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Issue #666: Revert "simplify karg in device/grid of split-k op (#644)" (#665)

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This reverts commit bb5530af91.
This commit is contained in:
Jun Liu
2023-04-06 17:14:11 -07:00
committed by GitHub
parent fde6d2742b
commit 3248387bbb
3 changed files with 539 additions and 438 deletions
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486
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4r2.hpp
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@@ -18,23 +18,60 @@
namespace ck {
template <typename GridwiseGemm,
bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation>
typename FloatAB,
typename FloatC,
typename AGridDesc_B_K0_M_K1,
typename BGridDesc_B_K0_N_K1,
typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename CBlockClusterAdaptor,
bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_gemm_xdlops_v2r4r2_simplified(typename GridwiseGemm::Argument karg)
kernel_gemm_xdlops_v2r4r2(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
c_grid_desc_mblock_mperblock_nblock_nperblock,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CElementwiseOperation c_element_op,
const CBlockClusterAdaptor c_block_cluster_adaptor)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
constexpr index_t shared_size = GridwiseGemm::GetSharedMemoryNumberOfByte();
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ uint8_t p_shared[shared_size];
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation>(
karg, static_cast<void*>(p_shared));
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
p_b_grid,
p_c_grid,
static_cast<void*>(p_shared_block),
a_b_k0_m_k1_grid_desc,
b_b_k0_n_k1_grid_desc,
c_grid_desc_mblock_mperblock_nblock_nperblock,
a_element_op,
b_element_op,
c_element_op,
c_block_cluster_adaptor);
#else
ignore = karg;
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_c_grid;
ignore = a_b_k0_m_k1_grid_desc;
ignore = b_b_k0_n_k1_grid_desc;
ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = a_element_op;
ignore = b_element_op;
ignore = c_element_op;
ignore = c_block_cluster_adaptor;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
}
@@ -42,13 +79,13 @@ template <index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
typename ALayout,
typename BLayout,
typename CLayout,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AGridDesc_B_K0_M_K1,
typename BGridDesc_B_K0_N_K1,
typename CMNGridDesc,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
tensor_operation::device::GemmSpecialization GemmSpec,
index_t MPerBlock,
index_t NPerBlock,
index_t K0PerBlock,
@@ -89,238 +126,10 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
static constexpr auto I7 = Number<7>{};
// K1 should be Number<...>
static constexpr auto K1 = Number<K1Value>{};
static constexpr auto M01 = 1;
static constexpr auto N01 = 1;
static constexpr auto K1 = Number<K1Value>{};
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
struct Argument : public ck::tensor_operation::device::BaseArgument
{
const FloatAB* p_a_grid;
const FloatAB* p_b_grid;
FloatC* p_c_grid;
index_t M;
index_t N;
index_t K;
index_t StrideA;
index_t StrideB;
index_t StrideC;
index_t MPadded;
index_t NPadded;
index_t KPadded;
index_t K0;
index_t k_batch;
Argument(const FloatAB* p_a_grid_,
const FloatAB* p_b_grid_,
FloatC* p_c_grid_,
index_t M_,
index_t N_,
index_t K_,
index_t StrideA_,
index_t StrideB_,
index_t StrideC_,
index_t MPadded_,
index_t NPadded_,
index_t KPadded_,
index_t K0_,
index_t k_batch_)
: p_a_grid(p_a_grid_),
p_b_grid(p_b_grid_),
p_c_grid(p_c_grid_),
M(M_),
N(N_),
K(K_),
StrideA(StrideA_),
StrideB(StrideB_),
StrideC(StrideC_),
MPadded(MPadded_),
NPadded(NPadded_),
KPadded(KPadded_),
K0(K0_),
k_batch(k_batch_)
{
}
void Print() const
{
std::cout << "arg {"
<< "M:" << M << ", "
<< "N:" << N << ", "
<< "K:" << K << ", "
<< "SA:" << StrideA << ", "
<< "SB:" << StrideB << ", "
<< "SC:" << StrideC << ", "
<< "MP:" << MPadded << ", "
<< "NP:" << NPadded << ", "
<< "KP:" << KPadded << ", "
<< "K0:" << K0 << ", "
<< "KB:" << k_batch << "}" << std::endl;
}
};
__host__ __device__ static auto CalculateGridSize(const Argument& karg)
{
return std::make_tuple(math::integer_divide_ceil(karg.N, NPerBlock),
math::integer_divide_ceil(karg.M, MPerBlock),
karg.k_batch);
}
// prefer this to be called on host
__host__ __device__ static auto CalculateMPadded(index_t M)
{
return (M + MPerBlock - 1) / MPerBlock * MPerBlock;
}
__host__ __device__ static auto CalculateNPadded(index_t N)
{
return (N + NPerBlock - 1) / NPerBlock * NPerBlock;
}
__host__ __device__ static auto CalculateK0(index_t K, index_t K_Batch = 1)
{
// k_batch * k0 * k0_per_block * k1
auto K_t = K_Batch * K0PerBlock * K1;
return (K + K_t - 1) / K_t * K0PerBlock;
}
__host__ __device__ static auto CalculateKPadded(index_t K, index_t K_Batch = 1)
{
