_C);
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr) + batch_offset_C;
this->RunGemm(a_ptr, b_ptr, c_ptr, kargs, i_m, i_n);
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];
if(kargs.k_batch == 1)
{
this->RunGemm(a_ptr, b_ptr, c_ptr, smem_ptr, kargs, splitk_batch_offset, i_m, i_n);
}
else
{
this->template RunGemm<memory_operation_enum::atomic_add>(
a_ptr, b_ptr, c_ptr, smem_ptr, kargs, splitk_batch_offset, i_m, i_n);
}
}
};
......
include/ck_tile/ops/gemm/kernel/gemm_kernel.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -8,7 +8,7 @@
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -69,18 +69,26 @@ struct GemmKernel
using ADataType = remove_cvref_t<typename GemmPipeline::ADataType>;
using BDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
// Below type is actually accumulation data type - the output of block GEMM.
using CDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
static constexpr auto I0 = number<0>();
static constexpr auto I1 = number<1>();
static constexpr auto I2 = number<2>();
__host__ static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
return TilePartitioner::GridSize(M, N, KBatch);
// clang-format off
return concat('_', "gemm", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
// clang-format on
}
__host__ static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
{
return dim3(TilePartitioner::GridSize(M, N), 1, KBatch);
}
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
struct GemmKernelArgs
{
......@@ -93,6 +101,7 @@ struct GemmKernel
index_t stride_A;
index_t stride_B;
index_t stride_C;
index_t k_batch;
};
CK_TILE_HOST static constexpr GemmKernelArgs MakeKernelArgs(const GemmHostArgs& hostArgs)
......@@ -105,121 +114,188 @@ struct GemmKernel
hostArgs.K,
hostArgs.stride_A,
hostArgs.stride_B,
hostArgs.stride_C};
hostArgs.stride_C,
hostArgs.k_batch};
}
// CK_TILE_HOST static constexpr GemmKernelArgs MakeKernelArgs(const void* a_ptr,
// const void* b_ptr,
// void* c_ptr,
// index_t M,
// index_t N,
// index_t K,
// index_t stride_A,
// index_t stride_B,
// index_t stride_C)
// {
// return GemmKernelArgs{a_ptr, b_ptr, c_ptr, M, N, K, stride_A, stride_B, stride_C};
// }
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
}
struct SplitKBatchOffset
{
__device__ SplitKBatchOffset(const GemmKernelArgs& kargs,
const std::size_t k_id = blockIdx.z)
{
constexpr auto K1 = TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{});
const index_t K_t = kargs.k_batch * K1;
const index_t KRead = (kargs.K + K_t - 1) / K_t * K1;
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
a_k_split_offset = k_id * KRead;
}
else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
{
a_k_split_offset = k_id * KRead * kargs.stride_A;
}
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
{
b_k_split_offset = k_id * KRead * kargs.stride_B;
}
else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
{
b_k_split_offset = k_id * KRead;
}
if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
{
splitted_k = KRead;
}
else
{
splitted_k = kargs.K - KRead * (kargs.k_batch - 1);
}
}
index_t a_k_split_offset;
index_t b_k_split_offset;
index_t splitted_k;
};
CK_TILE_HOST static bool IsSupportedArgument(const GemmKernelArgs& kargs)
{
if constexpr(EpiloguePipeline::template GetVectorSizeC<CDataType>() % 2 != 0 &&
is_any_of<CDataType, fp16_t, bf16_t>::value)
{
if(kargs.k_batch != 1)
{
std::cerr << "Conditions not met for Kbatch >1 !" << std::endl;
return false;
}
}
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
if(kargs.K % TilePartitioner::kK != 0 && GemmPipeline::kPadK == false)
if(kargs.K % TilePartitioner::KPerBlock != 0 && GemmPipeline::kPadK == false)
{
std::cerr << "Can't support K that is not a multiple of KPerBlock"
" without padding!"
<< std::endl;
return false;
}
if(kargs.K % GemmPipeline::VectorSizeA != 0)
if(kargs.K % GemmPipeline::GetVectorSizeA() != 0)
{
std::cerr << "K is not a multiple of vector load size for A tensor!" << std::endl;
return false;
}
}
else
{
if(kargs.M % TilePartitioner::kM != 0 && GemmPipeline::kPadM == false)
if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
{
std::cerr << "Can't support M that is not a multiple of MPerBlock"
" without padding!"
<< std::endl;
return false;
}
if(kargs.M % GemmPipeline::VectorSizeA != 0)
if(kargs.M % GemmPipeline::GetVectorSizeA() != 0)
{
std::cerr << "M is not a multiple of vector load size for A tensor!" << std::endl;
return false;
}
}
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
{
if(kargs.N % TilePartitioner::kN != 0 && GemmPipeline::kPadN == false)
if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
{
std::cerr << "Can't support N that is not a multiple of NPerBlock"
" without padding!"
<< std::endl;
return false;
}
if(kargs.N % GemmPipeline::VectorSizeB != 0)
if(kargs.N % GemmPipeline::GetVectorSizeB() != 0)
{
std::cerr << "N is not a multiple of vector load size for B tensor!" << std::endl;
return false;
}
}
else
{
if(kargs.K % TilePartitioner::kK != 0 && GemmPipeline::kPadK == false)
if(kargs.K % TilePartitioner::KPerBlock != 0 && GemmPipeline::kPadK == false)
{
std::cerr << "Can't support K that is not a multiple of KPerBlock"
" without padding!"
<< std::endl;
return false;
}
if(kargs.K % GemmPipeline::VectorSizeB != 0)
if(kargs.K % GemmPipeline::GetVectorSizeB() != 0)
{
std::cerr << "K is not a multiple of vector load size for B tensor!" << std::endl;
return false;
}
}
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
if(kargs.N % TilePartitioner::kN != 0 && GemmPipeline::kPadN == false)
if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
{
std::cerr << "Can't support N that is not a multiple of NPerBlock"
" without padding!"
<< std::endl;
return false;
}
if(kargs.N % GemmPipeline::VectorSizeC != 0)
if(kargs.N % EpiloguePipeline::template GetVectorSizeC<CDataType>() != 0)
{
std::cerr << "N is not a multiple of vector load size for C tensor!" << std::endl;
return false;
}
}
else
{
if(kargs.M % TilePartitioner::kM != 0 && GemmPipeline::kPadM == false)
if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
{
std::cerr << "Can't support M that is not a multiple of MPerBlock"
" without padding!"
<< std::endl;
return false;
}
if(kargs.M % GemmPipeline::VectorSizeC != 0)
if(kargs.M % EpiloguePipeline::template GetVectorSizeC<CDataType>() != 0)
{
std::cerr << "M is not a multiple of vector load size for C tensor!" << std::endl;
return false;
}
}
return true;
}
CK_TILE_DEVICE auto MakeGemmTensorViews(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
const GemmKernelArgs& kargs) const
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
CK_TILE_DEVICE static auto MakeGemmTensorViews(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
const GemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset)
{
const auto& a_tensor_view = [&]() {
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
a_ptr,
make_tuple(kargs.M, kargs.K),
make_tuple(kargs.M, splitk_batch_offset.splitted_k),
make_tuple(kargs.stride_A, 1),
number<GemmPipeline::VectorSizeA>{},
number<GemmPipeline::GetVectorSizeA()>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
a_ptr,
make_tuple(kargs.M, kargs.K),
make_tuple(1, kargs.stride_A),
number<1>{},
make_tuple(splitk_batch_offset.splitted_k, kargs.M),
make_tuple(kargs.stride_A, 1),
number<GemmPipeline::GetVectorSizeA()>{},
number<1>{});
}
}();
......@@ -229,35 +305,36 @@ struct GemmKernel
{
return make_naive_tensor_view<address_space_enum::global>(
b_ptr,
make_tuple(kargs.N, kargs.K),
make_tuple(1, kargs.stride_B),
number<1>{},
make_tuple(splitk_batch_offset.splitted_k, kargs.N),
make_tuple(kargs.stride_B, 1),
number<GemmPipeline::GetVectorSizeB()>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
b_ptr,
make_tuple(kargs.N, kargs.K),
make_tuple(kargs.N, splitk_batch_offset.splitted_k),
make_tuple(kargs.stride_B, 1),
number<GemmPipeline::VectorSizeB>{},
number<GemmPipeline::GetVectorSizeB()>{},
number<1>{});
}
}();
// TODO: enable vector write for C in ColMajor
const auto& c_tensor_view = [&]() {
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
c_ptr,
make_tuple(kargs.M, kargs.N),
make_tuple(kargs.stride_C, 1),
number<GemmPipeline::VectorSizeC>{},
number<EpiloguePipeline::template GetVectorSizeC<CDataType>()>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
c_ptr,
make_tuple(kargs.M, kargs.N),
make_tuple(1, kargs.stride_C),
......@@ -270,23 +347,23 @@ struct GemmKernel
}
template <typename TensorView>
CK_TILE_DEVICE auto MakeGemmPadViews(const TensorView& views) const
CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
{
const auto& a_pad_view = [&]() {
const auto& a_tensor_view = views.at(I0);
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return pad_tensor_view(
a_tensor_view,
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
sequence<false, GemmPipeline::kPadK>{});
return pad_tensor_view(a_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<false, GemmPipeline::kPadK>{});
}
else
{
return pad_tensor_view(
a_tensor_view,
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
sequence<GemmPipeline::kPadM, false>{});
return pad_tensor_view(a_tensor_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::MPerBlock>{}),
sequence<false, GemmPipeline::kPadM>{});
}
}();
......@@ -294,35 +371,36 @@ struct GemmKernel
const auto& b_tensor_view = views.at(I1);
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
{
return pad_tensor_view(
b_tensor_view,
make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
sequence<false, GemmPipeline::kPadK>{});
return pad_tensor_view(b_tensor_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<false, GemmPipeline::kPadK>{});
}
else
{
return pad_tensor_view(
b_tensor_view,
make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
sequence<GemmPipeline::kPadN, false>{});
return pad_tensor_view(b_tensor_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<false, GemmPipeline::kPadN>{});
}
}();
// TODO vector write in for C in ColMajor
const auto& c_pad_view = [&]() {
const auto& c_tensor_view = views.at(I2);
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
return pad_tensor_view(
c_tensor_view,
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kN>{}),
sequence<false, GemmPipeline::kPadN>{});
return pad_tensor_view(c_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<false, GemmPipeline::kPadN>{});
}
else
{
return pad_tensor_view(
c_tensor_view,
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kN>{}),
sequence<GemmPipeline::kPadM, false>{});
return pad_tensor_view(c_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<GemmPipeline::kPadM, false>{});
}
}();
......@@ -330,25 +408,50 @@ struct GemmKernel
}
template <typename PadView>
CK_TILE_DEVICE auto
MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n) const
CK_TILE_DEVICE static auto
MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
{
const auto& a_pad_view = views.at(I0);
const auto& a_block_window = make_tile_window(
a_pad_view,
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
{i_m, 0});
const auto& b_pad_view = views.at(I1);
const auto& b_block_window = make_tile_window(
b_pad_view,
make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
{i_n, 0});
const auto& a_pad_view = views.at(I0);
const auto& b_pad_view = views.at(I1);
const auto& c_pad_view = views.at(I2);
auto c_block_window = make_tile_window(
const auto& a_block_window = [&]() {
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return make_tile_window(a_pad_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
{i_m, 0});
}
else
{
return make_tile_window(a_pad_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::MPerBlock>{}),
{0, i_m});
}
}();
const auto& b_block_window = [&]() {
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
{
return make_tile_window(b_pad_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
{i_n, 0});
}
else
{
return make_tile_window(b_pad_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
{0, i_n});
}
}();
auto c_block_window = make_tile_window(
c_pad_view,
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kN>{}),
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
{i_m, i_n});
return make_tuple(a_block_window, b_block_window, c_block_window);
......@@ -360,47 +463,162 @@ struct GemmKernel
* @param a_ptr input A pointer
* @param b_ptr input B pointer
* @param c_ptr output C pointer
* @param smem_ptr_0 The start memory pointer of the shared memory block.
* @param kargs GEMM kernel arguments
* @param splitk_batch_offset splitk_batch_offset Utility structure used to calculate k batch.
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
*
* @tparam DstInMemOp Destination memory operation (default: set).
*/
CK_TILE_DEVICE void RunGemm(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
const GemmKernelArgs& kargs,
const index_t block_idx_m,
const index_t block_idx_n) const
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
CK_TILE_DEVICE static void RunGemm(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
void* smem_ptr_0,
const GemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset,
const index_t block_idx_m,
const index_t block_idx_n)
{
// Create Gemm tensor views, pad views and tile windows
const auto& gemm_tensor_views_tuple = MakeGemmTensorViews(a_ptr, b_ptr, c_ptr, kargs);
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const auto& gemm_tensor_views_tuple =
MakeGemmTensorViews<DstInMemOp>(a_ptr, b_ptr, c_ptr, kargs, splitk_batch_offset);
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k);
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
const auto& b_block_window = gemm_tile_windows.at(I1);
const auto& c_block_tile = GemmPipeline{}.template operator()(
a_block_window, b_block_window, num_loop, smem_ptr_0);
// Run Epilogue Pipeline
auto& c_block_window = gemm_tile_windows.at(I2);
EpiloguePipeline{}
.template operator()<decltype(c_block_window), decltype(c_block_tile), DstInMemOp>(
c_block_window, c_block_tile, smem_ptr_0);
}
/**
* @brief Runs single GEMM problem cooperatively by whole workgroup.
*
* @note RunGEMM2LDS in with two shared memory buffers using the ping pong buffer mechanism.
*
* @param a_ptr input A pointer
* @param b_ptr input B pointer
* @param c_ptr output C pointer
* @param smem_ptr_0 The starting pointer of 1st shared memory block.
* @param smem_ptr_1 The starting pointer of 2nd shared memory block.
* @param kargs GEMM kernel arguments
* @param splitk_batch_offset Utility structure used to calculate k batch.
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
*
* @tparam DstInMemOp Destination memory operation (default: set).
*/
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
CK_TILE_DEVICE static void RunGemm2LDS(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
void* __restrict__ smem_ptr_0,
void* __restrict__ smem_ptr_1,
const GemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset,
const index_t block_idx_m,
const index_t block_idx_n)
{
// Create Gemm tensor views, pad views and tile windows
const auto& gemm_tensor_views_tuple =
MakeGemmTensorViews<DstInMemOp>(a_ptr, b_ptr, c_ptr, kargs, splitk_batch_offset);
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = TilePartitioner::GetLoopNum(kargs.K);
const index_t num_loop = TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k);
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
const auto& b_block_window = gemm_tile_windows.at(I1);
const auto& c_block_tile =
GemmPipeline{}.template operator()(a_block_window, b_block_window, num_loop, smem_ptr);
const auto& c_block_tile = GemmPipeline{}.template operator()(
a_block_window, b_block_window, num_loop, smem_ptr_0, smem_ptr_1);
// Run Epilogue Pipeline
auto& c_block_window = gemm_tile_windows.at(I2);
EpiloguePipeline{}(c_block_window, c_block_tile);
EpiloguePipeline{}
.template operator()<decltype(c_block_window), decltype(c_block_tile), DstInMemOp>(
c_block_window, c_block_tile, smem_ptr_0);
}
CK_TILE_DEVICE void operator()(GemmKernelArgs kargs) const
{
const auto [i_m, i_n] = TilePartitioner{}();
const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockIdx.x);
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
const SplitKBatchOffset splitk_batch_offset(kargs);
// options
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr);
const BDataType* b_ptr = static_cast<const BDataType*>(kargs.b_ptr);
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
const ADataType* a_ptr =
static_cast<const ADataType*>(kargs.a_ptr) + splitk_batch_offset.a_k_split_offset;
const BDataType* b_ptr =
static_cast<const BDataType*>(kargs.b_ptr) + splitk_batch_offset.b_k_split_offset;
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
// allocate LDS
__shared__ char smem_ptr_0[GetSmemSize()];
__shared__ char smem_ptr_1[GetSmemSize()];
RunGemm(a_ptr, b_ptr, c_ptr, kargs, i_m, i_n);
if(kargs.k_batch == 1)
{
if constexpr(GemmPipeline::DoubleSmemBuffer == true)
{
RunGemm2LDS(a_ptr,
b_ptr,
c_ptr,
smem_ptr_0,
smem_ptr_1,
kargs,
splitk_batch_offset,
i_m,
i_n);
}
else
{
RunGemm(a_ptr, b_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
}
}
else
{
// Do not compile in case where we have unsupported
// VectorSizeC & data type configuration.
if constexpr(!(EpiloguePipeline::template GetVectorSizeC<CDataType>() % 2 != 0 &&
is_any_of<CDataType, fp16_t, bf16_t>::value))
{
if constexpr(GemmPipeline::DoubleSmemBuffer == true)
{
RunGemm2LDS<memory_operation_enum::atomic_add>(a_ptr,
b_ptr,
c_ptr,
smem_ptr_0,
smem_ptr_1,
kargs,
splitk_batch_offset,
i_m,
i_n);
}
else
{
RunGemm<memory_operation_enum::atomic_add>(
a_ptr, b_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
}
}
}
}
};
......
include/ck_tile/ops/gemm/kernel/gemm_tile_partitioner.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
/**
* @file
* GemmTilePartitioner allows customized mapping between a workgroup and the C-tile it computes.
