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Commit 64fbf5a2 authored by wangshaojie6's avatar wangshaojie6
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add prefetch 4 global load version. perf seems not good

parent 428ae72a
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Showing with 1920 additions and 7 deletions
example/01_gemm/CMakeLists.txt
View file @ 64fbf5a2
...@@ -5,5 +5,6 @@ add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp) ...@@ -5,5 +5,6 @@ add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp) add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp) add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp) add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp)
add_example_executable(example_gemm_xdl_skip_all_lds_fp16 gemm_xdl_skip_all_lds_fp16.cpp)
# FIXME: re-enable this exampe as test when SWDEV-335738 is fixed # FIXME: re-enable this exampe as test when SWDEV-335738 is fixed
add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp) add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
example/01_gemm/gemm_xdl_skip_all_lds_fp16.cpp 0 → 100644
View file @ 64fbf5a2
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_gemm_xdl_skip_b_lds.hpp"
#include "device_gemm_xdl_skip_all_lds.hpp"
#include "device_gemm_xdl.hpp"
#include "device_gemm_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp"
#include "reference_gemm.hpp"
#include "gemm_specialization.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSkipAllLds
//###########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BThreadTransfer| CThreadTransfer| CThreadTransfer|
//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| SrcScalar| SrcDstVectorDim| DstScalar|
//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| | PerVector|
//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
//< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 64, 4, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 7, 1>;
< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 4, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 7, 1>;
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, float, AElementOp, BElementOp, CElementOp>;
template <typename DataType>
std::ostream& show_2d_matrix(std::ostream& os, Tensor<DataType>& matrix)
{
os << "[" << std::endl;
for(size_t x = 0; x < matrix.mDesc.GetLengths()[0]; x++)
{
os << "[";
for(size_t y = 0; y < matrix.mDesc.GetLengths()[1]; y++)
{
os << std::setw(5) << static_cast<float>(matrix(x, y));
}
os << "]" << std::endl;
}
os << "]";
return os;
}
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
bool time_kernel = false;
// GEMM shape
#if 1
ck::index_t M = 128 * 360;
ck::index_t N = 64;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = N;
#else
ck::index_t M = 16;
ck::index_t N = 16;
ck::index_t K = 8;
ck::index_t StrideA = 8;
ck::index_t StrideB = 8;
ck::index_t StrideC = 16;
#endif
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
//a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
//a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
a_m_k.GenerateTensorValue(GeneratorTensor_2<BDataType>{0, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{0, 2});
//b_k_n.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
// a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
}
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
if(do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
#if 0
{
show_2d_matrix(std::cout << "a : ", a_m_k) << std::endl;
show_2d_matrix(std::cout << "b: ", b_k_n) << std::endl;
show_2d_matrix(std::cout << "c_device: ", c_m_n_device_result) << std::endl;
show_2d_matrix(std::cout << "c_host :", c_m_n_host_result) << std::endl;
}
#endif
ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
}
return 0;
}
example/01_gemm/gemm_xdl_skip_b_lds_fp16.cpp
View file @ 64fbf5a2
...@@ -47,16 +47,17 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSkipBLds ...@@ -47,16 +47,17 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSkipBLds
//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| SrcScalar| SrcDstVectorDim| DstScalar| //###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| SrcScalar| SrcDstVectorDim| DstScalar|
//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| | PerVector| //###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| | PerVector|
//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
#if 0 #if 1
< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 7, 1>; //< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 64, 4, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 7, 1>;
< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 4, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 7, 1>;
using ADataType = ck::half_t; using ADataType = ck::half_t;
using BDataType = ck::half_t; using BDataType = ck::half_t;
using CDataType = ck::half_t; using CDataType = ck::half_t;
using AccDataType = float; using AccDataType = float;
#else #else
// < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 16, 4, 4, 16, 16, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>; < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 4, 4, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>;
// < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 32, 4, 4, 32, 32, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>; // < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 32, 4, 4, 32, 32, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>;
< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>; // < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>;
using ADataType = float; using ADataType = float;
using BDataType = float; using BDataType = float;
using CDataType = float; using CDataType = float;
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_xdlops_skip_all_lds.hpp 0 → 100644
View file @ 64fbf5a2
#pragma once
#include "common_header.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "xdlops_gemm.hpp"
#include "tensor_adaptor.hpp"
namespace ck {
template <index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename AK0AM0M1M2M3K1BlockDesc,
typename BK0BN0N1N2N3K1BlockDesc,
index_t MPerBlock,
index_t NPerBlock,
index_t K0PerBlock,
index_t MPerXDL,
index_t NPerXDL,
index_t MRepeat,
index_t NRepeat,
index_t KPack>
struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_non_lds
{
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 WaveSize = 64;
static constexpr index_t KPerBlock = K0PerBlock * KPack;
static constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerXDL, NPerXDL, KPack>{};
static constexpr index_t KPerThread = KPerBlock / xdlops_gemm.K0PerXdlops;
static constexpr index_t K0PerThread = K0PerBlock / xdlops_gemm.K0PerXdlops;
static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
FloatAcc,
MRepeat * NRepeat,
xdlops_gemm.GetRegSizePerXdlops(),
true>
c_thread_buf_;
__host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
__device__ static auto GetWaveIdx()
{
const index_t thread_id = get_thread_local_1d_id();
constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
make_tuple(Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}));
return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
}
__device__ static auto CalculateAThreadOriginDataIndex()
{
const auto wave_idx = GetWaveIdx();
const auto waveId_m = wave_idx[I0];
const auto xdlops_a_idx = xdlops_gemm.CalculateAThreadOriginDataIndex();
return make_tuple(0, waveId_m, xdlops_a_idx[I1], KPerThread * xdlops_a_idx[I0]);
}
__device__ static auto CalculateBThreadOriginDataIndex()
{
const auto wave_idx = GetWaveIdx();
const auto waveId_n = wave_idx[I1];
const auto xdlops_b_idx = xdlops_gemm.CalculateBThreadOriginDataIndex();
return make_tuple(0, waveId_n, xdlops_b_idx[I1], KPerThread * xdlops_b_idx[I0]);
}
template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
__device__ static auto
CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
{
const auto wave_idx = GetWaveIdx();
const auto waveId_m = wave_idx[I0];
const auto waveId_n = wave_idx[I1];
const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
constexpr auto mrepeat_mwave_mperxdl_to_m_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerXDL))),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0, 1, 2>{}));
constexpr auto nrepeat_nwave_nperxdl_to_n_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerXDL))),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0, 1, 2>{}));
const index_t c_thread_m = mrepeat_mwave_mperxdl_to_m_adaptor.CalculateBottomIndex(
make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
const index_t c_thread_n = nrepeat_nwave_nperxdl_to_n_adaptor.CalculateBottomIndex(
make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
return make_tuple(c_thread_m, c_thread_n);
}
__host__ __device__ BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_non_lds()
{
static_assert(AK0AM0M1M2M3K1BlockDesc::IsKnownAtCompileTime() &&
BK0BN0N1N2N3K1BlockDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
static_assert(BlockSize == MWaves * NWaves * WaveSize,
"BlockSize != MWaves * NWaves * WaveSize\n");
static_assert(MPerBlock % (MPerXDL * MRepeat) == 0 && NPerBlock % (NPerXDL * NRepeat) == 0,
"wrong!");
}
__host__ __device__ static constexpr auto GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
{
constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
constexpr auto N = c_m0_m1_m2_n_tblk_lens[I3];
return make_naive_tensor_descriptor_packed(
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
}
__host__ __device__ static constexpr auto GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
{
constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
constexpr auto N = c_m0_m1_m2_n_tblk_lens[I3];
return make_naive_tensor_descriptor_packed(
make_tuple(I1, Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
}
__host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
{
constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
Number<NRepeat>{},
Number<MWaves>{},
Number<NWaves>{},
Number<MPerXDL>{},
Number<NPerXDL>{}));
return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_block_desc_m0_n0_m1_n1_m2_n2);
}
__host__ __device__ static constexpr auto GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
{
constexpr auto c_block_desc_g_m0_n0_m1_n1_m2_n2 =
make_naive_tensor_descriptor_packed(make_tuple(I1,
Number<MRepeat>{},
Number<NRepeat>{},
Number<MWaves>{},
Number<NWaves>{},
Number<MPerXDL>{},
Number<NPerXDL>{}));
return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
c_block_desc_g_m0_n0_m1_n1_m2_n2);
}
template <typename CGridDesc_M_N>
__host__ __device__ static constexpr auto
MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_M_N& c_grid_desc_m_n)
{
const auto M = c_grid_desc_m_n.GetLength(I0);
const auto N = c_grid_desc_m_n.GetLength(I1);
const auto c_grid_desc_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}));
return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m0_n0_m1_n1_m2_n2);
}
template <typename CGridDesc_G_M_N>
__host__ __device__ static constexpr auto
MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
