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Unverified Commit 30072aec authored by Chao Liu's avatar Chao Liu Committed by GitHub
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Restructure gridwise and blockwise GEMM, add tensor contraction and FWD-v4r5 (#36) 

* experimenting magic number division

* overhauling fwd-v4r4 to clearly reflect transformation graph

* added fwd-v4r5

* bug fix for make_dynamic_naive_tensor_descriptor_aligned_v2

* bug fix and added sanity-check in transform_dynamic_tensor_descriptor

* added conv_driver_v2
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composable_kernel/include/driver/driver_dynamic_contraction_v1r1.hpp 0 → 100644
View file @ 30072aec
#ifndef CK_DRIVER_DYNAMIC_CONTRACTION_V1R1_HPP
#define CK_DRIVER_DYNAMIC_CONTRACTION_V1R1_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_contraction_v1r1.hpp"
namespace ck {
template <index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
InMemoryDataOperation CGlobalMemoryDataOperation,
typename AGKGM0GM1GridDesc,
typename BGKGN0GN1GridDesc,
typename CGM0GM1GN0GN1GridDesc,
index_t GM1PerBlockGM11,
index_t GN1PerBlockGN11,
index_t KPerBlock,
index_t M1PerThread,
index_t N1PerThread,
index_t KPerThread,
index_t M1N1ThreadClusterM10,
index_t M1N1ThreadClusterN10,
index_t M1N1ThreadClusterM11,
index_t M1N1ThreadClusterN11,
typename ABlockTransferThreadSliceLengths_GK_GM0_GM10_GM11,
typename ABlockTransferThreadClusterLengths_GK_GM0_GM10_GM11,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_GM11,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_GK_GN0_GN10_GN11,
typename BBlockTransferThreadClusterLengths_GK_GN0_GN10_GN11,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_GN11,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
__host__ float
driver_dynamic_contraction_v1r1(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AGKGM0GM1GridDesc& a_gk_gm0_gm1_grid_desc,
const BGKGN0GN1GridDesc& b_gk_gn0_gn1_grid_desc,
const CGM0GM1GN0GN1GridDesc& c_gm0_gm1_gn0_gn1_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
index_t nrepeat)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseContraction = GridwiseDynamicContraction_km0m1_kn0n1_m0m1n0n1_v1r1<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AGKGM0GM1GridDesc,
BGKGN0GN1GridDesc,
CGM0GM1GN0GN1GridDesc,
GM1PerBlockGM11,
GN1PerBlockGN11,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_GK_GM0_GM10_GM11,
ABlockTransferThreadClusterLengths_GK_GM0_GM10_GM11,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_GM11,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_GK_GN0_GN10_GN11,
BBlockTransferThreadClusterLengths_GK_GN0_GN10_GN11,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_GN11,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
const auto K = a_gk_gm0_gm1_grid_desc.GetLength(I0);
if(!GridwiseContraction::CheckValidity(
a_gk_gm0_gm1_grid_desc, b_gk_gn0_gn1_grid_desc, c_gm0_gm1_gn0_gn1_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicContraction_km_kn0n1_mn0n1_v1r1 has invalid setting");
}
const auto a_gk_gm0_gm10_gm11_grid_desc =
GridwiseContraction::MakeAGKGM0GM10GM11GridDescriptor(a_gk_gm0_gm1_grid_desc);
const auto b_gk_gn0_gn10_gn11_grid_desc =
GridwiseContraction::MakeBGKGN0GN10GN11GridDescriptor(b_gk_gn0_gn1_grid_desc);
using AGKGM0GM10GM11GridDesc = decltype(a_gk_gm0_gm10_gm11_grid_desc);
using BGKGN0GN10GN11GridDesc = decltype(b_gk_gn0_gn10_gn11_grid_desc);
// c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc
const auto c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc =
GridwiseContraction::MakeCGM10BM0BM1GN10BN0BN1GridDescriptor(c_gm0_gm1_gn0_gn1_grid_desc);
using CGM10BM0BM1GN10BN0BN1GridDesc = decltype(c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc);
// c_blockid_to_gm10_gn10_block_cluster_adaptor
const auto c_blockid_to_gm10_gn10_block_cluster_adaptor =
GridwiseContraction::MakeCBlockIdToGM10GN10BlockClusterAdaptor(c_gm0_gm1_gn0_gn1_grid_desc);
using CBlockIdToGM10GN10BlockClusterAdaptor =
decltype(c_blockid_to_gm10_gn10_block_cluster_adaptor);
const index_t grid_size = GridwiseContraction::CalculateGridSize(c_gm0_gm1_gn0_gn1_grid_desc);
const bool has_main_k_block_loop = GridwiseContraction::CalculateHasMainKBlockLoop(K);
const bool has_double_tail_k_block_loop =
GridwiseContraction::CalculateHasDoubleTailKBlockLoop(K);
{
std::cout << "a_gk_gm0_gm10_gm11_grid_desc{" << a_gk_gm0_gm10_gm11_grid_desc.GetLength(I0)
<< ", " << a_gk_gm0_gm10_gm11_grid_desc.GetLength(I1) << ", "
<< a_gk_gm0_gm10_gm11_grid_desc.GetLength(I2) << ", "
<< a_gk_gm0_gm10_gm11_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "b_gk_gn0_gn10_gn11_grid_desc{" << b_gk_gn0_gn10_gn11_grid_desc.GetLength(I0)
<< ", " << b_gk_gn0_gn10_gn11_grid_desc.GetLength(I1) << ", "
<< b_gk_gn0_gn10_gn11_grid_desc.GetLength(I2) << ", "
<< b_gk_gn0_gn10_gn11_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc{ "
<< c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc.GetLength(I0) << ", "
<< c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc.GetLength(I1) << ", "
<< c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc.GetLength(I2) << ", "
<< c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc.GetLength(I3) << ", "
<< c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc.GetLength(I4) << ", "
<< c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc.GetLength(I5) << "}" << std::endl;
}
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = kernel_dynamic_contraction_v1r1<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGKGM0GM10GM11GridDesc>,
remove_reference_t<BGKGN0GN10GN11GridDesc>,
remove_reference_t<CGM10BM0BM1GN10BN0BN1GridDesc>,
remove_reference_t<CBlockIdToGM10GN10BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_gk_gm0_gm10_gm11_grid_desc,
b_gk_gn0_gn10_gn11_grid_desc,
c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc,
c_blockid_to_gm10_gn10_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel = kernel_dynamic_contraction_v1r1<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGKGM0GM10GM11GridDesc>,
remove_reference_t<BGKGN0GN10GN11GridDesc>,
remove_reference_t<CGM10BM0BM1GN10BN0BN1GridDesc>,
remove_reference_t<CBlockIdToGM10GN10BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_gk_gm0_gm10_gm11_grid_desc,
b_gk_gn0_gn10_gn11_grid_desc,
c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc,
c_blockid_to_gm10_gn10_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = kernel_dynamic_contraction_v1r1<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGKGM0GM10GM11GridDesc>,
remove_reference_t<BGKGN0GN10GN11GridDesc>,
remove_reference_t<CGM10BM0BM1GN10BN0BN1GridDesc>,
remove_reference_t<CBlockIdToGM10GN10BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_gk_gm0_gm10_gm11_grid_desc,
b_gk_gn0_gn10_gn11_grid_desc,
c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc,
c_blockid_to_gm10_gn10_block_cluster_adaptor);
}
else
{
const auto kernel = kernel_dynamic_contraction_v1r1<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGKGM0GM10GM11GridDesc>,
remove_reference_t<BGKGN0GN10GN11GridDesc>,
remove_reference_t<CGM10BM0BM1GN10BN0BN1GridDesc>,
remove_reference_t<CBlockIdToGM10GN10BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_gk_gm0_gm10_gm11_grid_desc,
b_gk_gn0_gn10_gn11_grid_desc,
c_gm10_bm0_bm1_gn10_bn0_bn1_grid_desc,
c_blockid_to_gm10_gn10_block_cluster_adaptor);
}
return ave_time;
}
} // namespace ck
#endif
composable_kernel/include/driver/driver_dynamic_gemm_v1r2.hpp 0 → 100644
View file @ 30072aec
#ifndef CK_DRIVER_DYNAMIC_GEMM_V1R2
#define CK_DRIVER_DYNAMIC_GEMM_V1R2
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_v1r2.hpp"
namespace ck {
template <index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
InMemoryDataOperation CGlobalMemoryDataOperation,
typename AKMGridDesc,
typename BKNGridDesc,
typename CMNGridDesc,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t M1PerThread,
index_t N1PerThread,
index_t KPerThread,
index_t M1N1ThreadClusterM10,
index_t M1N1ThreadClusterN10,
index_t M1N1ThreadClusterM11,
index_t M1N1ThreadClusterN11,
typename ABlockTransferThreadSliceLengths_K_M0_M1,
typename ABlockTransferThreadClusterLengths_K_M0_M1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_M1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K_N0_N1,
typename BBlockTransferThreadClusterLengths_K_N0_N1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_N1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
