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Commit bc45fd98 authored by Jing Zhang's avatar Jing Zhang
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clean code

parent ee1c88b1
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example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_bias_fp16.cpp
View file @ bc45fd98
......@@ -59,7 +59,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl_F
//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
struct ProblemSize final
......@@ -202,8 +202,13 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
gemm_descs.push_back(
{1, problem_size.Ns[i], problem_size.Ks[i], 1, problem_size.stride_Bs[i], 1, {0}});
gemm_descs.push_back({sum_of_m,
problem_size.Ns[i],
problem_size.Ks[i],
1,
problem_size.stride_Bs[i],
1,
{0}});
grouped_gemm_kernel_args_.push_back(
{a_tensors_device[i]->GetDeviceBuffer(),
......@@ -249,6 +254,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
}
gemm.SetDeviceKernelArgs(argument, gemm_desc_workspace.GetDeviceBuffer());
gemm.SetKBatch(argument, 1);
invoker.Run(argument, StreamConfig{nullptr, false});
......
example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16.cpp
View file @ bc45fd98
......@@ -55,10 +55,10 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl_F
//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
//< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
//< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
//< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 64, 128, 64, 8, 8, 32, 32, 1, 2, S<1, 8, 32, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, S<1, 8, 32, 1>, S<2, 0, 1, 3>, S<2, 0, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 64, 128, 64, 8, 8, 32, 32, 1, 2, S<1, 8, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 8, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
//< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 64, 128, 64, 8, 8, 32, 32, 1, 2, S<1, 8, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 8, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
struct ProblemSize final
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp
View file @ bc45fd98
......@@ -13,8 +13,6 @@
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
//#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
//#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_splitk_cshuffle.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
......@@ -419,7 +417,7 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
throw std::runtime_error("wrong! k_batch must be > 0");
}
const index_t AverM = sum_of_m / group_count_;
const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);
const index_t StrideE = gemm_desc_kernel_arg_[0].StrideE_;
const index_t N = gemm_desc_kernel_arg_[0].N_;
......@@ -481,7 +479,7 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
index_t group_id = 0;
sum_of_m = gemm_descs[0].M_;
const index_t AverM = sum_of_m / group_count_;
const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);
const index_t N = gemm_descs[0].N_;
const index_t K = gemm_descs[0].K_;
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_splitk_cshuffle.hpp
View file @ bc45fd98
......@@ -200,42 +200,6 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
c_block_size * sizeof(CShuffleDataType));
}
#if 0
// A desc for source in blockwise copy
template <typename AGridDesc_M_K>
__host__ __device__ static constexpr auto
MakeDefaultAGridDescriptor_AK0_M_AK1(const AGridDesc_M_K& a_grid_desc_m_k)
{
const auto M = a_grid_desc_m_k.GetLength(I0);
const auto K = a_grid_desc_m_k.GetLength(I1);
const auto AK0 = K / AK1;
return transform_tensor_descriptor(a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
make_pass_through_transform(M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
// B desc for source in blockwise copy
template <typename BGridDesc_N_K>
__host__ __device__ static constexpr auto
MakeDefaultBGridDescriptor_BK0_N_BK1(const BGridDesc_N_K& b_grid_desc_n_k)
{
const auto N = b_grid_desc_n_k.GetLength(I0);
const auto K = b_grid_desc_n_k.GetLength(I1);
const auto BK0 = K / BK1;
return transform_tensor_descriptor(b_grid_desc_n_k,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_pass_through_transform(N)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
#endif
__host__ __device__ static auto CalculateMPadded(index_t M)
{
return math::integer_least_multiple(M, MPerBlock);
......@@ -414,16 +378,6 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
MakeBGridDescriptor_KBatch_BK0_N_BK1<BLayout, GemmSpec>(K, N, StrideB, KBatch);
ignore = StrideDs;
// using DsGridDesc_M_N =
// remove_cvref_t<decltype(MakeDsGridDescriptor_M_N<DsLayout, GemmSpec>({}, {}, {}))>;
// DsGridDesc_M_N ds_grid_desc_m_n;
// static_for<0, NumDTensor, 1>{}([&](auto j) {
// using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
// ds_grid_desc_m_n(j) = MakeEGridDescriptor_M_N<DLayout, GemmSpec>(M, N, StrideDs[j]);
//});
const auto e_grid_desc_m_n = MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
......@@ -457,80 +411,6 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
return true;
}
#if 0
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
template <typename AGridDesc_M_K,
typename BGridDesc_N_K,
typename DsGridDesc_M_N,
typename EGridDesc_M_N,
typename Block2ETileMap>
__host__ __device__ static constexpr bool CheckValidity(const AGridDesc_M_K& a_grid_desc_m_k,
const BGridDesc_N_K& b_grid_desc_n_k,
const DsGridDesc_M_N& ds_grid_desc_m_n,
const EGridDesc_M_N& e_grid_desc_m_n,
const Block2ETileMap& block_2_etile_map)
{
static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
(NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
"Invalid tuning param!");
const auto M = a_grid_desc_m_k.GetLength(I0);
const auto N = b_grid_desc_n_k.GetLength(I0);
const auto K = a_grid_desc_m_k.GetLength(I1);
// check consistency of desc
if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1)))
{
return false;
}
bool valid = true;
static_for<0, NumDTensor, 1>{}([&](auto i) {
valid = valid && (M == ds_grid_desc_m_n[i].GetLength(I0) &&
N == ds_grid_desc_m_n[i].GetLength(I1));
});
if(!valid)
{
return false;
}
// check tile size
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
{
return false;
}
// check gridwise gemm pipeline
const auto num_k_loop = K / KPerBlock;
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
{
return false;
}
// check block-to-E-tile
if(!block_2_etile_map.CheckValidity(e_grid_desc_m_n))
{
return false;
}
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
// check tensor size: cannot be larger than 2GB each
constexpr long_index_t TwoGB = (long_index_t{1} << 31);
if(!(a_grid_desc_m_k.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB &&
b_grid_desc_n_k.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB &&
e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
{
return false;
}
return true;
}
#endif
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
{
const index_t num_loop = K / KPerBlock;
......@@ -540,56 +420,6 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
using DsGridPointer = decltype(MakeDsGridPointer());
#if 0
template <typename ALayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
{
constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock};
const auto a_grid_desc_mraw_kraw = [&]() {
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(StrideA, I1));
}
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(I1, StrideA));
}
}();
return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
}
template <typename BLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
{
constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock};
const auto b_grid_desc_nraw_kraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(I1, StrideB));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(StrideB, I1));
}
}();
return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
}
#endif
template <typename ELayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
......@@ -683,15 +513,6 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
const index_t n_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
// if(get_thread_local_1d_id() == 0)
//{
// printf("%d %d %d %d\n",
// get_block_1d_id(),
// kbatch_id,
// block_work_idx[I1],
// block_work_idx[I2]);
//}
// lds max alignment
constexpr auto max_lds_align = math::lcm(AK1, BK1);
......
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