mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-03-27 18:47:39 +00:00
349 lines
14 KiB
C++
349 lines
14 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
|
// SPDX-License-Identifier: MIT
|
|
|
|
#include <hip/hip_runtime.h>
|
|
|
|
#include <cstring>
|
|
#include <iostream>
|
|
#include <ostream>
|
|
#include <string>
|
|
#include <tuple>
|
|
#include <type_traits>
|
|
|
|
#include "ck_tile/host.hpp"
|
|
#include "mx_flatmm.hpp"
|
|
|
|
template <typename Layout>
|
|
static constexpr inline auto is_row_major(Layout layout_)
|
|
{
|
|
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
|
|
ck_tile::tensor_layout::gemm::RowMajor>>{};
|
|
}
|
|
|
|
template <typename FlatmmConfig,
|
|
typename ADataType,
|
|
typename BDataType,
|
|
typename DsDatatype,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename DsLayout,
|
|
typename CLayout,
|
|
typename ScaleA,
|
|
typename ScaleB,
|
|
bool UsePersistentKernel = false,
|
|
typename CDEElementWise = ck_tile::element_wise::PassThrough>
|
|
float invoke_mx_flatmm(ck_tile::DeviceMem& a_dev_buf,
|
|
ck_tile::DeviceMem& b_shuffle_dev_buf,
|
|
ck_tile::DeviceMem& c_dev_buf,
|
|
ck_tile::index_t M,
|
|
ck_tile::index_t N,
|
|
ck_tile::index_t K,
|
|
ck_tile::index_t stride_A,
|
|
ck_tile::index_t stride_B,
|
|
ck_tile::index_t stride_C,
|
|
ck_tile::index_t kbatch,
|
|
ScaleA scale_a,
|
|
ScaleB scale_b,
|
|
int n_warmup,
|
|
int n_repeat)
|
|
{
|
|
ck_tile::ScaleFlatmmHostArgs<ScaleA, ScaleB> args = {a_dev_buf.GetDeviceBuffer(),
|
|
b_shuffle_dev_buf.GetDeviceBuffer(),
|
|
{},
|
|
c_dev_buf.GetDeviceBuffer(),
|
|
kbatch,
|
|
M,
|
|
N,
|
|
K,
|
|
stride_A,
|
|
stride_B,
|
|
{},
|
|
stride_C,
|
|
scale_a,
|
|
scale_b};
|
|
|
|
using FlatmmShape = ck_tile::TileGemmShape<
|
|
ck_tile::sequence<FlatmmConfig::M_Tile, FlatmmConfig::N_Tile, FlatmmConfig::K_Tile>,
|
|
ck_tile::sequence<FlatmmConfig::M_Warp, FlatmmConfig::N_Warp, FlatmmConfig::K_Warp>,
|
|
ck_tile::sequence<FlatmmConfig::M_Warp_Tile,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
FlatmmConfig::K_Warp_Tile>>;
|
|
|
|
using TilePartitioner =
|
|
ck_tile::GemmSpatiallyLocalTilePartitioner<FlatmmShape,
|
|
FlatmmConfig::TileParitionerGroupNum,
|
|
FlatmmConfig::TileParitionerM01>;
|
|
|
|
using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
|
|
FlatmmConfig::kPadN,
|
|
FlatmmConfig::kPadK,
|
|
ALayout,
|
|
BLayout,
|
|
CLayout,
|
|
FlatmmConfig::NumWaveGroups>;
|
|
using GemmPipelineProblem =
|
|
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, FlatmmShape, Traits>;
|
|
|
|
using BaseFlatmmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;
|
|
|
|
const ck_tile::index_t k_grain = args.k_batch * FlatmmConfig::K_Tile;
|
|
const ck_tile::index_t k_split = (K + k_grain - 1) / k_grain * FlatmmConfig::K_Tile;
|
|
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(k_split);
|
|
const bool has_hot_loop = BaseFlatmmPipeline::BlockHasHotloop(num_loop);
|
|
const ck_tile::TailNumber tail_num = BaseFlatmmPipeline::GetBlockLoopTailNum(num_loop);
|
|
|
|
float ave_time = BaseFlatmmPipeline::template TailHandler<true>(
|
|
[&](auto has_hot_loop_, auto tail_num_) {
|
|
constexpr auto has_hot_loop_v = has_hot_loop_.value;
|
|
constexpr auto tail_num_v = tail_num_.value;
|
|
auto invoke_splitk_path = [&](auto split_k_) {
|
|
return mx_flatmm_calc<FlatmmConfig,
|
|
ADataType,
|
|
BDataType,
|
|
DsDatatype,
|
|
AccDataType,
|
|
CDataType,
|
|
ALayout,
|
|
BLayout,
|
|
DsLayout,
|
|
CLayout,
|
|
ScaleA,
|
|
ScaleB,
|
|
UsePersistentKernel,
|
|
CDEElementWise,
|
|
split_k_.value,
|
|
has_hot_loop_v,
|
|
tail_num_v>(
|
|
args,
|
|
ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
|
|
};
|
|
return (args.k_batch == 1) ? invoke_splitk_path(std::false_type{})