__host__ float driver_dynamic_gemm_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AKMGridDesc& a_k_m_grid_desc,
const BKNGridDesc& b_k_n_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
index_t nrepeat)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm =
GridwiseDynamicGemm_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
const auto M = a_k_m_grid_desc.GetLength(I1);
const auto N = b_k_n_grid_desc.GetLength(I1);
const auto K = a_k_m_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k_m_grid_desc, b_k_n_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error("wrong! GridwiseDynamicGemm_km_kn_mn_v1r2 has invalid setting");
}
const auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
const auto b_k_n0_n1_grid_desc = GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
using AKM0M1GridDesc = decltype(a_k_m0_m1_grid_desc);
using BKN0N1GridDesc = decltype(b_k_n0_n1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// c_blockid_to_m0_n0_block_cluster_adaptor
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(c_blockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K);
{
std::cout << "a_k_m0_m1_grid_desc{" << a_k_m0_m1_grid_desc.GetLength(I0) << ", "
<< a_k_m0_m1_grid_desc.GetLength(I1) << ", " << a_k_m0_m1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_k_n0_n1_grid_desc{" << b_k_n0_n1_grid_desc.GetLength(I0) << ", "
<< b_k_n0_n1_grid_desc.GetLength(I1) << ", " << b_k_n0_n1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_dynamic_gemm_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
}
} // namespace ck
#endif
composable_kernel/include/kernel_algorithm/transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp
View file @ 30072aec
......@@ -10,11 +10,7 @@ namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmM1,
index_t GemmN1,
typename... Wei,
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
......@@ -100,74 +96,11 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto GemmM = out_gemmm_gemmn_global_desc.GetLength(I0);
const auto GemmN = out_gemmm_gemmn_global_desc.GetLength(I1);
const auto GemmK = wei_gemmk_gemmm_global_desc.GetLength(I0);
assert(GemmM % GemmMPerBlock == 0 && GemmN % GemmNPerBlock == 0 && GemmK % GemmKPerBlock == 0);
const auto GemmM0 = GemmM / Number<GemmM1>{};
const auto GemmN0 = GemmN / Number<GemmN1>{};
const auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc = transform_dynamic_tensor_descriptor(
out_gemmm_gemmn_global_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmM0, GemmM1)),
make_unmerge_transform(make_tuple(GemmN0, GemmN1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
// out_gemm_block_cluster_desc
const auto out_gemm_block_cluster_desc = make_cluster_descriptor_v2(
make_tuple(GemmM / Number<GemmMPerBlock>{}, GemmN / Number<GemmNPerBlock>{}));
// hack to control index calculation when iterating over wei_gemmk_gemmm_global tensor
constexpr auto wei_gemmk_gemmm_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto wei_gemmk_gemmm_global_move_slice_window_iterator_hacks = Sequence<0, 0, 0>{};
// hack to control index calculation when iterating over in_gemmk_gemmn_global tensor
constexpr auto in_gemmk_gemmn_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2>{}));
constexpr auto in_gemmk_gemmn_global_move_slice_window_iterator_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2>{};
// hack to control index calculation when iterating over out_gemmm0_gemmm1_gemmn0_gemmn1_global
// tensor hack for NKHW format
constexpr auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
return make_tuple(wei_gemmk_gemmm_global_desc,
in_gemmk_gemmn_global_desc,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc,
out_gemm_block_cluster_desc,
wei_gemmk_gemmm_global_iterator_hacks,
in_gemmk_gemmn_global_iterator_hacks,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks,
wei_gemmk_gemmm_global_move_slice_window_iterator_hacks,
in_gemmk_gemmn_global_move_slice_window_iterator_hacks);
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmM1,
index_t GemmN1,
typename... Wei,
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
......@@ -247,72 +180,11 @@ transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_no_pad(
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto GemmM = out_gemmm_gemmn_global_desc.GetLength(I0);
const auto GemmN = out_gemmm_gemmn_global_desc.GetLength(I1);
const auto GemmK = wei_gemmk_gemmm_global_desc.GetLength(I0);
assert(GemmM % GemmMPerBlock == 0 && GemmN % GemmNPerBlock == 0 && GemmK % GemmKPerBlock == 0);
const auto GemmM0 = GemmM / Number<GemmM1>{};
const auto GemmN0 = GemmN / Number<GemmN1>{};
const auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc = transform_dynamic_tensor_descriptor(
out_gemmm_gemmn_global_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmM0, GemmM1)),
make_unmerge_transform(make_tuple(GemmN0, GemmN1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
// out_gemm_block_cluster_desc
const auto out_gemm_block_cluster_desc = make_cluster_descriptor_v2(
make_tuple(GemmM / Number<GemmMPerBlock>{}, GemmN / Number<GemmNPerBlock>{}));
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto wei_gemmk_gemmm_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto wei_gemmk_gemmm_global_move_slice_window_iterator_hacks = Sequence<0, 0, 0>{};
// hack to control index calculation when iterating over b_k_n_global tensor
constexpr auto in_gemmk_gemmn_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 1, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 1>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 2, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 2>{}));
constexpr auto in_gemmk_gemmn_global_move_slice_window_iterator_hacks =
Sequence<0, 0, 0, 0, 0, 1, 2>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
// hack for NKHW format
constexpr auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
return make_tuple(wei_gemmk_gemmm_global_desc,
in_gemmk_gemmn_global_desc,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc,
out_gemm_block_cluster_desc,
wei_gemmk_gemmm_global_iterator_hacks,
in_gemmk_gemmn_global_iterator_hacks,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks,
wei_gemmk_gemmm_global_move_slice_window_iterator_hacks,
in_gemmk_gemmn_global_move_slice_window_iterator_hacks);
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmM1,
index_t GemmN1,
typename... Wei,
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
......@@ -383,60 +255,8 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto GemmM = out_gemmm_gemmn_global_desc.GetLength(I0);
const auto GemmN = out_gemmm_gemmn_global_desc.GetLength(I1);
const auto GemmK = wei_gemmk_gemmm_global_desc.GetLength(I0);
assert(GemmM % GemmMPerBlock == 0 && GemmN % GemmNPerBlock == 0 && GemmK % GemmKPerBlock == 0);
const auto GemmM0 = GemmM / Number<GemmM1>{};
const auto GemmN0 = GemmN / Number<GemmN1>{};
const auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc = transform_dynamic_tensor_descriptor(
out_gemmm_gemmn_global_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmM0, GemmM1)),
make_unmerge_transform(make_tuple(GemmN0, GemmN1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
// out_gemm_block_cluster_desc
const auto out_gemm_block_cluster_desc = make_cluster_descriptor_v2(
make_tuple(GemmM / Number<GemmMPerBlock>{}, GemmN / Number<GemmNPerBlock>{}));
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto wei_gemmk_gemmm_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto wei_gemmk_gemmm_global_move_slice_window_iterator_hacks = Sequence<0, 0, 0>{};
// hack to control index calculation when iterating over b_k_n_global tensor
constexpr auto in_gemmk_gemmn_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0>{}, Sequence<0, 0, 1>{}),
make_tuple(Sequence<0, 2, 0>{}, Sequence<0, 0, 2>{}));
constexpr auto in_gemmk_gemmn_global_move_slice_window_iterator_hacks = Sequence<0, 1, 2>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
constexpr auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
return make_tuple(wei_gemmk_gemmm_global_desc,
in_gemmk_gemmn_global_desc,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc,
out_gemm_block_cluster_desc,
wei_gemmk_gemmm_global_iterator_hacks,
in_gemmk_gemmn_global_iterator_hacks,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks,
wei_gemmk_gemmm_global_move_slice_window_iterator_hacks,
in_gemmk_gemmn_global_move_slice_window_iterator_hacks);
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
} // namespace ck
......