*/
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
template <typename BlockGemmShape_>
struct GemmTilePartitioner
/**
* @brief Class providing 2D workgroup index mapping into 2D output GEMM C-tile space.
*
*/
template <typename BlockGemmShapeType>
struct GemmTile2DPartitioner
{
using BlockGemmShape = remove_cvref_t<BlockGemmShape_>;
using BlockGemmShape = remove_cvref_t<BlockGemmShapeType>;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t kM = BlockGemmShape::kM;
static constexpr index_t kN = BlockGemmShape::kN;
static constexpr index_t kK = BlockGemmShape::kK;
CK_TILE_HOST_DEVICE GemmTile2DPartitioner() noexcept = delete;
CK_TILE_HOST_DEVICE GemmTile2DPartitioner([[maybe_unused]] index_t M,
[[maybe_unused]] index_t N) noexcept;
CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t batch_size)
/**
* @brief Calculates GEMM kernel grid size.
*
* @param M GEMM's M dimension.
* @param N GEMM's N dimension.
* @return dim3 Structure holding grid's X,Y and Z dimensions.
*/
CK_TILE_HOST static auto
GridSize(index_t M, index_t N) noexcept(noexcept(MPerBlock != 0 && NPerBlock != 0)) -> dim3
{
index_t GridDimX = (M + kM - 1) / kM;
index_t GridDimY = (N + kN - 1) / kN;
index_t GridDimZ = batch_size;
return dim3(GridDimX, GridDimY, GridDimZ);
const index_t GridDimX = (M + MPerBlock - 1) / MPerBlock;
const index_t GridDimY = (N + NPerBlock - 1) / NPerBlock;
return dim3(GridDimX, GridDimY, 1);
}
CK_TILE_HOST_DEVICE static constexpr auto GetLoopNum(index_t K)
/**
* @brief Calculate number of loop iterations over GEMM's K dimension.
*
* @param K GEMM's K dimension.
* @return index_t The number of loop iterations over K dimension.
*/
CK_TILE_HOST_DEVICE static auto GetLoopNum(index_t K) noexcept -> index_t
{
return integer_divide_ceil(K, kK);
return integer_divide_ceil(K, KPerBlock);
}
CK_TILE_DEVICE auto operator()()
/**
* @brief The function returns 2D output tile space.
* @param [in] blockIdx is blockIdx.x
* @param [in] blockIdy is blockIdx.y
* @return Returns the output tile indexes.
*/
/**
* @brief Calculate workgroup 2D index mapping into 2D output C-tile space.
*
* @param blockIdx WGP's X index.
* @param blockIdy WGP's Y index.
* @return const tuple<index_t, index_t> Tuple containing 2D output C-tile index.
*/
CK_TILE_DEVICE static auto GetOutputTileIndex(index_t blockIdx, index_t blockIdy) noexcept
-> const tuple<index_t, index_t>
{
const index_t iM = __builtin_amdgcn_readfirstlane(blockIdx.x * kM);
const index_t iN = __builtin_amdgcn_readfirstlane(blockIdx.y * kN);
const index_t iM = __builtin_amdgcn_readfirstlane(blockIdx);
const index_t iN = __builtin_amdgcn_readfirstlane(blockIdy);
return make_tuple(iM, iN);
}
};
/**
* @brief Class providing 1D WGP index mapping into 2D output C-tile space.
*
* @tparam BlockGemmShape_ A class providing basic GEMM parameters. \link TileGemmShape
*/
template <typename BlockGemmShape_>
struct GemmTile1DPartitioner
{
......@@ -45,30 +92,261 @@ struct GemmTile1DPartitioner
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N)
CK_TILE_HOST_DEVICE GemmTile1DPartitioner() noexcept = delete;
/**
* @brief Construct a new GemmTile1DPartitioner object.
*
* @param M GEMM's M dimension.
* @param N GEMM's N dimension.
*/
CK_TILE_HOST_DEVICE GemmTile1DPartitioner([[maybe_unused]] index_t M, index_t N) noexcept
{
index_t GridDimX = (M + MPerBlock - 1) / MPerBlock;
index_t GridDimY = (N + NPerBlock - 1) / NPerBlock;
return dim3(GridDimX * GridDimY, 1, 1);
N_ = N;
}
CK_TILE_HOST_DEVICE static constexpr auto GetNBlock(index_t N)
/**
* @brief Calculates GEMM kernel grid size.
*
* @param M GEMM's M dimension.
* @param N GEMM's N dimension.
* @return dim3 Structure holding grid's X,Y and Z dimensions.
*/
CK_TILE_HOST static auto
GridSize(index_t M, index_t N) noexcept(noexcept(MPerBlock != 0 && NPerBlock != 0)) -> index_t
{
return integer_divide_ceil(N, NPerBlock);
const index_t GridDimX = (M + MPerBlock - 1) / MPerBlock;
const index_t GridDimY = (N + NPerBlock - 1) / NPerBlock;
return GridDimX * GridDimY;
}
CK_TILE_HOST_DEVICE static constexpr auto GetLoopNum(index_t K)
/**
* @brief Calculate number of loop iterations over GEMM's K dimension.
*
* @param K GEMM's K dimension.
* @return index_t The number of loop iterations over K dimension.
*/
CK_TILE_HOST_DEVICE static auto GetLoopNum(index_t K) noexcept -> index_t
{
return integer_divide_ceil(K, KPerBlock);
}
CK_TILE_DEVICE auto operator()(index_t blockOffset, index_t NBlockSize)
/**
* @brief Calculate workgroup 1D index mapping into 2D output C-tile space.
*
* @param blockIdx WGP's index.
* @return const tuple<index_t, index_t> Tuple containing 2D output C-tile index.
*/
CK_TILE_DEVICE static auto GetOutputTileIndex(index_t blockIdx) noexcept
-> const tuple<index_t, index_t>
{
const index_t NBlocks = integer_divide_ceil(N_, NPerBlock);
const index_t iM = __builtin_amdgcn_readfirstlane(blockIdx / NBlocks);
const index_t iN = __builtin_amdgcn_readfirstlane(blockIdx - iM * NBlocks);
return make_tuple(iM, iN);
}
private:
CK_TILE_DEVICE static index_t N_;
};
/**
* @brief `GemmTile1DPartitioner::GetOutputTileIndex`'s std::false specialization,
* checking expression validity in-place for ill-formed.
*/
template <typename, typename = void>
struct HasFnOneArgImpl : std::false_type
{
};
/**
* @brief `GemmTile1DPartitioner::GetOutputTileIndex`'s std::true specialization,
* checking expression validity in-place for well-formed.
* @note: `1` - a constant value indicating the number of parameters in the function.
*/
template <typename T>
struct HasFnOneArgImpl<T, std::void_t<decltype(std::declval<T>().GetOutputTileIndex(1))>>
: std::true_type
{
};
/**
* @brief Struct used to calculate offseted tile indexes.
* @note: The struct supports the 1D-Partitioner mechanism,
* enable-if `GetOutputTileIndex`-fn is std::true_type when `GetOutputTileIndex`-fn is well-formed,
* otherwise std::false_type.
*/
template <typename TilePartitioner,
typename = typename std::enable_if_t<HasFnOneArgImpl<TilePartitioner>{}>>
struct OffsettedTile1DPartitioner
{
/**
* @brief The function subtracts the block's start (offset) from 1D raw-indexes.
* @param [in] block_start Workgroup offset.
* @param [in] M Gemm's M dimension.
* @param [in] N Gemm's N dimension.
* @return Returns a `tuple` [Im, In] with shifted index.
*/
[[nodiscard]] CK_TILE_DEVICE static auto
GetOffsetedTileIndex(index_t block_start, index_t M, index_t N) noexcept
-> const tuple<index_t, index_t>
{
index_t iM = __builtin_amdgcn_readfirstlane((blockIdx.x - blockOffset) /
GetNBlock(NBlockSize) * MPerBlock);
index_t iN = __builtin_amdgcn_readfirstlane((blockIdx.x - blockOffset) %
GetNBlock(NBlockSize) * NPerBlock);
const auto [iM, iN] = TilePartitioner{M, N}.GetOutputTileIndex(blockIdx.x - block_start);
return make_tuple(iM, iN);
}
};
/**
* @brief Class mapping 1D block index into 2D output tile space.
*
* @note It groups spatially workgroups in order to better utilize caches.
* It is using grouped Rows of column-vectors WGP pattern. It's optimized
* for gfx94x-like multiple-die chip.
*
* @tparam GroupNum - The number of big groups.
* @tparam M01 - The number of groups in M dim within spatially local WGPs,
*
*/
template <typename BlockGemmShapeType, index_t GroupNum, index_t M01>
struct GemmSpatiallyLocalTilePartitioner
{
using BlockGemmShape = remove_cvref_t<BlockGemmShapeType>;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
CK_TILE_HOST_DEVICE GemmSpatiallyLocalTilePartitioner() noexcept = delete;
CK_TILE_HOST_DEVICE GemmSpatiallyLocalTilePartitioner(index_t M_, index_t N_) noexcept
: M(M_), N(N_)
{
}
/**
* @brief Calculates GEMM kernel grid size.
*
* @param M GEMM's M dimension.
* @param N GEMM's N dimension.
* @return index_t A total number of workgroups.
*/
CK_TILE_HOST static auto
GridSize(index_t M, index_t N) noexcept(noexcept(MPerBlock != 0 && NPerBlock != 0)) -> index_t
{
const index_t GridDimX = integer_divide_ceil(M, MPerBlock);
const index_t GridDimY = integer_divide_ceil(N, NPerBlock);
return GridDimX * GridDimY;
}
/**
* @brief Calculate number of loop iterations over GEMM's K dimension.
*
* @param K GEMM's K dimension.
* @return index_t The number of loop iterations over K dimension.
*/
CK_TILE_HOST_DEVICE static auto GetLoopNum(index_t K) noexcept -> index_t
{
return integer_divide_ceil(K, KPerBlock);
}
/**
* @brief Calculate workgroup 1D index mapping into 2D output C-tile space.
*
* @param [in] block_1d_id WGP's index.
* @return const tuple<index_t, index_t> Tuple containing 2D output C-tile index.
*/
CK_TILE_DEVICE auto GetOutputTileIndex(index_t block_1d_id) noexcept
-> const tuple<index_t, index_t>
{
const auto M0 = integer_divide_ceil(M, MPerBlock);
const auto N0 = integer_divide_ceil(N, NPerBlock);
if(M0 == 1)
{
return make_tuple(0, block_1d_id);
}
else if(N0 == 1)
{
return make_tuple(block_1d_id, 0);
}
// block_1d_id = block_1d_id % (M0 * N0); // swallow batch index
else
{
const auto group_size = integer_divide_ceil(M0 * N0, GroupNum);
const auto big_group_num = GroupNum - (group_size * GroupNum - M0 * N0);
const auto group_id_y = block_1d_id / GroupNum;
const auto group_id_x = block_1d_id - group_id_y * GroupNum;
const auto remap_block_1d_id =
group_id_x <= big_group_num
? group_id_x * group_size + group_id_y
: group_id_x * group_size + big_group_num - group_id_x + group_id_y;
const index_t idx_M0 = remap_block_1d_id / N0;
const index_t idx_N0 = remap_block_1d_id - idx_M0 * N0;
const index_t M0_tmp = M0 / M01;
const index_t M0_mod_M01 = M0 - M0_tmp * M01;
const auto M01_adapt = (idx_M0 < M0 - M0_mod_M01) ? M01 : M0_mod_M01;
const index_t idx_M00 = idx_M0 / M01;
const index_t idx_M01 = idx_M0 - idx_M00 * M01;
const index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
/**
* idxN0
*
* |< mtx N >|
*
* NPerBlock NPerBlock NPerBlock NPerBlock
* N_0 N_1 N_2 N_3
* - |-----------|-----------|-----------|-----|-----|-
* ^ | - - 0 |/----> 2 | | | |
* | | | / | | | | | M_0 MPerBlock
* | M | /| | | | | |
* |-0---|---/-|-----|-----|-----------|-----|-----|-
* | 1 | / | | | blockid | | |
* idxM0 | | | / | V | 5 | | | M_1 MPerBlock
* | - V 1 | - 3 | | | |
* |-----------|-----------|-----------|-----|-----|-
* mtx M | | | | | |
* | | | | | | M_2 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* | | | | | |
* | | | | | | M_3 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* V | | | | | |
* - |-----------|-----------|-----------|-----|-----|- M_4 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* Example:
* assume:
* M0 = 5
* N0 = 4
* block_1d_id = 5
* M01 = 2
*
* idx_N0 = 1
* idx_M0 = 1
* M01_adapt = 2
* idx_M00 = 0
* idx_M01 = 1
* idx_N0_M01_local = 5
* output {1, 2}
*/
const index_t N_out = idx_N0_M01_local / M01_adapt;
const index_t idx_loc_mod_M01 = idx_N0_M01_local - N_out * M01_adapt;
return make_tuple(idx_loc_mod_M01 + idx_M00 * M01, N_out);
}
}
private:
index_t M;
index_t N;
};
} // namespace ck_tile
include/ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <string>
#include "ck_tile/core/numeric/math.hpp"
#include "ck_tile/core/utility/literals.hpp"
#include "ck_tile/core/utility/amd_address_space.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
#include "ck_tile/host.hpp"
namespace ck_tile {
struct GroupedGemmHostArgs
struct GroupedGemmHostArgs : public ck_tile::GemmHostArgs
{
const void* a_ptr;
const void* b_ptr;
void* c_ptr;
index_t M;
index_t N;
index_t K;
index_t stride_A;
index_t stride_B;
index_t stride_C;
CK_TILE_HOST GroupedGemmHostArgs() noexcept = default;
CK_TILE_HOST GroupedGemmHostArgs(const void* a_ptr_,
const void* b_ptr_,
void* c_ptr_,
ck_tile::index_t M_,
ck_tile::index_t N_,
ck_tile::index_t K_,
ck_tile::index_t stride_A_,
ck_tile::index_t stride_B_,
ck_tile::index_t stride_C_)
: GemmHostArgs(a_ptr_, b_ptr_, c_ptr_, KBatch, M_, N_, K_, stride_A_, stride_B_, stride_C_)
{
}
private:
static constexpr index_t KBatch = 1;
};
template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
struct GroupedGemmKernel
struct GroupedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, EpiloguePipeline_>
{
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
using ALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
using BLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
using CLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
static constexpr index_t KernelBlockSize = GemmPipeline::BlockSize;
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
using ALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
using BLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
using CLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
using ADataType = remove_cvref_t<typename GemmPipeline::ADataType>;
using BDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
using CDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
using OffsetTile1DPartitioner = OffsettedTile1DPartitioner<TilePartitioner>;
using Base = GemmKernel<TilePartitioner_, GemmPipeline_, EpiloguePipeline_>;
using GemmKernelArgs = typename Base::GemmKernelArgs;
static constexpr index_t KernelBlockSize = GemmPipeline::BlockSize;
struct GemmTransKernelArg
{
GroupedGemmHostArgs group_karg;
GemmKernelArgs group_karg;
ck_tile::index_t block_start;
ck_tile::index_t block_end;
GemmTransKernelArg() = default;
GemmTransKernelArg(GroupedGemmHostArgs&& karg, index_t bl_start, index_t bl_end)
GemmTransKernelArg(GemmKernelArgs&& karg, index_t bl_start, index_t bl_end)
: group_karg{karg}, block_start{bl_start}, block_end{bl_end}
{
}
};
__host__ static size_t GetWorkSpaceSize(const std::vector<GroupedGemmHostArgs>& gemm_descs)
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
return gemm_descs.size() * sizeof(GemmTransKernelArg);
// clang-format off