{
const auto G = c_grid_desc_g_m_n.GetLength(I0);
const auto M = c_grid_desc_g_m_n.GetLength(I1);
const auto N = c_grid_desc_g_m_n.GetLength(I2);
const auto c_grid_desc_g_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
c_grid_desc_g_m_n,
make_tuple(make_pass_through_transform(G),
make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 3, 5>{}, Sequence<2, 4, 6>{}));
return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
c_grid_desc_g_m0_n0_m1_n1_m2_n2);
}
template <typename ABlockBuffer, typename BBlockBuffer, typename CThreadBuffer>
__device__ void Run(const ABlockBuffer& a_thread_buf,
const BBlockBuffer& b_thread_buf,
CThreadBuffer& c_thread_buf) const
{
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
// read B
static_for<0, KPerThread, KPack>{}([&](auto k) {
vector_type<FloatAB, KPack> a_thread_vec;
vector_type<FloatAB, KPack> b_thread_vec;
constexpr index_t k0 = k / KPack;
static_for<0, KPack, 1>{}([&](auto i) {
a_thread_vec.template AsType<FloatAB>()(i) = a_thread_buf
[Number<a_thread_desc_.CalculateOffset(make_tuple(k0, m0, i))>{}];
b_thread_vec.template AsType<FloatAB>()(i) = b_thread_buf
[Number<b_thread_desc_.CalculateOffset(make_tuple(k0, n0, i))>{}];
});
using mfma_input_type =
typename vector_type<FloatAB, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
xdlops_gemm.template Run(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
});
});
});
}
private:
// A[M0, M1, M2, KPerThread]
static constexpr auto a_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(Number<K0PerThread>{}, // KPerThread
Number<MRepeat>{}, // repeat
Number<KPack>{}));
// B[N0, N1, N2, KPerThread]
static constexpr auto b_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(Number<K0PerThread>{}, // KPerThread
Number<NRepeat>{}, // repeat
Number<KPack>{}));
// C[M, N, NumRegXdlops]
static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, xdlops_gemm.GetRegSizePerXdlops()));
};
} // namespace ck
include/ck/tensor_operation/gpu/device/device_gemm_xdl_skip_all_lds.hpp 0 → 100644
View file @ 64fbf5a2
#pragma once
#include <iostream>
#include <sstream>
#include "device.hpp"
#include "device_base.hpp"
#include "device_gemm.hpp"
#include "common_header.hpp"
#include "tensor_layout.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_skip_b_lds_v1.hpp"
#include "gridwise_gemm_xdlops_skip_all_lds_v1.hpp"
#include "gemm_specialization.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename ADataType,
typename BDataType,
typename CDataType,
typename AccDataType,
typename ALayout,
typename BLayout,
typename CLayout,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
GemmSpecialization GemmSpec,
ck::index_t BlockSize,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t K0PerBlock,
ck::index_t K1,
ck::index_t MPerXDL,
ck::index_t NPerXDL,
ck::index_t MXdlPerWave,
ck::index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
bool ABlockLdsAddExtraM,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector>
struct DeviceGemmXdlSkipAllLds
: public DeviceGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto K1Number = Number<K1>{};
static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
{
assert(K % K1 == 0);
const index_t K0 = K / K1;
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));
}
}();
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
{
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
return transform_tensor_descriptor(
a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
make_right_pad_transform(M, PadM)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
else
{
return transform_tensor_descriptor(
a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
make_pass_through_transform(M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
}
static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
{
assert(K % K1 == 0);
const index_t K0 = K / K1;
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));
}
}();
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
{
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
return transform_tensor_descriptor(
b_grid_desc_k_n,
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
make_right_pad_transform(N, PadN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
else
{
return transform_tensor_descriptor(
b_grid_desc_k_n,
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
}
static auto MakeCGridDescriptor_M_N(index_t M, index_t N, 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 == GemmSpecialization::MNPadding)
{
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
return transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
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>{}));
}
}
using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_K0_M_K1(1, 1, 1));
using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
// GridwiseGemm
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_skip_all_lds_v1<
BlockSize,
ADataType, // TODO: distinguish A/B datatype
AccDataType,
CDataType,
InMemoryDataOperationEnum::Set,
AGridDesc_K0_M_K1,
BGridDesc_K0_N_K1,
CGridDesc_M_N,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
MPerBlock,
NPerBlock,
K0PerBlock,
MPerXDL,
NPerXDL,
K1,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
false, // AThreadTransferSrcResetCoordinateAfterRun,
ABlockLdsAddExtraM,
BBlockTransferSrcScalarPerVector,
false, // BThreadTransferSrcResetCoordinateAfterRun,
Sequence<0, 2, 4, 5, 6, 1, 3, 7>, // CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector>;
// Argument