composable_kernel/include/kernel_algorithm/transform_forward_convolution_into_gemm_v4r4_nhwc_kyxc_nhwk.hpp
View file @ 30072aec
......@@ -10,11 +10,7 @@ namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmM1,
index_t GemmN1,
typename... Wei,
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
......@@ -100,74 +96,11 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto GemmM = out_gemmm_gemmn_global_desc.GetLength(I0);
const auto GemmN = out_gemmm_gemmn_global_desc.GetLength(I1);
const auto GemmK = wei_gemmk_gemmm_global_desc.GetLength(I0);
assert(GemmM % GemmMPerBlock == 0 && GemmN % GemmNPerBlock == 0 && GemmK % GemmKPerBlock == 0);
const auto GemmM0 = GemmM / Number<GemmM1>{};
const auto GemmN0 = GemmN / Number<GemmN1>{};
const auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc = transform_dynamic_tensor_descriptor(
out_gemmm_gemmn_global_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmM0, GemmM1)),
make_unmerge_transform(make_tuple(GemmN0, GemmN1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
// out_gemm_block_cluster_desc
const auto out_gemm_block_cluster_desc = make_cluster_descriptor_v2(
make_tuple(GemmM / Number<GemmMPerBlock>{}, GemmN / Number<GemmNPerBlock>{}));
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto wei_gemmk_gemmm_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto wei_gemmk_gemmm_global_move_slice_window_iterator_hacks = Sequence<0, 0, 0>{};
// hack to control index calculation when iterating over b_k_n_global tensor
constexpr auto in_gemmk_gemmn_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2>{}));
constexpr auto in_gemmk_gemmn_global_move_slice_window_iterator_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
// hack for NKHW format
constexpr auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
return make_tuple(wei_gemmk_gemmm_global_desc,
in_gemmk_gemmn_global_desc,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc,
out_gemm_block_cluster_desc,
wei_gemmk_gemmm_global_iterator_hacks,
in_gemmk_gemmn_global_iterator_hacks,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks,
wei_gemmk_gemmm_global_move_slice_window_iterator_hacks,
in_gemmk_gemmn_global_move_slice_window_iterator_hacks);
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmM1,
index_t GemmN1,
typename... Wei,
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
......@@ -238,61 +171,8 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto GemmM = out_gemmm_gemmn_global_desc.GetLength(I0);
const auto GemmN = out_gemmm_gemmn_global_desc.GetLength(I1);
const auto GemmK = wei_gemmk_gemmm_global_desc.GetLength(I0);
assert(GemmM % GemmMPerBlock == 0 && GemmN % GemmNPerBlock == 0 && GemmK % GemmKPerBlock == 0);
const auto GemmM0 = GemmM / Number<GemmM1>{};
const auto GemmN0 = GemmN / Number<GemmN1>{};
const auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc = transform_dynamic_tensor_descriptor(
out_gemmm_gemmn_global_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmM0, GemmM1)),
make_unmerge_transform(make_tuple(GemmN0, GemmN1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
// out_gemm_block_cluster_desc
const auto out_gemm_block_cluster_desc = make_cluster_descriptor_v2(
make_tuple(GemmM / Number<GemmMPerBlock>{}, GemmN / Number<GemmNPerBlock>{}));
// hack to control index calculation when iterating over wei_gemmk_gemmm_global_iterator_hacks
// tensor
constexpr auto wei_gemmk_gemmm_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto wei_gemmk_gemmm_global_move_slice_window_iterator_hacks = Sequence<0, 0, 0>{};
// hack to control index calculation when iterating over b_k_n_global tensor
constexpr auto in_gemmk_gemmn_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto in_gemmk_gemmn_global_move_slice_window_iterator_hacks = Sequence<0, 0, 0>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
constexpr auto out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}));
return make_tuple(wei_gemmk_gemmm_global_desc,
in_gemmk_gemmn_global_desc,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_desc,
out_gemm_block_cluster_desc,
wei_gemmk_gemmm_global_iterator_hacks,
in_gemmk_gemmn_global_iterator_hacks,
out_gemmm0_gemmm1_gemmn0_gemmn1_global_iterator_hacks,
wei_gemmk_gemmm_global_move_slice_window_iterator_hacks,
in_gemmk_gemmn_global_move_slice_window_iterator_hacks);
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
} // namespace ck
......
composable_kernel/include/kernel_algorithm/transform_forward_convolution_into_gemm_v4r5_nchw_kcyx_nkhw.hpp 0 → 100644
View file @ 30072aec
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_CONTRACTION_V4R5_NCHW_KCYX_NKHW_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_CONTRACTION_V4R5_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <index_t N0_,
typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto
transform_forward_convolution_into_contraction_v4r5_nchw_kcyx_nkhw_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
const DynamicTensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_c_hi_wi_grid_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_grid_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_grid_desc.GetLength(I1);
const auto Hi = in_n_c_hi_wi_grid_desc.GetLength(I2);
const auto Wi = in_n_c_hi_wi_grid_desc.GetLength(I3);
const auto Ho = out_n_k_ho_wo_grid_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_grid_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_grid_desc.GetLength(I2);
const auto X = wei_k_c_y_x_grid_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_gk_gm0_gm1_grid_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C * Y * X)),
make_tuple(make_unmerge_transform(make_tuple(I1, K)),
make_pass_through_transform(C * Y * X)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1, 2>{}, Sequence<0>{}));
// input tensor
const auto in_n_c_hip_wip_grid_desc = transform_dynamic_tensor_descriptor(
in_n_c_hi_wi_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
constexpr auto N0 = Number<N0_>{};
const auto N1 = N / N0;
const auto in_n0_n1_c_y_ho_x_wo_grid_desc = transform_dynamic_tensor_descriptor(
in_n_c_hip_wip_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(N0, N1)),
make_pass_through_transform(C),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3, 4>{}, Sequence<5, 6>{}));
const auto in_gk_gn0_gn1_grid_desc = transform_dynamic_tensor_descriptor(
in_n0_n1_c_y_ho_x_wo_grid_desc,
make_tuple(make_merge_transform(make_tuple(C, Y, X)),
make_pass_through_transform(N0),
make_merge_transform(make_tuple(N1, Ho, Wo))),
make_tuple(Sequence<2, 3, 5>{}, Sequence<0>{}, Sequence<1, 4, 6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
// output tensor
const auto out_n_k_howo_grid_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho * Wo));
const auto out_n0_n1_1_k_howo_grid_desc = transform_dynamic_tensor_descriptor(
out_n_k_howo_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(Number<N0>{}, N1)),
make_unmerge_transform(make_tuple(I1, K)),
make_pass_through_transform(Ho * Wo)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}, Sequence<4>{}));
const auto out_gm0_gm1_gn0_gn1_grid_desc = transform_dynamic_tensor_descriptor(
out_n0_n1_1_k_howo_grid_desc,
make_tuple(make_pass_through_transform(I1),
make_pass_through_transform(K),
make_pass_through_transform(Number<N0>{}),
make_merge_transform_v2_magic_division(make_tuple(N1, Ho * Wo))),
make_tuple(Sequence<2>{}, Sequence<3>{}, Sequence<0>{}, Sequence<1, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
return make_tuple(
wei_gk_gm0_gm1_grid_desc, in_gk_gn0_gn1_grid_desc, out_gm0_gm1_gn0_gn1_grid_desc);
}
} // namespace ck
#endif
composable_kernel/include/tensor_description/dynamic_multi_index_transform.hpp
View file @ 30072aec
......@@ -1164,6 +1164,165 @@ struct DynamicMerge_v2_magic_division
}
};
// Implementation of "Merge" transformation primitive that uses magic-number-division to do lowering
// of both multi-index and delta of multi-index
// Caution:
// 1. The magic number division implementation being used would produce correct result if the
// dividended is uint32_t and its value is with in 31-bit value range of uint32_t.
// 2. The magic number division for int32_t dividened has not been implemented, the int32_t
// dividend would be bit-wise interpreted as uint32_t and magic number division implementation for
// uint32_t is then used.
// 3. For Merge primitive, upper-index is the dividend.
// 4. When upper-index is uint32_t, its value need to be within 31-bit range.