using P_ = GemmPipeline;
return concat('_', "gemm_grouped", gemm_prec_str<ADataType, BDataType>,
concat('x', P_::kMPerBlock, P_::kNPerBlock, P_::kKPerBlock),
concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
// clang-format on
}
__host__ static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
__host__ static auto GetWorkSpaceSize(const std::vector<GroupedGemmHostArgs>& gemm_descs)
-> std::size_t
{
return gemm_descs.size() * sizeof(GemmTransKernelArg);
}
using Hargs = GroupedGemmHostArgs;
__host__ static constexpr auto BlockSize() -> dim3 { return dim3(KernelBlockSize); }
__host__ static constexpr auto GridSize(const std::vector<Hargs>& gemm_descs)
__host__ static constexpr auto GridSize(const std::vector<GroupedGemmHostArgs>& gemm_descs)
{
index_t grid_size = 0;
for(const auto& it_desc : gemm_descs)
{
const auto dim3 = TilePartitioner::GridSize(it_desc.M, it_desc.N);
grid_size += dim3.x * dim3.y * 1;
const auto local_grid_size = TilePartitioner::GridSize(it_desc.M, it_desc.N);
grid_size += local_grid_size * it_desc.k_batch;
}
return dim3(grid_size, 1, 1);
}
CK_TILE_HOST static auto MakeKargs(const std::vector<Hargs>& gemm_descs)
CK_TILE_HOST static auto MakeKargs(const std::vector<GroupedGemmHostArgs>& gemm_descs)
-> std::vector<GemmTransKernelArg>
{
std::vector<GemmTransKernelArg> gemm_kernel_args_;
index_t group_count = ck_tile::type_convert<ck_tile::index_t>(gemm_descs.size());
......@@ -99,23 +118,23 @@ struct GroupedGemmKernel
const index_t stride_b = gemm_descs[i].stride_B;
const index_t stride_c = gemm_descs[i].stride_C;
const auto dim3 = TilePartitioner::GridSize(M, N);
const index_t grid_size_grp = dim3.x * 1 * 1;
const index_t grid_size_grp = TilePartitioner::GridSize(M, N) * gemm_descs[i].k_batch;
const index_t block_start = grid_size;
const index_t block_end = grid_size + grid_size_grp;
grid_size += grid_size_grp;
auto karg = GroupedGemmHostArgs{type_convert<const ADataType*>(gemm_descs[i].a_ptr),
type_convert<const BDataType*>(gemm_descs[i].b_ptr),
type_convert<CDataType*>(gemm_descs[i].c_ptr),
M,
N,
K,
stride_a,
stride_b,
stride_c};
auto karg = GemmKernelArgs{type_convert<const ADataType*>(gemm_descs[i].a_ptr),
type_convert<const BDataType*>(gemm_descs[i].b_ptr),
type_convert<CDataType*>(gemm_descs[i].c_ptr),
M,
N,
K,
stride_a,
stride_b,
stride_c,
gemm_descs[i].k_batch};
gemm_kernel_args_.emplace_back(std::move(karg), block_start, block_end);
}
......@@ -123,162 +142,34 @@ struct GroupedGemmKernel
return gemm_kernel_args_;
}
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
CK_TILE_HOST_DEVICE static constexpr auto GetSmemSize() -> index_t
{
return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
}
CK_TILE_DEVICE void Run(const Hargs& kargs, const index_t block_start) const
CK_TILE_DEVICE void Run(const GemmTransKernelArg& kargs) const
{
const auto [i_m, i_n] = TilePartitioner{}(block_start, kargs.N);
// options
const ADataType* a_start = static_cast<const ADataType*>(kargs.a_ptr);
const BDataType* b_start = static_cast<const BDataType*>(kargs.b_ptr);
// Convert pointers to tensor views
auto a_tensor_view = [&]() {
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
a_start,
make_tuple(kargs.M, kargs.K),
make_tuple(kargs.stride_A, 1),
number<GemmPipeline::VectorSizeA>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
a_start,
make_tuple(kargs.M, kargs.K),
make_tuple(1, kargs.stride_A),
number<1>{},
number<1>{});
}
}();
const auto [iM, iN] = OffsetTile1DPartitioner::GetOffsetedTileIndex(
kargs.block_start, kargs.group_karg.M, kargs.group_karg.N);
auto b_tensor_view = [&]() {
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
b_start,
make_tuple(kargs.N, kargs.K),
make_tuple(1, kargs.stride_B),
number<1>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
b_start,
make_tuple(kargs.N, kargs.K),
make_tuple(kargs.stride_B, 1),
number<GemmPipeline::VectorSizeB>{},
number<1>{});
}
}();
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
auto a_pad_view = [&]() {
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return pad_tensor_view(a_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<false, GemmPipeline::kPadK>{});
}
else
{
return pad_tensor_view(a_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<GemmPipeline::kPadM, false>{});
}
}();
// clang-format on
const typename Base::SplitKBatchOffset splitk_batch_offset(kargs.group_karg, blockIdx.z);
auto a_block_window = make_tile_window(
a_pad_view,
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::KPerBlock>{}),
{i_m, 0});
auto b_pad_view = [&]() {
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
{
return pad_tensor_view(b_tensor_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<false, GemmPipeline::kPadK>{});
}
else
{
return pad_tensor_view(b_tensor_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<GemmPipeline::kPadN, false>{});
}
}();
auto b_block_window = make_tile_window(
b_pad_view,
make_tuple(number<TilePartitioner::NPerBlock>{}, number<TilePartitioner::KPerBlock>{}),
{i_n, 0});
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.group_karg.a_ptr);
const BDataType* b_ptr = static_cast<const BDataType*>(kargs.group_karg.b_ptr);
CDataType* c_ptr = static_cast<CDataType*>(kargs.group_karg.c_ptr);
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];
const index_t num_loop = TilePartitioner::GetLoopNum(kargs.K);
// Run GEMM cooperatively by whole wokrgroup.
auto c_block_tile =
GemmPipeline{}.template operator()(a_block_window, b_block_window, num_loop, smem_ptr);
CDataType* c_start = static_cast<CDataType*>(kargs.c_ptr);
auto c_tensor_view = [&]() {
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
c_start,
make_tuple(kargs.M, kargs.N),
make_tuple(kargs.stride_C, 1),
number<GemmPipeline::VectorSizeC>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
c_start,
make_tuple(kargs.M, kargs.N),
make_tuple(1, kargs.stride_C),
number<1>{},
number<1>{});
}
}();
auto c_pad_view = [&]() {
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
return pad_tensor_view(c_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<false, GemmPipeline::kPadN>{});
}
else
{
return pad_tensor_view(c_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<GemmPipeline::kPadM, false>{});
}
}();
auto CBlockWindow_pad = make_tile_window(
c_pad_view,
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
{i_m, i_n});
EpiloguePipeline{}(CBlockWindow_pad, c_block_tile);
this->RunGemm(
a_ptr, b_ptr, c_ptr, smem_ptr, kargs.group_karg, splitk_batch_offset, i_m, i_n);
}
CK_TILE_DEVICE void operator()(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
int group_count) const
index_t group_count) const
{
const index_t block_id = ck_tile::get_block_1d_id();
const auto gemm_desc_ptr = reinterpret_cast<const GemmTransKernelArg*>(
......@@ -286,7 +177,7 @@ struct GroupedGemmKernel
index_t left = 0;
index_t right = group_count;
index_t group_id = index_t((left + right) / 2);
index_t group_id = index_t((left + right) >> 1);
while((!(block_id >= gemm_desc_ptr[group_id].block_start &&
block_id < gemm_desc_ptr[group_id].block_end)) &&
......@@ -300,10 +191,10 @@ struct GroupedGemmKernel
{
left = group_id;
}
group_id = index_t((left + right) / 2);
group_id = index_t((left + right) >> 1);
}
Run(gemm_desc_ptr[group_id].group_karg, gemm_desc_ptr[group_id].block_start);
Run(gemm_desc_ptr[group_id]);
}
};
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
namespace ck_tile {
......@@ -12,18 +13,23 @@ struct GemmPipelineAgBgCrImplBase
{
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
template <typename DstBlockTile, typename SrcTileWindow>
CK_TILE_HOST_DEVICE static constexpr auto TransposeC() { return Problem::TransposeC; }
template <typename DstBlockTile, typename SrcTileWindow, typename DramTileWindowStep>
CK_TILE_DEVICE void GlobalPrefetch(DstBlockTile& dst_block_tile,
SrcTileWindow& dram_tile_window) const
SrcTileWindow& dram_tile_window,
const DramTileWindowStep& dram_tile_window_step) const
{
load_tile(dst_block_tile, dram_tile_window);
move_tile_window(dram_tile_window, {0, KPerBlock});
move_tile_window(dram_tile_window, dram_tile_window_step);
}
template <typename DstTileWindow, typename SrcBlockTile, typename ElementFunction>
......@@ -35,20 +41,26 @@ struct GemmPipelineAgBgCrImplBase
store_tile(lds_tile_window, block_tile_tmp);
}
template <typename DstBlockTile, typename SrcTileWindow>
CK_TILE_DEVICE void LocalPrefetch(DstBlockTile& dst_block_tile,
const SrcTileWindow& lds_tile_window) const
{
load_tile(dst_block_tile, lds_tile_window);
}
CK_TILE_DEVICE auto GetABLdsTensorViews(void* p_smem) const
{
// A tile in LDS
ADataType* p_a_lds = static_cast<ADataType*>(p_smem);
ADataType* __restrict__ p_a_lds = static_cast<ADataType*>(p_smem);
constexpr auto a_lds_block_desc = Policy::template MakeALdsBlockDescriptor<Problem>();
auto a_lds_block = make_tensor_view<address_space_enum::lds>(p_a_lds, a_lds_block_desc);
// TODO: LDS alignment should come from Policy!
constexpr index_t a_lds_block_space_size_aligned =
integer_divide_ceil(sizeof(ADataType) * a_lds_block_desc.get_element_space_size(), 16) *
16;
constexpr index_t a_lds_block_space_size_aligned = integer_least_multiple(
sizeof(ADataType) * a_lds_block_desc.get_element_space_size(), 16);
// B tile in LDS
BDataType* p_b_lds = static_cast<BDataType*>(
BDataType* __restrict__ p_b_lds = static_cast<BDataType*>(
static_cast<void*>(static_cast<char*>(p_smem) + a_lds_block_space_size_aligned));
constexpr auto b_lds_block_desc = Policy::template MakeBLdsBlockDescriptor<Problem>();
auto b_lds_block = make_tensor_view<address_space_enum::lds>(p_b_lds, b_lds_block_desc);
......@@ -60,19 +72,21 @@ struct GemmPipelineAgBgCrImplBase
CK_TILE_DEVICE auto GetAWindows(const ADramBlockWindowTmp& a_dram_block_window_tmp,
const ALdsTensorView& a_lds_block_view) const
{
constexpr bool is_col_major = std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
using YPerTile = std::conditional_t<is_col_major, number<KPerBlock>, number<MPerBlock>>;
using XPerTile = std::conditional_t<is_col_major, number<MPerBlock>, number<KPerBlock>>;
// A DRAM tile window for load
auto a_copy_dram_window =
make_tile_window(a_dram_block_window_tmp.get_bottom_tensor_view(),
make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
make_tuple(YPerTile{}, XPerTile{}),
a_dram_block_window_tmp.get_window_origin(),
Policy::template MakeADramTileDistribution<Problem>());
// A LDS tile window for store
auto a_copy_lds_window =
make_tile_window(a_lds_block_view,
make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
{0, 0},
a_copy_dram_window.get_tile_distribution());
auto a_copy_lds_window = make_tile_window(
a_lds_block_view, make_tuple(number<MPerBlock>{}, number<KPerBlock>{}), {0, 0});
auto a_lds_gemm_window = make_tile_window(
a_lds_block_view, make_tuple(number<MPerBlock>{}, number<KPerBlock>{}), {0, 0});
......@@ -86,18 +100,22 @@ struct GemmPipelineAgBgCrImplBase
CK_TILE_DEVICE auto GetBWindows(const BDramBlockWindowTmp& b_dram_block_window_tmp,
const BLdsTensorView& b_lds_block_view) const
{
constexpr bool is_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
using YPerTile = std::conditional_t<is_row_major, number<KPerBlock>, number<NPerBlock>>;
using XPerTile = std::conditional_t<is_row_major, number<NPerBlock>, number<KPerBlock>>;
auto b_copy_dram_window =
make_tile_window(b_dram_block_window_tmp.get_bottom_tensor_view(),
make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
make_tuple(YPerTile{}, XPerTile{}),
b_dram_block_window_tmp.get_window_origin(),
Policy::template MakeBDramTileDistribution<Problem>());
// TODO: Do we really need those two tile windows???
// They're exactly same...
// B LDS tile window for store
auto b_copy_lds_window =
make_tile_window(b_lds_block_view,
make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
{0, 0},
b_copy_dram_window.get_tile_distribution());
auto b_copy_lds_window = make_tile_window(
b_lds_block_view, make_tuple(number<NPerBlock>{}, number<KPerBlock>{}), {0, 0});
auto b_lds_gemm_window = make_tile_window(
b_lds_block_view, make_tuple(number<NPerBlock>{}, number<KPerBlock>{}), {0, 0});
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v3.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <sstream>
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -20,6 +24,8 @@ struct BaseGemmPipelineAgBgCrCompV3
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
CK_TILE_HOST_DEVICE static constexpr auto TransposeC() { return Problem::TransposeC; }
CK_TILE_HOST static constexpr bool BlockHasHotloop(index_t num_loop)
{
return num_loop > PrefetchStages;
......@@ -37,7 +43,7 @@ struct BaseGemmPipelineAgBgCrCompV3
// LocalPreFillStages: 1
// LocalPreFetchStages: 1
// LocalSharedMemoryBuffer: 1
template <typename Problem, typename Policy = GemmPipelineAGmemBGmemCRegV1DefaultPolicy>
template <typename Problem, typename Policy = UniversalGemmPipelineAgBgCrPolicy>
struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
{
using Base = BaseGemmPipelineAgBgCrCompV3<Problem>;
......@@ -62,26 +68,86 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t VectorSizeA = Problem::VectorSizeA;
static constexpr index_t VectorSizeB = Problem::VectorSizeB;
static constexpr index_t VectorSizeC = Problem::VectorSizeC;
static constexpr index_t GetVectorSizeA() { return Policy::template GetVectorSizeA<Problem>(); }
static constexpr index_t GetVectorSizeB() { return Policy::template GetVectorSizeB<Problem>(); }
static constexpr index_t GetVectorSizeC() { return Policy::template GetVectorSizeC<Problem>(); }
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
static constexpr bool kPadK = Problem::kPadK;
// Where is the right place for HasHotLoop and TailNum ???