|
|
: invoke_splitk_path(std::true_type{});
|
|
},
|
|
has_hot_loop,
|
|
tail_num);
|
|
|
|
constexpr int APackedSize = ck_tile::numeric_traits<ADataType>::PackedSize;
|
|
constexpr int BPackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;
|
|
|
|
std::size_t flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / 32;
|
|
std::size_t num_byte = sizeof(ADataType) * M * K / APackedSize +
|
|
sizeof(BDataType) * N * K / BPackedSize + sizeof(CDataType) * M * N +
|
|
sizeof(ck_tile::e8m0_t) * M * K / 32 +
|
|
sizeof(ck_tile::e8m0_t) * N * K / 32;
|
|
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
|
|
|
std::cout << "Run " << ck_tile::gemm_prec_str<ADataType, BDataType>() << " Flatmm kernel " //
|
|
<< " M = " << M << " N = " << N << " K = " << K << " StrideA = " << stride_A
|
|
<< " StrideB = " << stride_B << " StrideC = " << stride_C << " : " << ave_time
|
|
<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
|
|
|
|
return ave_time;
|
|
}
|
|
|
|
auto create_args(int argc, char* argv[])
|
|
{
|
|
ck_tile::ArgParser arg_parser;
|
|
arg_parser.insert("m", "32", "m dimension")
|
|
.insert("n", "128", "n dimension")
|
|
.insert("k", "256", "k dimension")
|
|
.insert("a_layout", "R", "A tensor data layout - Row by default")
|
|
.insert("b_layout", "C", "B tensor data layout - Row by default")
|
|
.insert("c_layout", "R", "C tensor data layout - Row by default")
|
|
.insert("stride_a", "0", "Tensor A stride")
|
|
.insert("stride_b", "0", "Tensor B stride")
|
|
.insert("stride_c", "0", "Tensor C stride")
|
|
.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
|
|
.insert(
|
|
"mx_prec", "fp4xfp4", "data type for activation and weight, support: fp4xfp4, fp8xfp8")
|
|
.insert("warmup", "50", "number of iterations before benchmark the kernel")
|
|
.insert("repeat", "100", "number of iterations to benchmark the kernel")
|
|
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
|
|
.insert("split_k", "1", "splitK value")
|
|
.insert("init", "0", "0:random, 1:constant(1)")
|
|
.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
|
|
.insert("warp_tile",
|
|
"0",
|
|
"0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
|
|
bool result = arg_parser.parse(argc, argv);
|
|
return std::make_tuple(result, arg_parser);
|
|
}
|
|
|
|
template <ck_tile::index_t N_Warp_Tile, typename dtype>
|
|
auto preShuffleWeight(ck_tile::HostTensor<dtype>& src)
|
|
{
|
|
auto src_lengths = src.get_lengths();
|
|
const int K = src_lengths[0];
|
|
const int N = src_lengths[1];
|
|
constexpr int packed_size = ck_tile::numeric_traits<dtype>::PackedSize;
|
|
int KPack = 16 * packed_size; // fp4:32 or fp8:16
|
|
int NLane = N_Warp_Tile;
|
|
int KLane = 64 / NLane;
|
|
int K0 = K / (KLane * KPack);
|
|
|
|
ck_tile::HostTensor<dtype> shuffled(ck_tile::HostTensorDescriptor({N * K}, {1}));
|
|
|
|
// K -> K0 KLane KPack
|
|
// N -> N0 NLane
|
|
// N, K -> N0 K0 KLane NLane KPack
|
|
for(int n = 0; n < N; ++n)
|
|
{
|
|
for(int k = 0; k < K; k += packed_size)
|
|
{
|
|
int n0 = n / NLane;
|
|
int n1 = n % NLane;
|
|
|
|
int k0 = k / (KLane * KPack);
|
|
int tempk = k % (KLane * KPack);
|
|
int k1 = tempk / KPack;
|
|
int k2 = tempk % KPack;
|
|
|
|
int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
|
|
k1 * KPack * NLane + n1 * KPack + k2;
|
|
|
|
shuffled(outputIndex) = src(k, n);
|
|
}
|
|
}
|
|
return shuffled;
|
|
}
|
|
|
|
template <class FlatmmConfig, bool KLast, typename dtype>
|
|
auto preShuffleScale(ck_tile::HostTensor<dtype>& src)
|
|
{
|
|
auto src_lengths = src.get_lengths();
|
|
const auto MN = KLast ? src_lengths[0] : src_lengths[1];
|
|
const auto K = KLast ? src_lengths[1] : src_lengths[0];
|
|
|
|
size_t MNXdlPack = 2;
|
|
size_t KXdlPack = 2;
|
|