// 5. When upper-index is int32_t type (when index_t is int32_t), its value need to be
// non-negative.
template <typename LowLengths>
struct DynamicMerge_v2r2_magic_division
{
static constexpr index_t NDimLow = LowLengths::Size();
using LowerIndex = MultiIndex<NDimLow>;
using UpperIndex = MultiIndex<1>;
using LowLengthsScan = decltype(
container_reverse_exclusive_scan(LowLengths{}, math::multiplies_v2{}, Number<1>{}));
using UpLengths =
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies_v2{}, Number<1>{})));
using LowLengthsScanMagicDivisorMultipiler = decltype(generate_tuple(
lambda_merge_generate_MagicDivision_calculate_magic_multiplier<LowLengthsScan>{},
Number<NDimLow>{}));
using LowLengthsScanMagicDivisorShift = decltype(
generate_tuple(lambda_merge_generate_MagicDivision_calculate_magic_shift<LowLengthsScan>{},
Number<NDimLow>{}));
LowLengths low_lengths_;
LowLengthsScan low_lengths_scan_;
LowLengthsScanMagicDivisorMultipiler low_lengths_scan_magic_divisor_multiplier_;
LowLengthsScanMagicDivisorShift low_lengths_scan_magic_divisor_shift_;
UpLengths up_lengths_;
__host__ __device__ constexpr DynamicMerge_v2r2_magic_division() = default;
__host__ __device__ constexpr DynamicMerge_v2r2_magic_division(const LowLengths& low_lengths)
: low_lengths_{low_lengths},
low_lengths_scan_{
container_reverse_exclusive_scan(low_lengths, math::multiplies_v2{}, Number<1>{})},
low_lengths_scan_magic_divisor_multiplier_{generate_tuple(
[&](auto i) { return MagicDivision::CalculateMagicMultiplier(low_lengths_scan_[i]); },
Number<NDimLow>{})},
low_lengths_scan_magic_divisor_shift_{generate_tuple(
[&](auto i) { return MagicDivision::CalculateMagicShift(low_lengths_scan_[i]); },
Number<NDimLow>{})},
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies_v2{}, Number<1>{}))}
{
static_assert(LowerIndex::Size() == NDimLow, "wrong!");
}
__host__ __device__ static constexpr index_t GetNumOfLowerDimension() { return NDimLow; }
__host__ __device__ static constexpr index_t GetNumOfUpperDimension() { return 1; }
__host__ __device__ constexpr const auto& GetUpperLengths() const { return up_lengths_; }
template <typename LowIdx, typename UpIdx>
__host__ __device__ constexpr void CalculateLowerIndex(LowIdx& idx_low,
const UpIdx& idx_up) const
{
static_assert(LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
"wrong! inconsistent # of dimension");
index_t tmp = idx_up[Number<0>{}];
static_for<0, NDimLow - 1, 1>{}([&, this](auto i) {
idx_low(i) =
MagicDivision::DoMagicDivision(tmp,
this->low_lengths_scan_magic_divisor_multiplier_[i],
this->low_lengths_scan_magic_divisor_shift_[i]);
tmp -= idx_low[i] * this->low_lengths_scan_[i];
});
idx_low(Number<NDimLow - 1>{}) = tmp;
}
template <typename LowIdxDiff,
typename UpIdxDiff,
typename LowIdx,
typename UpIdx,
index_t Hack>
__host__ __device__ void UpdateLowerIndex(LowIdxDiff& idx_diff_low,
const UpIdxDiff& idx_diff_up,
LowIdx& idx_low,
const UpIdx& idx_up_new,
Number<Hack>) const
{
static_assert(LowIdxDiff::Size() == NDimLow && UpIdxDiff::Size() == 1 &&
LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
"wrong! inconsistent # of dimension");
index_t tmp = idx_up_new[Number<0>{}];
static_for<0, NDimLow - 1, 1>{}([&, this](auto i) {
index_t idx_low_old = idx_low[i];
idx_low(i) =
MagicDivision::DoMagicDivision(tmp,
this->low_lengths_scan_magic_divisor_multiplier_[i],
this->low_lengths_scan_magic_divisor_shift_[i]);
idx_diff_low(i) = idx_low[i] - idx_low_old;
tmp -= idx_low[i] * this->low_lengths_scan_[i];
});
idx_diff_low(Number<NDimLow - 1>{}) = tmp - idx_low[Number<NDimLow - 1>{}];
idx_low(Number<NDimLow - 1>{}) = tmp;
}
__host__ __device__ static constexpr bool IsLinearTransform() { return false; }
__host__ __device__ static constexpr bool IsValidUpperIndexAlwaysMappedToValidLowerIndex()
{
return true;
}
__host__ __device__ static constexpr bool IsKnownAtCompileTime()
{
return is_known_at_compile_time<LowLengths>::value &&
is_known_at_compile_time<LowLengthsScanMagicDivisorMultipiler>::value &&
is_known_at_compile_time<LowLengthsScanMagicDivisorShift>::value &&
is_known_at_compile_time<UpLengths>::value;
}
template <typename UpIdx>
__host__ __device__ static constexpr bool
IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& /* idx_up */)
{
return true;
}
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicMerge_v2r2_magic_division, ");
printf("low_lengths_ ");
print_multi_index(low_lengths_);
printf("low_lengths_scan ");
print_multi_index(low_lengths_scan_);
printf("low_lengths_scan_magic_divisor_multiplier_ ");
print_multi_index(low_lengths_scan_magic_divisor_multiplier_);
printf("low_lengths_scan_magic_divisor_shift_ ");
print_multi_index(low_lengths_scan_magic_divisor_shift_);
printf("up_lengths_ ");
print_multi_index(up_lengths_);
printf("}");
}
};
template <typename UpLengths, bool Use24BitIntegerCalculation>
struct DynamicUnMerge
{
......
composable_kernel/include/tensor_description/dynamic_multi_index_transform_helper.hpp
View file @ 30072aec
......@@ -56,10 +56,21 @@ __host__ __device__ constexpr auto make_merge_transform(const LowLengths& low_le
#if !CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION
return DynamicMerge_v1_carry_check<LowLengths>{low_lengths};
#else
#if 1
return DynamicMerge_v2_magic_division<LowLengths>{low_lengths};
#else
return DynamicMerge_v2r2_magic_division<LowLengths>{low_lengths};
#endif
#endif
}
template <typename LowLengths>
__host__ __device__ constexpr auto
make_merge_transform_v2_magic_division(const LowLengths& low_lengths)
{
return DynamicMerge_v2_magic_division<LowLengths>{low_lengths};
}
template <typename UpLengths, bool Use24BitIntegerCalculation = false>
__host__ __device__ constexpr auto make_unmerge_transform(
const UpLengths& up_lengths,
......
composable_kernel/include/tensor_description/dynamic_tensor_descriptor.hpp
View file @ 30072aec
......@@ -308,6 +308,19 @@ transform_dynamic_tensor_descriptor(const OldTensorDescriptor& old_tensor_desc,
NewLowerDimensionOldVisibleIdss,
NewUpperDimensionNewVisibleIdss)
{
// sanity check
{
constexpr auto all_old_top_ids = unpack([](auto... xs) { return merge_sequences(xs...); },
NewLowerDimensionOldVisibleIdss{});
constexpr auto all_new_top_ids = unpack([](auto... xs) { return merge_sequences(xs...); },
NewUpperDimensionNewVisibleIdss{});
static_assert(is_valid_sequence_map<decltype(all_old_top_ids)>::value &&
is_valid_sequence_map<decltype(all_old_top_ids)>::value,
"wrong!");
}
// lower dimension's hidden idss
// convert lower dimension visible idss (tuple of sequences) to hidden idss (tuple of
// sequences)
......
composable_kernel/include/tensor_description/dynamic_tensor_descriptor_helper.hpp
View file @ 30072aec
......@@ -115,26 +115,30 @@ template <typename... Lengths, typename Align>
__host__ __device__ constexpr auto
make_dynamic_naive_tensor_descriptor_aligned_v2(const Tuple<Lengths...>& lengths, Align align)
{
constexpr auto I1 = Number<1>{};
constexpr index_t N = sizeof...(Lengths);
const auto stride_n_minus_2 = math::integer_least_multiple(lengths[Number<N - 1>{}], align);
auto strides = generate_tuple(
[&](auto i) {
if constexpr(i.value == N - 1)
{
return Number<1>{};
return I1;
}
else if constexpr(i.value == N - 2)
{
return math::lcm(lengths[Number<N - 1>{}], align);
return Number<stride_n_minus_2>{};
}
else
{
return container_reduce(lengths,
math::multiplies_v2{},
math::lcm(lengths[Number<N - 1>{}], align),
i,
Number<stride_n_minus_2>{},
i + I1,
Number<N - 2>{},
Number<1>{});
I1);
}
},
Number<N>{});
......