static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
static constexpr bool HasHotLoop = Problem::HasHotLoop;
static constexpr auto TailNum = Problem::TailNum;
static constexpr auto Scheduler = Problem::Scheduler;
using Base::PrefetchStages;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "pipeline_AgBgCrCompV3", BlockSize,
concat('x', GetVectorSizeA(), GetVectorSizeB(), GetVectorSizeC()),
concat('x', kPadM, kPadN, kPadK));
// clang-format on
}
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
}
CK_TILE_HOST static std::string Print()
{
constexpr index_t MPerXDL = BlockGemm::WarpGemm::kM;
constexpr index_t NPerXDL = BlockGemm::WarpGemm::kN;
constexpr index_t KPerXDL = BlockGemm::WarpGemm::WarpGemmAttribute::Impl::kK;
constexpr index_t WaveSize = 64;
constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
// Below should be equal to AK1|BK1
constexpr index_t A_LDS_Read_Width = Policy::template GetSmemPackA<Problem>();
constexpr index_t B_LDS_Read_Width = Policy::template GetSmemPackB<Problem>();
constexpr index_t A_LDS_Write_Width = Policy::template GetSmemPackA<Problem>();
constexpr index_t B_LDS_Write_Width = Policy::template GetSmemPackB<Problem>();
constexpr index_t A_Buffer_Load_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * GetVectorSizeA());
constexpr index_t B_Buffer_Load_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * GetVectorSizeB());
constexpr index_t A_LDS_Write_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * A_LDS_Write_Width);
constexpr index_t B_LDS_Write_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * B_LDS_Write_Width);
constexpr index_t A_LDS_Read_Inst_Num =
WaveNumN * MPerBlock * KPerBlock / (BlockSize * A_LDS_Read_Width);
constexpr index_t B_LDS_Read_Inst_Num =
WaveNumM * MPerBlock * KPerBlock / (BlockSize * B_LDS_Read_Width);
constexpr index_t C_MFMA_Inst_Num = MPerBlock * NPerBlock * KPerBlock /
(BlockSize / WaveSize) / (MPerXDL * NPerXDL * KPerXDL);
auto str = std::stringstream{};
str << "A/B vector size: " << GetVectorSizeA() << ", " << GetVectorSizeB() << "\n"
<< "A/B LDS read/write width: " << A_LDS_Read_Width << ", " << B_LDS_Read_Width << "\n"
<< "A/B buffer load inst: " << A_Buffer_Load_Inst_Num << ", " << B_Buffer_Load_Inst_Num
<< "\n"
<< "A/B LDS write inst: " << A_LDS_Write_Inst_Num << ", " << B_LDS_Write_Inst_Num
<< "\n"
<< "A/B LDS read inst: " << A_LDS_Read_Inst_Num << ", " << B_LDS_Read_Inst_Num << "\n"
<< "C MFMA inst: " << C_MFMA_Inst_Num << "\n"
<< "KPack: " << BlockGemm::Traits::KPack << "\n"
<< "PrefetchStages: " << PrefetchStages << "\n";
return str.str();
}
template <GemmPipelineScheduler Scheduler>
struct PipelineImpl : public PipelineImplBase
{
......@@ -94,29 +160,35 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
CK_TILE_DEVICE static constexpr auto HotLoopScheduler()
{
constexpr index_t MPerXDL = BlockGemmShape::WarpTile::at(I0{});
constexpr index_t NPerXDL = BlockGemmShape::WarpTile::at(I1{});
constexpr index_t KPerXDL = BlockGemmShape::WarpTile::at(I2{});
constexpr index_t MPerXDL = BlockGemm::WarpGemm::kM;
constexpr index_t NPerXDL = BlockGemm::WarpGemm::kN;
constexpr index_t KPerXDL = BlockGemm::WarpGemm::WarpGemmAttribute::Impl::kK;
constexpr index_t WaveSize = 64;
constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
constexpr index_t A_LDS_Read_Width = KPerXDL;
constexpr index_t B_LDS_Read_Width = KPerXDL;
// Below should be equal to AK1|BK1
constexpr index_t A_LDS_Read_Width = Policy::template GetSmemPackA<Problem>();
constexpr index_t B_LDS_Read_Width = Policy::template GetSmemPackB<Problem>();
constexpr index_t A_LDS_Write_Width = Policy::template GetSmemPackA<Problem>();
constexpr index_t B_LDS_Write_Width = Policy::template GetSmemPackB<Problem>();
constexpr index_t A_Buffer_Load_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * VectorSizeA);
MPerBlock * KPerBlock / (BlockSize * GetVectorSizeA());
constexpr index_t B_Buffer_Load_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * VectorSizeB);
NPerBlock * KPerBlock / (BlockSize * GetVectorSizeB());
constexpr index_t A_LDS_Write_Inst_Num = MPerBlock * KPerBlock / (BlockSize * KPerXDL);
constexpr index_t B_LDS_Write_Inst_Num = NPerBlock * KPerBlock / (BlockSize * KPerXDL);
constexpr index_t A_LDS_Write_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * A_LDS_Write_Width);
constexpr index_t B_LDS_Write_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * B_LDS_Write_Width);
constexpr index_t A_LDS_Read_Inst_Num =
WaveNumN * MPerBlock * KPerBlock / (BlockSize * KPerXDL);
WaveNumN * MPerBlock * KPerBlock / (BlockSize * A_LDS_Read_Width);
constexpr index_t B_LDS_Read_Inst_Num =
WaveNumM * MPerBlock * KPerBlock / (BlockSize * KPerXDL);
WaveNumM * MPerBlock * KPerBlock / (BlockSize * B_LDS_Read_Width);
constexpr index_t C_MFMA_Inst_Num = MPerBlock * NPerBlock * KPerBlock /
(BlockSize / WaveSize) /
......@@ -246,11 +318,22 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
"A/B Dram block window should have the same data type as appropriate "
"([A|B]DataType) defined in Problem definition!");
static_assert(MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}],
"A/B block window appropriate sizes must be equal to MPerBlock/NPerblock"
" or KPerBlock!");
constexpr bool is_a_col_major =
std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
static_assert(is_a_col_major
? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"A block window has incorrect lengths for defined ALayout!");
static_assert(is_b_row_major
? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"B block window has incorrect lengths for defined BLayout!");
// ------------------------------------------------------------------------------------
// Definitions of all needed tiles
......@@ -285,23 +368,51 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
ABlockTile a_block_tile;
BBlockTile b_block_tile;
using ADramTileWindowStep = typename ADramBlockWindowTmp::BottomTensorIndex;
using BDramTileWindowStep = typename BDramBlockWindowTmp::BottomTensorIndex;
constexpr ADramTileWindowStep a_dram_tile_window_step =
is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
constexpr BDramTileWindowStep b_dram_tile_window_step =
is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
// -----------------------------------------------------------------------------------------
// Gemm pipeline start
// prefetch
// global read 0
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window);
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
// initialize C
tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
// LDS write 0
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tile);
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tile);
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
}
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window);
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
......@@ -316,11 +427,31 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
{
block_sync_lds();
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tile);
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tile);
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
}
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
block_gemm(c_block_tile, a_lds_gemm_window, b_lds_gemm_window);
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4.hpp 0 → 100644
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4_default_policy.hpp"
namespace ck_tile {
// A Tile Window: global memory
// B Tile Window: global memory
// C Distributed tensor: register
template <typename Problem>
struct BaseGemmPipelineAgBgCrCompV4
{
static constexpr index_t PrefetchStages = 2;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
CK_TILE_HOST static constexpr bool BlockHasHotloop(index_t num_loop)
{
return num_loop > PrefetchStages;
}
CK_TILE_HOST static constexpr TailNumber GetBlockLoopTailNum(index_t num_loop)
{
if(num_loop % PrefetchStages == 1)
{
return TailNumber::Three;
}
else
{
return TailNumber::Two;
}
}
};
/**
* @brief Compute optimized pipeline version 4
*
* This version introduces a dual LDS window mechanism using a ping-pong buffer approach
* for more efficient data handling from global memory. Unlike compute version 3, this method
* allows one LDS to fetch data from global memory while the other LDS executes warps for MFMA
* matrix multiplication. This dual operation helps in keeping the Warp unit continuously busy,
* thereby significantly reducing memory load times and enhancing overall performance.
*
* @note This version shows improved performance over Compute Version 3 with the same block tile.
* It is particularly more efficient for large matrices where M, N, and K are greater than 8K,
* even when Compute Version 3's block size is twice that of Compute Version 4.
*/
template <typename Problem, typename Policy = GemmPipelineAgBgCrCompV4DefaultPolicy>
struct GemmPipelineAgBgCrCompV4 : public BaseGemmPipelineAgBgCrCompV4<Problem>
{
using Base = BaseGemmPipelineAgBgCrCompV4<Problem>;
using PipelineImplBase = GemmPipelineAgBgCrImplBase<Problem, Policy>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using CDataType = remove_cvref_t<typename Problem::CDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using CLayout = remove_cvref_t<typename Problem::CLayout>;
using BlockGemm = remove_cvref_t<decltype(Policy::template GetBlockGemm<Problem>())>;
using I0 = number<0>;
using I1 = number<1>;
using I2 = number<2>;
static constexpr index_t BlockSize = Problem::kBlockSize;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t GetVectorSizeA() { return Policy::template GetVectorSizeA<Problem>(); }
static constexpr index_t GetVectorSizeB() { return Policy::template GetVectorSizeB<Problem>(); }
static constexpr index_t GetVectorSizeC() { return Policy::template GetVectorSizeC<Problem>(); }
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
static constexpr bool kPadK = Problem::kPadK;
static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
static constexpr bool HasHotLoop = Problem::HasHotLoop;
static constexpr auto TailNum = Problem::TailNum;
static constexpr auto Scheduler = Problem::Scheduler;
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
}
CK_TILE_HOST_DEVICE static constexpr auto IsTransposeC()
{
return Policy::template IsTransposeC<Problem>();
}
template <GemmPipelineScheduler Scheduler>
struct PipelineImpl : public PipelineImplBase
{
};
template <>
struct PipelineImpl<GemmPipelineScheduler::Intrawave> : public PipelineImplBase
{
using Base = PipelineImplBase;
CK_TILE_DEVICE static constexpr auto HotLoopScheduler()
{
constexpr index_t MPerXDL = BlockGemmShape::WarpTile::at(I0{});
constexpr index_t NPerXDL = BlockGemmShape::WarpTile::at(I1{});
constexpr index_t KPerXDL = BlockGemmShape::WarpTile::at(I2{});
constexpr index_t WaveSize = 64;
constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
constexpr index_t A_LDS_Read_Width = KPerXDL;
constexpr index_t B_LDS_Read_Width = KPerXDL;
constexpr index_t A_Buffer_Load_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * GetVectorSizeA());
constexpr index_t B_Buffer_Load_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * GetVectorSizeB());
constexpr index_t A_LDS_Write_Inst_Num = MPerBlock * KPerBlock / (BlockSize * KPerXDL);
constexpr index_t B_LDS_Write_Inst_Num = NPerBlock * KPerBlock / (BlockSize * KPerXDL);
constexpr index_t A_LDS_Read_Inst_Num =
WaveNumN * MPerBlock * KPerBlock / (BlockSize * KPerXDL);
constexpr index_t B_LDS_Read_Inst_Num =
WaveNumM * MPerBlock * KPerBlock / (BlockSize * KPerXDL);
constexpr index_t C_MFMA_Inst_Num = MPerBlock * NPerBlock * KPerBlock /
(BlockSize / WaveSize) /
(MPerXDL * NPerXDL * KPerXDL);
constexpr auto num_ds_read_inst_a = A_LDS_Read_Width * sizeof(ADataType) == 16
? A_LDS_Read_Inst_Num
: A_LDS_Read_Inst_Num / 2;
constexpr auto num_ds_read_inst_b = B_LDS_Read_Width * sizeof(BDataType) == 16
? B_LDS_Read_Inst_Num
: B_LDS_Read_Inst_Num / 2;
constexpr auto num_ds_read_inst = num_ds_read_inst_a + num_ds_read_inst_b;
constexpr auto num_ds_write_inst = A_LDS_Write_Inst_Num + B_LDS_Write_Inst_Num;
constexpr auto num_buffer_load_inst = A_Buffer_Load_Inst_Num + B_Buffer_Load_Inst_Num;
constexpr auto num_issue = num_buffer_load_inst;
static_for<0, num_buffer_load_inst, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA : 1
__builtin_amdgcn_sched_group_barrier(
0x100, num_ds_read_inst / num_issue, 0); // DS read : 2
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA: 1
__builtin_amdgcn_sched_group_barrier(
0x200, num_ds_write_inst / num_issue, 0); // DS write : 1
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA : 1
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read :1
__builtin_amdgcn_sched_group_barrier(
0x008, C_MFMA_Inst_Num / num_issue - 3, 0); // MFMA : 5
});
__builtin_amdgcn_sched_barrier(0);
}
template <bool HasHotLoop,
TailNumber TailNum,
typename ADramBlockWindowTmp,
typename BDramBlockWindowTmp,
typename AElementFunction,
typename BElementFunction>
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
const AElementFunction& a_element_func,
const BDramBlockWindowTmp& b_dram_block_window_tmp,
const BElementFunction& b_element_func,
index_t num_loop,
void* __restrict__ p_smem_0,
void* __restrict__ p_smem_1) const
{
static_assert(
std::is_same_v<ADataType, remove_cvref_t<typename ADramBlockWindowTmp::DataType>> &&
std::is_same_v<BDataType,
remove_cvref_t<typename BDramBlockWindowTmp::DataType>>,
"Data Type conflict on A and B matrix input data type.");
constexpr bool is_a_col_major =
std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
static_assert(is_a_col_major
? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"A block window has incorrect lengths for defined ALayout!");
static_assert(is_b_row_major
? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"B block window has incorrect lengths for defined BLayout!");
////////////// global window & register /////////////////
// A DRAM tile window for load
auto a_copy_dram_window =
make_tile_window_linear(a_dram_block_window_tmp.get_bottom_tensor_view(),
make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
a_dram_block_window_tmp.get_window_origin(),
Policy::template MakeADramTileDistribution<Problem>());
// B DRAM tile window for load
auto b_copy_dram_window =
make_tile_window_linear(b_dram_block_window_tmp.get_bottom_tensor_view(),
make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
b_dram_block_window_tmp.get_window_origin(),
Policy::template MakeBDramTileDistribution<Problem>());
// A register tile for global load
constexpr auto ABlockTileDistr = a_copy_dram_window.get_tile_distribution();
constexpr auto BBlockTileDistr = b_copy_dram_window.get_tile_distribution();
using ABlockTile = decltype(make_static_distributed_tensor<ADataType>(ABlockTileDistr));
using BBlockTile = decltype(make_static_distributed_tensor<BDataType>(BBlockTileDistr));
ABlockTile a_global_load_tile;
BBlockTile b_global_load_tile;
using ADramTileWindowStep = typename ADramBlockWindowTmp::BottomTensorIndex;
using BDramTileWindowStep = typename BDramBlockWindowTmp::BottomTensorIndex;
constexpr ADramTileWindowStep a_dram_tile_window_step =
is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
constexpr BDramTileWindowStep b_dram_tile_window_step =
is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
// global prefetch 0
// global read 0
Base::GlobalPrefetch(a_global_load_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_global_load_tile, b_copy_dram_window, b_dram_tile_window_step);
////////////// LDS desc, window & register /////////////////
auto&& [a_lds_block0, b_lds_block0] = Base::GetABLdsTensorViews(p_smem_0);
auto&& [a_lds_block1, b_lds_block1] = Base::GetABLdsTensorViews(p_smem_1);
auto a_copy_lds_window0 = make_tile_window(
a_lds_block0, make_tuple(number<MPerBlock>{}, number<KPerBlock>{}), {0, 0});
auto a_copy_lds_window1 = make_tile_window(
a_lds_block1, make_tuple(number<MPerBlock>{}, number<KPerBlock>{}), {0, 0});
auto b_copy_lds_window0 = make_tile_window(
b_lds_block0, make_tuple(number<NPerBlock>{}, number<KPerBlock>{}), {0, 0});
auto b_copy_lds_window1 = make_tile_window(
b_lds_block1, make_tuple(number<NPerBlock>{}, number<KPerBlock>{}), {0, 0});
// Block GEMM
auto block_gemm = BlockGemm();
auto c_block_tile = block_gemm.MakeCBlockTile();
// initialize C
tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
// LDS write 0
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_global_load_tile);
Base::LocalPrefill(a_copy_lds_window0, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window0, a_global_load_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_global_load_tile);
Base::LocalPrefill(b_copy_lds_window0, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window0, b_global_load_tile, b_element_func);
}
// global read 1
Base::GlobalPrefetch(a_global_load_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_global_load_tile, b_copy_dram_window, b_dram_tile_window_step);
block_sync_lds();
constexpr auto ALdsTileDistr = decltype(make_static_tile_distribution(
BlockGemm::MakeABlockDistributionEncode())){};
constexpr auto BLdsTileDistr = decltype(make_static_tile_distribution(
BlockGemm::MakeBBlockDistributionEncode())){};
using ALdsTile = decltype(make_static_distributed_tensor<ADataType>(ALdsTileDistr));
using BLdsTile = decltype(make_static_distributed_tensor<BDataType>(BLdsTileDistr));
ALdsTile a_block_tile0;
ALdsTile a_block_tile1;
BLdsTile b_block_tile0;
BLdsTile b_block_tile1;
auto a_lds_ld_window0 =
make_tile_window_linear(a_lds_block0,
make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
{0, 0},
ALdsTileDistr);
auto a_lds_ld_window1 =
make_tile_window_linear(a_lds_block1,
make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
{0, 0},
ALdsTileDistr);
auto b_lds_ld_window0 =
make_tile_window_linear(b_lds_block0,
make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
{0, 0},
BLdsTileDistr);
auto b_lds_ld_window1 =
make_tile_window_linear(b_lds_block1,
make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
{0, 0},
BLdsTileDistr);
Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
Base::LocalPrefetch(b_block_tile0, b_lds_ld_window0);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_global_load_tile);
Base::LocalPrefill(a_copy_lds_window1, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window1, a_global_load_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_global_load_tile);
Base::LocalPrefill(b_copy_lds_window1, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window1, b_global_load_tile, b_element_func);
}
Base::GlobalPrefetch(a_global_load_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_global_load_tile, b_copy_dram_window, b_dram_tile_window_step);
if(HasHotLoop)
{
// minus 2 because we have ping-pong double buffer.