struct Argument : public BaseArgument
{
Argument(const ADataType* p_a_grid,
const BDataType* p_b_grid,
CDataType* p_c_grid,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
index_t StrideC,
index_t M01,
index_t N01,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
: p_a_grid_{p_a_grid},
p_b_grid_{p_b_grid},
p_c_grid_{p_c_grid},
a_grid_desc_k0_m_k1_{},
b_grid_desc_k0_n_k1_{},
c_grid_desc_m_n_{},
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_{},
block_2_ctile_map_{},
M01_{M01},
N01_{N01},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
c_element_op_{c_element_op}
{
a_grid_desc_k0_m_k1_ =
DeviceGemmXdlSkipAllLds::MakeAGridDescriptor_K0_M_K1(M, K, StrideA);
b_grid_desc_k0_n_k1_ =
DeviceGemmXdlSkipAllLds::MakeBGridDescriptor_K0_N_K1(K, N, StrideB);
c_grid_desc_m_n_ = DeviceGemmXdlSkipAllLds::MakeCGridDescriptor_M_N(M, N, StrideC);
if(GridwiseGemm::CheckValidity(
a_grid_desc_k0_m_k1_, b_grid_desc_k0_n_k1_, c_grid_desc_m_n_, M01_, N01_))
{
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
block_2_ctile_map_ =
GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3_ =
GridwiseGemm::MakeAGridDescriptor_K0_K1_K2_M0_M1_M2_M3_K3(a_grid_desc_k0_m_k1_);
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_ =
GridwiseGemm::MakeBGridDescriptor_K0_K1_K2_N0_N1_N2_N3_K3(b_grid_desc_k0_n_k1_);
}
}
// private:
const ADataType* p_a_grid_;
const BDataType* p_b_grid_;
CDataType* p_c_grid_;
AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
CGridDesc_M_N c_grid_desc_m_n_;
typename GridwiseGemm::AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3
a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3_;
typename GridwiseGemm::BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_;
typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_;
typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
index_t M01_;
index_t N01_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CElementwiseOperation c_element_op_;
};
// Invoker
struct Invoker : public BaseInvoker
{
using Argument = DeviceGemmXdlSkipAllLds::Argument;
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
{
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
<< ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
<< arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
<< ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
<< arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
}
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
arg.b_grid_desc_k0_n_k1_,
arg.c_grid_desc_m_n_,
arg.M01_,
arg.N01_))
{
throw std::runtime_error(
"wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
}
const index_t grid_size = GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_);
const auto K0 = arg.a_grid_desc_k0_m_k1_.GetLength(I0);
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
float ave_time = 0;
if(has_main_k0_block_loop)
{
const auto kernel = kernel_gemm_xdlops_skip_all_lds_v1<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
CDataType,
remove_reference_t<DeviceGemmXdlSkipAllLds::AGridDesc_K0_M_K1>,
remove_reference_t<typename GridwiseGemm::AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3>,
remove_reference_t<DeviceGemmXdlSkipAllLds::BGridDesc_K0_N_K1>,
remove_reference_t<typename GridwiseGemm::BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3>,
remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
true>;
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_,
arg.a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3_,
arg.b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
else
{
const auto kernel = kernel_gemm_xdlops_skip_all_lds_v1<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
CDataType,
remove_reference_t<DeviceGemmXdlSkipAllLds::AGridDesc_K0_M_K1>,
remove_reference_t<typename GridwiseGemm::AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3>,
remove_reference_t<DeviceGemmXdlSkipAllLds::BGridDesc_K0_N_K1>,
remove_reference_t<typename GridwiseGemm::BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3>,
remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
false>;
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_,
arg.a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3_,
arg.b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
return ave_time;
}
// polymorphic
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
static bool IsSupportedArgument(const Argument& arg)
{
return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
arg.b_grid_desc_k0_n_k1_,
arg.c_grid_desc_m_n_,
arg.M01_,
arg.N01_);
}
// polymorphic
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto MakeArgument(const ADataType* p_a,
const BDataType* p_b,
CDataType* p_c,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
index_t StrideC,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
{
return Argument{p_a,
p_b,
p_c,
M,
N,
K,
StrideA,
StrideB,
StrideC,
1,
1,
a_element_op,
b_element_op,
c_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
// polymorphic
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
const void* p_b,
void* p_c,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
index_t StrideC,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
index_t /* KBatch */ = 1) override
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b),
static_cast<CDataType*>(p_c),
M,
N,
K,
StrideA,
StrideB,
StrideC,
1,
1,
a_element_op,
b_element_op,
c_element_op);
}
// polymorphic
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>(Invoker{});
}
// polymorphic
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceGemmXdlSkipAllLds"
<< "<"
<< BlockSize << ", "
<< MPerBlock << ", "
<< NPerBlock << ", "
<< K0PerBlock << ", "
<< K1 << ", "
<< MPerXDL << ", "
<< NPerXDL << ", "
<< MXdlPerWave << ", "
<< NXdlPerWave
<< ">";
// clang-format on
return str.str();
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_skip_all_lds_v1.hpp 0 → 100644
View file @ 64fbf5a2
#pragma once
#include "common_header.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "blockwise_gemm_xdlops.hpp"
#include "blockwise_gemm_xdlops_skip_b_lds.hpp"
#include "blockwise_gemm_xdlops_skip_all_lds.hpp"
#include "thread_group_tensor_slice_transfer_v4r1.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "gridwise_gemm_pipeline_v1.hpp"
namespace ck {
template <typename GridwiseGemm,
typename FloatAB,
typename FloatC,
typename AGridDesc_K0_M_K1,
typename AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3,
typename BGridDesc_K0_N_K1,
typename BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3,
typename CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename Block2CTileMap,
bool HasMainK0BlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_gemm_xdlops_skip_all_lds_v1(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1,
const AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3 a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
const BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3 b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CElementwiseOperation c_element_op,
const Block2CTileMap block_2_ctile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
//__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
GridwiseGemm::template Run<HasMainK0BlockLoop>(p_a_grid,
p_b_grid,
p_c_grid,
//p_shared,
a_grid_desc_k0_m_k1,
a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
a_element_op,
b_element_op,
c_element_op,
block_2_ctile_map);
#else
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_c_grid;
ignore = a_grid_desc_k0_m_k1;
ignore = b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3;
ignore = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2;
ignore = a_element_op;
ignore = b_element_op;
ignore = c_element_op;
ignore = block_2_ctile_map;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
}
template <index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AGridDesc_K0_M_K1,
typename BGridDesc_K0_N_K1,
typename CGridDesc_M_N,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
index_t MPerBlock,
index_t NPerBlock,
index_t K0PerBlock,
index_t MPerXDL,
index_t NPerXDL,
index_t K1Value,
index_t MXdlPerWave,
index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_K1,
bool AThreadTransferSrcResetCoordinateAfterRun,
bool ABlockLdsExtraM,
index_t BBlockTransferSrcScalarPerVector,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector>
struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_skip_all_lds_v1
{
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 auto I4 = Number<4>{};
static constexpr auto I5 = Number<5>{};
static constexpr auto I6 = Number<6>{};
static constexpr auto I7 = Number<7>{};
static constexpr auto MultiK0 = 4 * 1;
// K1 should be Number<...>
static constexpr auto K1 = Number<K1Value>{};
static constexpr index_t WaveSize = 64;
static constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXDL);
static constexpr index_t NWaves = NPerBlock / (NXdlPerWave * NPerXDL);
static constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerXDL, NPerXDL, K1>{};
static constexpr index_t K0PerThread = K0PerBlock / xdlops_gemm.K0PerXdlops;
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
__host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
{
constexpr auto max_lds_align = K1;
// A matrix in LDS memory, dst of blockwise copy
constexpr auto a_block_desc_k0_m_k1 = [&]() {
if constexpr(ABlockLdsExtraM)
{
return make_naive_tensor_descriptor(
make_tuple(Number<K0PerBlock * MultiK0>{}, Number<MPerBlock>{}, K1),
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
}
else
{
return make_naive_tensor_descriptor_aligned(
make_tuple(Number<K0PerBlock * MultiK0>{}, Number<MPerBlock>{}, K1),
max_lds_align);
}
}();
return a_block_desc_k0_m_k1;
}
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_desc_k0_m_k1 = GetABlockDescriptor_K0PerBlock_MPerBlock_K1();
constexpr auto max_lds_align = K1;
constexpr auto a_block_space_size_aligned =
math::integer_least_multiple(a_block_desc_k0_m_k1.GetElementSpaceSize(), max_lds_align);
return (a_block_space_size_aligned) * sizeof(FloatAB);
}
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
__host__ __device__ static constexpr bool
CheckValidity(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1,
const CGridDesc_M_N& c_grid_desc_m_n,
index_t M01,
index_t N01)
{
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 * MXdlPerWave) == 0) &&
(NPerBlock % (NXdlPerWave * NPerXDL)) == 0,
"Invalid tuning param!");
const auto M = a_grid_desc_k0_m_k1.GetLength(I1);
const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
if(!(M == c_grid_desc_m_n.GetLength(I0) && N == c_grid_desc_m_n.GetLength(I1) &&
K0 == b_grid_desc_k0_n_k1.GetLength(I0) && K1 == a_grid_desc_k0_m_k1.GetLength(I2) &&
K1 == b_grid_desc_k0_n_k1.GetLength(I2)))
return false;
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
return false;
// 2-stage prefetch currently only support even number of K0 loop
// TODO: add support for odd number of K0 loop
if(!((K0 / K0PerBlock) % 2 == 0))
{
return false;
}
// check M01, N01
constexpr auto M1 = Number<MPerBlock>{};
constexpr auto N1 = Number<NPerBlock>{};