composable_kernel/include/tensor_operation/blockwise_dynamic_tensor_slice_transfer.hpp
View file @ 30072aec
......@@ -31,8 +31,8 @@ template <index_t BlockSize,
index_t DstScalarPerVector,
index_t SrcScalarStrideInVector,
index_t DstScalarStrideInVector,
index_t ThreadTransferSrcResetCoordinateAfterRun,
index_t ThreadTransferDstResetCoordinateAfterRun>
bool ThreadTransferSrcResetCoordinateAfterRun,
bool ThreadTransferDstResetCoordinateAfterRun>
struct BlockwiseDynamicTensorSliceTransfer_v4
{
static constexpr index_t nDim = remove_reference_t<SrcDesc>::GetNumOfDimension();
......
composable_kernel/include/tensor_operation/blockwise_gemm_v2.hpp
View file @ 30072aec
......@@ -29,10 +29,10 @@ template <index_t BlockSize,
index_t M1PerThread,
index_t N1PerThread,
index_t KPerThread,
index_t MLevel0ThreadCluster,
index_t NLevel0ThreadCluster,
index_t MLevel1ThreadCluster,
index_t NLevel1ThreadCluster,
index_t M1N1ThreadClusterM10,
index_t M1N1ThreadClusterN10,
index_t M1N1ThreadClusterM11,
index_t M1N1ThreadClusterN11,
index_t AThreadCopyScalarPerVector_M1,
index_t BThreadCopyScalarPerVector_N1,
typename std::enable_if<ABlockDesc::IsKnownAtCompileTime() &&
......@@ -62,8 +62,8 @@ struct BlockwiseGemm_km0m1_kn0n1_m0m1n0n1_v1
CThreadDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
static_assert(BlockSize == MLevel0ThreadCluster * MLevel1ThreadCluster *
NLevel0ThreadCluster * NLevel1ThreadCluster,
static_assert(BlockSize == M1N1ThreadClusterM11 * M1N1ThreadClusterM10 *
M1N1ThreadClusterN11 * M1N1ThreadClusterN10,
"wrong! blocksize and cluster size not consistent");
static_assert(ABlockDesc{}.GetLength(I0) == BBlockDesc{}.GetLength(I0),
......@@ -78,6 +78,8 @@ struct BlockwiseGemm_km0m1_kn0n1_m0m1n0n1_v1
constexpr index_t N1 = BBlockDesc{}.GetLength(I2);
// 4-d data space into 4-d thread space
// upper: {1, M1N1ThreadClusterM10 * M1N1ThreadClusterM11, 1, M1N1ThreadClusterN10 *
// M1N1ThreadClusterN11} lower: {M0, M1, N0, N1}
constexpr auto adaptor0 = make_single_stage_tensor_adaptor(
make_tuple(make_vectorize_transform(M0, 1),
make_vectorize_transform(M1PerThread, M1 / M1PerThread),
......@@ -87,21 +89,27 @@ struct BlockwiseGemm_km0m1_kn0n1_m0m1n0n1_v1
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// thread position 4-d thread space
// upper: {M1N1ThreadClusterM10, M1N1ThreadClusterM11, M1N1ThreadClusterN10,
// M1N1ThreadClusterN11} lower: {1, M1N1ThreadClusterM10 * M1N1ThreadClusterM11, 1,
// M1N1ThreadClusterN10 * M1N1ThreadClusterN11}
constexpr auto adaptor1 = make_single_stage_tensor_adaptor(
make_tuple(
make_freeze_transform(make_multi_index(0)),
make_unmerge_transform(make_tuple(MLevel1ThreadCluster, MLevel0ThreadCluster)),
make_unmerge_transform(make_tuple(M1N1ThreadClusterM10, M1N1ThreadClusterM11)),
make_freeze_transform(make_multi_index(0)),
make_unmerge_transform(make_tuple(NLevel1ThreadCluster, NLevel0ThreadCluster))),
make_unmerge_transform(make_tuple(M1N1ThreadClusterN10, M1N1ThreadClusterN11))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<>{}, Sequence<0, 1>{}, Sequence<>{}, Sequence<2, 3>{}));
// 4-d thread space to 1-d thread space
// upper: {BlockSize}
// lower: {M1N1ThreadClusterM10, M1N1ThreadClusterM11, M1N1ThreadClusterN10,
// M1N1ThreadClusterN11}
constexpr auto adaptor2 = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(MLevel1ThreadCluster,
NLevel1ThreadCluster,
MLevel0ThreadCluster,
NLevel0ThreadCluster))),
make_tuple(make_merge_transform(make_tuple(M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11))),
make_tuple(Sequence<0, 2, 1, 3>{}),
make_tuple(Sequence<0>{}));
......@@ -221,10 +229,10 @@ template <index_t BlockSize,
index_t M1PerThread,
index_t N1PerThread,
index_t KPerThread,
index_t MLevel0ThreadCluster,
index_t NLevel0ThreadCluster,
index_t MLevel1ThreadCluster,
index_t NLevel1ThreadCluster,
index_t M1N1ThreadClusterM10,
index_t M1N1ThreadClusterN10,
index_t M1N1ThreadClusterM11,
index_t M1N1ThreadClusterN11,
index_t AThreadCopyScalarPerVector_M1,
index_t BThreadCopyScalarPerVector_N1,
typename std::enable_if<ABlockDesc::IsKnownAtCompileTime() &&
......@@ -254,8 +262,8 @@ struct BlockwiseGemm_km0m1_kn0n1_m0m1n0n1_v2_pipeline_2x2
CThreadDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
static_assert(BlockSize == MLevel0ThreadCluster * MLevel1ThreadCluster *
NLevel0ThreadCluster * NLevel1ThreadCluster,
static_assert(BlockSize == M1N1ThreadClusterM11 * M1N1ThreadClusterM10 *
M1N1ThreadClusterN11 * M1N1ThreadClusterN10,
"wrong! blocksize and cluster size not consistent");
static_assert(ABlockDesc{}.GetLength(I0) == BBlockDesc{}.GetLength(I0),
......@@ -287,18 +295,18 @@ struct BlockwiseGemm_km0m1_kn0n1_m0m1n0n1_v2_pipeline_2x2
constexpr auto adaptor1 = make_single_stage_tensor_adaptor(
make_tuple(
make_freeze_transform(make_multi_index(0)),
make_unmerge_transform(make_tuple(MLevel1ThreadCluster, MLevel0ThreadCluster)),
make_unmerge_transform(make_tuple(M1N1ThreadClusterM10, M1N1ThreadClusterM11)),
make_freeze_transform(make_multi_index(0)),
make_unmerge_transform(make_tuple(NLevel1ThreadCluster, NLevel0ThreadCluster))),
make_unmerge_transform(make_tuple(M1N1ThreadClusterN10, M1N1ThreadClusterN11))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<>{}, Sequence<0, 1>{}, Sequence<>{}, Sequence<2, 3>{}));
// 4-d thread space to 1-d thread space
constexpr auto adaptor2 = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(MLevel1ThreadCluster,
NLevel1ThreadCluster,
MLevel0ThreadCluster,
NLevel0ThreadCluster))),
make_tuple(make_merge_transform(make_tuple(M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11))),
make_tuple(Sequence<0, 2, 1, 3>{}),
make_tuple(Sequence<0>{}));
......