index_t iCounter = __builtin_amdgcn_readfirstlane(num_loop - 2);
do
{
// ping
{
block_sync_lds();
Base::LocalPrefetch(a_block_tile1, a_lds_ld_window1);
Base::LocalPrefetch(b_block_tile1, b_lds_ld_window1);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_global_load_tile);
Base::LocalPrefill(a_copy_lds_window0, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(
a_copy_lds_window0, a_global_load_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_global_load_tile);
Base::LocalPrefill(b_copy_lds_window0, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(
b_copy_lds_window0, b_global_load_tile, b_element_func);
}
Base::GlobalPrefetch(
a_global_load_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(
b_global_load_tile, b_copy_dram_window, b_dram_tile_window_step);
// gemm
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
HotLoopScheduler();
__builtin_amdgcn_sched_barrier(0);
}
// pong
{
block_sync_lds();
Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
Base::LocalPrefetch(b_block_tile0, b_lds_ld_window0);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_global_load_tile);
Base::LocalPrefill(a_copy_lds_window1, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(
a_copy_lds_window1, a_global_load_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_global_load_tile);
Base::LocalPrefill(b_copy_lds_window1, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(
b_copy_lds_window1, b_global_load_tile, b_element_func);
}
Base::GlobalPrefetch(
a_global_load_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(
b_global_load_tile, b_copy_dram_window, b_dram_tile_window_step);
// gemm
block_gemm(c_block_tile, a_block_tile1, b_block_tile1);
HotLoopScheduler();
__builtin_amdgcn_sched_barrier(0);
}
iCounter -= 2;
} while(iCounter > 1);
}
// tail 3
if(TailNum == TailNumber::Three)
{
// 3
{
block_sync_lds();
Base::LocalPrefetch(a_block_tile1, a_lds_ld_window1);
Base::LocalPrefetch(b_block_tile1, b_lds_ld_window1);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_global_load_tile);
Base::LocalPrefill(a_copy_lds_window0, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window0, a_global_load_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_global_load_tile);
Base::LocalPrefill(b_copy_lds_window0, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window0, b_global_load_tile, b_element_func);
}
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
}
// 2
{
block_sync_lds();
Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
block_gemm(c_block_tile, a_block_tile1, b_block_tile1);
}
// 1
{
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
__builtin_amdgcn_sched_barrier(0);
}
}
else
{
// 2
{
block_sync_lds();
Base::LocalPrefetch(a_block_tile1, a_lds_ld_window1);
Base::LocalPrefetch(b_block_tile1, b_lds_ld_window1);
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
static_for<0, 8, 1>{}([&](auto i) {
ignore = i;
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
__builtin_amdgcn_sched_group_barrier(0x008, 8, 0); // MFMA
});
__builtin_amdgcn_sched_barrier(0);
}
// 1
{
block_gemm(c_block_tile, a_block_tile1, b_block_tile1);
__builtin_amdgcn_sched_barrier(0);
}
}
return c_block_tile;
}
};
template <typename ADramBlockWindowTmp,
typename BDramBlockWindowTmp,
typename AElementFunction,
typename BElementFunction>
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
const AElementFunction& a_element_func,
const BDramBlockWindowTmp& b_dram_block_window_tmp,
const BElementFunction& b_element_func,
index_t num_loop,
void* p_smem_0,
void* p_smem_1) const
{
return PipelineImpl<Scheduler>{}.template operator()<HasHotLoop, TailNum>(
a_dram_block_window_tmp,
a_element_func,
b_dram_block_window_tmp,
b_element_func,
num_loop,
p_smem_0,
p_smem_1);
}
public:
template <typename ADramBlockWindowTmp, typename BDramBlockWindowTmp>
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
const BDramBlockWindowTmp& b_dram_block_window_tmp,
const index_t num_loop,
void* __restrict__ p_smem_0,
void* __restrict__ p_smem_1) const
{
return PipelineImpl<Scheduler>{}.template operator()<HasHotLoop, TailNum>(
a_dram_block_window_tmp,
[](const ADataType& a) { return a; },
b_dram_block_window_tmp,
[](const BDataType& b) { return b; },
num_loop,
p_smem_0,
p_smem_1);
}
};
} // namespace ck_tile
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4_default_policy.hpp 0 → 100644
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
namespace ck_tile {
// Default policy for GemmPipelineAGmemBGmemCregComputeV4, except the block gemm method, it shares
// the same vector size implementation, SmemSize, Global memory tile distiribution as the
// UniversalGemm Pipeline Policy.
// Default policy class should not be templated, put template on
// member functions instead.
struct GemmPipelineAgBgCrCompV4DefaultPolicy
: public UniversalGemmBasePolicy<GemmPipelineAgBgCrCompV4DefaultPolicy>
{
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
{
using namespace ck_tile;
constexpr index_t kMPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPack = GetSmemPackA<Problem>();
constexpr auto a_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kKPerBlock / KPack>{}, number<kMPerBlock>{}, number<KPack>{}),
make_tuple(number<kMPerBlock * KPack>{}, number<KPack>{}, number<1>{}),
number<KPack>{},
number<1>{});
constexpr auto a_lds_block_desc = transform_tensor_descriptor(
a_lds_block_desc_0,
make_tuple(
make_pass_through_transform(number<kMPerBlock>{}),
make_merge_transform(make_tuple(number<kKPerBlock>{} / KPack, number<KPack>{}))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return a_lds_block_desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor()
{
constexpr index_t kNPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPack = GetSmemPackB<Problem>();
constexpr auto b_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kKPerBlock / KPack>{}, number<kNPerBlock>{}, number<KPack>{}),
make_tuple(number<(kNPerBlock)*KPack>{}, number<KPack>{}, number<1>{}),
number<KPack>{},
number<1>{});
constexpr auto b_lds_block_desc = transform_tensor_descriptor(
b_lds_block_desc_0,
make_tuple(
make_pass_through_transform(number<kNPerBlock>{}),
make_merge_transform(make_tuple(number<kKPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1>{}, sequence<0, 2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return b_lds_block_desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetBlockGemm()
{
using AccDataType = float;
using BlockWarps = typename Problem::BlockGemmShape::BlockWarps;
using WarpTile = typename Problem::BlockGemmShape::WarpTile;
using WarpGemm = WarpGemmMfmaDispatcher<typename Problem::ADataType,
typename Problem::BDataType,
AccDataType,
WarpTile::at(I0),
WarpTile::at(I1),
WarpTile::at(I2),
Problem::TransposeC>;
using BlockGemmPolicy = BlockGemmARegBRegCRegV1CustomPolicy<typename Problem::ADataType,
typename Problem::BDataType,
typename Problem::CDataType,
BlockWarps,
WarpGemm>;
return BlockGemmARegBRegCRegV1<Problem, BlockGemmPolicy>{};
}
};
} // namespace ck_tile
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_mem.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -7,6 +7,7 @@
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -20,6 +21,8 @@ struct BaseGemmPipelineAgBgCrMem
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
CK_TILE_HOST_DEVICE static constexpr auto TransposeC() { return Problem::TransposeC; }
static constexpr index_t BlockSize = Problem::kBlockSize;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
......@@ -88,7 +91,7 @@ struct BaseGemmPipelineAgBgCrMem
// LocalPreFillStages: 1
// LocalPreFetchStages: 0
// LocalSharedMemoryBuffer: 1
template <typename Problem, typename Policy = GemmPipelineAGmemBGmemCRegV1DefaultPolicy>
template <typename Problem, typename Policy = UniversalGemmPipelineAgBgCrPolicy>
struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
{
using Base = BaseGemmPipelineAgBgCrMem<Problem>;
......@@ -104,27 +107,40 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
using CLayout = remove_cvref_t<typename Problem::CLayout>;
using BlockGemm = remove_cvref_t<decltype(Policy::template GetBlockGemm<Problem>())>;
using I0 = number<0>;
using I1 = number<1>;
using I2 = number<2>;
using I0 = number<0>;
using I1 = number<1>;
using I2 = number<2>;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t VectorSizeA = Problem::VectorSizeA;
static constexpr index_t VectorSizeB = Problem::VectorSizeB;
static constexpr index_t VectorSizeC = Problem::VectorSizeC;
static constexpr index_t GetVectorSizeA() { return Policy::template GetVectorSizeA<Problem>(); }
static constexpr index_t GetVectorSizeB() { return Policy::template GetVectorSizeB<Problem>(); }
static constexpr index_t GetVectorSizeC() { return Policy::template GetVectorSizeC<Problem>(); }
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
static constexpr bool kPadK = Problem::kPadK;
static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
// Where is the right place for HasHotLoop and TailNum ???
static constexpr bool HasHotLoop = Problem::HasHotLoop;
static constexpr auto TailNum = Problem::TailNum;
static constexpr auto Scheduler = Problem::Scheduler;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "pipeline_AgBgCrMe",
concat('x', MPerBlock, NPerBlock, KPerBlock),
concat('x', GetVectorSizeA(), GetVectorSizeB(), GetVectorSizeC()),
concat('x', kPadM, kPadN, kPadK));
// clang-format on
}
using Base::PrefetchStages;
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
......@@ -162,11 +178,22 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
"A/B Dram block window should have the same data type as appropriate "
"([A|B]DataType) defined in Problem definition!");
static_assert(MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}],
"A/B block window appropriate sizes must be equal to MPerBlock/NPerblock"
" or KPerBlock!");
constexpr bool is_a_col_major =
std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
static_assert(is_a_col_major
? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"A block window has incorrect lengths for defined ALayout!");
static_assert(is_b_row_major
? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"B block window has incorrect lengths for defined BLayout!");
// ------------------------------------------------------------------------------------
// Definitions of all needed tiles
......@@ -210,25 +237,59 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
tuple_array<ABlockTile, PrefetchStages> a_block_tiles;
tuple_array<BBlockTile, PrefetchStages> b_block_tiles;
using ADramTileWindowStep = typename ADramBlockWindowTmp::BottomTensorIndex;
using BDramTileWindowStep = typename BDramBlockWindowTmp::BottomTensorIndex;
constexpr ADramTileWindowStep a_dram_tile_window_step =
is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
constexpr BDramTileWindowStep b_dram_tile_window_step =
is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
// -----------------------------------------------------------------------------------------
// Gemm pipeline start
// prefetch
// global read 0
Base::GlobalPrefetch(a_block_tiles.get(I0{}), a_copy_dram_window);
Base::GlobalPrefetch(b_block_tiles.get(I0{}), b_copy_dram_window);
Base::GlobalPrefetch(
a_block_tiles.get(I0{}), a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(
b_block_tiles.get(I0{}), b_copy_dram_window, b_dram_tile_window_step);
// initialize C
tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
// LDS write 0
Base::LocalPrefill(a_copy_lds_window, a_block_tiles.get(I0{}), a_element_func);
Base::LocalPrefill(b_copy_lds_window, b_block_tiles.get(I0{}), b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tiles.get(I0{}));
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tiles.get(I0{}), a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tiles.get(I0{}));
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tiles.get(I0{}), b_element_func);
}
// Global prefetch [1, PrefetchStages]
static_for<1, PrefetchStages, 1>{}([&](auto prefetch_idx) {
Base::GlobalPrefetch(a_block_tiles.get(number<prefetch_idx>{}), a_copy_dram_window);
Base::GlobalPrefetch(b_block_tiles.get(number<prefetch_idx>{}), b_copy_dram_window);
Base::GlobalPrefetch(a_block_tiles.get(number<prefetch_idx>{}),
a_copy_dram_window,
a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tiles.get(number<prefetch_idx>{}),
b_copy_dram_window,
b_dram_tile_window_step);
});
// main body
......@@ -244,19 +305,45 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
block_sync_lds();
Base::LocalPrefill(
a_copy_lds_window,
a_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
a_element_func);
Base::LocalPrefill(
b_copy_lds_window,
b_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(
a_shuffle_tmp,
a_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}));
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(
a_copy_lds_window,
a_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(
b_shuffle_tmp,
b_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}));
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(
b_copy_lds_window,
b_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
b_element_func);
}
Base::GlobalPrefetch(a_block_tiles.get(number<prefetch_idx>{}),
a_copy_dram_window);
a_copy_dram_window,
a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tiles.get(number<prefetch_idx>{}),
b_copy_dram_window);
b_copy_dram_window,
b_dram_tile_window_step);
});
i += PrefetchStages;
......@@ -272,12 +359,32 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
block_sync_lds();
Base::LocalPrefill(a_copy_lds_window,
a_block_tiles.get(number<prefetch_idx>{}),
a_element_func);
Base::LocalPrefill(b_copy_lds_window,
b_block_tiles.get(number<prefetch_idx>{}),
b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tiles.get(number<prefetch_idx>{}));
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window,
a_block_tiles.get(number<prefetch_idx>{}),
a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tiles.get(number<prefetch_idx>{}));
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window,
b_block_tiles.get(number<prefetch_idx>{}),
b_element_func);
}
});
block_sync_lds();
......@@ -349,11 +456,22 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
"A/B Dram block window should have the same data type as appropriate "
"([A|B]DataType) defined in Problem definition!");
static_assert(MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}],
"A/B block window appropriate sizes must be equal to MPerBlock/NPerblock"
" or KPerBlock!");
constexpr bool is_a_col_major =
std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
static_assert(is_a_col_major
? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"A block window has incorrect lengths for defined ALayout!");
static_assert(is_b_row_major
? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"B block window has incorrect lengths for defined BLayout!");
// ------------------------------------------------------------------------------------
// Definitions of all needed tiles
......@@ -397,25 +515,58 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
tuple_array<ABlockTile, PrefetchStages> a_block_tiles;
tuple_array<BBlockTile, PrefetchStages> b_block_tiles;
using ADramTileWindowStep = typename ADramBlockWindowTmp::BottomTensorIndex;
using BDramTileWindowStep = typename BDramBlockWindowTmp::BottomTensorIndex;
constexpr ADramTileWindowStep a_dram_tile_window_step =
is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
constexpr BDramTileWindowStep b_dram_tile_window_step =
is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
// -----------------------------------------------------------------------------------------
// Gemm pipeline start
// prefetch
// global read 0
Base::GlobalPrefetch(a_block_tiles.get(I0{}), a_copy_dram_window);
Base::GlobalPrefetch(b_block_tiles.get(I0{}), b_copy_dram_window);
Base::GlobalPrefetch(
a_block_tiles.get(I0{}), a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(
b_block_tiles.get(I0{}), b_copy_dram_window, b_dram_tile_window_step);
// initialize C
tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
// LDS write 0
Base::LocalPrefill(a_copy_lds_window, a_block_tiles.get(I0{}), a_element_func);
Base::LocalPrefill(b_copy_lds_window, b_block_tiles.get(I0{}), b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tiles.get(I0{}));
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tiles.get(I0{}), a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tiles.get(I0{}));
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tiles.get(I0{}), b_element_func);
}
// Global prefetch [1, PrefetchStages]
static_for<1, PrefetchStages, 1>{}([&](auto prefetch_idx) {
Base::GlobalPrefetch(a_block_tiles.get(number<prefetch_idx>{}), a_copy_dram_window);
Base::GlobalPrefetch(b_block_tiles.get(number<prefetch_idx>{}), b_copy_dram_window);
Base::GlobalPrefetch(a_block_tiles.get(number<prefetch_idx>{}),
a_copy_dram_window,
a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tiles.get(number<prefetch_idx>{}),
b_copy_dram_window,
b_dram_tile_window_step);
});
// main body
......@@ -429,19 +580,45 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
block_gemm(c_block_tile, a_lds_gemm_window, b_lds_gemm_window);
// no second block_sync_lds because it's interwave
Base::LocalPrefill(
a_copy_lds_window,
a_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
a_element_func);
Base::LocalPrefill(
b_copy_lds_window,
b_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(
a_shuffle_tmp,
a_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}));
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(
a_copy_lds_window,
a_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(
b_shuffle_tmp,
b_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}));
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(
b_copy_lds_window,
b_block_tiles.get(number<(prefetch_idx + 1) % PrefetchStages>{}),
b_element_func);
}
Base::GlobalPrefetch(a_block_tiles.get(number<prefetch_idx>{}),
a_copy_dram_window);
a_copy_dram_window,
a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tiles.get(number<prefetch_idx>{}),
b_copy_dram_window);
b_copy_dram_window,
b_dram_tile_window_step);
});
i += PrefetchStages;
......@@ -454,12 +631,32 @@ struct GemmPipelineAgBgCrMem : public BaseGemmPipelineAgBgCrMem<Problem>
block_gemm(c_block_tile, a_lds_gemm_window, b_lds_gemm_window);
// no second block_sync_lds because it's interwave
Base::LocalPrefill(a_copy_lds_window,
a_block_tiles.get(number<prefetch_idx>{}),
a_element_func);
Base::LocalPrefill(b_copy_lds_window,
b_block_tiles.get(number<prefetch_idx>{}),
b_element_func);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tiles.get(number<prefetch_idx>{}));
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window,
a_block_tiles.get(number<prefetch_idx>{}),
a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tiles.get(number<prefetch_idx>{}));
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window,
b_block_tiles.get(number<prefetch_idx>{}),
b_element_func);
}
});
block_sync_lds();
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <ostream>
#include <sstream>
#include "ck_tile/core.hpp"
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1_default_policy.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -23,31 +24,39 @@ struct GemmPipelineAGmemBGmemCRegV1
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using CLayout = remove_cvref_t<typename Problem::CLayout>;
using BlockGemm = remove_cvref_t<decltype(Policy::template GetBlockGemm<Problem>())>;
static constexpr index_t BlockSize = Problem::kBlockSize;
static constexpr index_t kMPerBlock = BlockGemmShape::kM;
static constexpr index_t kNPerBlock = BlockGemmShape::kN;
static constexpr index_t kKPerBlock = BlockGemmShape::kK;
static constexpr index_t VectorSizeA = Problem::VectorSizeA;
static constexpr index_t VectorSizeB = Problem::VectorSizeB;
static constexpr index_t VectorSizeC = Problem::VectorSizeC;
static constexpr index_t GetVectorSizeA() { return Problem::VectorSizeA; }
static constexpr index_t GetVectorSizeB() { return Problem::VectorSizeB; }
static constexpr index_t GetVectorSizeC() { return Problem::VectorSizeC; }
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
static constexpr bool kPadK = Problem::kPadK;
CK_TILE_HOST_DEVICE static constexpr index_t GetStaticLdsSize()
static constexpr index_t kLdsAlignmentInBytes = 16;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
return integer_divide_ceil(
sizeof(ADataType) *
Policy::template MakeALdsBlockDescriptor<Problem>().get_element_space_size(),
16) *
16 +
sizeof(BDataType) *
Policy::template MakeBLdsBlockDescriptor<Problem>().get_element_space_size();
// clang-format off
return concat('_', "pipeline_AGmemBGmemCRegV1",
concat('x', kMPerBlock, kNPerBlock, kKPerBlock, BlockSize),
concat('x', GetVectorSizeA(), GetVectorSizeB(), GetVectorSizeC()),
concat('x', kPadM, kPadN, kPadK));
// clang-format on
}
// For the basic gemm pipelien DoubleSmemBuffer set to be false naturally.