size_t XdlMNThread = FlatmmConfig::N_Warp_Tile; // 16
|
|
size_t XdlKThread = 64 / XdlMNThread;
|
|
|
|
const auto MN_Paded = ck_tile::integer_least_multiple(MN, XdlMNThread * MNXdlPack);
|
|
|
|
ck_tile::HostTensor<dtype> shuffled(ck_tile::HostTensorDescriptor({MN_Paded * K}, {1}));
|
|
|
|
size_t K0 = K / KXdlPack / XdlKThread; // KRepeat
|
|
|
|
// The 4 16x128 building blocks will be packed into 1 32x256 for F4
|
|
// The 8 16x16x128 mfma will be packed into 1 32x32x256 for F4
|
|
|
|
// unfold the MN32xK(256/32) scale buffer
|
|
// 4 16 2 2
|
|
// To XdlKThread-> XdlMNThread -> KXdlPack -> MNXdlPack
|
|
// Then, MNRepeat->KRepeat
|
|
|
|
for(size_t n = 0; n < MN_Paded; ++n)
|
|
{
|
|
for(size_t k = 0; k < K; ++k)
|
|
{
|
|
auto n0 = n / (XdlMNThread * MNXdlPack); // i MNRepeat
|
|
auto tempn = n % (XdlMNThread * MNXdlPack);
|
|
auto n1 = tempn % XdlMNThread; // i XdlMNThread
|
|
auto n2 = tempn / XdlMNThread; // i MNXdlPack
|
|
|
|
auto k0 = k / (XdlKThread * KXdlPack); // i KRepeat
|
|
auto tempk = k % (XdlKThread * KXdlPack);
|
|
auto k1 = tempk % XdlKThread; // i XdlKThread
|
|
auto k2 = tempk / XdlKThread; // i KXdlPack
|
|
|
|
auto outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
|
|
k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
|
|
k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
|
|
k2 * MNXdlPack + n2;
|
|
|
|
if constexpr(KLast)
|
|
shuffled(outputIndex) = n < MN ? src(n, k) : dtype{};
|
|
else
|
|
shuffled(outputIndex) = n < MN ? src(k, n) : dtype{};
|
|
}
|
|
}
|
|
return shuffled;
|
|
}
|
|
|
|
#include "run_mx_flatmm.inc"
|
|
|
|
int run_mx_flatmm_example(int argc, char* argv[])
|
|
{
|
|
auto [result, arg_parser] = create_args(argc, argv);
|
|
if(!result)
|
|
return -1;
|
|
|
|
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
|
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
|
|
|
std::string mx_prec = arg_parser.get_str("mx_prec");
|
|
std::string a_layout = arg_parser.get_str("a_layout");
|
|
std::string b_layout = arg_parser.get_str("b_layout");
|
|
int persistent_opt = arg_parser.get_int("persistent");
|
|
|
|
if(a_layout == "R" && b_layout == "C")
|
|
{
|
|
if(mx_prec == "fp4" || mx_prec == "fp4xfp4")
|
|
{
|
|
if(persistent_opt == 0)
|
|
return run_mx_flatmm_with_layouts<ck_tile::pk_fp4_t,
|
|
ck_tile::pk_fp4_t,
|
|
ck_tile::fp16_t,
|
|
MXfp4_FlatmmConfig16,
|
|
false>(argc, argv, Row{}, Col{}, Row{});
|
|
else
|
|
throw std::runtime_error("Only non-persistent kernels are supported currently!");
|
|
}
|
|
else if(mx_prec == "fp6" || mx_prec == "fp6xfp6")
|
|
{
|
|
throw std::runtime_error("fp6xfp6 is not supported.");
|
|
}
|
|
else if(mx_prec == "fp8" || mx_prec == "fp8xfp8")
|
|
{
|
|
if(persistent_opt == 0)
|
|
return run_mx_flatmm_with_layouts<ck_tile::fp8_t,
|
|
ck_tile::fp8_t,
|
|
ck_tile::fp16_t,
|
|
MXfp8_FlatmmConfig16,
|
|
false>(argc, argv, Row{}, Col{}, Row{});
|
|
else
|
|
throw std::runtime_error("Only support non-persistent kernel now!");
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported data_type!");
|
|
}
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
|
|
}
|
|
}
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
auto [result, arg_parser] = create_args(argc, argv);
|
|
if(!result)
|
|
return EXIT_FAILURE;
|
|
try
|
|
{
|
|
int warp_tile = arg_parser.get_int("warp_tile");
|
|
if(warp_tile == 0)
|
|
{
|
|
return run_mx_flatmm_example(argc, argv);
|
|
}
|
|
else if(warp_tile == 1)
|
|
{
|
|
throw std::runtime_error("Only support MFMA_16x16x128 now!");
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported warp_tile!");
|
|
}
|
|
}
|
|
catch(const std::runtime_error& e)
|
|
{
|
|
std::cerr << "Runtime error: " << e.what() << '\n';
|
|
return EXIT_FAILURE;
|
|
}
|
|
}
|