composable_kernel/include/tensor_operation/blockwise_gemm_v2r2.hpp 0 → 100644
View file @ 30072aec
#ifndef CK_BLOCKWISE_GEMM_V2R2_HPP
#define CK_BLOCKWISE_GEMM_V2R2_HPP
#include "common_header.hpp"
#include "tensor_adaptor.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_gemm_v2.hpp"
namespace ck {
// C[M0, M1, N0, N1] += transpose(A[K, M0, M1]) * B[K, N0, N1]
// A and B are visable to the whole block, C is distributed among each thread
// Assume:
// 1. A:
// 1. AKMBlockDesc is known at compile-time
// 2. ABlockBuffer is DynamicBuffer
// 2. B:
// 1. BKNBlockDesc is known at compile-time
// 2. BBlockBuffer is DynamicBuffer
// 3. C:
// 1. CM0M1N0N1ThreadDesc is known at compile-time
// 2. CThreadBuffer is StaticBuffer
// Also assume:
// M0 = N0 = 2. It will do 2x2 pipelined read and fma (ABBA optimization)
template <index_t BlockSize,
typename FloatA,
typename FloatB,
typename FloatC,
typename AKMBlockDesc,
typename BKNBlockDesc,
index_t M1PerThreadM11,
index_t N1PerThreadN11,
index_t KPerThread,
index_t M1N1ThreadClusterM100,
index_t M1N1ThreadClusterN100,
index_t M1N1ThreadClusterM101,
index_t M1N1ThreadClusterN101,
index_t AThreadCopyScalarPerVector_M11,
index_t BThreadCopyScalarPerVector_N11,
typename std::enable_if<AKMBlockDesc::IsKnownAtCompileTime() &&
BKNBlockDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct BlockwiseGemm_km_kn_m0m1n0n1_v2r2_pipeline_2x2
{
using AIndex = MultiIndex<3>;
using BIndex = MultiIndex<3>;
using CIndex = MultiIndex<4>;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
static constexpr index_t K = AKMBlockDesc{}.GetLength(I0);
static constexpr index_t M = AKMBlockDesc{}.GetLength(I1);
static constexpr index_t N = BKNBlockDesc{}.GetLength(I1);
static constexpr index_t M100 = M1N1ThreadClusterM100;
static constexpr index_t N100 = M1N1ThreadClusterN100;
static constexpr index_t M101 = M1N1ThreadClusterM101;
static constexpr index_t N101 = M1N1ThreadClusterN101;
static constexpr index_t M11 = M1PerThreadM11;
static constexpr index_t N11 = N1PerThreadN11;
static constexpr index_t M1 = M1N1ThreadClusterM100 * M1N1ThreadClusterM101 * M1PerThreadM11;
static constexpr index_t N1 = M1N1ThreadClusterN100 * M1N1ThreadClusterN101 * N1PerThreadN11;
static constexpr index_t M0 = M / M1;
static constexpr index_t N0 = N / N1;
__host__ __device__ static constexpr auto
MakeAKM0M1BlockDescriptor(const AKMBlockDesc& a_k_m_block_desc)
{
const auto a_k_m0_m1_block_desc = transform_dynamic_tensor_descriptor(
AKMBlockDesc{},
make_tuple(make_pass_through_transform(Number<K>{}),
make_unmerge_transform(make_tuple(Number<M0>{}, Number<M1>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}));
return a_k_m0_m1_block_desc;
}
__host__ __device__ static constexpr auto
MakeBKN0N1BlockDescriptor(const BKNBlockDesc& b_k_n_block_desc)
{
const auto b_k_n0_n1_block_desc = transform_dynamic_tensor_descriptor(
BKNBlockDesc{},
make_tuple(make_pass_through_transform(Number<K>{}),
make_unmerge_transform(make_tuple(Number<N0>{}, Number<N1>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}));
return b_k_n0_n1_block_desc;
}
__host__ __device__ static constexpr auto MakeCM0M100M101M11N0N100N101N11ToMNBlockAdaptor()
{
// upper: [M0, M100, M101, M11, N0, N100, N101, N11]
// lower: [M, N]
constexpr auto c_m0_m100_m101_m11_n0_n100_n101_n11_to_m_n_block_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_unmerge_transform(make_tuple(
Number<M0>{}, Number<M100>{}, Number<M101>{}, Number<M11>{})),
make_unmerge_transform(make_tuple(
Number<N0>{}, Number<N100>{}, Number<N101>{}, Number<N11>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1, 2, 3>{}, Sequence<4, 5, 6, 7>{}));
return c_m0_m100_m101_m11_n0_n100_n101_n11_to_m_n_block_adaptor;
}
__host__ __device__ static constexpr auto
MakeCM0M100M101M11N0N100N101N11ToM0M1N0N1BlockAdaptor()
{
// upper: [M0, M100, M101, M11, N0, N100, N101, N11]
// lower: [M0, M1, N0, N1]
constexpr auto c_m0_m100_m101_m11_n0_n100_n101_n11_to_m0_m1_n0_n1_block_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_pass_through_transform(Number<M0>{}),
make_unmerge_transform(
make_tuple(Number<M100>{}, Number<M101>{}, Number<M11>{})),
make_pass_through_transform(Number<N0>{}),
make_unmerge_transform(
make_tuple(Number<N100>{}, Number<N101>{}, Number<N11>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}, Sequence<5, 6, 7>{}));
return c_m0_m100_m101_m11_n0_n100_n101_n11_to_m0_m1_n0_n1_block_adaptor;
}
__host__ __device__ static constexpr auto GetCM0M1N0N1ThreadTensorLengths()
{
return Sequence<M0, M11, N0, N11>{};
}
static constexpr auto a_k_m0_m1_block_desc_ = MakeAKM0M1BlockDescriptor(AKMBlockDesc{});
static constexpr auto b_k_n0_n1_block_desc_ = MakeBKN0N1BlockDescriptor(BKNBlockDesc{});
public:
__device__ BlockwiseGemm_km_kn_m0m1n0n1_v2r2_pipeline_2x2()
: c_thread_origin_data_idx_{CalculateCM0M1N0N1ThreadOriginOnBlock(
get_thread_local_1d_id())},
a_thread_copy_{
make_tuple(0, c_thread_origin_data_idx_[I0], c_thread_origin_data_idx_[I1])},
b_thread_copy_{
make_tuple(0, c_thread_origin_data_idx_[I2], c_thread_origin_data_idx_[I3])}
{
static_assert(AKMBlockDesc::IsKnownAtCompileTime() && BKNBlockDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
static_assert(BlockSize == M101 * M100 * N101 * N100,
"wrong! blocksize and cluster size not consistent");
static_assert(M % M1 == 0 && N % N1 == 0, "wrong!");
static_assert(AKMBlockDesc{}.GetLength(I0) == BKNBlockDesc{}.GetLength(I0),
"wrong! K dimension not consistent");
// TODO: remove this restriction
static_assert(M0 == 2 && N0 == 2, "wrong");
}
__device__ static CIndex CalculateCM0M1N0N1ThreadOriginOnBlock(index_t thread_id)
{
// lower: [M0, M1, N0, N1]
// upper: [M0, M100, M101, M11, N0, N100, N101, N11]
constexpr auto adaptor0 = MakeCM0M100M101M11N0N100N101N11ToM0M1N0N1BlockAdaptor();
// lower: [M0, M100, M101, M11, N0, N100, N101, N11]
// upper: [Tid, M0, M11, N0, N11]
constexpr auto adaptor1 = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(M100, N100, M101, N101)),
make_pass_through_transform(M0),
make_pass_through_transform(M11),
make_pass_through_transform(N0),
make_pass_through_transform(N11)),
make_tuple(
Sequence<1, 5, 2, 6>{}, Sequence<0>{}, Sequence<3>{}, Sequence<4>{}, Sequence<7>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
constexpr auto adaptor = chain_tensor_adaptors(adaptor0, adaptor1);
return adaptor.CalculateBottomIndex(make_multi_index(get_thread_local_1d_id(), 0, 0, 0, 0));
}
__host__ __device__ static constexpr index_t GetABlockAlignment() { return M1PerThreadM11; }
__host__ __device__ static constexpr auto GetBBlockAlignment() { return N1PerThreadN11; }
template <typename CM0M1N0N1ThreadDesc,
typename ABlockBuffer,
typename BBlockBuffer,
typename CThreadBuffer>
__device__ void Run(const CM0M1N0N1ThreadDesc& c_m0_m1_n0_n1_thread_desc,
const ABlockBuffer& a_block_buf,
const BBlockBuffer& b_block_buf,
CThreadBuffer& c_thread_buf) const