static constexpr bool DoubleSmemBuffer = false;
CK_TILE_HOST_DEVICE static constexpr auto TransposeC() { return Problem::TransposeC; }
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
......@@ -82,8 +91,9 @@ struct GemmPipelineAGmemBGmemCRegV1
auto a_lds_block = make_tensor_view<address_space_enum::lds>(p_a_lds, a_lds_block_desc);
constexpr index_t a_lds_block_space_size_aligned =
integer_divide_ceil(sizeof(ADataType) * a_lds_block_desc.get_element_space_size(), 16) *
16;
integer_divide_ceil(sizeof(ADataType) * a_lds_block_desc.get_element_space_size(),
kLdsAlignmentInBytes) *
kLdsAlignmentInBytes;
// B tile in LDS
BDataType* p_b_lds = static_cast<BDataType*>(
......@@ -124,7 +134,7 @@ struct GemmPipelineAGmemBGmemCRegV1
b_lds_block, make_tuple(number<kNPerBlock>{}, number<kKPerBlock>{}), {0, 0});
// Block GEMM
auto block_gemm = Policy::template GetBlockGemm<Problem>();
auto block_gemm = BlockGemm();
// Acc register tile
auto c_block_tile = decltype(block_gemm(a_lds_gemm_window, b_lds_gemm_window)){};
......@@ -146,7 +156,7 @@ struct GemmPipelineAGmemBGmemCRegV1
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegBlockDescriptor<Problem>());
Policy::template MakeShuffledARegBlockDistribution<Problem>());
shuffle_tile(a_shuffle_tmp, a_block_tile);
const auto a_block_tile_tmp = tile_elementwise_in(a_element_func, a_shuffle_tmp);
store_tile(a_copy_lds_window, a_block_tile_tmp);
......@@ -160,7 +170,7 @@ struct GemmPipelineAGmemBGmemCRegV1
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegBlockDescriptor<Problem>());
Policy::template MakeShuffledBRegBlockDistribution<Problem>());
shuffle_tile(b_shuffle_tmp, b_block_tile);
const auto b_block_tile_tmp = tile_elementwise_in(b_element_func, b_shuffle_tmp);
store_tile(b_copy_lds_window, b_block_tile_tmp);
......@@ -197,7 +207,7 @@ struct GemmPipelineAGmemBGmemCRegV1
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
{
auto b_shuffle_tmp_loop = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegBlockDescriptor<Problem>());
Policy::template MakeShuffledBRegBlockDistribution<Problem>());
shuffle_tile(b_shuffle_tmp_loop, b_block_tile);
store_tile(b_copy_lds_window,
tile_elementwise_in(b_element_func, b_shuffle_tmp_loop));
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1_default_policy.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -12,38 +12,10 @@ namespace ck_tile {
// Default policy class should not be templated, put template on member functions instead
struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
{
static constexpr auto I0 = number<0>{};
static constexpr auto I1 = number<1>{};
static constexpr auto I2 = number<2>{};
#if 0
// 2d
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
{
using namespace ck_tile;
constexpr index_t kMPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
constexpr auto a_lds_block_desc =
make_naive_tensor_descriptor_packed(make_tuple(kMPerBlock, kKPerBlock), number<32>{});
return a_lds_block_desc;
}
// 2d
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor()
{
using namespace ck_tile;
constexpr index_t kNPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
constexpr auto b_lds_block_desc =
make_naive_tensor_descriptor_packed(make_tuple(kNPerBlock, kKPerBlock), number<32>{});
return b_lds_block_desc;
}
#elif 1
// 3d + padding
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
......@@ -53,7 +25,6 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
constexpr index_t kMPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
// TODO: this 8 is AK1! should be a policy parameter!
constexpr auto a_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<kKPerBlock / 8>{}, number<kMPerBlock>{}, number<8>{}),
make_tuple(number<(kMPerBlock + 1) * 8>{}, number<8>{}, number<1>{}),
......@@ -114,8 +85,7 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
{
constexpr index_t smem_size_a = GetSmemSizeA<Problem>();
constexpr index_t smem_size_b = GetSmemSizeB<Problem>();
index_t smem_size = 0;
smem_size += smem_size_a + smem_size_b;
constexpr index_t smem_size = smem_size_a + smem_size_b;
return smem_size;
}
......@@ -123,88 +93,15 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemPackA()
{
using ADataType = remove_cvref_t<typename Problem::ADataType>;
return Problem::VectorLoadSize / sizeof(ADataType);
return Problem::VectorLoadSize;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemPackB()
{
using BDataType = remove_cvref_t<typename Problem::BDataType>;
return Problem::VectorLoadSize / sizeof(BDataType);
}
#elif 1
// fake XOR
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
{
using namespace ck_tile;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
constexpr index_t kMPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
constexpr auto a_lds_block_desc_d1_d2_d3 = make_naive_tensor_descriptor_packed(
make_tuple(number<kMPerBlock / 2>{}, number<2>{}, number<kKPerBlock>{}),
number<kKPerBlock>{});
constexpr index_t kK1 = 16 / sizeof(ADataType);
constexpr auto a_lds_block_desc_d4_d5_d6 = transform_tensor_descriptor(
a_lds_block_desc_d1_d2_d3,
make_tuple(
make_xor_transform(make_tuple(number<kMPerBlock / 2>{}, number<kKPerBlock>{}), kK1),
make_pass_through_transform(2)),
make_tuple(sequence<0, 2>{}, sequence<1>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}));
constexpr auto a_lds_block_desc_m_k = transform_tensor_descriptor(
a_lds_block_desc_d4_d5_d6,
make_tuple(make_merge_transform(make_tuple(number<kMPerBlock / 2>{}, number<2>{})),
make_pass_through_transform(kKPerBlock)),
make_tuple(sequence<0, 1>{}, sequence<2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return a_lds_block_desc_m_k;
return Problem::VectorLoadSize;
}
// fake XOR
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor()
{
using namespace ck_tile;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
constexpr index_t kNPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
constexpr auto b_lds_block_desc_d1_d2_d3 = make_naive_tensor_descriptor_packed(
make_tuple(number<kNPerBlock / 2>{}, number<2>{}, number<kKPerBlock>{}),
number<kKPerBlock>{});
constexpr index_t kK1 = 16 / sizeof(BDataType);
constexpr auto b_lds_block_desc_d4_d5_d6 = transform_tensor_descriptor(
b_lds_block_desc_d1_d2_d3,
make_tuple(
make_xor_transform(make_tuple(number<kNPerBlock / 2>{}, number<kKPerBlock>{}), kK1),
make_pass_through_transform(2)),
make_tuple(sequence<0, 2>{}, sequence<1>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}));
constexpr auto b_lds_block_desc_n_k = transform_tensor_descriptor(
b_lds_block_desc_d4_d5_d6,
make_tuple(make_merge_transform(make_tuple(number<kNPerBlock / 2>{}, number<2>{})),
make_pass_through_transform(kKPerBlock)),
make_tuple(sequence<0, 1>{}, sequence<2>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return b_lds_block_desc_n_k;
}
#endif
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeADramTileDistribution()
{
......@@ -269,7 +166,6 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
static_assert(M0 * M1 * M2 == MPerBlock,
"Incorrect M0, M2, M1 configuration! "
"M0, M1, M2 must cover whole MPerBlock!");
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
......@@ -390,7 +286,7 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledBRegBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledBRegBlockDistribution()
{
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
......@@ -438,11 +334,11 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledARegBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledARegBlockDistribution()
{
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
static_assert(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::RowMajor>);
static_assert(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
constexpr index_t kBlockSize = Problem::kBlockSize;
constexpr index_t kMPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t kKPerBlock = Problem::BlockGemmShape::kK;
......@@ -488,11 +384,6 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetBlockGemm()
{
constexpr bool TransposeC = false;
constexpr auto I0 = number<0>{};
constexpr auto I1 = number<1>{};
constexpr auto I2 = number<2>{};
using AccDataType = float;
using BlockWarps = typename Problem::BlockGemmShape::BlockWarps;
using WarpTile = typename Problem::BlockGemmShape::WarpTile;
......@@ -502,7 +393,7 @@ struct GemmPipelineAGmemBGmemCRegV1DefaultPolicy
WarpTile::at(I0),
WarpTile::at(I1),
WarpTile::at(I2),
TransposeC>;
Problem::TransposeC>;
using BlockGemmPolicy = BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::ADataType,
typename Problem::BDataType,
typename Problem::CDataType,
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v2.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v2_default_policy.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -25,6 +26,15 @@ struct GemmPipelineAGmemBGmemCRegV2
static constexpr index_t kNPerBlock = BlockGemmShape::kN;
static constexpr index_t kKPerBlock = BlockGemmShape::kK;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "pipeline_AGmemBGmemCRegV2",
concat('x', kMPerBlock, kNPerBlock, kKPerBlock, kBlockSize));
// clang-format on
}
CK_TILE_HOST_DEVICE static constexpr auto TransposeC() { return Problem::TransposeC; }
CK_TILE_HOST_DEVICE static constexpr index_t GetStaticLdsSize()
{
return integer_divide_ceil(
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_problem.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -11,10 +13,10 @@ template <typename ADataType_,
typename BDataType_,
typename CDataType_,
typename BlockGemmShape_,
typename TileGemmTraits_>
typename Traits_>
struct GemmPipelineProblemBase
{
using GemmTraits = remove_cvref_t<TileGemmTraits_>;
using Traits = remove_cvref_t<Traits_>;
using ADataType = remove_cvref_t<ADataType_>;
using BDataType = remove_cvref_t<BDataType_>;
......@@ -22,18 +24,32 @@ struct GemmPipelineProblemBase
using BlockGemmShape = remove_cvref_t<BlockGemmShape_>;
using ALayout = remove_cvref_t<typename GemmTraits::ALayout>;
using BLayout = remove_cvref_t<typename GemmTraits::BLayout>;
using CLayout = remove_cvref_t<typename GemmTraits::CLayout>;
using ALayout = remove_cvref_t<typename Traits::ALayout>;
using BLayout = remove_cvref_t<typename Traits::BLayout>;
using CLayout = remove_cvref_t<typename Traits::CLayout>;
static constexpr index_t VectorLoadSize = GemmTraits::_VectorSize;
static constexpr index_t kBlockSize = BlockGemmShape::NumWarps * get_warp_size();
static constexpr bool TransposeC = Traits::TransposeC;
static constexpr bool kPadM = GemmTraits::kPadM;
static constexpr bool kPadN = GemmTraits::kPadN;
static constexpr bool kPadK = GemmTraits::kPadK;
static constexpr index_t kBlockSize = BlockGemmShape::NumWarps * get_warp_size();
static constexpr auto Scheduler = GemmPipelineScheduler::Default;
static constexpr bool kPadM = Traits::kPadM;
static constexpr bool kPadN = Traits::kPadN;
static constexpr bool kPadK = Traits::kPadK;
static constexpr bool DoubleSmemBuffer = Traits::DoubleSmemBuffer;
static constexpr auto Scheduler = GemmPipelineScheduler::Default;
static constexpr index_t VectorLoadSize = Traits::_VectorSize;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "gemm_problem",
concat('x', VectorLoadSize, kBlockSize),
concat('x', kPadM, kPadN, kPadK),
Scheduler);
// clang-format on
}
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentA()
{
......@@ -110,7 +126,6 @@ struct GemmPipelineProblemBase
return kPadK ? 1 : GetAlignmentB();
}
}();
static constexpr index_t VectorSizeC = []() {
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
......@@ -128,27 +143,45 @@ template <typename ADataType_,
typename BDataType_,
typename CDataType_,
typename BlockGemmShape_,
typename TileGemmTraits_>
typename Traits_>
using GemmPipelineProblem =
GemmPipelineProblemBase<ADataType_, BDataType_, CDataType_, BlockGemmShape_, TileGemmTraits_>;
GemmPipelineProblemBase<ADataType_, BDataType_, CDataType_, BlockGemmShape_, Traits_>;
template <typename ADataType_,
typename BDataType_,
typename CDataType_,
typename BlockGemmShape_,
typename TileGemmTraits_,
typename Traits_,
GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
bool HasHotLoop_ = true,
TailNumber TailNum_ = TailNumber::Full>
struct UniversalGemmPipelineProblem : public GemmPipelineProblemBase<ADataType_,
BDataType_,
CDataType_,
BlockGemmShape_,
TileGemmTraits_>
struct UniversalGemmPipelineProblem
{
using Traits = remove_cvref_t<Traits_>;
using ADataType = remove_cvref_t<ADataType_>;
using BDataType = remove_cvref_t<BDataType_>;
using CDataType = remove_cvref_t<CDataType_>;
using BlockGemmShape = remove_cvref_t<BlockGemmShape_>;
using ALayout = remove_cvref_t<typename Traits::ALayout>;
using BLayout = remove_cvref_t<typename Traits::BLayout>;
using CLayout = remove_cvref_t<typename Traits::CLayout>;
static constexpr index_t kBlockSize = BlockGemmShape::NumWarps * get_warp_size();
static constexpr bool kPadM = Traits::kPadM;
static constexpr bool kPadN = Traits::kPadN;
static constexpr bool kPadK = Traits::kPadK;
static constexpr bool DoubleSmemBuffer = Traits::DoubleSmemBuffer;
static constexpr auto Scheduler = Scheduler_;
static constexpr auto HasHotLoop = HasHotLoop_;
static constexpr auto TailNum = TailNum_;
static constexpr bool TransposeC = Traits::TransposeC;
};
} // namespace ck_tile
include/ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
namespace ck_tile {
// UniversalGemm Policy
struct UniversalGemmPipelineAgBgCrPolicy
template <typename Derived>
struct UniversalGemmBasePolicy
{
static constexpr auto I0 = number<0>{};
static constexpr auto I1 = number<1>{};
static constexpr auto I2 = number<2>{};
static constexpr bool TransposeC = true;
template <typename Problem, typename DataType, index_t MNPerBlock>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorLoadSize()
static constexpr auto ATileAccessPattern = tile_distribution_pattern::thread_raked;
static constexpr auto BTileAccessPattern = tile_distribution_pattern::thread_raked;
/**
* @brief Get the maximum global memory vector load size.
*
* @tparam Problem The UniversalGemmPipelineProblem object.
* @tparam DataType The tensor data type we're considering.
* @tparam MNPerBlock The MPerBlock or NPerBlock value depending on tensor (A/B).
* @tparam XPerTile The contiguous Tile dimension size.
* @return Maximum DRAM vector load size.