const auto M0 = M / M1;
const auto N0 = N / N1;
if(!(M0 % M01 == 0 && N0 % N01 == 0))
return false;
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
return true;
}
__host__ __device__ static constexpr index_t
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n)
{
const auto M = c_grid_desc_m_n.GetLength(I0);
const auto N = c_grid_desc_m_n.GetLength(I1);
const index_t grid_size = (M / MPerBlock) * (N / NPerBlock);
return grid_size;
}
// TODO move this function into GEMM-pipeline class
__host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
{
const bool has_main_k0_block_loop = (K0 / (MultiK0 * K0PerBlock)) > 1;
return has_main_k0_block_loop;
}
__host__ __device__ static constexpr auto
MakeAGridDescriptor_K0_K1_K2_M0_M1_M2_M3_K3(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1)
{
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
const auto M = a_grid_desc_k0_m_k1.GetLength(I1);
const auto a_griddesc_k0_mblockid_mrepeat_mwaves_mperxdlops_k1 = transform_tensor_descriptor(
a_grid_desc_k0_m_k1,
make_tuple(make_unmerge_transform(
make_tuple(K0 / K0PerBlock, xdlops_gemm.K0PerXdlops, K0PerThread)),
make_unmerge_transform(make_tuple(
M / (MXdlPerWave * MWaves * MPerXDL), MXdlPerWave, MWaves, MPerXDL)),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4, 5, 6>{}, Sequence<7>{}));
return a_griddesc_k0_mblockid_mrepeat_mwaves_mperxdlops_k1;
}
__host__ __device__ static constexpr auto
MakeBGridDescriptor_K0_K1_K2_N0_N1_N2_N3_K3(const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1)
{
const auto K0 = b_grid_desc_k0_n_k1.GetLength(I0);
const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
const auto b_griddesc_k0_nblockid_nrepeat_nwaves_nperxdlops_k1 = transform_tensor_descriptor(
b_grid_desc_k0_n_k1,
make_tuple(make_unmerge_transform(
make_tuple(K0 / K0PerBlock, xdlops_gemm.K0PerXdlops, K0PerThread)),
make_unmerge_transform(make_tuple(
N / (NXdlPerWave * NWaves * NPerXDL), NXdlPerWave, NWaves, NPerXDL)),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4, 5, 6>{}, Sequence<7>{}));
return b_griddesc_k0_nblockid_nrepeat_nwaves_nperxdlops_k1;
}
__device__ static auto GetWaveIdx()
{
const index_t thread_id = get_thread_local_1d_id();
constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
make_tuple(Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}));
return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
}
__device__ static auto GetWaveKNIdx(const index_t thread_id)
{
constexpr auto wave_threadid_to_nk_idx_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(xdlops_gemm.K0PerXdlops, NPerXDL))),
make_tuple(Sequence<0, 1>{}),
make_tuple(Sequence<0>{}));
return wave_threadid_to_nk_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
}
__device__ static auto GetWaveKMIdx(const index_t thread_id)
{
constexpr auto wave_threadid_to_mk_idx_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(xdlops_gemm.K0PerXdlops, MPerXDL))),
make_tuple(Sequence<0, 1>{}),
make_tuple(Sequence<0>{}));
return wave_threadid_to_mk_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
}
__host__ __device__ static constexpr auto
MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_M_N& c_grid_desc_m_n)
{
constexpr auto max_lds_align = K1;
// A matrix in LDS memory, dst of blockwise copy
constexpr auto a_block_desc_k0_m_k1 = [&]() {
if constexpr(ABlockLdsExtraM)
{
return make_naive_tensor_descriptor(
make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
}
else
{
return make_naive_tensor_descriptor_aligned(
make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
}
}();
// B matrix threadwise copy
constexpr auto b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3 =
make_naive_tensor_descriptor_packed(make_tuple(I1,
I1,
Number<K0PerThread>{}, // K0PerThread
I1, // NBlockId
Number<NXdlPerWave>{}, // repeat
I1, // waves
I1, // NPerXdlops
Number<K1>{}));
using BlockwiseGemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1r1<
BlockSize,
FloatAB,
FloatAcc,
decltype(a_block_desc_k0_m_k1),
decltype(b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3),
MPerBlock,
NPerBlock,
K0PerBlock,
MPerXDL,
NPerXDL,
MXdlPerWave,
NXdlPerWave,
K1>;
return BlockwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n);
}
// return block_id to C matrix tile idx (m0, n0) mapping
__host__ __device__ static constexpr auto
MakeDefaultBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n, index_t M01, index_t N01)
{
const auto M = c_grid_desc_m_n.GetLength(I0);
const auto N = c_grid_desc_m_n.GetLength(I1);
constexpr auto M1 = Number<MPerBlock>{};
constexpr auto N1 = Number<NPerBlock>{};
const auto M0 = M / M1;
const auto N0 = N / N1;
const auto M00 = M0 / M01;
const auto N00 = N0 / N01;
const auto m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_unmerge_transform(make_tuple(M00, M01)),
make_unmerge_transform(make_tuple(N00, N01))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1, 3>{}));
const auto cblockid_to_m00_m01_n00_n01_block_cluster_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(M00, N00, M01, N01))),
make_tuple(Sequence<0, 1, 2, 3>{}),
make_tuple(Sequence<0>{}));
const auto cblockid_to_m0_n0_block_cluster_adaptor =
chain_tensor_adaptors(m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
cblockid_to_m00_m01_n00_n01_block_cluster_adaptor);
return cblockid_to_m0_n0_block_cluster_adaptor;
}
using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
decltype(MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{}));
using DefaultBlock2CTileMap = decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}, 1, 1));
using BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3 =
decltype(MakeBGridDescriptor_K0_K1_K2_N0_N1_N2_N3_K3(BGridDesc_K0_N_K1{}));
using AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3 =
decltype(MakeAGridDescriptor_K0_K1_K2_M0_M1_M2_M3_K3(AGridDesc_K0_M_K1{}));
template <bool HasMainK0BlockLoop, typename Block2CTileMap = DefaultBlock2CTileMap>
__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,
const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
const AGridDesc_K0_K1_K2_M0_M1_M2_M3_K3 a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
const BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3 b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2& c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CElementwiseOperation& c_element_op,
const Block2CTileMap& block_2_ctile_map)
{
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_a_grid, a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3.GetElementSpaceSize());
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_b_grid, b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3.GetElementSpaceSize());
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_c_grid, c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetElementSpaceSize());
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
// divide block work by [M, N]
const auto block_work_idx =
block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
// 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_work_idx[I0] * MPerBlock);
const index_t n_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
// A matrix blockwise copy
// a thread wise copy
ignore = a_element_op;
constexpr auto a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3 =
make_naive_tensor_descriptor_packed(make_tuple(I1,
I1,
Number<K0PerThread>{}, // K0PerThread
I1, // NBlockId
Number<MXdlPerWave>{}, // repeat
I1, // waves
I1, // NPerXdlops
Number<K1>{}));
StaticBuffer<AddressSpaceEnum::Vgpr,
FloatAB,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3.GetElementSpaceSize(),
true>
a_thread_buf_0, a_thread_buf_1, a_thread_buf_2, a_thread_buf_3;
ignore = b_element_op;
// B matrix threadwise copy
constexpr auto b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3 =
make_naive_tensor_descriptor_packed(make_tuple(I1,
I1,
Number<K0PerThread>{}, // K0PerThread
I1, // NBlockId
Number<NXdlPerWave>{}, // repeat
I1, // waves
I1, // NPerXdlops
Number<K1>{}));
StaticBuffer<AddressSpaceEnum::Vgpr,
FloatAB,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3.GetElementSpaceSize(),
true>
b_thread_buf_0, b_thread_buf_1, b_thread_buf_2, b_thread_buf_3;
const auto wave_id = GetWaveIdx();
const auto wave_k_n_id = GetWaveKNIdx(wave_id[I2]);
const auto wave_k_m_id = GetWaveKMIdx(wave_id[I2]);
#if 0
const index_t block_id = get_block_1d_id();
const index_t thread_id = get_thread_local_1d_id();
printf("block id: %d m blockid: %d n block id: %d ,thread id: %d, wave id :{%d %d %d} "
"kn id: {%d %d}, km id: {%d %d}\n",
block_id,
block_work_idx[I0],
block_work_idx[I1],
thread_id,
wave_id[I0],
wave_id[I1],
wave_id[I2],
wave_k_n_id[I0],
wave_k_n_id[I1],
wave_k_m_id[I0],
wave_k_m_id[I1]);
printf("mfma thread k per xdlops: %d K0PerThread: %d HasMainK0BlockLoop: %d K0: %d \t",
xdlops_gemm.K0PerXdlops, K0PerThread, HasMainK0BlockLoop, b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3.GetLength(I0));
#endif
auto a_threadwise_copy =
ThreadwiseTensorSliceTransfer_v2<FloatAB,
FloatAB,
decltype(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3),
decltype(a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3),
Sequence<I1,
I1,
Number<K0PerThread>{},
I1,
Number<MXdlPerWave>{},
I1,
I1,
Number<K1>{}>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
ABlockTransferSrcScalarPerVector,
AThreadTransferSrcResetCoordinateAfterRun,
true>(
a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_multi_index(
0, wave_k_m_id[I0], 0, block_work_idx[I0], 0, wave_id[I1], wave_k_m_id[I1], 0));
auto b_threadwise_copy =
ThreadwiseTensorSliceTransfer_v2<FloatAB,
FloatAB,
decltype(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3),
decltype(b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3),
Sequence<I1,
I1,
Number<K0PerThread>{},
I1,
Number<NXdlPerWave>{},
I1,
I1,
Number<K1>{}>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
BBlockTransferSrcScalarPerVector,
BThreadTransferSrcResetCoordinateAfterRun,
true>(
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_multi_index(
0, wave_k_n_id[I0], 0, block_work_idx[I1], 0, wave_id[I1], wave_k_n_id[I1], 0));
// GEMM definition
// c_mtx += transpose(a_mtx) * b_mtx
// a_mtx[K0PerBlock, MPerBlock] is in LDS
// b_mtx[K0PerBlock, NPerBlock] is in LDS
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_non_lds<
BlockSize,
FloatAB,
FloatAcc,
decltype(a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3),
decltype(b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3),
MPerBlock,
NPerBlock,
K0PerBlock,
MPerXDL,
NPerXDL,
MXdlPerWave,
NXdlPerWave,
K1>{};
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
// gridwise GEMM pipeline
//constexpr auto a_block_slice_copy_step = make_multi_index(K0PerBlock * MultiK0, 0, 0);
constexpr auto a_thread_slice_copy_step = make_multi_index(1, 0, 0, 0, 0, 0, 0, 0);
constexpr auto b_thread_slice_copy_step = make_multi_index(1, 0, 0, 0, 0, 0, 0, 0);
// preload data to regiester and LDS
{
// Read
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_0);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_0);