auto K0 = CalculateK0(K, K_Batch);
return K_Batch * K0 * K1;
}
__host__ __device__ static auto MakeAGridDescriptor_KBatch_K0_M_K1(index_t M,
index_t MPad,
index_t K,
index_t StrideA,
index_t KBatch,
index_t K0,
index_t KPad)
{
const auto a_grid_desc_m_k = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
}
}();
const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
a_grid_desc_m_k,
make_tuple(make_pass_through_transform(M), make_right_pad_transform(K, KPad - K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
{
// const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
return transform_tensor_descriptor(
a_grid_desc_m_kpad,
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
make_right_pad_transform(M, MPad - M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
}
else
{
return transform_tensor_descriptor(
a_grid_desc_m_kpad,
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
make_pass_through_transform(M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
}
}
__host__ __device__ static auto MakeBGridDescriptor_KBatch_K0_N_K1(index_t K,
index_t NPad,
index_t N,
index_t StrideB,
index_t KBatch,
index_t K0,
index_t KPad)
{
const auto b_grid_desc_k_n = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
}
}();
const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
b_grid_desc_k_n,
make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
{
// const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
return transform_tensor_descriptor(
b_grid_desc_kpad_n,
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
make_right_pad_transform(N, NPad - N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
}
else
{
return transform_tensor_descriptor(
b_grid_desc_kpad_n,
make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
}
}
__host__ __device__ static auto
MakeCGridDescriptor_M_N(index_t M, index_t N, index_t MPad, index_t NPad, index_t StrideC)
{
const auto c_grid_desc_m_n = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
}
}();
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
{
return transform_tensor_descriptor(c_grid_desc_m_n,
make_tuple(make_right_pad_transform(M, MPad - M),
make_right_pad_transform(N, NPad - N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
else
{
return transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
}
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
constexpr auto max_lds_align = K1;
@@ -369,68 +178,45 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
c_block_size * sizeof(FloatC));
}
__host__ __device__ static constexpr bool CheckValidity(const Argument& karg)
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
template <typename Block2CTileMap>
__host__ __device__ static constexpr bool
CheckValidity(const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
const Block2CTileMap& block_2_ctile_map)
{
if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
"wrong! K1 need to be known at compile-time");
static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
(NPerBlock % (NRepeat * NPerXDL)) == 0,
"Invalid tuning param!");
const auto M = a_b_k0_m_k1_grid_desc.GetLength(I2);
const auto N = b_b_k0_n_k1_grid_desc.GetLength(I2);
const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
return false;
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
return false;
if(!block_2_ctile_map.CheckValidity(c_m_n_grid_desc))
{
if(!(karg.M % MPerBlock == 0))
return false;
}
if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
{
if(!(karg.N % NPerBlock == 0))
return false;
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
{
if(karg.K % ABlockTransferSrcScalarPerVector != 0)
return false;
}
else
{
if(karg.M % ABlockTransferSrcScalarPerVector != 0)
return false;
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
if(karg.N % BBlockTransferSrcScalarPerVector != 0)
return false;
}
else
{
if(karg.K % BBlockTransferSrcScalarPerVector != 0)
return false;
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
{
if(karg.N % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
return false;
}
else
{
if(karg.M % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
return false;
return false;
}
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
return true;
}
__host__ __device__ static auto GetKPad(index_t K, index_t KBatch)
{
const index_t K0 = math::integer_divide_ceil(K, K1 * K0PerBlock * KBatch) * K0PerBlock;
const index_t KPad = KBatch * K0 * K1;
return KPad;
}
__host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
{
const bool has_main_k0_block_loop = K0 > K0PerBlock;
@@ -438,9 +224,8 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
return has_main_k0_block_loop;
}
template <typename CGridDesc>
__host__ __device__ static constexpr auto
MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CGridDesc& c_m_n_grid_desc)
MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CMNGridDesc& c_m_n_grid_desc)