*/
template <typename Problem, typename DataType, index_t MNPerBlock, index_t XPerTile>
CK_TILE_HOST_DEVICE static constexpr auto GetGlobalVectorLoadSize()
{
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t elements_per_thread = MNPerBlock * KPerBlock / BlockSize;
if constexpr(elements_per_thread % (16 / sizeof(DataType)) == 0)
// Assume DataType is even!
if constexpr(XPerTile % (16 / sizeof(DataType)) == 0 &&
elements_per_thread % (16 / sizeof(DataType)) == 0)
{
return (16 / sizeof(DataType));
}
else if constexpr(elements_per_thread % (8 / sizeof(DataType)) == 0)
else if constexpr(XPerTile % (8 / sizeof(DataType)) == 0 &&
elements_per_thread % (8 / sizeof(DataType)) == 0)
{
return (8 / sizeof(DataType));
}
else if constexpr(elements_per_thread % (4 / sizeof(DataType)) == 0 &&
sizeof(DataType) >= 4)
else if constexpr(sizeof(DataType) >= 4 && XPerTile % (4 / sizeof(DataType)) == 0 &&
elements_per_thread % (4 / sizeof(DataType)) == 0)
{
return (4 / sizeof(DataType));
}
else if constexpr(elements_per_thread % (2 / sizeof(DataType)) == 0 &&
sizeof(DataType) >= 2)
else if constexpr(sizeof(DataType) >= 2 && XPerTile % (2 / sizeof(DataType)) == 0 &&
elements_per_thread % (2 / sizeof(DataType)) == 0)
{
return (2 / sizeof(DataType));
}
......@@ -50,119 +63,246 @@ struct UniversalGemmPipelineAgBgCrPolicy
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeA()
{
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPack = GetVectorLoadSize<Problem, ADataType, MPerBlock>();
constexpr auto DataTypeSize = sizeof(ADataType);
constexpr auto MLdsLayer =
(32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
return GetGlobalVectorLoadSize<Problem, ADataType, MPerBlock, KPerBlock>();
}
else
{
return GetGlobalVectorLoadSize<Problem, ADataType, MPerBlock, MPerBlock>();
}
}
constexpr auto a_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack * MLdsLayer>{},
number<MPerBlock / MLdsLayer>{},
number<KPack>{}),
make_tuple(number<KPack>{}, number<KPerBlock * MLdsLayer>{}, number<1>{}),
number<KPack>{},
number<1>{});
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeB()
{
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
a_lds_block_desc_0,
make_tuple(make_xor_transform(make_tuple(number<MPerBlock / MLdsLayer>{},
number<KPerBlock / KPack * MLdsLayer>{})),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{}),
make_tuple(sequence<1, 0>{}, sequence<2>{}));
if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
return GetGlobalVectorLoadSize<Problem, BDataType, NPerBlock, NPerBlock>();
}
else
{
return GetGlobalVectorLoadSize<Problem, BDataType, NPerBlock, KPerBlock>();
}
}
constexpr auto a_lds_block_desc_xk0_mnldslayer_mn_xk1 = transform_tensor_descriptor(
a_lds_block_desc_permuted,
make_tuple(make_unmerge_transform(
make_tuple(number<KPerBlock / KPack>{}, number<MLdsLayer>{})),
make_pass_through_transform(number<MPerBlock / MLdsLayer>{}),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
/**
* @brief Get the vector store size for C tensor.
*
* @tparam Problem - Gemm pipeline problem class.
*
* @note The vector store size for output C tensor would depend on multiple factors
* like its data layout and warp gemm C transposition. In general it would
* be the number of consecutive elements in contiguous C dimension hold by
* single thread.
*
* @return The vector store size for C tensor.
*/
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeC()
{
using BlockGemm = remove_cvref_t<decltype(Derived::template GetBlockGemm<Problem>())>;
using WG = typename BlockGemm::WarpGemm;
constexpr auto a_lds_block_desc = transform_tensor_descriptor(
a_lds_block_desc_xk0_mnldslayer_mn_xk1,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<MPerBlock / MLdsLayer>{}, number<MLdsLayer>{})),
make_merge_transform_v3_division_mod(
make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
constexpr bool TransposeC = Problem::TransposeC;
using CLayout = typename Problem::CLayout;
using CWarpDstr = typename WG::CWarpDstr;
return a_lds_block_desc;
// N is contiguous dimension
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
if constexpr(TransposeC)
{
// In this case each thread has multiple consecutive elements in
// N dimension, however consecutive threads' elements have stride.
constexpr index_t NDimY = CWarpDstr::NDimY;
constexpr auto c_warp_y_lengths =
CWarpDstr{}.get_ys_to_d_descriptor().get_lengths();
static_assert(WG::WarpGemmAttribute::Impl::kCM1PerLane ==
c_warp_y_lengths.get(number<NDimY - 1>{}));
return c_warp_y_lengths.get(number<NDimY - 1>{});
}
else
{
// In this case each thread has just a single item in Ndim
return WG::WarpGemmAttribute::Impl::kCNLane / WG::kN;
}
}
// M is contiguous dimension
else if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::ColumnMajor>)
{
if constexpr(TransposeC)
{
// In this case each thread has just a single item in Mdim
return WG::WarpGemmAttribute::Impl::kCNLane / WG::kN;
}
else
{
// In this case each thread has multiple consecutive elements in
// M dimension, however consecutive threads' elements have stride.
constexpr index_t NDimY = CWarpDstr::NDimY;
constexpr auto c_warp_y_lengths =
CWarpDstr{}.get_ys_to_d_descriptor().get_lengths();
static_assert(WG::WarpGemmAttribute::Impl::kCM1PerLane ==
c_warp_y_lengths.get(number<NDimY - 1>{}));
return c_warp_y_lengths.get(number<NDimY - 1>{});
}
}
else
{
static_assert(false, "Unsupported CLayout!");
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto IsTransposeC()
{
return Problem::TransposeC;
}
using BDataType = remove_cvref_t<typename Problem::BDataType>;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeADramTileDistribution()
{
using ALayout = remove_cvref_t<typename Problem::ALayout>;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPack = GetVectorLoadSize<Problem, BDataType, NPerBlock>();
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t VecLoadSize = GetVectorSizeA<Problem>();
constexpr auto DataTypeSize = sizeof(BDataType);
constexpr auto NLdsLayer =
(32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
// Tile: MPerBlock X KPerBlock
if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
MPerBlock,
KPerBlock,
VecLoadSize,
ATileAccessPattern>;
return TileEncodingPattern::Make2DStaticTileDistribution();
}
// Tile: KPerBlock X MPerBlock
else
{
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
KPerBlock,
MPerBlock,
VecLoadSize,
ATileAccessPattern>;
return TileEncodingPattern::Make2DStaticTileDistribution();
}
}
constexpr auto b_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack * NLdsLayer>{},
number<NPerBlock / NLdsLayer>{},
number<KPack>{}),
make_tuple(number<KPack>{}, number<KPerBlock * NLdsLayer>{}, number<1>{}),
number<KPack>{},
number<1>{});
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBDramTileDistribution()
{
using BLayout = remove_cvref_t<typename Problem::BLayout>;
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
b_lds_block_desc_0,
make_tuple(make_xor_transform(make_tuple(number<NPerBlock / NLdsLayer>{},
number<KPerBlock / KPack * NLdsLayer>{})),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{}),
make_tuple(sequence<1, 0>{}, sequence<2>{}));
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t VecLoadSize = GetVectorSizeB<Problem>();
constexpr auto b_lds_block_desc_xk0_mnldslayer_mn_xk1 = transform_tensor_descriptor(
b_lds_block_desc_permuted,
make_tuple(make_unmerge_transform(
make_tuple(number<KPerBlock / KPack>{}, number<NLdsLayer>{})),
make_pass_through_transform(number<NPerBlock / NLdsLayer>{}),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
// Tile: KPerBlock X NPerBlock
if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
KPerBlock,
NPerBlock,
VecLoadSize,
BTileAccessPattern>;
return TileEncodingPattern::Make2DStaticTileDistribution();
}
// Tile: NPerBlock X KPerBlock
else
{
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
NPerBlock,
KPerBlock,
VecLoadSize,
BTileAccessPattern>;
return TileEncodingPattern::Make2DStaticTileDistribution();
}
}
constexpr auto b_lds_block_desc = transform_tensor_descriptor(
b_lds_block_desc_xk0_mnldslayer_mn_xk1,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<NPerBlock / NLdsLayer>{}, number<NLdsLayer>{})),
make_merge_transform_v3_division_mod(
make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return b_lds_block_desc;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledARegTileDistribution()
{
using ALayout = remove_cvref_t<typename Problem::ALayout>;
static_assert(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t VecLoadSize = GetVectorSizeA<Problem>();
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
KPerBlock,
MPerBlock,
VecLoadSize,
ATileAccessPattern>;
return TileEncodingPattern::MakeShuffled2DStaticTileDistribution();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledBRegTileDistribution()
{
using BLayout = remove_cvref_t<typename Problem::BLayout>;
static_assert(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>);
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t VecLoadSize = GetVectorSizeB<Problem>();
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
KPerBlock,
NPerBlock,
VecLoadSize,
BTileAccessPattern>;
return TileEncodingPattern::MakeShuffled2DStaticTileDistribution();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemPackA()
{
using BlockGemm = remove_cvref_t<decltype(Derived::template GetBlockGemm<Problem>())>;
constexpr index_t KPack = BlockGemm::Traits::KPack;
return KPack;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetSmemPackB()
{
using BlockGemm = remove_cvref_t<decltype(Derived::template GetBlockGemm<Problem>())>;
constexpr index_t KPack = BlockGemm::Traits::KPack;
return KPack;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeA()
{
constexpr index_t smem_size_a = sizeof(typename Problem::ADataType) *
MakeALdsBlockDescriptor<Problem>().get_element_space_size();
constexpr auto a_lds_desc = Derived::template MakeALdsBlockDescriptor<Problem>();
constexpr index_t smem_size_a = integer_least_multiple(
sizeof(typename Problem::ADataType) * a_lds_desc.get_element_space_size(), 16);
return smem_size_a;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSizeB()
{
constexpr index_t smem_size_b = sizeof(typename Problem::BDataType) *
MakeBLdsBlockDescriptor<Problem>().get_element_space_size();
constexpr auto b_lds_desc = Derived::template MakeBLdsBlockDescriptor<Problem>();
constexpr index_t smem_size_b = integer_least_multiple(
sizeof(typename Problem::BDataType) * b_lds_desc.get_element_space_size(), 16);
return smem_size_b;
}
......@@ -171,298 +311,272 @@ struct UniversalGemmPipelineAgBgCrPolicy
{
constexpr index_t smem_size_a = GetSmemSizeA<Problem>();
constexpr index_t smem_size_b = GetSmemSizeB<Problem>();
index_t smem_size = 0;
smem_size += smem_size_a + smem_size_b;
return smem_size;
return smem_size_a + smem_size_b;
}
};
// UniversalGemm Policy
struct UniversalGemmPipelineAgBgCrPolicy
: public UniversalGemmBasePolicy<UniversalGemmPipelineAgBgCrPolicy>
{
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeADramTileDistribution()
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
{
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using ALayout = remove_cvref_t<typename Problem::ALayout>;
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPack = GetSmemPackA<Problem>();
if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
{
constexpr index_t M1 = Problem::VectorLoadSize / sizeof(ADataType);
constexpr index_t M0 = MPerBlock / M1;
constexpr index_t total_pixels = MPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % M1 == 0);
constexpr index_t K3 = total_pixels / M1;
constexpr index_t KPack = GetVectorLoadSize<Problem, ADataType, MPerBlock>();
static_assert(KPack % K3 == 0);
constexpr index_t K2 = KPack / K3;
if constexpr(get_warp_size() % (K2 * M0) == 0)
{
constexpr index_t K1 = get_warp_size() / (K2 * M0);
constexpr index_t K0 = BlockSize / get_warp_size();
static_assert(KPerBlock == K0 * K1 * K2 * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
else
{
constexpr index_t K1 = (K2 * M0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
}
else
{
constexpr index_t K1 = Problem::VectorLoadSize / sizeof(ADataType);
constexpr index_t K0 = KPerBlock / K1;
constexpr index_t M2 = get_warp_size() / K0;
if constexpr(get_warp_size() % (M2 * K0) == 0)
{
constexpr index_t M1 = BlockSize / get_warp_size();
static_assert(M2 != 0, "M2 is zero, which will lead to a division by zero error.");
static_assert(M1 != 0, "M1 is zero, which will lead to a division by zero error.");
constexpr index_t M0 = MPerBlock / (M2 * M1);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
else
{
constexpr index_t M0 = BlockSize / get_warp_size();
constexpr index_t M1 = MPerBlock / (M2 * M0);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<2, 0>>,
sequence<1, 2>,
sequence<1, 1>>{});
}
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBDramTileDistribution()
{
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using BLayout = remove_cvref_t<typename Problem::BLayout>;
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr auto DataTypeSize = sizeof(ADataType);
constexpr auto MLdsLayer =
(32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr auto a_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<KPerBlock / KPack * MLdsLayer>{},
number<MPerBlock / MLdsLayer>{},
number<KPack>{}),
make_tuple(number<KPack>{}, number<KPerBlock * MLdsLayer>{}, number<1>{}),
number<KPack>{},
number<1>{});
if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
constexpr index_t N1 = Problem::VectorLoadSize / sizeof(BDataType);
constexpr index_t N0 = NPerBlock / N1;
constexpr index_t total_pixels = NPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % N1 == 0);
constexpr index_t K3 = total_pixels / N1;
constexpr index_t KPack = GetVectorLoadSize<Problem, BDataType, NPerBlock>();
static_assert(KPack % K3 == 0);
constexpr index_t K2 = KPack / K3;
if constexpr(get_warp_size() % (K2 * N0) == 0)
{
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = BlockSize / get_warp_size();
static_assert(KPerBlock == K0 * K1 * K2 * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
else
{
constexpr index_t K1 = (K2 * N0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
}
else
{
constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
a_lds_block_desc_0,
make_tuple(make_xor_transform(make_tuple(number<MPerBlock / MLdsLayer>{},
number<KPerBlock / KPack * MLdsLayer>{})),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{}),
make_tuple(sequence<1, 0>{}, sequence<2>{}));
constexpr index_t K1 = Problem::VectorLoadSize / sizeof(BDataType);
constexpr index_t K0 = KPerBlock / K1;
constexpr index_t N2 = get_warp_size() / K0;
// coalesce reading for each blocks
if constexpr(get_warp_size() % (N2 * K0) == 0)
{
constexpr index_t N1 = BlockSize / get_warp_size();
static_assert(N2 != 0, "N2 is zero, which will lead to a division by zero error.");
static_assert(N1 != 0, "N1 is zero, which will lead to a division by zero error.");
constexpr index_t N0 = NPerBlock / (N2 * N1);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
// coalesce reading for each warps
else
{
constexpr index_t N0 = BlockSize / get_warp_size();
constexpr index_t N1 = NPerBlock / (N2 * N0);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<2, 0>>,
sequence<1, 2>,
sequence<1, 1>>{});
}
}
}
constexpr auto a_lds_block_desc_xk0_mnldslayer_mn_xk1 = transform_tensor_descriptor(
a_lds_block_desc_permuted,
make_tuple(make_unmerge_transform(
make_tuple(number<KPerBlock / KPack>{}, number<MLdsLayer>{})),
make_pass_through_transform(number<MPerBlock / MLdsLayer>{}),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledARegBlockDescriptor()
{
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
static_assert(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr auto a_lds_block_desc = transform_tensor_descriptor(
a_lds_block_desc_xk0_mnldslayer_mn_xk1,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<MPerBlock / MLdsLayer>{}, number<MLdsLayer>{})),
make_merge_transform_v3_division_mod(
make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
constexpr index_t M1 = Problem::VectorLoadSize / sizeof(ADataType);
constexpr index_t M0 = MPerBlock / M1;
constexpr index_t total_pixels = MPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % M1 == 0);
constexpr index_t K3 = total_pixels / M1;
constexpr index_t kKPack = GetVectorLoadSize<Problem, ADataType, MPerBlock>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t warp_size = get_warp_size();
if constexpr(warp_size % (K2 * M0) == 0)
{
constexpr index_t K1 = warp_size / (K2 * M0);
constexpr index_t K0 = BlockSize / warp_size;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
}
else
{
constexpr index_t K1 = (K2 * M0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
}
return a_lds_block_desc;
}
/**
* @brief Create LDS block descriptor for B tensor.
*
* @tparam Problem Gemm pipeline problem.
* @return B tensor LDS block descriptor.