{
static_assert(CM0M1N0N1ThreadDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
// TODO: remove this restriction
static_assert(M0 == 2 && N0 == 2 && CM0M1N0N1ThreadDesc{}.GetLength(I0) == M0 &&
CM0M1N0N1ThreadDesc{}.GetLength(I2) == N0,
"wrong");
auto a_thread_buf = make_static_buffer<AddressSpace::Vgpr, FloatA>(
a_k_m0_m1_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpace::Vgpr, FloatB>(
b_k_n0_n1_thread_desc_.GetElementSpaceSize());
constexpr auto threadwise_gemm =
ThreadwiseGemm_km0m1_kn0n1_m0m1n0n1<FloatA,
FloatB,
FloatC,
decltype(a_k_m0_m1_thread_desc_),
decltype(b_k_n0_n1_thread_desc_),
CM0M1N0N1ThreadDesc,
Sequence<KPerThread>,
Sequence<1, M1PerThreadM11>,
Sequence<1, N1PerThreadN11>>{};
// read A_sub_0
a_thread_copy_.Run(a_k_m0_m1_block_desc_,
make_tuple(I0, I0, I0),
a_block_buf,
a_k_m0_m1_thread_desc_,
make_tuple(I0, I0, I0),
a_thread_buf);
// read B_sub_0
b_thread_copy_.Run(b_k_n0_n1_block_desc_,
make_tuple(I0, I0, I0),
b_block_buf,
b_k_n0_n1_thread_desc_,
make_tuple(I0, I0, I0),
b_thread_buf);
// read B_sub_1
b_thread_copy_.Run(b_k_n0_n1_block_desc_,
make_tuple(I0, I1, I0),
b_block_buf,
b_k_n0_n1_thread_desc_,
make_tuple(I0, I1, I0),
b_thread_buf);
// read A_sub_1
a_thread_copy_.Run(a_k_m0_m1_block_desc_,
make_tuple(I0, I1, I0),
a_block_buf,
a_k_m0_m1_thread_desc_,
make_tuple(I0, I1, I0),
a_thread_buf);
// C_sub_00 += transpose(A_sub_0) * B_sub_0
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I0, I0),
b_thread_buf,
make_tuple(I0, I0, I0),
c_thread_buf,
make_tuple(I0, I0, I0, I0));
// C_sub_01 += transpose(A_sub_0) * B_sub_1
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I0, I0),
b_thread_buf,
make_tuple(I0, I1, I0),
c_thread_buf,
make_tuple(I0, I0, I1, I0));
// loop over rest of k
static_for<KPerThread, K, KPerThread>{}([&](auto k) {
// read A_sub_0
a_thread_copy_.Run(a_k_m0_m1_block_desc_,
make_tuple(k, I0, I0),
a_block_buf,
a_k_m0_m1_thread_desc_,
make_tuple(I0, I0, I0),
a_thread_buf);
// C_sub_10 += transpose(A_sub_1) * B_sub_0
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I1, I0),
b_thread_buf,
make_tuple(I0, I0, I0),
c_thread_buf,
make_tuple(I1, I0, I0, I0));
// read B_sub_0
b_thread_copy_.Run(b_k_n0_n1_block_desc_,
make_tuple(k, I0, I0),
b_block_buf,
b_k_n0_n1_thread_desc_,
make_tuple(I0, I0, I0),
b_thread_buf);
// C_sub_11 += transpose(A_sub_1) * B_sub_1
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I1, I0),
b_thread_buf,
make_tuple(I0, I1, I0),
c_thread_buf,
make_tuple(I1, I0, I1, I0));
// read B_sub_1
b_thread_copy_.Run(b_k_n0_n1_block_desc_,
make_tuple(k, I1, I0),
b_block_buf,
b_k_n0_n1_thread_desc_,
make_tuple(I0, I1, I0),
b_thread_buf);
// read A_sub_1
a_thread_copy_.Run(a_k_m0_m1_block_desc_,
make_tuple(k, I1, I0),
a_block_buf,
a_k_m0_m1_thread_desc_,
make_tuple(I0, I1, I0),
a_thread_buf);
// C_sub_00 += transpose(A_sub_0) * B_sub_0
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I0, I0),
b_thread_buf,
make_tuple(I0, I0, I0),
c_thread_buf,
make_tuple(I0, I0, I0, I0));
// C_sub_01 += transpose(A_sub_0) * B_sub_1
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I0, I0),
b_thread_buf,
make_tuple(I0, I1, I0),
c_thread_buf,
make_tuple(I0, I0, I1, I0));
});
// C_sub_10 += transpose(A_sub_1) * B_sub_0
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I1, I0),
b_thread_buf,
make_tuple(I0, I0, I0),
c_thread_buf,
make_tuple(I1, I0, I0, I0));
// C_sub_11 += transpose(A_sub_1) * B_sub_1
threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I1, I0),
b_thread_buf,
make_tuple(I0, I1, I0),
c_thread_buf,
make_tuple(I1, I0, I1, I0));
}
private:
// A[K, M0, M1]
static constexpr auto a_k_m0_m1_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<KPerThread>{}, Number<M0>{}, Number<M1PerThreadM11>{}));
// B[K, N0, N1]
static constexpr auto b_k_n0_n1_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<KPerThread>{}, Number<N0>{}, Number<N1PerThreadN11>{}));
using AThreadCopy =
ThreadwiseDynamicTensorSliceTransfer_v4<FloatA,
FloatA,
decltype(a_k_m0_m1_block_desc_),
decltype(a_k_m0_m1_thread_desc_),
Sequence<KPerThread, 1, M1PerThreadM11>,
Sequence<0, 1, 2>,
2,
AThreadCopyScalarPerVector_M11,
1>;
using BThreadCopy =
ThreadwiseDynamicTensorSliceTransfer_v4<FloatB,
FloatB,
decltype(b_k_n0_n1_block_desc_),
decltype(b_k_n0_n1_thread_desc_),
Sequence<KPerThread, 1, N1PerThreadN11>,
Sequence<0, 1, 2>,
2,
BThreadCopyScalarPerVector_N11,
1>;
CIndex c_thread_origin_data_idx_;
AThreadCopy a_thread_copy_;
BThreadCopy b_thread_copy_;
};
} // namespace ck
#endif
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm.hpp → composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_v1r1.hpp
View file @ 30072aec
......@@ -27,13 +27,13 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_v1(const FloatA* __restrict__ p_a_global,
const FloatB* __restrict__ p_b_global,
FloatC* __restrict__ p_c_global,
const AGlobalDesc a_k_m_global_desc,
const BGlobalDesc b_k_n_global_desc,
const CGlobalDesc c_m0_m1_n0_n1_global_desc,
const CBlockClusterDesc c_block_cluster_desc)
kernel_dynamic_gemm_v1r1(const FloatA* __restrict__ p_a_global,
const FloatB* __restrict__ p_b_global,
FloatC* __restrict__ p_c_global,
const AGlobalDesc a_k_m_global_desc,
const BGlobalDesc b_k_n_global_desc,
const CGlobalDesc c_m0_m1_n0_n1_global_desc,
const CBlockClusterDesc c_block_cluster_desc)
{
GridwiseGemm::Run(p_a_global,
p_b_global,
......@@ -63,13 +63,13 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_v1(const FloatA* __restrict__ p_a_global,
const FloatB* __restrict__ p_b_global,
FloatC* __restrict__ p_c_global,
const void __CONSTANT__* p_a_k_m_global_desc,
const void __CONSTANT__* p_b_k_n_global_desc,
const void __CONSTANT__* p_c_m0_m1_n0_n1_global_desc,
const void __CONSTANT__* p_c_block_cluster_desc)
kernel_dynamic_gemm_v1r1(const FloatA* __restrict__ p_a_global,
const FloatB* __restrict__ p_b_global,
FloatC* __restrict__ p_c_global,
const void __CONSTANT__* p_a_k_m_global_desc,
const void __CONSTANT__* p_b_k_n_global_desc,
const void __CONSTANT__* p_c_m0_m1_n0_n1_global_desc,
const void __CONSTANT__* p_c_block_cluster_desc)
{
// first cast void __CONSTANT__ void* to void*
// second cast void* to Desc*
......@@ -108,13 +108,13 @@ template <index_t BlockSize,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t MPerThread,
index_t NPerThread,
index_t M1PerThread,
index_t N1PerThread,
index_t KPerThread,
index_t MLevel0Cluster,
index_t NLevel0Cluster,
index_t MLevel1Cluster,
index_t NLevel1Cluster,
index_t M1N1ThreadClusterM10,
index_t M1N1ThreadClusterN10,
index_t M1N1ThreadClusterM11,
index_t M1N1ThreadClusterN11,
typename ABlockTransferThreadSliceLengths_K_M,
typename ABlockTransferThreadClusterLengths_K_M,
typename ABlockTransferThreadClusterArrangeOrder,
......@@ -139,14 +139,14 @@ template <index_t BlockSize,
typename CGlobalIteratorHacks,
typename AGlobalMoveSliceWindowIteratorHacks,