{
const auto M = c_m_n_grid_desc.GetLength(I0);
const auto N = c_m_n_grid_desc.GetLength(I1);
@@ -457,11 +242,10 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
}
// return block_id to C matrix tile idx (m0, n0) mapping
template <typename CGridDesc>
__host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
const CGridDesc& c_m_n_grid_desc, index_t /* M01 */, index_t /* N01 */, index_t KBatch)
const CMNGridDesc& c_m_n_grid_desc, index_t /* M01 */, index_t /* N01 */, index_t KBatch)
{
return BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc>(
return BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CMNGridDesc>(
c_m_n_grid_desc, 8, KBatch);
}
@@ -478,25 +262,24 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
Number<CShuffleNRepeatPerShuffle * NWave * NPerXDL>{}));
}
template <bool HasMainKBlockLoop, InMemoryDataOperationEnum CGlobalMemoryDataOperation>
__device__ static void Run(const Argument& karg, void* __restrict__ p_shared_block)
using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
decltype(MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CMNGridDesc{}));
using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
template <bool HasMainKBlockLoop>
__device__ static void Run(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
void* __restrict__ p_shared_block,
const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
c_grid_desc_mblock_mperblock_nblock_nperblock,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CElementwiseOperation& c_element_op,
const CBlockClusterAdaptor& c_block_cluster_adaptor)
{
const FloatAB* p_a_grid = karg.p_a_grid;
const FloatAB* p_b_grid = karg.p_b_grid;
FloatC* p_c_grid = karg.p_c_grid;
const auto a_b_k0_m_k1_grid_desc = MakeAGridDescriptor_KBatch_K0_M_K1(
karg.M, karg.MPadded, karg.K, karg.StrideA, karg.k_batch, karg.K0, karg.KPadded);
const auto b_b_k0_n_k1_grid_desc = MakeBGridDescriptor_KBatch_K0_N_K1(
karg.K, karg.NPadded, karg.N, karg.StrideB, karg.k_batch, karg.K0, karg.KPadded);
const auto c_grid_desc_m_n =
MakeCGridDescriptor_M_N(karg.M, karg.N, karg.MPadded, karg.NPadded, karg.StrideC);
const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
const AElementwiseOperation a_element_op = AElementwiseOperation{};
const BElementwiseOperation b_element_op = BElementwiseOperation{};
const CElementwiseOperation c_element_op = CElementwiseOperation{};
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
@@ -506,16 +289,26 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
const index_t block_m_id = __builtin_amdgcn_readfirstlane(blockIdx.y);
const index_t block_n_id = __builtin_amdgcn_readfirstlane(blockIdx.x);
const index_t k_batch_id = __builtin_amdgcn_readfirstlane(blockIdx.z);
// divide block work by [M, N]
const auto block_work_idx =
c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
if(!c_block_cluster_adaptor.ValidCTileIndex(
make_tuple(block_work_idx[I1], block_work_idx[I2]),
make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
{
return;
}
const index_t k_batch_id = block_work_idx[I0];
// HACK: this force m/n_block_data_idx_on_grid into SGPR
const index_t m_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
const index_t n_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_n_id * NPerBlock);
__builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
// lds max alignment
constexpr auto max_lds_align = K1;
@@ -651,6 +444,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
auto blockwise_gemm =
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
FloatAB,
@@ -853,7 +647,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
{c_block_desc_mblock_mperblock_nblock_nperblock,
make_multi_index(0, 0, 0, 0),
c_grid_desc_mblock_mperblock_nblock_nperblock,
make_multi_index(block_m_id, 0, block_n_id, 0),
make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0),
c_element_op};
constexpr auto mxdlperwave_forward_step =
@@ -922,48 +716,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
});
}
}
template <typename Layout>
struct LStr
{
static std::string Get() { return ""; }
};
template <>
struct LStr<ck::tensor_layout::gemm::RowMajor>
{
static std::string Get() { return "R"; }
};
template <>
struct LStr<ck::tensor_layout::gemm::ColumnMajor>
{
static std::string Get() { return "C"; }
};
static std::string GetTypeString()
{
auto str = std::stringstream();
// clang-format off
str << "GemmXdlSplitKCShuffle_"
<< getGemmSpecializationString(GemmSpec) << "_"
<< std::string(ALayout::name)[0]
<< std::string(BLayout::name)[0]
<< std::string(CLayout::name)[0]
<< "_"
<< "B" << BlockSize << "_"
<< "Vec" << ABlockTransferSrcScalarPerVector << "x"
<< BBlockTransferSrcScalarPerVector << "x"
<< CBlockTransferScalarPerVector_NWaveNPerXDL << "_"
<< MPerBlock << "x"
<< NPerBlock << "x"
<< K0PerBlock << "x"
<< K1 ;
// clang-format on
return str.str();
}
};
} // namespace ck