*/
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledBRegBlockDescriptor()
CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor()
{
using BLayout = remove_cvref_t<typename Problem::BLayout>;
// using BLayout = remove_cvref_t<typename Problem::BLayout>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
static_assert(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>);
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t N1 = Problem::VectorLoadSize / sizeof(BDataType);
constexpr index_t N0 = NPerBlock / N1;
constexpr index_t total_pixels = NPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % N1 == 0);
constexpr index_t K3 = total_pixels / N1;
constexpr index_t kKPack = GetVectorLoadSize<Problem, BDataType, NPerBlock>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t warp_size = get_warp_size();
if constexpr(warp_size % (K2 * N0) == 0)
#if 1
// if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
{
constexpr index_t K1 = warp_size / (K2 * N0);
constexpr index_t K0 = BlockSize / warp_size;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
constexpr index_t KPack = GetSmemPackB<Problem>();
constexpr auto BK0 = number<KPerBlock / KPack>{};
constexpr auto DataTypeSize = sizeof(BDataType);
constexpr auto NLdsLayer =
(32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
constexpr auto b_lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(
BK0 * number<NLdsLayer>{}, number<NPerBlock / NLdsLayer>{}, number<KPack>{}),
make_tuple(number<KPack>{}, number<KPerBlock * NLdsLayer>{}, number<1>{}),
number<KPack>{},
number<1>{});
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
b_lds_block_desc_0,
make_tuple(make_xor_transform(make_tuple(number<NPerBlock / NLdsLayer>{},
BK0 * number<NLdsLayer>{})),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{}),
make_tuple(sequence<1, 0>{}, sequence<2>{}));
constexpr auto b_lds_block_desc_bk0_nldslayer_n_bk1 = transform_tensor_descriptor(
b_lds_block_desc_permuted,
make_tuple(make_unmerge_transform(make_tuple(BK0, number<NLdsLayer>{})),
make_pass_through_transform(number<NPerBlock / NLdsLayer>{}),
make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
constexpr auto b_lds_block_desc = transform_tensor_descriptor(
b_lds_block_desc_bk0_nldslayer_n_bk1,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<NPerBlock / NLdsLayer>{}, number<NLdsLayer>{})),
make_merge_transform_v3_division_mod(make_tuple(BK0, number<KPack>{}))),
make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return b_lds_block_desc;
}
else
#else
else // B is Row Major
{
constexpr index_t K1 = (K2 * N0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t VecLoadSize = GetVectorSizeB<Problem>();
using TileEncodingPattern = TileDistributionEncodingPattern2D<BlockSize,
KPerBlock,
NPerBlock,
VecLoadSize,
BTileAccessPattern>;
constexpr auto BK0 = number<TileEncodingPattern::X1>{};
constexpr auto BK1 = number<TileEncodingPattern::Y0>{};
// constexpr auto N0 = BBlockTransferThreadClusterLengths_BK0_N_BK1{}.At(I1);
constexpr auto N0 = TileEncodingPattern::X0;
constexpr auto N1 = NPerBlock / N0;
using WarpTile = typename Problem::BlockGemmShape::WarpTile;
constexpr auto NPerXdl = number<WarpTile::at(I1)>{};
// constexpr auto KThreadWrite =
// BBlockTransferThreadClusterLengths_BK0_N_BK1{}.At(I0);
constexpr auto KThreadWrite = TileEncodingPattern::Y2;
constexpr auto K0PerThreadWrite = BK0 / KThreadWrite;
constexpr auto KThreadRead = 64 / NPerXdl;
constexpr auto K0PerThreadRead = BK0 / KThreadRead;
constexpr auto kfold =
(BK1 * N0 * sizeof(BDataType) > 128) ? 1 : 128 / (BK1 * N0 * sizeof(BDataType));
constexpr auto KThreadReadPerm =
(kfold * K0PerThreadWrite / K0PerThreadRead) > 1
? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
: KThreadRead;
// 1<=npair<=n0
constexpr auto npair = (BK1 * NPerXdl * sizeof(BDataType) > 128)
? 1
: ((128 / (BK1 * NPerXdl * sizeof(BDataType))) > N0
? N0
: 128 / (BK1 * NPerXdl * sizeof(BDataType)));
constexpr auto b_lds_block_desc = make_naive_tensor_descriptor_packed(
make_tuple(number<KThreadWrite / kfold / KThreadReadPerm>{},
number<K0PerThreadWrite>{},
number<KThreadReadPerm * N1>{},
number<kfold * N0 / npair>{},
number<npair>{},
BK1));
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
b_lds_block_desc,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_xor_transform(
make_tuple(number<KThreadReadPerm * N1>{}, number<kfold * N0 / npair>{})),
make_pass_through_transform(number<npair>{}),
make_pass_through_transform(BK1)),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}));
constexpr auto b_lds_block_desc_unmerged = transform_tensor_descriptor(
b_lds_block_desc_permuted,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_unmerge_transform(make_tuple(number<KThreadReadPerm>{}, number<N1>{})),
make_unmerge_transform(make_tuple(number<kfold>{}, number<N0 / npair>{})),
make_pass_through_transform(number<npair>{}),
make_pass_through_transform(BK1)),
make_tuple(sequence<0>{},
sequence<1>{},
sequence<2>{},
sequence<3>{},
sequence<4>{},
sequence<5>{}),
make_tuple(sequence<1>{},
sequence<2>{},
sequence<0, 3>{},
sequence<4, 5>{},
sequence<6>{},
sequence<7>{}));
// constexpr auto b_lds_block_desc_bk0_n_bk1 = transform_tensor_descriptor(
// b_lds_block_desc_unmerged,
// make_tuple(make_merge_transform_v3_division_mod(
// make_tuple(number<KThreadReadPerm>{},
// number<KThreadWrite / kfold / KThreadReadPerm>{},
// number<kfold>{},
// number<K0PerThreadWrite>{})),
// make_merge_transform_v3_division_mod(
// make_tuple(number<N0 / npair>{}, number<npair>{}, number<N1>{})),
// make_pass_through_transform(BK1)),
// make_tuple(sequence<0, 1, 4, 2>{}, sequence<5, 6, 3>{}, sequence<7>{}),
// make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}));
constexpr auto b_lds_block_desc_kn = transform_tensor_descriptor(
b_lds_block_desc_unmerged,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<KThreadReadPerm>{},
number<KThreadWrite / kfold / KThreadReadPerm>{},
number<kfold>{},
number<K0PerThreadWrite>{},
BK1)),
make_merge_transform_v3_division_mod(
make_tuple(number<N0 / npair>{}, number<npair>{}, number<N1>{}))),
make_tuple(sequence<0, 1, 4, 2, 7>{}, sequence<5, 6, 3>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
// return b_lds_block_desc_bk0_n_bk1;
return b_lds_block_desc_kn;
// constexpr auto b_lds_block_desc_bk0_n_bk1 = make_naive_tensor_descriptor(
// make_tuple(BK0, number<NPerBlock>{}, number<KPack>{}),
// make_tuple(number<KPack>{}, number<KPerBlock>{}, number<1>{}),
// number<KPack>{},
// number<1>{});
// constexpr auto b_lds_block_desc = transform_tensor_descriptor(
// b_lds_block_desc_bk0_n_bk1,
// make_tuple(make_pass_through_transform(number<NPerBlock>{}),
// make_merge_transform_v3_division_mod(make_tuple(BK0,
// number<KPack>{}))),
// make_tuple(sequence<1>{}, sequence<0, 2>{}),
// make_tuple(sequence<0>{}, sequence<1>{}));
// return b_lds_block_desc;
}
#endif
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetBlockGemm()
{
using AccDataType = float;
using BlockWarps = typename Problem::BlockGemmShape::BlockWarps;
using WarpTile = typename Problem::BlockGemmShape::WarpTile;
using WarpGemm = WarpGemmMfmaDispatcher<typename Problem::ADataType,
typename Problem::BDataType,
AccDataType,
typename Problem::CDataType,
WarpTile::at(I0),
WarpTile::at(I1),
WarpTile::at(I2),
TransposeC>;
Problem::TransposeC>;
using BlockGemmPolicy = BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::ADataType,
typename Problem::BDataType,
typename Problem::CDataType,
BlockWarps,
WarpGemm>;
return BlockGemmASmemBSmemCRegV1<Problem, BlockGemmPolicy>{};
return BlockUniversalGemmAsBsCr<Problem, BlockGemmPolicy>{};
}
};
......
include/ck_tile/ops/gemm/pipeline/tile_gemm_shape.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -19,6 +20,16 @@ struct TileGemmShape
static constexpr index_t kM = BlockTile::at(number<0>{});
static constexpr index_t kN = BlockTile::at(number<1>{});
static constexpr index_t kK = BlockTile::at(number<2>{});
CK_TILE_HOST static std::string GetName()
{
// clang-format off
return concat('_', "tile_gemm_shape",
concat('x', kM, kN, kK, NumWarps),
concat('x', BlockWarps::at(number<0>{}), BlockWarps::at(number<1>{}), BlockWarps::at(number<2>{})),
concat('x', (WarpTile::at(number<0>{})), WarpTile::at(number<1>{}), WarpTile::at(number<2>{})));
// clang-format on
}
};
} // namespace ck_tile
include/ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp
View file @ 7572a691
......@@ -19,11 +19,37 @@ struct TileGemmTraits
static constexpr bool kPadN = kPadN_;
static constexpr bool kPadK = kPadK_;
// TODO this can't be hardcoded here! Should be in policy!
static constexpr int _VectorSize = 16;
using ALayout = ALayout_;
using BLayout = BLayout_;
using CLayout = CLayout_;
static constexpr bool TransposeC = false;
};
template <bool kPadM_,
bool kPadN_,
bool kPadK_,
bool DoubleSmemBuffer_,
typename ALayout_,
typename BLayout_,
typename CLayout_,
bool TransposeC_ = false>
struct TileGemmUniversalTraits
{
static constexpr bool kPadM = kPadM_;
static constexpr bool kPadN = kPadN_;
static constexpr bool kPadK = kPadK_;
static constexpr bool DoubleSmemBuffer = DoubleSmemBuffer_;
using ALayout = ALayout_;
using BLayout = BLayout_;
using CLayout = CLayout_;
static constexpr bool TransposeC = TransposeC_;
};
} // namespace ck_tile
include/ck_tile/ops/image_to_column.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -8,3 +8,4 @@
#include "ck_tile/ops/image_to_column/pipeline/tile_image_to_column_shape.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/layernorm2d.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -11,3 +11,4 @@
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_traits.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/layernorm2d/kernel/layernorm2d_fwd_kernel.hpp
View file @ 7572a691
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -14,7 +14,8 @@ struct Layernorm2dFwdHostArgs
{
const void* p_x; // [m ,n], input, fp16/bf16
const void* p_x_residual; // [m ,n], shortcut input, prec same as input, nullptr if not used
const void* p_x_scale; // [1 ,n], smooth scale input, fp32, nullptr if not used
const void* p_sm_scale; // [1 ,n], smooth scale input, fp32, nullptr if not used
const void* p_x_bias; // [1, n], bias, prec same as input
const void* p_gamma; // [1, n], gamma, prec same as input
const void* p_beta; // [1, n], beta, prec same as input
......@@ -42,15 +43,16 @@ struct Layernorm2dFwd
using Epilogue = remove_cvref_t<Epilogue_>;
using Problem = typename Pipeline::Problem;
using XDataType = remove_cvref_t<typename Problem::XDataType>;
using GammaDataType = remove_cvref_t<typename Problem::GammaDataType>;
using BetaDataType = remove_cvref_t<typename Problem::BetaDataType>;
using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
using YDataType = remove_cvref_t<typename Problem::YDataType>;
using MeanDataType = remove_cvref_t<typename Problem::MeanDataType>;
using InvStdDataType = remove_cvref_t<typename Problem::InvStdDataType>;
using XScaleDataType = remove_cvref_t<typename Problem::XScaleDataType>;
using YScaleDataType = remove_cvref_t<typename Problem::YScaleDataType>;
using XDataType = remove_cvref_t<typename Problem::XDataType>;
using XBiasDataType = remove_cvref_t<typename Problem::XBiasDataType>;
using GammaDataType = remove_cvref_t<typename Problem::GammaDataType>;
using BetaDataType = remove_cvref_t<typename Problem::BetaDataType>;
using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
using YDataType = remove_cvref_t<typename Problem::YDataType>;
using MeanDataType = remove_cvref_t<typename Problem::MeanDataType>;
using InvStdDataType = remove_cvref_t<typename Problem::InvStdDataType>;
using SmoothScaleDataType = remove_cvref_t<typename Problem::SmoothScaleDataType>;
using YScaleDataType = remove_cvref_t<typename Problem::YScaleDataType>;
// for simplicity, shortcut input/output type is same as X
using XResidualDataType = XDataType;
......@@ -67,6 +69,7 @@ struct Layernorm2dFwd
static constexpr bool kPadM = false; // always no need to pad along M
static constexpr bool kPadN = Problem::Traits::kPadN;
static constexpr bool kTwoPass = Problem::Traits::kTwoPass;
static constexpr auto kXbias = Problem::Traits::kXbias;
static constexpr auto kFusedAdd = Problem::Traits::kFusedAdd;
static constexpr auto kFusedQuant = Problem::Traits::kFusedQuant;
......@@ -81,7 +84,8 @@ struct Layernorm2dFwd
{
const void* p_x; // [m ,n], input, fp16/bf16
const void* p_x_residual; // [m ,n], shortcut input, prec same as input, nullptr if not used
const void* p_x_scale; // [1 ,n], smooth scale input, fp32, nullptr if not used
const void* p_sm_scale; // [1 ,n], smooth scale input, fp32, nullptr if not used
const void* p_x_bias; // [1, n], bias, prec same as input
const void* p_gamma; // [1, n], gamma, prec same as input
const void* p_beta; // [1, n], beta, prec same as input
......@@ -107,7 +111,8 @@ struct Layernorm2dFwd
{
return Kargs{hargs.p_x,
hargs.p_x_residual,
hargs.p_x_scale,
hargs.p_sm_scale,
hargs.p_x_bias,
hargs.p_gamma,
hargs.p_beta,
hargs.p_y,
......@@ -152,6 +157,7 @@ struct Layernorm2dFwd
using S_ = typename Problem::BlockShape;
auto surfix = [&] () {
std::string n;
if (kXbias != Layernorm2dXBiasEnum::NO_BIAS) n += _SS_("_") + Layernorm2dXBiasEnumName<kXbias>::name;
if (kFusedAdd != Layernorm2dFusedAddEnum::NO_ADD) n += _SS_("_") + Layernorm2dFusedAddEnumName<kFusedAdd>::name;
if (kFusedQuant != Layernorm2dFusedQuantEnum::NO_SWEEP) n += _SS_("_") + Layernorm2dFusedQuantEnumName<kFusedQuant>::name;
if (kPadN) n += "_pn";
......@@ -165,7 +171,7 @@ struct Layernorm2dFwd
base_str += _SS_("_") + _SS_(t2s<YDataType>::name);
}
if (kFusedQuant == Layernorm2dFusedQuantEnum::SMOOTH_DYNAMIC_QUANT) {
base_str += _SS_("_sx") + _SS_(t2s<XScaleDataType>::name);
base_str += _SS_("_sx") + _SS_(t2s<SmoothScaleDataType>::name);
base_str += _SS_("_sy") + _SS_(t2s<YScaleDataType>::name);
}
if (kFusedQuant == Layernorm2dFusedQuantEnum::DYNAMIC_QUANT) {
......@@ -228,6 +234,27 @@ struct Layernorm2dFwd
}
}();
const auto x_bias_window = [&]() {
if constexpr(kXbias == Layernorm2dXBiasEnum::ADD_BIAS)
{
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const XBiasDataType*>(kargs.p_x_bias),
make_tuple(kargs.n),
make_tuple(1),
number<Vector_N>{},
number<1>{});
const auto tmp2_ =
pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<false>{});
return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {0});
}
else
{
return make_null_tile_window(make_tuple(number<Block_N>{}));
}
}();
const auto gamma_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const GammaDataType*>(kargs.p_gamma),
......@@ -329,18 +356,18 @@ struct Layernorm2dFwd
return make_null_tile_window(make_tuple(number<Block_M>{}));
}();
auto x_scale_window = [&]() {
auto sm_scale_window = [&]() {
if constexpr(kFusedQuant == Layernorm2dFusedQuantEnum::SMOOTH_DYNAMIC_QUANT)
{
const auto win_ = [&]() {
const auto tmp_0_ = make_naive_tensor_view_packed<address_space_enum::global>(
static_cast<const XScaleDataType*>(kargs.p_x_scale),
static_cast<const SmoothScaleDataType*>(kargs.p_sm_scale),
make_tuple(kargs.n),
number<Vector_N>{});
return pad_tensor_view(tmp_0_,
make_tuple(number<Block_N>{}),
sequence<false>{}); // x_scale no need pad
sequence<false>{}); // sm_scale no need pad
}();
return make_tile_window(win_, make_tuple(number<Block_N>{}), {0});
}
......@@ -371,13 +398,14 @@ struct Layernorm2dFwd
Pipeline{}(x_window,
x_residual_window,
x_bias_window,
gamma_window,
beta_window,
y_window,
y_residual_window,
mean_window,
inv_std_window,
x_scale_window,
sm_scale_window,
y_scale_window,
static_cast<const ComputeDataType>(kargs.epsilon),
kargs.n,
......
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