typename BGlobalMoveSliceWindowIteratorHacks>
struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1r1
{
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
constexpr auto max_lds_align = math::lcm(Number<ABlockTransferDstScalarPerVector_M>{},
Number<BBlockTransferDstScalarPerVector_N>{},
Number<MPerThread>{},
Number<NPerThread>{});
Number<M1PerThread>{},
Number<N1PerThread>{});
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
......@@ -210,8 +210,8 @@ struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
// lds max alignment
constexpr auto max_lds_align = math::lcm(Number<ABlockTransferDstScalarPerVector_M>{},
Number<BBlockTransferDstScalarPerVector_N>{},
Number<MPerThread>{},
Number<NPerThread>{});
Number<M1PerThread>{},
Number<N1PerThread>{});
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
......@@ -284,34 +284,39 @@ struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
static_assert(MPerBlock % (MPerThread * MLevel0Cluster * MLevel1Cluster) == 0 &&
NPerBlock % (NPerThread * NLevel0Cluster * NLevel1Cluster) == 0,
"wrong!");
static_assert(
MPerBlock % (M1PerThread * M1N1ThreadClusterM11 * M1N1ThreadClusterM10) == 0 &&
NPerBlock % (N1PerThread * M1N1ThreadClusterN11 * M1N1ThreadClusterN10) == 0,
"wrong!");
constexpr index_t MRepeat = MPerBlock / (MPerThread * MLevel0Cluster * MLevel1Cluster);
constexpr index_t NRepeat = NPerBlock / (NPerThread * NLevel0Cluster * NLevel1Cluster);
constexpr index_t M0PerThread =
MPerBlock / (M1PerThread * M1N1ThreadClusterM11 * M1N1ThreadClusterM10);
constexpr index_t N0PerThread =
NPerBlock / (N1PerThread * M1N1ThreadClusterN11 * M1N1ThreadClusterN10);
constexpr auto a_k_m0_m1_block_desc = transform_dynamic_tensor_descriptor(
a_k_m_block_desc,
make_tuple(
make_pass_through_transform(Number<KPerBlock>{}),
make_unmerge_transform(make_tuple(
Number<MRepeat>{}, Number<MPerThread * MLevel0Cluster * MLevel1Cluster>{}))),
make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
make_unmerge_transform(make_tuple(
Number<M0PerThread>{},
Number<M1PerThread * M1N1ThreadClusterM11 * M1N1ThreadClusterM10>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}));
constexpr auto b_k_n0_n1_block_desc = transform_dynamic_tensor_descriptor(
b_k_n_block_desc,
make_tuple(
make_pass_through_transform(Number<KPerBlock>{}),
make_unmerge_transform(make_tuple(
Number<NRepeat>{}, Number<NPerThread * NLevel0Cluster * NLevel1Cluster>{}))),
make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
make_unmerge_transform(make_tuple(
Number<N0PerThread>{},
Number<N1PerThread * M1N1ThreadClusterN11 * M1N1ThreadClusterN10>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}));
constexpr auto c_m0_m1_n0_n1_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
Number<MRepeat>{}, Number<MPerThread>{}, Number<NRepeat>{}, Number<NPerThread>{}));
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(Number<M0PerThread>{},
Number<M1PerThread>{},
Number<N0PerThread>{},
Number<N1PerThread>{}));
const auto blockwise_gemm =
BlockwiseGemm_km0m1_kn0n1_m0m1n0n1_v2_pipeline_2x2<BlockSize,
......@@ -321,15 +326,15 @@ struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
decltype(a_k_m0_m1_block_desc),
decltype(b_k_n0_n1_block_desc),
decltype(c_m0_m1_n0_n1_thread_desc),
MPerThread,
NPerThread,
M1PerThread,
N1PerThread,
KPerThread,
MLevel0Cluster,
NLevel0Cluster,
MLevel1Cluster,
NLevel1Cluster,
MPerThread,
NPerThread>{};
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
M1PerThread,
N1PerThread>{};
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_space_size =
......@@ -345,9 +350,10 @@ struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
auto c_thread_buf = make_static_buffer<AddressSpace::Vgpr, FloatAcc>(
c_m0_m1_n0_n1_thread_desc.GetElementSpaceSize());
ThreadwiseDynamicTensorSliceSet_v1<FloatAcc,
decltype(c_m0_m1_n0_n1_thread_desc),
Sequence<MRepeat, MPerThread, NRepeat, NPerThread>>{}
ThreadwiseDynamicTensorSliceSet_v1<
FloatAcc,
decltype(c_m0_m1_n0_n1_thread_desc),
Sequence<M0PerThread, M1PerThread, N0PerThread, N1PerThread>>{}
.Run(c_m0_m1_n0_n1_thread_desc, make_tuple(I0, I0, I0, I0), c_thread_buf, FloatAcc{0});
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock, 0);
......@@ -479,8 +485,8 @@ struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
// output: register to global memory
{
constexpr auto M1 = Number<MPerThread * MLevel0Cluster * MLevel1Cluster>{};
constexpr auto N1 = Number<NPerThread * NLevel0Cluster * NLevel1Cluster>{};
constexpr auto M1 = Number<M1PerThread * M1N1ThreadClusterM10 * M1N1ThreadClusterM11>{};
constexpr auto N1 = Number<N1PerThread * M1N1ThreadClusterN10 * M1N1ThreadClusterN11>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
constexpr auto c_m0_m1_n0_n1_global_tensor_iterator_hacks = CGlobalIteratorHacks{};
......@@ -493,7 +499,7 @@ struct GridwiseDynamicGemm_km_kn_m0m1n0n1_v1
FloatC,
decltype(c_m0_m1_n0_n1_thread_desc),
decltype(c_m0_m1_n0_n1_global_desc),
Sequence<MRepeat, MPerThread, NRepeat, NPerThread>,
Sequence<M0PerThread, M1PerThread, N0PerThread, N1PerThread>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
......
composable_kernel/include/utility/common_header.hpp
View file @ 30072aec
......@@ -6,6 +6,7 @@
#include "container_helper.hpp"
#include "statically_indexed_array.hpp"
#include "container_element_picker.hpp"
#include "multi_index.hpp"
#include "data_type.hpp"
#include "float_type.hpp"
#include "functional.hpp"
......
composable_kernel/include/utility/config.amd.hpp.in
View file @ 30072aec
......@@ -14,7 +14,7 @@
#define CK_DEVICE_BACKEND_AMD 1
// GPU ID
#if 0
#if 1
#define CK_AMD_GPU_GFX906 1
#elif 0
#define CK_AMD_GPU_GFX908 1
......@@ -28,7 +28,7 @@
#endif
// launch bounds
#define CK_USE_LAUNCH_BOUNDS 1
#define CK_USE_LAUNCH_BOUNDS 0
#ifdef CK_USE_LAUNCH_BOUNDS
#define CK_MAX_THREAD_PER_BLOCK 256
......
composable_kernel/include/utility/magic_division.hpp
View file @ 30072aec
......@@ -118,6 +118,7 @@ struct MagicDivision
return (tmp + dividend) >> shift;
}
#if 1 // debug
// HACK: magic division for int32_t
// HACK: use dividend_i32 as if it's uint32_t, dividend_i32 need to be
// non-negative for result to be correct
......@@ -127,8 +128,25 @@ struct MagicDivision
{
uint32_t dividend_u32 = as_type<uint32_t>(dividend_i32);
uint32_t tmp = ((uint64_t)dividend_u32 * (uint64_t)multiplier) >> 32;
return (tmp + dividend_i32) >> shift;
return (tmp + dividend_u32) >> shift;
}
#else
// the inline ASM is producing wrong result
__host__ __device__ static int32_t
DoMagicDivision(int32_t dividend_i32, uint32_t multiplier, uint32_t shift)
{
uint32_t r;
asm volatile("\n \
v_mul_hi_u32 %0, %1, %2 \n \
v_add_u32_e32 %0, %1, %0 \n \
v_lshrrev_b32_e32 %0, %3, %0 \n \
"
: "=v"(r)
: "v"(as_type<uint32_t>(dividend_i32)), "s"(multiplier), "s"(shift));
return as_type<int32_t>(r);
}
#endif
};
} // namespace ck
......
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