// Move
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
// Read
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_1);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_1);
// Move
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
// Read
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_2);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_2);
// Move
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
// Initialize C
c_thread_buf.Clear();
// a data write to lds
// main body
if constexpr(HasMainK0BlockLoop)
{
index_t K0BlockMainLoop =
__builtin_amdgcn_readfirstlane(K0 / (MultiK0 * K0PerBlock));
index_t i = 0;
do
{
static_for<0, MultiK0, 4>{}([&](auto) {
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_3);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_3);
blockwise_gemm.Run(a_thread_buf_0, b_thread_buf_0, c_thread_buf);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_0);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_0);
blockwise_gemm.Run(a_thread_buf_1, b_thread_buf_1, c_thread_buf);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_1);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_1);
blockwise_gemm.Run(a_thread_buf_2, b_thread_buf_2, c_thread_buf);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_2);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_2);
blockwise_gemm.Run(a_thread_buf_3, b_thread_buf_3, c_thread_buf);
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
});
i += 1;
} while(i < (K0BlockMainLoop - 1));
}
// tail
{
static_for<0, MultiK0, 4>{}([&](auto i) {
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_3);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_3);
blockwise_gemm.Run(a_thread_buf_0, b_thread_buf_0, c_thread_buf);
if constexpr(i < MultiK0 - 4)
{ // only move b windows
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_0);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_0);
}
blockwise_gemm.Run(a_thread_buf_1, b_thread_buf_1, c_thread_buf);
if constexpr(i < MultiK0 - 4)
{
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_1);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_1);
}
blockwise_gemm.Run(a_thread_buf_2, b_thread_buf_2, c_thread_buf);
if constexpr(i < MultiK0 - 4)
{
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_grid_buf,
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
b_thread_buf_2);
a_threadwise_copy.Run(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_grid_buf,
a_thread_desc_k0_k1_k2_m0_m1_m2_m3_k3,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
a_thread_buf_2);
}
blockwise_gemm.Run(a_thread_buf_3, b_thread_buf_3, c_thread_buf);
if constexpr(i < MultiK0 - 4)
{
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
b_thread_slice_copy_step);
a_threadwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_k1_k2_m0_m1_m2_m3_k3,
a_thread_slice_copy_step);
}
});
}
}
// output: register to global memory
{
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I0);
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2);
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4);
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
// calculate origin of thread output tensor on global memory
// blockwise GEMM c matrix starting index
const auto c_thread_mtx_on_block =
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
const index_t m_thread_data_on_grid =
m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
const index_t n_thread_data_on_grid =
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
const auto m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
make_tuple(Sequence<0>{}));
const auto m_thread_data_on_grid_idx =
m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
make_multi_index(m_thread_data_on_grid));
const auto n_thread_data_on_grid_to_n0_n1_n2_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
make_tuple(Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}));
const auto n_thread_data_on_grid_idx =
n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
make_multi_index(n_thread_data_on_grid));
auto c_thread_copy =
ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
FloatC,
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
decltype(c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2),
CElementwiseOperation,
Sequence<M0, N0, I1, I1, M2, I1, M4, I1>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
CGlobalMemoryDataOperation,
1,
true>{
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_multi_index(m_thread_data_on_grid_idx[I0],
n_thread_data_on_grid_idx[I0],
m_thread_data_on_grid_idx[I1],
n_thread_data_on_grid_idx[I1],
m_thread_data_on_grid_idx[I2],
m_thread_data_on_grid_idx[I3],
m_thread_data_on_grid_idx[I4],
n_thread_data_on_grid_idx[I2]),
c_element_op};
c_thread_copy.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
c_thread_buf,
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
c_grid_buf);
}
}
};
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_skip_b_lds_v1.hpp
View file @ 64fbf5a2
#ifndef CK_GRIDWISE_GEMM_XDLOPS_SKIP_B_LDS_V1_HPP #pragma once
#define CK_GRIDWISE_GEMM_XDLOPS_SKIP_B_LDS_V1_HPP
#include "common_header.hpp" #include "common_header.hpp"
#include "multi_index_transform_helper.hpp" #include "multi_index_transform_helper.hpp"
...@@ -707,4 +706,3 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_skip_b_lds_v1 ...@@ -707,4 +706,3 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_skip_b_lds_v1
}; };
} // namespace ck } // namespace ck
#endif
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